The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblie...The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.展开更多
ICESat-2(Ice,Cloud,and Land Elevation Satellite-2)激光卫星作为当前最先进的激光测高卫星之一,通过发射532 nm波长的激光,能够有效获取浅海区域的水深数据,极大地推进了浅海测深技术的发展。然而,ICESat-2的原始数据常受到噪声点云...ICESat-2(Ice,Cloud,and Land Elevation Satellite-2)激光卫星作为当前最先进的激光测高卫星之一,通过发射532 nm波长的激光,能够有效获取浅海区域的水深数据,极大地推进了浅海测深技术的发展。然而,ICESat-2的原始数据常受到噪声点云的干扰,给数据的后期处理带来了不小的挑战。为提高数据处理的准确性和效率,本研究针对ICESat-2点云在水平方向上比垂直方向更为密集的特性,开发了一种基于多层感知机(Multilayer Perceptron,MLP)的去噪算法。该算法综合考虑了水平椭圆搜索区域内的点密度、点与点之间的平均距离、最近邻点间的距离(分别为3和5)等特征值,实现对噪声点的有效识别和去除。通过选取澳大利亚某岛礁区域的ICESat-2数据作为训练集,同时使用经过我国西沙群岛玉琢礁和东岛的数据对所提出的去噪模型进行验证。实验结果表明,本研究所提出的去噪方法正确率达到90%以上,显著优于现有的OPTICS去噪算法以及基于置信度的去噪结果。这一成果不仅为ICESat-2数据的噪声去除提供了一种新的解决方案,也为相关领域的研究提供了可靠的数据支持。展开更多
文摘The present study is to develop a new user-defined function using artificial neural networks intent Computational Fluid Dynamics(CFD)simulation for the prediction of water-vapor multiphase flows through fuel assemblies of nuclear reactor.Indeed,the provision of accurate material data especially for water and steam over a wider range of temperatures and pressures is an essential requirement for conducting CFD simulations in nuclear engineering thermal hydraulics.Contrary to the commercial CFD solver ANSYS-CFX,where the industrial standard IAPWS-IF97(International Association for the Properties of Water and Steam-Industrial Formulation 1997)is implemented in the ANSYS-CFX internal material database,the solver ANSYS-FLUENT provides only the possibility to use equation of state(EOS),like ideal gas law,Redlich-Kwong EOS and piecewise polynomial interpolations.For that purpose,new approach is used to implement the thermophysical properties of water and steam for subcooled water in CFD solver ANSYS-FLUENT.The technique is based on artificial neural networks of multi-layer type to accurately predict 10 thermodynamic and transport properties of the density,specific heat,dynamic viscosity,thermal conductivity and speed of sound on saturated liquid and saturated vapor.Temperature is used as single input parameter,the maximum absolute error predicted by the artificial neural networks ANNs,was around 3%.Thus,the numerical investigation under CFD solver ANSYSFLUENT becomes competitive with other CFD codes of which ANSYS-CFX in this area.In fact,the coupling of the Rensselaer Polytechnical Institute(RPI)wall boiling model and the developed Neural-UDF(User Defined Function)was found to be useful in predicting the vapor volume fraction in subcooled boiling flow.
