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Optimal design of responsive structures

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Abstract

With recent advances in both responsive materials and fabrication techniques, it is now possible to construct integrated functional structures, composed of both structural and active materials. We investigate the robust design of such structures through topology optimization. By applying a typical interpolation scheme and filtering technique, we prove existence of an optimal design to a class of objective functions which depend on the compliances of the stimulated and unstimulated states. In particular, we consider the actuation work and the blocking load as objectives, both of which may be written in terms of compliances. We study numerical results for the design of a 2D rectangular lifting actuator for both of these objectives, and discuss some intuition behind the features of the converged designs. We formulate the optimal design of these integrated responsive structures with the introduction of voids or holes in the domain, and show that our existence result holds in this setting. We again consider the design of the 2D lifting actuator now with voids. Finally, we investigate the optimal design of an integrated 3D torsional actuator for maximum blocking torque.

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Notes

  1. Precisely, it is bounded by a constant that depends on \(\rho _{min}\) and becomes unbounded as \(\rho _{min} \rightarrow 0\)

References

  • Allaire G (2002) Shape optimization by the homogenization method. Springer, New York

    Book  MATH  Google Scholar 

  • Allaire G, Aubry S (1999) On optimal microstructures for a plane shape optimization problem. Struct Optim 17:86–94

    Article  Google Scholar 

  • Allaire G, Bogosel B (2018) Optimizing supports for additive manufacturing. Struct Multidisc Optim 58(6):2493–2515

    Article  MathSciNet  Google Scholar 

  • Allaire G, Bonnetier É, Francfort G, Jouve F (1997) Shape optimization by the homogenization method. Numer Math 76:27–68

    Article  MathSciNet  MATH  Google Scholar 

  • Allaire G, Jouve F, Michailidis G (2016) Variational analysis and aerospace engineering, volume 116 of Springer optimization and its applications book series, chapter molding direction constraints in structural optimization via a level-set method. Springer, Berlin, pp 1–39

    Google Scholar 

  • Allaire G, Dapogny C, Estevez R, Faure A, Michailidis G (2017a) Structural optimization under overhang constraints imposed by additive manufacturing technologies. J Comput Phys 351:295–328

    Article  MathSciNet  MATH  Google Scholar 

  • Allaire G, Dapogny C, Faure A, Michailidis G (2017b) Shape optimization of a layer by layer mechanical constraint for additive manufacturing. C R Math 355(6):699–717

    Article  MathSciNet  MATH  Google Scholar 

  • Ambrosio L, Buttazzo G (1993) An optimal design problem with perimeter penalization. Calc Var Partial Differ Equ 1:55–69

    Article  MathSciNet  MATH  Google Scholar 

  • Ambulo CP, Burroughs JJ, Boothby JM, Kim H, Shankar MR, Ware TH (2017) Four-dimensional printing of liquid crystal elastomers. ACS Appl Mater Interfaces 9:37332–37339

    Article  Google Scholar 

  • Bangerth W, Hartmann R, Kanschat G (2007) deal.I —a general purpose object oriented finite element library. ACM Trans Math Softw 33(4):24/1–24/27

    Article  MathSciNet  MATH  Google Scholar 

  • Bendsøe M, Sigmund O (1999) Material interpolation schemes in topology optimization. Arch Appl Mech 69:635–654

    Article  MATH  Google Scholar 

  • Bendsøe M, Sigmund O (2003) Topology optimization: theory, methods and applications, 2nd edn. Springer, Berlin

    MATH  Google Scholar 

  • Bhattacharya K (2003) Microstructure of martensite: how it forms and how it gives rise to the shape-memory effect. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Bourdin B (2001) Filters in topology optimization. Int J Numer Methods Eng 50:2143–2158

    Article  MathSciNet  MATH  Google Scholar 

  • Bourdin B, Chambolle A (2003) Design-dependent loads in topology optimization. ESAIM Control Optimd Calc Var 9:19–48

    Article  MathSciNet  MATH  Google Scholar 

  • Bourdin B, Kohn RV (2008) Optimization of structural topology in the high-porosity regime. J Mech Phys Solids 56:1043–1064

    Article  MathSciNet  MATH  Google Scholar 

  • Cesana P, De Simone A (2011) Quasiconvex envelopes of energies for nematic elastomers in the small strain regime and applications. J Mech Phys Solids 59:787–803

    Article  MathSciNet  MATH  Google Scholar 

  • Cherkaev A (2000) Variational methods for structural optimization. Springer, New York

    Book  MATH  Google Scholar 

  • Elahinia M, Moghaddam NS, Andani MT, Amerinatanzi A, Bimber BA, Hamilton RF (2016) Fabrication of NiTi through additive manufacturing: a review. Prog Mater Sci 83:630–663

    Article  Google Scholar 

  • Francfort GA, Murat F (1986) Homogenization and optimal bounds in linear elasticity. Arch Ration Mech Anal 94:307–334

    Article  MathSciNet  MATH  Google Scholar 

  • Geoffroy-Donders P, Allaire G, Michailidis G, Pantz O (2020) Coupled optimization of macroscopic structures and lattice infill. Int J Numer Methods Eng 61:2253–2269

    Google Scholar 

  • Haber R, Jog C, Bendsøe M (1996) A new approach to variable-topology shape design using a constraint on the perimeter. Struct Optim 11:1–12

    Article  Google Scholar 

  • Kotikian A, Truby RL, Boley JW, White TJ, Lewis JA (2018) 3D printing of liquid crystal elastomeric actuators with spatially programed nematic order. Adv Mater 30:1706164

    Article  Google Scholar 

  • Lazarov BS, Sigmund O (2011) Filters in topology optimization based on Helmholtz-type differential equations. Int J Numer Methods Eng 86(6):765–781

    Article  MathSciNet  MATH  Google Scholar 

  • Milton GW (1986) Homogenization and effective moduli of materials and media, volume 1 of the IMA volumes in mathematics and its applications. Chapter Modelling the properties of composite by laminates. Springer, Berlin, pp 150–174

    Google Scholar 

  • Milton GW (2002) The theory of composite. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Nematollahi M, Toker G, Saghaian SE, Salazar J, Mahtabi M, Benafan O, Karaca H, Elahinia M (2019) Additive manufacturing of Ni-Rich NiTiHf20: manufacturability, composition, density, and transformation behavior. Shape Mem Superelast 5:113–124

    Article  Google Scholar 

  • Panetta J, Zhou Q, Malomo L, Pietroni N, Cignoni P, Zorin D (2015) Elastic textures for additive fabrication. ACM Trans Gr 34:1–12

    Article  MATH  Google Scholar 

  • Park S, Yoo J (2012) Structural optimization of a multi-physics problem considering thermal and magnetic effects. IEEE Trans Magn 48(11):3883–3886

    Article  Google Scholar 

  • Phillips BT, Becker KP, Kuruyama S, Galloway KC, Whittredge G, Vogt DM, Clark BT, Rosen MH, Pieribone VA, Gruber DF, Wood RJ (2018) A dexterous, glove-based teleoperable low-power soft robotic arm for delicate deep-sea biological exploration. Sci Rep 8:14779

    Article  Google Scholar 

  • Qian X (2017) Undercut and overhang angle control in topology optimization: a density gradient based integral approach. Int J Numer Methods Eng 111(3):247–272

    Article  MathSciNet  Google Scholar 

  • Rodrigue H, Wei W, Bhandari B, Ahn S-H (2015) Fabrication of wrist-like SMA-based actuator by double smart soft composite casting. Smart Mater Struct 24:125003

    Article  Google Scholar 

  • Rodrigues H, Fernandes P (1995) A material based model for topology optimization of thermoelastic structures. Int J Numer Methods Eng 38(12):1951–1965

    Article  MathSciNet  MATH  Google Scholar 

  • Rosental T, Magdassi S (2019) A new approach to 3D printing dense ceramics by ceramic precursor binders. Adv Eng Mater 21:1900604

    Article  Google Scholar 

  • Ruiz D, Sigmund O (2018) Optimal design of robust piezoelectric microgrippers undergoing large displacements. Struct Multidisc Optim 57(1):71–82

    Article  MathSciNet  Google Scholar 

  • Schumacher C, Bickel B, Rys J, Marschner S, Daraio C, Gross M (2015) Microstructures to control elasticity in 3D printing. ACM Trans Gr 34:136:1-136:13

    Article  Google Scholar 

  • Sigmund O (1997) On the design of compliant mechanisms using topology optimization. Mech Struct Mach 25:495–526

    Article  Google Scholar 

  • Sigmund O (1998) Systematic design of microactuators using topology optimization. In: Varadan VK, McWhorter PJ, Singer RA, Vellekoop MJ (eds) Smart structures and materials 1998: smart electronics and MEMS, vol 3328. International Society for Optics and Photonics, SPIE, Bellingham pp 23–31

  • Sigmund O (2001a) Design of multiphysics actuators using topology optimization—part i: one-material structures. Comput Methods Appl Mech Eng 190(49):6577–6604

    Article  MATH  Google Scholar 

  • Sigmund O (2001b) Design of multiphysics actuators using topology optimization—part ii: two-material structures. Comput Methods Appl Mech Eng 190(49):6605–6627

    Article  MATH  Google Scholar 

  • Svanberg K (1987) The method of moving asymptotes–a new method for structural optimization. Int J Numer Methods Eng 24(2):359–373

    Article  MathSciNet  MATH  Google Scholar 

  • Tabrizi M, Ware TH, Shankar MR (2019) Voxelated molecular patterning in three-dimensional freeforms. ACS Appl Mater Interfaces 11:28236

    Google Scholar 

  • Wang Y, Kang Z (2017) Structural shape and topology optimization of cast parts using level set method. Int J Numer Methods Eng 111(13):1252–1273

    Article  MathSciNet  Google Scholar 

  • Yoon GH (2012) Topological layout design of electro-fluid-thermal-compliant actuator. Comput Methods Appl Mech Eng 209–212:28–44

    Article  Google Scholar 

  • York PA, Wood RJ (2019) Nitinol living hinges for millimeter-sized robots and medical devices. In: International Conference on Robotics and Automation, pp 889–893

Download references

Acknowledgements

We are grateful for the financial support of the U.S. National Science Foundation through “Collaborative Research: Optimal Design of Responsive Materials and Structures” (DMS:2009289 at Caltech and DMS:2009303 at LSU and McMaster University). Part of this work was performed while BB was the A.K. & Shirley Barton Professor of Mathematics at Louisiana State University (USA).

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Correspondence to Kaushik Bhattacharya.

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Appendices

Appendix 1: A proof of Lemma 3.2

Here, we provide the proof of Lemma 3.2.

Proof

We will first show by compactness that there exists a \(u^{\infty } \in \mathcal {U}\) such that \(u_k \rightharpoonup u^{\infty }\) in \(W^{1,2}(\Omega )\). Then, we will show that we must have \(u^{\infty } = \bar{u}\).

Since \(u_k\) is the equillibrium solution corresponding to \(\phi _k\) for some fixed S, it satisfies

$$\begin{aligned} u_k = \text {arg}\min _{u \in \mathcal {U}} \ \mathcal {E}_f(\phi _k, u, S), \end{aligned}$$
(65)

and for any \(\tilde{u} \in \mathcal {U}\), we have

$$\begin{aligned} \mathcal {E}_f (\phi _k, u_k, S) \le \mathcal {E}_f (\phi _k, \tilde{u}, S). \end{aligned}$$
(66)

Furthermore,

$$\begin{aligned}&\mathcal {E}_f (\phi _k, \tilde{u}, S) \le \int _{\Omega } \frac{1}{2} \left[ \mathbb {C}_s \varepsilon (\tilde{u}) \cdot \varepsilon (\tilde{u}) + \mathbb {C}_r(S) (\varepsilon (\tilde{u}) - \varepsilon ^*(S)) \cdot (\varepsilon (\tilde{u}) - \varepsilon ^*(S)) \right] \ \text {d}x \nonumber \\&\quad - \int _{\partial _f \Omega } f \cdot \tilde{u} \ \text {d}s = M \end{aligned}$$
(67)

where M is some constant, independent of k. So,

$$\begin{aligned} \mathcal {E}_f (\phi _k, u_k, S) \le M. \end{aligned}$$
(68)

Now, expanding the energy functional

$$\begin{aligned} \begin{aligned} \mathcal {E}_f (\phi _k, u_k, S) =&\int _{\Omega } \frac{1}{2} \big [(1 - (F \ast \phi _k)^p) \mathbb {C}_s \varepsilon (u_k) \cdot \varepsilon (u_k) + (F \ast \phi _k)^p \mathbb {C}_r(S) \varepsilon (u_k) \cdot \varepsilon (u_k) \\&+ (F \ast \phi _k)^p \mathbb {C}_r(S)\varepsilon ^*(S) \cdot \varepsilon ^*(S) - 2(F \ast \phi _k)^p \mathbb {C}_r (S) \varepsilon ^*(S) \cdot \varepsilon (u_k) \big ] \ \text{d}x \\&- \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s, \end{aligned} \end{aligned}$$
(69)

and using the ellipticity from Remark 1

$$\begin{aligned} \begin{aligned}&m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - \left| \int _{\Omega } (F \ast \phi _k)^p \mathbb {C}_r (S) \varepsilon ^*(S) \cdot \varepsilon (u_k) \ \text {d}x \right| \\&\quad - \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - \int _{\Omega } (F \ast \phi _k)^p \left| \mathbb {C}_r (S) \varepsilon ^*(S) \cdot \varepsilon (u_k) \right| \ \text {d}x \\&\quad - \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - \int _{\Omega } (F \ast 1)^p \left| \mathbb {C}_r (S) \varepsilon ^*(S) \cdot \varepsilon (u_k) \right| \ \text {d}x\\&\quad - \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - \int _{\Omega } \left| \mathbb {C}_r (S) \varepsilon ^*(S) \cdot \varepsilon (u_k) \right| \ \text {d}x\\&\quad - \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \le \mathcal {E}(\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - \left\Vert \mathbb {C}_r (S) \varepsilon ^*(S)\right\Vert _{L^2(\Omega )} \left\Vert \varepsilon (u_k)\right\Vert _{L^2(\Omega )}\\&\quad -\int _{\partial _f \Omega } f \cdot u_k \ \text{d}s \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - c \left\Vert u_k\right\Vert _{W^{1,2}( \Omega )} \\&\quad - \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \le \mathcal {E}_f (\phi _k, u_k, S) \end{aligned} \end{aligned}$$
(70)

for some constants \(m,c > 0\), independent of k. Additionally,

$$\begin{aligned} \begin{aligned}&m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - c \left\Vert u_k\right\Vert _{W^{1,2}( \Omega )} - \left| \int _{\partial _f \Omega } f \cdot u_k \ \text {d}s \right| \\&\quad \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - c \left\Vert u_k\right\Vert _{W^{1,2}( \Omega )} - \left\Vert f\right\Vert _{L^2(\partial _f \Omega )} \left\Vert u_k\right\Vert _{L^2(\partial _f \Omega )}\\&\quad \le \mathcal {E}(\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - c \left\Vert u_k\right\Vert _{W^{1,2}( \Omega )} - \left\Vert f\right\Vert _{L^2(\partial _f \Omega )} \left\Vert u_k\right\Vert _{L^2(\partial \Omega )}\\&\quad \le \mathcal {E}_f (\phi _k, u_k, S), \\&\quad m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - c \left\Vert u_k\right\Vert _{W^{1,2}( \Omega )} - a \left\Vert u_k\right\Vert _{W^{1,2}(\Omega )}\\&\quad&\le \mathcal {E}_f (\phi _k, u_k, S), \end{aligned} \end{aligned}$$
(71)

for some constant \(a > 0\). Then,

$$\begin{aligned} m \left\Vert u_k\right\Vert ^2_{W^{1,2}(\Omega )} - b \left\Vert u_k\right\Vert _{W^{1,2}(\Omega )} \le M \implies \left\Vert u_k\right\Vert _{W^{1,2}(\Omega )} \le d, \end{aligned}$$
(72)

for some constant \(b > 0\), where \(d > 0\) is a constant independent of k. Thus, \(u_k\) is a bounded sequence in \(W^{1,2}(\Omega )\), and there exists a \(u^\infty \in \mathcal {U}\) such that

$$\begin{aligned} u_{k} \rightharpoonup u^{\infty } \text { in } W^{1,2}(\Omega ) \text { when } k \rightarrow + \infty , \end{aligned}$$
(73)

up to a subsequence. Next, consider \(\bar{u} \in \mathcal {U}\) such that

$$\begin{aligned} \bar{u} = \text {arg}\min _{u \in \mathcal {U}} \mathcal {E}_f (\bar{\phi }, u, S). \end{aligned}$$
(74)

Then

$$\begin{aligned} \mathcal {E}_f (\bar{\phi }, \bar{u}, S) \le \mathcal {E}_f (\bar{\phi }, u^{\infty }, S). \end{aligned}$$
(75)

Similarly,

$$\begin{aligned}&\mathcal {E}_f(\phi _k, u_k, S) \le \mathcal {E}_f(\phi _k, \bar{u}, S) = \mathcal {E}_f(\phi _k, \bar{u}, S) - \mathcal {E}_f(\bar{\phi }, \bar{u}, S) + \mathcal {E}_f(\bar{\phi }, \bar{u}, S) \nonumber \\&\quad - \mathcal {E}_f(\bar{\phi }, u_k, S) + \mathcal {E}_f(\bar{\phi }, u_k, S), \end{aligned}$$
(76)

or

$$\begin{aligned} \mathcal {E}_f(\bar{\phi }, u_k, S) \le \mathcal {E}_f(\bar{\phi }, \bar{u}, S) + \mathcal {E}_f(\phi _k, \bar{u}, S) - \mathcal {E}_f(\bar{\phi }, \bar{u}, S) + \mathcal {E}_f(\bar{\phi }, u_k, S) - \mathcal {E}_f(\phi _k, u_k, S). \end{aligned}$$
(77)

Then taking limits, and using the strong convergence of the convolution gives

$$\begin{aligned} \lim _{k \rightarrow \infty } \ \mathcal {E}_f(\bar{\phi }, u_k, S) \le \mathcal {E}_f(\bar{\phi }, \bar{u}, S). \end{aligned}$$
(78)

The convexity of the energy integrand in \(\nabla u\) and u for a given \(\phi\) and S gives lower semi-continuity of our energy function

$$\begin{aligned} \mathcal {E}_f(\bar{\phi }, u^{\infty }, S) \le \lim _{k \rightarrow \infty } \ \mathcal {E}_f(\bar{\phi }, u_k, S), \end{aligned}$$
(79)

so

$$\begin{aligned} \mathcal {E}_f(\bar{\phi }, u^{\infty }, S) \le \mathcal {E}_f(\bar{\phi }, \bar{u}, S). \end{aligned}$$
(80)

Then from (75),

$$\begin{aligned} \mathcal {E}_f(\bar{\phi }, u^{\infty }, S) = \mathcal {E}_f(\bar{\phi }, \bar{u}, S). \end{aligned}$$
(81)

From the uniqueness of the minimizer of \(\mathcal {E}_f(\bar{\phi }, \times , S)\) we have

$$\begin{aligned} u^\infty = \bar{u}. \end{aligned}$$
(82)

Then, as desired,

$$\begin{aligned} u_{k} \rightharpoonup \bar{u} \text { in } W^{1,2}(\Omega ) \text { when } k \rightarrow + \infty . \end{aligned}$$
(83)

\(\square\)

Appendix 2: Workpiece objective as force in spring

Here we show the workpiece objective is equivalent to maximizing the load of a point spring. This is equivalent to the objective used by Sigmund in an earlier work to study thermal actuators (Sigmund 1998). However, we include the derivation here for completeness. Consider a linear spring in direction \(\hat{n}\) of spring constant \(\kappa > 0\) connected to the boundary of the domain at some point of interest \(x_0 \in \partial _f \Omega\). The aim is to maximize the load carried by this spring upon actuation. Thus, we look to maximize the load in the spring:

$$\begin{aligned} \sup \{ f_0 : f_0 = -\kappa u(x_0) \cdot \hat{n}, \Phi \in \mathcal {D} \} \end{aligned}$$
(84)

where u is the equilibrium solution corresponding to \(S = 1\) and \(f = f_0 \delta (x - x_0) \hat{n}\). Assuming homogeneous Dirichlet conditions \(u_0 = 0\) on \(\partial _u \Omega\), it is easy to see using the linearity of the Euler-Lagrange equations

$$\begin{aligned} u = v + u_{S = 0, f_0 \hat{n}} \end{aligned}$$
(85)

where \(u_{S = 0, f_0 \hat{n}}\) minimizes the elastic energy (4) with \(S = 0\) and \(f = f_0 \delta (x - x_0) \hat{n}\). Invoking linearity again gives \(u_{S = 0, f_0 \hat{n}} = f_0 u_{S = 0, \hat{n}}\). The displacement can then be written as

$$\begin{aligned} u = v + f_0 u_{S = 0, \hat{n}}. \end{aligned}$$
(86)

Evaluating at \(x = x_0\), taking an inner product with \(\hat{n}\), and using the constraint that \(f_0 = - \kappa u(x_0) \cdot \hat{n}\) gives

$$\begin{aligned} -\frac{f_0}{\kappa } = v(x_0) \cdot \hat{n} + f_0 u_{S = 0, \hat{n}}(x_0) \cdot \hat{n}. \end{aligned}$$
(87)

Rearranging gives

$$\begin{aligned} f_0 = \frac{-\kappa v(x_0) \cdot \hat{n} }{\kappa u_{S = 0, \hat{n}}(x_0) \cdot \hat{n} + 1} = \frac{ - \kappa v(x_0) \cdot \hat{n} - \kappa u_{S = 0, \hat{n}}(x_0) \cdot \hat{n} - 1}{ \kappa u_{S = 0, \hat{n}}(x_0) \cdot \hat{n} + 1} - 1 \end{aligned}$$
(88)

or

$$\begin{aligned} f_0 = \frac{ - \kappa u_{S = 1, \hat{n}}(x_0) \cdot \hat{n} - 1}{\kappa u_{S = 0, \hat{n}}(x_0) \cdot \hat{n} + 1} - 1. \end{aligned}$$
(89)

We recognize \(u_{S = 0, \hat{n}}(x_0) \cdot \hat{n}\) and \(u_{S = 1, \hat{n}}(x_0) \cdot \hat{n}\) as the unactuated and actuated compliances under loading \(f = \delta (x - x_0) \hat{n}\). Thus, the workpiece objective can then be written as a function of compliancies,

$$\begin{aligned} \inf _{\Phi \in \mathcal {D}} \mathcal {O}(\Phi ) = \frac{\kappa \mathcal {C}_{\hat{n}}(\Phi , 1) + 1}{\kappa \mathcal {C}_{\hat{n}}(\Phi , 0) + 1}. \end{aligned}$$
(90)

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Akerson, A., Bourdin, B. & Bhattacharya, K. Optimal design of responsive structures. Struct Multidisc Optim 65, 111 (2022). https://doi.org/10.1007/s00158-022-03200-5

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