Machine Learning-Based Thermo-performance Prediction and Evaluation of Automotive Heat Exchangers

the following nine variables were measured: inlet air temperature T_(Air,in), inlet air humidity H_(Air,in), inlet air mass flow rate m ̇_Air, outlet air temperature T_(Air,out), cooling water inlet temperature T_(Water,in), cooling water outlet temperature T_(Water,out), cooling water mass flow rate m ̇_Water, air-side pressure drop 〖Δp〗_Air, and water-side pressure drop 〖Δp〗_Water——in order to本数据集共测量了以下九项变量：进气空气温度（inlet air temperature）T_(Air,in)、进气空气湿度（inlet air humidity）H_(Air,in)、进气空气质量流量（inlet air mass flow rate）ṁ_Air、出气空气温度（outlet air temperature）T_(Air,out)、冷却水进口温度（cooling water inlet temperature）T_(Water,in)、冷却水出口温度（cooling water outlet temperature）T_(Water,out)、冷却水质量流量（cooling water mass flow rate）ṁ_Water、空气侧压降（air-side pressure drop）Δp_Air以及水侧压降（water-side pressure drop）Δp_Water——以用于