Technologies for Enabling System Architecture Optimization

Optimization of complex system architectures can support the non-biased search for novel architectures in the early design phase. Four aspects needed to enable architecture optimization and the author's views on how to solve them are discussed: formalization of the architecture design space, systematic exploration of the design space, conversion from architecture model to simulation model, and flexible simulation of architecture performance. Modeling the design space is done using the Architecture Design Space Graph (ADSG) implemented in ADORE. Systematic exploration can be done using evolutionary or surrogate-based optimization algorithms. Architecture to simulation model conversion can be done using an object-oriented approach using class factories, or using theMultiLinQ tool to synchronize a central data repository. Finally, simulation environments should expose a flexible and modular interface to be used in architecture optimization. A jet engine architecting problem is presented that demonstrates various aspects of system architecture optimization

The Influence of Architectural Design Decisions on the Formulation of MDAO Problems

Formulating system architecture design problems as optimization problems has the potential to help reduce bias in searching the combinatorial design space, and help find novel system architectures to better meet the challenges of the future. Typical types of design decisions present in architecture optimization problems, however, pose some special challenges to currently existing MDO problem formulations. Design variables are mixed-discrete, a hierarchy might exist between design variables where one design variable can deactivate another, and there might be multiple conflicting objectives to optimize for simultaneously. This poster explores some of the impacts these effects can have on MDO problems, in particular regarding the inclusion or exclusion of analysis blocks

SYSTEM ARCHITECTURE DESIGN SPACE MODELING AND OPTIMIZATION ELEMENTS

Optimization of complex system architectures can support the non-biased search for novel architectures in the early design phase. Four aspects needed to enable architecture optimization are discussed: formalization of the architecture design space, systematic exploration of the design space, conversion from architecture model to simulation model, and flexible simulation of architecture performance. Modeling the design space is driven by system requirements and simulation capabilities and should be based on functional decomposition. Systematic exploration can be done using enumeration, design of experiments, or optimization. Various approaches for converting architectures to simulation models are discussed. Finally, simulation environments should expose a flexible and modular interface to be used in architecture optimization. A jet engine architecting problem is presented that demonstrates various aspects of system architecture optimization

From System Architecting to System Design and Optimization: A Link Between MBSE and MDAO

Optimization of system architectures can help deal with finding better system architectures in a large design space plagued by combinatorial explosion of alternatives. To enable architecture optimization, the design space should therefore be formalized into a numerical optimization problem, and it should be possible to quantitatively evaluate architecture alternatives. This paper presents a methodology for generating and modeling architecture design spaces using the Architecture Design Space Graph (ADSG), and using collaborative Multidisciplinary Design Analysis and Optimization (MDAO) techniques to evaluate architectures. Collaborative MDAO leverages disciplinary expertise while ensuring that analysis tools exchange data consistently and correctly using a central data schema. The problem solved in this paper is the missing link between architecture optimization and collaborative MDAO: the reflection of generated architectures in the central data schema. It is solved by the authors by mapping architecture components and Quantities of Interest (QOIs) to the central data schema using Data Schema Operations (DSOs). Such a mapping also assists the user in identifying missing or unnecessary disciplinary analysis tools. Three web-based software tools implementing the methodology are presented. Finally, the methodology and tools are demonstrated using the design of a supersonic business jet as an example

Systems Architecting: A Practical Example of Design Space Modeling and Safety-Based Filtering within the AGILE4.0 Project

The aerospace industry strives toward innovative aircraft concepts that feature increasing electrification to meet environmental and business targets. Advanced Multi-Disciplinary Design Analysis and Optimization (MDAO) frameworks have been developed to help evaluate these aircraft and their systems. However, the system architecting process still relies on a system architecture baseline from past aircraft programs or historical data, thereby precluding the exploration of a larger design space and identifying optimal solutions for further development. Furthermore, the evolution of system safety is a critical factor in establishing the feasibility of a system architecture solution. Therefore, there is a need to explore a large design space of system architectures for safety, certification, and performance requirements in an efficient manner. This paper presents a rule-based safety assessment approach within a systems architecting framework that demonstrates the ability to generate and filter a large design space based on safety heuristics. This approach is demonstrated using a case study for an aircraft landing gear braking system

Effectiveness of Surrogate-Based Optimization Algorithms for System Architecture Optimization

The design of complex system architectures brings with it a number of challenging issues, among others large combinatorial design spaces. Optimization can be applied to explore the design space, however gradient-based optimization algorithms cannot be applied due to the mixed-discrete nature of the design variables. It is investigated how effective surrogate-based optimization algorithms are for solving the black-box, hierarchical, mixed-discrete, multi-objective system architecture optimization problems. Performance is compared to the NSGA-II multi-objective evolutionary algorithm. An analytical benchmark problem that exhibits most important characteristics of architecture optimization is defined. First, an investigation into algorithm effectiveness is performed by measuring how accurately a known Pareto-front can be approximated for a fixed number of function evaluations. Then, algorithm efficiency is investigated by applying various multi-objective convergence criteria to the algorithms and establishing the possible trade-off between result quality and function evaluations needed. Finally, the impact of hidden constraints on algorithm performance is investigated. The code used for this paper has been published

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

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

SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multifidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license.Comment: version

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

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