基于迁移学习的纳米流体导热系数替代模型
Transfer-learning-based Surrogate Model for Thermal Conductivity of Nanofluids
摘要：
自20世纪90年代以来，纳米流体的传热特性得到了广泛的研究。研究表明，悬浮的纳米颗粒显着改变悬浮液的热性能。纳米流体的热导率是通常发现大于基础流体的热导率的性质之一。发现这种热导率的增加取决于几个参数。目前已经提出了几种理论来模拟纳米流体的热导率，但还没有可靠的通用理论来模拟纳米流体的异常热导率。近年来，监督数据驱动的方法已成功地用于创建跨各种科学学科的代理模型，特别是用于建模难以理解的现象。这些监督学习方法允许模型捕捉高度非线性的现象。在这项工作中，我们利用现有的相关性，并同时使用它们与现有的实验结果，以开发更强大的替代模型预测纳米流体的热导率。人工神经网络使用迁移学习方法进行训练，以预测32种不同颗粒-流体组合（8种颗粒材料和4种流体）的球形颗粒纳米流体的热导率增强。从相关性生成的大量较低精度的数据用于粗调模型参数，并且有限量的更可信的实验数据用于微调模型参数。将基于迁移学习的模型的结果与仅使用拟合优度指标在实验数据上训练的基线模型的结果进行比较。据发现，迁移学习模型表现更好，拟合优度拟合优度值为0.93，而基线模型为0.83。
英文摘要：
Heat transfer characteristics of nanofluids have been extensively studied since the 1990s. Research investigations show that the suspended nanoparticles significantly alter the suspension's thermal properties. The thermal conductivity of nanofluids is one of the properties that is generally found to be greater than that of the base fluid. This increase in thermal conductivity is found to depend on several parameters. Several theories have been proposed to model the thermal conductivities of nanofluids, but there is no reliable universal theory yet to model the anomalous thermal conductivity of nanofluids. In recent years, supervised data-driven methods have been successfully employed to create surrogate models across various scientific disciplines, especially for modeling difficult-to-understand phenomena. These supervised learning methods allow the models to capture highly non-linear phenomena. In this work, we have taken advantage of existing correlations and used them concurrently with available experimental results to develop more robust surrogate models for predicting the thermal conductivity of nanofluids. Artificial neural networks are trained using the transfer learning approach to predict the thermal conductivity enhancement of nanofluids with spherical particles for 32 different particle-fluid combinations (8 particles materials and 4 fluids). The large amount of lower accuracy data generated from correlations is used to coarse-tune the model parameters, and the limited amount of more trustworthy experimental data is used to fine-tune the model parameters. The transfer learning-based models' results are compared with those from baseline models which are trained only on experimental data using a goodness of fit metric. It is found that the transfer learning models perform better with goodness of fit values of 0.93 as opposed to 0.83 from the baseline models.
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