The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously.The process variables(Mach number(M),nozzle pressure ratio(η),a...The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously.The process variables(Mach number(M),nozzle pressure ratio(η),area ratio(α),and length to diameter ratio(γ))were numerically explored to address several aspects of this process,namely base pressure(β)and base pressure with cavity(βcav).In this work,the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy,thereby regulating the base drag required for smooth flow of aerodynamic vehicles.Based on the identical dataset,the GA-BP and PSO-BP algorithms are also compared to thePCA-BAS-ENNalgorithm.The data for training and testing the algorithmswas derived using the regression equation developed using the Box-Behnken Design(BBD).The results show that the PCA-BAS-ENN model delivered highly accurate predictions when compared to the other two models.As a result,the advantages of these results are two-fold,providing:(i)a detailed examination of the efficiency of different neural network algorithms in dealing with a genuine aerodynamic problem,and(ii)helpful insights for regulating process variables to improve technological,operational,and financial factors,simultaneously.展开更多
Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let...Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let decompose. According to the feature of transient fault, the author proposes for the first time the automatic determination technology of the threshold by us e of the adaptive filter characteristic of wavelet transform. Based on profound researches on steady fault feature, this dissertation makes an effective token o f steady fault feature by using wavelet energy method, and proposes the new idea to identify cut-in case and cut-out case, thereby successfully gives an uniqu e description quantitatively on the characterization of the variation of fault a nd cutting condition in the monitoring system.展开更多
基金This research is supported by the Structures and Materials(S&M)Research Lab of Prince Sultan University.
文摘The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously.The process variables(Mach number(M),nozzle pressure ratio(η),area ratio(α),and length to diameter ratio(γ))were numerically explored to address several aspects of this process,namely base pressure(β)and base pressure with cavity(βcav).In this work,the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy,thereby regulating the base drag required for smooth flow of aerodynamic vehicles.Based on the identical dataset,the GA-BP and PSO-BP algorithms are also compared to thePCA-BAS-ENNalgorithm.The data for training and testing the algorithmswas derived using the regression equation developed using the Box-Behnken Design(BBD).The results show that the PCA-BAS-ENN model delivered highly accurate predictions when compared to the other two models.As a result,the advantages of these results are two-fold,providing:(i)a detailed examination of the efficiency of different neural network algorithms in dealing with a genuine aerodynamic problem,and(ii)helpful insights for regulating process variables to improve technological,operational,and financial factors,simultaneously.
文摘Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let decompose. According to the feature of transient fault, the author proposes for the first time the automatic determination technology of the threshold by us e of the adaptive filter characteristic of wavelet transform. Based on profound researches on steady fault feature, this dissertation makes an effective token o f steady fault feature by using wavelet energy method, and proposes the new idea to identify cut-in case and cut-out case, thereby successfully gives an uniqu e description quantitatively on the characterization of the variation of fault a nd cutting condition in the monitoring system.
