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 03052nam 2200541 450 001 9910821514003321 005 20230808192638.0 010 $a1-78450-197-2 035 $a(CKB)3710000000644944 035 $a(EBL)4441868 035 $a(SSID)ssj0001651833 035 $a(PQKBManifestationID)16426289 035 $a(PQKBTitleCode)TC0001651833 035 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Getting work ; 2. Getting on with the job ; 3. Communication ; 4. Social interaction; 5. Sensory issues ; 6. Organising work; 7. The trouble with managers ; 8. Bullying, harassment, discrimination ; 9. All change! ; 10. Job insecurity in a time of austerity; Chapter 3 - Remove those Barriers!; 1. Getting work 327 $a2. Getting on with the job 3. Communication ; 4. Social interaction ; 5. Sensory issues ; 6. Organising work ; 7. The trouble with managers ; 8. Bullying, harassment, discrimination ; 9. All change! ; 10. Job insecurity in a time of austerity ; Chapter 4 - Autism, Work and the Law; A global convention for rights ; European Union; United Kingdom ; United States of America ; Canada ; Australia ; Ten key legal concepts; Chapter 5 - Organising for Change; A progressive workplace policy ; Collective bargaining and trade unions ; Nothing about us without us ; References ; Subject Index 327 $aAuthor IndexBlank Page 330 $aPeople with autism often find themselves excluded from working life. This practical handbook lays out reasonable, achievable ways in which working environments can be adapted and people with autism included as valuable members of the workforce. 606 $aPeople with disabilities$xEmployment 606 $aAutism 615 0$aPeople with disabilities$xEmployment. 615 0$aAutism. 676 $a331.5/94 700 $aBooth$b Janine$01672854 702 $aMcDonnell$b John 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821514003321 996 $aAutism equality in the workplace$94036481 997 $aUNINA