{"product_id":"computational-approaches-for-aerospace-design-the-pursuit-of-excellence-9780470855409","title":"Computational Approaches for Aerospace Design The","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe ability to carry out analysis as a precursor to decision making in engineering design has increased. This text explores how computer aided analysis has revolutionized aerospace engineering, providing a comprehensive coverage of the technologies underpinning advanced computational design.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eForeword.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Preliminaries.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Objectives.\u003c\/p\u003e \u003cp\u003e1.2 Road Map –What is Covered and What is Not.\u003c\/p\u003e \u003cp\u003e1.3 An Historical Perspective on Aerospace Design.\u003c\/p\u003e \u003cp\u003e1.4 Traditional Manual Approaches to Design and Design Iteration, Design Teams.\u003c\/p\u003e \u003cp\u003e1.5 Advances in Modeling Techniques: Computational Engineering.\u003c\/p\u003e \u003cp\u003e1.6 Trade-offs in Aerospace System Design.\u003c\/p\u003e \u003cp\u003e1.7 Design Automation, Evolution and Innovation.\u003c\/p\u003e \u003cp\u003e1.8 Design Search and Optimization (DSO).\u003c\/p\u003e \u003cp\u003e1.9 The Take-up of Computational Methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Design-oriented Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Geometry Modeling and Design Parameterization.\u003c\/p\u003e \u003cp\u003e2.2 Computational Mesh Generation.\u003c\/p\u003e \u003cp\u003e2.3 Analysis and Design of Coupled Systems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Elements of Numerical Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Single Variable Optimizers – Line Search.\u003c\/p\u003e \u003cp\u003e3.2 Multivariable Optimizers.\u003c\/p\u003e \u003cp\u003e3.3 Constrained Optimization.\u003c\/p\u003e \u003cp\u003e3.4 Metamodels and Response Surface Methods.\u003c\/p\u003e \u003cp\u003e3.5 Combined Approaches – Hybrid Searches, Metaheuristics.\u003c\/p\u003e \u003cp\u003e3.6 Multiobjective Optimization.\u003c\/p\u003e \u003cp\u003e3.7 Robustness.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Sensitivity Analysis and Approximation Concepts.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sensitivity Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Finite-difference Methods.\u003c\/p\u003e \u003cp\u003e4.2 Complex Variable Approach.\u003c\/p\u003e \u003cp\u003e4.3 Direct Methods.\u003c\/p\u003e \u003cp\u003e4.4 Adjoint Methods.\u003c\/p\u003e \u003cp\u003e4.5 Semianalytical Methods.\u003c\/p\u003e \u003cp\u003e4.6 Automatic Differentiation.\u003c\/p\u003e \u003cp\u003e4.7 Mesh Sensitivities for Complex Geometries.\u003c\/p\u003e \u003cp\u003e4.8 Sensitivity of Optima to Problem Parameters.\u003c\/p\u003e \u003cp\u003e4.9 Sensitivity Analysis of Coupled Systems.\u003c\/p\u003e \u003cp\u003e4.10 Comparison of Sensitivity Analysis Techniques.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 General Approximation Concepts and Surrogates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Local Approximations.\u003c\/p\u003e \u003cp\u003e5.2 Multipoint Approximations.\u003c\/p\u003e \u003cp\u003e5.3 Black-box Modeling: a Statistical Perspective.\u003c\/p\u003e \u003cp\u003e5.4 Generalized Linear Models.\u003c\/p\u003e \u003cp\u003e5.5 Sparse Approximation Techniques.\u003c\/p\u003e \u003cp\u003e5.6 Gaussian Process Interpolation and Regression.\u003c\/p\u003e \u003cp\u003e5.7 Data Parallel Modeling.\u003c\/p\u003e \u003cp\u003e5.8 Design of Experiments (DoE).\u003c\/p\u003e \u003cp\u003e5.9 Visualization and Screening.\u003c\/p\u003e \u003cp\u003e5.10 Black-box Surrogate Modeling in Practice.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Physics-based Approximations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Surrogate Modeling using Variable-fidelity Models.\u003c\/p\u003e \u003cp\u003e6.2 An Introduction to Reduced Basis Methods.\u003c\/p\u003e \u003cp\u003e6.3 Reduced Basis Methods for Linear Static Reanalysis.\u003c\/p\u003e \u003cp\u003e6.4 Reduced Basis Methods for Reanalysis of Eigenvalue Problems.\u003c\/p\u003e \u003cp\u003e6.5 Reduced Basis Methods for Nonlinear Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Frameworks for Design Space Exploration.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Managing Surrogate Models in Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Trust-region Methods.\u003c\/p\u003e \u003cp\u003e7.2 The Space Mapping Approach.\u003c\/p\u003e \u003cp\u003e7.3 Surrogate-assisted Optimization using Global Models.\u003c\/p\u003e \u003cp\u003e7.4 Managing Surrogate Models in Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003e7.5 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Design in the Presence of Uncertainty.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Uncertainty Modeling and Representation.\u003c\/p\u003e \u003cp\u003e8.2 Uncertainty Propagation.\u003c\/p\u003e \u003cp\u003e8.3 Taguchi Methods.\u003c\/p\u003e \u003cp\u003e8.4 The Welch–Sacks Method.\u003c\/p\u003e \u003cp\u003e8.5 Design for Six.\u003c\/p\u003e \u003cp\u003e8.6 Decision-theoretic Formulations.\u003c\/p\u003e \u003cp\u003e8.7 Reliability-based Optimization.\u003c\/p\u003e \u003cp\u003e8.8 Robust Design using Information-gap Theory.\u003c\/p\u003e \u003cp\u003e8.9 Evolutionary Algorithms for Robust Design.\u003c\/p\u003e \u003cp\u003e8.10 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Architectures for Multidisciplinary Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Preliminaries.\u003c\/p\u003e \u003cp\u003e9.2 Fully Integrated Optimization (FIO).\u003c\/p\u003e \u003cp\u003e9.3 System Decomposition and Optimization.\u003c\/p\u003e \u003cp\u003e9.4 Simultaneous Analysis and Design (SAND).\u003c\/p\u003e \u003cp\u003e9.5 Distributed Analysis Optimization Formulation.\u003c\/p\u003e \u003cp\u003e9.6 Collaborative Optimization.\u003c\/p\u003e \u003cp\u003e9.7 Concurrent Subspace Optimization.\u003c\/p\u003e \u003cp\u003e9.8 Coevolutionary Architectures.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Case Studies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Problem in Satellite Design 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 A Problem in Structural Dynamics.\u003c\/p\u003e \u003cp\u003e10.2 Initial Passive Redesign in Three Dimensions.\u003c\/p\u003e \u003cp\u003e10.3 A Practical Three-dimensional Design.\u003c\/p\u003e \u003cp\u003e10.4 Active Control Measures.\u003c\/p\u003e \u003cp\u003e10.5 Combined Active and Passive Methods.\u003c\/p\u003e \u003cp\u003e10.6 Robustness Measures.\u003c\/p\u003e \u003cp\u003e10.7 Adjoint-based Approaches.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Airfoil Section Design.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Analysis Methods.\u003c\/p\u003e \u003cp\u003e11.2 Drag-estimation Methods.\u003c\/p\u003e \u003cp\u003e11.3 Calculation Methods Adopted.\u003c\/p\u003e \u003cp\u003e11.4 Airfoil Parameterization.\u003c\/p\u003e \u003cp\u003e11.5 Multiobjective Optimization.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Aircraft Wing Design – Data Fusion between Codes 447\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Overall Wing Design.\u003c\/p\u003e \u003cp\u003e12.3 An Example and Some Basic Searches.\u003c\/p\u003e \u003cp\u003e12.4 Direct Multifidelity Searches.\u003c\/p\u003e \u003cp\u003e12.5 Response Surface Modeling.\u003c\/p\u003e \u003cp\u003e12.6 Data Fusion.\u003c\/p\u003e \u003cp\u003e12.7 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Turbine Blade Design (I) – Guide-vane SKE Control.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Design of Experiment Techniques, Response Surface Models and Model\u003c\/p\u003e \u003cp\u003eRefinement.\u003c\/p\u003e \u003cp\u003e13.2 Initial Design.\u003c\/p\u003e \u003cp\u003e13.3 Seven-variable Trials without Capacity Constraint.\u003c\/p\u003e \u003cp\u003e13.4 Twenty-one-variable Trial with Capacity Constraint.\u003c\/p\u003e \u003cp\u003e13.5 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Turbine Blade Design (II) – Fir-tree Root Geometry.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Modeling and Optimization of Traditional Fir-tree Root Shapes.\u003c\/p\u003e \u003cp\u003e14.3 Local Shape Parameterization using NURBS.\u003c\/p\u003e \u003cp\u003e14.4 Finite Element Analysis of the Fir-tree Root.\u003c\/p\u003e \u003cp\u003e14.5 Formulation of the Optimization Problem and Two-stage Search Strategy.\u003c\/p\u003e \u003cp\u003e14.6 Optimum Notch Shape and Stress Distribution.\u003c\/p\u003e \u003cp\u003e14.7 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Aero-engine Nacelle Design Using the Geodise Toolkit.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 The Geodise System.\u003c\/p\u003e \u003cp\u003e15.2 Gas-turbine Noise Control.\u003c\/p\u003e \u003cp\u003e15.3 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Getting the Optimization Process Started.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Problem Classification.\u003c\/p\u003e \u003cp\u003e16.2 Initial Search Process Choice.\u003c\/p\u003e \u003cp\u003e16.3 Assessment of Initial Results.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49525394964823,"sku":"9780470855409","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470855409.jpg?v=1731860347","url":"https:\/\/bookcurl.com\/products\/computational-approaches-for-aerospace-design-the-pursuit-of-excellence-9780470855409","provider":"Book Curl","version":"1.0","type":"link"}