Abstract
View Video Presentation: https://doi-org.tudelft.idm.oclc.org/10.2514/6.2021-3078.vid Decisions regarding the system architecture are important and taken early in the design process, however suffer from large design spaces and expert bias. Systematic design space exploration techniques, like optimization, can be applied to system architecting. Realistic engineering benchmark problems are needed to enable development of optimization algorithms that can successfully solve these black-box, hierarchical, mixed-discrete, multi-objective architecture optimization problems. Such benchmark problems support the development of more capable optimization algorithms, more suitable methods for modeling system architecture design space, and educating engineers and other stakeholders on system architecture optimization in general. In this paper, an engine architecting benchmark problem is presented that exhibits all this behavior and is based on the open-source simulation tools pyCycle and OpenMDAO. Next to thermodynamic cycle analysis, the proposed benchmark problem includes modules for the estimation of engine weight, length, diameter, noise and NOx emissions. The problem is defined using modular interfaces, allowing to tune the complexity of the problem, by varying the number of design variables, objectives and constraints. The benchmark problem is validated by comparing to pyCycle example cases and existing engine performance data, and demonstrated using both a simple and a realistic problem formulation, solved using the multi-objective NSGA-II algorithm. It is shown that realistic results can be obtained, even though the design space is subject to hidden constraints due to the engine evaluation not converging for all design points.
Highlights
D taken early on in the design process of complex systems greatly impact the performance of the final design
A realistic system architecture optimization benchmark based on an aircraft jet engine architecting problem was presented
Benchmark problem requirements are defined with the aim to aid research into optimization algorithms capable of solving system architecture optimization problems, research into modeling methods for system architecture design space, and education on system architecture optimization in general
Summary
D taken early on in the design process of complex systems greatly impact the performance of the final design. System design spaces are often extremely large due to the combinatorial explosion of alternatives To solve these problems, systematic design space exploration techniques need to be applied during the design of a complex system, at the earlier design stage when the architecture of the system has not been fixed yet [1]. Common to any collaborative MDO pursue, is that the performance analysis model generally consists of an expensive black-box function This means that no a-priori information is available about the shape and topology of the design space, and that the number of function evaluations needed for finding the optimal designs should be as low as possible. This work aims at filling said gap with the development of a benchmark optimization problem based on a realistic aircraft jet engine architecting framework, for the specific purposes of: 1) Supporting the development, evaluation, and comparison of optimization algorithms suitable for system architecture optimization; 2) Supporting the development, evaluation, and comparison of system architecture design space modeling methods; 3) Educating researchers, engineers, and other stakeholders on the behavior and characteristics of system architecture optimization problems
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