Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management / Edition 1

Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management / Edition 1

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by Etienne de Rocquigny
     
 

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment

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Overview

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment in any type of industry. It is concerned with the quantification of uncertainties in the presence of data, model(s) and knowledge about the system, and offers a technical contribution to decision-making processes whilst acknowledging industrial constraints. The approach presented can be applied to a range of different business contexts, from research or early design through to certification or in-service processes. The authors aim to foster optimal trade-offs between literature-referenced methodologies and the simplified approaches often inevitable in practice, owing to data, time or budget limitations of technical decision-makers.

Uncertainty in Industrial Practice:

  • Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework.
  • Presents methods for organizing and treating uncertainties in a generic and prioritized perspective.
  • Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints.
  • Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods.
  • Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries.

    This book provides a guide to dealing with quantitative uncertainty in engineering and modelling and is aimed at practitioners, including risk-industry regulators and academics wishing to develop industry-realistic methodologies. 

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  • Product Details

    ISBN-13:
    9780470994474
    Publisher:
    Wiley
    Publication date:
    06/30/2008
    Pages:
    364
    Sales rank:
    1,177,955
    Product dimensions:
    6.10(w) x 9.20(h) x 1.00(d)

    Table of Contents

    Preface xiii

    Contributors and Acknowledgements xv

    Introduction xvii

    Notation – Acronyms and abbreviations xxi

    Part I Common Methodological Framework 1

    1 Introducing the common methodological framework 3

    1.1 Quantitative uncertainty assessment in industrial practice: a wide variety of contexts 3

    1.2 Key generic features, notation and concepts 4

    1.2.1 Pre-existing model, variables of interest and uncertain/fixed inputs 4

    1.2.2 Main goals of the uncertainty assessment 6

    1.2.3 Measures of uncertainty and quantities of interest 7

    1.2.4 Feedback process 9

    1.2.5 Uncertainty modelling 10

    1.2.6 Propagation and sensitivity analysis processes 10

    1.3 The common conceptual framework 11

    1.4 Using probabilistic frameworks in uncertainty quantification – preliminary comments 13

    1.4.1 Standard probabilistic setting and interpretations 13

    1.4.2 More elaborate level-2 settings and interpretations 14

    1.5 Concluding remarks 17

    References 18

    2 Positioning of the case studies 21

    2.1 Main study characteristics to be specified in line with the common framework 21

    2.2 Introducing the panel of case studies 21

    2.3 Case study abstracts 27

    Part II Case Studies 33

    3 CO2 emissions: estimating uncertainties in practice for power plants 35

    3.1 Introduction and study context 35

    3.2 The study model and methodology 36

    3.2.1 Three metrological options: common features in the preexisting models 36

    3.2.2 Differentiating elements of the fuel consumption models 38

    3.3 Underlying framework of the uncertainty study 39

    3.3.1 Specification of the uncertainty study 39

    3.3.2 Description and modelling of the sources of uncertainty 40

    3.3.3 Uncertainty propagation and sensitivity analysis 42

    3.3.4 Feedback process 44

    3.4 Practical implementation and results 44

    3.5 Conclusions 47

    References 47

    4 Hydrocarbon exploration: decision-support through uncertainty treatment 49

    4.1 Introduction and study context 49

    4.2 The study model and methodology 50

    4.2.1 Basin and petroleum system modelling 50

    4.3 Underlying framework of the uncertainty study 54

    4.3.1 Specification of the uncertainty study 54

    4.3.2 Description and modelling of the sources of uncertainty 56

    4.3.3 Uncertainty propagation and sensitivity analysis 57

    4.3.4 Feedback process 57

    4.4 Practical implementation and results 59

    4.4.1 Uncertainty analysis 59

    4.4.2 Sensitivity analysis 62

    4.5 Conclusions 63

    References 64

    5 Determination of the risk due to personal electronic devices (PEDs) carried out on radio-navigation systems aboard aircraft 65

    5.1 Introduction and study context 65

    5.2 The study model and methodology 66

    5.2.1 Electromagnetic compatibility modelling and analysis 66

    5.2.2 Setting the EMC problem 67

    5.2.3 A model-based approach 68

    5.2.4 Regulatory and industrial stakes 69

    5.3 Underlying framework of the uncertainty study 71

    5.3.1 Specification of the uncertainty study 71

    5.3.2 Description and modelling of the sources of uncertainty 72

    5.3.3 Uncertainty propagation and sensitivity analysis 75

    5.3.4 Feedback process 76

    5.4 Practical implementation and results 76

    5.4.1 Limitations of the results of the study 76

    5.4.2 Scenario no.1: effects of one emitter in the aircraft on ILS antenna (realistic data-set) 76

    5.4.3 Scenario no. 2: effects of one emitter in the aircraft on ILS antenna with penalized susceptibility 78

    5.4.4 Scenario no. 3: 10 coherent emitters in the aircraft, ILS antenna with a realistic data set 79

    5.4.5 Scenario no. 4: new model considering the effect of one emitter in the aircraft on ILS antenna and safety factors 79

    5.5 Conclusions 80

    References 80

    6 Safety assessment of a radioactive high-level waste repository – comparison of dose and peak dose 81

    6.1 Introduction and study context 81

    6.2 Study model and methodology 82

    6.2.1 Source term model 83

    6.2.2 Geosphere model 83

    6.2.3 The biosphere model 84

    6.3 Underlying framework of the uncertainty study 84

    6.3.1 Specification of the uncertainty study 84

    6.3.2 Sources of uncertainty, model inputs and uncertainty model developed 85

    6.3.3 Uncertainty propagation and sensitivity analysis 86

    6.3.4 Feedback process 87

    6.4 Practical implementation and results 87

    6.4.1 Uncertainty analysis 87

    6.4.2 Sensitivity analysis 91

    6.5 Conclusions 95

    References 96

    7 A cash flow statistical model for airframe accessory maintenance contracts 97

    7.1 Introduction and study context 97

    7.2 The study model and methodology 97

    7.2.1 Generalities 97

    7.2.2 Level-1 uncertainty 98

    7.2.3 Computation 98

    7.2.4 Stock size 100

    7.3 Underlying framework of the uncertainty study 100

    7.3.1 Specification of the uncertainty study 100

    7.3.2 Description and modelling of the sources of uncertainty 101

    7.3.3 Uncertainty propagation and sensitivity analysis 103

    7.3.4 Feedback process 104

    7.4 Practical implementation and results 104

    7.4.1 Design of experiments results 105

    7.4.2 Sobol’s sensitivity indices 107

    7.4.3 Comparison between DoE and Sobol’ methods 108

    7.5 Conclusions 108

    References 109

    8 Uncertainty and reliability study of a creep law to assess the fuel cladding behaviour of PWR spent fuel assemblies during interim dry storage 111

    8.1 Introduction and study context 111

    8.2 The study model and methodology 112

    8.2.1 Failure limit strain and margin 113

    8.2.2 The temperature scenario 113

    8.3 Underlying framework of the uncertainty study 114

    8.3.1 Specification of the uncertainty study 114

    8.3.2 Description and modelling of the sources of uncertainty 115

    8.3.3 Uncertainty propagation and sensitivity analysis 116

    8.3.4 Feedback process 116

    8.4 Practical implementation and results 117

    8.4.1 Dispersion of the minimal margin 117

    8.4.2 Sensitivity analysis 119

    8.4.3 Exceedance probability analysis 120

    8.5 Conclusions 121

    References 122

    9 Radiological protection and maintenance 123

    9.1 Introduction and study context 123

    9.2 The study model and methodology 124

    9.3 Underlying framework of the uncertainty study 128

    9.3.1 Specification of the uncertainty study 128

    9.3.2 Description and modelling of the sources of uncertainty 129

    9.3.3 Uncertainty propagation and sensitivity analysis 131

    9.3.4 Feedback process 131

    9.4 Practical implementation and results 132

    9.5 Conclusions 134

    References 134

    10 Partial safety factors to deal with uncertainties in slope stability of river dykes 135

    10.1 Introduction and study context 135

    10.2 The study model and methodology 136

    10.2.1 Slope stability models 136

    10.2.2 Incorporating slope stability in dyke design 137

    10.2.3 Uncertainties in design process 138

    10.3 Underlying framework of the uncertainty study 138

    10.3.1 Specification of the uncertainty study 139

    10.3.2 Description and modelling of the sources of uncertainty 142

    10.3.3 Uncertainty propagation and sensitivity analysis 144

    10.3.4 Feedback process 149

    10.4 Practical implementation and results 150

    10.5 Conclusions 153

    References 153

    11 Probabilistic assessment of fatigue life 155

    11.1 Introduction and study context 155

    11.2 The study model and methodology 155

    11.2.1 Fatigue criteria 155

    11.2.2 System model 156

    11.3 Underlying framework of the uncertainty study 157

    11.3.1 Outline of current practice in fatigue design 157

    11.3.2 Specification of the uncertainty study 158

    11.3.3 Description and modelling of the sources of uncertainty 160

    11.3.4 Uncertainty propagation and sensitivity analysis 161

    11.3.5 Feedback process 161

    11.4 Practical implementation and results 162

    11.4.1 Identification of the macro fatigue resistance β(N) 162

    11.4.2 Uncertainty analysis 164

    11.5 Conclusions 167

    References 167

    12 Reliability modelling in early design stages using the Dempster-Shafer Theory of Evidence 169

    12.1 Introduction and study context 169

    12.2 The study model and methodology 170

    12.2.1 The system 170

    12.2.2 The system fault tree model 171

    12.2.3 The IEC 61508 guideline: a framework for safety requirements 172

    12.3 Underlying framework of the uncertainty study 173

    12.3.1 Specification of the uncertainty study 173

    12.3.2 Description and modelling of the sources of uncertainty 176

    12.4 Practical implementation and results 178

    12.5 Conclusions 182

    References 182

    Part III Methodological Review and Recommendations 185

    13 What does uncertainty management mean in an industrial context? 187

    13.1 Introduction 187

    13.2 A basic distinction between ‘design’ and ‘in-service operations’ in an industrial estate 188

    13.2.1 Design phases 188

    13.2.2 In-service operations 189

    13.3 Failure-driven risk management and option-exploring approaches at company level 190

    13.4 Survey of the main trends and popular concepts in industry 191

    13.5 Links between uncertainty management studies and a global industrial context 192

    13.5.1 Internal/endogenous context 193

    13.5.2 External/exogenous uncertainty 194

    13.5.3 Layers of uncertainty 195

    13.6 Developing a strategy to deal with uncertainties 195

    References 197

    14 Uncertainty settings and natures of uncertainty 199

    14.1 A classical distinction 199

    14.2 Theoretical distinctions, difficulties and controversies in practical applications 202

    14.3 Various settings deemed acceptable in practice 205

    References 210

    15 Overall approach 213

    15.1 Recalling the common methodological framework 213

    15.2 Introducing the mathematical formulation and key steps of a study 214

    15.2.1 The specification step – measure of uncertainty, quantities of interest and setting 214

    15.2.2 The uncertainty modelling (or source quantification) step 215

    15.2.3 The uncertainty propagation step 218

    15.2.4 The sensitivity analysis step, or importance ranking 219

    15.3 Links between final goals, study steps and feedback process 220

    15.4 Comparison with applied system identification or command/control classics 221

    15.5 Pre-existing or system model validation and model uncertainty 222

    15.6 Links between decision theory and the criteria of the overall framework 223

    References 224

    16 Uncertainty modelling methods 225

    16.1 Objectives of uncertainty modelling and important issues 225

    16.2 Recommendations in a standard probabilistic setting 227

    16.2.1 The case of independent variables 228

    16.2.2 Building an univariate probability distribution via expert/engineering judgement 229

    16.2.3 The case of dependent uncertain model inputs 234

    16.3 Comments on level-2 probabilistic settings 236

    References 237

    17 Uncertainty propagation methods 239

    17.1 Recommendations per quantity of interest 240

    17.1.1 Variance, moments 240

    17.1.2 Probability density function 243

    17.1.3 Quantiles 245

    17.1.4 Exceedance probability 247

    17.2 Meta-models 250

    17.2.1 Building a meta-model 251

    17.2.2 Validation of a meta-model 252

    17.3 Summary 253

    References 256

    18 Sensitivity analysis methods 259

    18.1 The role of sensitivity analysis in quantitative uncertainty assessment 260

    18.1.1 Understanding influence and ranking importance of uncertainties (goal U) 261

    18.1.2 Calibrating, simplifying and validating a numerical model (goal A) 262

    18.1.3 Comparing relative performances and decision support (goal S) 263

    18.1.4 Demonstrating compliance with a criterion or a regulatory threshold (goal C) 264

    18.2 Towards the choice of an appropriate Sensitivity Analysis framework 264

    18.3 Scope, potential and limitations of the various techniques 269

    18.3.1 Differential methods 269

    18.3.2 Approximate reliability methods 270

    18.3.3 Regression/correlation 271

    18.3.4 Screening methods 273

    18.3.5 Variance analysis of Monte Carlo simulations 274

    18.3.6 Non-variance analysis of Monte Carlo simulations 276

    18.3.7 Graphical methods 278

    18.4 Conclusions 280

    References 281

    19 Presentation in a deterministic format 285

    19.1 How to present in a deterministic format? 286

    19.1.1 (Partial) safety factors in a deterministic approach 286

    19.1.2 Safety factors in a probabilistic approach 287

    19.2 On the reliability target 290

    19.3 Final comments 291

    References 292

    20 Recommendations on the overall process in practice 293

    20.1 Recommendations on the key specification step 293

    20.1.1 Choice of the system model 294

    20.1.2 Choice of the uncertainty setting 294

    20.1.3 Choice of the quantity of interest 296

    20.1.4 Choice of the model input representation (‘x’ and ‘d’) 297

    20.2 Final comments regarding dissemination challenges 297

    References 298

    Conclusion 299

    Appendices 303

    Appendix A A selection of codes and standards 305

    Appendix B A selection of tools and websites 307

    Appendix C Towards non-probabilistic settings: promises and industrial challenges 313

    Index 329

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