JB
J.H. Bussemaker
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System Architecture Optimization
Function-Based Modeling, Optimization Algorithms, and Multidisciplinary Evaluation
When designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed. ...
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed. ...
When designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed.
A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.
SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.
Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed.
Conference paper
(2022)
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Erik H. Baalbergen, Jos Vankan, Luca Boggero, J.H. Bussemaker, Thierry Lefebvre, Bastiaan Beijer, A.M.R.M. Bruggeman, Massimo Mandorino
Collaboration is a key enabler for the development of modern aircraft and its systems and components. Because of the highly complex and integrated nature of many aircraft systems, effective collaboration requires well-organized, multi-disciplinary, multi-engineer, and multi-organization development processes. These processes require data-driven and computer-supported tools and methodologies. Collaboration may seem as simple as working together, thereby adopting standards and tools, and freely sharing data, information, and knowledge. However, in the development of complex systems such as aircraft, collaboration is not that straightforward. For example, aircraft engineers across disciplines and organizations commonly face challenges such as firewalls, data and tool heterogeneity, and intellectual property protection. In this paper, we review the collaboration challenges, describe how the EU-funded research project AGILE 4.0 addresses these challenges, and detail the application of, and experiences with, AGILE 4.0’s collaboration-enabling technologies.
...
Collaboration is a key enabler for the development of modern aircraft and its systems and components. Because of the highly complex and integrated nature of many aircraft systems, effective collaboration requires well-organized, multi-disciplinary, multi-engineer, and multi-organization development processes. These processes require data-driven and computer-supported tools and methodologies. Collaboration may seem as simple as working together, thereby adopting standards and tools, and freely sharing data, information, and knowledge. However, in the development of complex systems such as aircraft, collaboration is not that straightforward. For example, aircraft engineers across disciplines and organizations commonly face challenges such as firewalls, data and tool heterogeneity, and intellectual property protection. In this paper, we review the collaboration challenges, describe how the EU-funded research project AGILE 4.0 addresses these challenges, and detail the application of, and experiences with, AGILE 4.0’s collaboration-enabling technologies.
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. ...
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. ...
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.
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.