Search Results

The primary objective of this investigation is the optimum design of lightweight foam material systems for controlled energy absorption under blast impact. The ultimate goal of these systems is to increase the safety and integrity of occupants and critical components in structural systems such as automotive vehicles, buildings, ships, and aircrafts. Although outstanding results have been achieved with the use of foams in blast protective systems, current design practices rely on trial and error as there is an absence of a systematic design method. While the governing equations are known for a variety of physical phenomena in appropriate length scales, there are no suitable methodologies to accomplish the aforementioned objectives. A promising approach to systematically design the material's microstructure is the use of structural optimization methods. This investigation presents an appropriate design methodology to optimally design foam material systems for blast mitigation.

This paper presents a framework to incorporate the notion of reliability into crashworthy designs of automotive vehicle components. Optimal design for crashworthiness is a challenging task in itself as it involves time dependent complex interactions among bodies along with material and geometric nonlinearities. Maximum energy absorption in the structure is widely used as a design criteria in crashworthiness designs as long as penetration levels are kept under allowable limits. These designs behave well and are safe as long as loading conditions and material properties are deterministic however failure can happen due to excessive penetration under uncertain conditions. Hence a semi coupled reliability based crashworthiness design methodology is proposed in which independent reliability assessments are done on the designs at various intermediate levels during an iterative design cycle of a crashworthy structure.

Formula SAE is a very competitive event in which collegiate engineering teams design, build, and race open-wheeled vehicles. With teams representing the best engineering programs from around the world, small decreases in weight on every component can mean large overall decreased weight for the entire vehicle, leading to faster lap times. The goal of this work is to redesign a wheel upright for a Formula SAE racing vehicle. Being that the vehicle uprights are considered un-sprung weight, any weight savings achieved on their design is worth twice as much as weight savings achieved on sprung vehicle weight. The structural design optimization problem is expressed in terms of two conflicting objectives: minimize the compliance and minimize the weight of the component. The optimization process is performed through a topology optimization approach. Given the loading conditions, tractions, and null elements for the manufacturing of the wheel upright, a new topology is generated.

In this paper, a Hybrid Cellular Automaton (HCA) algorithm has been utilized to develop an efficient methodology for synthesizing structures under a dynamic loading event. This method utilizes the cellular automata computing paradigm with the nonlinear transient finite element analysis. Previous work in topology optimization for structural design has concentrated on modeling assuming static loading conditions due to the complexities associated with dynamic loading. An HCA method has been developed to address more complicated cases that involve dynamic events, such as impacts and collisions. To illustrate this technology, a generic design domain that represents an automotive crush structure is presented to demonstrate the efficiency of the proposed methodology in synthesizing crashworthy, constant cross section structures. Influence of mass fraction on final topology and application of this methodology for multiple load cases is presented.

In this investigation a robotic system's dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations allow designers to predict the dynamic performance of a robotic system for a particular configuration (i.e.,point design). While the dynamic capability equations are a powerful tool, they can not account for performance variations due to aleatory uncertainties inherent in the system. To account for the inherent aleatory uncertainties, a reliability-based design optimization (RBDO) strategy is employed to design robotic systems with robust dynamic performance. RBDO has traditionally been implemented as a nested multilevel optimization process in which reliability constraints require solution to an optimization problem (i.e., reliability analysis). In this work a robust unilevel performance measure approach(PMA) is developed for performing reliability-based design optimization which eliminates the lower level problem in RBDO.