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Non-iterative estimation of heat transfer coefficients using artificial neural network models

机译：使用人工神经网络模型的热系数非迭代估计

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摘要

The Inverse Heat Conduction Problem (IHCP) dealing with the estimation of the heat transfer coefficient for a solid/ fluid assembly from the knowledge of inside temperature was accomplished using an artificial neural network (ANN). Two cases were considered: (a) a cube with constant thermophysical properties and (b) a semi-infinite plate with temperature dependent thermal conductivity resulting in linear and nonlinear problem, respectively. The Direct Heat Conduction Problems (DHCP) of transient heat conduction in a cube and in a semi-infinite plate with a convective boundary condition were solved. The dimensionless temperature-time history at a known location was then correlated with the corresponding dimensionless heat transfer coefficient/Bio t number using appropriate ANN models. Two different models were developed for each case i.e. for a cube and a semi-infinite plate. In the first one, the ANN model was trained to predict Biot number from the slope of the dimensionless temperature ratio versus Fourier number. In the second, an ANN model was developed to predict the dimensionless heat transfer coefficient from non-dimensional temperature. In addition, the training data sets were transformed using a trigonometric function to improve the prediction performance of the ANN model. The developed models may offer significant advantages when dealing with repetitive estimation of heat transfer coefficient. The proposed approach was tested for transient experiments. A 'parameter estimation' approach was used to obtain Biot number from experimental data.
机译：使用内部神经网络（ANN）完成了逆向导热问题（IHCP），该逆向导热问题（IHCP）用于根据内部温度知识估算固体/流体组件的传热系数。考虑了两种情况：（a）具有恒定热物理性质的立方体，（b）具有与温度相关的导热率的半无限板，分别导致线性和非线性问题。解决了具有对流边界条件的立方体和半无限板中瞬态热传导的直接热传导问题（DHCP）。然后使用适当的ANN模型，将已知位置的无量纲温度-时间历史与相应的无量纲传热系数/ Bio t数相关联。针对每种情况开发了两种不同的模型，即立方体和半无限大的板。在第一个中，训练了ANN模型，以从无量纲温度比对傅立叶数的斜率预测比奥数。第二，建立了一个ANN模型，可以从无量纲温度预测无量纲传热系数。此外，使用三角函数对训练数据集进行了转换，以提高ANN模型的预测性能。当处理传热系数的重复估计时，开发的模型可能会提供明显的优势。所提出的方法已经过瞬态实验测试。 “参数估计”方法用于从实验数据中获得比奥数。

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