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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
C. Audet, J.E. Dennis Jr., Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17, 2–11 (2006)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Y. Bazilevs, K. Takizawa, T.E. Tezduyar, Computational Fluid–Structure Interaction. Methods and Applications (Wiley, Hoboken, 2013)
T. Belytschko, W.K. Liu, B. Moran, Nonlinear Finite Elements for Continua and Structures (Wiley, Chichester, 2000)
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)
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)
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)
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)
J.A. Cottrell, T.J.R. Hughes, Y. Bazilevs, Isogeometric Analysis: Toward Integration of CAD and FEA (Wiley, Chichester, 2009)
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
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)
M.-C. Hsu, Y. Bazilevs, Fluid structure interaction modeling of wind turbines: simulating the full machine. Comput. Mech. 50, 821–833 (2012)
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)
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)
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)
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)
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
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)
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.”
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)
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)
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)
L. Piegl, W. Tiller, The NURBS Book (Springer, Berlin/Heidelberg, 1997)
T. Richter, Goal-oriented error estimation for fluid–structure interaction problems. Comput. Methods Appl. Mech. Eng. 223–224, 28–42 (2012)
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)
S. Sankaran, Stochastic optimization using a sparse grid collocation scheme. Probab. Eng. Mech. 24, 382–396 (2009)
S. Sankaran, A.L. Marsden, A stochastic collocation method for uncertainty quantification in cardiovascular simulations. J. Biomech. Eng. 133, 031001 (2011)
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)
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)
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)
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)
Acknowledgements
This work was supported by the AFOSR Grant FA9550-12-1-0005. The authors greatly acknowledge this support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-95504-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95503-2
Online ISBN: 978-3-319-95504-9
eBook Packages: Computer ScienceComputer Science (R0)