Vehicle Dynamic and NVH Laboratory
This research group is focused on sound and vibration problems related to automotive industries. These studies include both analytical and experimental approaches using the latest techniques and equipments. In one study, coir fiber is used as an acoustic material. Acoustic treatment using absorbing materials and system are widely used to reduce reverberation effect in closed spaces and to improve the acoustics performance of an enclosed space like automobile compartment. An innovative semi-active acoustic absorption panel is developed using treated coir fiber as sound absorber materials. The perforated spaces and the air gap behind the panels can be adjusted to give different settings for a wide range of acoustic conditions.
The combination of high quality natural organic fiber and different perforated cross-sectional areas give the panel an optimum absorption characteristic for specific frequency range. Incorporating coir fiber which is a reusable, recyclable, biodegradable building material in the design of the panel can reduce waste, pollution. Utilization of agricultural waste materials enhances economics by booming retrieve industry in one hand and reducing the need for non-degradable synthetic matters on the other hand Gold Medal of the IENA 2007 Competition (Germany) was just one of the achievements.
ASIS stands for (A)utomotive (S)imulator for dr(I)ver behaviour analysi(S). It is a system of two integrated modules that must be installed and run together to achieve the objective of evaluating and training driver for better driving competency. Its special features are tailor made driving environment, automated training and testing for driver, integration with psychology data measurement, Logging and tracking of driving data, automated evaluation of driver and driving competency and behavior. It benefits drivers by familiarizing them with driving environment before introducing the driver to a real car. Therefore it provides a safe training environment for driver. Moreover it can be used to profile driver based on their driving behavior. This is a key feature to identify the suitable drivers for public transport.