Bi-Level Integrated System Synthesis Research Articles

Developing a Multi-objective Multi-Disciplinary Robust Design Optimization Framework

The purpose of this study is to provide an efficient Multi-Objective Multidisciplinary Robust Design Optimization (MOMRDO) framework. To this end, Bi-Level Integrated System Synthesis (BLISS) framework is implemented as a fast Multi-disciplinary Design Optimization (MDO) framework. Progressive Latin Hypercube Sampling (PLHS) is developed as a Design of Experiment (DOE) of the Uncertainty Analysis (UA). This systematic approach leads to a fast, adaptive and efficient framework for Robust Design Optimization (RDO) of complex systems. The accuracy and performance of the proposed algorithm have been evaluated with various tests. Finally, the RDO of a hydrazine monopropellant thruster is defined as a case study. The results show that the proposed method is a fast and efficient method for the multi-objective optimization design of complex systems, and this approach can be used for other engineering applications as well.

Multidisciplinary design optimization architecture to concurrent design of satellite systems

The design of space systems is a complex and multidisciplinary process with multiple conflicting objectives, large number of design variables, and constraints that limits application of the existing multidisciplinary design optimization architectures to this class of design problems. This paper presents an enhanced multidisciplinary design optimization architecture to concurrent holistic design optimization of a satellite system. The proposed multidisciplinary design optimization architecture extends concepts of multidiscipline feasible and bi-level integrated system synthesis into a unified architecture using metamodels. The proposed architecture was evaluated and compared with the existing multidisciplinary design optimization architectures that include all-at-once, bi-level integrated system synthesis, and multidisciplinary design optimization using a remote sensing small satellite in low earth orbit. The satellite design optimization problem deals with the minimization of the total mass of the satellite, involving disciplines of mission analysis, payload, structures, attitude determination and control, communication, command and data handling, power and thermal. The computational performance and accuracy of the proposed architecture were compared with multidisciplinary design optimization benchmark problems. Then the proposed architecture is successfully applied to the satellite system design problem. The results obtained show that metamodel-based bi-level integrated system synthesis-multidisciplinary design optimization architecture presented in this paper provides an effective way of solving large-scale design problems.

Evaluation of Multidisciplinary Design Optimization Methods and Applying Single Level Frameworks in Unmanned Aerial Vehicle (UAV) Design

Objectives: Evaluation of Multidisciplinary Design Optimization (MDO) methods, comparison of advantages and disadvantages of them by applying single level frameworks in design of UAVs using genetic algorithm optimization and Sequential Quadratic Programming (SQP). Methods/Statistical Analysis: Multidisciplinary design optimization methods are divided into 2 overall single level and multilevel frameworks, mathematically. Frameworks under investigation here are: Multi Disciplinary Feasibility (MDF), All At Once (AAO) as single level frameworks and Collaborative Optimization (CO) in addition to Bi-Level Integrated System Synthesis (BLISS) as bi-level frameworks. Selection criteria of appropriate design framework include simplicity of algorithm, quick response as well as degree of convergence in finding optimal answers. Findings: in accordance with the aforementioned criteria, single level methods are superior to multilevel ones. Anyway, no method or framework could be considered as superior; instead, appropriate design framework according to each application should be selected. Finally, single level methods have been used in UAVs design according to which, MDF framework holds higher convergence capability than AAO, though with higher run time in comparison to AAO. Applications /Improvements: single level methods have been used in UAVs design and Global Hawk (RQ-4B) is redesigned for implementation and comparison.

A multi-objective bi-level optimisation algorithm with global and local design variables

An optimisation algorithm that uses a bi-level structure in order to consolidate the solution among multiple discipline optimisations with both local and global design variables is presented. This algorithm is following the bi-level integrated system synthesis decomposition scheme but each discipline level optimisation has its own distinct objective function and constraints. The constraints from all the disciplines are accumulated at the system level optimisation in order to ensure that the final solution is feasible for all disciplines. The structure and the computational process of the algorithm are presented. Two analytical examples are solved for demonstrating the utility of the new algorithm. In all analyses, the optimal results converge on the Pareto front. It is further demonstrated that utilising different weights in the system level objective function allows constructing points on the Pareto front.

A Strategy for Reliability-Based Multidisciplinary Design Optimization of Wind Turbine Using BLISS and PMA

Performance of wind turbines can be negatively affected by uncertainties. Uncertainty-based multi-disciplinary design optimization (UMDO) techniques have been successfully applied in the aerospace industry and given the similarities to wind turbine design problem, application of UMDO techniques is an opportunity to improve wind turbine design. However, the major challenges of UMDO, namely computational complexity and organizational complexity caused by both time-consuming disciplinary analysis models and UMDO algorithms, still greatly hamper its usage in wind engineering. In recent years, there is a surge of research aiming at solving these problems. The purpose of this paper is to review these approaches and with the gathered information, a strategy with bi-level integrated system synthesis (BLISS) and performance measurement approach (PMA) for a reliability-based multidisciplinary design optimization of a wind turbine is proposed.

CO-KR-5 Multidisciplinary Robust Design Optimization for a Deep-sea Test Miner

Multidisciplinary design optimization(MDO) is developed from aerospace and automotive engineering encountered in the design that incorporates a number of interacting disciplines.Although MDO has been studied for deterministic parameters and variables,the likelihood of system failure can be often caused by variation of environmental or operating factors acting on the system.Thus,robust design which can find the best design solution minimizing the variance of response is necessary to obtain reliability of the system.This research is called multidisciplinary robust design optimization(MRDO).In this study,robust design of multidisciplinary systems,coupled with robust design using multiplicative decomposition method and bi-level integrated system synthesis method,is proposed.To validate proposed method,MRDO for a test deep-seamining vehicle system is conducted.

A method to improve computational efficiency for CSSO and BLISS

A considerable portion of the computational cost results from the solution of sensitivity information in concurrent subspace optimization and bi-level integrated system synthesis. A novel method to update sensitivity information is suggested to improve the computational efficiency in this brief note. Firstly, both the approximate insensitive terms and approximate linear terms in sensitivity information are identified, and then their values are temporarily kept constant for multiple multidisciplinary design optimization cycles. A practical engineering case study is presented to demonstrate the effectiveness of the proposed method.

On the development of Bi-Level Integrated System Collaborative Optimization

Bi-Level Integrated System Collaborative Optimization (BLISCO) is a new multidisciplinary design optimization (MDO) method based on Bi-Level Integrated System Synthesis (BLISS) and Collaborative Optimization (CO). The key ideas of BLISCO are to replace compatibility constraint with the sum of coupled outputs as an integrated objective of subsystems and to decompose design variables into system design variables and subsystem design variables, while maintaining the collaborative mechanism of CO. One mathematical example and two engineering problems are used to test the effectiveness of BLISCO under the platform of iSIGHTTM. Results from the test cases show that BLISCO has satisfactory convergence, accurate result and reliable robustness.

Integrated System-of-Systems Synthesis

This paper defines a system of systems as an assemblage of components organized in three levels, each level featuring both analysis and optimization. The choice of only three levels is deliberate, based on examination of the nature of a system of systems, a sample of references, and the range of potential applications. The solution methodology introduced in the paper derives as an extension of an existing bilevel integrated system synthesis method for which an application and implementation experience, as well as rigorous proof of correctness, are available. The new method, called trilevel integrated system synthesis, addresses the engineering practice requirements discussed in the paper. It builds on the formal representation of the system-of-systems coupling data known as the data dependency matrix, on the use of surrogate models, and on a recursive similarity of the data coupling among the systems in a system of systems and among the components within the system. The paper defines three variants of trilevel integrated system synthesis and identifies one of the three for implementation by the criteria of computational cost and engineering practicality. The closing discussion points out that because of extraordinary cost development and doubtful utility of simplified test cases, a system-of-systems optimization method ought to be developed in conjunction with a real, full-scale, application.

Comparison of MDO methods with mathematical examples

Recently, engineering systems are quite large and complicated. The design requirements are fairly complex and it is not easy to satisfy them by considering only one discipline. Therefore, a design methodology that can consider various disciplines is needed. Multidisciplinary design optimization (MDO) is an emerging optimization method that considers a design environment with multiple disciplines. Seven methods have been proposed for MDO. They are Multiple-discipline-feasible (MDF), Individual-discipline-feasible (IDF), All-at-once (AAO), Concurrent subspace optimization (CSSO), Collaborative optimization (CO), Bi-level integrated system synthesis (BLISS), and Multidisciplinary design optimization based on independent subspaces (MDOIS). Through several mathematical examples, the performances of the methods are evaluated and compared. Specific requirements are defined for comparison and new types of mathematical problems are defined based on the requirements. All the methods are coded and the performances of the methods are compared qualitatively and quantitatively.

Evaluation of Multidisciplinary Optimization Techniques Applied to a Reusable Launch Vehicle

Three multilevel multidisciplinary optimization techniques, Bi-Level Integrated System Synthesis, Collaborative Optimization, and Modified Collaborative Optimization, are applied to the design of a reusable launch vehicle, evaluated, and compared in this study. In addition to comparing the techniques against each other, they are also compared with designs reached via fixed-point iteration of disciplines with local optimization and the industry accepted multidisciplinary optimization technique, All-at-Once. The new multidisciplinary optimization techniques, particularly Bi-Level Integrated System Synthesis, showed greater ability than fixed-point iteration to design for a global objective and were more applicable to complex systems than All-at-Once. This study was the first time that the novel multidisciplinary optimization methods were compared qualitatively and quantitatively under controlled experimentation practices. It is still impossible to statistically determine whether any one of the novel multidisciplinary optimization techniques is better than another, because more studies using different test problems corroborating the conclusions made here are needed.

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.

Optimization design for scramjet and analysis of its operation performance

Flow field numerical simulation methods of scramjet introduced in this paper, include using CFD method to analyze the flow phenomenon in each component of scramjet, i.e. forebody/inlet, combustor and afterbody/nozzle. Design and optimization of a generic scramjet model are investigated based on the bilevel integrated system synthesis method. Analysis and design procedures of each component are treated as a black box in scramjet system design. Components of scramjet are designed and optimized at subsystem level in the first step, and then a system level optimization is performed for the whole scramjet. Multi-fidelity simulation models are developed and applied to analyze and design the scramjet model. According to the requirement of design, appropriate fidelity simulation models are adopted in design cycle under the limitation of time and budget. A design and optimization example is presented in which some parameters are chosen as scramjet system level variables by sensitivity analysis of all possible design parameters; other parameters become component level variables of forebody/inlet, combustor and afterbody/nozzle. The design of experiment method and genetic algorithm arc applied to explore design space and to obtain optimal or near optimal solution. Operation performance of scramjet is also analyzed in this paper, considering the interaction between scramjet and vehicle airframe.

Bilevel Integrated System Synthesis for Concurrent and Distributed Processing

The paper introduces a new version of the Bi-Level Integrated System Synthesis (BLISS) methods intended for optimization of engineering systems conducted by distributed specialty groups working concurrently and using a multiprocessor computing environment. The method decomposes the overall optimization task into subtasks associated with disciplines or subsystems where the local design variables are numerous and a single, system-level optimization whose design variables are relatively few. The subtasks are fully autonomous as to their inner operations and decision making. Their purpose is to eliminate the local design variables and generate a wide spectrum of feasible designs whose behavior is represented by Response Surfaces to be accessed by a system-level optimization. It is shown that, if the problem is convex, the solution of the decomposed problem is the same as that obtained without decomposition. A simplified example of an aircraft design shows the method working as intended. The paper includes a discussion of the method merits and demerits and recommendations for further research.

Bilevel Integrated System Synthesis with Response Surfaces

Two variants of the BLISS method are proposed for multidisciplinary design optimization (MDO). These variants utilize experimental design methods and response surfaces to reduce the number of expensive system analysis required for solution of the MDO problem. BLISS is an MDO method for decomposition-based optimization of engineering systems that involves system optimization with a relatively small number of design variables and a number of subsystem optimizations that could each have a large number of local variables. In BLISS, the optimum sensitivity analysis data are used to relate the subsystem optimization solutions with the system optimizations. Instead, with the proposed variants, polynomial response surface approximations using either the system analysis or the subsystem optimization results are used. Additionally, the response surface construction process is well suited for computing in a concurrent processing environment. The proposed variants are implemented and evaluated on a conceptual-level aircraft and ship design problem.

Bilevel Integrated System Synthesis

BLISS is a method for optimization of engineering systems by decomposition. It separates the system level optimization, having a relatively small number of design variables, from the potentially numerous subsystem optimizations that may each have a large number of local design variables. The subsystem optimizations are autonomous and may be conducted concurrently. Subsystem and system optimizations alternate, linked by sensitivity data, producing a design improvement in each iteration. Starting from a best guess initial design, the method improves that design in iterative cycles, each cycle comprised of two steps. In step one, the system level variables are frozen and the improvement is achieved by separate, concurrent, and autonomous optimizations in the local variable subdomains. In step two, further improvement is sought in the space of the system level variables. Optimum sensitivity data link the second step to the first. The method prototype was implemented using MATLAB and iSIGHT programming software and tested on a simplified, conceptural level supersonic business jet design, and a detailed design of an electronic device. Satisfactory convergence and favorable agreement with the benchmark results were observed. Modularity of the method is intended to fit the human organization and map well on the computing technology of concurrent processing.

Bilevel integrated system synthesis with response surfaces

Bilevel integrated system synthesis with response surfaces

Bilevel integrated system synthesis

Bilevel integrated system synthesis