Research shows that due to enhanced properties IoNanofluids have the potential of being used as heat transfer fluids (HTFs). A significant amount of experimental work has been done to determine the thermophysical and rheological properties of IoNanofluids; however, the number of intelligent models is still limited. In this work, we have experimentally determined the thermal conductivity and viscosity of MXene-doped [MMIM][DMP] ionic liquid. The size of the MXene nanoflakes was determined to be less than 100 nm. The concentration was varied from 0.05 mass% to 0.2 mass%, whereas the temperature varied from 19 °C to 60 °C. The maximum thermal conductivity enhancement of 1.48 was achieved at 0.2 mass% and 30 °C temperature. For viscosity, the maximum relative viscosity of 1.145 was obtained at 0.2 mass% and 23 °C temperature. After the experimental data for thermal conductivity and viscosity were obtained, two multiple linear regression (MLR) models were developed. The MLR models’ performances were found to be poor, which further called for the development of more accurate models. Then two feedforward multilayer perceptron models were developed. The Levenberg–Marquardt algorithm was used to train the models. The optimum models had 4 and 10 neurons for thermal conductivity and viscosity model, respectively. The values of statistical indices showed the models to be well-fit models. Further, relative deviations values were also accessed for training data and testing data, which further showed the models to be well fit.

中文翻译：

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基于 MXene 的水性离子纳米流体的热导率和粘度的 ANN 建模

研究表明，由于增强的特性，离子纳米流体具有用作传热流体 (HTF) 的潜力。已经进行了大量的实验工作来确定 IoNanofluids 的热物理和流变特性；然而，智能模型的数量仍然有限。在这项工作中，我们通过实验确定了 MXene 掺杂的 [MMIM][DMP] 离子液体的热导率和粘度。MXene 纳米薄片的尺寸被确定为小于 100 nm。浓度从 0.05 质量％变化到 0.2 质量％，而温度从 19°C 变化到 60°C。在 0.2 质量％和 30 °C 温度下实现了 1.48 的最大热导率增强。对于粘度，在 0.2 质量％和 23 °C 温度下获得的最大相对粘度为 1.145。在获得热导率和粘度的实验数据后，开发了两个多元线性回归 (MLR) 模型。发现 MLR 模型的性能很差，这进一步要求开发更准确的模型。然后开发了两个前馈多层感知器模型。Levenberg-Marquardt 算法用于训练模型。最佳模型分别具有 4 个和 10 个神经元用于热导率和粘度模型。统计指标的值表明模型是拟合良好的模型。此外，还访问了训练数据和测试数据的相对偏差值，这进一步表明模型拟合良好。发现 MLR 模型的性能很差，这进一步要求开发更准确的模型。然后开发了两个前馈多层感知器模型。Levenberg-Marquardt 算法用于训练模型。最佳模型分别具有 4 个和 10 个神经元用于热导率和粘度模型。统计指标的值表明模型是拟合良好的模型。此外，还访问了训练数据和测试数据的相对偏差值，这进一步表明模型拟合良好。发现 MLR 模型的性能很差，这进一步要求开发更准确的模型。然后开发了两个前馈多层感知器模型。Levenberg-Marquardt 算法用于训练模型。最佳模型分别具有 4 个和 10 个神经元用于热导率和粘度模型。统计指标的值表明模型是拟合良好的模型。此外，还访问了训练数据和测试数据的相对偏差值，这进一步表明模型拟合良好。分别。统计指标的值表明模型是拟合良好的模型。此外，还访问了训练数据和测试数据的相对偏差值，这进一步表明模型拟合良好。分别。统计指标的值表明模型是拟合良好的模型。此外，还访问了训练数据和测试数据的相对偏差值，这进一步表明模型拟合良好。