The wind tunnel simulations of wind loading on a solid structure of revolution with one smooth and five rough surfaces were conducted using wind tunnel tests. Timemean and fluctuating pressure distributions on the sur...The wind tunnel simulations of wind loading on a solid structure of revolution with one smooth and five rough surfaces were conducted using wind tunnel tests. Timemean and fluctuating pressure distributions on the surface were obtained, and the relationships between the roughness Reynolds number and pressure distributions were analyzed and discussed. The results show that increasing the surface roughness can significantly affect the pressure distribution, and the roughness Reynolds numbers play an important role in the change of flow patterns. The three flow patterns of subcritical, critical and supercritical flows can be classified based on the changing patterns of both the mean and the fluctuating pressure distributions. The present study suggests that the wind tunnel results obtained in the supercritical pattern reflect more closely those of full-scale solid structure of revolution at the designed wind speed.展开更多
This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 ...This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.展开更多
文摘The wind tunnel simulations of wind loading on a solid structure of revolution with one smooth and five rough surfaces were conducted using wind tunnel tests. Timemean and fluctuating pressure distributions on the surface were obtained, and the relationships between the roughness Reynolds number and pressure distributions were analyzed and discussed. The results show that increasing the surface roughness can significantly affect the pressure distribution, and the roughness Reynolds numbers play an important role in the change of flow patterns. The three flow patterns of subcritical, critical and supercritical flows can be classified based on the changing patterns of both the mean and the fluctuating pressure distributions. The present study suggests that the wind tunnel results obtained in the supercritical pattern reflect more closely those of full-scale solid structure of revolution at the designed wind speed.
基金This project was funded by grants from the National Natural Science Foundation of China(No.51778072 and No.51408062)Practice Innovation and Entrepreneurship Enhancement Plan of CSUST(SJCX202021).
文摘This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.
