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Computational cost plays a major role in the performance of scientific and engineering simulation. This in turn makes the virtual validation process complex and time consuming. In the simulation process, achievement of appropriate level of accurate models as close as physical testing is the root for increase in the computational cost. During preliminary phase of product development, it is difficult to identify the appropriate size, shape and other parameters of the component and they will undergo several modifications in concept and other stages. An approximation model called metamodel or surrogate model has developed for reducing these effects and minimizing the computational cost. Metamodel can be used in the place of actual simulation models. Metamodel can be an algorithm or a mathematical relation representing the relations between input and output parameters.

In current scenario, there is an increasing need to have faster product development and achieve the optimum design quickly. In an automobile air conditioning system, the main function of HVAC third row floor duct is to get the sufficient airflow from the rear heating ventilating and air-conditioning (HVAC) system and to provide the sufficient airflow within the leg locations of passenger. Apart from airflow and temperature, fatigue strength of the duct is one of the important factors that need to be considered while designing and optimizing the duct. The challenging task is to package the duct below the carpet within the constrained space and the duct should withstand the load applied by the passenger leg and the luggage. Finite element analysis (FEA) has been used extensively to validate the stress and deformation of the duct under different loading conditions applied over the duct system.

The main function of mobile air conditioning system in a vehicle is to provide the thermal comfort to the occupants sitting inside the vehicle at all environmental conditions. The function of ducts is to get the sufficient airflow from the HVAC system and distribute the airflow evenly throughout the cabin. In this paper, the focus is to optimize the rear passenger floor duct system to meet the target requirements through design for six sigma (DFSS) methodology. Computational fluid dynamics analysis (CFD) has been used extensively to optimize system performance and shorten the product development time. In this methodology, a parametric modeling of floor duct design using the factors such as crossectional area, duct length, insulation type, insulation thickness and thickness of duct were created using CATIA. L12 orthogonal design array matrix has been created and the 3D CFD analysis has been carried out individually to check the velocity and temperature.

In recent years, there is increasing demand for every CAE engineer on their confidence level of the virtual simulation results due to the upfront robust design requirement during early stage of an automotive product development. Apart from vehicle feel factor NVH characteristics, there are certain vibration target requirements at system or component level which need to be addressed during design stage itself in order to achieve the desired functioning during vehicle operating conditions. Vehicle passive safety system is one which primarily consists of acceleration sensors, control module and air-bag deployment system. Control module’s decision is based on accelerometer sensor signals so that its mounting locations should meet the sufficient inertance or dynamic stiffness performance in order to avoid distortion in signals due to its structural resonances.

Modern day customer awareness on noise and comfort is extremely increasing, which demands OEM manufacturers to focus on NVH attributes and to meet environmental legislative requirements. Noise generation mechanism in Air Intake System (AIS) is one of the major sources for vehicle interior noise and it occurs mainly because of air column oscillation by sharp pressure pulsation from opening and closing of valves in engine cylinders. Air intake system designer has immense challenges to attenuate intake noise during design stage, in order to meet the vehicle interior noise requirements by using multiple resonators to tune the desired broad band frequencies and to choose the optimum number of resonators. The placement of resonator on both the clean duct and dirty sides is also a key challenge for better noise reduction from air intake system.

The thermal comfort for the passenger inside the cabin is maintained by the HVAC system. To ensure a comfort for the 2nd row passengers in the cabin, it is very essential to design an efficient HVAC and rear console duct system which can deliver sufficient airflow with less pressure drop. The primary focus of the study is to assess existing airflow of the center console duct using CFD and propose improvement in its duct shape to meet the passenger comfort sitting in the rear seat. In this study, the vehicle cabin model, HVAC system and duct design was modeled using the design software UG. To analyze and estimate the behavior of the air flow of the system, a steady state simulation was performed using STAR CCM CFD software. The performance of the console duct system is judged by parameters like distribution of airflow, velocity at console duct outlet, pressure drop through the duct and the uniformity of the air flow at the passenger locations.

Current generation passenger vehicles are built with several electronic sensors and modules which are required for the functioning of passive safety systems. These sensors and modules are mounted on the vehicle body at locations chosen to meet safety functionality requirements. They are mounted on pillars or even directly on panels based on specific packaging requirements. The body panel or pillar poses local structural resonances and its dynamic behavior can directly affect the functioning of these sensors and modules. Hence a specific inertance performance level at the mounting locations is required for the proper functioning of those sensors and modules. Drive point modal frequency response function (FRF) analysis, at full vehicle model for the frequency range up to 1000 Hz, is performed using finite element method (FEM) and verified against the target level along with test correlation.