Computational Approaches for Aerospace Design: The Pursuit of Excellence

Andy Keane, Prasanth Nair

  • 出版商: Wiley
  • 出版日期: 2005-08-05
  • 售價: $1,560
  • 貴賓價: 9.8$1,529
  • 語言: 英文
  • 頁數: 602
  • 裝訂: Hardcover
  • ISBN: 0470855401
  • ISBN-13: 9780470855409
  • 相關分類: Excel
  • 下單後立即進貨 (約5~7天)

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Description

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.

 

Table of Contents

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 Welch–Sacks 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.

10 A Problem in Satellite Design 391

10.1 A Problem in Structural Dynamics.

10.2 Initial Passive Redesign in Three Dimensions.

10.3 A Practical Three-dimensional Design.

10.4 Active Control Measures.

10.5 Combined Active and Passive Methods.

10.6 Robustness Measures.

10.7 Adjoint-based Approaches.

11 Airfoil Section Design.

11.1 Analysis Methods.

11.2 Drag-estimation Methods.

11.3 Calculation Methods Adopted.

11.4 Airfoil Parameterization.

11.5 Multiobjective Optimization.

12 Aircraft Wing Design – Data Fusion between Codes 447

12.1 Introduction.

12.2 Overall Wing Design.

12.3 An Example and Some Basic Searches.

12.4 Direct Multifidelity Searches.

12.5 Response Surface Modeling.

12.6 Data Fusion.

12.7 Conclusions.

13 Turbine Blade Design (I) – Guide-vane SKE Control.

13.1 Design of Experiment Techniques, Response Surface Models and Model

Refinement.

13.2 Initial Design.

13.3 Seven-variable Trials without Capacity Constraint.

13.4 Twenty-one-variable Trial with Capacity Constraint.

13.5 Conclusions.

14 Turbine Blade Design (II) – Fir-tree Root Geometry.

14.1 Introduction.

14.2 Modeling and Optimization of Traditional Fir-tree Root Shapes.

14.3 Local Shape Parameterization using NURBS.

14.4 Finite Element Analysis of the Fir-tree Root.

14.5 Formulation of the Optimization Problem and Two-stage Search Strategy.

14.6 Optimum Notch Shape and Stress Distribution.

14.7 Summary.

15 Aero-engine Nacelle Design Using the Geodise Toolkit.

15.1 The Geodise System.

15.2 Gas-turbine Noise Control.

15.3 Conclusions.

16 Getting the Optimization Process Started.

16.1 Problem Classification.

16.2 Initial Search Process Choice.

16.3 Assessment of Initial Results.

Bibliography.

Index.

商品描述(中文翻譯)

描述

在過去的五十年中,工程設計中進行分析以作為決策的先驅能力大幅增加。特別是現代計算系統的出現和先進數值方法的發展,使得計算建模成為生產優化設計的重要工具。本書探討了計算機輔助分析如何革命了航空航天工程,全面介紹了支撐先進計算設計的最新技術。通過實例研究和超過500個主要研究文獻的參考,讀者可以全面了解這項技術,深入了解世界上最複雜的工程系統。

主要特點:
- 包括航空設計的歷史背景和已建立的優化、幾何和數學建模技術,將最近的工程發展置於相關背景中。
- 探討了最新的方法,如演化和響應曲面優化、伴隨和數值微分敏感度代碼、不確定性分析以及使用基於網格的計算的並行系統集成方案。
- 通過結構靜力學、動力學和流體力學在衛星、飛機和航空發動機設計問題中的實際應用來說明方法。

這本書對於修讀航空、車輛和發動機設計課程的高年級本科生和研究生來說是一個寶貴的資源。對於從事計算方法設計的實踐工程師和研究人員也很有用。

目錄

- 前言
- 前言
- 致謝
- 第一部分:初步
- 第1章:介紹
- 1.1 目標
- 1.2 路線圖-涵蓋內容和未涵蓋內容
- 1.3 航空航天設計的歷史透視
- 1.4 傳統手動設計方法和設計迭代,設計團隊
- 1.5 建模技術的進展:計算工程
- 1.6 航空航天系統設計中的權衡
- 1.7 設計自動化、演化和創新
- 1.8 設計搜索和優化(DSO)
- 1.9 計算方法的應用
- 第2章:以設計為導向的分析
- 2.1 幾何建模和設計參數化
- 2.2 計算網格生成
- 2.3 耦合系統的分析和設計
- 第3章:數值優化的要素
- 3.1 單變量優化