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首页> 外文期刊>Heat transfer >A comparison between radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model the free convection in an open round cavity
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A comparison between radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model the free convection in an open round cavity

机译：径向基函数（RBF）与自适应神经模糊推理系统（ANFIS）之间的比较，以模拟开放圆形腔中的自由对流

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摘要

This study demonstrates the capability of radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model and predict the free convection heat transfer in an open round cavity. In fact, the effects of the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (WD), on the free convection in the cavity, are modeled via the RBF and ANFIS models. To start modeling, sufficient data are gathered. Here, data are experimentally generated using a Mach-Zehnder interferometer. In the next step, the RBF and ANFIS models are trained. According to the results, there is an optimum ratio (WD), in which the heat transfer is maximum. This maximum value increases by increasing the Rayleigh number (Ra). Moreover, based on the results obtained by the RBF and ANFIS, the predicted results for the convection heat transfer are in good agreement with similar ones obtained experimentally. The mean relative errors of the training, testing, and checking data for the RBF model were found as 0.1348%, 1.1972%, and 2.4967%, respectively. Moreover, for the ANFIS model, the error values were 0.0731%, 0.9110%, and 1.9144%, which shows that RBF and ANFIS can predict the results precisely.
机译：这项研究证明了径向基函数（RBF）和自适应神经模糊推理系统（ANFIS）能够建模和预测开放式圆腔中自由对流传热的能力。实际上，通过RBF和ANFIS模型可以模拟瑞利数（Ra）和从腔体底部到腔体直径的非导体势垒距离之比（WD）对腔体内自由对流的影响。为了开始建模，需要收集足够的数据。在这里，数据是使用Mach-Zehnder干涉仪实验产生的。下一步，将训练RBF和ANFIS模型。根据结果​​，有一个最佳比率（WD），其中传热最大。通过增加瑞利数（Ra）来增加该最大值。此外，基于RBF和ANFIS的结果，对流换热的预测结果与实验获得的相似结果吻合良好。 RBF模型的训练，测试和检查数据的平均相对误差分别为0.1348％，1.127％和2.4967％。此外，对于ANFIS模型，误差值为0.0731％，0.9110％和1.9144％，这表明RBF和ANFIS可以准确地预测结果。

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