Multidisciplinary Design Optimization in Computational Mechanics
Piotr Breitkopf, Rajan Filomeno Coelho
John Wiley & Sons, Feb 4, 2013 - Technology & Engineering - 549 pages
This book provides a comprehensive introduction to the mathematical and algorithmic methods for the Multidisciplinary Design Optimization (MDO) of complex mechanical systems such as aircraft or car engines. We have focused on the presentation of strategies efficiently and economically managing the different levels of complexity in coupled disciplines (e.g. structure, fluid, thermal, acoustics, etc.), ranging from Reduced Order Models (ROM) to full-scale Finite Element (FE) or Finite Volume (FV) simulations. Particular focus is given to the uncertainty quantification and its impact on the robustness of the optimal designs. A large collection of examples from academia, software editing and industry should also help the reader to develop a practical insight on MDO methods.
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Reducedorder Models for Coupled Problems
Uncertaint uantification for Robust Desi
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3D wing aerodynamic airfoil application approach approximation basis functions bilevel calculation Chapter coefficients component computer experiment consider constraints convergence correlation corresponding cost function coupling variables criterion database defined denote design variables dimension disciplinary disciplines discrete domain equations error estimation evaluations example Figure finite element fluid formulation Gaussian geometry global gradient Hessian implementation initial input interface interpolation ISAT iterations kernel kriging Latin hypercube sampling learning set least-squares linear matrix MDO process mesh metamodel method minimization model reduction modes Monte-Carlo multidisciplinary analysis multidisciplinary design optimization multidisciplinary optimization Nash equilibrium non-linear objective function obtained optimization problem optimization process optimum output parameters Pareto sets performed Poisson problem polynomial proper orthogonal decomposition quadratic reduced-order model regression response surface sampling scalar second-order shape optimization simulations snapshots solution solving space step strategy structural surrogate models techniques values variance wing