Engineering Design Reliability Handbook

Researchers in industry and academia are making important advances on various fronts including reliability-based design and modeling of uncertainty when data is limited. Still, industry continues to lose billions of dollars each year because of unexpected system failures. Engineering Design Reliability Handbook is a valuable addition to the reliability literature. The book presents an industrial perspective of non-deterministic approaches and documented stories and quantifies the benefits of these approaches. It considers the issue of modeling uncertainty when data is scarce and provides a comparison of non-deterministic approaches, both numerically and experimentally in design problems. Engineering Design Reliability HANDBOOK......Page 1 Reliability Handbook......Page 4 Preface......Page 6 About the Editors......Page 8 Contributors......Page 9 Disclaimer......Page 16 Contents......Page 12 Section I Status and Future of Nondeterministic Approaches ( NDAs)......Page 17 1.1.2 Part II: Nondeterministic Modeling: Critical Issues and Recent Advances......Page 19 1.1.2.2 Reliability Assessment and Uncertainty Propagation......Page 20 1.1.2.4 Reliability Certification......Page 21 1.1.3 Part III: Applications......Page 22 2.1 Introduction......Page 23 2.1.3 Chapter Outline......Page 25 2.2 Types of Uncertainties and Uncertainty Measures......Page 26 2.4 Nondeterministic Analysis Approaches: Categories......Page 27 2.4.1 Bounding Uncertainties in Simulation Models......Page 28 2.5.1 Advanced Virtual Product Development Facilities......Page 29 2.5.3 Uncertainty Quantification......Page 30 2.6 Verification and Validation of Numerical Simulations......Page 31 2.7.3 Performance......Page 32 2.8 Response Surface Methodology (RSM)......Page 33 2.10 Robustness......Page 34 2.12 Key Components of Advanced Simulation and Modeling Environments......Page 35 2.12.1 Intelligent Tools and Facilities......Page 36 2.13 Nondeterministic Approaches Research and Learning Network......Page 37 References......Page 39 3.1 Introduction......Page 42 3.2 The Future Nondeterministic Design Environment......Page 43 3.3.1 Current Nondeterministic Design Technology......Page 47 3.3.2 Some Transition History......Page 48 3.3.2.2 Use of Weibull Models in Aircraft Engine Field Support......Page 49 3.3.2.4 Probability Integration Algorithms......Page 50 3.3.3 Finite Element Based Probabilistic Analysis......Page 51 3.4.1 Neural Networks [61Ò66]......Page 52 3.4.3 Interval Arithmetic [82-85]......Page 53 3.4.7 Expert Systems......Page 54 3.5.2 Verification and Validation of NDA Methods......Page 55 3.5.3 NDA Error Estimation......Page 57 3.5.4 Confidence Interval Modeling......Page 58 3.5.5 Traditional Reliability and Safety Assessment Methods......Page 59 Acknowledgments......Page 60 References......Page 61 4.1 Introduction......Page 66 4.2 The Business Case......Page 67 4.3 Strategies for Design Approach for Low-Cost Development......Page 69 4.4 Role of Design Space Exploration Approaches (Deterministic and Nondeterministic) in the Product Development Phase......Page 70 4.5 Sensitivity Analysis......Page 73 4.6 Probabilistic Analysis Approaches......Page 74 4.7 The Need and Role of Multidisciplinary Analysis in Deterministic and Nondeterministic Analysis......Page 76 4.8 Technology Transition and Software Implementation......Page 77 4.9 Needed Technology Advances......Page 78 Acknowledgments......Page 79 5.1 Introduction......Page 80 5.2 The Vehicle Development Process......Page 81 5.3 Vehicle Development Process: A Decision-Analytic View......Page 82 5.4 The Decision Analysis Cycle......Page 83 5 .4.1 Illustration: Door Seal Selection......Page 84 5.4.1.1 The Door Sealing System......Page 85 5.4.2 Deterministic Phase......Page 86 5.4.2.1 Door Seal Selection Continued: Deterministic Phase......Page 87 5.4.3.1 Uncertainty Characterization Methods......Page 89 5.4.3.2 Door Seal Selection Continued: Probabilistic Phase......Page 90 5.4.4.2 Model Validation: Current Practice......Page 92 5.4.4.3 Model Validation: A Formal Process......Page 93 5.4.4.4 Door Seal Selection Continued: Information Phase......Page 94 5.5 Concluding Comments and Challenges......Page 95 References......Page 96 6.1 Introduction......Page 98 6.2.2 Research Needs and Trends......Page 99 6.3.1 Current Methods......Page 101 6.3.2 Research Needs and Trends......Page 102 6.4.2 Research Needs and Trends......Page 104 6.5.1 Current Methods......Page 105 6.5.2 Research Needs and Trends......Page 106 References......Page 107 7.1 Background......Page 109 7.1.1 Critical Element of the Design Process......Page 110 7.1.2 The Need for a New Approach......Page 111 7.2 An Inductive Approach......Page 113 7.2.1.2 Hierarchical Bayes......Page 114 7.3 Information Aggregation......Page 115 7.3.2 Data Congeries......Page 116 7.4.1 Distribution or Process Model?......Page 117 T......Page 118 7.5 Value of Information......Page 119 7.7 Summary......Page 121 References......Page 122 Section II Nondeterministic Modeling: Critical Issues and Recent Advances......Page 124 uncertainty......Page 127 X......Page 128 X, X......Page 130 Example 3: Uncertainty in Design of the Automotive Component in Example 1......Page 131 of an Automotive Component......Page 132 Example 5: Different Types of Uncertainty in Automotive Component Design......Page 135 8.2.2 Taxonomies According to the Nature of Uncertainty......Page 136 Randomly from a Batch......Page 137 8.3.1 Types of Uncertainty......Page 138 of an Automotive Component......Page 139 8.3.2 Types of Information......Page 140 When We Only Know the Stress Range......Page 141 Y,......Page 142 of a Chemical Solvent......Page 143 8.4 Chapters in This Book on Methods for Collecting Information and for Constructing Models of Uncertainty......Page 144 References......Page 145 9.1 Introduction: Uncertainty-Based Information Theory in Modeling and Simulation......Page 147 9.2.1 Logical and Set Theoretical Approaches......Page 149 Borel field......Page 150 normalization......Page 152 cumulative distribution......Page 153 9.2.3.2 Interpretations of Probability......Page 154 Law of Total Probability......Page 155 9.2.3.4 Distribution Function Formulation of Bayes Theorem......Page 156 9.2.3.5 Binomial/Beta Reliability Example......Page 157 9.3.1 Historical Development of GIT......Page 158 Norms and Conorms:......Page 159 membership function......Page 160 Proposition 7:......Page 161 Implication:......Page 162 fuzzy quantity......Page 163 U......Page 164 UU......Page 165 linguistic variable.......Page 166 normal......Page 167 plausibility......Page 168 C......Page 169 random variable.......Page 170 9.3.5.3 The Information Content of a Random Set......Page 171 specific:......Page 172 U(......Page 173 p-box......Page 174 possibility distribution......Page 175 necessity measure:......Page 176 C=......Page 177 C=......Page 178 9.3.6.4 Relations between Probabilistic and Possibilistic Concepts......Page 179 9.4 Conclusion and Summary......Page 180 UNKNOWN......Page 0 R......Page 182 53,......Page 184 4,......Page 185 30:......Page 186 10.1.1 Background......Page 187 10.1.2 Improved Models for Epistemic Uncertainty......Page 189 10.2.1 Belief, Plausibility, and BPA Functions......Page 190 10.2.2 Cumulative and Complementary Cumulative Functions......Page 192 10.2.3 Input/Output Uncertainty Mapping......Page 194 10.2.4 Simple Conceptual Examples......Page 196 Example 2......Page 197 10.3.1 Problem Description......Page 198 10.3.2.1 Combination of Evidence......Page 200 10.3.2.2 Construction of Probabilistic Response......Page 201 10.3.3.1 Construction of Basic Probability Assignments for Individual Inputs......Page 203 10.3.3.2 Combination of Evidence......Page 204 10.3.3.3 Construction of Basic Probability Assignments for the Product Space......Page 205 10.3.3.4 Construction of Belief and Plausibility for the System Response......Page 206 10.3.4 Comparison and Interpretation of Results......Page 211 10.4 Research Topics in the Application of Evidence Theory......Page 212 References......Page 213 11.1 Introduction and Overview......Page 217 11.2 Design of a Cantilever with Uncertain Load......Page 218 11.2.1 Performance Optimization......Page 219 11.2.2 Robustness to Uncertain Load......Page 221 11.2.3 Info-Gap Robust-Optimal Design: Clash with Performance- Optimal Design......Page 223 11.2.4 Resolving the Clash......Page 224 11.2.5 Opportunity from Uncertain Load......Page 225 11.3.1 Model Uncertainty......Page 227 11.3.2 Performance Optimization with the Best Model......Page 228 11.3.3 Robustness Function......Page 229 11.3.4 Example......Page 231 11.4.2 Uncertainty and Robustness......Page 233 11.4.3 Example......Page 235 11.5.1 Info-Gap Robustness as a Decision Monitor......Page 238 11.5.2 Nonlinear Spring......Page 239 11.6.1 The Basic Lemma......Page 241 11.6.2 Optimal-Performance vs. Optimal Robustness: The Theorem......Page 243 11.6.3 Information-Gap Models of Uncertainty......Page 244 11.7 Conclusion: A Historical Perspective......Page 245 References......Page 246 12.1 Introduction......Page 247 12.2.1 Definitions, Applications, and Scope......Page 248 12.2.2 Dependency......Page 251 12.2.4 Linear Interval Equations......Page 252 12.3 Interval Methods for Predicting System Response Due to Uncertain Parameters......Page 254 12.3.2 Interval Finite Element Methods......Page 255 but physically inconsistent)......Page 257 underlying physics......Page 258 12.4.1 Approximation Errors......Page 263 12.4.2 Rounding-off Errors......Page 264 12.5 Future Developments of Interval Methods for Reliable Engineering Computations......Page 265 References......Page 267 Appendix 12.1 Interval Arithmetic Operations......Page 269 13.1 Introduction......Page 271 13.2.1.2 Knowledge Concepts......Page 273 13.2.1.3 Roles......Page 274 13.2.2.1 Criteria for an Expert Elicitation Approach......Page 275 13.2.3.1 Identifying the Adviser-Expert......Page 276 13.2.3.2 Defining a Collaborative Strategy......Page 277 13.2.4.1 What Is a Knowledge Model?......Page 278 13.2.4.2 Process for Eliciting Expertise......Page 279 13.2.4.3 Knowledge Representation Techniques for Modeling Expertise......Page 280 13.2.4.4 Success and Failure for the Problem Area......Page 283 13.3.1.1 What Is Expert Judgment?......Page 284 13.3.1.2 What to Elicit?......Page 285 13.3.1.3 Identifying the Experts......Page 286 13.3.1.5 Expert Cognition and the Problem of Bias......Page 287 13.3.1.7 Question Phrasing......Page 290 13.3.2 A Modified Delphi for Reliability Analysis......Page 292 13.4.1 Characterizing Uncertainties......Page 293 13.4.1.3 Aggregation of Expert Judgments......Page 296 13.4.2.1 How Expert Knowledge Combines with Other Information......Page 298 References......Page 299 14.1 Introduction......Page 302 14.2.1 Statistically Independent Random Variables......Page 304 14.2.3 Random Variables with Nataf Distribution......Page 305 14.2.4 Dependent Nonnormal Random Variables......Page 306 14.3.1 Component Reliability by FORM......Page 307 14.3.2 System Reliability by FORM......Page 310 14.3.2.1 Example: Series System Reliability Analysis of a Frame by FORM......Page 313 14.3.3 FORM Importance and Sensitivity Measures......Page 314 14.4 The Second-Order Reliability Method......Page 316 14.5 Time-Variant Reliability Analysis......Page 320 14.5.1 Example: Mean Out-Crossing Rate of a Column under Stochastic Loads......Page 322 References......Page 323 15.1 Introduction......Page 326 15.2.2 Fundamental Systems......Page 327 15.2.3 Modeling of Systems at Level N......Page 329 15.2.5 Formal Representation of Systems......Page 331 15.3.1 Introduction......Page 335 15.3.2 Assessment of the Probability of Failure of Series Systems......Page 337 15.3.3 Reliability Bounds for Series Systems......Page 338 15.3.4 Series Systems with Equally Correlated Elements......Page 340 15.3.5 Series Systems with Unequally Correlated Elements......Page 342 15.4.1 Introduction......Page 343 15.4.2 Assessment of the Probability of Failure of Parallel Systems......Page 344 15.4.3 Reliability Bounds for Parallel Systems......Page 345 15.4.4 Equivalent Linear Safety Margins for Parallel Systems......Page 347 15.4.5 Parallel Systems with Equally Correlated Elements......Page 349 15.5.1 Introduction......Page 351 15.5.3 Assessment of System Reliability at Level 2......Page 352 15.5.4 Assessment of System Reliability at Level N > 2......Page 353 15.5.5 Assessment of System Reliability at Mechanism Level......Page 354 15.5.6 Examples......Page 357 15.6.1 Reliability of a Tubular Joint......Page 363 15.6.2 Reliability-Based Optimal Design of a Steel-Jacket Offshore Structure......Page 365 15.6.3 Reliability-Based Optimal Maintenance Strategies......Page 366 References......Page 368 16.1 Introduction......Page 371 16.3 Probability Distributions of the ÏQuantum Mass RatioÓ and Its Logarithm......Page 372 16.4 Logarithmic Variance and Other Statistics of the Ï Quantum Mass RatioÓ......Page 374 16.5 Probability Distribution of the ÏQuantum Size RatioÓ......Page 379 16.6 Extensions and Applications to Reliability Analysis......Page 381 16.7 Conclusion......Page 382 References......Page 383 17.1 Introduction and Objectives......Page 384 17.2.1 Modeling Random Processes......Page 385 17.2.2 Calculation of the Response......Page 388 17.2.3 Failure Analysis......Page 391 17.3 Evaluation of Stochastic Response and Failure Analysis: Linear Systems......Page 393 17.3.1 Evaluation of Stochastic Response......Page 394 17.3.2 Failure Analysis......Page 395 17.4 Evaluation of the Stochastic Response of Nonlinear Systems......Page 400 17.6 Conclusion......Page 401 References......Page 402 18.1 Introduction......Page 403 18.2 Loads as Processes: Upcrossings......Page 404 18.4 First Passage Probability......Page 405 18.5 Estimation of Upcrossing Rates......Page 406 18.6 Estimation of the Outcrossing Rate......Page 407 18.8 Time-Dependent Structural Reliability......Page 408 18.9.2 Gaussian Processes and Linear Limit State Functions......Page 409 18.9.4 Directional Simulation......Page 410 18.9.5 Ensembled Upcrossing Rate (EUR) Approach......Page 411 18.9.7 Summary of Solution Methods......Page 412 18.10 Load Combinations......Page 413 18.11 Some Remaining Research Problems......Page 415 References......Page 416 19.1 Introduction......Page 418 19.2.1 Basic Formulation......Page 420 19.2.2 Linear Models and Regression......Page 421 19.2.3 Analysis of Variance......Page 422 19.2.4 First- and Second-Order Polynomials......Page 425 19.2.5 Exponential Relationships......Page 426 19.3.1 Transformations......Page 427 19.3.2 Saturated Designs......Page 428 19.3.3 Redundant Designs......Page 429 19.3.4 Comparison......Page 430 19.4.2 Error Checking and Adaptation of the Response Surface......Page 431 19.5.1 Linear Response Surface......Page 432 19.5.2 Nonlinear Response Surface......Page 434 19.5.3 Nonlinear Finite Element Structure......Page 436 19.6 Recommendations......Page 438 References......Page 439 20.1 Introduction......Page 441 20.2.2 Inverse Probability Transformation......Page 443 20.2.5 Density Decomposition......Page 444 20.3.1 Gaussian Vectors......Page 445 20.3.2 NonGaussian Vectors......Page 446 20.4 Stochastic Fields (or Processes)......Page 448 20.4.1 One-Level Hierarchical Simulation Models......Page 451 20.4.2 Two-Level Hierarchical Simulation Models......Page 456 20.5.1 Sequential Importance Sampling (SIS)......Page 461 20.5.2 Dynamic Monte Carlo (DMC)......Page 462 20.5.3 Computing Tail Distribution Probabilities......Page 465 20.5.4 Employing Stochastic Linear PDE Solutions......Page 468 20.5.5 Incorporating Modeling Uncertainties......Page 470 20.6 Summary......Page 471 References......Page 472 Researchers in the engineering industry and academia are making important advances on reliability-based design and modeling of uncertainty when data is limited. Non deterministic approaches have enabled industries to save billions by reducing design and warranty costs and by improving quality. Considering the lack of comprehensive and definitive presentations on the subject, Engineering Design Reliability Handbook is a valuable addition to the reliability literature. It presents the perspectives of experts from the industry, national labs, and academia on non-deterministic approaches including probabilistic, interval and fuzzy sets-based methods, generalized information theory, Dempster-Shaffer evidence theory, and robust reliability. It also presents recent advances in all important fields of reliability design including modeling of uncertainty, reliability assessment of both static and dynamic components and systems, design decision making in the face of uncertainty, and reliability validation. The editors and the authors also discuss documented success stories and quantify the benefits of these approaches. With contributions from a team of respected international authors and the guidance of esteemed editors, this handbook is a distinctive addition to the acclaimed line of handbooks from CRC Press. "Engineering Design Reliability Handbook is a valuable addition to the reliability literature. It presents the perspectives of experts from the industry, national labs, and academia on non-deterministic approaches including probabilistic, interval and fuzzy sets-based methods, generalized information theory, Dempster-Shaffer evidence theory, and robust reliability. It also presents recent advances in all important fields of reliability design including modeling of uncertainty, reliability assessment of both static and dynamic components and systems, design decision making in the face of uncertainty, and reliability validation. The editors and the authors also discuss documented success stories and quantify the benefits of these approaches."--Jacket

"Compiles nearly 400 fully assigned NMR spectra of approximately 300 polymers and polymer additives, representing all major clases of materials: polyolefins, styrenics, acrylates, methacrylates, vinyl polymers, elastomers, polyethers, polyesters, polymides, silicones, cellulosics, polyurethanes, plasticizers, and antioxidants."

Polymer News

...A major task in polymer NMR studies is to interpret the NMR spectra....Over the years, only a few attempts have been made....the latest effort, by Brandolini and Hills, is the most up-to-date and the bestin this genre.

"Organized by chemical structure, this practical reference compiles nearly 400 fully assigned NMR spectra of some 300 polymers and polymer additives representing all major classes of materials - polyolefins, styrenics, acrylates, methacrylates, vinyl polymers, elastomers, polyethers, polyesters, polyamides, silicones, cellulosics, polyurethanes, plasticizers, and antioxidants."