Michael Kokkolaras

Combined optimisation of design and power management of the hydraulic hybrid propulsion system for the 6 × 6 medium truck

Hybrid propulsion systems are one of the critical technologies on the roadmap to future ultra-efficient trucks. While there is a significant body of work related to hybrid passenger cars and light ...

Target cascading in vehicle redesign: a class VI truck study

The analytical target cascading process is applied to the redesign of a U.S. class VI truck. Necessary simulation and analysis models for predicting vehicle dynamics, powertrain, and suspension behaviour are developed. Vehicle design targets that include improved fuel economy, ride quality, driveability, and performance metrics are translated into system design specifications, and a consistent final design is obtained. Trade-offs between conflicting targets are identified. The study illustrates how the analytical target cascading process can reduce vehicle design cycle time while ensuring physical prototype matching, and how costly design iterations late in the development process can be avoided.

DESIGN OPTIMIZATION OF HIERARCHICALLY DECOMPOSED MULTILEVEL SYSTEMS UNDER UNCERTAINTY

This paper presents a methodology for design optimization of hierarchically decomposed systems under uncertainty. We propose an extended, probabilistic version of the deterministic analytical target cascading (ATC) formulation by treating uncertain quantities as random variables and posing probabilistic design constraints. A bottom-to-top coordination strategy is used for the ATC process. Given that first-order approximations may introduce un-acceptably large errors, we use a technique based on the advanced mean value method to estimate uncertainty propagation through the multilevel hierarchy of elements that comprise the decomposed system. A simple yet illustrative hierarchical bilevel engine design problem is used to demonstrate the proposed methodology. The results confirm the applicability of the proposed probabilistic ATC formulation and the accuracy of the uncertainty propagation technique.

Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation.

Platform selection under performance bounds in optimal design of product families

Designing a family of product variants that share some components usually requires a compromise in performance relative to the individually optimized variants due to the commonality constraints. Choosing components for sharing may depend on what performance losses can be tolerated. In this article an optimal design problem is formulated to choose product components to be shared without exceeding user-specified bounds on performance. This enables the designer to control tradeoffs and obtain optimal product family designs for maximizing commonality at different levels of acceptable performance. A family of automotive body side frames is used to demonstrate the approach.

Mixed Variable Optimization of the Number and Composition of Heat Intercepts in a Thermal Insulation System

In the literature, thermal insulation systems with a fixed number of heat intercepts have been optimized with respect to intercept locations and temperatures. The number of intercepts and the types of insulators that surround them were chosen by parametric studies. This was because the optimization methods used could not treat such categorical variables. Discrete optimization variables are categorical if the objective function or the constraints can not be evaluated unless the variables take one of a prescribed enumerable set of values. The key issue is that categorical variables can not be treated as ordinary discrete variables are treated by relaxing them to continuous variables with a side constraint that they be discrete at the solution.A new mixed variable programming (MVP) algorithm makes it possible to optimize directly with respect to mixtures of discrete, continuous, and categorical decision variables. The result of applying MVP is shown here to give a 65% reduction in the objective function over the previously published result for a thermal insulation model from the engineering literature. This reduction is largely because MVP optimizes simultaneously with respect to the number of heat intercepts and the choices from a list of insulator types as well as intercept locations and temperatures. The main purpose of this paper is to show that the mixed variable optimization algorithm can be applied effectively to a broad class of optimization problems in engineering that could not be easily solved with earlier methods.

Optimal partitioning and coordination decisions in decomposition-based design optimization

The solution of complex system design problems using decomposition-based optimization methods requires determination of appropriate problem partitioning and coordination strategies. Previous optimal partitioning techniques have not addressed the coordination issue explicitly. This article presents a formal approach to simultaneous partitioning and coordination strategy decisions that can provide insights on whether a decomposition-based method will be effective for a given problem. Pareto-optimal solutions are generated to quantify tradeoffs between the sizes of subproblems and coordination problems as measures of the computational costs resulting from different partitioning and coordination strategies. Promising preliminary results with small test problems are presented. The approach is illustrated on an electric water pump design problem.

PLATFORM SELECTION UNDER PERFORMANCE LOSS CONSTRAINTS IN OPTIMAL DESIGN OF PRODUCT FAMILIES

Designing a family of product variants that share some components usually entails a performance loss relative to the individually optimized variants due to the commonality constraints. Choosing com ...

PLATFORM-BASED DESIGN AND DEVELOPMENT: CURRENT TRENDS AND NEEDS IN INDUSTRY

Many companies constantly struggle to find cost-effective solutions to satisfy the diverse demands of their customers. In this paper, we report on two recent industry-focused conferences that emphasized platform design, development, and deployment as a means to increase variety, shorten lead-times, and reduce development and production costs. The first conference, Platform Management for Continued Growth, was held November-December 2004 in Atlanta, Georgia, and the second, 2005 Innovations in Product Development Conference - Product Families and Platforms: From Strategic Innovation to Implementation, was held in November 2005 in Cambridge, Massachusetts. The two conferences featured presentations from academia and more than 20 companies who shared their successes and frustrations with platform design and deployment, platform-based product development, and product family planning. Our intent is to provide a summary of the common themes that we observed in these two conferences. Based on this discussion, we extrapolate upon industry’s needs in platform design, development, and deployment to stimulate and catalyze future work in this important area of research.

Comparing Time Histories for Validation of Simulation Models: Error Measures and Metrics

Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing measures and a metric to compare time histories obtained from simulation model outputs and experimental tests. The focus of the work is on vehicle safety applications. We restrict attention to quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First, we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Three independent error measures are proposed for vehicle safety applications, associated with three physically meaningful characteristics (phase, magnitude, and slope), which utilize norms, cross-correlation measures, and algorithms such as dynamic time warping to quantify discrepancies. A combined use of these three measures can serve as a metric that encapsulates the important aspects of time history comparison. It is also shown how these measures can be used in conjunction with ratings from subject matter experts to build regression-based validation metrics.