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A scalable problem to benchmark robust multidisciplinary design optimization techniques
A scalable problem to benchmark robust multidisciplinary design optimization (RMDO) algorithms is proposed. This allows the user to choose the number...
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Interval Multidisciplinary Design Optimization
This chapter introduces the interval model into the multidisciplinary design optimization (MDO) problem, and whereby constructs an interval MDO model...
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Uncertainty-Based Multidisciplinary Design Optimization (UMDO)
This chapter is devoted to the description of the MDO formulations in the presence of uncertainty. In Chapter 1 , deterministic MDO formulations...
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A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis
The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and...
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Hyperloop system optimization
Hyperloop system design is a uniquely coupled problem because it involves the simultaneous design of a complex, high-performance vehicle and its...
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A taxonomy of constraints in black-box simulation-based optimization
The types of constraints encountered in black-box simulation-based optimization problems differ significantly from those addressed in nonlinear...
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MDO Related Issues: Multi-Objective and Mixed Continuous/Discrete Optimization
In addition to the multi-fidelity aspects in MDO discussed in Chapter 8 , two additional topics of interest to solve complex MDO problems are...
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Multi-Fidelity for MDO Using Gaussian Processes
The challenges of handling uncertainties within an MDO process have been discussed in Chapters 6 and 7 . Related concepts to multi-fidelity are...
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A Pareto Front Numerical Reconstruction Strategy Applied to a Satellite System Conceptual Design
A satellite system conceptual design problem is addressed in this work. A multi-objective parametric optimization problem is formulated and...
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Deep Gaussian process for multi-objective Bayesian optimization
Bayesian Optimization has become a widely used approach to perform optimization involving computationally intensive black-box functions, such as the...
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Multidisciplinary System Modeling and Optimization
With the increasing complexity of systems such as aerospace vehicles, it has become more and more necessary to adopt a global and integrated approach...
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Quantifying uncertainty with ensembles of surrogates for blackbox optimization
Blackbox optimization tackles problems where the functions are expensive to evaluate and where no analytical information is available. In this...
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Aerospace System Analysis and Optimization in Uncertainty
Spotlighting the field of Multidisciplinary Design Optimization (MDO), this book illustrates and implements state-of-the-art methodologies within the...
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Uncertainty Propagation for Multidisciplinary Problems
In Chapter 3 , several uncertainty propagation techniques for black-box functions have been introduced. In order to take into account the specific...
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Introduction
This chapter introduces the engineering background and research significance of uncertain optimization and analyzes the research status of several...
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Uncertainty Propagation and Sensitivity Analysis
The uncertainty propagation consists in determining the impact of the input uncertainties of a simulation code on the outputs of this model. In the...
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Expendable and Reusable Launch Vehicle Design
For many countries (United States of America, Russia, Europe, Japan, etc.), the launch vehicles are cornerstones of an independent access to space....
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Monotonic grey box direct search optimization
We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when...
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Dynamic improvements of static surrogates in direct search optimization
The present work is in a context of derivative-free optimization involving direct search algorithms guided by surrogate models of the original...
