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A parallel computing framework for performing structural-acoustic optimization with stochastic forcing

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Abstract

Structural-acoustic optimization procedures can be used to find the optimal design for reduced noise or vibration in many real-world scenarios. However, the time required to compute the structural-acoustic quantity of interest often limits the size of the model. Additionally, structural-acoustic optimization using state-of-the-art evolutionary algorithms may require tens of thousands of system solutions, which add to the limitations for large full-scale systems. To reduce the time required for each function evaluation, parallel processing techniques are used to solve the system in a highly scalable fashion. The approach reduces the analysis time by solving the system using a frequency-domain formulation and distributing solution frequencies amongst processors to solve in parallel. To demonstrate, the sound radiated from a curved panel under the influence of a turbulent boundary layer is minimized in the presence of added point masses, which are varied during the optimization procedure. The total mass is also minimized and the Pareto front relating the trade-off between added mass and reduced noise is determined. Solver scaling information is provided that demonstrates the utility of the parallel processing approach.

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References

  • Aage N, Andreassen E, Lazarov BS (2015) Topology optimization using PETSc: an easy-to-use, fully, parallel, open source topology optimization framework. Struct Multi Discip Optim, 51

  • Aage N, Andreassen E, Lazarov BS, Sigmund O (2017) Giga-voxel computational morphogensis for structural design. Nature, 550

  • Aiello R, Auriemma F (2018) Optimized vibro-acoustic design of suspended glass panels. Struct MultiDiscip Optim 58:2253–2268

    Article  Google Scholar 

  • Belegundu AD, Salagame RR, Koopmann GH (1994) A general optimization strategy for sound power minimization. Struct MultiDiscip Optim 8(2–3):113–119

    Article  Google Scholar 

  • Bendat JS, Piersol AG (2000) Random data. Wiley, New York

    MATH  Google Scholar 

  • Benthien GW (1989) Application of frequency interpolation to acoustic-structure interaction problems, Naval Ocean System Center TR-1323

  • Bonness WK, Fahnline JB, Lysak PD, Shepherd MR (2017) Modal forcing functions for structural vibration from turbulent boundary layer flow at low and high speed. J Sound Vib 395:224–239

    Article  Google Scholar 

  • Butkewitsch S, Steffen V (2001) A case study on frequency response optimization. Int J Solids Str 38:1737–1748

    Article  Google Scholar 

  • Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, New York

    MATH  Google Scholar 

  • Constans EW, Koopmann GH, Belegundu AD (1998) The use of modal tailoring to minimize the radiated sound power of vibrating shells: theory and experiment. J Sound Vib 217(2):335–350

    Article  Google Scholar 

  • Corcos GM (1963) Resolution of pressure in turbulence. J Acoust Soc Am 35(2):192–199

    Article  Google Scholar 

  • Cunefare KA (1994) Optimization techniques in structural acoustic design, J Acoust Soc Am, 95

  • de Souza MZ, Ramirez Pozo AT (2014) A GPU implementation of MOEA/D-ACO for the multiobjective traveling salesman problem. In: Proceedings of the 2014 Brazilian conference on intelligent systems

  • Durillo JJ, Nebro AJ, Luna F, Alba E (2008) A study of master-slave approaches to parallelize NSGA-II. In: Proceedings of 2008 IEEE international symbosium on parallel and distributed processing

  • Fahnline JB, Koopmann GH (1996) A lumped parameter model for the acoustic power output from a vibrating structure. J Acoust Soc Am 100(6):3539–3547

    Article  Google Scholar 

  • Fahnline JB, McDevitt TE, Whitney EJ, Capone DE (2006) Structural-acoustic tailoring of metal structures by laser free-forming Noise. Control Eng J 54(2):124–129

    Google Scholar 

  • Giuliani M, Quinn J, Herman J, Castelletti A, Reed PM (2018) Scalable multi-objective control for large scale water resources systems under uncertainty, IEEE Trans Cont Syst Tech 26(4)

    Article  Google Scholar 

  • Hadka D, Reed P (2013) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comp 21(2):231– 259

    Article  Google Scholar 

  • Hadka D, Reed P (2015) Large-scale parallelization of the borg multiobjective evolutionary algorithm to enhance the management of complex environmental systems. Env Model & Soft 69:353– 369

    Article  Google Scholar 

  • Hambric SA, Boger DA, Fahnline JB, Campbell RL (2010) Structure- and fluid-borne acoustic power sources induced by turbulent flow in 90∘ piping elbows. J Fl Str 26:121–147

    Article  Google Scholar 

  • He MX, Sun JQ (2018) Multi-objective structural-acoustic optimization of beams made of functionally graded materials, Comp Str 185

    Article  Google Scholar 

  • He MX, Xiong FR, Sun JQ (2017) Multi-objective optimization of elastic beams for noise reduction, J Vib Acs, 139

  • Howe MS (1998) Acoustics of fluid-structure interaction. Cambridge Univ Press, Cambridge

    Book  Google Scholar 

  • Jeon JY, Okuma M (2008) An optimum embossment of rectangular section in panel to minimize noise power. J Vib Acoust 130:021012–1–021012-7

    Article  Google Scholar 

  • Johnson WM, Cunefare KA (2002) Structural acoustic optimization of a composite cylindrical shell using FEM/BEM. J Vib Acoust 124:410–414

    Article  Google Scholar 

  • Joshi P, Mulani SB, Slemp WCH, Kapania RK (2012) Vibro-acoustic optimization of turbulent boundary excited panel with curvilinear stiffeners. J Aircraft 49(1):52–65

    Article  Google Scholar 

  • Joshi P, Mulani SB, Kapania RK (2015) Multi-objective vibro-acoustic optimization of stiffened panels. Struct MultiDiscip Optim 51:835–848

    Article  MathSciNet  Google Scholar 

  • Klaerner M, Wuehrl M, Kroll L, Marburg S (2017) FEA-based methods for optimising structure-borne sound radiation. Mech Sys Sig Proc 89:37–47

    Article  Google Scholar 

  • Koopmann GH, Fahnline JB (1997) Designing quiet structures, Academic Press

  • Lysak PD (2006) Modeling the wall pressure spectrum in turbulent pipe flows. J Fluids Eng 128:216–222

    Article  Google Scholar 

  • Marburg S (2002) Developments in structural–acoustic optimization for passive noise control. Archives of Computational Methods in Engineering State of the art reviews 9(4):291–370

    Article  Google Scholar 

  • Marburg S, Dienerowitz F, Fritze D, Hardtke HJ (2006) Case studies on structural–acoustic optimization of a finite beam. Acta Acustica united with Acustica 92(3):427–439

    Google Scholar 

  • Marburg S, Shepherd MR, Hambric SA (2016) Structural-acoustic optimization, engineering vibroacoustic analysis: methods and applications, Wiley

  • McCormick CA, Shepherd MR (2018) Optimal design and position of an embedded one-dimensional acoustic black hole. In: Proceedings of internoise 2018. Chicago

  • Mellen RH (1990) On modeling convective turbulence. J Acoust Soc Am 88(6):2891–2893

    Article  Google Scholar 

  • Ratle A, Berry A (1998) Use of genetic algorithm for the vibroacoustic optimization of a plate carrying point masses. J Acoust Soc Am 104(6):3385–3397

    Article  Google Scholar 

  • Reed P (2016) High performance computing with the MOEA framework and ignite, CreateSpace Independent Publishing Platform, USA

  • Roy N, Girard A (2005) Impact of residual modes in structural dynamics, spacecraft structures. Mater Mech Test 2005:581

    Google Scholar 

  • Shepherd MR, Hambric SA (2012) An approach for structural-acoustic optimization of ribbed panels using component mode synthesis. In: Proceedings of internoise 2012/ASME NCAD IN12-592

  • Shepherd MR, Hambric SA (2014) Minimization the acoustic power radiated by a fluid-loaded curved panel excited by turbulent boundary layer flow. J Acoust Soc Am 136(5):2575–2585

    Article  Google Scholar 

  • Shepherd MR, Fuertado FA, Conlon SC (2016) Multi-objective optimization of acoustic black hole vibration absorbers. J Acoust Soc Am 140(3):EL227–EL230

    Article  Google Scholar 

  • Vrugt JA, Robinson BA (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci 104(3):708–711

    Article  Google Scholar 

  • Walsh TF, Aquino W (2017) Massively parallel structal acoustics for forward and inverse problems. J Acoust Soc Am 143:3588

    Article  Google Scholar 

  • Walsh TF, Hammetter C, Sinclair MB, Shaklee-Brown H, Bishop J, Aquino W (2018) Design, optimization and fabrication of mechanical metamaterials for vibration control. J Acoust Soc Am 143:1917

    Article  Google Scholar 

  • Wixom AS, Walters GS, Martinelli SL, Williams DM (2019) Generalized polynomial chaos with optimized quadrature applied to a turbulent boundary layer forced plate. J Comp Nonlinear Dyn 14:021010–1–021010-9

    Google Scholar 

  • Wong WO (2016) Optimal design of a hysteretic vibration absorber using fixed-points theory. J Acoust Soc Am 139(6):3110–3115

    Article  Google Scholar 

  • Yuksel E, Kamci G, Basdogan I (2012) Vibro-acoustic design optimization study to improve the sound pressure level inside the passenger cabin, vol 134

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Acknowledgments

The authors would like to thank Dr. Peter Lysak for his recommendations regarding the forcing functions used in this paper.

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Authors and Affiliations

  1. The Pennsylvania State University, State College, PA, USA

    Micah R. Shepherd, Robert L. Campbell & Stephen A. Hambric

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  1. Micah R. Shepherd
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  2. Robert L. Campbell
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  3. Stephen A. Hambric
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Correspondence to Micah R. Shepherd.

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Shepherd, M.R., Campbell, R.L. & Hambric, S.A. A parallel computing framework for performing structural-acoustic optimization with stochastic forcing. Struct Multidisc Optim 61, 675–685 (2020). https://doi.org/10.1007/s00158-019-02389-2

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