LEADER 05497nam 2200661 a 450 001 9910877607803321 005 20200520144314.0 010 $a0-470-77073-2 010 $a1-281-84099-8 010 $a9786611840990 010 $a0-470-77074-0 035 $a(CKB)1000000000556207 035 $a(EBL)366872 035 $a(OCoLC)437234453 035 $a(SSID)ssj0000263926 035 $a(PQKBManifestationID)11217805 035 $a(PQKBTitleCode)TC0000263926 035 $a(PQKBWorkID)10283146 035 $a(PQKB)11279627 035 $a(MiAaPQ)EBC366872 035 $a(PPN)204738563 035 $a(EXLCZ)991000000000556207 100 $a20080229d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aUncertainty in industrial practice $ea guide to quantitative uncertainty management /$fedited by Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola 210 $aChichester, England ;$aHoboken, NJ $cJ. Wiley$dc2008 215 $a1 online resource (365 p.) 300 $aDescription based upon print version of record. 311 $a0-470-99447-9 320 $aIncludes bibliographical references and index. 327 $aUncertainty in Industrial Practice; Contents; Preface; Contributors and Acknowledgements; Introduction; Notation - Acronyms and abbreviations; Part I Common Methodological Framework; 1 Introducing the common methodological framework; 1.1 Quantitative uncertainty assessment in industrial practice: a wide variety of contexts; 1.2 Key generic features, notation and concepts; 1.2.1 Pre-existing model, variables of interest and uncertain/fixed inputs; 1.2.2 Main goals of the uncertainty assessment; 1.2.3 Measures of uncertainty and quantities of interest; 1.2.4 Feedback process 327 $a1.2.5 Uncertainty modelling1.2.6 Propagation and sensitivity analysis processes; 1.3 The common conceptual framework; 1.4 Using probabilistic frameworks in uncertainty quantification - preliminary comments; 1.4.1 Standard probabilistic setting and interpretations; 1.4.2 More elaborate level-2 settings and interpretations; 1.5 Concluding remarks; References; 2 Positioning of the case studies; 2.1 Main study characteristics to be specified in line with the common framework; 2.2 Introducing the panel of case studies; 2.3 Case study abstracts; Part II Case Studies 327 $a3 CO2 emissions: estimating uncertainties in practice for power plants3.1 Introduction and study context; 3.2 The study model and methodology; 3.2.1 Three metrological options: common features in the pre-existing models; 3.2.2 Differentiating elements of the fuel consumption models; 3.3 Underlying framework of the uncertainty study; 3.3.1 Specification of the uncertainty study; 3.3.2 Description and modelling of the sources of uncertainty; 3.3.3 Uncertainty propagation and sensitivity analysis; 3.3.4 Feedback process; 3.4 Practical implementation and results; 3.5 Conclusions; References 327 $a4 Hydrocarbon exploration: decision-support through uncertainty treatment4.1 Introduction and study context; 4.2 The study model and methodology; 4.2.1 Basin and petroleum system modelling; 4.3 Underlying framework of the uncertainty study; 4.3.1 Specification of the uncertainty study; 4.3.2 Description and modelling of the sources of uncertainty; 4.3.3 Uncertainty propagation and sensitivity analysis; 4.3.4 Feedback process; 4.4 Practical implementation and results; 4.4.1 Uncertainty analysis; 4.4.2 Sensitivity analysis; 4.5 Conclusions; References 327 $a5 Determination of the risk due to personal electronic devices (PEDs) carried out on radio-navigation systems aboard aircraft5.1 Introduction and study context; 5.2 The study model and methodology; 5.2.1 Electromagnetic compatibility modelling and analysis; 5.2.2 Setting the EMC problem; 5.2.3 A model-based approach; 5.2.4 Regulatory and industrial stakes; 5.3 Underlying framework of the uncertainty study; 5.3.1 Specification of the uncertainty study; 5.3.2 Description and modelling of the sources of uncertainty; 5.3.3 Uncertainty propagation and sensitivity analysis; 5.3.4 Feedback process 327 $a5.4 Practical implementation and results 330 $aManaging 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 acknowledgin 606 $aIndustrial management$xMathematical models 606 $aUncertainty$xMathematical models 606 $aRisk management 615 0$aIndustrial management$xMathematical models. 615 0$aUncertainty$xMathematical models. 615 0$aRisk management. 676 $a658.001 701 $aRocquigny$b Etienne de$0522144 701 $aDevictor$b Nicolas$01753106 701 $aTarantola$b Stefano$01753107 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877607803321 996 $aUncertainty in industrial practice$94188697 997 $aUNINA