Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management / Edition 1 available in Hardcover
Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management / Edition 1
- ISBN-10:
- 0470994479
- ISBN-13:
- 9780470994474
- Pub. Date:
- 06/09/2008
- Publisher:
- Wiley
Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management / Edition 1
Hardcover
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$153.95Overview
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.
Product Details
| ISBN-13: | 9780470994474 |
|---|---|
| Publisher: | Wiley |
| Publication date: | 06/09/2008 |
| Pages: | 364 |
| Product dimensions: | 6.10(w) x 9.20(h) x 1.00(d) |
About the Author
Nicolas Devictor, Commissariat a l'Energie Atomique.
Stefano Tarantola, J.R.C. Ispra.
Authors: The 10 members of the Uncertainty Project Group, part of ESReDA: European Safety, Reliability and Data Association.
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Table of Contents
Preface xiiiContributors 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