Reliability Based Design Optimization Of Multiphysics Aerospace Systems

Spotlighting the field of Multidisciplinary Design Optimization (MDO), this book illustrates and implements state-of-the-art methodologies within the complex process of aerospace system design under uncertainties. The book provides approaches to integrating a multitude of components and constraints with the ultimate goal of reducing design cycles. Insights on a vast assortment of problems are provided, including discipline modeling, sensitivity analysis, uncertainty propagation, reliability analysis, and global multidisciplinary optimization. The extensive range of topics covered include areas of current open research. This Work is destined to become a fundamental reference for aerospace systems engineers, researchers, as well as for practitioners and engineers working in areas of optimization and uncertainty. Part I is largely comprised of fundamentals. Part II presents methodologies for single discipline problems with a review of existing uncertainty propagation, reliability analysis, and optimization techniques. Part III is dedicated to the uncertainty-based MDO and related issues. Part IV deals with three MDO related issues: the multifidelity, the multi-objective optimization and the mixed continuous/discrete optimization and Part V is devoted to test cases for aerospace vehicle design.

To make coupled multi-physics-informed design decisions, multidisciplinary analysis, design optimization and uncertainty quantification must be present to accurately represent the full system under investigation. Unfortunately, all of these processes are computationally demanding, requiring a large number of system evaluations with identified uncertain variables, and iterative system evaluations with respect to the design variables of interest. Surrogate or metamodels are used to alleviate the computational burden in both these design exploration activities by trading accuracy with efficiency. The primary objective of this dissertation is to develop a flexible surrogate modeling technique capable of quantifying the uncertainty of multidisciplinary systems in an iterative and efficient procedure.In this work, the Non-Deterministic Kriging (NDK) method is derived. This surrogate model represents a flexible approach for approximating epistemic and aleatory uncertainty. To achieve an iterative and efficient computational framework additional tasks were established: (1) characterize and develop a unified stochastic process incorporating incomplete and mixed uncertainty data; (2) develop a novel adaptive sampling method that effectively and efficiently updates the NDK model to enable a global multidisciplinary design optimization technique under uncertainty; (3) derive analytic sensitivities to achieve non-deterministic sensitivities with respect to the design variables; (4) propose an efficient reliability-based design optimization framework for multidisciplinary systems using NDK to reduce the design space.

Featuring aerospace examples and applications, Reliability Analysis of Dynamic Systems presents the very latest probabilistic techniques for accurate and efficient dynamic system reliability analysis. While other books cover more broadly the reliability techniques and challenges related to large systems, Dr Bin Wu presents a focused discussion of new methods particularly relevant to the reliability analysis of large aerospace systems under harmonic loads in the low frequency range. Developed and written to help you respond to challenges such as non-linearity of the failure surface, intensive computational costs and complexity in your dynamic system, Reliability Analysis of Dynamic Systems is a specific, detailed and application-focused reference for engineers, researchers and graduate students looking for the latest modeling solutions. The Shanghai Jiao Tong University Press Aerospace Series publishes titles that cover the latest advances in research and development in aerospace. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The readership for the series is broad, reflecting the wide range of aerospace interest and application, but focuses on engineering. Forthcoming titles in the Shanghai Jiao Tong University Press Aerospace Series: Reliability Analysis of Dynamic Systems • Wake Vortex Control • Aeroacoustics: Fundamentals and Applications in Aeropropulsion Systems • Computational Intelligence in Aerospace Design • Unsteady Flow and Aeroelasticity in Turbomachinery Authored by a leading figure in Chinese aerospace with 20 years’ professional experience in reliability analysis and engineering simulation. Offers solutions to the challenges of non-linearity, intensive computational cost and complexity in reliability assessment. Aerospace applications and examples used throughout to illustrate accuracy and efficiency achieved with new methods.

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.

This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.Crespo, Luis G. and Bushnell, Dennis M. (Technical Monitor)Langley Research CenterOPTIMIZATION; PROBABILITY THEORY; UNCERTAIN SYSTEMS; ROBUSTNESS (MATHEMATICS); RELIABILITY; DESIGN ANALYSIS; PROBABILITY DENSITY FUNCTIONS

This book highlights a unique combination of numerical tools and strategies for handling the challenges of multiphysics simulation, with a specific focus on electromechanical systems as the target application. Features: introduces the concept of design via simulation, along with the role of multiphysics simulation in today’s engineering environment; discusses the importance of structural optimization techniques in the design and development of electromechanical systems; provides an overview of the physics commonly involved with electromechanical systems for applications such as electronics, magnetic components, RF components, actuators, and motors; reviews the governing equations for the simulation of related multiphysics problems; outlines relevant (topology and parametric size) optimization methods for electromechanical systems; describes in detail several multiphysics simulation and optimization example studies in both two and three dimensions, with sample numerical code.

In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.