LEADER 11189nam 2200541 450 001 996466732803316 005 20231110224603.0 010 $a3-030-80542-5 035 $a(MiAaPQ)EBC6875874 035 $a(Au-PeEL)EBL6875874 035 $a(CKB)21022420300041 035 $a(PPN)269154663 035 $a(EXLCZ)9921022420300041 100 $a20220917d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in uncertainty quantification and optimization under uncertainty with aerospace applications $eproceedings of the 2020 UQOP international conference /$fedited by Massimiliano Vasile and Domenico Quagliarella 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$dİ2022 215 $a1 online resource (448 pages) 225 1 $aSpace Technology Proceedings ;$vv.8 311 08$aPrint version: Vasile, Massimiliano Advances in Uncertainty Quantification and Optimization under Uncertainty with Aerospace Applications Cham : Springer International Publishing AG,c2022 9783030805418 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Contents -- Part I Applications of Uncertainty in Aerospace & -- Engineering (ENG) -- From Uncertainty Quantification to Shape Optimization: Cross-Fertilization of Methods for Dimensionality Reduction -- 1 Introduction -- 2 Design-Space Dimensionality Reduction in Shape Optimization -- 2.1 Geometry-Based Formulation -- 2.2 Physics-Informed Formulation -- 3 Example Application -- 4 Concluding Remarks -- References -- Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems -- Nomenclature -- 1 Introduction -- 2 Modelling of an AI-ETIPS -- 2.1 Computational Model -- 2.2 Case of Study -- 3 Cloud Uncertainty Characterization -- 4 Uncertainty Propagation Methodologies -- 4.1 Monte Carlo Sampling Methods -- 4.2 Generalized Polynomial Chaos Expansion -- 5 Numerical Results -- 6 Concluding Remarks -- References -- Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation -- 1 Introduction -- 2 Multi-fidelity Gaussian Process Regression -- 3 Aerodynamic Computational Chain -- 4 Far-Field Drag Coefficient Calculation -- 5 Deterministic Design Optimisation Problem -- 6 Probabilistic Design Optimisation Problem -- 7 Optimisation Pipeline -- 8 Results -- 8.1 Deterministic Optimisation -- 8.2 Probabilistic Optimisation -- 9 Conclusion -- References -- Scalable Dynamic Asynchronous Monte Carlo Framework Applied to Wind Engineering Problems -- 1 Introduction -- 2 Monte Carlo Methods -- 2.1 Monte Carlo -- 2.2 Asynchronous Monte Carlo -- 2.3 Scheduling -- 3 Wind Engineering Benchmark -- 3.1 Problem Description -- 3.2 Source of Uncertainty -- 3.3 Results -- 4 Conclusion -- References -- Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE -- 1 Introduction. 327 $a2 Methodology and Description of the Proposed Model -- 2.1 Extracting Availability and Economic Cost from Functionability Profiles -- 2.2 Multi-Objective Optimization Approach -- 2.3 Building Functionability Profiles -- 3 The Application Case -- 4 Results and Discussion -- 5 Conclusions -- References -- Multi-objective Robustness Analysis of the Polymer Extrusion Process -- 1 Introduction -- 2 Robustness in Polymer Extrusion -- 2.1 Extrusion Process -- 2.2 Robustness Methodology -- 2.3 Multi-objective Optimization with Robustness -- 3 Results and Discussion -- 4 Conclusion -- References -- Quantification of Operational and Geometrical Uncertainties of a 1.5-Stage Axial Compressor with Cavity Leakage Flows -- 1 Motivation and Test Case Description -- 1.1 Geometry and Operating Regime -- 1.2 Uncertainty Definition -- Correlated Fields at the Main Inlet -- Secondary Inlets -- Rotor Blade Tip Gap -- 2 Uncertainty Quantification Method -- 2.1 Scaled Sensitivity Derivatives -- 3 Simulation Setup and Computational Cost -- 4 Results and Discussion -- 4.1 Non-deterministic Performance Curve -- 4.2 Scaled Sensitivity Derivatives -- 5 Conclusions -- References -- Can Uncertainty Propagation Solve the Mysterious Case of Snoopy? -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Dynamics Modelling -- 3.2 Using the TDA Structure to Solve ODE -- 3.3 Performing Numerical Analysis -- 3.4 Propagator Implementation and Validation -- 3.5 Monte-Carlo Estimation -- 4 Results and Discussion -- 4.1 Performing Numerical Analysis on the Trajectory of Snoopy -- 4.2 Computing Snoopy's Trajectory -- 4.3 Estimating the Probability of Snoopy's Presence -- 5 Conclusions and Future Work -- References -- Part II Imprecise Probability, Theory and Applications (IP) -- Robust Particle Filter for Space Navigation Under EpistemicUncertainty -- 1 Introduction. 327 $a2 Filtering Under Epistemic Uncertainty -- 2.1 Imprecise Formulation -- 2.2 Expectation Estimator -- 2.3 Bound Estimator -- 3 Test Case -- 3.1 Initial State Uncertainty -- 3.2 Observation Model and Errors -- 3.3 Results -- 4 Conclusions -- References -- Computing Bounds for Imprecise Continuous-Time Markov Chains Using Normal Cones -- 1 Introduction -- 2 Imprecise Markov Chains in Continuous Time -- 2.1 Imprecise Distributions over States -- 2.2 Imprecise Transition Rate Matrices -- 2.3 Distributions at Time t -- 3 Numerical Methods for Finding Lower Expectations -- 3.1 Lower Expectation and Transition Operators as Linear Programming Problems -- 3.2 Computational Approaches to Estimating Lower Expectation Functionals -- 4 Normal Cones of Imprecise Q-Operators -- 5 Norms of Q-Matrices -- 6 Numerical Methods for CTIMC Bounds Calculation -- 6.1 Matrix Exponential Method -- 6.2 Checking Applicability of the Matrix Exponential Method -- 6.3 Checking the Normal Cone Inclusion -- 6.4 Approximate Matrix Exponential Method -- 7 Error Estimation -- 7.1 General Error Bounds -- 7.2 Error Estimation for a Single Step -- 7.3 Error Estimation for the Uniform Grid -- 8 Algorithm and Examples -- 8.1 Parts of the Algorithm -- 8.2 Examples -- 9 Concluding Remarks -- References -- Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference -- 1 Introduction -- 2 Markov Chain Monte Carlo -- 3 Simultaneous Sampling -- 4 Markov Chain Monte Carlo for Imprecise Models -- 5 Practical Implementation -- 6 Linear Representation for Exponential Families -- 7 Computer Representation of the Credal Sets -- 8 Credal Set Merging -- 9 Discussion -- Reference -- Computing Expected Hitting Times for Imprecise Markov Chains -- 1 Introduction -- 2 Existence of Solutions -- 3 A Computational Method -- 4 Complexity Analysis -- References. 327 $aPart III Robust and Reliability-Based Design Optimisation in Aerospace Engineering (RBDO) -- Multi-Objective Robust Trajectory Optimization of Multi-Asteroid Fly-By Under Epistemic Uncertainty -- 1 Introduction -- 2 Problem Formulation -- 3 Lower Expectation -- 3.1 Minimizing the Expectation -- 3.2 Estimating the Expectation -- 4 Multi-Objective Optimization -- 4.1 Control Mapping for Dimensionality Reduction -- Deterministic Control Map -- Max-Min Control Map -- Min-Max Control Map -- 4.2 Threshold Mapping -- 5 Asteroid Tour Test Case -- 6 Results -- 6.1 Control Map and Threshold Map -- 6.2 Lower Expectation -- 6.3 Expectation and Sampling Methods -- 6.4 Execution Times -- 7 Conclusions -- References -- Reliability-Based Robust Design Optimization of a Jet Engine Nacelle -- 1 Introduction -- 2 Definition of Aeronautical Optimization Under Uncertainties -- 2.1 Nacelle Acoustic Liner and Manufacturing Tolerances -- 2.2 Nacelle Acoustic Liner FEM Model -- 3 Adaptive Sparse Polynomial Chaos for Reliability Problems -- 3.1 Basic Formulation of Adaptive PCE -- 3.2 Adaptive Sparse Polynomial Chaos Expansion -- 3.3 Application of Adaptive PCE to Reliability-Based Optimization -- 4 Reliability-Based Optimization of the Engine Nacelle -- 4.1 Optimization Platform -- 4.2 Optimization Results -- 5 Conclusion -- References -- Bayesian Optimization for Robust Solutions Under Uncertain Input -- 1 Introduction -- 2 Literature Review -- 3 Problem Definition -- 4 Methodology -- 4.1 Gaussian Process -- 4.2 Robust Bayesian Optimization -- Direct Robustness Approximation -- Robust Knowledge Gradient -- 4.3 Stochastic Kriging -- 5 Experiments -- 5.1 Benchmark Problems -- Test Functions -- Experimental Setup -- 5.2 Results -- Latin Hypercube Sampling -- Stochastic Kriging -- Uncontrollable Input -- 6 Conclusions -- References. 327 $aOptimization Under Uncertainty of Shock Control Bumps for Transonic Wings -- 1 Introduction -- 2 Gradient-Based Robust Design Framework -- 2.1 Motivation -- 2.2 Surrogate-Based Uncertainty Quantification -- 2.3 Obtaining the Gradients of the Statistics -- 2.4 Optimization Architecture -- 2.5 Application to Analytical Test Function -- 3 Application to the Robust Design of Shock Control Bumps: Problem Definition -- 3.1 Test Case -- 3.2 Numerical Model -- 3.3 Parametrization of Shock Control Bumps -- 3.4 Optimization Formulations -- 4 Results -- 4.1 Single-Point (Deterministic) Results -- 4.2 Uncertainty Quantification -- 4.3 Robust Results -- 5 Conclusions -- References -- Multi-Objective Design Optimisation of an Airfoil with Geometrical Uncertainties Leveraging Multi-Fidelity Gaussian Process Regression -- 1 Introduction -- 2 Design Optimisation Problem of Airfoil -- 3 Solvers -- 4 Multi-Fidelity Gaussian Process Regression -- 5 Uncertainty Treatment -- 6 Multi-Objective Optimisation Framework for Airfoil Optimisation Under Uncertainty -- 7 Results -- 8 Conclusion -- References -- High-Lift Devices Topology Robust Optimisation Using Machine Learning Assisted Optimisation -- 1 Introduction -- 2 Machine Learning Assisted Optimisation -- 2.1 Surrogate Model -- 2.2 Classifier -- 3 Quadrature Approach for Uncertainty Quantification -- 4 Problem Formulation -- 4.1 Optimisation Design Variables -- 4.2 High-Lift Devices Robust Optimisation Problem -- Original Objective Function -- Artificial Objective Function -- 5 Optimisation Setup -- 6 Results -- 7 Conclusions and Future Work -- References -- Network Resilience Optimisation of Complex Systems -- 1 Introduction -- 2 Evidence Theory as Uncertainty Framework -- 3 System Network Model -- 4 Complexity Reduction of Uncertainty Quantification -- 4.1 Network Decomposition -- 4.2 Tree-Based Exploration. 327 $a4.3 Combined Method. 410 0$aSpace Technology Proceedings 606 $aMeasurement uncertainty (Statistics) 606 $aMathematical optimization 607 $aOuter space 615 0$aMeasurement uncertainty (Statistics) 615 0$aMathematical optimization. 676 $a629.101519544 702 $aVasile$b Massimiliano 702 $aQuagliarella$b D. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466732803316 996 $aAdvances in uncertainty quantification and optimization under uncertainty with aerospace applications$92914970 997 $aUNISA LEADER 06493nam 2200721 450 001 9910830371203321 005 20190319151240.0 010 $a1-119-20473-9 010 $a1-280-99534-3 010 $a9786613766953 010 $a1-118-28696-0 035 $a(CKB)2670000000229344 035 $a(EBL)843659 035 $a(OCoLC)783862075 035 $a(SSID)ssj0000687963 035 $a(PQKBManifestationID)12289554 035 $a(PQKBTitleCode)TC0000687963 035 $a(PQKBWorkID)10755460 035 $a(PQKB)10331937 035 $a(PQKBManifestationID)16033927 035 $a(PQKB)21259610 035 $a(MiAaPQ)EBC843659 035 $a(DLC) 2012013818 035 $a(PPN)178030910 035 $a(CaSebORM)9781118282885 035 $a(EXLCZ)992670000000229344 100 $a20160817h20122012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCloudonomics $ethe business value of cloud computing /$fJoe Weinman 205 $a1st edition 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d2012. 210 4$dİ2012 215 $a1 online resource (417 p.) 300 $aIncludes index. 311 $a1-118-28288-4 311 $a1-118-22996-7 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCloudonomics: The Business Value of Cloud Computing; Contents; Preface; Acknowledgments; Chapter 1: A Cloudy Forecast; Clouds Everywhere; Cashing In on the Cloud; Beyond Business; Clarifying the Cloud; Farther On; Summary; Notes; Chapter 2: Does the Cloud Matter?; Productivity Paradox; Competitiveness Confrontation; Summary; Notes; Chapter 3: Cloud Strategy; Insanity or Inevitability?; Democratization of IT; Industrialization of IT; Strategy; The Cloud: More than IT; The Networked Organization; Form Follows Function, IT Follows Form; Aligning Cloud with Strategy; Everyware, Anywhere; Summary 327 $aNotesChapter 4: Challenging Convention; What Is the Cloud?; Economies of Scale; Competitive Advantage and Customer Value; Cloud Ecosystem Dynamics; IT Spend; Issues with the Cloud; Summary; Notes; Chapter 5: What Is a Cloud?; Defining the Cloud; On-Demand Resources; Utility Pricing; Common Infrastructure; Location Independence; Online Accessibility; Difference from Traditional Purchase and Ownership; Cloud Criteria and Implications; Is the Cloud New or a New Buzzword?; Summary; Notes; Chapter 6: Strategy and Value; Access to Competencies; Availability; Capacity 327 $aComparative Advantage and Core versus ContextUnit Cost; Delivered Cost; Total Solution Cost; Opportunity Cost and Cost Avoidance; Agility; Time Compression; Margin Expansion; Customer and User Experience and Loyalty; Employee Satisfaction; Revenue Growth; Community and Sustainability; Risk Reduction; Competitive Vitality and Survival; Summary; Notes; Chapter 7: When-and When Not-to Use the Cloud; Use Cases for the Cloud; Complementary Capabilities and Competencies; Communications; Conversations, Connections, and Communities; Congregations, Commons, and Collections; Consolidation 327 $aCollaboration, Competition, and CrowdsourcingCommerce and Clearing; Collaborative Consumption; Coordination, Currency, Consistency, and Control; Cross-Device Access and Synchronization; Cash Flow; Capacity; Continuity; Checkpoints; Chokepoints; Context; Celerity; Customer Experience; Combinations of the Above; Inappropriate Cloud Use Cases; Constant; Custom; Classic; Close Coupling; Content Capture, Creation, and Consumption; Cryptography; Compression; Caching; Covert; Continuity; Summary; Notes; Chapter 8: Demand Dilemma; A Diversity of Demands; Examples of Variability 327 $aChase Demand or Shape It?Summary; Notes; Chapter 9: Capacity Conundrum; Service Quality Impacts; Fixed Capacity versus Variable Demand; Splitting the Difference; Better Safe than Sorry; Capacity Inertia; Summary; Notes; Chapter 10: Significance of Scale; Is the Cloud Like Electricity?; Distributed Power Generation; Is the Cloud Like Rental Cars?; Capital Expenditures versus Operating Expenses; Benchmark Data; Cost Factors; Benchmarking the Leaders; Size Matters; Summary; Notes; Chapter 11: More Is Less; Is the Cloud Less Expensive?; Characterizing Relative Costs and Workload Variability 327 $aWhen Clouds Cost Less or the Same 330 $a"The ultimate guide to assessing and exploiting the customer value and revenue potential of the CloudA new business model is sweeping the world--the Cloud. And, as with any new technology, there is a great deal of fear, uncertainty, and doubt surrounding cloud computing. Cloudonomics radically upends the conventional wisdom, clearly explains the underlying principles and illustrates through understandable examples how Cloud computing can create compelling value--whether you are a customer, a provider, a strategist, or an investor. Cloudonomics covers everything you need to consider for the delivery of business solutions, opportunities, and customer satisfaction through the Cloud, so you can understand it--and put it to work for your business. Cloudonomics also delivers insight into when to avoid the cloud, and why. Quantifies how customers, users, and cloud providers can collaborate to create win-wins Reveals how to use the Laws of Cloudonomics to define strategy and guide implementation Explains the probable evolution of cloud businesses and ecosystemsDemolishes the conventional wisdom on cloud usage, IT spend, community clouds, and the enterprise-provider cloud balance Whether you're ready for it or not, Cloud computing is here to stay. Cloudonomics shows how the business model of the Cloud offers insights to executives, practitioners, and strategists in virtually any industry--not just technology executives but also those in the marketing, operations, economics, venture capital, and financial fields"--$cProvided by publisher. 606 $aCloud computing$xEconomic aspects 606 $aInformation technology$xManagement 615 0$aCloud computing$xEconomic aspects. 615 0$aInformation technology$xManagement. 676 $a004.6782 676 $a658.05 686 $aCOM000000$2bisacsh 700 $aWeinman$b Joe$f1958-$0949366 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830371203321 996 $aCloudonomics$94047773 997 $aUNINA