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As the main actuator in hydraulic systems, motors generate significant noise radiation that increasingly fails to meet low-noise requirements. Due to unresolved issues such as unclear primary noise sources and low localization accuracy, noise reduction in motors remains challenging. Therefore, to identify the main noise sources and improve localization accuracy, this study employed multiple approaches: A fluid simulation model of the motor was established using Pumplinx software to obtain the variation of fluid excitation forces at the motor’s port plate. A cosimulation using ADAMS and AMESim was conducted to acquire the variation of excitation forces caused by pistons impacting the cylinder block during motor operation. Combined with transient finite element analysis, the transient analysis method in ANSYS was used to obtain the vibration displacement response on the surfaces of the motor housing and rear end cover. Using this vibration data from ANSYS as acoustic boundary conditions, a boundary element analysis was performed in LMS Virtual Lab to simulate the motor’s acoustic field, thereby identifying the main noise sources and primary noise generation areas. Subsequently, a motor sound intensity noise test bench was designed to obtain sound intensity variation cloud maps, verifying the accuracy of the multi-physics simulation results. Then, considering the relationships among the observation matrix, sparse representation, and reconstruction algorithm, the regularized orthogonal matching pursuit reconstruction algorithm was adopted to determine the localization areas of the main motor noise. Finally, the feasibility of the optimized reconstruction algorithm in improving localization accuracy was verified with the sound intensity test bench. The results show that the multi-physics field simulation of the motor model is correct, the main noise sources are the pressure impact at the valve plate and piston collisions, the main noise area is distributed around the valve plate, and the new localization accuracy reaches 25 mm, achieving the determination of the main motor noise sources and an improvement in localization accuracy.
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