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A Computational Steering Framework for Large-Scale Composite Structures

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Handbook of Dynamic Data Driven Applications Systems

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Abstract

Recent advances in simulation, optimization, structural health monitoring, and high-performance computing create a unique opportunity to combine the developments in these fields to formulate a Dynamic Data-Driven Applications Systems (DDDAS) Interactive Structure Composite Element Relation Network (DISCERN) framework. DISCERN consists of the following items and features: a structural health monitoring (SHM) system, an advanced fluid-structure interaction (FSI) simulation, and sensitivity analysis, optimization and control software. High-performance computing (HPC) is employed to enhance the efficiency and effectiveness of the system. The intended application of the DISCERN framework is the analysis of medium-to-large-scale composite structures. These include aerospace structures, such as military aircraft fuselage and wings, helicopter blades, and unmanned aerial vehicles, and civil structures, such as wind turbine blades and towers. The proposed DISCERN framework continuously and dynamically integrates the SHM data into the FSI analysis of these structures. This capability allows one to: (1) Shelter the structures from excessive stress levels during operation; (2) Make informed decisions to perform structural maintenance and repair; and (3) Predict the remaining fatigue life of the structure. The primal and adjoint, time-dependent FSI formulations are presented. A simple control strategy for FSI problems is formulated based on the information provided by the solution of the primal and adjoint FSI problems. Such control strategies presented are useful for computational steering simulations of interest in this work.

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References

  1. I. Akkerman, Y. Bazilevs, V.M. Calo, T.J.R. Hughes, S. Hulshoff, The role of continuity in residual-based variational multiscale modeling of turbulence. Comput. Mech. 41, 371–378 (2008)

    Article  MathSciNet  Google Scholar 

  2. C. Audet, J.E. Dennis Jr., Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17, 2–11 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Y. Bazilevs, L. Beirao da Veiga, J.A. Cottrell, T.J.R. Hughes, G. Sangalli, Isogeometric analysis: approximation, stability and error estimates for h-refined meshes. Math. Methods Mod. Appl. Sci. 16, 1031–1090 (2006)

    Article  MathSciNet  Google Scholar 

  4. Y. Bazilevs, V.M. Calo, J.A. Cottrell, T.J.R. Hughes, A. Reali, G. Scovazzi, Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows. Comput. Methods Appl. Mech. Eng. 197, 173–201 (2007)

    Article  MathSciNet  Google Scholar 

  5. Y. Bazilevs, C. Michler, V.M. Calo, T.J.R. Hughes, Weak Dirichlet boundary conditions for wall-bounded turbulent flows. Comput. Methods Appl. Mech. Eng. 196, 4853–4862 (2007)

    Article  MathSciNet  Google Scholar 

  6. Y. Bazilevs, V.M. Calo, T.J.R. Hughes, Y. Zhang, Isogeometric fluid–structure interaction: theory, algorithms and computations. Comput. Mech. 43, 3–37 (2008)

    Article  MathSciNet  Google Scholar 

  7. Y. Bazilevs, V.M. Calo, J.A. Cottrell, J. Evans, T.J.R. Hughes, S. Lipton, M.A. Scott, T.W. Sederberg, Isogeometric analysis using t-splines. Comput. Methods Appl. Mech. Eng. 199, 229–263 (2010)

    Article  MathSciNet  Google Scholar 

  8. Y. Bazilevs, M.-C. Hsu, I. Akkerman, S. Wright, K. Takizawa, B. Henicke, T. Spielman, T.E. Tezduyar, 3D simulation of wind turbine rotors at full scale. Part I: geometry modeling and aerodynamics. Int. J. Numer. Methods Fluids 65, 207–235 (2011)

    MATH  Google Scholar 

  9. Y. Bazilevs, M.-C. Hsu, J. Kiendl, R. Wuechner, K.-U. Bletzinger, 3D simulation of wind turbine rotors at full scale. Part II: fluid-structure interaction. Int. J. Numer. Methods Fluids 65, 236–253 (2011)

    MATH  Google Scholar 

  10. Y. Bazilevs, A.L. Marsden, F. Lanza di Scalea, A. Majumdar, M. Tatineni, Toward a computational steering framework for large-scale composite structures based on continually and dynamically injected sensor data. Proc. Comput. Sci. 9, 1149–1158 (2012)

    Article  Google Scholar 

  11. Y. Bazilevs, M.-C. Hsu, M.T Bement, Adjoint-based control of fluid-structure interaction for computational steering applications. Proc. Comput. Sci. 18, 1989–1998 (2013)

    Article  Google Scholar 

  12. Y. Bazilevs, K. Takizawa, T.E. Tezduyar, Computational Fluid–Structure Interaction. Methods and Applications (Wiley, Hoboken, 2013)

    Google Scholar 

  13. T. Belytschko, W.K. Liu, B. Moran, Nonlinear Finite Elements for Continua and Structures (Wiley, Chichester, 2000)

    MATH  Google Scholar 

  14. M.T. Bement, T.R. Bewley, Excitation design for damage detection using iterative adjoint-based optimization–Part 1: method development. Mech. Syst. Signal Process. 23, 783–793 (2009)

    Article  Google Scholar 

  15. D.J. Benson, Y. Bazilevs, M.-C. Hsu, T.J.R. Hughes, Isogeometric shell analysis: the Reissner–Mindlin shell. Comput. Methods Appl. Mech. Eng. 199, 276–289 (2010)

    Article  MathSciNet  Google Scholar 

  16. D.J. Benson, Y. Bazilevs, M.-C. Hsu, T.J.R. Hughes, A large-deformation, rotation-free isogeometric shell. Comput. Methods Appl. Mech. Eng. 200, 1367–1378 (2011)

    Article  MathSciNet  Google Scholar 

  17. A.J. Booker, J.E. Dennis Jr., P.D. Frank, D.B. Serafini, V. Torczon, M.W. Trosset, A rigorous framework for optimization of expensive functions by surrogates. Struct. Optim. 17, 1–13 (1999)

    Article  Google Scholar 

  18. J.A. Cottrell, T.J.R. Hughes, Y. Bazilevs, Isogeometric Analysis: Toward Integration of CAD and FEA (Wiley, Chichester, 2009)

    Book  Google Scholar 

  19. F. Darema, Dynamic data driven applications systems: a new paradigm for application simulations and measurements, in Proceedings of ICCS 2004 4th International Conference on Computational Science, 2004, pp. 662–669

    Google Scholar 

  20. F. Lanza di Scalea, H. Matt, I. Bartoli, S. Coccia, G. Park, C. Farrar, Health monitoring of uav wing skin-to-spar joints using guided waves and macro fiber composite transducers. J. Intell. Mater. Syst. Struct. 18, 373–388 (2007)

    Article  Google Scholar 

  21. M.-C. Hsu, Y. Bazilevs, Fluid structure interaction modeling of wind turbines: simulating the full machine. Comput. Mech. 50, 821–833 (2012)

    Article  Google Scholar 

  22. T.J.R. Hughes, W.K. Liu, T.K. Zimmerman, Arbitrary Lagrangian–Eulerian finite element formulation for incompressible viscous flows. Comput. Methods Appl. Mech. Eng. 29, 329–349 (1981)

    Article  Google Scholar 

  23. T.J.R. Hughes, J.A. Cottrell, Y. Bazilevs, Isogeometric analysis: Cad, finite elements, NURBS, exact geometry, and mesh refinement. Comput. Methods Appl. Mech. Eng. 194, 4135–4195 (2005)

    Article  MathSciNet  Google Scholar 

  24. A.A. Johnson, T.E. Tezduyar, Mesh update strategies in parallel finite element computations of flow problems with moving boundaries and interfaces. Comput. Methods Appl. Mech. Eng. 119, 73–94 (1994)

    Article  Google Scholar 

  25. J. Kiendl, Y. Bazilevs, M.-C. Hsu, R. Wuechner, K.-U. Bletzinger, The bending strip method for isogeometric analysis of Kirchhoff-Love shell structures comprised of multiple patches. Comput. Methods Appl. Mech. Eng. 199, 2403–2416 (2010)

    Article  MathSciNet  Google Scholar 

  26. A. Korobenko, M.C. Hsu, I. Akkerman, J. Tippmann, Y. Bazilevs, Structural mechanics modeling and FSI simulation of wind turbines. Math. Models Methods Appl. Sci. 23, 249–272 (2012). https://doi.org/10.1142/S0218202513400034

    Article  MathSciNet  Google Scholar 

  27. A. Manohar, F. Lanza di Scalea, Wavelet aided multivariate outlier analysis to enhance defect contrast in thermal images. Exp. Tech. Soc. Exp. Mech. 38(1), 28–37 (2014)

    Google Scholar 

  28. A.L. Marsden, M. Wang, J.E. Dennis Jr., P. Moin, Optimal aeroacoustic shape design using the surrogate management framework. Optim. Eng. 5, 235–262 (2004). Special Issue on “Surrogate Optimization.”

    Google Scholar 

  29. H. Matt, I. Bartoli, F. Lanza di Scalea, Ultrasonic guided wave monitoring of composite wing skin-to-spar bonded joints in aerospace structures. J. Acoust. Soc. Am. 118, 2240–2252 (2005)

    Article  Google Scholar 

  30. J.T. Oden, K.R. Diller, C. Bajaj, J.C. Browne, J. Hazle, I. Babuska, J. Bass, L. Demkowicz, Y. Feng, D. Fuentes, S. Prudhomme, M.N. Rylander, R.J. Stafford, Y. Zhang, Dynamic data-driven finite element models for laser treatment of prostate cancer. Numer. Methods PDE 23, 904–922 (2007)

    Article  Google Scholar 

  31. G. Park, C. Farrar, F. Lanza di Scalea, S. Coccia, Performance assessment and validation of piezoelectric active-sensors in structural health monitoring. Smart Mater. Struct. 15, 1673–1683 (2006)

    Article  Google Scholar 

  32. L. Piegl, W. Tiller, The NURBS Book (Springer, Berlin/Heidelberg, 1997)

    Book  Google Scholar 

  33. T. Richter, Goal-oriented error estimation for fluid–structure interaction problems. Comput. Methods Appl. Mech. Eng. 223–224, 28–42 (2012)

    Article  MathSciNet  Google Scholar 

  34. Y. Saad, M. Schultz, GMRES: a generalized minimal residual algorithm for solving non- symmetric linear systems. SIAM J. Sci. Stat. Comput. 7, 856–869 (1986)

    Article  Google Scholar 

  35. S. Sankaran, Stochastic optimization using a sparse grid collocation scheme. Probab. Eng. Mech. 24, 382–396 (2009)

    Article  Google Scholar 

  36. S. Sankaran, A.L. Marsden, A stochastic collocation method for uncertainty quantification in cardiovascular simulations. J. Biomech. Eng. 133, 031001 (2011)

    Article  Google Scholar 

  37. S. Sankaran, C. Audet, A.L. Marsden, A method for stochastic constrained optimization using derivative-free surrogate pattern search and collocation. J. Comput. Phys. 229, 4664–4682 (2010)

    Article  Google Scholar 

  38. T.W. Sederberg, D.L. Cardon, G.T. Finnigan, N.S. North, J. Zheng, T. Lyche, T-spline simplification and local refinement. ACM Trans. Graph. 23, 276–283 (2004)

    Article  Google Scholar 

  39. K.G. van der Zee, E.H. van Brummelen, I. Akkerman, R. de Borst, Goal-oriented error estimation and adaptivity for fluid–structure interaction using exact linearized adjoints. Comput. Methods Appl. Mech. Eng. 200, 2738–2757 (2011)

    Article  MathSciNet  Google Scholar 

  40. N. Zabaras, B. Ganapathysubramanian, A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach. J. Comput. Phys. 227, 4697–4735 (2008)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by the AFOSR Grant FA9550-12-1-0005. The authors greatly acknowledge this support.

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada

    A. Korobenko

  2. Department of Mechanical Engineering, Iowa State University, Ames, IA, USA

    M.-C. Hsu

  3. School of Engineering, Brown University, Providence, RI, USA

    Y. Bazilevs

Authors
  1. A. Korobenko
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  2. M.-C. Hsu
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  3. Y. Bazilevs
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Corresponding author

Correspondence to A. Korobenko .

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Editors and Affiliations

  1. Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA

    Erik Blasch

  2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  3. Information Directorate, Air Force Research Laboratory, Rome, NY, USA

    Alex Aved

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Korobenko, A., Hsu, MC., Bazilevs, Y. (2018). A Computational Steering Framework for Large-Scale Composite Structures. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-95504-9_8

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  • Online ISBN: 978-3-319-95504-9

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