Exploring mass trade-offs in preliminary vehicle design using Pareto sets

Our goal in this work is to develop analytical tools to support the definition of balanced and compatible sets of vehicle specifications in the early stages of vehicle development. In this paper, we discuss the development and application of a Technical Feasibility Model (TFM) that may be used in preliminary design to assess the technical feasibility and optimality of specified combinations of vehicle performance targets. For this paper, we have exercised the TFM specifically to explore the relationships between vehicle mass, vehicle performance measures, (such as acceleration, fuel efficiency, and interior roominess), and high-level vehicle design parameters (such as overall exterior dimensions, occupant positions, and selection of a powertrain). The TFM is developed by first applying a Multi-Objective Genetic Algorithm to a multidisciplinary design framework to generate a set of Pareto-optimal design solutions, then applying response surface methods to generate a smooth mathematical representation of the Pareto set, and finally using geometric construction to analyze the position of a test point relative to the representation of the Pareto set. Results of this analysis include an assessment of the feasibility and optimality of the test point as well as a variety of projections from the test point to the representation of the Pareto set that may be used to identify opportunities for refining, relaxing, improving, or prioritizing performance specifications. The mapping between performance space and design space has been preserved, allowing for investigation of relationships between performance specifications and design variable settings. In this paper we broadly demonstrate the application of the TFM, beginning with its basic capabilities of testing the feasibility of a specified combination of performance measures, quantifying the available amount of design freedom for a specified combination of performance measures, and quantifying the change in each performance measure required to attain a Pareto-optimal solution. In addition, we will demonstrate how the capabilities of the TFM may be leveraged specifically for exploring relationships between vehicle mass, vehicle performance measures, and vehicle design parameters by generating response surfaces to identify compatible sets of vehicle performance targets at specified levels of vehicle mass and quantifying the sensitivity of performance measures to changes in vehicle mass. Collectively, these capabilities make the TFM a powerful tool for managing vehicle mass and ensuring vehicle design feasibility in the earliest stages of the vehicle development process.