Kurt Hacker

A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design

In this paper we introduce a technique to reduce the effects of uncertainty and incorporate flexibility in the design of complex engineering systems involving multiple decisionmakers. We focus on the uncertainty that is created when a disciplinary designer or design team must try to predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and design teams, each of which may only have control over a portion of the total set of system design variables. Modeling the interaction among these decisionmakers and reducing the effect caused by lack of global control by any one designer is the focus of this paper. We use concepts from robust design to reduce the effects of decisions made during the design of one subsystem on the performance of the rest of the system. Thus, in a situation where the cost of uncertainty is high, these tools can be used to increase the robustness, or independence, of the subsystems, enabling designers to make more effective decisions. This approach includes uncertainty caused by control factor variation (Type II robust design) and uncertainty caused by unknown nonlocal design information (Type I robust design). To demonstrate the usefulness of this approach, we consider a case study involving the design of a passenger aircraft.

Robust Design Through the Use of a Hybrid Genetic Algorithm

In this paper we present a hybrid optimization approach to perform robust design. The motivation for this work is the fact that many realistic engineering systems are mutimodal in nature with multiple local optima, and moreover may have one or more uncertain design parameters. The approach that is presented utilizes both local and global optimization algorithms to find good design points more efficiently than either could alone. The mean and variance of the objective function at a design point is calculated using Monte Carlo simulation and is used to drive the optimization process. To demonstrate the usefulness of this approach a case study is considered involving the design of a beam with dimensional uncertainty.

Racecar Optimization and Tradeoff Analysis in a Parallel Computing Environment

In this paper, we present an approach to the optimization of a racecar using vehicle dynamics simulation in a parallel-computing environment. The use of vehicle dynamics simulations in the automotive and auto racing industries is widespread. Complex vehicle simulations can include hundreds of parameters and be very computationally expensive to perform. This limits the number of design configurations that can be considered within a reasonable time, preventing thorough exploration of the design space. It also limits the usefulness of these simulations during the course of a race weekend when time is of the essence. In this paper, we present results from work to overcome this problem. Our results focus on determining the Pareto optimal designs for a vehicle model with three design variables running simulated races around corners of different radii.

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.

Comparison of design methodologies in the preliminary design of a passenger aircraft

In this paper we seek to compare the configurations and quality of passenger aircraft resulting from the use of different design methodologies. First, we will optimize the design in the traditional iterative framework that is generally followed in practice. To this we will compare the results obtained following a design methodology based in part on game theoretic principles. We will compare not only the quality of the resulting designs, but also the efficiency of the design process in terms of time and cost.