Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (eBook, PDF)

Proceedings of the 2020 UQOP International Conference
Redaktion: Vasile, Massimiliano; Quagliarella, Domenico

• Format: PDF

Produktbeschreibung

The 2020 International Conference on Uncertainty Quantification & Optimization gathered together internationally renowned researchers in the fields of optimization and uncertainty quantification. The resulting proceedings cover all related aspects of computational uncertainty management and optimization, with particular emphasis on aerospace engineering problems.

The book contributions are organized under four major themes:

• Applications of Uncertainty in Aerospace & Engineering
• Imprecise Probability, Theory and Applications
• Robust and Reliability-Based Design Optimisation in Aerospace Engineering
• Uncertainty Quantification, Identification and Calibration in Aerospace Models

This proceedings volume is useful across disciplines, as it brings the expertise of theoretical and application researchers together in a unified framework.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 457
• Erscheinungstermin: 27. Januar 2022
• Englisch
• ISBN-13: 9783030805425
• Artikelnr.: 63360413

Autorenporträt

Massimiliano Vasile is Professor of Space Systems Engineering in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde. Previously, he was a Senior Lecturer in the Department of Aerospace Engineering and Head of Research for the Space Advanced Research Team at the University of Glasgow. He developed Direct Transcription by Finite Elements on Spectral Basis for optimal control, implemented in the ESA software DITAN for low-thrust trajectory design. He has worked on the global optimization of space trajectories developing innovative single and multi-objective optimization algorithms, and on the combination of optimization and imprecise probabilities to mitigate the effect of uncertainty in decision making and autonomous planning. More recently, he has undertaken extensive research on the development of effective techniques for asteroid deflection and manipulation. His research has been funded by the European Space Agency, the EPSRC, the Planetary Society and the European Commission. Prof Vasile is currently leading Stardust, an EU-funded international research and training network on active debris removal and asteroid manipulation. Domenico Quagliarella received in May 1988 the MS degree in Aeronautical Engineering and in July 1993 the Ph.D. in Aerospace Engineering from the University "Federico II" in Naples, Italy. In July 1988, he got a research engineer position at CIRA, where he is currently head of the Multidisciplinary Analysis and Design Group of Fluid Mechanics Department. His research interests are the application of hybrid multi-objective optimization methods to aerodynamic and multidisciplinary design, the use of approximate fitness evaluators for efficiency improvement in optimization, and uncertainty quantification for robust and reliability-based design. He is the author of about 80 international journal and conference papers. He is also editor of four edited books and two special issues of academic journals. He participated in several EU projects, and he also carried out research activity in the framework of "Clean Sky" and "Clean Sky 2" public-private partnerships between the European Commission and the Aeronautical Industry.

Inhaltsangabe

Chapter 1. Cloud Uncertainty Quantification for Runback Ice Formations in Anti-Ice Electro-Thermal Ice Protection Systems.- Chapter 2. Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty using Far-Field Drag Approximation.- Chapter 3. Scalable dynamic asynchronous Monte Carlo framework applied to wind engineering problems.- Chapter 4. Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE.- Chapter 5. From Uncertainty Quanti cation to Shape Optimization: Cross-Fertilization of Methods for Dimensionality Reduction.- Chapter 6. Multi-Objective Robustness Analysis of the Polymer Extrusion Process.- Chapter 7. Quantification of operational and geometrical uncertainties of a 1.5 stage axial compressor with cavity leakage flows.- Chapter 8. Can Uncertainty Propagation Solve the Mysterious Case of Snoopy ?.- Chapter 9. Robust Particle Filter for Space Navigation under Epistemic Uncertainty.- Chapter 10. Computingbounds for imprecise continuous-time Markov chains using normal cones.- Chapter 11. Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference.- Chapter 12. Computing Expected Hitting Times for Imprecise Markov Chains.- Chapter 13. Multi-Objective Robust Trajectory Optimization of Multi Asteroid Fly-By Under Epistemic Uncertainty.- Chapter 14. Reliability-based Robust Design Optimization of a Jet Engine Nacelle.- Chapter 15. Bayesian Optimization for Robust Solutions under Uncertain Input.- Chapter 16. Optimization under Uncertainty of Shock Control Bumps for Transonic Wings.- Chapter 17. Multi-objective design optimisation of an airfoil with geometrical uncertainties leveraging multi- delity Gaussian process regression.- Chapter 18. High-Lift Devices Topology Robust Optimisation using Machine Learning Assisted Optimisation.- Chapter 19. Network Resilience Optimisation of Complex Systems.- Chapter 20. Gaussian Processes for CVaR approximation in Robust Aerodynamic Shape Design.- Chapter 21. Inference methods for gas/surface interaction models: from deterministic approaches to Bayesian techniques.- Chapter 22. Bayesian Adaptive Selection Under Prior Ignorance.- Chapter 23. A Machine-Learning Framework for Plasma-Assisted Combustion using Principal Component Analysis and Gaussian Process Regression.- Chapter 24. Estimating exposure fraction from radiation biomarkers: a comparison of frequentist and Bayesian approaches.- Chapter 25. A Review of some recent advancements in Non-Ideal Compressible Fluid Dynamics.- Chapter 26. Dealing with high dimensional inconsistent measurements in inverse problems using surrogate modeling: an approach based on sets and intervals.- Chapter 27. Stochastic Preconditioners for Domain Decomposition Methods.- Index.