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Uncertainty in Industrial Practice


Uncertainty in Industrial Practice

A Guide to Quantitative Uncertainty Management
1. Aufl.

von: Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola

99,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 15.09.2008
ISBN/EAN: 9780470770740
Sprache: englisch
Anzahl Seiten: 364

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Beschreibungen

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. <p>Uncertainty in Industrial Practice:</p> <ul> <li>Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework.</li> </ul> <ul> <li> <p>Presents methods for organizing and treating uncertainties in a generic and prioritized perspective.</p> </li> <li>Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints.</li> <li>Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods.</li> </ul> <ul> <li>Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries.</li> </ul> <p>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.</p>
<p>Preface xiii</p> <p>Contributors and Acknowledgements xv</p> <p>Introduction xvii</p> <p>Notation – Acronyms and abbreviations xxi</p> <p><b>Part I Common Methodological Framework 1</b></p> <p><b>1 Introducing the common methodological framework 3</b></p> <p>1.1 Quantitative uncertainty assessment in industrial practice: a wide variety of contexts 3</p> <p>1.2 Key generic features, notation and concepts 4</p> <p>1.2.1 Pre-existing model, variables of interest and uncertain/fixed inputs 4</p> <p>1.2.2 Main goals of the uncertainty assessment 6</p> <p>1.2.3 Measures of uncertainty and quantities of interest 7</p> <p>1.2.4 Feedback process 9</p> <p>1.2.5 Uncertainty modelling 10</p> <p>1.2.6 Propagation and sensitivity analysis processes 10</p> <p>1.3 The common conceptual framework 11</p> <p>1.4 Using probabilistic frameworks in uncertainty quantification – preliminary comments 13</p> <p>1.4.1 Standard probabilistic setting and interpretations 13</p> <p>1.4.2 More elaborate level-2 settings and interpretations 14</p> <p>1.5 Concluding remarks 17</p> <p>References 18</p> <p><b>2 Positioning of the case studies 21</b></p> <p>2.1 Main study characteristics to be specified in line with the common framework 21</p> <p>2.2 Introducing the panel of case studies 21</p> <p>2.3 Case study abstracts 27</p> <p><b>Part II Case Studies 33</b></p> <p><b>3 CO2 emissions: estimating uncertainties in practice for power plants 35</b></p> <p>3.1 Introduction and study context 35</p> <p>3.2 The study model and methodology 36</p> <p>3.2.1 Three metrological options: common features in the preexisting models 36</p> <p>3.2.2 Differentiating elements of the fuel consumption models 38</p> <p>3.3 Underlying framework of the uncertainty study 39</p> <p>3.3.1 Specification of the uncertainty study 39</p> <p>3.3.2 Description and modelling of the sources of uncertainty 40</p> <p>3.3.3 Uncertainty propagation and sensitivity analysis 42</p> <p>3.3.4 Feedback process 44</p> <p>3.4 Practical implementation and results 44</p> <p>3.5 Conclusions 47</p> <p>References 47</p> <p><b>4 Hydrocarbon exploration: decision-support through uncertainty treatment 49</b></p> <p>4.1 Introduction and study context 49</p> <p>4.2 The study model and methodology 50</p> <p>4.2.1 Basin and petroleum system modelling 50</p> <p>4.3 Underlying framework of the uncertainty study 54</p> <p>4.3.1 Specification of the uncertainty study 54</p> <p>4.3.2 Description and modelling of the sources of uncertainty 56</p> <p>4.3.3 Uncertainty propagation and sensitivity analysis 57</p> <p>4.3.4 Feedback process 57</p> <p>4.4 Practical implementation and results 59</p> <p>4.4.1 Uncertainty analysis 59</p> <p>4.4.2 Sensitivity analysis 62</p> <p>4.5 Conclusions 63</p> <p>References 64</p> <p><b>5 Determination of the risk due to personal electronic devices (PEDs) carried out on radio-navigation systems aboard aircraft 65</b></p> <p>5.1 Introduction and study context 65</p> <p>5.2 The study model and methodology 66</p> <p>5.2.1 Electromagnetic compatibility modelling and analysis 66</p> <p>5.2.2 Setting the EMC problem 67</p> <p>5.2.3 A model-based approach 68</p> <p>5.2.4 Regulatory and industrial stakes 69</p> <p>5.3 Underlying framework of the uncertainty study 71</p> <p>5.3.1 Specification of the uncertainty study 71</p> <p>5.3.2 Description and modelling of the sources of uncertainty 72</p> <p>5.3.3 Uncertainty propagation and sensitivity analysis 75</p> <p>5.3.4 Feedback process 76</p> <p>5.4 Practical implementation and results 76</p> <p>5.4.1 Limitations of the results of the study 76</p> <p>5.4.2 Scenario no.1: effects of one emitter in the aircraft on ILS antenna (realistic data-set) 76</p> <p>5.4.3 Scenario no. 2: effects of one emitter in the aircraft on ILS antenna with penalized susceptibility 78</p> <p>5.4.4 Scenario no. 3: 10 coherent emitters in the aircraft, ILS antenna with a realistic data set 79</p> <p>5.4.5 Scenario no. 4: new model considering the effect of one emitter in the aircraft on ILS antenna and safety factors 79</p> <p>5.5 Conclusions 80</p> <p>References 80</p> <p><b>6 Safety assessment of a radioactive high-level waste repository – comparison of dose and peak dose 81</b></p> <p>6.1 Introduction and study context 81</p> <p>6.2 Study model and methodology 82</p> <p>6.2.1 Source term model 83</p> <p>6.2.2 Geosphere model 83</p> <p>6.2.3 The biosphere model 84</p> <p>6.3 Underlying framework of the uncertainty study 84</p> <p>6.3.1 Specification of the uncertainty study 84</p> <p>6.3.2 Sources of uncertainty, model inputs and uncertainty model developed 85</p> <p>6.3.3 Uncertainty propagation and sensitivity analysis 86</p> <p>6.3.4 Feedback process 87</p> <p>6.4 Practical implementation and results 87</p> <p>6.4.1 Uncertainty analysis 87</p> <p>6.4.2 Sensitivity analysis 91</p> <p>6.5 Conclusions 95</p> <p>References 96</p> <p><b>7 A cash flow statistical model for airframe accessory maintenance contracts 97</b></p> <p>7.1 Introduction and study context 97</p> <p>7.2 The study model and methodology 97</p> <p>7.2.1 Generalities 97</p> <p>7.2.2 Level-1 uncertainty 98</p> <p>7.2.3 Computation 98</p> <p>7.2.4 Stock size 100</p> <p>7.3 Underlying framework of the uncertainty study 100</p> <p>7.3.1 Specification of the uncertainty study 100</p> <p>7.3.2 Description and modelling of the sources of uncertainty 101</p> <p>7.3.3 Uncertainty propagation and sensitivity analysis 103</p> <p>7.3.4 Feedback process 104</p> <p>7.4 Practical implementation and results 104</p> <p>7.4.1 Design of experiments results 105</p> <p>7.4.2 Sobol’s sensitivity indices 107</p> <p>7.4.3 Comparison between DoE and Sobol’ methods 108</p> <p>7.5 Conclusions 108</p> <p>References 109</p> <p><b>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</b></p> <p>8.1 Introduction and study context 111</p> <p>8.2 The study model and methodology 112</p> <p>8.2.1 Failure limit strain and margin 113</p> <p>8.2.2 The temperature scenario 113</p> <p>8.3 Underlying framework of the uncertainty study 114</p> <p>8.3.1 Specification of the uncertainty study 114</p> <p>8.3.2 Description and modelling of the sources of uncertainty 115</p> <p>8.3.3 Uncertainty propagation and sensitivity analysis 116</p> <p>8.3.4 Feedback process 116</p> <p>8.4 Practical implementation and results 117</p> <p>8.4.1 Dispersion of the minimal margin 117</p> <p>8.4.2 Sensitivity analysis 119</p> <p>8.4.3 Exceedance probability analysis 120</p> <p>8.5 Conclusions 121</p> <p>References 122</p> <p><b>9 Radiological protection and maintenance 123</b></p> <p>9.1 Introduction and study context 123</p> <p>9.2 The study model and methodology 124</p> <p>9.3 Underlying framework of the uncertainty study 128</p> <p>9.3.1 Specification of the uncertainty study 128</p> <p>9.3.2 Description and modelling of the sources of uncertainty 129</p> <p>9.3.3 Uncertainty propagation and sensitivity analysis 131</p> <p>9.3.4 Feedback process 131</p> <p>9.4 Practical implementation and results 132</p> <p>9.5 Conclusions 134</p> <p>References 134</p> <p><b>10 Partial safety factors to deal with uncertainties in slope stability of river dykes 135</b></p> <p>10.1 Introduction and study context 135</p> <p>10.2 The study model and methodology 136</p> <p>10.2.1 Slope stability models 136</p> <p>10.2.2 Incorporating slope stability in dyke design 137</p> <p>10.2.3 Uncertainties in design process 138</p> <p>10.3 Underlying framework of the uncertainty study 138</p> <p>10.3.1 Specification of the uncertainty study 139</p> <p>10.3.2 Description and modelling of the sources of uncertainty 142</p> <p>10.3.3 Uncertainty propagation and sensitivity analysis 144</p> <p>10.3.4 Feedback process 149</p> <p>10.4 Practical implementation and results 150</p> <p>10.5 Conclusions 153</p> <p>References 153</p> <p><b>11 Probabilistic assessment of fatigue life 155</b></p> <p>11.1 Introduction and study context 155</p> <p>11.2 The study model and methodology 155</p> <p>11.2.1 Fatigue criteria 155</p> <p>11.2.2 System model 156</p> <p>11.3 Underlying framework of the uncertainty study 157</p> <p>11.3.1 Outline of current practice in fatigue design 157</p> <p>11.3.2 Specification of the uncertainty study 158</p> <p>11.3.3 Description and modelling of the sources of uncertainty 160</p> <p>11.3.4 Uncertainty propagation and sensitivity analysis 161</p> <p>11.3.5 Feedback process 161</p> <p>11.4 Practical implementation and results 162</p> <p>11.4.1 Identification of the macro fatigue resistance β(N) 162</p> <p>11.4.2 Uncertainty analysis 164</p> <p>11.5 Conclusions 167</p> <p>References 167</p> <p><b>12 Reliability modelling in early design stages using the Dempster-Shafer Theory of Evidence 169</b></p> <p>12.1 Introduction and study context 169</p> <p>12.2 The study model and methodology 170</p> <p>12.2.1 The system 170</p> <p>12.2.2 The system fault tree model 171</p> <p>12.2.3 The IEC 61508 guideline: a framework for safety requirements 172</p> <p>12.3 Underlying framework of the uncertainty study 173</p> <p>12.3.1 Specification of the uncertainty study 173</p> <p>12.3.2 Description and modelling of the sources of uncertainty 176</p> <p>12.4 Practical implementation and results 178</p> <p>12.5 Conclusions 182</p> <p>References 182</p> <p><b>Part III Methodological Review and Recommendations 185</b></p> <p><b>13 What does uncertainty management mean in an industrial context? 187</b></p> <p>13.1 Introduction 187</p> <p>13.2 A basic distinction between ‘design’ and ‘in-service operations’ in an industrial estate 188</p> <p>13.2.1 Design phases 188</p> <p>13.2.2 In-service operations 189</p> <p>13.3 Failure-driven risk management and option-exploring approaches at company level 190</p> <p>13.4 Survey of the main trends and popular concepts in industry 191</p> <p>13.5 Links between uncertainty management studies and a global industrial context 192</p> <p>13.5.1 Internal/endogenous context 193</p> <p>13.5.2 External/exogenous uncertainty 194</p> <p>13.5.3 Layers of uncertainty 195</p> <p>13.6 Developing a strategy to deal with uncertainties 195</p> <p>References 197</p> <p><b>14 Uncertainty settings and natures of uncertainty 199</b></p> <p>14.1 A classical distinction 199</p> <p>14.2 Theoretical distinctions, difficulties and controversies in practical applications 202</p> <p>14.3 Various settings deemed acceptable in practice 205</p> <p>References 210</p> <p><b>15 Overall approach 213</b></p> <p>15.1 Recalling the common methodological framework 213</p> <p>15.2 Introducing the mathematical formulation and key steps of a study 214</p> <p>15.2.1 The specification step – measure of uncertainty, quantities of interest and setting 214</p> <p>15.2.2 The uncertainty modelling (or source quantification) step 215</p> <p>15.2.3 The uncertainty propagation step 218</p> <p>15.2.4 The sensitivity analysis step, or importance ranking 219</p> <p>15.3 Links between final goals, study steps and feedback process 220</p> <p>15.4 Comparison with applied system identification or command/control classics 221</p> <p>15.5 Pre-existing or system model validation and model uncertainty 222</p> <p>15.6 Links between decision theory and the criteria of the overall framework  223</p> <p>References 224</p> <p><b>16 Uncertainty modelling methods 225</b></p> <p>16.1 Objectives of uncertainty modelling and important issues 225</p> <p>16.2 Recommendations in a standard probabilistic setting 227</p> <p>16.2.1 The case of independent variables 228</p> <p>16.2.2 Building an univariate probability distribution via expert/engineering judgement 229</p> <p>16.2.3 The case of dependent uncertain model inputs 234</p> <p>16.3 Comments on level-2 probabilistic settings 236</p> <p>References 237</p> <p><b>17 Uncertainty propagation methods 239</b></p> <p>17.1 Recommendations per quantity of interest 240</p> <p>17.1.1 Variance, moments 240</p> <p>17.1.2 Probability density function 243</p> <p>17.1.3 Quantiles 245</p> <p>17.1.4 Exceedance probability 247</p> <p>17.2 Meta-models 250</p> <p>17.2.1 Building a meta-model 251</p> <p>17.2.2 Validation of a meta-model 252</p> <p>17.3 Summary 253</p> <p>References 256</p> <p><b>18 Sensitivity analysis methods 259</b></p> <p>18.1 The role of sensitivity analysis in quantitative uncertainty assessment 260</p> <p>18.1.1 Understanding influence and ranking importance of uncertainties (goal U) 261</p> <p>18.1.2 Calibrating, simplifying and validating a numerical model (goal A) 262</p> <p>18.1.3 Comparing relative performances and decision support (goal S) 263</p> <p>18.1.4 Demonstrating compliance with a criterion or a regulatory threshold (goal C) 264</p> <p>18.2 Towards the choice of an appropriate Sensitivity Analysis framework 264</p> <p>18.3 Scope, potential and limitations of the various techniques 269</p> <p>18.3.1 Differential methods 269</p> <p>18.3.2 Approximate reliability methods 270</p> <p>18.3.3 Regression/correlation 271</p> <p>18.3.4 Screening methods 273</p> <p>18.3.5 Variance analysis of Monte Carlo simulations 274</p> <p>18.3.6 Non-variance analysis of Monte Carlo simulations 276</p> <p>18.3.7 Graphical methods 278</p> <p>18.4 Conclusions 280</p> <p>References 281</p> <p><b>19 Presentation in a deterministic format 285</b></p> <p>19.1 How to present in a deterministic format? 286</p> <p>19.1.1 (Partial) safety factors in a deterministic approach 286</p> <p>19.1.2 Safety factors in a probabilistic approach 287</p> <p>19.2 On the reliability target 290</p> <p>19.3 Final comments 291</p> <p>References 292</p> <p><b>20 Recommendations on the overall process in practice 293</b></p> <p>20.1 Recommendations on the key specification step 293</p> <p>20.1.1 Choice of the system model 294</p> <p>20.1.2 Choice of the uncertainty setting 294</p> <p>20.1.3 Choice of the quantity of interest 296</p> <p>20.1.4 Choice of the model input representation (‘x’ and ‘d’) 297</p> <p>20.2 Final comments regarding dissemination challenges 297</p> <p>References 298</p> <p>Conclusion 299</p> <p>Appendices 303</p> <p>Appendix A A selection of codes and standards 305</p> <p>Appendix B A selection of tools and websites 307</p> <p>Appendix C Towards non-probabilistic settings: promises and industrial challenges 313</p> <p>Index 329</p>
<p><strong>Editors: Etienne de Rocquigny</strong>, Electricite de France, R&D (Senior Research Fellow). <p><strong>Nicolas Devictor</strong>, Commissariat a l'Energie Atomique. <p><strong>Stefano Tarantola</strong>, J.R.C. Ispra. <p><strong>Authors:</strong> The 10 members of the Uncertainty Project Group, part of ESReDA: European Safety, Reliability and Data Association.
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. <p>Uncertainty in Industrial Practice:</p> <ul class="noindent"> <li>Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework.</li> </ul> <ul class="noindent"> <li>Presents methods for organizing and treating uncertainties in a generic and prioritized perspective.</li> </ul> <ul class="noindent"> <li>Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints.</li> </ul> <ul class="noindent"> <li>Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods.</li> </ul> <ul class="noindent"> <li>Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries.</li> </ul> <p>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. </p>

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