Computational Approaches for Aerospace Design

Over the last fifty years, the ability to carry out analysis as a
precursor to decision making in engineering design has increased
dramatically. In particular, the advent of modern computing systems
and the development of advanced numerical methods have made
computational modelling a vital tool for producing optimized
designs.

This text explores how computer-aided analysis has
revolutionized aerospace engineering, providing a comprehensive
coverage of the latest technologies underpinning advanced
computational design. Worked case studies and over 500 references
to the primary research literature allow the reader to gain a full
understanding of the technology, giving a valuable insight into the
world's most complex engineering systems.

Key Features:

• Includes background information on the history of aerospace
  design and established optimization, geometrical and mathematical
  modelling techniques, setting recent engineering developments in a
  relevant context.

• Examines the latest methods such as evolutionary and response
  surface based optimization, adjoint and numerically differentiated
  sensitivity codes, uncertainty analysis, and concurrent systems
  integration schemes using grid-based computing.

• Methods are illustrated with real-world applications of
  structural statics, dynamics and fluid mechanics to satellite,
  aircraft and aero-engine design problems.

Senior undergraduate and postgraduate engineering students
taking courses in aerospace, vehicle and engine design will find
this a valuable resource. It will also be useful for practising
engineers and researchers working on computational approaches to
design.

Autorentext

Andy Keane is the author of Computational Approaches for Aerospace Design: The Pursuit of Excellence, published by Wiley. Prasanth Nair is the author of Computational Approaches for Aerospace Design: The Pursuit of Excellence, published by Wiley.

Zusammenfassung
Over the last fifty years, the ability to carry out analysis as a precursor to decision making in engineering design has increased dramatically. In particular, the advent of modern computing systems and the development of advanced numerical methods have made computational modelling a vital tool for producing optimized designs. This text explores how computer-aided analysis has revolutionized aerospace engineering, providing a comprehensive coverage of the latest technologies underpinning advanced computational design. Worked case studies and over 500 references to the primary research literature allow the reader to gain a full understanding of the technology, giving a valuable insight into the world's most complex engineering systems.

Key Features:

• Includes background information on the history of aerospace design and established optimization, geometrical and mathematical modelling techniques, setting recent engineering developments in a relevant context.
• Examines the latest methods such as evolutionary and response surface based optimization, adjoint and numerically differentiated sensitivity codes, uncertainty analysis, and concurrent systems integration schemes using grid-based computing.
• Methods are illustrated with real-world applications of structural statics, dynamics and fluid mechanics to satellite, aircraft and aero-engine design problems.
  Senior undergraduate and postgraduate engineering students taking courses in aerospace, vehicle and engine design will find this a valuable resource. It will also be useful for practising engineers and researchers working on computational approaches to design.

Inhalt
Foreword. Preface.

Acknowledgments.

I Preliminaries.

1 Introduction.

1.1 Objectives.

1.2 Road Map What is Covered and What is Not.

1.3 An Historical Perspective on Aerospace Design.

1.4 Traditional Manual Approaches to Design and Design Iteration, Design Teams.

1.5 Advances in Modeling Techniques: Computational Engineering.

1.6 Trade-offs in Aerospace System Design.

1.7 Design Automation, Evolution and Innovation.

1.8 Design Search and Optimization (DSO).

1.9 The Take-up of Computational Methods.

2 Design-oriented Analysis.

2.1 Geometry Modeling and Design Parameterization.

2.2 Computational Mesh Generation.

2.3 Analysis and Design of Coupled Systems.

3 Elements of Numerical Optimization.

3.1 Single Variable Optimizers Line Search.

3.2 Multivariable Optimizers.

3.3 Constrained Optimization.

3.4 Metamodels and Response Surface Methods.

3.5 Combined Approaches Hybrid Searches, Metaheuristics.

3.6 Multiobjective Optimization.

3.7 Robustness.

II Sensitivity Analysis and Approximation Concepts.

4 Sensitivity Analysis.

4.1 Finite-difference Methods.

4.2 Complex Variable Approach.

4.3 Direct Methods.

4.4 Adjoint Methods.

4.5 Semianalytical Methods.

4.6 Automatic Differentiation.

4.7 Mesh Sensitivities for Complex Geometries.

4.8 Sensitivity of Optima to Problem Parameters.

4.9 Sensitivity Analysis of Coupled Systems.

4.10 Comparison of Sensitivity Analysis Techniques.

5 General Approximation Concepts and Surrogates.

5.1 Local Approximations.

5.2 Multipoint Approximations.

5.3 Black-box Modeling: a Statistical Perspective.

5.4 Generalized Linear Models.

5.5 Sparse Approximation Techniques.

5.6 Gaussian Process Interpolation and Regression.

5.7 Data Parallel Modeling.

5.8 Design of Experiments (DoE).

5.9 Visualization and Screening.

5.10 Black-box Surrogate Modeling in Practice.

6 Physics-based Approximations.

6.1 Surrogate Modeling using Variable-fidelity Models.

6.2 An Introduction to Reduced Basis Methods.

6.3 Reduced Basis Methods for Linear Static Reanalysis.

6.4 Reduced Basis Methods for Reanalysis of Eigenvalue Problems.

6.5 Reduced Basis Methods for Nonlinear Problems.

III Frameworks for Design Space Exploration.

7 Managing Surrogate Models in Optimization.

7.1 Trust-region Methods.

7.2 The Space Mapping Approach.

7.3 Surrogate-assisted Optimization using Global Models.

7.4 Managing Surrogate Models in Evolutionary Algorithms.

7.5 Concluding Remarks.

8 Design in the Presence of Uncertainty.

8.1 Uncertainty Modeling and Representation.

8.2 Uncertainty Propagation.

8.3 Taguchi Methods.

8.4 The WelchSacks Method.

8.5 Design for Six.

8.6 Decision-theoretic Formulations.

8.7 Reliability-based Optimization.

8.8 Robust Design using Information-gap Theory.

8.9 Evolutionary Algorithms for Robust Design.

8.10 Concluding Remarks.

9 Architectures for Multidisciplinary Optimization.

9.1 Preliminaries.

9.2 Fully Integrated Optimization (FIO).

9.3 System Decomposition and Optimization.

9.4 Simultaneous Analysis and Design (SAND).

9.5 Distributed Analysis Optimization Formulation.

9.6 Collaborative Optimization.

9.7 Concurrent Subspace Optimization.

9.8 Coevolutionary Architectures. **IV Case Studies.</b&gt...