基于Pope修正的有效黏度假设,张量基神经网络(tensor based neural network,TBNN)构建了从雷诺平均方程湍流模型(RANS)的平均应变率张量和平均旋转率张量到高精度数值解的雷诺应力各向异性张量的映射.将高精度数值解用于TBNN的训练,从而...基于Pope修正的有效黏度假设,张量基神经网络(tensor based neural network,TBNN)构建了从雷诺平均方程湍流模型(RANS)的平均应变率张量和平均旋转率张量到高精度数值解的雷诺应力各向异性张量的映射.将高精度数值解用于TBNN的训练,从而使TBNN根据RANS求解的湍动能、湍流耗散率和速度梯度预测其雷诺应力各向异性张量,并与对应的高精度数值模拟结果以及风洞实验结果对比以评估TBNN的预测能力.本工作将TBNN的预测能力从低速域拓展至高超声速工况,分别对低速槽道流、低速NACA0012翼型以及高超声速平板边界层3种工况进行了小样本的训练并成功预测,并以槽道流训练的TBNN较好地预测了低速平板边界层,验证了模型的泛化能力.对于外推的低速槽道流算例,TBNN预测的结果在y^(+)>5的区域与直接数值模拟(DNS)以及实验的误差均在10%以内,预测结果揭示了TBNN对雷诺应力各向异性张量的良好预测能力;对于翼型的预测效果尽管相较于槽道流略有下降,但近壁关键区域较RANS结果仍有显著提升;对于高超声速平板,TBNN在边界层内展现出了良好的预测能力,在y^(+)>5的区域与DNS的误差同样在10%以内.基于Pope本构关系的TBNN方法在平板的高超声速工况下仍能较准确预测边界层内的雷诺应力各向异性张量,方法在宽速域下的预测能力具有较好的表现,且模型泛化能力亦得到了验证.展开更多
The aim of this work is to evaluate how the building distribution influences the cooling effect of water bodies. Different turbulence models, including the S-A, SKE, RNG, Realizable, Low-KE and RSM model, were evaluat...The aim of this work is to evaluate how the building distribution influences the cooling effect of water bodies. Different turbulence models, including the S-A, SKE, RNG, Realizable, Low-KE and RSM model, were evaluated, and the CFD results were compared with wind tunnel experiment. The effects of the water body were detected by analyzing the water vapor distribution around it. It is found that the RNG model is the most effective model in terms of accuracy and computational economy. Next, the RNG model was used to simulate four waterfront planning cases to predict the wind, thermal and moisture environment in urban areas around urban water bodies. The results indicate that the building distribution, especially the height of the frontal building, has a larger effect on the water vapor dispersion, and indicate that the column-type distribution has a better performance than the enclosed-type distribution.展开更多
The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numer...The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.展开更多
文摘基于Pope修正的有效黏度假设,张量基神经网络(tensor based neural network,TBNN)构建了从雷诺平均方程湍流模型(RANS)的平均应变率张量和平均旋转率张量到高精度数值解的雷诺应力各向异性张量的映射.将高精度数值解用于TBNN的训练,从而使TBNN根据RANS求解的湍动能、湍流耗散率和速度梯度预测其雷诺应力各向异性张量,并与对应的高精度数值模拟结果以及风洞实验结果对比以评估TBNN的预测能力.本工作将TBNN的预测能力从低速域拓展至高超声速工况,分别对低速槽道流、低速NACA0012翼型以及高超声速平板边界层3种工况进行了小样本的训练并成功预测,并以槽道流训练的TBNN较好地预测了低速平板边界层,验证了模型的泛化能力.对于外推的低速槽道流算例,TBNN预测的结果在y^(+)>5的区域与直接数值模拟(DNS)以及实验的误差均在10%以内,预测结果揭示了TBNN对雷诺应力各向异性张量的良好预测能力;对于翼型的预测效果尽管相较于槽道流略有下降,但近壁关键区域较RANS结果仍有显著提升;对于高超声速平板,TBNN在边界层内展现出了良好的预测能力,在y^(+)>5的区域与DNS的误差同样在10%以内.基于Pope本构关系的TBNN方法在平板的高超声速工况下仍能较准确预测边界层内的雷诺应力各向异性张量,方法在宽速域下的预测能力具有较好的表现,且模型泛化能力亦得到了验证.
基金Project(51438005)supported by the National Natural Science Foundation of China
文摘The aim of this work is to evaluate how the building distribution influences the cooling effect of water bodies. Different turbulence models, including the S-A, SKE, RNG, Realizable, Low-KE and RSM model, were evaluated, and the CFD results were compared with wind tunnel experiment. The effects of the water body were detected by analyzing the water vapor distribution around it. It is found that the RNG model is the most effective model in terms of accuracy and computational economy. Next, the RNG model was used to simulate four waterfront planning cases to predict the wind, thermal and moisture environment in urban areas around urban water bodies. The results indicate that the building distribution, especially the height of the frontal building, has a larger effect on the water vapor dispersion, and indicate that the column-type distribution has a better performance than the enclosed-type distribution.
文摘The main purpose of this research is the second-order modeling of flow and turbulent heat flux in nonpremixed methane-air combustion.A turbulent stream of non-premixed combustion in a stoichiometric condition,is numerically analyzed through the Reynolds averaged Navier-Stokes(RANS) equations.For modeling radiation and combustion,the discrete ordinates(DO) and eddy dissipation concept model have been applied.The Reynolds stress transport model(RSM) also was used for turbulence modeling.For THF in the energy equation,the GGDH model and high order algebraic model of HOGGDH with simple eddy diffusivity model have been applied.Comparing the numerical results of the SED model(with the turbulent Prandtl 0.85) and the second-order heat flux models with available experimental data follows that applying the second-order models significantly led to the modification of predicting temperature distribution and species mass fraction distribution in the combustion chamber.Calculation of turbulent Prandtl number in the combustion chamber shows that the assumption of Pr_(t) of 0.85 is far from reality and Pr_(t) in different areas varies from 0.4 to 1.2.
