System Architecture Optimization: An Open Source Multidisciplinary Aircraft Jet Engine Architecting Problem

Abstract

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 multiobjective 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.

Effectiveness of Surrogate-Based Optimization Algorithms for System Architecture Optimization

“…Up to 60% of evaluations in an optimization run may result in violated hidden constraints. A realistic engine architecting problem published in [80] shows similar results: in the initial design of experiments of 205 design points, approximately 67% did not yield a meaningful result due to an unconverged design loop. The number of infeasible design points may be reduced by choosing a design space parameterization that reduces the chance for representing infeasible designs: for example, much effort has been invested in developing parametric geometry representations in the aircraft design discipline [81].…”

“…Future research will focus on hidden constraints in architecture optimization, to enable the application on realistic simulation-based optimization problems that may be subject to analysis convergence issues. The engine benchmark problem from [80] will be used to test specific approaches for dealing with hidden constraints in surrogate-based optimization. Surrogate-based algorithms that can deal with mixed-discrete, hierarchical and multi-objective design space will be applied in various industry-provided use-cases in the AGILE 4.0 project.…”

“…Evolutionary algorithms like NSGA-II are able to solve problems where large parts of the design space are subject to hidden constraints, however need many function evaluations to do so. For example, in [80] a Pareto front of optimal engine architectures is found in 4000 function evaluations. Applying the SBO algorithms investigated in previous sections to the engine architecting problem shows that these algorithms are unable to produce a large improvement over the initial DOE.…”

Value‐driven Systems Engineering Approach addressing Manufacturing, Supply‐chain and Aircraft Design in the Decision‐Making Process

“…Briefly, once selected all the stakeholders, needs and requirements, functional requirements have been collected to generate the system architectures in ADORE ) . To check the consistency between the architectures and the MDO problems, the DLR internal tool called MULTILINQ has been used (Bussemaker, Ciampa, & Nagel, 2022). VALORISE, which stands for Value-driven trAdespace visuaLizatiOn, exploRatIon and asSEssment is an interactive dashboard, developed by DLR, leveraging the value-model theory to simplify the multi-criteria decision-making process.…”

From Requirements to Product: an MBSE Approach for the Digitalization of the Aircraft Design Process

“…Once the architectural model is complete, architectural design choices must be made to obtain one system architecture instance. The architectural design choices can be made either by the designer or by a computer during the analysis or optimization of the logical system architecture (Bussemaker et al 2021). An example of a selected system architecture is shown in green in Figure 2.…”