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The present work is concerned with the objective of multi disciplinary design optimization (MDO) of an automotive front end structure using truncated finite element model. A truncated finite element model of a real world vehicle is developed and its efficacy for use in design optimization is demonstrated. The main goal adopted here is minimizing the weight of the front end structure meeting NVH, durability and crash safety targets. Using the Response Surface Method (RSM) and the Design Of Experiments (DOE) technique, second order polynomial response surfaces are generated for prediction of the structural performance parameters such as lowest modal frequency, fatigue life, and peak deceleration value.

The present work is concerned with the objective of developing a process for practical multi-disciplinary design optimization (MDO). The main goal adopted here is to minimize the weight of a vehicle body structure meeting NVH (Noise, Vibration and Harshness), durability, and crash safety targets. Initially, for simplicity a square tube is taken for the study. The design variables considered in the study are width, thickness and yield strength of the tube. Using the Response Surface Method (RSM) and the Design Of Experiments (DOE) technique, second order polynomial response surfaces are generated for prediction of the structural performance parameters such as lowest modal frequency, fatigue life, and peak deceleration value. The optimum solution is then obtained by using traditional gradient-based search algorithm functionality “fmincon” in commercial Matlab package.

This paper examines the theoretical worst case of normal headform impact on an infinitely rigid surface with the help of a dynamic spring-mass model. It is pointed out that the current approach is not an actual representation of any vehicle upper interior but is useful in gaining insight into the headform impact phenomenon and determining how to further enhance design. After considering force-deflection characteristics of a variety of commonly used headform impact protection countermeasures, a mathematical model is set up with spring properties that approximate those of physical countermeasures. Closed-form solutions are derived for various dynamic elasto-plastic phases including elastic unloading and contact. A parametric study is then carried out with HIC(d) as the dependent variable, and spring stiffness, yield force and spring length (representing countermeasure crush space) as the design variables.

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements.

Idealized mathematical models, also known as lumped parameter models (LPMs), are widely used in analyzing vehicles for ride comfort and driving attributes. However, the limitations of some of these LPMs are sometimes not apparent and a rigorous comparative study of common LPMs is necessary in ascertaining their suitability for various dynamic situations. In the present study, the mathematical descriptions of three common LPMs, viz. quarter, half and full car models, are systematically presented and solved for the appropriate response parameters such as body acceleration, body displacement, and, pitch and roll angles using representative passive suspension system properties. By carrying out a comparison of the three stated LPMs for hump-type road profiles, important quantitative insights, not previously reported in the literature, are generated into their behaviors so that their applications can be judicious and efficient.