Following high-profile government and industry studies, electric aircraft propulsion has emerged as an important research topic. This article surveys the scholarly and business literature on ...fixed-wing aircraft propelled in whole or in part by electricity. This includes all-electric, hybrid electric, and turboelectric architectures. We introduce a classification of electric aircraft, technology factors, and performance parameters. Next, we present an overview of electrical components and electric propulsion architectures. We survey existing commercial products, prototypes, demonstrators, and conceptual studies, and develop a list of potential benefits and disadvantages of electric propulsion with estimates of potential benefit. We present an introduction to power electronics, electric machines, and batteries for aircraft designers, and explore the emerging problem of aircraft thermal management. We review modeling, simulation, and multidisciplinary optimization capabilities, and identify current shortcomings. We conclude that the electric aircraft design problem introduces new coupling between previously distinct disciplines, such as aerodynamics and propulsion, which may only become apparent with high-fidelity, physics-based analysis. High-fidelity multidisciplinary design analysis and optimization of electric aircraft, including safety and economic analysis, remains an open challenge.
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the ...applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems.
The DIRECT algorithm: 25 years Later Jones, Donald R.; Martins, Joaquim R. R. A.
Journal of global optimization,
03/2021, Letnik:
79, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Introduced in 1993, the DIRECT global optimization algorithm provided a fresh approach to minimizing a black-box function subject to lower and upper bounds on the variables. In contrast to the ...plethora of nature-inspired heuristics, DIRECT was
deterministic
and had only one hyperparameter (the desired accuracy). Moreover, the algorithm was simple, easy to implement, and usually performed well on low-dimensional problems (up to six variables). Most importantly, DIRECT balanced local and global search (exploitation vs. exploration) in a unique way: in each iteration, several points were sampled, some for global and some for local search. This approach eliminated the need for “tuning parameters” that set the balance between local and global search. However, the very same features that made DIRECT simple and conceptually attractive also created weaknesses. For example, it was commonly observed that, while DIRECT is often fast to find the basin of the global optimum, it can be slow to fine-tune the solution to high accuracy. In this paper, we identify several such weaknesses and survey the work of various researchers to extend DIRECT so that it performs better. All of the extensions show substantial improvement over DIRECT on various test functions. An outstanding challenge is to improve performance
robustly
across problems of different degrees of difficulty, ranging from simple (unimodal, few variables) to very hard (multimodal, sharply peaked, many variables). Opportunities for further improvement may lie in combining the best features of the different extensions.
Multidisciplinary design optimization (MDO) is concerned with solving design problems involving coupled numerical models of complex engineering systems. While various MDO software frameworks exist, ...none of them take full advantage of state-of-the-art algorithms to solve coupled models efficiently. Furthermore, there is a need to facilitate the computation of the derivatives of these coupled models for use with gradient-based optimization algorithms to enable design with respect to large numbers of variables. In this paper, we present the theory and architecture of OpenMDAO, an open-source MDO framework that uses Newton-type algorithms to solve coupled systems and exploits problem structure through new hierarchical strategies to achieve high computational efficiency. OpenMDAO also provides a framework for computing coupled derivatives efficiently and in a way that exploits problem sparsity. We demonstrate the framework’s efficiency by benchmarking scalable test problems. We also summarize a number of OpenMDAO applications previously reported in the literature, which include trajectory optimization, wing design, and structural topology optimization, demonstrating that the framework is effective in both coupling existing models and developing new multidisciplinary models from the ground up. Given the potential of the OpenMDAO framework, we expect the number of users and developers to continue growing, enabling even more diverse applications in engineering analysis and design.
Antony Jameson pioneered CFD-based aerodynamic design optimization in the late 1980s. In addition to developing the fundamental theory, Jameson implemented that theory in codes that were practical ...enough to be used in industry. As a result of Jameson’s seminal efforts, a research community has been established in aerodynamic design optimization. This research area has experienced sustained improvements in CFD solvers, mesh deformation, sensitivity computation, and optimization tools. We review recent developments for each of these components and present open-source tools available for aerodynamic shape optimization. A variety of applications is presented, including the optimization of a supercritical airfoil starting from a circle, a web application that optimizes airfoils within a few seconds, aircraft aerodynamic and aerostructural optimization, and aeropropulsive optimization. We also review the Aerodynamic Design Optimization Discussion Group (ADODG) benchmarks and other aerodynamic shape optimization problems. Among the ADODG benchmarks, we focus on the RANS-based problems and discuss some of the issues encountered, including comparing Euler and RANS results and design-space multimodality. The availability of these benchmarks and the open-source tools is expected to enable further studies and benchmarks in CFD-based aerodynamic design optimization and MDO.
•Jameson made aerodynamic design optimization possible through the adjoint method.•Many design optimization challenges identified in a 2014 study have been addressed.•Aerodynamic optimization benchmarks made it easier to compare different approaches.•Open-source aerodynamic optimization software opens the door to widespread use.
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