Prediction of flow field and mass flow rate in a solar chimney at different heights using ANFIS technique

T. Doan, Minh-Thu T. Huynh, Y. Nguyen
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Abstract

Natural ventilation for buildings using solar chimney is increasingly attracting the attention of many researchers. Many techniques have been introduced to research on solar chimneys such as experimental, analytical, computational methods. Recently, with the development of computer technology, computational method, particularly, Computational Fluid Dynamics (CFD) becomes more common and widely applied in solar chimney, but this method still exists limitation. One of the main limitations is using much computational source. In this study, CFD was combined with Adaptive Neuro-Fuzzy Inference System (ANFIS) to prevail against this limitation when predicting flow field and mass flow rate in a chimney. In particular, the fluid flow and heat transfer in chimney were simulated with CFD to create dataset. Two ANFIS models were built, trained, and validated using dataset from CFD. After the training, ANFIS models can predict flow temperature, velocity and induced mass flow rate, respectively, with R-squared (R2) of 0.97, 0.997 and 0.9996 for training set, while 0.9715, 0.994 and 0.9996 for testing set; similarly, root mean squared error (RMSE) are 0.032, 1.703, 3.45x10−5 for training set, and 0.042, 1.713 and 2.95x10−5 for testing set. It is expected that the combination of CFD and ANFIS can estimate more different scenarios but using less computational time.
利用ANFIS技术预测不同高度太阳能烟囱内的流场和质量流量
利用太阳能烟囱进行建筑自然通风越来越受到研究者的关注。太阳能烟囱的研究采用了实验、分析、计算等多种方法。近年来,随着计算机技术的发展,计算方法,特别是计算流体力学(CFD)在太阳能烟囱中的应用越来越普遍和广泛,但这种方法仍然存在局限性。其中一个主要的限制是使用大量的计算源。在本研究中,CFD与自适应神经模糊推理系统(ANFIS)相结合,在预测烟囱内流场和质量流量时克服了这一局限性。利用CFD对烟囱内的流体流动和换热进行了数值模拟,建立了数据集。利用CFD数据集建立、训练和验证了两个ANFIS模型。经过训练,ANFIS模型可以分别预测流体温度、流速和诱导质量流量,训练集的r平方(R2)分别为0.97、0.997和0.9996,测试集的r平方(R2)分别为0.9715、0.994和0.9996;同样,训练集的均方根误差(RMSE)分别为0.032、1.703、3.45x10−5,测试集的均方根误差分别为0.042、1.713、2.95x10−5。预计CFD和ANFIS的结合可以在使用更少的计算时间的同时估计更多不同的场景。
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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