Abstract
We are introducing the Carrier-Domain Method (CDM) for high-resolution computation of time-periodic long-wake flows, with cost-effectives that makes the computations practical. The CDM is closely related to the Multidomain Method, which was introduced 24 years ago, originally intended also for cost-effective computation of long-wake flows and later extended in scope to cover additional classes of flow problems. In the CDM, the computational domain moves in the free-stream direction, with a velocity that preserves the outflow nature of the downstream computational boundary. As the computational domain is moving, the velocity at the inflow plane is extracted from the velocity computed earlier when the plane’s current position was covered by the moving domain. The inflow data needed at an instant is extracted from one or more instants going back in time as many periods. Computing the long-wake flow with a high-resolution moving mesh that has a reasonable length would certainly be far more cost-effective than computing it with a fixed mesh that covers the entire length of the wake. We are also introducing a CDM version where the computational domain moves in a discrete fashion rather than a continuous fashion. To demonstrate how the CDM works, we compute, with the version where the computational domain moves in a continuous fashion, the 2D flow past a circular cylinder at Reynolds number 100. At this Reynolds number, the flow has an easily discernible vortex shedding frequency and widely published lift and drag coefficients and Strouhal number. The wake flow is computed up to 350 diameters downstream of the cylinder, far enough to see the secondary vortex street. The computations are performed with the Space–Time Variational Multiscale method and isogeometric discretization; the basis functions are quadratic NURBS in space and linear in time. The results show the power of the CDM in high-resolution computation of time-periodic long-wake flows.
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1 Introduction
In this article, we are introducing the Carrier-Domain Method (CDM) for high-resolution computation of time-periodic long-wake flows. The CDM has both the cost-effectives needed to make the computations practical and the accuracy needed in the long-wake computations to correctly represent the vortex patterns far downstream. It is closely related to, and was actually inspired by, the Multidomain Method (MDM) [1], which was introduced 24 years ago, originally intended also for cost-effective computation of long-wake flows and later extended in scope to cover additional classes of flow problems.
In computation of long-wake flows with the MDM, the wake computation alone might be the final objective, or the ultimate objective might the computation of the wake influence on a secondary object placed far downstream. The computation is based on a sequence of subdomains with slight overlap between them. The first one covers the object producing the wake, and that would be the primary object if there is a secondary object downstream. The secondary object would be in the last subdomain. The velocity specified at the inflow boundary of the first subdomain is the free-stream velocity. The inflow velocity for each subsequent subdomain is extracted from the preceding subdomain. If the outflow boundary of a subsequent subdomain is also in the preceding subdomain, for example when the subsequent subdomain is fully inside the preceding subdomain, then the stress at the outflow boundary is also extracted from the preceding subdomain.
The first computations [1] with the MDM were 2D long-wake flow for a circular cylinder, which was a verification study, and 3D wake influence on a wing placed downstream of a larger wing. Other 3D computations conducted soon after that were a cylinder wake extending 300 diameters downstream [2], aerodynamics of a parachute crossing an aircraft wake [3], and fluid–structure interactions (FSI) of that parachute as it is crossing the aircraft wake [4]. The 3D cylinder computation was at Reynolds number 140. The secondary vortex street, appearing far downstream and known from laboratory experiments, was successfully captured. In the parachute computations, the first subdomain was for the aircraft and the near-wake flow. The second subdomain was for the long-wake flow. The third subdomain, which was for the parachute, was translating fully inside the second subdomain.
A different MDM version was used in the thermo-fluids computations reported in [5] for a freight truck and its rear tires. It was a spatially multiscale version with global and local domains. The global domain, as the primary domain, contained the entire truck, including all the tires. The local domain, as the secondary domain, contained the four rear (left) tires. The secondary domain was fully inside the primary domain, with their boundaries on the tire, truck, and road surfaces coinciding. A mesh with reasonable resolution was used in the primary computation. At the inflow boundary, the velocity and temperature were the free-stream values. At the outflow boundary, the stress and the normal component of the heat flux vector were zero. At the top and side computational boundaries, the normal component of the velocity, the tangential stress, and the normal component of the heat flux vector were zero. The computation generated a large amount of time-history data, which was stored with the “ST-C” [6] data compression, to be used in the secondary computation. A mesh with higher resolution was used in the secondary computation. The higher resolution was intended to increase the accuracy of the thermo-fluids analysis and tire heat transfer rates. At the inflow, top, and side computational boundaries, the velocity and temperature were specified, with the values specified at each time step extracted from the primary-computation data stored. The extraction was done by evaluating the temporal NURBS representation of the primary-computation velocity and temperature at the corresponding time. At the outflow boundary, the stress was extracted from the primary-computation data, and the normal component of the heat flux vector was set to zero. Where the primary- and secondary-domain boundaries coincided, the conditions specified for the secondary domain were the same as the conditions specified for the primary domain. The data extraction from the primary-computation data was by the least-squares projection, because, the nodal points of the secondary-domain boundaries cannot be expected to coincide with nodal points in the primary domain.
The MDM version used in the thermo-fluids computations of the freight truck and its tires was also used recently [7] in high-resolution isogeometric analysis of car and tire aerodynamics. The computational model had much of the complexities of the actual car and tire, such as the near-actual tire geometry, road contact, and tire deformation and rotation.The focus was on the tire aerodynamics, with high-resolution in both space and time. The influence of the aerodynamics of the car body was included, in the framework of the MDM, from the global computation with near-actual car body and tire geometries, carried out earlier [8] with a reasonable mesh resolution. The high-resolution local computation was for the left set of tires. It was performed in a nested MDM sequence over three subdomains. The first subdomain was for the front tire, the second for the front-tire wake flow, and the third for the rear tire. The inflow velocities were extracted from the global, first-subdomain, and second-subdomain computations. All remaining boundary conditions for the three subdomains were extracted from the global computation.
In [9, 10], the MDM was used in computation of flow over a complex terrain. The first subdomain was for flow over a flat plate, and the second subdomain for flow over the complex terrain. There was no overlap between the two subdomains. The inflow velocity of the second subdomain was coming, by weak enforcement, from the outflow velocity of the first subdomain.
The MDM in its originally intended way was used in [11] in aerodynamic and FSI analysis of two wind turbines that were placed back to back in an atmospheric boundary layer flow. The two turbines were the primary and secondary objects. There were three subdomains. The first one was for the first turbine and the near-wake flow, the second one for the long-wake flow, and third one for the second turbine. The computations in all subdomains were based on finite element discretization.
The velocity data from a plane located at 10 m downstream of the primary turbine in [11] was used in [12,13,14] as the inflow velocity in the IGA-based two-domain MDM computations of the wind turbine wake. The computational framework, together with a comprehensive set of proof of concept computations of the wake, was presented in [12], studies on spatial and temporal resolution in [13], and studies on spatial-refinement directional preference in [14]. In the MDM version introduced and tested in [12], a subsequent-subdomain computation starts some duration after the preceding-subdomain computation does, thus reducing the computational cost even more. The lag in the start time is from recognizing that there is no flow information for the subsequent-subdomain computation to advect before the preceding-subdomain computation advects the information from its inflow plane to its outflow plane. This version was used in all the studies reported in [13, 14]. The CDM was inspired by this version of the MDM.
In the CDM, the computational domain moves in the free-stream direction, with a velocity that preserves the outflow nature of the downstream computational boundary. As the computational domain is moving, the velocity at the inflow plane is extracted from the velocity computed earlier when the plane’s current position was covered by the moving domain (see Fig. 1). The inflow data needed at an instant is extracted from one or more instants going back in time as many periods. It goes without saying that computing the long-wake flow with a high-resolution moving mesh that has a reasonable length would be far more cost-effective than computing it with a fixed mesh that covers the entire length of the wake. We are also introducing a CDM version where the computational domain moves in a discrete fashion rather than a continuous fashion. We will use the abbreviations “CDM-C” and “CDM-D” to identify the versions with continuous and discrete motion of the computational domain.
To demonstrate how the CDM works, we compute, with the CDM-C, the 2D flow past a circular cylinder at Reynolds number 100. At this Reynolds number, the flow has an easily discernible vortex shedding frequency and widely published lift and drag coefficients and Strouhal number. We compute the wake flow up to 350 diameters downstream of the cylinder, far enough to see the secondary vortex street. The computational platform is made of, in addition to the CDM-C, the Space–Time Variational Multiscale (ST-VMS) method [5, 15, 16], ST Isogeometric Analysis (ST-IGA) [15, 17, 18], and methods for calculating the stabilization parameters and related element lengths targeting IGA discretization [19, 20]. In the ST-IGA, the basis functions are quadratic NURBS in space and linear in time. The ST context of the ST-VMS is bringing higher-order accuracy (see [15, 16]), and the VMS feature is bringing better representation of the multiscale flow patterns. The ST-IGA is bringing higher accuracy in representing the cylinder geometry and flow solution.
1.1 ST-VMS
This subsection, included for completeness, is mostly from [7, 21, 22]. The ST-VMS is the core method used in the wake computations. It can serve as a moving-mesh method in computation of flow problems with FSI and moving boundaries and interfaces (MBI). It originated from and subsumes its precursor the Deforming-Spatial-Domain/Stabilized ST (DSD/SST) method [23,24,25]. The DSD/SST is mostly called “ST-SUPS,” with the abbreviation “SUPS” denoting the stabilization components SUPG and PSPG, which stand for the Streamline-Upwind/Petrov–Galerkin [26] and Pressure-Stabilizing/Petrov–Galerkin [23]. The ST-SUPS, in broader interpretation of the terminology, includes the stabilization component coming from Least-Squares on the Incompressibility Constraint (LSIC), as the DSD/SST in its form in [24] did. The VMS components of the ST-VMS are from the residual-based VMS (RBVMS) method [27,28,29,30]. The increased accuracy associated with the ST framework (see [15, 16, 31]) makes the ST-SUPS and ST-VMS appealing also in flow computations without MBI. Furthermore, the framework, naturally, makes it possible to use IGA basis functions also in time [31].
The arbitrary Lagrangian–Eulerian (ALE) moving-mesh framework is older, though its use in 3D finite element flow computations with modern stabilized methods like the SUPG is somewhat newer compared to the ST-SUPS. The ram-air parachute FSI analysis in [32] was one of the earliest computations with the ALE-SUPS. In the category of moving-mesh methods with the stabilization components coming from the RBVMS, however, the ALE-VMS method [33,34,35,36] precedes the ST-VMS. The ST-SUPS, ALE-SUPS, ALE-VMS, and ST-VMS, as methods, have much in common, and so do the classes of problems computed with them since their inception.
The classes of problems computed with the ALE-SUPS, RBVMS, and ALE-VMS include wind turbines [11, 37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], turbomachinery [58,59,60,61,62,63,64], stratified flows [9, 65], bridges [66,67,68,69,70], marine applications [71,72,73], free-surface flows [74,75,76,77,78], two-phase flows [79,80,81,82,83,84,85], additive manufacturing [86], aircraft applications [87, 88], hypersonic flows [89], parachutes [32], cardiovascular medicine [33, 90,91,92,93,94,95,96,97,98,99,100,101,102,103], mixed ALE-VMS immersogeometric analysis [100, 104] (ALE-VMS/IMGA) computations [99,100,101, 105,106,107,108,109,110,111,112,113] in the framework of the fluid–solid interface-tracking/interface-capturing technique [114], and IMGA FSI and flow analysis [104, 115,116,117,118].
The classes of problems computed with the ST-SUPS and ST-VMS include those summarized in [119] (all computed in 1993–2018), wind turbines [10, 12,13,14, 35, 50, 52, 53, 120,121,122], turbomachinery [18, 52, 53, 123,124,125,126], ground vehicles and tires [5, 7, 8, 10, 54, 55, 127,128,129,130,131,132], fluid films [130, 132, 133], disk brakes [134], flapping-wing aerodynamics [17, 35, 135,136,137,138,139], spacecraft [140, 141], parachutes [35, 54, 55, 140, 142,143,144,145,146], cardiovascular medicine [22, 102, 103, 137, 147,148,149,150,151,152,153,154,155,156,157,158,159], Taylor–Couette flow [160, 161], U-ducts [162], higher-order temporal IGA discretization [31], and boundary-layer mesh resolution studies [21].
The ST-SUPS, ALE-SUPS, ALE-VMS, and ST-VMS, like all moving-mesh methods, need to be complemented with mesh update methods in FSI and MBI computations. The mesh update most of the time consists of moving the mesh to accommodate the motion of the boundaries and interfaces and to control the mesh resolution near solid surfaces that are moving, and remeshing if the element distortion exceeds an acceptable level. Since the inception of the ST-SUPS, a large number of special- and general-purpose mesh moving methods have been developed for computations with the ST-SUPS and ST-VMS. Some of them have also been used with the ALE-SUPS and ALE-VMS. A recent article [139] on mesh moving methods provides an overview. The general-purpose methods include, as the first one, the linear-elasticity mesh moving method with mesh-Jacobian-based stiffening [160, 163] and, as the most recent ones, the element-based mesh relaxation [142], where the mesh motion is determined by using the large-deformation mechanics equations and an element-based zero-stress state (ZSS) [164,165,166,167,168,169], mesh relaxation and mesh moving methods [170] based on fiber-reinforced hyperelasticity and optimized ZSS, and the linear-elasticity mesh moving method with no cycle-to-cycle accumulated distortion [157, 171].
1.2 ST-IGA
This subsection, included for completeness, is mostly from [7, 21]. The IGA basis functions in space brought major accuracy increases in fluid and solid mechanics computations [33, 90, 172, 173]. That made IGA basis functions appealing for the ST-SUPS and ST-VMS computations and led to the introduction of the ST-IGA, at the same time the ST-VMS was introduced. It is IGA discretization in the ST framework. The terminology “ST-IGA” implies, depending on the context, discretization with IGA basis functions in space or time or both. The test computations reported in [15], which were in 2D, were for flow past an airfoil and for pure advection of a scalar. The flow computation was with IGA basis functions in space, and the advection computations with IGA basis functions in both space and time, accompanied by a stability and accuracy analysis for the pure advection equation. The advection computations and stability and accuracy analysis showed what can naturally be expected, and that is, higher-order basis functions in space will deliver more if they are used together with higher-order basis functions in time. Keeping in mind that the increased accuracy the ST-IGA with IGA basis functions in space brings is attained with fewer control points, the effective element sizes will be larger. With that, larger time steps can be taken while still keeping the Courant number at or below the levels we target for good accuracy.
Using IGA basis functions in time is uniquely offered by the ST framework, and partly because of that the effort was focused on that track in the early years of the ST-IGA computations [15,16,17]. Taking advantage of that opportunity brings higher accuracy in representing the motion of a solid surface, a mesh motion consistent with that surface motion, and better efficiency in representing the mesh motion and in remeshing. The ST/NURBS Mesh Update Method (STNMUM) [17, 120] was built around these positive attributes of the ST-IGA. The ST-C is another example of the good things that come out of the ST-IGA with IGA basis functions in time. The letter “C” in “ST-C” means “continuous.” This is a method for extracting time-continuous data from the computed data, and it can work as a data compression method in dealing with large data volumes [5,6,7, 12,13,14, 52,53,55, 124, 125, 127, 134]. The classes of problems computed by using the ST-IGA with IGA basis functions in time include wind turbines [10, 50, 52, 53, 120,121,122], turbomachinery [18, 52, 53, 123,124,125,126], flapping-wing aerodynamics [17, 35, 135,136,137,138,139], spacecraft cover separation aerodynamics [140], and higher-order temporal IGA discretization [31].
The classes of problems computed by using the ST-IGA with IGA basis functions in space include wind turbines [10, 12,13,14, 52, 53, 122], turbomachinery [18, 52, 53, 123,124,125,126], ground vehicles and tires [7, 8, 10, 128,129,130,131,132], fluid films [130, 132, 133], parachutes [54, 55, 144, 146], cardiovascular medicine [22, 102, 103, 152,153,154,155,156,157,158,159], Taylor–Couette flow [161], U-ducts [161]. higher-order temporal IGA discretization [31], and boundary-layer mesh resolution studies [21]. It was pointed out as early as in 2007 (see [174]) that the image-based geometries used in patient-specific arterial FSI computations are not for the ZSS of the artery and that a ZSS estimation method is needed. The ZSS estimation methods introduced in and after 2016 [102, 166,167,168,169] stand on the IGA basis functions in space, and so does the related hyperelastic shell analysis [175, 176]. The IGA basis functions in space have also been a part of quite a few advanced computational methods targeting design and structural analysis, those reported in [177,178,179,180,181,182,183,184,185,186] are examples of that, and turbine blades and heart valves are among the examples.
1.3 Stabilization parameters and local length scales targeting IGA discretization
This subsection, included for completeness, is mostly from [7, 21, 22]. The stabilization terms of the ST-SUPS, ALE-SUPS, RBVMS, ALE-VMS, ST-VMS, and most stabilized methods have some factors called “stabilization parameters” [35] that multiply the residuals. Some of the parameters have the units of time, and some the units of kinematic viscosity. Those with the units of time are called “\(\tau _{{{\mathrm {SUPG}}}}\)” and “\(\tau _{{{\mathrm {PSPG}}}}\),” and the one with the units of kinematic viscosity is called “\(\nu _{{{\mathrm {LSIC}}}}\)” [187] (see Sect. 1.1 for the abbreviation “LSIC”). That is the terminology used in the context of the ST-SUPS, ALE-SUPS, RBVMS, ALE-VMS, and ST-VMS. The expressions introduced in [188] for stabilization parameters were for separate \(\tau _{{{\mathrm {SUPG}}}}\) and \(\tau _{{{\mathrm {PSPG}}}}\), and test computations with them were successful. Leaving that aside, a single parameter, “\(\tau _{{{\mathrm {SUPS}}}}\)” [15, 35], is used instead of two.
The expressions for the stabilization parameters involve, among other constituents, local lengths scales, also called “element lengths.” Precursors of the element lengths and stabilization parameters used with the SUPG, PSPG, ST-SUPS, ALE-SUPS, RBVMS, ALE-VMS, and ST-VMS can be found in [26, 189,190,191]. Designing good element lengths and stabilization parameters continued as a significant part of the research on residual-based stabilized methods. That generated a good number favorite expressions for the element lengths and stabilization parameters (see, for example, [5, 17, 24, 25, 120, 188, 192,193,194,195,196,197]), and until late 2017 they were all intended for finite element discretization. For about a decade, the element length was a local length scale in the flow direction, which is an advection length scale. A second local length scale, in the solution-gradient direction, was introduced in [194] and was identified as the diffusion length scale in [24]. Element lengths and stabilization parameters in the ST context were introduced in [24, 194], those specific to the VMS stabilization in [5], and those for coupled incompressible-flow and thermal-transport equations in [5]. Direction-dependent element lengths that have node-numbering invariance also for simplex elements were introduced in [197]. All these local length scales and stabilization parameters were intended for finite element discretization but have also been in use in computations with IGA discretization.
Local length scales and stabilization parameters targeting IGA discretization were introduced in 2017 in [19]. They would certainly also be suitable for use in computations with finite element discretization as a special case of IGA discretization. The expression introduced in [19] for the direction-dependent local length scale is from a conceptually simple three-step derivation. In the first step, the direction vector is mapped from the physical element to the parent element; in the second step, the discretization spacing along each of the parametric coordinates is accounted for; in the third step, what has been obtained in the parent element is mapped back to the physical element. Although the derivation steps were in the ST framework, reducing them to space only is straightforward. The stabilization parameters given in [129], which are largely from [19], are the current ones that have been in use in ST-VMS and ST-SUPS computations. In deriving the expressions for the local length scales, we do not have to use the standard integration parametric space. Instead, we can use a preferred parametric space that more effectively serves the purpose, which is obtaining good expressions. This idea was introduced and shown to work well in [20, 197, 198]. The expressions for the local local length scales include a transformation tensor that relates the two parametric spaces. Based on this idea, expressions for the direction-dependent local length scales targeting complex-geometry B-spline meshes were introduced in [20]. We require the local length scales to be invariant with respect to element splitting in B-spline meshes. Without that invariance, the flow solution would be influenced by something that it should not be influenced by. The expressions introduced in [20] meet that requirement, and the proof was given in [198].
The local-length-scale expressions introduced in [19, 20] have been used in computing the following classes of problems: wind turbines [10, 12,13,14, 122], turbomachinery [126], ground vehicles and tires [7, 8, 10, 129,130,131,132], fluid films [130, 132, 133], parachutes [146], cardiovascular medicine [22, 156,157,158,159], Taylor–Couette flow [161], U-ducts [162], higher-order temporal IGA discretization [31], and boundary-layer mesh resolution studies [21]. They have also been used in [63], following a gas turbine flow computation with IGA discretization, to calculate the Courant number from the local flow speed, time-step size, and mesh local length scale in the flow direction.
1.4 Outline of the remaining sections
The CDM is described in Sect. 2, the computations are presented in Sects. 3 and 4, and the concluding remarks are given in Sect. 5.
2 CDM
2.1 MDM and computational cost
Let us consider a time-periodic solution at a plane, with period T, which propagates with the free-stream velocity U. Just for illustration, we use a 1D context, and set the plane location as \(x=0\). This was the case, for example, in the wind turbine computations in [12,13,14]. We set our length scale as \(L = U T\). We assume that we want to know the time-dependent solution for the long domain 0 to kL. The example shown in Fig. 2 is for \(k=4\). We note that k does not have to be an integer. The figure shows how the computation can be carried over two ST domains with slight spatial overlap. This configuration represents the MDM version introduced in [12], where a subsequent-subdomain computation starts some duration after the preceding-subdomain computation does, instead of starting from \(t = 0\). The lag in the start time is from recognizing that there is no flow information for the subsequent-subdomain computation to advect before the preceding-subdomain computation advects the information from its inflow plane to its outflow plane. The total area of the ST subdomains, which is a rough estimate of the computing time, is \(Q = 42 LT = 42 U T^2\) (actually slightly more than that because of the overlap). That represents about 1/4 reduction in the computational cost. Going beyond that, we can reduce the computational cost further by stopping the preceding-subdomain computation at \(t=7T\) (see Fig. 3), and by completing the data set from the later periods of the computed data. That is under the assumption that by then the computed data is time-periodic or nearly time-periodic (see, for example, Fig. 4), from which a time-periodic data can somehow be built. Then, the total area of the ST subdomains is \(Q = 28 U T^2\). That represents about 1/2 reduction in the computational cost.
Generally, if we compute with a full computational domain, we need to compute for a duration of \((k+1)T\), making the area of the ST domain
Based on the advection speed and the periodicity (see Fig. 4), we can compute over only a portion of the ST domain to obtain the time-periodic solution. Figure 5 shows, as an example, the ideal ST domain portion. Its area is
Remark 1
Comparing Eqs. (2) and (4), the reduction factor for the computational cost is \(\frac{1}{k+1}\).
Remark 2
We define T to be in general the highest time period we expect to see in the wake flow. In our specific case in this article, the relationship between T and the lift period \(T^{{\mathrm {L}}}\) is \(T = 2 T^{{\mathrm {L}}}\) (see Sect. 3.5).
2.2 CDM-C
Let us consider a computational domain with length \(L_{{\mathrm {c}}}\), meant to move with the velocity U (see Fig. 6). That is our “carrier domain.” The computation starts at \(t=2T\), with the carrier domain being stationary at the beginning and at the end. The area of the ST domain swept is \(Q = L \left( T + \frac{k L}{U}\right) = (k+1) U T^2\). In a typical computation, the flow information exists the domain from the outflow boundary. This means that even if the solution near the outflow plane is not as accurate as in the interior, the influence will not accumulate over time. However, in the example of Fig. 6, the flow information will not exit from the outflow plane. Therefore, moving the computational domain with the velocity U would not be practical.
CDM-C. The carrier domain (red) at an instant and the ST domain (enclosed by the red dashed lines) swept by that. In this illustration, \(L_{{\mathrm {c}}} = L\). The red circle with white filling indicates the current position of the inflow plane. The black circle with red filling indicates going back in time one period. The flow information at the left boundary will reach x at \(t = 2T + x/U\), therefore, from \(t=2T\) to 3T we are not showing below that line the solution we have in Fig. 4. (Color figure online)
The carrier domain will move with velocity V, satisfying the condition
We represent it as \(V = \alpha _{{\mathrm {C}}} U\), where \(0< \alpha _{{\mathrm {C}}} < 1\), and the exit speed is \((1-\alpha _{{\mathrm {C}}})U\). To extract the inflow data going back at least one period, the domain length needs to be
Based on that, we express \(L_{{\mathrm {c}}}\) as
where \(\beta _{{\mathrm {C}}} \ge 1\). Figure 7 is for the case \(\beta _{{\mathrm {C}}} = 1\). The area of the ST domain is
We examine the extraction of the inflow data further. In the cases like those in Figs. 6 and 7, we can go back only one period, and the extraction would be from the outflow plane of the carrier domain. Because the solution near the outflow plane would not be as accurate as in the interior, \(\beta _{{\mathrm {C}}} = 1\) would not serve our purpose well.
CDM-C. The carrier domain (red) at an instant and the ST domain (enclosed by the red dashed lines) swept by that. This is for the case \(\beta _{{\mathrm {C}}}=1\). As an example, we set \(\alpha _{{\mathrm {C}}} = 0.5\). The red circle with white filling indicates the current position of the inflow plane. The black circle with red filling indicates going back in time one period. The orange dotted line is \(t = 2T + x/U\); we are not showing below that line the solution we have in Fig. 4. (Color figure online)
Given the observations above, we now introduce a version of CDM-C that would work without these shortcomings. How far we go back in time to extract the inflow data has to be less than \(L_{{\mathrm {c}}}/V = \beta _{{\mathrm {C}}} T\). To make the data extraction clearer, we split \(\beta _{{\mathrm {C}}}\) as \(\beta _{{\mathrm {C}}} = m + r\), where m is an integer and \(0 < r \le 1\). As an example, for \(\beta _{{\mathrm {C}}} = 2\), we would set \(m=1\) and \(r=1\) instead of \(m=2\) and \(r=0\) to avoid extracting the inflow data from the outflow plane of an earlier computation. Practically, using the smallest r is cost-effective, as long as the solution at rVT upstream of the outflow plane is reliable. In the example in Fig. 8, \(m = 2\) and \(r = 0.4\).
CDM-C. The carrier domain (red) at an instant and the ST domain (enclosed by the red dashed lines) swept by that. In this illustration, \(\alpha _{{\mathrm {C}}} = 0.5\), \(m=2\), and \(r=0.4\), making \(L_{{\mathrm {c}}} = 1.2 L\). The red circle with white filling indicates the current position of the inflow plane. Each black circle with red filling indicates going back in time one period. The orange dotted line is \(t = 2T + x/U\); we are not showing below that line the solution we have in Fig. 4. (Color figure online)
To make the inflow-data extraction and time-periodicity of the solution consistent during the entire computation, we compute \((m+r) T\) with the carrier domain being stationary at the beginning and at the end. The area of the ST domain is
Now, we explain how the inflow boundary condition is extracted. We denote the inflow location at a given time t as \(x_{{\mathrm {inf}}}(t)\), and the computed solutions at t and x as u(x, t). With that, the inflow condition \(g(x_{{\mathrm {inf}}}(t), t)\) is
if we can go back in time only one period. If we can go back in time m periods, then we set
where \(w_k\) are weights, and \(\sum \limits _{k=1}^m w_k = 1\). We can think of more complicated ways of doing this and enforcing periodicity; we will leave that as future investigation.
Remark 3
Even if \(k/\alpha _{{\mathrm {D}}}\) is an integer, because \(\beta _{{\mathrm {D}}} > 1\), the subdomains will have overlap, and that is how we wanted our method to be.
Remark 4
In reporting the data from the CDM-C computation, we will use the ST domain shown in Fig. 9.
Remark 5
An additional good feature of the CDM-C is that the relative velocity with respect to the mesh becomes smaller, making the Courant number smaller. This leads to better accuracy under most flow conditions.
2.3 CDM-D
We are also introducing a CDM version where the computational domain moves in a discrete fashion. Before we describe the method we are actually proposing, like we did for the CDM-C, we go over some basic cases that come to mind first. Similar to what we did in the CDM-C with \(\alpha _{{\mathrm {c}}}=1\) and \(\beta _{{\mathrm {C}}}=1\), we split the domain into k subdomains of length \(L_{{\mathrm {c}}} = U T\). The computation over each is for a duration of 2T (see Fig. 10). The total area of the ST subdomains is \(Q = L (k 2 T) = 2 k U T^2\).
CDM-D. The ST subdomains (red). For this case, \(L_{{\mathrm {c}}} = U T\). The orange dotted line is \(t = 2T + x/U\); we are not showing below that line the solution we have in Fig. 4. (Color figure online)
Remark 6
Unless k is an integer, we need to have subdomains with either different sizes or overlap. If there is overlap, Q would be somewhat larger.
CDM-D. The ST subdomains (red). This for the case \(\alpha _{{\mathrm {D}}}<1\). As an example, in this illustration, \(L_{{\mathrm {c}}} = 0.5 U T\). The orange dotted line is \(t = 2T + x/U\); we are not showing below that line the solution we have in Fig. 4. (Color figure online)
Similar to what we did in CDM-C with \(\alpha _{{\mathrm {C}}}=0.5\) and \(\beta _{{\mathrm {C}}}=1\), in \(L_{{\mathrm {c}}} = \alpha _{{\mathrm {D}}} L\), we set \(0<\alpha _{{\mathrm {D}}}<1\). There are \(k/\alpha _{{\mathrm {D}}}\) ST subdomains. The computation over each is for a duration of \((1+\alpha _{{\mathrm {D}}})T\) (see Fig. 11). The start time for a subsequent-subdomain computation is arbitrary beyond \(\alpha _{{\mathrm {D}}} T\) because of the periodicity. The total area of the ST subdomains is
which implies that \(\alpha _{{\mathrm {D}}} < 1\) is more cost-effective.
Remark 7
If \(k/\alpha _{{\mathrm {D}}}\) is not an integer, Remark 6 for k applies to \(k/\alpha _{{\mathrm {D}}}\).
Neither \(\alpha _{{\mathrm {D}}}=1\) (first basic case) nor \(\alpha _{{\mathrm {D}}} < 1\) would serve our purpose well, because the inflow-data extraction is from the outflow plane of the earlier computation. We extend the length of the computational domain by a factor \(\beta _{{\mathrm {D}}} > 1\) to have overlap between the subdomains. The length becomes \(L_{{\mathrm {c}}} = \alpha _{{\mathrm {D}}} \beta _{{\mathrm {D}}} U T\), with the data extraction from \((\beta _{{\mathrm {D}}} - 1) \alpha _{{\mathrm {D}}} U T\) upstream of the outflow plane of the preceding-subdomain computation.
CDM-D. The ST subdomains (red). In this illustration, \(\alpha _{{\mathrm {D}}} = 0.5\), \(m=2\), \(r=0.2\), and \(\beta _{{\mathrm {D}}} = 1.2\). The blue lines denote the extent of the m periods, which is ending rT above the line \(t=2T+x/U\) (orange dotted line). We are not showing below that line the solution we have in Fig. 4. (Color figure online)
To avoid inflow-data extraction on \(t = t_0 + x/U\) (note that \(t_0\) is just an example-dependent starting time, which is \(t_0 = 2 T\) in Fig. 4), we extend the ST domain also in the time direction. We increase it from \((m + \alpha _{{\mathrm {D}}}) T\) to \((m + r + \alpha _{{\mathrm {D}}}) T\). With that, we can extract the inflow data for the full m periods, while avoiding the line \(t = t_0+ x/U\). This was implicitly accounted for in the CDM-C due to the direct relationship between the extraction position and extraction time, which, with the same direct relationship, would have been \((\beta _{{\mathrm {D}}}-1) \alpha _{{\mathrm {D}}} L = U r T\). Figure 12 shows an illustration of the CDM-D, as we are proposing. The total area of the ST subdomains is
Remark 8
Even if \(k/\alpha _{{\mathrm {D}}}\) is an integer, because \(\beta _{{\mathrm {D}}} > 1\), the subdomains will have overlap, and that is how we wanted our method to be.
Remark 9
In reporting the data from the CDM-D computation, we will use the ST domain shown in Fig. 13.
Remark 10
The inflow-data extraction is easier in the CDM-D than it is in the CDM-C. That is because the extractions are also done in a discrete fashion. We have m periods of the data and there is already a method to build from that an actually time-periodic data (see Section 2.6 in [12]).
Remark 11
We have the freedom to select the starting time for each subdomain on and above the line \(t = t_0 + x/U\), and in the illustration of Fig. 12, we selected that line.
Remark 12
From Eqs. (11) and (17), we get
Assuming that \(m \ll k\), \(1 + \frac{m+r}{k} \alpha _{{\mathrm {C}}} \sim 1\) and therefore the cost reduction is higher with the CDM-C than it is with the CDM-D.
3 Cylinder short-wake flow
We first compute the cylinder short-wake flow to establish the wake-flow time period and to generate the source data for the inflow velocity of the long-wake flow.
3.1 Domain and boundary conditions
Figure 14 shows the computational domain and boundary conditions. We are working with nondimensional numbers, where the cylinder diameter and uniform inflow velocity are \(D = 1\) and \(U = 1\). The Reynolds number based on U and D is 100. The top and bottom boundaries and slip surfaces, and the outflow boundary is stress-free.
3.2 Mesh
Figure 15 shows the quadratic NURBS mesh, which consists of 7292 control points and 6582 elements. There are 14 layers of elements from the cylinder surface, and the first layer thickness is 0.1.
Cylinder short-wake flow. Quadratic NURBS mesh. Full mesh (top) and the layered elements near the cylinder (bottom). The red circles are the control points. The checkerboard pattern is for differentiating between the elements, and the colors are for differentiating between the patches. (Color figure online)
3.3 Computational conditions
The flow computation method is the ST-VMS, and the stabilization parameters are those given by Eqs. (4)–(9) in [129]. We use the outflow stabilization given in [199]. The time-step size \(\varDelta t = 0.05\). The number of nonlinear iterations per time step is 4 and the number of GMRES iterations per nonlinear iteration is 300.
3.4 Results
The results are reported after reaching time-periodicity. Figure 16 shows the vorticity. The lift time period is established approximately as \(T^{{\mathrm {L}}} = 116 \varDelta t = 5.8\).
3.5 Time-periodic data
We use the velocity at \(x_1=2\) as the source data for the inflow velocity of the long-wake flow. The conversion from the source data to time-periodic data is with a method similar to the one described in Section 2.6 of [12]. Figure 17 shows the velocity magnitude at \({\mathbf {x}} = (2,\ 0)\). One of the objectives in our computations is to see the secondary vortex street, which has the time period of \(2 T^{{\mathrm {L}}}\). Therefore, our time-periodic data at the inflow plane has the period \(T = 2 T^{{\mathrm {L}}} = 232 \varDelta t = 11.6.\)
Cylinder short-wake flow. The velocity magnitude at \({\mathbf {x}} = (2,\ 0)\). Source data (\({\mathbf {u}}_{{\mathrm {SRCE}}}\)), time-periodic data (\({\mathbf {u}}_{{\mathrm {PERI}}}\)), and time-periodic data (\({\mathbf {u}}^{{\mathrm {L}}}_{{\mathrm {PERI}}}\)) based on \(T^{{\mathrm {L}}}\)
Remark 13
Although, the difference we see in Fig. 17 between \({\mathbf {u}}_{{\mathrm {PERI}}}\) and \({\mathbf {u}}^{{\mathrm {L}}}_{{\mathrm {PERI}}}\) is small, we see a significant difference between what we get from computations with them. We do not see the secondary vortex street in the computation with \({\mathbf {u}}^{{\mathrm {L}}}_{{\mathrm {PERI}}}\). This might be related to the observation in [200] that the secondary vortex street could only be obtained in the 3D computation.
4 Cylinder long-wake flow
Using the time-periodic data from Sect. 3.5, we compute the long-wake flow, first with a single, second subdomain in the framework of the MDM, and then with the CDM-C.
4.1 Wake domains and boundary conditions
Figure 18 shows the single, second subdomain (SD-2), the carrier domain (CD), and the boundary conditions. We note that, in the MDM terminology, the domain used in computation of the cylinder short-wake flow becomes “SD-1.” For both SD-2 and CD, as in Sect. 3.1, the top and bottom boundaries are slip surfaces, and the outflow boundary is stress-free. The inflow velocity for SD-2 is from Sect. 3.5, with the source data coming from the computation with SD-1. With CDM-C, we use \(\alpha _{{\mathrm {C}}} = 0.5\) and \(\beta _{{\mathrm {C}}} = 2.93\), which makes \(L_{{\mathrm {c}}} = 1.465UT\). The inflow velocity for CD is from Sect. 3.5 while it is stationary at its initial position, and from Eq. (12) while it is moving or at its final position.
Cylinder long-wake flow. Top: The single, second subdomain (SD-2) in the framework of the MDM. Bottom: The carrier domain (CD) at its initial and final positions. Boundary conditions: inflow (red), stress-free outflow (blue), and slip-surface top and bottom boundaries (green). The inflow velocity for SD-2 is from Sect. 3.5. The inflow velocity for CD is from Sect. 3.5 while it is stationary at its initial position, and from Eq. (12) while it is moving or at its final position
4.2 Meshes
The quadratic NURBS meshes in SD-2 and CD have almost the same resolution as the mesh in SD-1. The number of control points and elements are shown in Table 1.
4.3 Computational conditions
The computational conditions are the same as in Sect. 3.3, except the number of GMRES iterations per nonlinear iteration for the CDM-C computation, which is reduced to 80.
In the MDM computation, the initial condition is U everywhere, except the inflow boundary. We compute for 37T. The inflow velocity is time-periodic, coming from Sect. 3.5.
In the CDM-C computation, CD is stationary for \(0 \le t < 5 T\), a duration longer than \(\beta _{{\mathrm {C}}}T\). For this part of the computation, the initial condition is U everywhere, except the inflow boundary. Then, CD moves with the speed \(V = 0.5U\) for \(5 T \le t \le 62.07 T\), and becomes stationary again for \(62.07 T < t \le 65 T\). For \(0 \le t < 5 T\), the inflow velocity is time-periodic, coming from Sect. 3.5. After that, the inflow velocity is extracted in the framework of the CDM-C, as given by Eq. (12). Although we have data for \(m = 2\), we do not go back in time beyond \(m = 1\). This is because we wanted to reduce the influence of the outflow boundary condition, especially considering that we are computing at such a low Reynolds number.
4.4 Results
4.4.1 MDM computation
Figure 19 shows, for the MDM computation, the vorticity at \(t = 37 T\). The vortices have a vertical pattern in the near wake and gradually change to a horizontal pattern moving downstream. We consider the region from \(x_1 = 89\) to 176 to be the transition to the secondary vortex street. In the region from \(x_1 = 263\) to 350, the secondary vortex street is fully formed. The spacing between the vortices in the secondary vortex street is, as expected, about twice as large compared to the spacing in the first one.
4.4.2 CDM-C computation
Figures 20, 21, 22 and 23 show, for the CDM-C computation, the vorticity at various instants. We see the effect of the outflow boundary condition, especially when we compare frames with spatial overlap. As we report the data, we will be sufficiently far from the outflow (see Remark 4).
4.5 Discussion
In Fig. 24, we compare the vorticity from the MDM and CDM-C computations. The MDM data is repetition from Fig. 19. The CDM-C data is from patching together integer-T sections of the reporting ST domain shown in Fig. 9. They are in excellent agreement. The estimated cost reduction based on the ST areas is about 91 %, and the cost multiplier coming from solving the linear equation systems is about 27 %, because of the fewer number of GMRES iterations, and even less because of the smaller system size.
5 Concluding remarks
We have introduced the CDM for high-resolution computation of time-periodic long-wake flows. The CDM has both the cost-effectives needed to make the computations practical and the accuracy needed in the long-wake computations to correctly represent the vortex patterns far downstream. It is related to the MDM, which was introduced 24 years ago, with the original version intended also for cost-effective computation of long-wake flows. Some of the later versions of the MDM extended the scope to additional classes of flow problems, and some increased the efficiency in computing the long-wake flows. In the original MDM version, the wake flow is computed over a sequence of subdomains with slight overlap between them. The first one covers the object producing the wake, and that would be the primary object if there is a secondary object downstream. The secondary object would be in the last subdomain. The velocity specified at the inflow boundary of the first subdomain is the free-stream velocity. The inflow velocity for each subsequent subdomain is extracted from the preceding subdomain. In the latest MDM version, which was also intended for long-wake flows, in computing the wake flow over a sequence of subdomains, a subsequent-subdomain computation starts some duration after the preceding-subdomain computation does, thus reducing the computational cost even more. The lag in the start time is from recognizing that there is no flow information for the subsequent-subdomain computation to advect before the preceding-subdomain computation advects the information from its inflow plane to its outflow plane. The CDM was inspired by this version of the MDM.
In the CDM, the computational domain moves in the free-stream direction, with a velocity that preserves the outflow nature of the downstream computational boundary. As the computational domain is moving, the velocity at the inflow plane is extracted from the velocity computed earlier when the plane’s current position was covered by the moving domain. The inflow data needed at an instant is extracted from one or more instants going back in time as many periods. Naturally, computing the long-wake flow with a high-resolution moving mesh that has a reasonable length would be far more cost-effective than computing it with a fixed mesh that covers the entire length of the wake. We have also introduced a CDM version where the computational domain moves in a discrete fashion rather than a continuous fashion. The abbreviations CDM-C and CDM-D identify the versions with continuous and discrete motion of the computational domain.
We demonstrated how the CDM works by computing, with the CDM-C, the 2D flow past a circular cylinder at Reynolds number 100. At this Reynolds number, the flow has an easily discernible vortex shedding frequency and widely published lift and drag coefficients and Strouhal number. We computed the wake flow up to 350 diameters downstream of the cylinder, far enough to see the secondary vortex street. The computational platform was made of, in addition to the CDM-C, the ST-VMS, ST-IGA, and methods for calculating the stabilization parameters and related element lengths targeting IGA discretization. In the ST-IGA, the basis functions were quadratic NURBS in space and linear in time. The ST context of the ST-VMS brought higher-order accuracy, the VMS feature brought better representation of the multiscale flow patterns, and the ST-IGA brought higher accuracy in representing the cylinder geometry and flow solution. The results show the power of the CDM in high-resolution computation of time-periodic long-wake flows.
References
Osawa Y, Kalro V, Tezduyar T (1999) Multi-domain parallel computation of wake flows. Comput Methods Appl Mech Eng 174:371–391. https://doi.org/10.1016/S0045-7825(98)00305-3
Tezduyar T, Osawa Y (1999) Methods for parallel computation of complex flow problems. Parallel Comput 25:2039–2066. https://doi.org/10.1016/S0167-8191(99)00080-0
Tezduyar T, Osawa Y (2001) The multi-domain method for computation of the aerodynamics of a parachute crossing the far wake of an aircraft. Comput Methods Appl Mech Eng 191:705–716. https://doi.org/10.1016/S0045-7825(01)00310-3
Tezduyar T, Osawa Y (2001) Fluid–structure interactions of a parachute crossing the far wake of an aircraft. Comput Methods Appl Mech Eng 191:717–726. https://doi.org/10.1016/S0045-7825(01)00311-5
Takizawa K, Tezduyar TE, Kuraishi T (2015) Multiscale ST methods for thermo-fluid analysis of a ground vehicle and its tires. Math Models Methods Appl Sci 25:2227–2255. https://doi.org/10.1142/S0218202515400072
Takizawa K, Tezduyar TE (2014) Space–time computation techniques with continuous representation in time (ST-C). Comput Mech 53:91–99. https://doi.org/10.1007/s00466-013-0895-y
Kuraishi T, Xu Z, Takizawa K, Tezduyar TE, Yamasaki S (2022) High-resolution multi-domain space–time isogeometric analysis of car and tire aerodynamics with road contact and tire deformation and rotation. Comput Mech. https://doi.org/10.1007/s00466-022-02228-0
Kuraishi T, Yamasaki S, Takizawa K, Tezduyar TE, Xu Z, Kaneko R (2022) Space–time isogeometric analysis of car and tire aerodynamics with road contact and tire deformation and rotation. Comput Mech 70:49–72. https://doi.org/10.1007/s00466-022-02155-0
Ravensbergen M, Helgedagsrud TA, Bazilevs Y, Korobenko A (2020) A variational multiscale framework for atmospheric turbulent flows over complex environmental terrains. Comput Methods Appl Mech Eng 368:113182. https://doi.org/10.1016/j.cma.2020.113182
Bazilevs Y, Takizawa K, Tezduyar TE, Korobenko A, Kuraishi T, Otoguro Y (2022) Computational aerodynamics with isogeometric analysis. J Adv Eng Comput, submitted
Korobenko A, Yan J, Gohari SMI, Sarkar S, Bazilevs Y (2017) FSI simulation of two back-to-back wind turbines in atmospheric boundary layer flow. Comput Fluids 158:167–175. https://doi.org/10.1016/j.compfluid.2017.05.010
Kuraishi T, Zhang F, Takizawa K, Tezduyar TE (2021) Wind turbine wake computation with the ST-VMS method, isogeometric discretization and multidomain method: I. Computational framework. Comput Mech 68:113–130. https://doi.org/10.1007/s00466-021-02022-4
Kuraishi T, Zhang F, Takizawa K, Tezduyar TE (2021) Wind turbine wake computation with the ST-VMS method, isogeometric discretization and multidomain method: II. Spatial and temporal resolution. Comput Mech 68:175–184. https://doi.org/10.1007/s00466-021-02025-1
Zhang F, Kuraishi T, Takizawa K, Tezduyar TE (2022) Wind turbine wake computation with the ST-VMS method and isogeometric discretization: directional preference in spatial refinement. Comput Mech 69:1031–1040. https://doi.org/10.1007/s00466-021-02129-8
Takizawa K, Tezduyar TE (2011) Multiscale space–time fluid–structure interaction techniques. Comput Mech 48:247–267. https://doi.org/10.1007/s00466-011-0571-z
Takizawa K, Tezduyar TE (2012) Space–time fluid–structure interaction methods. Math Models Methods Appl Sci 22(supp02):1230001. https://doi.org/10.1142/S0218202512300013
Takizawa K, Henicke B, Puntel A, Spielman T, Tezduyar TE (2012) Space–time computational techniques for the aerodynamics of flapping wings. J Appl Mech 79:010903. https://doi.org/10.1115/1.4005073
Takizawa K, Tezduyar TE, Otoguro Y, Terahara T, Kuraishi T, Hattori H (2017) Turbocharger flow computations with the space–time isogeometric analysis (ST-IGA). Comput Fluids 142:15–20. https://doi.org/10.1016/j.compfluid.2016.02.021
Takizawa K, Tezduyar TE, Otoguro Y (2018) Stabilization and discontinuity-capturing parameters for space-time flow computations with finite element and isogeometric discretizations. Comput Mech 62:1169–1186. https://doi.org/10.1007/s00466-018-1557-x
Otoguro Y, Takizawa K, Tezduyar TE (2020) Element length calculation in B-spline meshes for complex geometries. Comput Mech 65:1085–1103. https://doi.org/10.1007/s00466-019-01809-w
Kuraishi T, Takizawa K, Tezduyar TE (2022) Boundary layer mesh resolution in flow computation with the space–time variational multiscale method and isogeometric discretization. Math Models Methods Appl Sci, to appear
Takizawa K, Bazilevs Y, Tezduyar TE, Hsu M-C, Terahara T (2022) Computational cardiovascular medicine with isogeometric analysis. J Adv Eng Comput, to appear. https://doi.org/10.55579/jaec.202263.381
Tezduyar TE (1992) Stabilized finite element formulations for incompressible flow computations. Adv Appl Mech 28:1–44. https://doi.org/10.1016/S0065-2156(08)70153-4
Tezduyar TE (2003) Computation of moving boundaries and interfaces and stabilization parameters. Int J Numer Methods Fluids 43:555–575. https://doi.org/10.1002/fld.505
Tezduyar TE, Sathe S (2007) Modeling of fluid–structure interactions with the space–time finite elements: solution techniques. Int J Numer Methods Fluids 54:855–900. https://doi.org/10.1002/fld.1430
Brooks AN, Hughes TJR (1982) Streamline upwind/Petrov–Galerkin formulations for convection dominated flows with particular emphasis on the incompressible Navier–Stokes equations. Comput Methods Appl Mech Eng 32:199–259
Hughes TJR (1995) Multiscale phenomena: Green’s functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles, and the origins of stabilized methods. Comput Methods Appl Mech Eng 127:387–401
Hughes TJR, Oberai AA, Mazzei L (2001) Large eddy simulation of turbulent channel flows by the variational multiscale method. Phys Fluids 13:1784–1799
Bazilevs Y, Calo VM, Cottrell JA, Hughes TJR, Reali A, Scovazzi G (2007) Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows. Comput Methods Appl Mech Eng 197:173–201
Bazilevs Y, Akkerman I (2010) Large eddy simulation of turbulent Taylor–Couette flow using isogeometric analysis and the residual-based variational multiscale method. J Comput Phys 229:3402–3414
Liu Y, Takizawa K, Otoguro Y, Kuraishi T, Tezduyar TE (2022) Flow computation with the space–time isogeometric analysis and higher-order basis functions in time. Math Models Methods Appl Sci, to appear
Kalro V, Tezduyar TE (2000) A parallel 3D computational method for fluid–structure interactions in parachute systems. Comput Methods Appl Mech Eng 190:321–332. https://doi.org/10.1016/S0045-7825(00)00204-8
Bazilevs Y, Calo VM, Hughes TJR, Zhang Y (2008) Isogeometric fluid–structure interaction: theory, algorithms, and computations. Comput Mech 43:3–37
Takizawa K, Bazilevs Y, Tezduyar TE (2012) Space–time and ALE-VMS techniques for patient-specific cardiovascular fluid–structure interaction modeling. Arch Comput Methods Eng 19:171–225. https://doi.org/10.1007/s11831-012-9071-3
Bazilevs Y, Takizawa K, Tezduyar TE (2013) Computational fluid–structure interaction: methods and applications. Wiley, Hoboken. ISBN 978-0470978771
Bazilevs Y, Takizawa K, Tezduyar TE (2019) Computational analysis methods for complex unsteady flow problems. Math Models Methods Appl Sci 29:825–838. https://doi.org/10.1142/S0218202519020020
Bazilevs Y, Hsu M-C, Akkerman I, Wright S, Takizawa K, Henicke B, Spielman T, Tezduyar TE (2011) 3D simulation of wind turbine rotors at full scale. Part I: geometry modeling and aerodynamics. Int J Numer Methods Fluids 65:207–235. https://doi.org/10.1002/fld.2400
Bazilevs Y, Hsu M-C, Kiendl J, Wüchner R, Bletzinger K-U (2011) 3D simulation of wind turbine rotors at full scale. Part II: fluid–structure interaction modeling with composite blades. Int J Numer Methods Fluids 65:236–253
Hsu M-C, Akkerman I, Bazilevs Y (2011) High-performance computing of wind turbine aerodynamics using isogeometric analysis. Comput Fluids 49:93–100
Bazilevs Y, Hsu M-C, Scott MA (2012) Isogeometric fluid–structure interaction analysis with emphasis on non-matching discretizations, and with application to wind turbines. Comput Methods Appl Mech Eng 249–252:28–41
Hsu M-C, Akkerman I, Bazilevs Y (2014) Finite element simulation of wind turbine aerodynamics: validation study using NREL Phase VI experiment. Wind Energy 17:461–481
Korobenko A, Hsu M-C, Akkerman I, Tippmann J, Bazilevs Y (2013) Structural mechanics modeling and FSI simulation of wind turbines. Math Models Methods Appl Sci 23:249–272
Korobenko A, Hsu M-C, Akkerman I, Bazilevs Y (2013) Aerodynamic simulation of vertical-axis wind turbines. J Appl Mech 81:021011. https://doi.org/10.1115/1.4024415
Bazilevs Y, Takizawa K, Tezduyar TE, Hsu M-C, Kostov N, McIntyre S (2014) Aerodynamic and FSI analysis of wind turbines with the ALE-VMS and ST-VMS methods. Arch Comput Methods Eng 21:359–398. https://doi.org/10.1007/s11831-014-9119-7
Bazilevs Y, Korobenko A, Deng X, Yan J, Kinzel M, Dabiri JO (2014) FSI modeling of vertical-axis wind turbines. J Appl Mech 81:081006. https://doi.org/10.1115/1.4027466
Bazilevs Y, Korobenko A, Deng X, Yan J (2015) Novel structural modeling and mesh moving techniques for advanced FSI simulation of wind turbines. Int J Numer Methods Eng 102:766–783. https://doi.org/10.1002/nme.4738
Bazilevs Y, Korobenko A, Yan J, Pal A, Gohari SMI, Sarkar S (2015) ALE-VMS formulation for stratified turbulent incompressible flows with applications. Math Models Methods Appl Sci 25:2349–2375. https://doi.org/10.1142/S0218202515400114
Bazilevs Y, Korobenko A, Deng X, Yan J (2016) FSI modeling for fatigue-damage prediction in full-scale wind-turbine blades. J Appl Mech 83(6):061010
Yan J, Korobenko A, Deng X, Bazilevs Y (2016) Computational free-surface fluid–structure interaction with application to floating offshore wind turbines. Comput Fluids 141:155–174. https://doi.org/10.1016/j.compfluid.2016.03.008
Korobenko A, Bazilevs Y, Takizawa K, Tezduyar TE (2018) Recent advances in ALE-VMS and ST-VMS computational aerodynamic and FSI analysis of wind turbines. In: Tezduyar TE (ed) Frontiers in computational fluid–structure interaction and flow simulation: research from lead investigators under forty—2018, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 253–336. https://doi.org/10.1007/978-3-319-96469-0_7 (ISBN 978-3-319-96468-3)
Korobenko A, Bazilevs Y, Takizawa K, Tezduyar TE (2019) Computer modeling of wind turbines: 1. ALE-VMS and ST-VMS aerodynamic and FSI analysis. Arch Comput Methods Eng 26:1059–1099. https://doi.org/10.1007/s11831-018-9292-1
Bazilevs Y, Takizawa K, Tezduyar TE, Hsu M-C, Otoguro Y, Mochizuki H, Wu MCH (2020) Wind turbine and turbomachinery computational analysis with the ALE and space–time variational multiscale methods and isogeometric discretization. J Adv Eng Comput 4:1–32. https://doi.org/10.25073/jaec.202041.278
Bazilevs Y, Takizawa K, Tezduyar TE, Hsu M-C, Otoguro Y, Mochizuki H, Wu MCH (2020) ALE and space–time variational multiscale isogeometric analysis of wind turbines and turbomachinery. In: Grama A, Sameh A (eds) Parallel algorithms in computational science and engineering, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 195–233. https://doi.org/10.1007/978-3-030-43736-7_7 (ISBN 978-3-030-43735-0)
Takizawa K, Bazilevs Y, Tezduyar TE, Korobenko A (2020) Variational multiscale flow analysis in aerospace, energy and transportation technologies. In: Grama A, Sameh A (eds) Parallel algorithms in computational science and engineering, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 235–280. https://doi.org/10.1007/978-3-030-43736-7_8 (ISBN 978-3-030-43735-0)
Takizawa K, Bazilevs Y, Tezduyar TE, Korobenko A (2020) Computational flow analysis in aerospace, energy and transportation technologies with the variational multiscale methods. J Adv Eng Comput 4:83–117. https://doi.org/10.25073/jaec.202042.279
Bayram AM, Bear C, Bear M, Korobenko A (2020) Performance analysis of two vertical-axis hydrokinetic turbines using variational multiscale method. Comput Fluids 200:104432. https://doi.org/10.1016/j.compfluid.2020.104432
Ravensbergen M, Bayram AM, Korobenko A (2020) The actuator line method for wind turbine modelling applied in a variational multiscale framework. Comput Fluids 201:104465. https://doi.org/10.1016/j.compfluid.2020.104465
Xu F, Moutsanidis G, Kamensky D, Hsu M-C, Murugan M, Ghoshal A, Bazilevs Y (2017) Compressible flows on moving domains: stabilized methods, weakly enforced essential boundary conditions, sliding interfaces, and application to gas-turbine modeling. Comput Fluids 158:201–220. https://doi.org/10.1016/j.compfluid.2017.02.006
Murugan M, Ghoshal A, Xu F, Hsu M-C, Bazilevs Y, Bravo L, Kerner K (2017) Analytical study of articulating turbine rotor blade concept for improved off-design performance of gas turbine engines. J Eng Gas Turbines Power 139:102601–6
Castorrini A, Corsini A, Rispoli F, Takizawa K, Tezduyar TE (2019) A stabilized ALE method for computational fluid–structure interaction analysis of passive morphing in turbomachinery. Math Models Methods Appl Sci 29:967–994. https://doi.org/10.1142/S0218202519410057
Kozak N, Xu F, Rajanna MR, Bravo L, Murugan M, Ghoshal A, Bazilevs Y, Hsu M-C (2020) High-fidelity finite element modeling and analysis of adaptive gas turbine stator-rotor flow interaction at off-design conditions. J Mech 36:595–606
Kozak N, Rajanna MR, Wu MCH, Murugan M, Bravo L, Ghoshal A, Hsu M-C, Bazilevs Y (2020) Optimizing gas turbine performance using the surrogate management framework and high-fidelity flow modeling. Energies 13:4283
Bazilevs Y, Takizawa K, Wu MCH, Kuraishi T, Avsar R, Xu Z, Tezduyar TE (2021) Gas turbine computational flow and structure analysis with isogeometric discretization and a complex-geometry mesh generation method. Comput Mech 67:57–84. https://doi.org/10.1007/s00466-020-01919-w
Zhu Q, Yan J (2021) A moving-domain CFD solver in FEniCS with applications to tidal turbine simulations in turbulent flows. Comput Math Appl 81:532–546
Yan J, Korobenko A, Tejada-Martinez AE, Golshan R, Bazilevs Y (2017) A new variational multiscale formulation for stratified incompressible turbulent flows. Comput Fluids 158:150–156. https://doi.org/10.1016/j.compfluid.2016.12.004
Helgedagsrud TA, Bazilevs Y, Mathisen KM, Oiseth OA (2019) Computational and experimental investigation of free vibration and flutter of bridge decks. Comput Mech 63:121–136. https://doi.org/10.1007/s00466-018-1587-4
Helgedagsrud TA, Bazilevs Y, Korobenko A, Mathisen KM, Oiseth OA (2019) Using ALE-VMS to compute aerodynamic derivatives of bridge sections. Comput Fluids 179:820–832. https://doi.org/10.1016/j.compfluid.2018.04.037
Helgedagsrud TA, Akkerman I, Bazilevs Y, Mathisen KM, Oiseth OA (2019) Isogeometric modeling and experimental investigation of moving-domain bridge aerodynamics. ASCE J Eng Mech 145:04019026. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001601
Helgedagsrud TA, Bazilevs Y, Mathisen KM, Yan J, Oiseth OA (2019) Modeling and simulation of bridge-section buffeting response in turbulent flow. Math Models Methods Appl Sci 29:939–966. https://doi.org/10.1142/S0218202519410045
Helgedagsrud TA, Bazilevs Y, Mathisen KM, Oiseth OA (2019) ALE-VMS methods for wind-resistant design of long-span bridges. J Wind Eng Ind Aerodyn 191:143–153. https://doi.org/10.1016/j.jweia.2019.06.001
Augier B, Yan J, Korobenko A, Czarnowski J, Ketterman G, Bazilevs Y (2015) Experimental and numerical FSI study of compliant hydrofoils. Comput Mech 55:1079–1090. https://doi.org/10.1007/s00466-014-1090-5
Yan J, Augier B, Korobenko A, Czarnowski J, Ketterman G, Bazilevs Y (2016) FSI modeling of a propulsion system based on compliant hydrofoils in a tandem configuration. Comput Fluids 141:201–211. https://doi.org/10.1016/j.compfluid.2015.07.013
Zhu Q, Xu F, Xu S, Hsu M-C, Yan J (2020) An immersogeometric formulation for free-surface flows with application to marine engineering problems. Comput Methods Appl Mech Eng 361:112748
Akkerman I, Bazilevs Y, Benson DJ, Farthing MW, Kees CE (2012) Free-surface flow and fluid-object interaction modeling with emphasis on ship hydrodynamics. J Appl Mech 79:010905
Akkerman I, Dunaway J, Kvandal J, Spinks J, Bazilevs Y (2012) Toward free-surface modeling of planing vessels: simulation of the Fridsma hull using ALE-VMS. Comput Mech 50:719–727
Yan J, Deng X, Korobenko A, Bazilevs Y (2017) Free-surface flow modeling and simulation of horizontal-axis tidal-stream turbines. Comput Fluids 158:157–166. https://doi.org/10.1016/j.compfluid.2016.06.016
Yan J, Deng X, Xu F, Xu S, Zhu Q (2020) Numerical simulations of two back-to-back horizontal axis tidal stream turbines in free-surface flows. J Appl Mech 10(1115/1):4046317
Zhu Q, Yan J, Tejada-Martínez A, Bazilevs Y (2020) Variational multiscale modeling of Langmuir turbulent boundary layers in shallow water using isogeometric analysis. Mech Res Commun 108:103570. https://doi.org/10.1016/j.mechrescom.2020.103570
Yan J, Yan W, Lin S, Wagner G (2018) A fully coupled finite element formulation for liquid–solid–gas thermo-fluid flow with melting and solidification. Comput Methods Appl Mech Eng 336:444–470
Yan J, Lin SS, Bazilevs Y, Wagner G (2019) Isogeometric analysis of multi-phase flows with surface tension and with application to dynamics of rising bubbles. Comput Fluids 179:777–789
Xu S, Liu N, Yan J (2019) Residual-based variational multi-scale modeling for particle-laden gravity currents over flat and triangular wavy terrains. Comput Fluids 188:114–124
Bayram AM, Korobenko A (2020) Variational multiscale framework for cavitating flows. Comput Mech 66:49–67. https://doi.org/10.1007/s00466-020-01840-2
Zhao Z, Yan J (2020) Variational multi-scale modeling of interfacial flows with a balanced-force surface tension model. Mech Res Commun. https://doi.org/10.1016/j.mechrescom.2020.103608
Cen H, Zhou Q, Korobenko A (2021) Variational multiscale framework for cavitating flows. Comput Fluids 214:104765. https://doi.org/10.1016/j.compfluid.2020.104765
Zhao Z, Zhu Q, Yan J (2021) A thermal multi-phase flow model for directed energy deposition processes via a moving signed distance function. Comput Methods Appl Mech Eng 373:113518
Zhu Q, Liu Z, Yan J (2021) Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput Mech 67:619–635. https://doi.org/10.1007/s00466-020-01952-9
Wang C, Wu MCH, Xu F, Hsu M-C, Bazilevs Y (2017) Modeling of a hydraulic arresting gear using fluid–structure interaction and isogeometric analysis. Comput Fluids 142:3–14. https://doi.org/10.1016/j.compfluid.2015.12.004
Wu MCH, Kamensky D, Wang C, Herrema AJ, Xu F, Pigazzini MS, Verma A, Marsden AL, Bazilevs Y, Hsu M-C (2017) Optimizing fluid–structure interaction systems with immersogeometric analysis and surrogate modeling: application to a hydraulic arresting gear. Comput Methods Appl Mech Eng 316:668–693
Codoni D, Moutsanidis G, Hsu M-C, Bazilevs Y, Johansen C, Korobenko A (2021) Stabilized methods for high-speed compressible flows: toward hypersonic simulations. Comput Mech 67:785–809. https://doi.org/10.1007/s00466-020-01963-6
Bazilevs Y, Calo VM, Zhang Y, Hughes TJR (2006) Isogeometric fluid–structure interaction analysis with applications to arterial blood flow. Comput Mech 38:310–322
Bazilevs Y, Gohean JR, Hughes TJR, Moser RD, Zhang Y (2009) Patient-specific isogeometric fluid–structure interaction analysis of thoracic aortic blood flow due to implantation of the Jarvik 2000 left ventricular assist device. Comput Methods Appl Mech Eng 198:3534–3550
Bazilevs Y, Hsu M-C, Benson D, Sankaran S, Marsden A (2009) Computational fluid–structure interaction: methods and application to a total cavopulmonary connection. Computat Mech 45:77–89
Bazilevs Y, Hsu M-C, Zhang Y, Wang W, Liang X, Kvamsdal T, Brekken R, Isaksen J (2010) A fully-coupled fluid–structure interaction simulation of cerebral aneurysms. Comput Mech 46:3–16
Bazilevs Y, Hsu M-C, Zhang Y, Wang W, Kvamsdal T, Hentschel S, Isaksen J (2010) Computational fluid–structure interaction: methods and application to cerebral aneurysms. Biomech Model Mechanobiol 9:481–498
Hsu M-C, Bazilevs Y (2011) Blood vessel tissue prestress modeling for vascular fluid–structure interaction simulations. Finite Elem Anal Des 47:593–599
Long CC, Marsden AL, Bazilevs Y (2013) Fluid–structure interaction simulation of pulsatile ventricular assist devices. Comput Mech 52:971–981. https://doi.org/10.1007/s00466-013-0858-3
Long CC, Esmaily-Moghadam M, Marsden AL, Bazilevs Y (2014) Computation of residence time in the simulation of pulsatile ventricular assist devices. Comput Mech 54:911–919. https://doi.org/10.1007/s00466-013-0931-y
Long CC, Marsden AL, Bazilevs Y (2014) Shape optimization of pulsatile ventricular assist devices using FSI to minimize thrombotic risk. Comput Mech 54:921–932. https://doi.org/10.1007/s00466-013-0967-z
Hsu M-C, Kamensky D, Bazilevs Y, Sacks MS, Hughes TJR (2014) Fluid–structure interaction analysis of bioprosthetic heart valves: significance of arterial wall deformation. Comput Mech 54:1055–1071. https://doi.org/10.1007/s00466-014-1059-4
Hsu M-C, Kamensky D, Xu F, Kiendl J, Wang C, Wu MCH, Mineroff J, Reali A, Bazilevs Y, Sacks MS (2015) Dynamic and fluid–structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models. Comput Mech 55:1211–1225. https://doi.org/10.1007/s00466-015-1166-x
Kamensky D, Hsu M-C, Schillinger D, Evans JA, Aggarwal A, Bazilevs Y, Sacks MS, Hughes TJR (2015) An immersogeometric variational framework for fluid–structure interaction: application to bioprosthetic heart valves. Comput Methods Appl Mech Eng 284:1005–1053
Takizawa K, Bazilevs Y, Tezduyar TE, Hsu M-C (2019) Computational cardiovascular flow analysis with the variational multiscale methods. J Adv Eng Comput 3:366–405. https://doi.org/10.25073/jaec.201932.245
Hughes TJR, Takizawa K, Bazilevs Y, Tezduyar TE, Hsu M-C (2020) Computational cardiovascular analysis with the variational multiscale methods and isogeometric discretization. In: Grama A, Sameh A (eds) Parallel algorithms in computational science and engineering, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 151–193. https://doi.org/10.1007/978-3-030-43736-7_6 (ISBN 978-3-030-43735-0)
Hsu M-C, Wang C, Xu F, Herrema AJ, Krishnamurthy A (2016) Direct immersogeometric fluid flow analysis using B-rep CAD models. Comput Aided Geom Des 43:143–158
Kamensky D, Evans JA, Hsu M-C (2015) Stability and conservation properties of collocated constraints in immersogeometric fluid–thin structure interaction analysis. Commun Comput Phys 18:1147–1180
Kamensky D, Evans JA, Hsu M-C, Bazilevs Y (2017) Projection-based stabilization of interface Lagrange multipliers in immersogeometric fluid–thin structure interaction analysis, with application to heart valve modeling. Comput Math Appl 74:2068–2088. https://doi.org/10.1016/j.camwa.2017.07.006
Kamensky D, Hsu M-C, Yu Y, Evans JA, Sacks MS, Hughes TJR (2017) Immersogeometric cardiovascular fluid–structure interaction analysis with divergence-conforming B-splines. Comput Methods Appl Mech Eng 314:408–472
Xu F, Morganti S, Zakerzadeh R, Kamensky D, Auricchio F, Reali A, Hughes TJR, Sacks MS, Hsu M-C (2018) A framework for designing patient-specific bioprosthetic heart valves using immersogeometric fluid–structure interaction analysis. Int J Numer Methods Biomed Eng 34:e2938
Yu Y, Kamensky D, Hsu M-C, Lu XY, Bazilevs Y, Hughes TJR (2018) Error estimates for projection-based dynamic augmented Lagrangian boundary condition enforcement, with application to fluid–structure interaction. Math Models Methods Appl Sci 28:2457–2509. https://doi.org/10.1142/S0218202518500537
Wu MCH, Zakerzadeh R, Kamensky D, Kiendl J, Sacks MS, Hsu M-C (2018) An anisotropic constitutive model for immersogeometric fluid–structure interaction analysis of bioprosthetic heart valves. J Biomech 74:23–31
Wu MCH, Muchowski HM, Johnson EL, Rajanna MR, Hsu M-C (2019) Immersogeometric fluid–structure interaction modeling and simulation of transcatheter aortic valve replacement. Comput Methods Appl Mech Eng 357:112556
Johnson EL, Wu MCH, Xu F, Wiese NM, Rajanna MR, Herrema AJ, Ganapathysubramanian B, Hughes TJR, Sacks MS, Hsu M-C (2020) Thinner biological tissues induce leaflet flutter in aortic heart valve replacements. Proc Natl Acad Sci 117:19007–19016
Xu F, Johnson EL, Wang C, Jafari A, Yang C-H, Sacks MS, Krishnamurthy A, Hsu M-C (2021) Computational investigation of left ventricular hemodynamics following bioprosthetic aortic and mitral valve replacement. Mech Res Commun. https://doi.org/10.1016/j.mechrescom.2020.103604
Tezduyar TE, Takizawa K, Moorman C, Wright S, Christopher J (2010) Space–time finite element computation of complex fluid–structure interactions. Int J Numer Methods Fluids 64:1201–1218. https://doi.org/10.1002/fld.2221
Xu F, Schillinger D, Kamensky D, Varduhn V, Wang C, Hsu M-C (2016) The tetrahedral finite cell method for fluids: immersogeometric analysis of turbulent flow around complex geometries. Comput Fluids 141:135–154
Wang C, Xu F, Hsu M-C, Krishnamurthy A (2017) Rapid B-rep model preprocessing for immersogeometric analysis using analytic surfaces. Comput Aided Geom Des 52–53:190–204
Xu S, Xu F, Kommajosula A, Hsu M-C, Ganapathysubramanian B (2019) Immersogeometric analysis of moving objects in incompressible flows. Comput Fluids 189:24–33
Xu S, Gao B, Lofquist A, Fernando M, Hsu M-C, Sundar H, Ganapathysubramanian B (2020) An octree-based immersogeometric approach for modeling inertial migration of particles in channels. Comput Fluids 214:104764
Tezduyar TE, Takizawa K (2019) Space–time computations in practical engineering applications: a summary of the 25-year history. Comput Mech 63:747–753. https://doi.org/10.1007/s00466-018-1620-7
Takizawa K, Tezduyar TE, McIntyre S, Kostov N, Kolesar R, Habluetzel C (2014) Space–time VMS computation of wind-turbine rotor and tower aerodynamics. Comput Mech 53:1–15. https://doi.org/10.1007/s00466-013-0888-x
Takizawa K, Tezduyar TE, Mochizuki H, Hattori H, Mei S, Pan L, Montel K (2015) Space–time VMS method for flow computations with slip interfaces (ST-SI). Math Models Methods Appl Sci 25:2377–2406. https://doi.org/10.1142/S0218202515400126
Otoguro Y, Mochizuki H, Takizawa K, Tezduyar TE (2020) Space–time variational multiscale isogeometric analysis of a tsunami-shelter vertical-axis wind turbine. Comput Mech 66:1443–1460. https://doi.org/10.1007/s00466-020-01910-5
Otoguro Y, Takizawa K, Tezduyar TE (2018) A general-purpose NURBS mesh generation method for complex geometries. In: Tezduyar TE (ed) Frontiers in computational fluid–structure interaction and flow simulation: research from lead investigators under forty—2018, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 399–434. https://doi.org/10.1007/978-3-319-96469-0_10 (ISBN 978-3-319-96468-3)
Komiya K, Kanai T, Otoguro Y, Kaneko M, Hirota K, Zhang Y, Takizawa K, Tezduyar TE, Nohmi M, Tsuneda T, Kawai M, Isono M (2019) Computational analysis of flow-driven string dynamics in a pump and residence time calculation. IOP Conf Ser Earth Environ Sci 240:062014. https://doi.org/10.1088/1755-1315/240/6/062014
Kanai T, Takizawa K, Tezduyar TE, Komiya K, Kaneko M, Hirota K, Nohmi M, Tsuneda T, Kawai M, Isono M (2019) Methods for computation of flow-driven string dynamics in a pump and residence time. Math Models Methods Appl Sci 29:839–870. https://doi.org/10.1142/S021820251941001X
Otoguro Y, Takizawa K, Tezduyar TE, Nagaoka K, Avsar R, Zhang Y (2019) Space–time VMS flow analysis of a turbocharger turbine with isogeometric discretization: computations with time-dependent and steady-inflow representations of the intake/exhaust cycle. Comput Mech 64:1403–1419. https://doi.org/10.1007/s00466-019-01722-2
Takizawa K, Tezduyar TE (2016) New directions in space–time computational methods. In: Bazilevs Y, Takizawa K (eds) Advances in computational fluid–structure interaction and flow simulation: new methods and challenging computations, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 159–178. https://doi.org/10.1007/978-3-319-40827-9_13 (ISBN 978-3-319-40825-5)
Kuraishi T, Takizawa K, Tezduyar TE (2018) Space–time computational analysis of tire aerodynamics with actual geometry, road contact and tire deformation. In: Tezduyar TE (ed) Frontiers in computational fluid–structure interaction and flow simulation: research from lead investigators under forty - 2018, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 337–376. https://doi.org/10.1007/978-3-319-96469-0_8 (ISBN 978-3-319-96468-3)
Kuraishi T, Takizawa K, Tezduyar TE (2019) Tire aerodynamics with actual tire geometry, road contact and tire deformation. Comput Mech 63:1165–1185. https://doi.org/10.1007/s00466-018-1642-1
Kuraishi T, Takizawa K, Tezduyar TE (2019) Space–time computational analysis of tire aerodynamics with actual geometry, road contact, tire deformation, road roughness and fluid film. Comput Mech 64:1699–1718. https://doi.org/10.1007/s00466-019-01746-8
Tezduyar TE, Takizawa K, Kuraishi T (2022) Space–time computational FSI and flow analysis: 2004 and beyond. In: Aldakheel F, Hudobivnik B, Soleimani M, Wessels H, Weissenfels C, Marino M (eds) Current trends and open problems in computational mechanics. Springer, Berlin, pp 537–544. https://doi.org/10.1007/978-3-030-87312-7_52 (ISBN 978-3-030-87312-7)
Kuraishi T, Terahara T, Takizawa K, Tezduyar TE (2022) Computational flow analysis with boundary layer and contact representation: I. Tire aerodynamics with road contact. J Mech 38:77–87. https://doi.org/10.1093/jom/ufac009
Kuraishi T, Takizawa K, Tezduyar TE (2019) Space–time isogeometric flow analysis with built-in Reynolds-equation limit. Math Models Methods Appl Sci 29:871–904. https://doi.org/10.1142/S0218202519410021
Takizawa K, Tezduyar TE, Kuraishi T, Tabata S, Takagi H (2016) Computational thermo-fluid analysis of a disk brake. Comput Mech 57:965–977. https://doi.org/10.1007/s00466-016-1272-4
Takizawa K, Henicke B, Puntel A, Kostov N, Tezduyar TE (2012) Space–time techniques for computational aerodynamics modeling of flapping wings of an actual locust. Comput Mech 50:743–760. https://doi.org/10.1007/s00466-012-0759-x
Takizawa K, Kostov N, Puntel A, Henicke B, Tezduyar TE (2012) Space–time computational analysis of bio-inspired flapping-wing aerodynamics of a micro aerial vehicle. Comput Mech 50:761–778. https://doi.org/10.1007/s00466-012-0758-y
Takizawa K, Tezduyar TE, Buscher A, Asada S (2014) Space–time interface-tracking with topology change (ST-TC). Comput Mech 54:955–971. https://doi.org/10.1007/s00466-013-0935-7
Takizawa K, Tezduyar TE, Buscher A (2015) Space–time computational analysis of MAV flapping-wing aerodynamics with wing clapping. Comput Mech 55:1131–1141. https://doi.org/10.1007/s00466-014-1095-0
Takizawa K, Bazilevs Y, Tezduyar TE (2022) Mesh moving methods in flow computations with the space–time and arbitrary Lagrangian–Eulerian methods. J Adv Eng Comput 6:85–112. https://doi.org/10.55579/jaec.202262.377
Takizawa K, Montes D, Fritze M, McIntyre S, Boben J, Tezduyar TE (2013) Methods for FSI modeling of spacecraft parachute dynamics and cover separation. Math Models Methods Appl Sci 23:307–338. https://doi.org/10.1142/S0218202513400058
Takizawa K, Montes D, McIntyre S, Tezduyar TE (2013) Space–time VMS methods for modeling of incompressible flows at high Reynolds numbers. Math Models Methods Appl Sci 23:223–248. https://doi.org/10.1142/s0218202513400022
Takizawa K, Tezduyar TE, Boben J, Kostov N, Boswell C, Buscher A (2013) Fluid–structure interaction modeling of clusters of spacecraft parachutes with modified geometric porosity. Comput Mech 52:1351–1364. https://doi.org/10.1007/s00466-013-0880-5
Takizawa K, Tezduyar TE, Kolesar R (2015) FSI modeling of the Orion spacecraft drogue parachutes. Comput Mech 55:1167–1179. https://doi.org/10.1007/s00466-014-1108-z
Takizawa K, Tezduyar TE, Terahara T (2016) Ram-air parachute structural and fluid mechanics computations with the space–time isogeometric analysis (ST-IGA). Comput Fluids 141:191–200. https://doi.org/10.1016/j.compfluid.2016.05.027
Takizawa K, Tezduyar TE, Kanai T (2017) Porosity models and computational methods for compressible-flow aerodynamics of parachutes with geometric porosity. Math Models Methods Appl Sci 27:771–806. https://doi.org/10.1142/S0218202517500166
Kanai T, Takizawa K, Tezduyar TE, Tanaka T, Hartmann A (2019) Compressible-flow geometric-porosity modeling and spacecraft parachute computation with isogeometric discretization. Comput Mech 63:301–321. https://doi.org/10.1007/s00466-018-1595-4
Takizawa K, Schjodt K, Puntel A, Kostov N, Tezduyar TE (2012) Patient-specific computer modeling of blood flow in cerebral arteries with aneurysm and stent. Comput Mech 50:675–686. https://doi.org/10.1007/s00466-012-0760-4
Takizawa K, Schjodt K, Puntel A, Kostov N, Tezduyar TE (2013) Patient-specific computational analysis of the influence of a stent on the unsteady flow in cerebral aneurysms. Comput Mech 51:1061–1073. https://doi.org/10.1007/s00466-012-0790-y
Takizawa K, Bazilevs Y, Tezduyar TE, Long CC, Marsden AL, Schjodt K (2014) ST and ALE-VMS methods for patient-specific cardiovascular fluid mechanics modeling. Math Models Methods Appl Sci 24:2437–2486. https://doi.org/10.1142/S0218202514500250
Takizawa K, Tezduyar TE, Buscher A, Asada S (2014) Space–time fluid mechanics computation of heart valve models. Comput Mech 54:973–986. https://doi.org/10.1007/s00466-014-1046-9
Suito H, Takizawa K, Huynh VQH, Sze D, Ueda T, Tezduyar TE (2016) A geometrical-characteristics study in patient-specific FSI analysis of blood flow in the thoracic aorta. In: Bazilevs Y, Takizawa K (eds) Advances in computational fluid–structure interaction and flow simulation: new methods and challenging computations, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 379–386. https://doi.org/10.1007/978-3-319-40827-9_29 (ISBN 978-3-319-40825-5)
Takizawa K, Tezduyar TE, Terahara T, Sasaki T (2018) Heart valve flow computation with the space–time slip interface topology change (ST-SI-TC) method and isogeometric analysis (IGA). In: Wriggers P, Lenarz T (eds) Biomedical technology: modeling, experiments and simulation, lecture notes in applied and computational mechanics. Springer, Berlin, pp 77–99. https://doi.org/10.1007/978-3-319-59548-1_6 (ISBN 978-3-319-59547-4)
Takizawa K, Tezduyar TE, Terahara T, Sasaki T (2017) Heart valve flow computation with the integrated space–time VMS, slip interface, topology change and isogeometric discretization methods. Comput Fluids 158:176–188. https://doi.org/10.1016/j.compfluid.2016.11.012
Takizawa K, Tezduyar TE, Uchikawa H, Terahara T, Sasaki T, Shiozaki K, Yoshida A, Komiya K, Inoue G (2018) Aorta flow analysis and heart valve flow and structure analysis. In: Tezduyar TE (ed) Frontiers in computational fluid–structure interaction and flow simulation: research from lead investigators under forty—2018, modeling and simulation in science, engineering and technology. Springer, Berlin, pp 29–89. https://doi.org/10.1007/978-3-319-96469-0_2 (ISBN 978-3-319-96468-3)
Takizawa K, Tezduyar TE, Uchikawa H, Terahara T, Sasaki T, Yoshida A (2019) Mesh refinement influence and cardiac-cycle flow periodicity in aorta flow analysis with isogeometric discretization. Comput Fluids 179:790–798. https://doi.org/10.1016/j.compfluid.2018.05.025
Terahara T, Takizawa K, Tezduyar TE, Bazilevs Y, Hsu M-C (2020) Heart valve isogeometric sequentially-coupled FSI analysis with the space–time topology change method. Comput Mech 65:1167–1187. https://doi.org/10.1007/s00466-019-01813-0
Terahara T, Takizawa K, Tezduyar TE, Tsushima A, Shiozaki K (2020) Ventricle-valve-aorta flow analysis with the space–time isogeometric discretization and topology change. Comput Mech 65:1343–1363. https://doi.org/10.1007/s00466-020-01822-4
Takizawa K, Terahara T, Tezduyar TE (2022) Space–time flow computation with contact between the moving solid surfaces. In: Aldakheel F, Hudobivnik B, Soleimani M, Wessels H, Weissenfels C, Marino M (eds) Current trends and open problems in computational mechanics. Springer, Berlin, pp 517–525. https://doi.org/10.1007/978-3-030-87312-7_50 (ISBN 978-3-030-87312-7)
Terahara T, Kuraishi T, Takizawa K, Tezduyar TE (2022) Computational flow analysis with boundary layer and contact representation: II. Heart valve flow with leaflet contact. J Mech 38:185–194. https://doi.org/10.1093/jom/ufac013
Tezduyar T, Aliabadi S, Behr M, Johnson A, Mittal S (1993) Parallel finite-element computation of 3D flows. Computer 26(10):27–36. https://doi.org/10.1109/2.237441
Aydinbakar L, Takizawa K, Tezduyar TE, Kuraishi T (2021) Space–time VMS isogeometric analysis of the Taylor–Couette flow. Comput Mech 67:1515–1541. https://doi.org/10.1007/s00466-021-02004-6
Aydinbakar L, Takizawa K, Tezduyar TE, Matsuda D (2021) U-duct turbulent-flow computation with the ST-VMS method and isogeometric discretization. Comput Mech 67:823–843. https://doi.org/10.1007/s00466-020-01965-4
Tezduyar TE, Behr M, Mittal S, Johnson AA (1992) Computation of unsteady incompressible flows with the finite element methods: space–time formulations, iterative strategies and massively parallel implementations. In: New methods in transient analysis, PVP-Vol.246/AMD, vol 143. ASME, New York, pp 7–24
Takizawa K, Takagi H, Tezduyar TE, Torii R (2014) Estimation of element-based zero-stress state for arterial FSI computations. Comput Mech 54:895–910. https://doi.org/10.1007/s00466-013-0919-7
Takizawa K, Torii R, Takagi H, Tezduyar TE, Xu XY (2014) Coronary arterial dynamics computation with medical-image-based time-dependent anatomical models and element-based zero-stress state estimates. Comput Mech 54:1047–1053. https://doi.org/10.1007/s00466-014-1049-6
Takizawa K, Tezduyar TE, Sasaki T (2018) Estimation of element-based zero-stress state in arterial FSI computations with isogeometric wall discretization. In: Wriggers P, Lenarz T (eds) Biomedical technology: modeling, experiments and simulation , lecture notes in applied and computational mechanics, lecture notes in applied and computational mechanics. Springer, Berlin, pp 101–122. https://doi.org/10.1007/978-3-319-59548-1_7 (ISBN 978-3-319-59547-4)
Takizawa K, Tezduyar TE, Sasaki T (2017) Aorta modeling with the element-based zero-stress state and isogeometric discretization. Comput Mech 59:265–280. https://doi.org/10.1007/s00466-016-1344-5
Sasaki T, Takizawa K, Tezduyar TE (2019) Aorta zero-stress state modeling with T-spline discretization. Comput Mech 63:1315–1331. https://doi.org/10.1007/s00466-018-1651-0
Sasaki T, Takizawa K, Tezduyar TE (2019) Medical-image-based aorta modeling with zero-stress-state estimation. Comput Mech 64:249–271. https://doi.org/10.1007/s00466-019-01669-4
Takizawa K, Tezduyar TE, Avsar R (2020) A low-distortion mesh moving method based on fiber-reinforced hyperelasticity and optimized zero-stress state. Comput Mech 65:1567–1591. https://doi.org/10.1007/s00466-020-01835-z
Tonon P, Sanches RAK, Takizawa K, Tezduyar TE (2021) A linear-elasticity-based mesh moving method with no cycle-to-cycle accumulated distortion. Comput Mech 67:413–434. https://doi.org/10.1007/s00466-020-01941-y
Hughes TJR, Cottrell JA, Bazilevs Y (2005) Isogeometric analysis: CAD, finite elements, NURBS, exact geometry, and mesh refinement. Comput Methods Appl Mech Eng 194:4135–4195
Bazilevs Y, Hughes TJR (2008) NURBS-based isogeometric analysis for the computation of flows about rotating components. Comput Mech 43:143–150
Tezduyar TE, Cragin T, Sathe S, Nanna B (2007) FSI computations in arterial fluid mechanics with estimated zero-pressure arterial geometry. In: Onate E, Garcia J, Bergan P, Kvamsdal T (eds) Marine 2007. CIMNE, Barcelona
Takizawa K, Tezduyar TE, Sasaki T (2019) Isogeometric hyperelastic shell analysis with out-of-plane deformation mapping. Comput Mech 63:681–700. https://doi.org/10.1007/s00466-018-1616-3
Taniguchi Y, Takizawa K, Otoguro Y, Tezduyar TE (2022) A hyperelastic extended Kirchhoff–Love shell model with out-of-plane normal stress: I. Out-of-plane deformation. Comput Mech 70:247–280. https://doi.org/10.1007/s00466-022-02166-x
Bazilevs Y, Hsu M-C, Kiendl J, Benson DJ (2012) A computational procedure for pre-bending of wind turbine blades. Int J Numer Methods Eng 89:323–336
Bazilevs Y, Deng X, Korobenko A, di Scalea FL, Todd MD, Taylor SG (2015) Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data. J Appl Mech 82:091008
Kiendl J, Hsu M-C, Wu MCH, Reali A (2015) Isogeometric Kirchhoff–Love shell formulations for general hyperelastic materials. Comput Methods Appl Mech Eng 291:280–303
Hsu M-C, Wang C, Herrema AJ, Schillinger D, Ghoshal A, Bazilevs Y (2015) An interactive geometry modeling and parametric design platform for isogeometric analysis. Comput Math Appl 70:1481–1500
Herrema AJ, Wiese NM, Darling CN, Ganapathysubramanian B, Krishnamurthy A, Hsu M-C (2017) A framework for parametric design optimization using isogeometric analysis. Comput Methods Appl Mech Eng 316:944–965
Benzaken J, Herrema AJ, Hsu M-C, Evans JA (2017) A rapid and efficient isogeometric design space exploration framework with application to structural mechanics. Comput Methods Appl Mech Eng 316:1215–1256
Kamensky D, Xu F, Lee C-H, Yan J, Bazilevs Y, Hsu M-C (2018) A contact formulation based on a volumetric potential: application to isogeometric simulations of atrioventricular valves. Comput Methods Appl Mech Eng 330:522–546
Herrema AJ, Johnson EL, Proserpio D, Wu MCH, Kiendl J, Hsu M-C (2019) Penalty coupling of non-matching isogeometric Kirchhoff–Love shell patches with application to composite wind turbine blades. Comput Methods Appl Mech Eng 346:810–840
Herrema AJ, Kiendl J, Hsu M-C (2019) A framework for isogeometric-analysis-based optimization of wind turbine blade structures. Wind Energy 22:153–170
Johnson EL, Hsu M-C (2020) Isogeometric analysis of ice accretion on wind turbine blades. Comput Mech 66:311–322
Tezduyar TE, Sathe S (2004) Enhanced-discretization space–time technique (EDSTT). Comput Methods Appl Mech Eng 193:1385–1401. https://doi.org/10.1016/j.cma.2003.12.029
Tezduyar TE, Osawa Y (2000) Finite element stabilization parameters computed from element matrices and vectors. Comput Methods Appl Mech Eng 190:411–430. https://doi.org/10.1016/S0045-7825(00)00211-5
Hughes TJR, Brooks AN (1979) A multi-dimensional upwind scheme with no crosswind diffusion. In: Hughes TJR (ed) Finite element methods for convection dominated flows, AMD, vol 34. ASME, New York, pp 19–35
Tezduyar TE, Hughes TJR (1982) Development of time-accurate finite element techniques for first-order hyperbolic systems with particular emphasis on the compressible Euler equations. NASA Technical Report NASA-CR-204772, NASA. http://www.researchgate.net/publication/24313718/
Tezduyar TE, Hughes TJR (1983) Finite element formulations for convection dominated flows with particular emphasis on the compressible Euler equations. In: Proceedings of AIAA 21st aerospace sciences meeting. AIAA Paper 83-0125, Reno, Nevada. https://doi.org/10.2514/6.1983-125
Hughes TJR, Mallet M, Mizukami A (1986) A new finite element formulation for computational fluid dynamics: II. Beyond SUPG. Comput Methods Appl Mech Eng 54:341–355
Tezduyar TE, Park YJ (1986) Discontinuity capturing finite element formulations for nonlinear convection–diffusion–reaction equations. Comput Methods Appl Mech Eng 59:307–325. https://doi.org/10.1016/0045-7825(86)90003-4
Tezduyar TE (2001) Adaptive determination of the finite element stabilization parameters. In: Proceedings of the ECCOMAS computational fluid dynamics conference 2001 (CD-ROM), Swansea, Wales, United Kingdom
Tezduyar TE (2004) Finite element methods for fluid dynamics with moving boundaries and interfaces. In: Stein E, Borst RD, Hughes TJR (eds) Encyclopedia of computational mechanics, volume 3: fluids, chapter 17. Wiley, Hoboken. https://doi.org/10.1002/0470091355.ecm069 (ISBN 978-0-470-84699-5)
Takizawa K, Henicke B, Montes D, Tezduyar TE, Hsu M-C, Bazilevs Y (2011) Numerical-performance studies for the stabilized space–time computation of wind-turbine rotor aerodynamics. Comput Mech 48:647–657. https://doi.org/10.1007/s00466-011-0614-5
Takizawa K, Ueda Y, Tezduyar TE (2019) A node-numbering-invariant directional length scale for simplex elements. Math Models Methods Appl Sci 29:2719–2753. https://doi.org/10.1142/S0218202519500581
Ueda Y, Otoguro Y, Takizawa K, Tezduyar TE (2020) Element-splitting-invariant local-length-scale calculation in B-spline meshes for complex geometries. Math Models Methods Appl Sci 30:2139–2174. https://doi.org/10.1142/S0218202520500402
Moghadam ME, Bazilevs Y, Hsia T-Y, Vignon-Clementel IE, Marsden AL, Modeling of Congenital Hearts Alliance (MOCHA) (2011) A comparison of outlet boundary treatments for prevention of backflow divergence with relevance to blood flow simulations. Comput Mech 48:277–291. https://doi.org/10.1007/s00466-011-0599-0
Osawa Y, Tezduyar T (1999) 3D simulation and visualization of unsteady wake flow behind a cylinder. J Vis 2:127–134. https://doi.org/10.1007/BF03181515
Acknowledgements
This work was supported in part by International Technology Center Indo-Pacific (ITC IPAC) Contract FA520921C0010; Grant-in-Aid for Scientific Research (A) 18H04100 from Japan Society for the Promotion of Science; and Rice–Waseda research agreement. The work was also supported in part by ARO Grant W911NF-17-1-0046 and Contract W911NF-21-C-0030 (third and fourth authors); Top Global University Project of Waseda University (third author); and Pioneering Research Program for a Waseda Open Innovation Ecosystem (W-SPRING) (first author). The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper.
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Liu, Y., Takizawa, K., Tezduyar, T.E. et al. Carrier-Domain Method for high-resolution computation of time-periodic long-wake flows. Comput Mech 71, 169–190 (2023). https://doi.org/10.1007/s00466-022-02230-6
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DOI: https://doi.org/10.1007/s00466-022-02230-6