In the present work, computational simulations was made using ANSYS CFX to predict the improvements in film cooling performance with dual trench. Dual-trench confguradon consists of two trenches together, one wider tr...In the present work, computational simulations was made using ANSYS CFX to predict the improvements in film cooling performance with dual trench. Dual-trench confguradon consists of two trenches together, one wider trench and the other is narrow trench that extruded from the wider one. Several blowing ratios in the range （0.5：5） were investigated. The pitch-to-diameter ratio of 2.775 is used. By using the dual trench configuration, the coolant jet impacted the trench wall two times allowing increasing the spreading of coolant laterally in the trench, reducing jet velocity and jet completely covered on the surface. The results indicate that this configuration increased adiabatic effectiveness as blowing ratio increased. The spatially averaged adiabatic effectiveness reached 57.6% for at M= 2. No observed film blow-off at all blowing ratios. The adiabatic film effectiveness of dual trench case outperformed the narrow trench case, laidback fan-shaped hole, fan-shaped hole and cylinder hole at different blowing ratios.展开更多
Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regres...Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. Residual centering (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpretation of moderator effects. I propose and validate residual centering in sequential re-estimations of a moderated regression—sequential residual centering (SRC)—by revealing unbiased multicollinearity conditioning across the interaction and its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used to derive a lower standard error of its regression coefficient. A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.展开更多
基金Supprted by Harbin Engineering University Scholarship under Grant No. 20100903D01
文摘In the present work, computational simulations was made using ANSYS CFX to predict the improvements in film cooling performance with dual trench. Dual-trench confguradon consists of two trenches together, one wider trench and the other is narrow trench that extruded from the wider one. Several blowing ratios in the range （0.5：5） were investigated. The pitch-to-diameter ratio of 2.775 is used. By using the dual trench configuration, the coolant jet impacted the trench wall two times allowing increasing the spreading of coolant laterally in the trench, reducing jet velocity and jet completely covered on the surface. The results indicate that this configuration increased adiabatic effectiveness as blowing ratio increased. The spatially averaged adiabatic effectiveness reached 57.6% for at M= 2. No observed film blow-off at all blowing ratios. The adiabatic film effectiveness of dual trench case outperformed the narrow trench case, laidback fan-shaped hole, fan-shaped hole and cylinder hole at different blowing ratios.
文摘Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. Residual centering (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpretation of moderator effects. I propose and validate residual centering in sequential re-estimations of a moderated regression—sequential residual centering (SRC)—by revealing unbiased multicollinearity conditioning across the interaction and its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used to derive a lower standard error of its regression coefficient. A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.
