In this study, rotational experiments are performed on a newly constructed rotating test rig using a wedge-shaped channel test box equipped with conventional circular pin-fin (CP) arrays and optimized guiding pin-fin (OGP) arrays. Numerical simulations are further employed to analyze the internal flow and heat transfer mechanisms. The experimental conditions cover Reynolds numbers ranging from 10,000 to 50,000 and rotation numbers from 0 to 0.3. The results indicate that the newly developed rotating heat transfer test platform satisfactorily meets the design specifications and can effectively simulate the high Reynolds number and high-rotation number conditions representative of actual turbine blade operation conditions in a laboratory environment. Moreover, this work confirms that the surrogate-based optimized guiding pin-fins cooling structure, developed in prior research, also enhances the overall heat transfer performance of the channel under rotating conditions. At a Reynolds number of 10,000 under stationary conditions, the CP and OGP arrays exhibit comparable heat transfer performance. As the rotation number increases, the OGP array demonstrates appreciably improved heat transfer than the CP array. Under other tested conditions, the channel-averaged Nusselt number of the OGP array is 5-8% higher than that of the CP array. The experiments also show that under rotations, the leading surface heat transfer of the CP array remains almost unchanged, whereas the trailing surface experiences a noticeable enhancement by about 14.5%. However, the novel OGP array shows a robust optimization effect with the consistent heat transfer improvement on both the leading and trailing surfaces under rotations. Further computational fluid dynamics (CFD) results reveal that the OGP array achieves a more uniform coolant distribution across the entire internal cooling passage, especially in the channel middle region under stationary conditions. As a result, the large portion area with high wall temperature is markedly reduced in the mid-span and tip regions of the channel by the OGP array, where the CP array exhibits inferior heat transfer performance there.
Turbine cooling design is challenged by the limited high-fidelity experimental data and the high computational cost of Computational Fluid Dynamics (CFD). A multi-source heterogeneous data fusion framework is proposed in the presented work to overcome the challenge based on a weighted Extreme Gradient Boosting (XGBoost) algorithm. The framework establishes a nonlinear mapping from geometric and operational parameters to the three-dimensional temperature field. By introducing adjustable sample weights, the model adaptively fuses sparse reference data with full-field simulation data. It is shown that by using a single data source, approximately 70 samples suffice to capture the dominant patterns within the design space, achieving predictions with a Mean Absolute Percentage Error (MAPE) below 0.5% relative to the numerical baseline in less than 0.5 s per field. Furthermore, it is revealed that sparse reference data can effectively correct systematic CFD biases through sample weight optimization, given a sufficient number of full-field data samples. It has been found that the proposed framework provides an efficient and accurate solution for evaluating turbine guide vane cooling performance rapidly.
Radiation intensity and panel ambient temperature affect solar power generation. Predicting solar panel temperature will enhance the power generation forecasts. This research work involves various data prediction models for estimating the temperature of photovoltaic (PV) modules, depending on ambient conditions like ambient temperature, wind speed, and radiation intensity. A total of 18,778 data samples were used to train and validate the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the temperature of hetero-junction with intrinsic thin-layer photovoltaic modules. Before implementing those algorithms, data preprocessing is performed to prepare the datasets, and the selection of input features is undertaken based on a correlation matrix. Results show the comparison of the coefficient of determination (R-2) score, mean square error, mean absolute error, and mean percentage error. Overall, the results show that ANFIS nonlinear models predict better than ANN models, as R-2 scores of ANN and ANFIS are 0.88 and 0.94, respectively. These two models are the best-fitting models on actual data. Compared to the model, the ANFIS model has a minimum root mean squared error of 3.75, and the model also achieved minimal deviations in efficiency (0.02%) and power (0.33%), performing better than ANN and other comparative models. The research reported in this study is valuable for predicting the temperature and power of photovoltaic modules. It will also be useful for energy management.
Efficient thermal management is crucial in compact electronic systems, where conventional cooling method often fails under high heat-flux conditions. This study numerically investigates the fluid flow and heat-transfer characteristics in a rectangular microchannel featuring a square rib modified with triangular extensions oriented either upward or downward, named as forward triangular rib connected with square rib (FTRS) and backward triangular rib connected with square rib (BTRS). Three-dimensional numerical investigations have been carried out for Reynolds numbers ranging from 4 to 800 to analyze the flow separation, recirculation, wall shear stress, pressure coefficient, heat-transfer coefficient, velocity deficit, and cooling uniformity index. The results reveal that the orientation of the rib has a significant effect on wake dynamics and thermal performance. The FTRS have a 17% higher returning velocity in the wake, resulting in a large wake and longer recirculation zones at Re = 300. Shear stress analysis revealed that, for a higher Re = 300, the FTRS yielded 44.61% reduction in the maximum bottom wall shear stress and 14.14% reduction in the maximum side-wall shear stress, reflecting a weaker velocity gradient near walls. The pressure distribution revealed a maximum pressure coefficient at the trailing edge for BTRS and at the leading edge for the FTRS. The BTRS achieved a 38.12% higher local heat-transfer coefficient at Re = 300, confirming its superior heat-transfer performance. For both cases, the cooling uniformity index increases with increasing Re, with the BTRS consistently providing a slightly higher cooling uniformity index. Since the BTRS has consistently outperformed the FTRS. Hence, the BTRS configuration has been further analyzed for Re > 500. In this regime, distinct transitions have been observed, at Re = 550, side-wall vortices have merged with the main wake. At Re = 600 and 700, the recirculation bubble has shortened while spreading laterally, and at Re = 800, the flow has become unsteady with periodic vortex shedding. Overall, the BTRS delivered enhanced wake stability, improved wall temperature, and superior thermal performance, making it a promising configuration for advanced microchannel cooling systems.
Abstract Efficient battery thermal management is essential for preventing thermal runaway and enhancing electric vehicle performance. Although refrigerant direct cooling technology offers superior heat transfer capabilities, its transient cooling characteristics and system energy efficiency under multi-gear regulation have not been fully characterized. This study combines experiments on a constructed hot water bath platform with numerical simulations to comprehensively investigate a refrigerant direct cooling system. A multi-point validated three-dimensional computational fluid dynamics (CFD) model incorporating the Mixture multiphase phase-change mechanism was established for both direct cooling and liquid cooling systems to evaluate thermal response and flow behavior. Comparison between direct cooling experiments and simulations shows close agreement in temperature curves, with nearly identical final temperatures, confirming the reliability of the simulation model. The results demonstrate that the direct cooling system achieves final temperatures 6.07–8.18 °C lower than those of the liquid cooling system, with the advantage becoming more pronounced at higher gear levels. Crucially, parametric energy efficiency analysis reveals that the system coefficient of performance (COP) varies between 3.24 and 4.35 across different compressor gears, highlighting a distinct trade-off between peak cooling rate and cumulative power consumption. These findings provide a useful quantitative basis and a theoretical reference for improving real-time control strategies in electric vehicle thermal management.
