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Py学习  »  机器学习算法

【Applied Energy最新原创论文】用于提升潜热存储罐设计的机器学习和多层感知器增强CFD方法

AEii国际应用能源 • 1 年前 • 110 次点击  

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?casa_token=kDRorceZHIgAAAAA:eXfKIVFZ7DzEVP8tyAPRTZUwnY9Pn4EEHsHsTd5-DQdgTwdHeYCuKq1OWfdYvLFefFUIQq5ABQ

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

移动式固液相变蓄热是解决废热利用过程中存在的废热产生与利用时空不匹配问题的有效方法。但固液相变蓄热罐在重力方向上存在熔化率和温度分布不均匀现象,这严重影响了蓄热罐的蓄热速率和稳定性。构建竖直方向水平半径非均匀的蓄热罐会对蓄热罐熔化率和温度分布均匀性产生影响。因此,本文设计了十种不同上、下半径比例的梯台形蓄热罐。在与实验结果进行比较验证了模型的准确性后,通过所设计的数值模型对十种梯台形蓄热罐的蓄热性能进行了研究。从整体区域、区域间均匀性和整体蓄热性能三个方面对蓄热性能进行评价。结果表明,上半径较大的蓄热罐的完全熔化时间比下半径较大的要短。熔化时间最短为17210 s,比基准蓄热罐缩短了11080 s,约为39.2%。从均匀性的角度进行评价也表明,适当增加上部相变材料的用量有利于改善熔化均匀性。温度和熔化率最均匀的case分别是case 5和case 4,分别改善了12.92%和38.28%。最后,为了节约传统CFD迭代计算消耗的资源,建立了多层感知机模型来预测熔化率和总蓄热量。经过训练,建立了一个预测偏差小于10%的预测模型。

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

Fig. 1. Schematic diagram for one solution to the mismatch of waste heat between plants and users based on mobile thermal energy storage.

Fig. 2. Summary of the methods used in this paper.

Fig. 3. Model display diagram: (a) The heat storage system in mobile latent heat storage, (b) Schematic of the shell-and-tube LHTES, (c) Computational domain of trapezoidal accumulator, (d) Representative mesh.

Fig. 11. Flow chart of operation schematic.

Fig. 12. Complete melting time of ten cases with different distribution ratio.

Fig. 16. The distribution proportion of temperature distribution at 15000 s.

关于Applied Energy

本期小编:胖丁顿;审核人:黄雨佳

《Applied Energy》是世界能源领域著名学术期刊,在全球出版巨头爱思唯尔 (Elsevier) 旗下,1975年创刊,影响因子11.2,CiteScore 20.4,高被引论文ESI全球工程期刊排名第4,谷歌学术全球学术期刊第50,本刊旨在为清洁能源转换技术、能源过程和系统优化、能源效率、智慧能源、环境污染物及温室气体减排、能源与其他学科交叉融合、以及能源可持续发展等领域提供交流分享和合作的平台。开源(Open Access)姊妹新刊《Advances in Applied Energy》现已正式上线。在《Applied Energy》的成功经验基础上,致力于发表应用能源领域顶尖科研成果,并为广大科研人员提供一个快速权威的学术交流和发表平台,欢迎关注!

公众号团队小编招募长期开放,欢迎发送自我简介(含教育背景、研究方向等内容)至wechat@applied-energy.org

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