Holdings: Computational approaches for aerospace design

Computational approaches for aerospace design: the pursuit of excellence

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Bibliographic Details
Main Author:	Keane, Andy J. (Author)
Format:	Book
Language:	English
Published:	Chichester, England Wiley 2005
Subjects:	Datenverarbeitung Mathematik Aerospace engineering > Data processing Aerospace engineering > Mathematics CAD Luft- und Raumfahrtindustrie
Online Access:	Publisher description Table of contents Inhaltsverzeichnis
Item Description:	Includes bibliographical references (p. [549]-573) and index
Physical Description:	XX, 582 S. Ill., graph Darst. 25 cm
ISBN:	0470855401

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adam_text	COMPUTATIONAL APPROACHES FOR AEROSPACE DESIGN THE PURSUIT OF EXCELLENCE ANDY J. KEANE PRASANTH B. NAIR UNIVERSITY OF SOUTHAMPTON JOHN WILEY &. SONS, LTD CONTENTS FOREWORD PREFACE ACKNOWLEDGMENTS XVII XIX XXI I PRELIMINARIES 1 1.6 1 INTRODUCTION 3 1.1 OBJECTIVES 3 1.2 ROAD MAP - WHAT IS COVERED AND WHAT IS NOT 4 1.3 AN HISTORICAL PERSPECTIVE ON AEROSPACE DESIGN 5 .3.1 A PAIR OF EARLY PIONEERS 6 .3.2 A PAIR OF GREAT DESIGNERS 8 .3.3 A PAIR OF GREAT RESEARCHERS 11 .3.4 TWO GREAT AEROSPACE COMPANIES 13 .3.5 RATIONALIZATION AND COOPERATION 15 .3.6 THE DAWN OF THE COMPUTATIONAL ERA 16 1.4 TRADITIONAL MANUAL APPROACHES TO DESIGN AND DESIGN ITERATION, DESIGN TEAMS 17 .4.1 DESIGN AS A DECISION-MAKING PROCESS 17 .4.2 CONCEPT DESIGN 19 .4.3 PRELIMINARY DESIGN 21 .4.4 DETAILED DESIGN 22 .4.5 IN-SERVICE DESIGN AND DECOMMISSIONING 24 .4.6 HUMAN ASPECTS OF DESIGN TEAMS 24 1.5 ADVANCES IN MODELING TECHNIQUES: COMPUTATIONAL ENGINEERING 25 .5.1 PARTIAL DIFFERENTIAL EQUATIONS (PDES) 25 .5.2 HARDWARE VERSUS SOFTWARE 26 .5.3 COMPUTATIONAL SOLID MECHANICS (CSM) 27 .5.4 COMPUTATIONAL FLUID DYNAMICS (CFD) 29 .5.5 MULTILEVEL APPROACHES OR ZOOM ANALYSIS 31 .5.6 COMPLEXITY 32 TRADE-OFFS IN AEROSPACE SYSTEM DESIGN 33 .6.1 BALANCED DESIGNS 33 .6.2 STRUCTURAL STRENGTH VERSUS WEIGHT 34 VIII CONTENTS 1.6.3 AERODYNAMICS VERSUS STRUCTURAL STRENGTH 37 1.6.4 STRUCTURES VERSUS CONTROL 39 1.6.5 ROBUSTNESS VERSUS NOMINAL PERFORMANCE 40 1.7 DESIGN AUTOMATION, EVOLUTION AND INNOVATION 42 1.7.1 INNOVATION 43 1.7.2 EVOLUTION 44 1.7.3 AUTOMATION 45 1.8 DESIGN SEARCH AND OPTIMIZATION (DSO) 45 1.8.1 BEGINNINGS 46 1.8.2 A TAXONOMY OF OPTIMIZATION 46 1.8.3 A BRIEF HISTORY OF OPTIMIZATION METHODS 48 1.8.4 THE PLACE OF OPTIMIZATION IN DESIGN - COMMERCIAL TOOLS 50 1.9 THE TAKE-UP OF COMPUTATIONAL METHODS 51 1.9.1 TECHNOLOGY TRANSFER 51 1.9.2 ACADEMIC DESIGN RESEARCH 52 1.9.3 SOCIO-TECHNICAL ISSUES 52 2 DESIGN-ORIENTED ANALYSIS 55 2. 1 GEOMETRY MODELING AND DESIGN PARAMETERIZATION 55 2.1.1 THE ROLE OF PARAMETERIZATION IN DESIGN 57 2.1.2 DISCRETE AND DOMAIN ELEMENT PARAMETERIZATIONS 59 2.1.3 NACA AIRFOILS 61 2.1.4 SPLINE-BASED APPROACHES 62 2.1.5 PARTIAL DIFFERENTIAL EQUATION AND OTHER ANALYTICAL APPROACHES ... 63 2.1.6 BASIS FUNCTION REPRESENTATION 65 2.1.7 MORPHING 66 2.1.8 SHAPE GRAMMARS 68 2.1.9 MESH-BASED EVOLUTIONARY ENCODINGS 69 2.1.10 CAD TOOLS VERSUS DEDICATED PARAMETERIZATION METHODS 70 2.2 COMPUTATIONAL MESH GENERATION 74 2.2.1 THE FUNCTION OF MESHES 74 2.2.2 MESH TYPES AND CELL/ELEMENT/VOLUME GEOMETRIES 78 2.2.3 MESH GENERATION, QUALITY AND ADAPTATION 86 2.2.4 MESHLESS APPROACHES 89 2.3 ANALYSIS AND DESIGN OF COUPLED SYSTEMS 91 2.3.1 INTERACTIONS BETWEEN GEOMETRY DEFINITION, MESHING AND SOLVERS - PARALLEL COMPUTATIONS 92 2.3.2 SIMPLE RELAXATION AND NEWTON TECHNIQUES 97 2.3.3 SYSTEMS INTEGRATION, WORKFLOW MANAGEMENT, DATA TRANSFER AND COMPRESSION 99 3 ELEMENTS OF NUMERICAL OPTIMIZATION 105 3.1 SINGLE VARIABLE OPTIMIZERS - LINE SEARCH 106 3.1.1 UNCONSTRAINED OPTIMIZATION WITH A SINGLE REAL VARIABLE 106 3.1.2 OPTIMIZATION WITH A SINGLE DISCRETE VARIABLE ILL 3.1.3 OPTIMIZATION WITH A SINGLE NONNUMERIC VARIABLE 113 3.2 MULTIVARIABLE OPTIMIZERS 114 3.2.1 POPULATION VERSUS SINGLE-POINT METHODS 116 CONTENTS IX 3.2.2 GRADIENT-BASED METHODS 117 3.2.3 NOISY/APPROXIMATE FUNCTION VALUES 123 3.2.4 NONGRADIENT ALGORITHMS 126 3.2.5 TERMINATION AND CONVERGENCE ASPECTS 139 3.3 CONSTRAINED OPTIMIZATION 141 3.3.1 PROBLEM TRANSFORMATIONS 142 3.3.2 LAGRANGIAN MULTIPLIERS 143 3.3.3 FEASIBLE DIRECTIONS METHOD 146 3.3.4 PENALTY FUNCTION METHODS 147 3.3.5 COMBINED LAGRANGIAN AND PENALTY FUNCTION METHODS 150 3.3.6 SEQUENTIAL QUADRATIC PROGRAMMING 150 3.3.7 CHROMOSOME REPAIR 151 3.4 METAMODELS AND RESPONSE SURFACE METHODS 152 3.4.1 GLOBAL VERSUS LOCAL METAMODELS 154 3.4.2 METAMODELING TOOLS 155 3.4.3 SIMPLE RSM EXAMPLES 157 3.5 COMBINED APPROACHES - HYBRID SEARCHES, METAHEURISTICS 158 3.5.1 GLOSSY - A HYBRID SEARCH TEMPLATE 161 3.5.2 METAHEURISTICS - SEARCH WORKFLOWS 163 3.6 MULTIOBJECTIVE OPTIMIZATION 165 3.6.1 MULTIOBJECTIVE WEIGHT ASSIGNMENT TECHNIQUES 166 3.6.2 METHODS FOR COMBINING GOAL FUNCTIONS, FUZZY LOGIC AND PHYSICAL PROGRAMMING 167 3.6.3 PARETO SET ALGORITHMS 169 3.6.4 NASH EQUILIBRIA 171 3.7 ROBUSTNESS 172 II SENSITIVITY ANALYSIS AND APPROXIMATION CONCEPTS 175 4 SENSITIVITY ANALYSIS 177 4.1 FINITE-DIFFERENCE METHODS 178 4.2 COMPLEX VARIABLE APPROACH 180 4.2.1 IMPLEMENTATION ISSUES 181 4.3 DIRECT METHODS 183 4.3.1 EXAMPLE: STATIC STRUCTURAL ANALYSIS 184 4.3.2 EXAMPLE: EIGENVALUE PROBLEMS 184 4.3.3 EXAMPLE: TRANSIENT DYNAMIC ANALYSIS 185 4.4 ADJOINT METHODS 187 4.4.1 DISCRETE ADJOINT FORMULATION 188 4.4.2 CONTINUOUS ADJOINT FORMULATION 190 4.4.3 IMPLEMENTATION ASPECTS 191 4.5 SEMIANALYTICAL METHODS 193 4.6 AUTOMATIC DIFFERENTIATION 193 4.6.1 FORWARD MODE 194 4.6.2 REVERSE MODE 196 4.6.3 AD SOFTWARE TOOLS AND IMPLEMENTATION ASPECTS 197 CONTENTS 4.7 MESH SENSITIVITIES FOR COMPLEX GEOMETRIES 199 4.8 SENSITIVITY OF OPTIMA TO PROBLEM PARAMETERS 201 4.9 SENSITIVITY ANALYSIS OF COUPLED SYSTEMS 203 4.10 COMPARISON OF SENSITIVITY ANALYSIS TECHNIQUES 205 4.10.1 CASE STUDY: AERODYNAMIC SENSITIVITY ANALYSIS 206 4.10.2 CASE STUDY: AEROSTRUCTURAL SENSITIVITY ANALYSIS 208 GENERAL APPROXIMATION CONCEPTS AND SURROGATES 211 5.1 LOCAL APPROXIMATIONS 213 5.1.1 TAYLOR SERIES APPROXIMATION 213 5.1.2 INTERVENING VARIABLES 214 5.2 MULTIPOINT APPROXIMATIONS 215 5.3 BLACK-BOX MODELING: A STATISTICAL PERSPECTIVE 217 5.3.1 DATA GENERATION 217 5.3.2 MODEL-STRUCTURE SELECTION 218 5.3.3 PARAMETER ESTIMATION 219 5.3.4 MODEL ASSESSMENT 220 5.3.5 ITERATIVE MODEL-BUILDING PROCEDURES 221 5.3.6 PERSPECTIVES FROM STATISTICAL LEARNING THEORY 221 5.3.7 INTERPOLATION VERSUS REGRESSION 223 5.4 GENERALIZED LINEAR MODELS 224 5.4.1 RESPONSE SURFACE METHODS: POLYNOMIAL MODELS 225 5.4.2 NEURAL NETWORK APPROXIMATIONS 226 5.4.3 RADIAL BASIS FUNCTION APPROXIMATIONS 227 5.4.4 HERMITE INTERPOLATION USING RADIAL BASIS FUNCTIONS 230 5.4.5 TUNING RBF SHAPE AND REGULARIZATION PARAMETERS 232 5.5 SPARSE APPROXIMATION TECHNIQUES 235 5.5.1 THE SUPPORT VECTOR MACHINE 235 5.5.2 GREEDY APPROXIMATIONS 238 5.5.3 STOPPING CRITERIA: EMPIRICAL RISK VERSUS COMPLEXITY TRADE-OFF . . . 241 5.6 GAUSSIAN PROCESS INTERPOLATION AND REGRESSION 243 5.6.1 BASIC FORMULATION 244 5.6.2 MAXIMUM LIKELIHOOD ESTIMATION 248 5.6.3 INCORPORATING SENSITIVITY INFORMATION 251 5.6.4 ASSESSMENT AND REFINEMENT OF GAUSSIAN PROCESS MODELS 252 5.7 DATA PARALLEL MODELING 254 5.7.1 DATA PARALLEL LOCAL LEARNING 255 5.7.2 DATA PARTITIONING 257 5.7.3 PREDICTION WITH MULTIPLE MODELS 259 5.7.4 COMPUTATIONAL ASPECTS 259 5.8 DESIGN OF EXPERIMENTS (DOE) 260 5.8.1 MONTE CARLO TECHNIQUES 262 5.8.2 LATIN HYPERCUBE SAMPLING 262 5.8.3 SAMPLING USING ORTHOGONAL ARRAYS 264 5.8.4 MINIMUM DISCREPANCY SEQUENCES 264 5.8.5 DOE USING OPTIMALITY CRITERIA 265 5.8.6 RECOMMENDATIONS FOR OPTIMIZATION STUDIES 266 CONTENTS XI 5.9 VISUALIZATION AND SCREENING 266 5.9.1 DESIGN SPACE VISUALIZATION 267 5.9.2 VARIABLE SCREENING 267 5.10 BLACK-BOX SURROGATE MODELING IN PRACTICE 268 6 PHYSICS-BASED APPROXIMATIONS 271 6.1 SURROGATE MODELING USING VARIABLE-FIDELITY MODELS 271 6.1.1 ZERO-ORDER SCALING 272 6.1.2 FIRST-ORDER SCALING 272 6.1.3 SECOND-ORDER SCALING 273 6.1.4 MULTIPOINT CORRECTIONS 273 6.1.5 GLOBAL SCALING USING SURROGATE MODELS 274 6.1.6 AN EXAMPLE 275 6.2 AN INTRODUCTION TO REDUCED BASIS METHODS 277 6.2.1 CHOICE OF BASIS VECTORS 277 6.2.2 SCHEMES FOR COMPUTING UNDETERMINED COEFFICIENTS 279 6.3 REDUCED BASIS METHODS FOR LINEAR STATIC REANALYSIS 280 6.3.1 CHOICE OF BASIS VECTORS 281 6.3.2 BUBNOV-GALERKIN AND PETROV-GALERKIN SCHEMES 283 6.3.3 TOPOLOGICALLY MODIFIED STRUCTURES 284 6.3.4 IMPLEMENTATION ISSUES 286 6.4 REDUCED BASIS METHODS FOR REANALYSIS OF EIGENVALUE PROBLEMS 287 6.4.1 IMPROVED FIRST-ORDER APPROXIMATION 287 6.4.2 GLOBAL REDUCED BASIS PROCEDURES 289 6.5 REDUCED BASIS METHODS FOR NONLINEAR PROBLEMS 291 6.5.1 PROPER ORTHOGONAL DECOMPOSITION 291 6.5.2 REDUCED-ORDER MODELING 293 6.5.3 CONCLUDING REMARKS 297 III FRAMEWORKS FOR DESIGN SPACE EXPLORATION 299 7 MANAGING SURROGATE MODELS IN OPTIMIZATION 301 7.1 TRUST-REGION METHODS 303 7.1.1 UNCONSTRAINED PROBLEMS 304 7.1.2 EXTENSION TO CONSTRAINED PROBLEMS 306 7.2 THE SPACE MAPPING APPROACH 307 7.2.1 MAPPING FUNCTIONS 308 7.2.2 GLOBAL SPACE MAPPING 310 7.3 SURROGATE-ASSISTED OPTIMIZATION USING GLOBAL MODELS 311 7.3.1 THE EXPECTED IMPROVEMENT CRITERION 312 7.3.2 THE GENERALIZED EXPECTED IMPROVEMENT CRITERION 315 7.3.3 THE WEIGHTED EXPECTED IMPROVEMENT CRITERION 315 7.3.4 EXTENSION TO CONSTRAINED PROBLEMS 316 7.3.5 CORRELATION MATRIX UPDATING 317 7.4 MANAGING SURROGATE MODELS IN EVOLUTIONARY ALGORITHMS 318 7.4.1 USING GLOBAL SURROGATE MODELS IN STANDARD EAS 318 XII CONTENTS 7.4.2 LOCAL SURROGATE-ASSISTED HYBRID EAS 319 7.4.3 NUMERICAL STUDIES ON TEST FUNCTIONS 322 7.5 CONCLUDING REMARKS 326 8 DESIGN IN THE PRESENCE OF UNCERTAINTY 327 8.1 UNCERTAINTY MODELING AND REPRESENTATION 330 8.1.1 PROBABILISTIC APPROACHES 331 8.1.2 NONPROBABILISTIC APPROACHES 332 8.2 UNCERTAINTY PROPAGATION 335 8.2.1 SIMULATION METHODS 335 8.2.2 TAYLOR SERIES APPROXIMATIONS 337 8.2.3 LAPLACE APPROXIMATION 338 8.2.4 RELIABILITY ANALYSIS 339 8.2.5 UNCERTAINTY ANALYSIS USING SURROGATE MODELS: THE BAYESIAN MONTE CARLO TECHNIQUE 342 8.2.6 POLYNOMIAL CHAOS EXPANSIONS 344 8.2.7 PHYSICS-BASED UNCERTAINTY PROPAGATION 346 8.2.8 OUTPUT BOUNDS AND ENVELOPES 348 8.3 TAGUCHI METHODS 348 8.4 THE WELCH-SACKS METHOD 350 8.5 DESIGN FOR SIX SIGMA 351 8.6 DECISION-THEORETIC FORMULATIONS 353 8.7 RELIABILITY-BASED OPTIMIZATION 354 8.8 ROBUST DESIGN USING INFORMATION-GAP THEORY 355 8.9 EVOLUTIONARY ALGORITHMS FOR ROBUST DESIGN 356 8.10 CONCLUDING REMARKS 357 9 ARCHITECTURES FOR MULTIDISCIPLINARY OPTIMIZATION 359 9.1 PRELIMINARIES 362 9.1.1 A MODEL PROBLEM 362 9.1.2 MULTIDISCIPLINARY ANALYSIS 365 9.2 FULLY INTEGRATED OPTIMIZATION (FIO) 368 9.3 SYSTEM DECOMPOSITION AND OPTIMIZATION 370 9.4 SIMULTANEOUS ANALYSIS AND DESIGN (SAND) 372 9.5 DISTRIBUTED ANALYSIS OPTIMIZATION FORMULATION 374 9.6 COLLABORATIVE OPTIMIZATION 376 9.6.1 COMPUTATIONAL ASPECTS 379 9.6.2 THEORETICAL PROPERTIES 379 9.7 CONCURRENT SUBSPACE OPTIMIZATION 379 9.8 COEVOLUTIONARY ARCHITECTURES 381 9.8.1 COEVOLUTIONARY GENETIC ALGORITHMS (CGAS) 381 9.8.2 SOME ISSUES IN COEVOLUTIONARY MDO 382 9.8.3 A COEVOLUTIONARY MDO (CMDO) ARCHITECTURE 383 9.8.4 DATA COORDINATION, SURROGATE MODELING, DECOMPOSITION, AND OTHER ISSUES 385 CONTENTS XIII IV CASE STUDIES 387 10 A PROBLEM IN SATELLITE DESIGN 391 10.1 A PROBLEM IN STRUCTURAL DYNAMICS 393 10.1.1 THE STRUCTURE 394 10.1.2 FINITE ELEMENT ANALYSIS 394 10.1.3 RECEPTANCE THEORY 396 10.2 INITIAL PASSIVE REDESIGN IN THREE DIMENSIONS 397 10.3 A PRACTICAL THREE-DIMENSIONAL DESIGN 400 10.3.1 THE REGULAR BOOM EXPERIMENT 401 10.3.2 PASSIVE OPTIMIZATION 402 10.3.3 THE OPTIMIZED BOOM EXPERIMENT 404 10.4 ACTIVE CONTROL MEASURES 406 10.4.1 ACTIVE VIBRATION CONTROL (AVC) 406 10.4.2 AVC EXPERIMENTAL SETUP 408 10.4.3 SELECTION OF OPTIMAL ACTUATOR POSITIONS ON THE BOOM STRUCTURE . . . 409 10.5 COMBINED ACTIVE AND PASSIVE METHODS 413 10.5.1 OPTIMAL ACTUATOR POSITIONS ON THE PASSIVELY OPTIMIZED BOOM . . . 413 10.5.2 SIMULTANEOUS ACTIVE AND PASSIVE OPTIMIZATION 413 10.5.3 OVERALL PERFORMANCE CURVES 415 10.5.4 SUMMARY 417 10.6 ROBUSTNESS MEASURES 417 10.6.1 THE OPTIMIZATION PROBLEM 418 10.6.2 ROBUSTNESS METRICS 418 10.6.3 RESULTS OF ROBUST OPTIMIZATION 421 10.7 ADJOINT-BASED APPROACHES 422 10.7.1 DIFFERENTIATION OF THE RECEPTANCE CODE 424 10.7.2 INITIAL RESULTS AND VALIDATION 426 10.7.3 PERFORMANCE ISSUES 426 11 AIRFOIL SECTION DESIGN 429 11.1 ANALYSIS METHODS 430 11.1.1 PANEL METHODS 430 11.1.2 FULL-POTENTIAL METHODS 430 11.1.3 FIELD PANEL METHODS 431 11.1.4 EULER METHODS 431 11.2 DRAG-ESTIMATION METHODS 431 11.2.1 SURFACE PRESSURE INTEGRATION 432 11.2.2 FAR-FIELD INTEGRATION OF INDUCED DRAG 432 11.2.3 CALCULATION OF WAVE DRAG BY INTEGRATION OVER THE SHOCK 433 11.3 CALCULATION METHODS ADOPTED 433 11.3.1 COMPARISON BETWEEN VGK AND MG2D ON RAE2822 AIRFOIL .... 434 11.4 AIRFOIL PARAMETERIZATION 434 11.4.1 PREVIOUS NONORTHOGONAL REPRESENTATIONS 435 11.4.2 PREVIOUS ORTHOGONAL REPRESENTATIONS 436 11.4.3 CHOICE OF SOURCE AIRFOIL SECTIONS 437 11.4.4 DERIVATION OF BASIS FUNCTIONS 438 XIV CONTENTS 11.4.5 MAPPING THE INFLUENCE OF THE FIRST THREE BASE FUNCTIONS ON DRAG . 442 11.4.6 SUMMARY OF AIRFOIL ANALYSIS 443 11.5 MULTIOBJECTIVE OPTIMIZATION 443 11.5.1 ROBUSTNESS AT FIXED MACH NUMBER 444 11.5.2 ROBUSTNESS AGAINST VARYING GEOMETRY AND MACH NUMBER 445 12 AIRCRAFT WING DESIGN - DATA FUSION BETWEEN CODES 447 12.1 INTRODUCTION 448 12.2 OVERALL WING DESIGN 450 12.2.1 SECTION GEOMETRY 450 12.2.2 LIFT AND DRAG RECOVERY 452 12.2.3 WING ENVELOPE DESIGN 453 12.3 AN EXAMPLE AND SOME BASIC SEARCHES 454 12.3.1 DRAG RESULTS 455 12.3.2 WING WEIGHT PREDICTION 456 12.3.3 WEIGHT SENSITIVITIES 459 12.3.4 DIRECT OPTIMIZATION 460 12.4 DIRECT MULTIFIDELITY SEARCHES 463 12.5 RESPONSE SURFACE MODELING 470 12.5.1 DESIGN OF EXPERIMENT METHODS AND KRIGING 470 12.5.2 APPLICATION OF DOE AND KRIGING 471 12.6 DATA FUSION 475 12.6.1 OPTIMIZATION USING THE FUSION MODEL 477 12.7 CONCLUSIONS 479 13 TURBINE BLADE DESIGN (I) - GUIDE-VANE SKE CONTROL 481 13.1 DESIGN OF EXPERIMENT TECHNIQUES, RESPONSE SURFACE MODELS AND MODEL REFINEMENT 481 13.1.1 DOE TECHNIQUES 482 13.1.2 RESPONSE SURFACE MODELS 482 13.1.3 MODEL REFINEMENT 484 13.2 INITIAL DESIGN 484 13.3 SEVEN-VARIABLE TRIALS WITHOUT CAPACITY CONSTRAINT 484 13.4 TWENTY-ONE-VARIABLE TRIAL WITH CAPACITY CONSTRAINT 490 13.5 CONCLUSIONS 495 14 TURBINE BLADE DESIGN (II) - FIR-TREE ROOT GEOMETRY 497 14.1 INTRODUCTION 497 14.2 MODELING AND OPTIMIZATION OF TRADITIONAL FIR-TREE ROOT SHAPES 499 14.3 LOCAL SHAPE PARAMETERIZATION USING NURBS 501 14.3.1 NURBS FILLET OF DEGREE TWO - CONIC FILLET 501 14.3.2 NURBS FILLET OF DEGREE THREE - CUBIC FILLET 502 14.4 FINITE ELEMENT ANALYSIS OF THE FIR-TREE ROOT 504 14.5 FORMULATION OF THE OPTIMIZATION PROBLEM AND TWO-STAGE SEARCH STRATEGY . 506 14.6 OPTIMUM NOTCH SHAPE AND STRESS DISTRIBUTION 507 14.6.1 COMPARISON BETWEEN CONIC FILLET AND SINGLE-ARC FILLET 507 14.6.2 COMPARISON BETWEEN DOUBLE-ARC FILLET AND CONIC FILLET 508 CONTENTS XV 14.6.3 COMPARISON BETWEEN CUBIC FILLET AND DOUBLE-ARC FILLET 508 14.7 SUMMARY 509 15 AERO-ENGINE NACELLE DESIGN USING THE GEODISE TOOLKIT 511 15.1 THE GEODISE SYSTEM 513 15.1.1 ARCHITECTURE 513 15.1.2 COMPUTE TOOLBOX 515 15.1.3 DATABASE TOOLBOX 517 15.1.4 OPTIMIZATION TOOLBOX 519 15.1.5 KNOWLEDGE TOOLBOX 521 15.1.6 XML TOOLBOX 521 15.1.7 GRAPHICAL WORKFLOW CONSTRUCTION ENVIRONMENT (WCE) 522 15.1.8 KNOWLEDGE SERVICES 525 15.1.9 SIMPLE TEST CASE 528 15.2 GAS-TURBINE NOISE CONTROL 530 15.2.1 CAD MODEL 531 15.2.2 MESH GENERATION AND CFD ANALYSIS 533 15.2.3 OPTIMIZATION STRATEGY 534 15.3 CONCLUSIONS 540 16 GETTING THE OPTIMIZATION PROCESS STARTED 541 16.1 PROBLEM CLASSIFICATION 542 16.1.1 RUN TIME 542 16.1.2 DETERMINISTIC VERSUS PROBABILISTIC ANALYZES 544 16.1.3 NUMBER OF VARIABLES TO BE EXPLORED 544 16.1.4 GOALS AND CONSTRAINTS 544 16.2 INITIAL SEARCH PROCESS CHOICE 544 16.2.1 PROBLEMS WHERE FUNCTION COST IS NOT AN ISSUE 545 16.2.2 MODERATELY DIFFICULT PROBLEMS 545 16.2.3 EXPENSIVE PROBLEMS 546 16.2.4 VERY EXPENSIVE PROBLEMS 546 16.3 ASSESSMENT OF INITIAL RESULTS 546 16.3.1 MULTIPLE SOLUTIONS FOUND FOR VERY CHEAP PROBLEMS 547 16.3.2 SINGLE SOLUTION FOUND FOR CHEAP OR VERY CHEAP PROBLEM 547 16.3.3 GOOD PREDICTIVE RSM BUILT 548 16.3.4 GRAPHICAL PRESENTATION OF RESULTS 548 BIBLIOGRAPHY 549 INDEX 575
adam_txt	COMPUTATIONAL APPROACHES FOR AEROSPACE DESIGN THE PURSUIT OF EXCELLENCE ANDY J. KEANE PRASANTH B. NAIR UNIVERSITY OF SOUTHAMPTON JOHN WILEY &. SONS, LTD CONTENTS FOREWORD PREFACE ACKNOWLEDGMENTS XVII XIX XXI I PRELIMINARIES 1 1.6 1 INTRODUCTION 3 1.1 OBJECTIVES 3 1.2 ROAD MAP - WHAT IS COVERED AND WHAT IS NOT 4 1.3 AN HISTORICAL PERSPECTIVE ON AEROSPACE DESIGN 5 .3.1 A PAIR OF EARLY PIONEERS 6 .3.2 A PAIR OF GREAT DESIGNERS 8 .3.3 A PAIR OF GREAT RESEARCHERS 11 .3.4 TWO GREAT AEROSPACE COMPANIES 13 .3.5 RATIONALIZATION AND COOPERATION 15 .3.6 THE DAWN OF THE COMPUTATIONAL ERA 16 1.4 TRADITIONAL MANUAL APPROACHES TO DESIGN AND DESIGN ITERATION, DESIGN TEAMS 17 .4.1 DESIGN AS A DECISION-MAKING PROCESS 17 .4.2 CONCEPT DESIGN 19 .4.3 PRELIMINARY DESIGN 21 .4.4 DETAILED DESIGN 22 .4.5 IN-SERVICE DESIGN AND DECOMMISSIONING 24 .4.6 HUMAN ASPECTS OF DESIGN TEAMS 24 1.5 ADVANCES IN MODELING TECHNIQUES: COMPUTATIONAL ENGINEERING 25 .5.1 PARTIAL DIFFERENTIAL EQUATIONS (PDES) 25 .5.2 HARDWARE VERSUS SOFTWARE 26 .5.3 COMPUTATIONAL SOLID MECHANICS (CSM) 27 .5.4 COMPUTATIONAL FLUID DYNAMICS (CFD) 29 .5.5 MULTILEVEL APPROACHES OR 'ZOOM' ANALYSIS 31 .5.6 COMPLEXITY 32 TRADE-OFFS IN AEROSPACE SYSTEM DESIGN 33 .6.1 BALANCED DESIGNS 33 .6.2 STRUCTURAL STRENGTH VERSUS WEIGHT 34 VIII CONTENTS 1.6.3 AERODYNAMICS VERSUS STRUCTURAL STRENGTH 37 1.6.4 STRUCTURES VERSUS CONTROL 39 1.6.5 ROBUSTNESS VERSUS NOMINAL PERFORMANCE 40 1.7 DESIGN AUTOMATION, EVOLUTION AND INNOVATION 42 1.7.1 INNOVATION 43 1.7.2 EVOLUTION 44 1.7.3 AUTOMATION 45 1.8 DESIGN SEARCH AND OPTIMIZATION (DSO) 45 1.8.1 BEGINNINGS 46 1.8.2 A TAXONOMY OF OPTIMIZATION 46 1.8.3 A BRIEF HISTORY OF OPTIMIZATION METHODS 48 1.8.4 THE PLACE OF OPTIMIZATION IN DESIGN - COMMERCIAL TOOLS 50 1.9 THE TAKE-UP OF COMPUTATIONAL METHODS 51 1.9.1 TECHNOLOGY TRANSFER 51 1.9.2 ACADEMIC DESIGN RESEARCH 52 1.9.3 SOCIO-TECHNICAL ISSUES 52 2 DESIGN-ORIENTED ANALYSIS 55 2. 1 GEOMETRY MODELING AND DESIGN PARAMETERIZATION 55 2.1.1 THE ROLE OF PARAMETERIZATION IN DESIGN 57 2.1.2 DISCRETE AND DOMAIN ELEMENT PARAMETERIZATIONS 59 2.1.3 NACA AIRFOILS 61 2.1.4 SPLINE-BASED APPROACHES 62 2.1.5 PARTIAL DIFFERENTIAL EQUATION AND OTHER ANALYTICAL APPROACHES . 63 2.1.6 BASIS FUNCTION REPRESENTATION 65 2.1.7 MORPHING 66 2.1.8 SHAPE GRAMMARS 68 2.1.9 MESH-BASED EVOLUTIONARY ENCODINGS 69 2.1.10 CAD TOOLS VERSUS DEDICATED PARAMETERIZATION METHODS 70 2.2 COMPUTATIONAL MESH GENERATION 74 2.2.1 THE FUNCTION OF MESHES 74 2.2.2 MESH TYPES AND CELL/ELEMENT/VOLUME GEOMETRIES 78 2.2.3 MESH GENERATION, QUALITY AND ADAPTATION 86 2.2.4 MESHLESS APPROACHES 89 2.3 ANALYSIS AND DESIGN OF COUPLED SYSTEMS 91 2.3.1 INTERACTIONS BETWEEN GEOMETRY DEFINITION, MESHING AND SOLVERS - PARALLEL COMPUTATIONS 92 2.3.2 SIMPLE RELAXATION AND NEWTON TECHNIQUES 97 2.3.3 SYSTEMS INTEGRATION, WORKFLOW MANAGEMENT, DATA TRANSFER AND COMPRESSION 99 3 ELEMENTS OF NUMERICAL OPTIMIZATION 105 3.1 SINGLE VARIABLE OPTIMIZERS - LINE SEARCH 106 3.1.1 UNCONSTRAINED OPTIMIZATION WITH A SINGLE REAL VARIABLE 106 3.1.2 OPTIMIZATION WITH A SINGLE DISCRETE VARIABLE ILL 3.1.3 OPTIMIZATION WITH A SINGLE NONNUMERIC VARIABLE 113 3.2 MULTIVARIABLE OPTIMIZERS 114 3.2.1 POPULATION VERSUS SINGLE-POINT METHODS 116 CONTENTS IX 3.2.2 GRADIENT-BASED METHODS 117 3.2.3 NOISY/APPROXIMATE FUNCTION VALUES 123 3.2.4 NONGRADIENT ALGORITHMS 126 3.2.5 TERMINATION AND CONVERGENCE ASPECTS 139 3.3 CONSTRAINED OPTIMIZATION 141 3.3.1 PROBLEM TRANSFORMATIONS 142 3.3.2 LAGRANGIAN MULTIPLIERS 143 3.3.3 FEASIBLE DIRECTIONS METHOD 146 3.3.4 PENALTY FUNCTION METHODS 147 3.3.5 COMBINED LAGRANGIAN AND PENALTY FUNCTION METHODS 150 3.3.6 SEQUENTIAL QUADRATIC PROGRAMMING 150 3.3.7 CHROMOSOME REPAIR 151 3.4 METAMODELS AND RESPONSE SURFACE METHODS 152 3.4.1 GLOBAL VERSUS LOCAL METAMODELS 154 3.4.2 METAMODELING TOOLS 155 3.4.3 SIMPLE RSM EXAMPLES 157 3.5 COMBINED APPROACHES - HYBRID SEARCHES, METAHEURISTICS 158 3.5.1 GLOSSY - A HYBRID SEARCH TEMPLATE 161 3.5.2 METAHEURISTICS - SEARCH WORKFLOWS 163 3.6 MULTIOBJECTIVE OPTIMIZATION 165 3.6.1 MULTIOBJECTIVE WEIGHT ASSIGNMENT TECHNIQUES 166 3.6.2 METHODS FOR COMBINING GOAL FUNCTIONS, FUZZY LOGIC AND PHYSICAL PROGRAMMING 167 3.6.3 PARETO SET ALGORITHMS 169 3.6.4 NASH EQUILIBRIA 171 3.7 ROBUSTNESS 172 II SENSITIVITY ANALYSIS AND APPROXIMATION CONCEPTS 175 4 SENSITIVITY ANALYSIS 177 4.1 FINITE-DIFFERENCE METHODS 178 4.2 COMPLEX VARIABLE APPROACH 180 4.2.1 IMPLEMENTATION ISSUES 181 4.3 DIRECT METHODS 183 4.3.1 EXAMPLE: STATIC STRUCTURAL ANALYSIS 184 4.3.2 EXAMPLE: EIGENVALUE PROBLEMS 184 4.3.3 EXAMPLE: TRANSIENT DYNAMIC ANALYSIS 185 4.4 ADJOINT METHODS 187 4.4.1 DISCRETE ADJOINT FORMULATION 188 4.4.2 CONTINUOUS ADJOINT FORMULATION 190 4.4.3 IMPLEMENTATION ASPECTS 191 4.5 SEMIANALYTICAL METHODS 193 4.6 AUTOMATIC DIFFERENTIATION 193 4.6.1 FORWARD MODE 194 4.6.2 REVERSE MODE 196 4.6.3 AD SOFTWARE TOOLS AND IMPLEMENTATION ASPECTS 197 CONTENTS 4.7 MESH SENSITIVITIES FOR COMPLEX GEOMETRIES 199 4.8 SENSITIVITY OF OPTIMA TO PROBLEM PARAMETERS 201 4.9 SENSITIVITY ANALYSIS OF COUPLED SYSTEMS 203 4.10 COMPARISON OF SENSITIVITY ANALYSIS TECHNIQUES 205 4.10.1 CASE STUDY: AERODYNAMIC SENSITIVITY ANALYSIS 206 4.10.2 CASE STUDY: AEROSTRUCTURAL SENSITIVITY ANALYSIS 208 GENERAL APPROXIMATION CONCEPTS AND SURROGATES 211 5.1 LOCAL APPROXIMATIONS 213 5.1.1 TAYLOR SERIES APPROXIMATION 213 5.1.2 INTERVENING VARIABLES 214 5.2 MULTIPOINT APPROXIMATIONS 215 5.3 BLACK-BOX MODELING: A STATISTICAL PERSPECTIVE 217 5.3.1 DATA GENERATION 217 5.3.2 MODEL-STRUCTURE SELECTION 218 5.3.3 PARAMETER ESTIMATION 219 5.3.4 MODEL ASSESSMENT 220 5.3.5 ITERATIVE MODEL-BUILDING PROCEDURES 221 5.3.6 PERSPECTIVES FROM STATISTICAL LEARNING THEORY 221 5.3.7 INTERPOLATION VERSUS REGRESSION 223 5.4 GENERALIZED LINEAR MODELS 224 5.4.1 RESPONSE SURFACE METHODS: POLYNOMIAL MODELS 225 5.4.2 NEURAL NETWORK APPROXIMATIONS 226 5.4.3 RADIAL BASIS FUNCTION APPROXIMATIONS 227 5.4.4 HERMITE INTERPOLATION USING RADIAL BASIS FUNCTIONS 230 5.4.5 TUNING RBF SHAPE AND REGULARIZATION PARAMETERS 232 5.5 SPARSE APPROXIMATION TECHNIQUES 235 5.5.1 THE SUPPORT VECTOR MACHINE 235 5.5.2 GREEDY APPROXIMATIONS 238 5.5.3 STOPPING CRITERIA: EMPIRICAL RISK VERSUS COMPLEXITY TRADE-OFF . . . 241 5.6 GAUSSIAN PROCESS INTERPOLATION AND REGRESSION 243 5.6.1 BASIC FORMULATION 244 5.6.2 MAXIMUM LIKELIHOOD ESTIMATION 248 5.6.3 INCORPORATING SENSITIVITY INFORMATION 251 5.6.4 ASSESSMENT AND REFINEMENT OF GAUSSIAN PROCESS MODELS 252 5.7 DATA PARALLEL MODELING 254 5.7.1 DATA PARALLEL LOCAL LEARNING 255 5.7.2 DATA PARTITIONING 257 5.7.3 PREDICTION WITH MULTIPLE MODELS 259 5.7.4 COMPUTATIONAL ASPECTS 259 5.8 DESIGN OF EXPERIMENTS (DOE) 260 5.8.1 MONTE CARLO TECHNIQUES 262 5.8.2 LATIN HYPERCUBE SAMPLING 262 5.8.3 SAMPLING USING ORTHOGONAL ARRAYS 264 5.8.4 MINIMUM DISCREPANCY SEQUENCES 264 5.8.5 DOE USING OPTIMALITY CRITERIA 265 5.8.6 RECOMMENDATIONS FOR OPTIMIZATION STUDIES 266 CONTENTS XI 5.9 VISUALIZATION AND SCREENING 266 5.9.1 DESIGN SPACE VISUALIZATION 267 5.9.2 VARIABLE SCREENING 267 5.10 BLACK-BOX SURROGATE MODELING IN PRACTICE 268 6 PHYSICS-BASED APPROXIMATIONS 271 6.1 SURROGATE MODELING USING VARIABLE-FIDELITY MODELS 271 6.1.1 ZERO-ORDER SCALING 272 6.1.2 FIRST-ORDER SCALING 272 6.1.3 SECOND-ORDER SCALING 273 6.1.4 MULTIPOINT CORRECTIONS 273 6.1.5 GLOBAL SCALING USING SURROGATE MODELS 274 6.1.6 AN EXAMPLE 275 6.2 AN INTRODUCTION TO REDUCED BASIS METHODS 277 6.2.1 CHOICE OF BASIS VECTORS 277 6.2.2 SCHEMES FOR COMPUTING UNDETERMINED COEFFICIENTS 279 6.3 REDUCED BASIS METHODS FOR LINEAR STATIC REANALYSIS 280 6.3.1 CHOICE OF BASIS VECTORS 281 6.3.2 BUBNOV-GALERKIN AND PETROV-GALERKIN SCHEMES 283 6.3.3 TOPOLOGICALLY MODIFIED STRUCTURES 284 6.3.4 IMPLEMENTATION ISSUES 286 6.4 REDUCED BASIS METHODS FOR REANALYSIS OF EIGENVALUE PROBLEMS 287 6.4.1 IMPROVED FIRST-ORDER APPROXIMATION 287 6.4.2 GLOBAL REDUCED BASIS PROCEDURES 289 6.5 REDUCED BASIS METHODS FOR NONLINEAR PROBLEMS 291 6.5.1 PROPER ORTHOGONAL DECOMPOSITION 291 6.5.2 REDUCED-ORDER MODELING 293 6.5.3 CONCLUDING REMARKS 297 III FRAMEWORKS FOR DESIGN SPACE EXPLORATION 299 7 MANAGING SURROGATE MODELS IN OPTIMIZATION 301 7.1 TRUST-REGION METHODS 303 7.1.1 UNCONSTRAINED PROBLEMS 304 7.1.2 EXTENSION TO CONSTRAINED PROBLEMS 306 7.2 THE SPACE MAPPING APPROACH 307 7.2.1 MAPPING FUNCTIONS 308 7.2.2 GLOBAL SPACE MAPPING 310 7.3 SURROGATE-ASSISTED OPTIMIZATION USING GLOBAL MODELS 311 7.3.1 THE EXPECTED IMPROVEMENT CRITERION 312 7.3.2 THE GENERALIZED EXPECTED IMPROVEMENT CRITERION 315 7.3.3 THE WEIGHTED EXPECTED IMPROVEMENT CRITERION 315 7.3.4 EXTENSION TO CONSTRAINED PROBLEMS 316 7.3.5 CORRELATION MATRIX UPDATING 317 7.4 MANAGING SURROGATE MODELS IN EVOLUTIONARY ALGORITHMS 318 7.4.1 USING GLOBAL SURROGATE MODELS IN STANDARD EAS 318 XII CONTENTS 7.4.2 LOCAL SURROGATE-ASSISTED HYBRID EAS 319 7.4.3 NUMERICAL STUDIES ON TEST FUNCTIONS 322 7.5 CONCLUDING REMARKS 326 8 DESIGN IN THE PRESENCE OF UNCERTAINTY 327 8.1 UNCERTAINTY MODELING AND REPRESENTATION 330 8.1.1 PROBABILISTIC APPROACHES 331 8.1.2 NONPROBABILISTIC APPROACHES 332 8.2 UNCERTAINTY PROPAGATION 335 8.2.1 SIMULATION METHODS 335 8.2.2 TAYLOR SERIES APPROXIMATIONS 337 8.2.3 LAPLACE APPROXIMATION 338 8.2.4 RELIABILITY ANALYSIS 339 8.2.5 UNCERTAINTY ANALYSIS USING SURROGATE MODELS: THE BAYESIAN MONTE CARLO TECHNIQUE 342 8.2.6 POLYNOMIAL CHAOS EXPANSIONS 344 8.2.7 PHYSICS-BASED UNCERTAINTY PROPAGATION 346 8.2.8 OUTPUT BOUNDS AND ENVELOPES 348 8.3 TAGUCHI METHODS 348 8.4 THE WELCH-SACKS METHOD 350 8.5 DESIGN FOR SIX SIGMA 351 8.6 DECISION-THEORETIC FORMULATIONS 353 8.7 RELIABILITY-BASED OPTIMIZATION 354 8.8 ROBUST DESIGN USING INFORMATION-GAP THEORY 355 8.9 EVOLUTIONARY ALGORITHMS FOR ROBUST DESIGN 356 8.10 CONCLUDING REMARKS 357 9 ARCHITECTURES FOR MULTIDISCIPLINARY OPTIMIZATION 359 9.1 PRELIMINARIES 362 9.1.1 A MODEL PROBLEM 362 9.1.2 MULTIDISCIPLINARY ANALYSIS 365 9.2 FULLY INTEGRATED OPTIMIZATION (FIO) 368 9.3 SYSTEM DECOMPOSITION AND OPTIMIZATION 370 9.4 SIMULTANEOUS ANALYSIS AND DESIGN (SAND) 372 9.5 DISTRIBUTED ANALYSIS OPTIMIZATION FORMULATION 374 9.6 COLLABORATIVE OPTIMIZATION 376 9.6.1 COMPUTATIONAL ASPECTS 379 9.6.2 THEORETICAL PROPERTIES 379 9.7 CONCURRENT SUBSPACE OPTIMIZATION 379 9.8 COEVOLUTIONARY ARCHITECTURES 381 9.8.1 COEVOLUTIONARY GENETIC ALGORITHMS (CGAS) 381 9.8.2 SOME ISSUES IN COEVOLUTIONARY MDO 382 9.8.3 A COEVOLUTIONARY MDO (CMDO) ARCHITECTURE 383 9.8.4 DATA COORDINATION, SURROGATE MODELING, DECOMPOSITION, AND OTHER ISSUES 385 CONTENTS XIII IV CASE STUDIES 387 10 A PROBLEM IN SATELLITE DESIGN 391 10.1 A PROBLEM IN STRUCTURAL DYNAMICS 393 10.1.1 THE STRUCTURE 394 10.1.2 FINITE ELEMENT ANALYSIS 394 10.1.3 RECEPTANCE THEORY 396 10.2 INITIAL PASSIVE REDESIGN IN THREE DIMENSIONS 397 10.3 A PRACTICAL THREE-DIMENSIONAL DESIGN 400 10.3.1 THE REGULAR BOOM EXPERIMENT 401 10.3.2 PASSIVE OPTIMIZATION 402 10.3.3 THE OPTIMIZED BOOM EXPERIMENT 404 10.4 ACTIVE CONTROL MEASURES 406 10.4.1 ACTIVE VIBRATION CONTROL (AVC) 406 10.4.2 AVC EXPERIMENTAL SETUP 408 10.4.3 SELECTION OF OPTIMAL ACTUATOR POSITIONS ON THE BOOM STRUCTURE . . . 409 10.5 COMBINED ACTIVE AND PASSIVE METHODS 413 10.5.1 OPTIMAL ACTUATOR POSITIONS ON THE PASSIVELY OPTIMIZED BOOM . . . 413 10.5.2 SIMULTANEOUS ACTIVE AND PASSIVE OPTIMIZATION 413 10.5.3 OVERALL PERFORMANCE CURVES 415 10.5.4 SUMMARY 417 10.6 ROBUSTNESS MEASURES 417 10.6.1 THE OPTIMIZATION PROBLEM 418 10.6.2 ROBUSTNESS METRICS 418 10.6.3 RESULTS OF ROBUST OPTIMIZATION 421 10.7 ADJOINT-BASED APPROACHES 422 10.7.1 DIFFERENTIATION OF THE RECEPTANCE CODE 424 10.7.2 INITIAL RESULTS AND VALIDATION 426 10.7.3 PERFORMANCE ISSUES 426 11 AIRFOIL SECTION DESIGN 429 11.1 ANALYSIS METHODS 430 11.1.1 PANEL METHODS 430 11.1.2 FULL-POTENTIAL METHODS 430 11.1.3 FIELD PANEL METHODS 431 11.1.4 EULER METHODS 431 11.2 DRAG-ESTIMATION METHODS 431 11.2.1 SURFACE PRESSURE INTEGRATION 432 11.2.2 FAR-FIELD INTEGRATION OF INDUCED DRAG 432 11.2.3 CALCULATION OF WAVE DRAG BY INTEGRATION OVER THE SHOCK 433 11.3 CALCULATION METHODS ADOPTED 433 11.3.1 COMPARISON BETWEEN VGK AND MG2D ON RAE2822 AIRFOIL . 434 11.4 AIRFOIL PARAMETERIZATION 434 11.4.1 PREVIOUS NONORTHOGONAL REPRESENTATIONS 435 11.4.2 PREVIOUS ORTHOGONAL REPRESENTATIONS 436 11.4.3 CHOICE OF SOURCE AIRFOIL SECTIONS 437 11.4.4 DERIVATION OF BASIS FUNCTIONS 438 XIV CONTENTS 11.4.5 MAPPING THE INFLUENCE OF THE FIRST THREE BASE FUNCTIONS ON DRAG . 442 11.4.6 SUMMARY OF AIRFOIL ANALYSIS 443 11.5 MULTIOBJECTIVE OPTIMIZATION 443 11.5.1 ROBUSTNESS AT FIXED MACH NUMBER 444 11.5.2 ROBUSTNESS AGAINST VARYING GEOMETRY AND MACH NUMBER 445 12 AIRCRAFT WING DESIGN - DATA FUSION BETWEEN CODES 447 12.1 INTRODUCTION 448 12.2 OVERALL WING DESIGN 450 12.2.1 SECTION GEOMETRY 450 12.2.2 LIFT AND DRAG RECOVERY 452 12.2.3 WING ENVELOPE DESIGN 453 12.3 AN EXAMPLE AND SOME BASIC SEARCHES 454 12.3.1 DRAG RESULTS 455 12.3.2 WING WEIGHT PREDICTION 456 12.3.3 WEIGHT SENSITIVITIES 459 12.3.4 DIRECT OPTIMIZATION 460 12.4 DIRECT MULTIFIDELITY SEARCHES 463 12.5 RESPONSE SURFACE MODELING 470 12.5.1 DESIGN OF EXPERIMENT METHODS AND KRIGING 470 12.5.2 APPLICATION OF DOE AND KRIGING 471 12.6 DATA FUSION 475 12.6.1 OPTIMIZATION USING THE FUSION MODEL 477 12.7 CONCLUSIONS 479 13 TURBINE BLADE DESIGN (I) - GUIDE-VANE SKE CONTROL 481 13.1 DESIGN OF EXPERIMENT TECHNIQUES, RESPONSE SURFACE MODELS AND MODEL REFINEMENT 481 13.1.1 DOE TECHNIQUES 482 13.1.2 RESPONSE SURFACE MODELS 482 13.1.3 MODEL REFINEMENT 484 13.2 INITIAL DESIGN 484 13.3 SEVEN-VARIABLE TRIALS WITHOUT CAPACITY CONSTRAINT 484 13.4 TWENTY-ONE-VARIABLE TRIAL WITH CAPACITY CONSTRAINT 490 13.5 CONCLUSIONS 495 14 TURBINE BLADE DESIGN (II) - FIR-TREE ROOT GEOMETRY 497 14.1 INTRODUCTION 497 14.2 MODELING AND OPTIMIZATION OF TRADITIONAL FIR-TREE ROOT SHAPES 499 14.3 LOCAL SHAPE PARAMETERIZATION USING NURBS 501 14.3.1 NURBS FILLET OF DEGREE TWO - CONIC FILLET 501 14.3.2 NURBS FILLET OF DEGREE THREE - CUBIC FILLET 502 14.4 FINITE ELEMENT ANALYSIS OF THE FIR-TREE ROOT 504 14.5 FORMULATION OF THE OPTIMIZATION PROBLEM AND TWO-STAGE SEARCH STRATEGY . 506 14.6 OPTIMUM NOTCH SHAPE AND STRESS DISTRIBUTION 507 14.6.1 COMPARISON BETWEEN CONIC FILLET AND SINGLE-ARC FILLET 507 14.6.2 COMPARISON BETWEEN DOUBLE-ARC FILLET AND CONIC FILLET 508 CONTENTS XV 14.6.3 COMPARISON BETWEEN CUBIC FILLET AND DOUBLE-ARC FILLET 508 14.7 SUMMARY 509 15 AERO-ENGINE NACELLE DESIGN USING THE GEODISE TOOLKIT 511 15.1 THE GEODISE SYSTEM 513 15.1.1 ARCHITECTURE 513 15.1.2 COMPUTE TOOLBOX 515 15.1.3 DATABASE TOOLBOX 517 15.1.4 OPTIMIZATION TOOLBOX 519 15.1.5 KNOWLEDGE TOOLBOX 521 15.1.6 XML TOOLBOX 521 15.1.7 GRAPHICAL WORKFLOW CONSTRUCTION ENVIRONMENT (WCE) 522 15.1.8 KNOWLEDGE SERVICES 525 15.1.9 SIMPLE TEST CASE 528 15.2 GAS-TURBINE NOISE CONTROL 530 15.2.1 CAD MODEL 531 15.2.2 MESH GENERATION AND CFD ANALYSIS 533 15.2.3 OPTIMIZATION STRATEGY 534 15.3 CONCLUSIONS 540 16 GETTING THE OPTIMIZATION PROCESS STARTED 541 16.1 PROBLEM CLASSIFICATION 542 16.1.1 RUN TIME 542 16.1.2 DETERMINISTIC VERSUS PROBABILISTIC ANALYZES 544 16.1.3 NUMBER OF VARIABLES TO BE EXPLORED 544 16.1.4 GOALS AND CONSTRAINTS 544 16.2 INITIAL SEARCH PROCESS CHOICE 544 16.2.1 PROBLEMS WHERE FUNCTION COST IS NOT AN ISSUE 545 16.2.2 MODERATELY DIFFICULT PROBLEMS 545 16.2.3 EXPENSIVE PROBLEMS 546 16.2.4 VERY EXPENSIVE PROBLEMS 546 16.3 ASSESSMENT OF INITIAL RESULTS 546 16.3.1 MULTIPLE SOLUTIONS FOUND FOR VERY CHEAP PROBLEMS 547 16.3.2 SINGLE SOLUTION FOUND FOR CHEAP OR VERY CHEAP PROBLEM 547 16.3.3 GOOD PREDICTIVE RSM BUILT 548 16.3.4 GRAPHICAL PRESENTATION OF RESULTS 548 BIBLIOGRAPHY 549 INDEX 575
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discipline	Theologie / Religionswissenschaften Verkehrstechnik Verkehr / Transport Maschinenbau
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id	DE-604.BV021625436
illustrated	Illustrated
index_date	2024-07-02T14:55:03Z
indexdate	2024-07-09T20:40:13Z
institution	BVB
isbn	0470855401
language	English
lccn	2005044335
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-014840430
oclc_num	60557355
open_access_boolean
owner	DE-91G DE-BY-TUM DE-634 DE-83
owner_facet	DE-91G DE-BY-TUM DE-634 DE-83
physical	XX, 582 S. Ill., graph Darst. 25 cm
publishDate	2005
publishDateSearch	2005
publishDateSort	2005
publisher	Wiley
record_format	marc
spelling	Keane, Andy J. Verfasser aut Computational approaches for aerospace design the pursuit of excellence Andy J. Keane, Prasanth B. Nair Chichester, England Wiley 2005 XX, 582 S. Ill., graph Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. [549]-573) and index Datenverarbeitung Mathematik Aerospace engineering Data processing Aerospace engineering Mathematics CAD (DE-588)4069794-0 gnd rswk-swf Luft- und Raumfahrtindustrie (DE-588)4048576-6 gnd rswk-swf Luft- und Raumfahrtindustrie (DE-588)4048576-6 s CAD (DE-588)4069794-0 s DE-604 Nair, Prasanth B. Sonstige oth http://www.loc.gov/catdir/enhancements/fy0624/2005044335-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0624/2005044335-t.html Table of contents HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014840430&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Keane, Andy J. Computational approaches for aerospace design the pursuit of excellence Datenverarbeitung Mathematik Aerospace engineering Data processing Aerospace engineering Mathematics CAD (DE-588)4069794-0 gnd Luft- und Raumfahrtindustrie (DE-588)4048576-6 gnd
subject_GND	(DE-588)4069794-0 (DE-588)4048576-6
title	Computational approaches for aerospace design the pursuit of excellence
title_auth	Computational approaches for aerospace design the pursuit of excellence
title_exact_search	Computational approaches for aerospace design the pursuit of excellence
title_exact_search_txtP	Computational approaches for aerospace design the pursuit of excellence
title_full	Computational approaches for aerospace design the pursuit of excellence Andy J. Keane, Prasanth B. Nair
title_fullStr	Computational approaches for aerospace design the pursuit of excellence Andy J. Keane, Prasanth B. Nair
title_full_unstemmed	Computational approaches for aerospace design the pursuit of excellence Andy J. Keane, Prasanth B. Nair
title_short	Computational approaches for aerospace design
title_sort	computational approaches for aerospace design the pursuit of excellence
title_sub	the pursuit of excellence
topic	Datenverarbeitung Mathematik Aerospace engineering Data processing Aerospace engineering Mathematics CAD (DE-588)4069794-0 gnd Luft- und Raumfahrtindustrie (DE-588)4048576-6 gnd
topic_facet	Datenverarbeitung Mathematik Aerospace engineering Data processing Aerospace engineering Mathematics CAD Luft- und Raumfahrtindustrie
url	http://www.loc.gov/catdir/enhancements/fy0624/2005044335-d.html http://www.loc.gov/catdir/enhancements/fy0624/2005044335-t.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014840430&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT keaneandyj computationalapproachesforaerospacedesignthepursuitofexcellence AT nairprasanthb computationalapproachesforaerospacedesignthepursuitofexcellence

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