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Trucks can carry heavy load and when applying the brakes during for example a mountain downhill or for an abrupt stop, the brake temperatures can rise significantly. Elevated temperatures in the drum brake region can reduce the braking efficiency or can even cause the brake system to fail, catch fire or even break. It therefore needs to be designed such to be able to transfer the heat out of its system by convection, conduction and/or radiation. All three heat transfer modes play an important role since the drum brakes of trucks are not much exposed to external airflow, a significant difference from disk brakes of passenger cars analyzed in previous studies. This makes it a complex heat transfer problem which is not easy to understand. Numerical methods provide insight by visualization of the different heat transfer modes. Presented is a numerical method that simulates the transient heat transfer of a truck drum brake system cooldown at constant driving speed.

A twist beam rear suspension system is modeled, analyzed and optimized in this paper. An ADAMS model is established based on the REC (Rigid-Elastic Coupling) Theory, which is verified by FEM (Finite Element Method) approach, the effects of the geometric parameters on the twist beam suspension performance are investigated. In order to increase the calculation efficiency and improve the simulation accuracy, a neural network model and NSGA II (Non-dominated Sorting Genetic Algorithm II) are adopted to conduct a multi-objective optimization on a twist beam rear suspension system.

An automotive vehicle should be designed to satisfy the wants of customers. The key is how to convert voices of customers into engineering languages. In other words, transfer the wants of customers into the right technical characteristics of a vehicle. A questionnaire of customer wants for a CUV (Crossover Utility Vehicle) is created and processed. Using QFD (Quality Function Deployment) and modified KANO model, the relative important degree is obtained from the original relative important degree of customer wants surveyed. Since some information gained is uncertain and the questionnaire sample is limited, a gray correlation analysis method is introduced, which calculates the competitive important degree of customer wants, then the final important degree of customer wants is gained by integrating the relative important degree and the competitive important degree.

This research proposes an automatic computer-aided design, analysis, and optimization process of a twist beam rear suspension system. The process combines CAD (Computer-Aided Design), CAE (Computer-Aided Engineering), and optimization technologies into an automation procedure, which includes: structural design, dynamic analysis, vibration analysis, durability analysis, and multidisciplinary optimization. The automation results shown the twist beam rear suspension weight reduced, the durability fatigue life increased, and the K&C (kinematics & compliance) characteristics are improved significantly.

Durability of automotive structures is a primary engineering consideration that is evaluated during a vehicle's design and development. In addition, it is a basic expectation of consumers, who demand ever-increasing levels of quality and dependability. Automakers have developed corporate requirements for vehicle system durability which must be met before a products is delivered to the customer. To provide early predictions of vehicle durability, prior to the construction and testing of prototypes, it is necessary to predict the forces generated in the vehicle structure due to road inputs. This paper describes an application of the “virtual proving ground” approach for vehicle durability load prediction for a vehicle on proving ground road surfaces. Correlation of the results of such a series of simulations will be described, and the modeling and simulation requirements to provide accurate simulations will be presented.