System Architecture Design Space Exploration: An Approach to Modeling and Optimization

Bussemaker, Jasper; Ciampa, Pier Davide; Nagel, Björn

doi:10.2514/6.2020-3172

Cited by 27 publications

(35 citation statements)

References 22 publications

(35 reference statements)

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“…Examples of ongoing works are the model of the "architecture design space" presented in Ref. [30], and the modeling environment for "design processes" presented in Ref. [31].…”

Section: B Framework Concept Structurementioning

confidence: 99%

A MBSE Approach to MDAO Systems for the Development of Complex Products

Ciampa

Nagel

Rocca

2020

Aiaa Aviation 2020 Forum

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The research and innovation AGILE project has developed an approach, the socalled AGILE Paradigm, focusing on the acceleration of the deployment and operation of collaborative Multidisciplinary Design Analysis Optimization systems, which in turns can be exploited to accelerate the development of complex products, such as novel aerospace systems. Although the technologies developed for the implementation of the paradigm, have proved to reduce the deployment and operational time to more than 40% with respect to conventional MDAO approaches, the AGILE Paradigm has not been formalized and model by digital design engineering practices. This work introduces a novel approach leveraging MBSE principles to streamline the development of agile MDAO design systems, and establishing a bridge between MBSE and MDAO. Major outcomes here presented are the MBSE-driven models of the so-called AGILE MDAO system, representing the architecture, the requirements, as well as the organizational aspects, and all the interactions and activities implemented during the life-cycle stages of the MDAO system. The MBSE Architectural Framework, which defines the underlying ontological concepts and perspectives driving the development of the AGILE MDAO system model, are modeled and presented as well. The paper introduces for the first time the overall approach, as well as the high-level elements of the models developed, here represented by making use of SysML standard. The described approach is at the core of the recently launched project AGILE4.0, in which its scope will be expanded to cover the entire life-cycle of the development of complex aeronautical systems.

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“…Examples of ongoing works are the model of the "architecture design space" presented in Ref. [30], and the modeling environment for "design processes" presented in Ref. [31].…”

Section: B Framework Concept Structurementioning

confidence: 99%

A MBSE Approach to MDAO Systems for the Development of Complex Products

Ciampa

Nagel

Rocca

2020

Aiaa Aviation 2020 Forum

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“…To determine the architecture decisions involved in the optimization problem, the architecture design space is modeled first. A novel methodology that enables modeling the design space from a functional perspective is used [25]. This methodology models the architecture design space using the Architecture Design Space Graph (ADSG), which maps functions to components.…”

Section: Modeling the Architecture Design Spacementioning

confidence: 99%

“…Therefore, if the ADSG contains a function node that points to two or more components, a decision node is inserted to represent the choice of architectures. For more details on the ADSG and its architecture decision mechanisms, the reader is referred to [25].…”

Section: Modeling the Architecture Design Spacementioning

confidence: 99%

Environmental and Flight Control System Architecture Optimization from a Family Concept Design Perspective

Hoz

Bussemaker

Fioriti

et al. 2020

Aiaa Aviation 2020 Forum

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One method an Original Equipment Manufacturer (OEM) can apply to reduce development and manufacturing costs is family concept design: each product family member is designed for a different design point, but a significant amount of components is shared among the family members. In this case, a trade-off exists between member performance and commonality. In the design of complex systems, often many different architectures are possible, and the design space is too large to explore exhaustively. In this work, we present an application of a new architecture optimization method to the design of a family of passenger transport jets, with a focus on the sizing of the Environmental Control System (ECS) and Flight Control System (FCS). The architecture design space is modeled using the Architecture Design Space Graph (ADSG), a novel method for constructing model-based system architecture optimization problems. Decisions are extracted and the multi-objective optimization problem is automatically formulated. Objectives used are commonality, representing acquisition costs, and fuel burn, representing a part of operation costs. These metrics are evaluated using a cross-organizational collaborative multidisciplinary analysis toolchain, and the resulting Multidisciplinary Design Optimization (MDO) problem is solved using a multi-objective evolutionary optimization algorithm. The results show that the trade-off between commonality and fuel burn is only present above a certain commonality level.

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“…Additionally, 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, also at the earlier design stage when the architecture of the system has not been fixed yet [1]. This would increase the understanding of the impact of important architectural decisions, enable the effective exploration of the design space, and reduce reliance on expert judgment suffering from bias, subjectivity, conservatism or overconfidence [2].…”

mentioning

confidence: 99%

“…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. Next, as shown in [1], the architecture optimization problem is a mixed-discrete, multi-objective, hierarchical problem: mixed-discrete because design decisions might consist of both categorical architecting decisions and integer or continuous sizing parameters, multi-objective because the evaluation of possible architectural choices is generally based on multiple conflicting stakeholder selection criteria, and hierarchical because design variables can be conditionally active based on other design variables. In fact, the hierarchical nature is generally a consequence of the aforementioned categorical variables, as the presence of certain component(s) in the system architecture comes with the necessary design variables to define said component(s).…”

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confidence: 99%

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

Bussemaker

Smedt

Rocca

et al. 2021

Aiaa Aviation 2021 Forum

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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.

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System Architecture Design Space Exploration: An Approach to Modeling and Optimization

Cited by 27 publications

References 22 publications

A MBSE Approach to MDAO Systems for the Development of Complex Products

A MBSE Approach to MDAO Systems for the Development of Complex Products

Environmental and Flight Control System Architecture Optimization from a Family Concept Design Perspective

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

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