• Energy Research

In the present scenario, the development of efficient lithium-ion energy storage system-based electric vehicles has been turned into the focus as an effective alternative to conventional transportation systems. However, correspondingly associated potential risks of thermal failure confirm the need for an effective thermal management system to mitigate the adverse effects of excessive heat generated during operation. This work takes an opportunity to present a comparative performance assessment of response surface method (RSM) and artificial neural networks (ANN)-based predictive models to analyze the thermal behavior of oscillating heat pipes (OHP) filled with binary working fluids (acetone-DI water, acetone-methanol, and acetone-ethanol) with mixing ratio (1:1 ≤ MR ≤ 2:1) and volumetric filling ratio (30%≤FR ≤ 70%) as independent variables at different battery discharge rates (1C ≤ DR ≤ 2C). The thermal performance is predicted in terms of maximum cell temperature (MCT), average cell temperature (ACT), and thermal uniformity (TD). The acetone-DI water binary mixture was identified as the optimal working fluid among the fluids evaluated. The ACT, MCT, and TD were determined to be 38.3°C, 40°C, and 3.4°C, respectively, under a filling ratio (FR) of 70%, mixing ratio (MR) of 2:1, and a discharge rate (DR) of 2C. The prediction model performance is measured using correlation coefficient ( R2) value and average absolute prediction error (APE) to acknowledge the best fit. The results confirm the applicability of both the prediction models with the ANN model ( R2-values ≥ 0.99998; average APE ≥ 0.0162%) slightly more accurate relative to the corresponding RSM approach ( R2-values ≥ 0.9814; average APE ≥ 0.2342%).

In the present scenario, the development of efficient lithium-ion energy storage system-based electric vehicles has been turned into the focus as an effective alternative to conventional transportation systems. However, correspondingly associated potential risks of thermal failure confirm the need for an effective thermal management system to mitigate the adverse effects of excessive heat generated during operation. This work takes an opportunity to present a comparative performance assessment of response surface method (RSM) and artificial neural networks (ANN)-based predictive models to analyze the thermal behavior of oscillating heat pipes (OHP) filled with binary working fluids (acetone-DI water, acetone-methanol, and acetone-ethanol) with mixing ratio (1:1 ≤ MR ≤ 2:1) and volumetric filling ratio (30%≤FR ≤ 70%) as independent variables at different battery discharge rates (1C ≤ DR ≤ 2C). The thermal performance is predicted in terms of maximum cell temperature (MCT), average cell temperature (ACT), and thermal uniformity (TD). The acetone-DI water binary mixture was identified as the optimal working fluid among the fluids evaluated. The ACT, MCT, and TD were determined to be 38.3°C, 40°C, and 3.4°C, respectively, under a filling ratio (FR) of 70%, mixing ratio (MR) of 2:1, and a discharge rate (DR) of 2C. The prediction model performance is measured using correlation coefficient ( R2) value and average absolute prediction error (APE) to acknowledge the best fit. The results confirm the applicability of both the prediction models with the ANN model ( R2-values ≥ 0.99998; average APE ≥ 0.0162%) slightly more accurate relative to the corresponding RSM approach ( R2-values ≥ 0.9814; average APE ≥ 0.2342%).

Solar air heaters (SAHs) are widely accepted and used for drying and heating purposes. Numerous methodologies are available for augmentation of the performance of a SAH but the application of artificial roughness underside the absorber plate is reported to bean efficient one. This study aims to present the effects of using hybrid artificial roughness combining the transverse ribs along with discrete inclined ribs, on the thermo-hydraulic performance of roughened SAH. The response surface methodology (RSM) is utilized for the design of experiments to investigate the influence of relative roughness height (0.018 ≤ e/ D ≤ 0.036), relative roughness pitch (5 ≤ p/ e ≤ 15), attack angle (45° ≤ α ≤ 75°), and Reynolds number (1,900 ≤ Re ≤ 20,300) as input parameters. The empirical equations are developed for Nusselt number, friction factor, and thermo-hydraulic performance parameter based on the regression analysis. An integrated analytical hierarchy process-based weighted aggregated sum product assessment (AHP-WASPAS) method is used to evaluate an optimal parametric combination ( e/ D = 0.036, p/ e = 15, α = 75°, and Re = 20,300). The results are supported by the corresponding experimental outcomes. Also, the sensitivity analysis and rank reversal tests performed as qualitative assessment procedures for the results obtained from the proposed strategy. The proposed decision framework is reported to be very sensitive to several criteria weight fluctuations with maximum and least deviations of 15.41% and 2.56% for δWjp = +50% and δWjp = −10%, respectively. Further, the obtained results confirm the robustness of aopted decisional scheme with just one rank reversal.

Solar air heaters (SAHs) are widely accepted and used for drying and heating purposes. Numerous methodologies are available for augmentation of the performance of a SAH but the application of artificial roughness underside the absorber plate is reported to bean efficient one. This study aims to present the effects of using hybrid artificial roughness combining the transverse ribs along with discrete inclined ribs, on the thermo-hydraulic performance of roughened SAH. The response surface methodology (RSM) is utilized for the design of experiments to investigate the influence of relative roughness height (0.018 ≤ e/ D ≤ 0.036), relative roughness pitch (5 ≤ p/ e ≤ 15), attack angle (45° ≤ α ≤ 75°), and Reynolds number (1,900 ≤ Re ≤ 20,300) as input parameters. The empirical equations are developed for Nusselt number, friction factor, and thermo-hydraulic performance parameter based on the regression analysis. An integrated analytical hierarchy process-based weighted aggregated sum product assessment (AHP-WASPAS) method is used to evaluate an optimal parametric combination ( e/ D = 0.036, p/ e = 15, α = 75°, and Re = 20,300). The results are supported by the corresponding experimental outcomes. Also, the sensitivity analysis and rank reversal tests performed as qualitative assessment procedures for the results obtained from the proposed strategy. The proposed decision framework is reported to be very sensitive to several criteria weight fluctuations with maximum and least deviations of 15.41% and 2.56% for δWjp = +50% and δWjp = −10%, respectively. Further, the obtained results confirm the robustness of aopted decisional scheme with just one rank reversal.