• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.292-299
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• 2018

The vehicle exterior design is the main parameter of aerodynamic wind noise, but the modification of it is nearly impossible at a proto-type stage. Therefore, it is very important to verify exterior design and estimate the correct wind noise level at the early vehicle design stages. The numerical simulations of aerodynamic wind noises around A-pillar and wiper were developed for specific vehicle exterior designs, but could not be directly used for the discussions with designers because these need complex modeling and simulation process. This study proposes new approach to A-pillar and wiper wind noise estimation at design stage using response surface methodology of modeFRONTIER, of which database is composed of PowerFLOW simulation, PowerCLAY modeling, SEA-Baced (Statistical Energy Analysis-Based) interior noise simulation, and turbulent acoustic power simulation. New design parameters are defined and their contributions are analyzed. A state-of-the-art, easy and reliable CAT (Computer Aided Test) tool for A-pillar and wiper wind noise are acquired from this study, which shows high usefulness in car development.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.57-66
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper proposes hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme, The validity of the proposed controller is verified by results at different dynamic operating conditions.