LEADER 05334nam 2200625 a 450 001 9910830099103321 005 20170815104200.0 010 $a1-118-60015-0 010 $a1-118-60002-9 035 $a(CKB)2670000000336677 035 $a(EBL)1124319 035 $a(SSID)ssj0000853121 035 $a(PQKBManifestationID)11498979 035 $a(PQKBTitleCode)TC0000853121 035 $a(PQKBWorkID)10866337 035 $a(PQKB)10483587 035 $a(MiAaPQ)EBC1124319 035 $a(PPN)22154836X 035 $a(OCoLC)829243605 035 $a(EXLCZ)992670000000336677 100 $a20100318d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultidisciplinary design optimization in computational mechanics$b[electronic resource] /$fedited by Piotr Breitkopf, Rajan Filomeno Coelho 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2010 215 $a1 online resource (573 p.) 225 1 $aISTE 300 $aDescription based upon print version of record. 311 $a1-84821-138-4 320 $aIncludes bibliographical references and index. 327 $aCover; Multidisciplinary Design Optimization in Computational Mechanics; Title Page; Copyright Page; Table of Contents; Foreword; Notes for Instructors; Acknowledgements; Chapter 1. Multilevel Multidisciplinary Optimization in Airplane Design; 1.1. Introduction; 1.2. Overview of the traditional airplane design process and expected MDO contributions; 1.3. First step toward MDO: local dimensioning by mathematical optimization; 1.4. Second step toward MDO: multilevel multidisciplinary dimensioning; 1.5. Elements of an MDO process; 1.6. Choice of optimizers; 1.6.1. Deterministic algorithms 327 $a1.6.2. Stochastic algorithms1.7. Coupling between levels; 1.7.1. Reduction of mathematical models; 1.7.2. Simplified physical models; 1.8. Post-processing; 1.8.1. Lagrange multipliers; 1.8.2. Pareto fronts; 1.8.3. Self-organizing maps; 1.9. Conclusion; Chapter 2. Response Surface Methodology and Reduced Order Models; 2.1. Introduction; 2.2. Introducing some more notations; 2.3. Linear regression; 2.3.1. Introduction to linear regression; 2.3.2. Leverage; 2.3.3. Generalized linear regression; 2.3.4. An implicit reduced order model: moving least-squares (MLS) method 327 $a2.3.5. Bias-variance trade-off2.4. Non-linear regression; 2.4.1. Neural networks as an example of non-linear models; 2.4.2. Another example of a non-linear model: parametrized RBFs; 2.4.3. Gradient algorithms; 2.4.4. Second-order methods; 2.5. Kriging interpolation; 2.5.1. Recall on Gaussian regression; 2.5.2. Basic principles of kriging algorithms; 2.5.3. Trend estimation; 2.5.4. Covariance estimation; 2.6. Non-parametric regression and kernel-based methods; 2.6.1. Introduction to non-parametric methods; 2.6.2. Parzen window regression; 2.6.3. Radial basis functions (RBFs) 327 $a2.6.4. EM estimation of a mixture2.6.5. How RBFs are used in this book; 2.7. Support vector regression; 2.7.1. Variational formulation of SVR; 2.7.2. Dual formulation of SVR; 2.7.3. Computation of SVR models; 2.7.4. Self-reproducing Hilbert space; 2.7.5. Regularizing properties of the kernel; 2.7.6. Margin selection and ?-regression; 2.7.7. Large databases and recursive learning; 2.8. Model selection; 2.8.1. Estimating generalization error; 2.8.2. Cross-validation methods; 2.8.3. Leverage methods; 2.9. Introduction to design of computer experiments (DoCE); 2.9.1. Classical techniques 327 $a2.9.2. Input space sampling2.9.3. Adaptive learning and sequential design; 2.10. Bibliography; Chapter 3. PDE Metamodeling using Principal Component Analysis; 3.1. Principal component analysis (PCA); 3.2. Truncation rank and projector error; 3.3. Application: POD reduction of velocity fields in an engine combustion chamber; 3.4. Reduced-basis methods, numerical analysis; 3.4.1. POD-Galerkin projection method; 3.4.2. Dual approach POD-Petrov-Galerkin; 3.5. Intrusive/non-intrusive aspects; 3.6. Double reduction in both space and parameter dimensions; 3.7. The weighted residual method 327 $a3.8. Non-linear problems 330 $aThis book provides a comprehensive introduction to the mathematical and algorithmic methods for the Multidisciplinary Design Optimization (MDO) of complex mechanical systems such as aircraft or car engines. We have focused on the presentation of strategies efficiently and economically managing the different levels of complexity in coupled disciplines (e.g. structure, fluid, thermal, acoustics, etc.), ranging from Reduced Order Models (ROM) to full-scale Finite Element (FE) or Finite Volume (FV) simulations. Particular focus is given to the uncertainty quantification and its impact on the robus 410 0$aISTE 606 $aEngineering design 606 $aEngineering mathematics 615 0$aEngineering design. 615 0$aEngineering mathematics. 676 $a620.100285 701 $aBreitkopf$b Piotr$0918057 701 $aCoelho$b Rajan Filomeno$01652155 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830099103321 996 $aMultidisciplinary design optimization in computational mechanics$94002610 997 $aUNINA