Design Of Large-scale Engineering Systems Research Articles

Response Surface Stochastic Finite Element Method of Composite Structure

Response Surface Method (RSM) has been applied to structural reliability problems successfully in many areas. Finite Element Method (FEM) is one of the most widely used computational methods, which permit the analysis and design of large-scale engineering systems. In order to obtain a reliability analysis method of composite structure with satisfactory accuracy and computational efficiency, RSM and FEM were combined by secondary development of ABAQUS. Response Surface Stochastic Finite Element Method (RSSFEM) which can solve the reliability problems of composite structure was developed. The numerical accuracy and the computational efficiency of the developed method were demonstrated by comparison with Monte-Carlo Stochastic Finite Element Method (MCSFEM).

Biased Information Passing Between Subsystems Over Time in Complex System Design

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

USE OF COUNTERPROPAGATION NEURAL NETWORKS TO ENHANCE THE CONCURRENT SUBSPACE OPTIMIZATION STRATEGY

The paper explores the use of artificial neural networks in a concurrent optimization strategy that derives from a decomposition based approach to design of large-scale engineering systems. These problems are characterized by complex couplings that render parametric design methods inappropriate as solution tools. Decomposition methods reduce the large dimensionality problem into a sequence of smaller, more tractable optimization problems, each with a smaller set of design variables and constraints. The decomposed subproblems are rarely decoupled completely, and design changes in one subproblem have a profound influence on changes in another subproblem. Essential components of decomposition based design methods are strategies to identify a topology for problem decomposition, and to develop coordination strategies which account for couplings among the decomposed problems. The paper examines the effectiveness of artificial neural networks as a tool to both account for the coupling, and to develop methods to coordinate the solution in the different subproblems to a converged optimal design