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Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences / / edited by Edmondo Minisci, Massimiliano Vasile, Jacques Periaux, Nicolas R. Gauger, Kyriakos C. Giannakoglou, Domenico Quagliarella
| Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences / / edited by Edmondo Minisci, Massimiliano Vasile, Jacques Periaux, Nicolas R. Gauger, Kyriakos C. Giannakoglou, Domenico Quagliarella |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (555 pages) |
| Disciplina | 519.3 |
| Collana | Computational Methods in Applied Sciences |
| Soggetto topico |
Engineering design
Computational intelligence Computer mathematics Engineering Design Computational Intelligence Computational Mathematics and Numerical Analysis |
| ISBN | 3-319-89988-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Keynote: Risk, Optimization and Meanfield Type Control, by Olivier Pironneau and Mathieu Laurière -- 2. Surrogate-Based Optimization in Aerodynamic Design -- A Review of Surrogate Modeling Techniques for Aerodynamic Analysis and Optimization: Current Limitations and Future Challenges in Industry, by Raul Yondo, Kamil Bobrowski, Esther Andrés and Eusebio Valero -- Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 wing through Evolutionary Programming and Support Vector Machines, by E. Andrés-Pérez, D. González-Juárez, M. J. Martin-Burgos, L. Carro-Calvo -- Enabling of Large Scale Aerodynamic Shape Optimization through POD-based Reduced-Order Modeling and Free Form Deformation, by A. Scardigli, R. Arpa, A. Chiarini and H. Telib -- Application of Surrogate-based Optimization Techniques to Aerodynamic Design Cases, by Emiliano Iuliano and Domenico Quagliarella -- Efficient Global Optimization method for multipoint airfoil design, by Davide Cinquegrana and Emiliano Iuliano -- 3. Adjoint Methods for Steady and Unsteady Optimization -- Checkpointing with time gaps for unsteady adjoint CFD, by Jan Christian Hueckelheim and Jens-Dominik Mueller -- Shape Optimization ofWind Turbine Blades using the Continuous Adjoint Method and Volumetric NURBS on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou -- Aerodynamic Shape Optimization Using the Adjoint-based Truncated Newton Method, by Evangelos M. Papoutsis-Kiachagias, Mehdi Ghavami Nejad, and Kyriakos C. Giannakoglou -- Application of the adjoint method for the reconstruction of the boundary condition in unsteady shallow water flow simulation, by Asier Lacasta, Daniel Caviedes-Voullième and Pilar García-Navarro -- Aerodynamic Optimization of Car Shapes using the Continuous Adjoint Method and an RBF Morpher, by E.M. Papoutsis-Kiachagias, S. Porziani, C. Groth, M.E. Biancolini, E. Costa and K.C. Giannakoglou -- 4. Holistic Optimization in Marine Design -- Upfront CAD – Parametric modeling techniques for shape optimization, by S. Harries, C. Abt and M. Brenner -- Simulation-based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions, by Matteo Diez, Silvia Volpi, Andrea Serani, Frederick Stern and Emilio F. Campana -- Application of Holistic Ship Optimization in Bulkcarrier Design and Operation, by Lampros Nikolopoulos, Evangelos Boulougouris -- 5. Game Strategies Combined with Evolutionary Computation -- Designing Networks in Cooperation with ACO, by E. D’Amato, E. Daniele and L. Mallozzi -- Augmented Lagrangian approach for constrained potential Nash games, by Lina Mallozzi and Domenico Quagliarella -- A Diversity Dynamic Territory Nash Strategy in Evolutionary Algorithms: Enhancing Performances in Reconstruction Problems in Structural Engineering, by David Greiner, Jacques Périaux, J.M. Emperador, B. Galván, G. Winter -- Interactive Inverse Modeling Based Multiobjective Evolutionary Algorithm, by Karthik Sindhya and Jussi Hakanen -- Multi-Disciplinary Design Optimization of Air-breathing Hypersonic Vehicle Using Pareto Games and Evolutionary Algorithms, by Peng Wu, Zhili Tang, Jacques Periaux -- 6. Optimisation under Uncertainty -- Innovative methodologies for Robust Design Optimization with large number of uncertainties using modeFRONTIER, by Alberto Clarich, Rosario Russo -- A Novel Method for Inverse Uncertainty Propagation, by Xin Chen, ArturoMolina-Crist ´obal,Marin D. Guenov, Varun C. Datta, Atif Riaz -- Uncertainty Sources in the Baseline Configuration for Robust Design of a Supersonic Natural Laminar Flow Wing-Body, by Domenico Quagliarella and Emiliano Iuliano -- Robust Airfoil Design in the Context of Multi-Objective Optimization, by Lisa Kusch and Nicolas R. Gauger -- An alternative formulation for design under uncertainty, by F. Fusi and P. M. Congedo and G. Geraci and G. Iaccarino -- Polynomial Representation of Model Uncertainty in Dynamical Systems, by Massimiliano Vasile -- 7. Algorithms and Industrial Applications -- Improved archiving and search strategies for Multi Agent Collaborative Search, by Lorenzo A. Ricciardi, Massimiliano Vasile -- Comparison of Multi-objective Approaches to the Real-World Production Scheduling, by Gregor Papa and Peter Korošec -- Elucidation of Influence of Fuels on Hybrid Rocket Using Visualization of Design-Space Structure, by Kazuhisa Chiba, Shin'ya Watanabe, Masahiro Kanazaki, Koki Kitagawa, and Toru Shimada -- Creating Optimised Employee Travel Plans, by Neil Urquhart and Emma Hart -- A New Rich Vehicle Routing Problem Model and Benchmark Resource, by Kevin Sim, Emma Hart, Neil Urquhart, and Tim Pigden -- Genetic Algorithm Applied to Design Knowledge Discovery of Launch Vehicle Using Clustered Hybrid Rocket Engine, by Masahiro Kanazaki, Kazuhisa Chiba, Shoma Ito, Masashi Nakamiya, Koki Kitagawa and Toru Shimada -- Topology Optimization of Flow Channels with Heat Transfer Using a Genetic Algorithm Assisted by the Kriging Model, by Mitsuo Yoshimura, Takashi Misaka, Koji Shimoyama, Shigeru Obayashi -- Topology Optimization using GPGPU, by Stefan Gavranovic, Dirk Hartmann, Utz Wever. . |
| Record Nr. | UNINA-9910337651603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in uncertainty quantification and optimization under uncertainty with aerospace applications : proceedings of the 2020 UQOP international conference / / edited by Massimiliano Vasile and Domenico Quagliarella
| Advances in uncertainty quantification and optimization under uncertainty with aerospace applications : proceedings of the 2020 UQOP international conference / / edited by Massimiliano Vasile and Domenico Quagliarella |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (448 pages) |
| Disciplina | 629.101519544 |
| Collana | Space Technology Proceedings |
| Soggetto topico |
Measurement uncertainty (Statistics)
Mathematical optimization |
| ISBN | 3-030-80542-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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.
2 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. 2 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. Part 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. Optimization 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. 4.3 Combined Method. |
| Record Nr. | UNISA-996466732803316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in uncertainty quantification and optimization under uncertainty with aerospace applications : proceedings of the 2020 UQOP international conference / / edited by Massimiliano Vasile and Domenico Quagliarella
| Advances in uncertainty quantification and optimization under uncertainty with aerospace applications : proceedings of the 2020 UQOP international conference / / edited by Massimiliano Vasile and Domenico Quagliarella |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (448 pages) |
| Disciplina | 629.101519544 |
| Collana | Space Technology Proceedings |
| Soggetto topico |
Measurement uncertainty (Statistics)
Mathematical optimization |
| ISBN | 3-030-80542-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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.
2 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. 2 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. Part 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. Optimization 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. 4.3 Combined Method. |
| Record Nr. | UNINA-9910523008803321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci
| Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci |
| Pubbl/distr/stampa | Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 |
| Descrizione fisica | 1 online resource (73 pages) : illustrations, graphs, tables |
| Disciplina | 523.44 |
| Collana | Progress in Astronautics and Aeronautics |
| Soggetto topico |
Asteroids
Space debris Space environment |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-5231-2071-1
1-62410-324-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910467029903321 |
| Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci
| Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci |
| Pubbl/distr/stampa | Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 |
| Descrizione fisica | 1 online resource (73 pages) : illustrations, graphs, tables |
| Disciplina | 523.44 |
| Collana | Progress in Astronautics and Aeronautics |
| Soggetto topico |
Asteroids
Space debris Space environment |
| ISBN |
1-5231-2071-1
1-62410-324-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910796027603321 |
| Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci
| Asteroid and space debris manipulation : advances from the Stardust Research Network / / edited by Massimiliano Vasile, Edmondo Minisci |
| Pubbl/distr/stampa | Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 |
| Descrizione fisica | 1 online resource (73 pages) : illustrations, graphs, tables |
| Disciplina | 523.44 |
| Collana | Progress in Astronautics and Aeronautics |
| Soggetto topico |
Asteroids
Space debris Space environment |
| ISBN |
1-5231-2071-1
1-62410-324-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910812528803321 |
| Reston, Virginia : , : American Institute of Aeronautics and Astronautics, Inc., , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bioinspired Optimization Methods and Their Applications [[electronic resource] ] : 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings / / edited by Bogdan Filipič, Edmondo Minisci, Massimiliano Vasile
| Bioinspired Optimization Methods and Their Applications [[electronic resource] ] : 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings / / edited by Bogdan Filipič, Edmondo Minisci, Massimiliano Vasile |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XI, 322 p. 18 illus.) |
| Disciplina | 004 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer networks
Artificial intelligence Computers, Special purpose Computers Computer Communication Networks Artificial Intelligence Special Purpose and Application-Based Systems Computing Milieux |
| ISBN | 3-030-63710-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996418220403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bioinspired Optimization Methods and Their Applications : 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings / / edited by Bogdan Filipič, Edmondo Minisci, Massimiliano Vasile
| Bioinspired Optimization Methods and Their Applications : 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings / / edited by Bogdan Filipič, Edmondo Minisci, Massimiliano Vasile |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XI, 322 p. 18 illus.) |
| Disciplina | 004 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer networks
Artificial intelligence Computers, Special purpose Computers Computer Communication Networks Artificial Intelligence Special Purpose and Application-Based Systems Computing Milieux |
| ISBN | 3-030-63710-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910427671503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational intelligence in aerospace sciences / / edited by Massimiliano Vasile, Victor M. Becerra
| Computational intelligence in aerospace sciences / / edited by Massimiliano Vasile, Victor M. Becerra |
| Pubbl/distr/stampa | [Place of publication not identified] : , : [American Institute of Aeronautics and Astronautics], , [2014] |
| Descrizione fisica | 1 online resource (115 pages) : illustrations |
| Disciplina | 629.1 |
| Collana | Progress in Astronautics and Aeronautics |
| Soggetto topico |
Computational intelligence
Aerospace engineering - Technological innovations Aerospace engineering |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-62410-271-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910466616903321 |
| [Place of publication not identified] : , : [American Institute of Aeronautics and Astronautics], , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational intelligence in aerospace sciences / / edited by Massimiliano Vasile, Victor M. Becerra
| Computational intelligence in aerospace sciences / / edited by Massimiliano Vasile, Victor M. Becerra |
| Pubbl/distr/stampa | [Place of publication not identified] : , : [American Institute of Aeronautics and Astronautics], , [2014] |
| Descrizione fisica | 1 online resource (115 pages) : illustrations |
| Disciplina | 629.1 |
| Collana | Progress in Astronautics and Aeronautics |
| Soggetto topico |
Computational intelligence
Aerospace engineering - Technological innovations Aerospace engineering |
| ISBN | 1-62410-271-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910796465103321 |
| [Place of publication not identified] : , : [American Institute of Aeronautics and Astronautics], , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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