Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.
Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Maurice Lemaire is Emeritus Professor at the French Institute for Advanced Mechanics in Clermont-Ferrand, France and Scientific Advisor of Phimeca Engineering S.A.
Inhaltsangabe
Foreword vii Preface xi Introduction xv Chapter 1. Understanding Uncertainty 1 1.1. Uncertainty and reality 1 1.1.1. Awareness of uncertainty 1 1.1.2. Territories of uncertainty 4 1.1.3. Conclusion 8 1.2. Robustness and reliability 9 1.2.1. Robustness 9 1.2.2. Reliability 13 1.2.3. Relationship between robustness and reliability 16 1.2.4. Optimizing robustness and reliability 19 1.2.5. Conclusion 21 1.3. Designing for robust production 22 1.3.1. Robustness and lifecycles 22 1.3.2. Description of the V cycle 23 1.3.3. Uncertainty in the V cycle 25 1.3.4. Uncertainty linked to a step in the V cycle 29 1.3.5. Robustness and uncertainty 33 1.3.6. Conclusion 38 Chapter 2. Modeling Uncertainty 41 2.1. Random uncertainty 41 2.1.1. Modeling uncertainty 41 2.1.2. Exploration of Mediocristan 42 2.1.3. From statistics to probabilities 47 2.1.4. Polynomial chaos 50 2.1.5. Exploration of Extremistan 52 2.1.6. Conclusion 55 2.2. Uncertainty in behavior models 55 2.2.1. Uncertainty and input data 56 2.2.2. Uncertainty in behavior models 61 2.3. Uncertainty propagation 70 2.3.1. The problem of uncertainty propagation 70 2.3.2. Analyzing sensitivity to uncertainty 71 2.3.3. Reliability analysis - classification methods 82 2.3.4. Model reductions 92 2.3.5. Quantifying uncertainty 98 2.3.6. Conclusion 100 Chapter 3. Decision Support under Uncertainty 101 3.1. Decision support in design 101 3.1.1. Decision support 101 3.1.2. Modeling decision support 103 3.1.3. Multi-criteria decision analysis (MCDA) 106 3.1.4. Conclusion 109 3.2. Summary and conclusion 110 3.2.1. Three perspectives 110 3.2.2. Challenges in engineering science 119 3.2.3. Industrial issues 123 Bibliography 125 Index 145
Foreword vii Preface xi Introduction xv Chapter 1. Understanding Uncertainty 1 1.1. Uncertainty and reality 1 1.1.1. Awareness of uncertainty 1 1.1.2. Territories of uncertainty 4 1.1.3. Conclusion 8 1.2. Robustness and reliability 9 1.2.1. Robustness 9 1.2.2. Reliability 13 1.2.3. Relationship between robustness and reliability 16 1.2.4. Optimizing robustness and reliability 19 1.2.5. Conclusion 21 1.3. Designing for robust production 22 1.3.1. Robustness and lifecycles 22 1.3.2. Description of the V cycle 23 1.3.3. Uncertainty in the V cycle 25 1.3.4. Uncertainty linked to a step in the V cycle 29 1.3.5. Robustness and uncertainty 33 1.3.6. Conclusion 38 Chapter 2. Modeling Uncertainty 41 2.1. Random uncertainty 41 2.1.1. Modeling uncertainty 41 2.1.2. Exploration of Mediocristan 42 2.1.3. From statistics to probabilities 47 2.1.4. Polynomial chaos 50 2.1.5. Exploration of Extremistan 52 2.1.6. Conclusion 55 2.2. Uncertainty in behavior models 55 2.2.1. Uncertainty and input data 56 2.2.2. Uncertainty in behavior models 61 2.3. Uncertainty propagation 70 2.3.1. The problem of uncertainty propagation 70 2.3.2. Analyzing sensitivity to uncertainty 71 2.3.3. Reliability analysis - classification methods 82 2.3.4. Model reductions 92 2.3.5. Quantifying uncertainty 98 2.3.6. Conclusion 100 Chapter 3. Decision Support under Uncertainty 101 3.1. Decision support in design 101 3.1.1. Decision support 101 3.1.2. Modeling decision support 103 3.1.3. Multi-criteria decision analysis (MCDA) 106 3.1.4. Conclusion 109 3.2. Summary and conclusion 110 3.2.1. Three perspectives 110 3.2.2. Challenges in engineering science 119 3.2.3. Industrial issues 123 Bibliography 125 Index 145
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