用于预测不同外部热通量条件下多孔介质传热的约束-纳入式深度学习模型

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-09-16 DOI:10.1016/j.egyai.2024.100425
Ziling Guo, Hui Wang, Huangyi Zhu, Zhiguo Qu
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引用次数: 0

摘要

多孔介质内部的温度场受不同边界条件的影响很大,有效热导率随空间结构形态的变化而变化。目前,传统的温度场预测方法成本高、耗时长,尤其是对于大型结构和尺寸,而深度学习代用模型存在与恒定边界条件和二维输入切片相关的局限性,缺乏三维拓扑和空间相关性。本文提出了一种以 U-Net 架构为骨干的约束融入模型,用于预测球状堆积多孔介质的温度场和有效热导率,并考虑了不同的外部热通量。通过晶格玻尔兹曼法(LBM)模拟共生成了 510 个原始温度场样本,并使用自放大法进一步增加到 33,150 个样本进行训练。通过在损失函数中添加物理约束项和自适应权重,将物理先验知识纳入模型,以限制训练方向。不同热通量和孔隙率的输入向量被嵌入潜特征中,用于预测不同的边界条件。结果表明,与测试集中的 LBM 结果相比,包含约束条件的模型的平均相对误差在 1.1 % 到 5.7 % 之间。该模型对数据库规模的依赖性较弱,并大大减少了计算时间,最大加速比为 7.14 × 106。本研究提出了一种带有物理约束的深度学习模型,用于预测多孔介质中的热传导,减轻了大量实验和模拟的负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Constraint-incorporated deep learning model for predicting heat transfer in porous media under diverse external heat fluxes

The temperature field within porous media is considerably affected by different boundary conditions, and effective thermal conductivity varies with spatial structure morphologies. At present, traditional prediction methods for the temperature field are expensive and time consuming, particularly for large structures and dimensions, whereas deep learning surrogate models have limitations related to constant boundary conditions and two-dimensional input slices, lacking the three-dimensional topology and spatial correlations. Herein, a constraint-incorporated model using U-Net architecture as the backbone is proposed to predict the temperature field and effective thermal conductivity of sphere-packed porous media, considering diverse external heat fluxes. A total of 510 original samples of temperature fields are generated through lattice Boltzmann method (LBM) simulations, which are further augmented to 33,150 samples using the self-amplification method for the training. Physical prior knowledge is incorporated into the model to constrain the training direction by adding physical constraint terms as well as adaptive weights to the loss function. Input vectors with different heat fluxes and porosities are embedded into latent features for predicting different boundary conditions. Results indicate that the constraint-incorporated model has a mean relative error ranging between 1.1 % and 5.7 % compared with the LBM results in the testing set. It exhibits weak dependence on the database size and substantially reduces computational time, with a maximum speedup ratio of 7.14 × 106. This study presents a deep learning model with physical constraints for predicting heat conduction in porous media, alleviating the burden of extensive experiments and simulations.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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