In this study, artificial neural networks (ANNs) are constructed and validated by using the bearing data generated numerically from a thermohydrodynamic (THD) lubrication model. In many tribological simulations, a surrogate model (meta-model) for obtaining a fast solution with sufficient accuracy is highly desired.,The THD model is represented by two coupled partial differential equations, a simplified generalized Reynolds equation, considering the viscosity variation across the film thickness direction and a transient energy equation for the 3-D film temperature distribution. The ANNs tested are having a single- or dual-hidden-layer with two inputs and one output. The root-mean-square error and maximum/minimum absolute errors of validation points, when comparing with the THD solutions, were used to evaluate the prediction accuracy of the ANNs.,It is demonstrated that a properly constructed ANN surrogate model can predict the THD lubrication performance almost instantly with accuracy adequately retained.,This study extends the use of ANNs to the applications other than the analyses dealing with experimental data. A similar procedure can be used to build a surrogate model for computationally intensive tribological models to have fast results. One of such applications is conducting extensive optimum design of tribological components or systems.,The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2020-0109/

中文翻译：

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用于热流体动力润滑分析的人工神经网络评估

在这项研究中，人工神经网络 (ANN) 是通过使用从热流体力学 (THD) 润滑模型中以数值方式生成的轴承数据构建和验证的。在许多摩擦学模拟中，非常需要用于获得具有足够精度的快速解决方案的替代模型（元模型）。THD 模型由两个耦合偏微分方程表示，这是一个简化的广义雷诺方程，考虑了整个过程中的粘度变化3-D 薄膜温度分布的薄膜厚度方向和瞬态能量方程。测试的人工神经网络具有单或双隐藏层，具有两个输入和一个输出。验证点的均方根误差和最大/最小绝对误差与 THD 解决方案相比，用于评估 ANN 的预测精度。结果表明，正确构建的 ANN 替代模型几乎可以立即预测 THD 润滑性能，并充分保留准确度。本研究将 ANN 的使用扩展到处理实验数据的分析以外的应用。类似的过程可用于构建计算密集型摩擦学模型的替代模型，以获得快速的结果。其中一项应用是对摩擦学组件或系统进行广泛的优化设计。本文的同行评审历史可在以下网址获得：https://publons.com/publon/10.1108/ILT-03-2020-0109/ 本研究将 ANN 的使用扩展到处理实验数据的分析以外的应用。类似的过程可用于构建计算密集型摩擦学模型的替代模型，以获得快速的结果。其中一项应用是对摩擦学组件或系统进行广泛的优化设计。本文的同行评审历史可在以下网址获得：https://publons.com/publon/10.1108/ILT-03-2020-0109/ 本研究将人工神经网络的使用扩展到处理实验数据的分析以外的应用。类似的过程可用于构建计算密集型摩擦学模型的替代模型，以获得快速的结果。其中一项应用是对摩擦学组件或系统进行广泛的优化设计。本文的同行评审历史可在以下网址获得：https://publons.com/publon/10.1108/ILT-03-2020-0109/