保温管道数值模拟及机器学习预测模型

通过模拟保温管道内流体的热传递过程，分析管道外径和保温层的热导率、密度、比热容、厚度对保温性能的影响规律，并使用机器学习对模拟所得数据进行训练，从而得到不同因素对保温性能的影响比重。结果表明，各参数特征中，管道外径占比39%、热导率占比37%、厚度占比13 %，密度及比热容两者共占比11%，故在影响管道保温性能的各因素中，管道外径、热导率、厚度占主要地位。各参数对保温性能的影响规律不同，多因素共同作用下，难以找到一个统一的函数模型来表达各参数对保温性能的影响规律。基于仿真模拟大量数据，利用机器学习建立预测模型，输入对应的参数即可预测相应的结果，该模型准确率达到99%，可以对实际应用进行指导。

Numerical simulation and machine learning prediction model of thermal insulation pipe

By simulating the heat transfer process of the fluid in the thermal insulation pipe, the influence of the pipe outer diameter and the thermal conductivity, density, specific heat capacity and thickness of the insulation layer on the insulation performance was analyzed, and the simulated data were trained by machine learning, so as to obtain the influence proportion of different factors on the thermal insulation performance. The results show that in all the parameters, the pipe outside diameter accounts for 39%, the thermal conductivity accounts for 37%, the thickness accounts for 13%, and the density and the specific heat capacity both account for 11%. Therefore, among the factors affecting the thermal insulation performance of the pipe, pipe outside diameter, the thermal conductivity and the thickness are the main factors. The influence law of each parameter on the thermal insulation performance is different, and it is difficult to find a unified functional model to express the influence law of each parameter on the thermal insulation performance under the joint action of multiple factors. Based on a large amount of simulation data, a prediction model is established by machine learning, and the corresponding results can be predicted by inputting the corresponding parameters. The accuracy of the model is up to 99%, which can guide the practical application.