Instrumentation and Data Filtering Method for Vehicle Underbelly Blast Testing

Mine blast explosion is known to be one of the most lethal threats to an armoured vehicle. With the recent advancement of computing power, many of the researchers have opted-in utilizing the numerical simulations to study the blast effect on military vehicles. Nonetheless, experimental work still required to validate the simulation models. The improvement of the instrumentations such as the sensor and data acquisition system also plays a vital role in achieving reliable results with high amplitude and finer data sampling. However, such instrumentations are expensive and increase the experimental cost. Sensors too have reading limitations and can be used up to a certain charge weight to avoid permanent damage to the sensors. In this paper, the challenges regarding the sensors used in an underbelly vehicle capsule mine blast are presented and the problem faced during the results filtering process is also addressed. The study suggests the use of strain gages to replace the accelerometers used for high explosive experiments as it is more robust and economical. Results filtered using Jackson Knee Method were found suitable to estimate the signal cut-off frequency for mine blast experiments.