The thermal management of cylindrical battery packs, widely used in electric vehicles and energy storage systems, is a critical aspect of ensuring their safety, performance, and longevity. As energy densities increase, effective cooling solutions become essential to address the challenges posed by excessive heat generation and uneven temperature distribution. This review has highlighted the promising potential of hybrid nanofluids and phase change materials (PCMs) in advancing thermal management systems for battery packs. Hybrid nanofluids, offering enhanced heat transfer properties, and PCMs, capable of storing and dissipating latent heat, represent a promising synergy for improving thermal management systems. This review provides a comprehensive analysis of the role of hybrid nanofluids and PCM in addressing the thermal challenges of cylindrical battery packs. The paper discusses heat generation mechanisms, the drawbacks of existing cooling methods, and the advantages of integrating these advanced materials into thermal management systems. By identifying research gaps and opportunities, this review offers a pathway for optimizing battery performance and highlights future research directions necessary for scalable and sustainable solutions. According to this review, future research should concentrate on creating hybrid cooling systems that effectively combine active, passive, and hybrid cooling techniques. Additional advancements in computer modeling, nanotechnology, and material science will be crucial to achieving the full potential of these innovative materials and overcoming existing limitations.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.