Outdoor power transformers are one of the most pervasive noise sources in power transmission and distribution systems.Accurate prediction of outdoor noise propagation plays a dominant role for the evaluation and contr...Outdoor power transformers are one of the most pervasive noise sources in power transmission and distribution systems.Accurate prediction of outdoor noise propagation plays a dominant role for the evaluation and control of noise relevant to the transformer stations.In this paper surface vibration tests are carried out on a scale model of a single-phase transformer tank wall at different excitation frequencies.The phase and amplitude of test data are found to be randomly distributed when the excitation frequency exceeds the seventh mode frequency,which allows the single-phase power transformer to be simplified as incoherent point sources.An outdoor-coherent model is subsequently developed and incorporated with the image source method to investigate noise propagation from single-phase power transformers,due to the occurrence of multiple reflections and diffractions in the propagation path of each point source.The proposed model is used to calculate the sound field of the power transformer group by exploiting the additional phase information.In comparison with the ISO9613 model and the boundary element method,it is found that the proposed coherent image source method leads to more accurate prediction results,and hence better performance for the prediction of the outdoor noise induced by single-phase power transformers.展开更多
Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics.The conventional prediction methods based on numerical simulation often demand huge computation...Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics.The conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and efficiency.Here,we present a data-driven deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining accuracy.The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions.Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is established.With reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure level.The root-mean-square-errors of prediction are below 0.82 dB in the test dataset,and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation.This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.展开更多
Reducing the radiated noise of a gearbox is a difficult problem in aviation,navigation,machinery,and other fields.Structural improvement is the main means of noise reduction for a gearbox,and it is realized primarily ...Reducing the radiated noise of a gearbox is a difficult problem in aviation,navigation,machinery,and other fields.Structural improvement is the main means of noise reduction for a gearbox,and it is realized primarily through contribution analysis and structure optimization.However,these approaches have certain limitations.In this study,a low-noise design method for a gearbox that combines the two approaches is proposed,and experimental verification is performed.First,a finite element/boundary element model is established using a single-stage herringbone gearbox.Considering the vibration excitation of the gear system,the radiation noise of a single-stage gearbox is predicted based on the modal acoustic transfer vector(MATV)method.Subsequently,the maximum field point of the radiated noise is determined,and the acoustic transfer vector(ATV)analysis and modal acoustic contribution(MAC)analysis are conducted to determine the region that contributes significantly to the radiated noise of the field point.The optimization region is selected through the panel acoustic contribution(PAC)analysis.Next,to reduce the normal speed in the optimization region,topology optimization is performed.According to the topology optimization results,four different noise reduction structures are added to the gearbox,and the low-noise optimization models are established respectively.Finally,by measuring the radiated noise of the gearbox before and after optimization under a given working condition,the validity of the radiated noise prediction method and the low-noise optimization design method are verified by comparing the simulation and experimental data.A comparison of the four optimization models proves that the noise reduction effect can be achieved only by adding a noise reduction structure to the center of the density nephogram.展开更多
Line-Spectrum noise of counter-rotation propellers has constructed the main part of the radiated noise of high speed vehicles in water. The line-spectrum noise of the counter-rotation propellers is due to the interact...Line-Spectrum noise of counter-rotation propellers has constructed the main part of the radiated noise of high speed vehicles in water. The line-spectrum noise of the counter-rotation propellers is due to the interaction between fore or aft propeller and wake of the vehicle,and the interaction between fore and aft propeller. Based on a combination of the lifting surface theory and acoustic method, the prediction of line-spectrum noise is presented in this paper.Theoretical calculation method, characteristics and numerical prediction of the line-spectrum noise are detailed too. The effect of different wake and different distance between fore and aft propeller on the propeller noise is also studied by numerical method. The agreement of predicted results compared with existing experimental data is quite satisfactory.展开更多
基金This work is funded by the Anhui Natural Science Foundation Project of China(under Grant KJ2016A201)the National Natural Science Foundation of China(under Grant 11774378).
文摘Outdoor power transformers are one of the most pervasive noise sources in power transmission and distribution systems.Accurate prediction of outdoor noise propagation plays a dominant role for the evaluation and control of noise relevant to the transformer stations.In this paper surface vibration tests are carried out on a scale model of a single-phase transformer tank wall at different excitation frequencies.The phase and amplitude of test data are found to be randomly distributed when the excitation frequency exceeds the seventh mode frequency,which allows the single-phase power transformer to be simplified as incoherent point sources.An outdoor-coherent model is subsequently developed and incorporated with the image source method to investigate noise propagation from single-phase power transformers,due to the occurrence of multiple reflections and diffractions in the propagation path of each point source.The proposed model is used to calculate the sound field of the power transformer group by exploiting the additional phase information.In comparison with the ISO9613 model and the boundary element method,it is found that the proposed coherent image source method leads to more accurate prediction results,and hence better performance for the prediction of the outdoor noise induced by single-phase power transformers.
基金supported by the National Key Research and Development Program of China(Grant No.2017YFA0303700)the National Natural Science Foundation of China(Grants Nos.12174190,11634006,12074286,and 81127901)the Innovation Special Zone of the National Defense Science and Technology,High-Performance Computing Center of Collaborative Innovation Center of Advanced Microstructures,and the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics.The conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and efficiency.Here,we present a data-driven deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining accuracy.The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions.Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is established.With reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure level.The root-mean-square-errors of prediction are below 0.82 dB in the test dataset,and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation.This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.
基金National Key R&D Program of China(Grant No.2018YFB2001501)Key Program of National Natural Science Foundation of China(Grant No.51535009).
文摘Reducing the radiated noise of a gearbox is a difficult problem in aviation,navigation,machinery,and other fields.Structural improvement is the main means of noise reduction for a gearbox,and it is realized primarily through contribution analysis and structure optimization.However,these approaches have certain limitations.In this study,a low-noise design method for a gearbox that combines the two approaches is proposed,and experimental verification is performed.First,a finite element/boundary element model is established using a single-stage herringbone gearbox.Considering the vibration excitation of the gear system,the radiation noise of a single-stage gearbox is predicted based on the modal acoustic transfer vector(MATV)method.Subsequently,the maximum field point of the radiated noise is determined,and the acoustic transfer vector(ATV)analysis and modal acoustic contribution(MAC)analysis are conducted to determine the region that contributes significantly to the radiated noise of the field point.The optimization region is selected through the panel acoustic contribution(PAC)analysis.Next,to reduce the normal speed in the optimization region,topology optimization is performed.According to the topology optimization results,four different noise reduction structures are added to the gearbox,and the low-noise optimization models are established respectively.Finally,by measuring the radiated noise of the gearbox before and after optimization under a given working condition,the validity of the radiated noise prediction method and the low-noise optimization design method are verified by comparing the simulation and experimental data.A comparison of the four optimization models proves that the noise reduction effect can be achieved only by adding a noise reduction structure to the center of the density nephogram.
文摘Line-Spectrum noise of counter-rotation propellers has constructed the main part of the radiated noise of high speed vehicles in water. The line-spectrum noise of the counter-rotation propellers is due to the interaction between fore or aft propeller and wake of the vehicle,and the interaction between fore and aft propeller. Based on a combination of the lifting surface theory and acoustic method, the prediction of line-spectrum noise is presented in this paper.Theoretical calculation method, characteristics and numerical prediction of the line-spectrum noise are detailed too. The effect of different wake and different distance between fore and aft propeller on the propeller noise is also studied by numerical method. The agreement of predicted results compared with existing experimental data is quite satisfactory.