【Applied Energy最新原创论文】基于机器学习和多层感知器的潜热储罐CFD优化设计方法 - 拷贝

原文信息：

Machine learning and multilayer perceptron enhanced CFD approach for improving design on latent heat storage tank

原文链接：

https://www.sciencedirect.com/science/article/pii/S030626192300822X

Highlights

• The effect of heat storage tank shape on heat storage efficiency is studied.

•Compared to basic case, the maximum saving ratio of melting time is 39.2%, which is 11080 s shorter than the basic case.

• Multilayer perceptron predicting model is built to predict the melting process.

•K-fold cross validation method is used to improve the accuracy of model prediction.

摘要

固液相变蓄热是解决余热产生与利用不匹配的重要方法，有利于脱碳和节能。然而，固液相变储热中相变材料的熔化和温度往往存在不均匀性。改变储热槽的形状会改变热量传递到相变材料的距离，从而改变储热性能。为此，本文设计了10种不同上下半径比例的梯形油箱。通过所设计的数值模型对10种情况下的蓄热性能进行了研究，并与实验结果进行了比较，验证了数值模型的准确性。从区域整体性、区域间均匀性和整体性等方面提出了评价潜热蓄热性能的指标。除了绘制质量分数、温度和流线等值线外，还提出了温度比和平均速度来定量评价温度分布和对流强度。结果表明，具有较大上半径的情况比具有较大下半径的情况短。最短熔化时间为17210 s，比基本情况下的11080 s缩短了39.2%。从均匀性的角度评价也表明，适当增加PCM的用量有利于改善熔融均匀性。最均匀的温度和质量分数是情况5和情况4，分别为12.92%和38.28%的改善。最后，建立了一个多层感知器（MLP）网络模型以预测熔化分数和总热存储，经过训练，模拟和预测之间的偏差小于10%。

更多关于"biorefinery"的研究请见：

https://www.sciencedirect.com/search?pub=Applied%20Energy&cid=271429&qs=biorefinery

Abstract

Solid-liquid phase change heat storage is an important method to solve the mismatch of the generation and usage of waste heat, which is conducive to decarbonization and energy conservation. However, there always exists inhomogeneity for the melting and temperature of the phase change material （PCM） in solid-liquid phase change heat storage. Changing the shape of the heat storage tank will change the distance of heat transfer to the phase change material, thus changing the heat storage performance.Therefore, this paper designed ten shapes of trapezoidal tank with different proportions of upper and lower radius. And the heat storage performance of ten cases were investigated through the designed numerical model, whose accuracy is convinced through comparison with experimental results. Many indexes are proposed to evaluate the properties of the latent heat thermal energy storage（LHTES）from the view of whole region, uniformity among subregions, and the storage heat performance of integrity. Except for drawing mass fraction, temperature, and streamline contours, the temperature proportion and the average velocity are proposed to quantitatively evaluate the temperature distribution and convection intensity. The results showed that the cases with larger upper radius are shorter than cases with larger lower radius. In addition, the shortest melting time is 17210 s, which is about 39.2% shorter by 11080 s than that of basic case. The evaluation from the view of uniformity also indicated that properly increasing the PCM amount is beneficial for improving the melting uniformity. The most uniform temperature and mass fraction are case 5 and case 4，respectively, with 12.92% and 38.28% improvement. Finally, one multilayer perceptron（MLP）network model is built to predict the melting fraction and total heat storage. After training, one model with a deviation lower than 10% between simulation and prediction is set up.

Keywords

Latent heat storage

Phase change material distribution

Multilayer perceptron prediction

Uniformity

Numerical investigation

Machine learning

Graphics

Graphical abstract

Fig. 1. Summary of the methods used in this paper.

Fig. 3. The average velocity of ten cases at 1000 s, 8000 s and 15000 s.

Fig. 3. Melting rate in five sub-regions under different proportions.

Fig. 4.Melting uniformity: (a) Temperature uniformity, (b) Mass fraction uniformity, (c) Integral average of temperature uniformity, (d) Integral average of mass

fraction uniformity.

Fig. 5. Evaluation of testing results: (a) Comparison of real melting fraction during testing process, (b) Comparison of real heat storage during testing process.

关于Applied Energy

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