Charles D. McAllister

Multidisciplinary Robust Design Optimization of an Internal Combustion Engine

In this paper, we introduce a multidisciplinary robust design optimization formulation to evaluate uncertainty encountered in the design process. The formulation is a combination of the bi-level Collaborative Optimization framework and the multiobjective approach of the compromise Decision Support Problem. To demonstrate the proposed framework, the design of a combustion chamber of an internal combustion engine containing two subsystem analyses is presented. The results indicate that the proposed Collaborative Optimization framework for multidisciplinary robust design optimization effectively attains solutions that are robust to variations in design variables and environmental conditions.

GOAL PROGRAMMING APPLICATIONS IN MULTIDISCIPLINARY DESIGN OPTIMIZATION

In this paper, we begin to explore the ramifications of a goal programming approach for multidisciplinary design optimization based on the Collaborative Optimization framework. Working within an existing computing architecture for simulation-based design, we propose a bi-level goal programming formulation of Collaborative Optimization to automate the design synthesis of complex systems, with particular emphasis on undersea vehicles. A brief overview of the simulation-based design computing architecture that has been developed is given. To demonstrate the proposed bi-level goal programming approach, the multidisciplinary design and optimization of an undersea vehicle containing four subsystem level analyses and a system-level analysis is presented. A traditional single-level optimization solution is also presented to provide a benchmark for comparison. While the bi-level goal programming approach is computationally more expensive, it does improve overall system performance by automating subsystem synthesis and arbitration during optimization.

Robust Multiobjective Optimization Through Collaborative Optimization and Linear Physical Programming

Rennselaer Polytechnic Institute, Troy, NY, 12180 Multidisciplinary design optimization (MDO) is a concurrent engineering design tool for large-scale, complex systems design that can be affected through the optimal design of several smaller functional units or subsystems. Due to the multiobjective nature of many MDO problems, recent work has focused on formulating the MDO problem to help resolve tradeoffs between multiple, conflicting objectives. In this paper, we describe the novel integration of Linear Physical Programming within the Collaborative Optimization framework to enable designers to formulate multiple system-level objectives in terms of physically meaningful parameters. The proposed formulation extends our previous multiobjective formulation of Collaborative Optimization, which uses Goal Programming at the system level and subsystem level to enable multiple objectives to be considered at both levels during optimization. The proposed framework is demonstrated using two MDO applications: (1) the design of a Formula 1 racecar and (2) the configuration of an autonomous underwater vehicle. Results obtained from the proposed formulation are compared against a traditional formulation without Collaborative Optimization or Linear Physical Programming.

MULTIDISCIPLINARY DESIGN OPTIMIZATION TESTBED BASED ON AUTONOMOUS UNDERWATER VEHICLE DESIGN

In this paper, we introduce the design of an autonomous underwater vehicle with the intent of conveying this testbed to the multidisciplinary design optimization research community for exploration of new and existing modeling and optimization methodologies. The design of an autonomous underwater vehicle poses a large- scale design scenario. We have decomposed the analyses into a system module and five subsystems: guidance and control, payload, power, machinery, and hydrodynamics/propulsion. Our initial formulation follows an Individual Discipline Feasible approach to multidisciplinary design optimization, and the included results provide a feasible design that maximizes payload length for exploratory electronic equipment. Nomenclature od outside diameter (in) depth operating depth (ft) speed maximum operating speed (m/s) missionTime time of operation (hr) thrustPower thrust power (hp) id

Implementation of response surface methodology using variance reduction techniques in semiconductor manufacturing

Semiconductor manufacturing is generally considered a cyclic industry. As such, individual producers able to react quickly and appropriately to market conditions will have a competitive advantage. Manufacturers who maintain low work in process inventory, ensure that specialized equipment is in good repair, and produce quality products at least possible cost will have the best opportunities to effectively compete and excel in these challenging venues. To support this nimble business model, our current efforts are directed toward creating efficient, accurate metamodels of the impact of maintenance policies on production efficiency. These validated polynomial approximations facilitate rapid exploration of the design region, compared with the original simulation models. The experiment design used for metamodel construction employed variance reduction techniques. When compared to a similar experiment design using independent streams, the variance reduction approach provided a decrease in standard error of the regression coefficients and smaller average error when validated against the simulation response.

Application of multidisciplinary design optimization to racecar design and analysis

Multidisciplinary design instances arise when the performance of large-scale, complex systems can be affected through the optimal design of several smaller functional units or subsystems. In this paper, we describe the use of multidisciplinary design optimization to resolve system-level tradeoffs during racecar design. Our implementation involves three design variables: weight distribution, aerodynamic downforce distribution, and roll stiffness distribution. The objective is to determine the racecar configuration that minimizes lap time around a skidpad of constant radius while satisfying a yaw balance constraint. The force and aerodynamic components of the design optimization problem provide the multidisciplinary setting in which Collaborative Optimization is implemented and compared with previous results obtained from a traditional optimization formulation.

RELATIVE RISK CHARACTERISTICS OF ROLLING HORIZON HEDGING HEURISTICS FOR CAPACITY EXPANSION

ABSTRACT This paper applies measures of risk to capacity expansion decisions made under uncertainty. Eight different decision making rules are constructed by varying both the frequency of the forecast updates and the hedge against uncertainty in a rolling horizon heuristic procedure. Using demand, capacity, and cost data from the utility division of a manufacturing company, the risk characteristics of each decision making rule are evaluated by simulation. The results indicate that annual forecast revisions hedged by ninety percent prediction limits are preferred over decision rules with less frequent forecast revisions or fixed-width hedges.