The growing interest in hybrid nanofluids is due to the synergistic effects of nanoparticles, which could give them better heat transfer properties as compared to base fluids, and conventional nanofluids. Several factors like temperature and volume fraction have been used in explaining the behaviours of hybrid nanofluids, however, the need to investigate comprehensively, the mixing ratios of hybrid nanofluids, remains a critical research area in the development of efficient nanofluids as heat transfer fluids. In this study, three mixture ratios of 1:1:1 (33.3% Al₂O₃, 33.3% ZnO, 33.3% Fe₃O₄), 1:2:1 (25% Al₂O₃, 50% ZnO, 25% Fe₃O₄), and 1:1:2 (25% Al₂O₃, 25% ZnO,50% Fe₃O₄) ternary hybrid nanofluid (THNF) are synthesized at volume concentrations of 0.5%, 0.75%, 1%, and 1.25%. All experiments were carried out at a temperature range between 25 °C-65 °C. The effect of temperature, volume concentration, mixture ratio, are examined, as well as the development of a machine learning model for accurate prediction. The thermal conductivity and dynamic viscosity behaviour of the THNF were investigated. The result showed that temperature and volume concentration significantly affected the thermophysical properties of the fluid. The optimum Thermal Conductivity enhancement (TCE) was retrieved for the 1:1:1 THNF, at 36.018%. The 2:1:1, and 1:2:1 mixture ratios had a 32.92%, and 31.68% TCE respectively. At 1% volume concentration, the optimum TCE (as compared with water) for the mono, hybrid, and THNF measured are 18.98%, 28.58%, and 32.45% respectively. It is seen that the least viscosity was recorded for the 1:1:1 mixture ratio (0.001 Pa.s), while the highest viscosity was measured for the 2:1:1 THNF mixture ratio (0.021 Pa. s). The Gaussian process regression (GPR) gave an excellent prediction showing an R² value of 0.9656, and 0.934 for the thermal conductivity and dynamic viscosity prediction respectively. In terms of application to solar thermal systems, the low viscosity of 1:1:1 Al₂O₃–ZnO-Fe₃O₄ THNF makes that a low-pressure drop and pump work is required in practical applications of the 1:1:1 Al₂O₃–ZnO-Fe₃O₄ THNF.

对混合纳米流体日益增长的兴趣是由于纳米粒子的协同效应，与基础流体和传统纳米流体相比，纳米粒子可以赋予它们更好的传热性能。温度和体积分数等几个因素已被用于解释混合纳米流体的行为，然而，综合研究混合纳米流体的混合比的需要仍然是开发高效纳米流体作为传热流体的关键研究领域。本研究采用1:1:1（33.3% Al ₂ O ₃、33.3% ZnO、33.3% Fe ₃ O ₄）、1:2:1（25% Al ₂ O ₃、50% ZnO、 25% Fe ₃ O ₄), 和 1:1:2 (25% Al ₂ O ₃ , 25% ZnO, 50% Fe ₃ O ₄) 三元混合纳米流体 (THNF) 以 0.5%、0.75%、1% 和 1.25% 的体积浓度合成。所有实验均在 25°C-65°C 之间的温度范围内进行。检查温度、体积浓度、混合比的影响，以及开发用于准确预测的机器学习模型。研究了 THNF 的热导率和动态粘度行为。结果表明，温度和体积浓度显着影响流体的热物理性质。1:1:1 THNF 的最佳热导率增强 (TCE) 为 36.018%。2:1:1 和 1:2:1 混合比分别具有 32.92% 和 31.68% 的 TCE。在 1% 体积浓度下，单、混合和 THNF 测量的最佳 TCE（与水相比）为 18.98%、28.58%、和 32.45%。可以看出，1:1:1 混合比 (0.001 Pa.s) 的粘度最低，而 THNF 混合比 2:1:1 (0.021 Pa.s) 的粘度最高。高斯过程回归 (GPR) 给出了一个很好的预测，显示了 R²的热导率和动态粘度预测值分别为 0.9656 和 0.934。在太阳能热系统的应用方面，1:1:1 Al ₂ O ₃ –ZnO-Fe ₃ O ₄ THNF的低粘度使得1:1的实际应用需要低压降和泵功。 :1 Al ₂ O ₃ –ZnO-Fe ₃ O ₄ THNF。