S. Jowkar

Bio: S. Jowkar is an academic researcher from Sharif University of Technology. The author has contributed to research in topics: Heat flux & Heat transfer. The author has an hindex of 3, co-authored 8 publications receiving 29 citations.

Rebounding suppression of droplet impact on hot surfaces: effect of surface temperature and concaveness.

Abstract: When a droplet impinges on a hot surface it is crucial to increase the contact time or decrease the rebounding distance if the heat transfer between the droplet and the surface is important. This will be more sensitive when the temperature regime is above the Leidenfrost values. The focus of the present experimental study is on the maximum height of drop bouncing after impinging on flat and semi-cylindrical concave surfaces, in particular in terms of surface temperature. It is shown that the behavior of the lamella during the spreading to its maximum diameter has a considerable impact on the maximum height of the drop bouncing. For different impact Weber numbers the map of thermal versus inertia effects is extracted for both the flat and concave surfaces for rebound and thermal atomization mid-regimes. It was shown that the thermal atomization mid-regime was eliminated in the case of drop impact on the concave surface in a wide range of impact Weber numbers and surface temperatures. The variations in rebounding and maximum heights at different regions of the maps are quantified and discussed. The morphology of drop impact on the concave surface was captured and the influence of its asymmetric deformation on extensive suppression of drop bouncing was discussed. Finally, the amount of dissipated energy due to drop deformation was obtained based on an energy balance analysis.

The effect of variable temperature and location on relative thermal conductivity (RTC) on the heat pipe in the presence of AL2O3 nanoparticles: Numerical and optimization approaches

Abstract: Background The cooling system is one of the important parts of new devices such as smartphones, servers, and other electrical devices. By employing the almost all heat transfer methods such as conduction, evaporation and condensation, heat pipes are the best choice to increase the heat transfer. The thermal conductivity of heat pipe is much higher than fins because they benefit from condensation and evaporation simultaneously. Methods This study tries to present relative thermal conductivity base on temperature and length of a heat pipe in optimized geometry. To achieve this aim, unsteady, multiphase fluid was considered inside the heat pipe. Evaporation, condensation, and conduction were assumed in the multi-functional simulation domain. Its also a combination of finite volume and differential evolutionary involved in simulation. This approach could improve the heat transfer efficiency and reduce the range of variables. Findings The numerical simulation of the evaporation and condensation indicated that relative thermal conductivity could improve the heat transfer accuracy of prediction Compared with standard methods up to 8%. The result also shows that by increasing the heat flux, relative thermal conductivity can play more efficient than constant thermal conductivity on the variation of Nu.

Numerical study on performance of double-fluid parabolic trough solar collector occupied with hybrid non-Newtonian nanofluids: Investigation of effects of helical absorber tube using deep learning

Abstract: Influences of a helical absorber tube on the thermal performances of a double-fluid parabolic trough solar collector (PTSC) occupied by non-Newtonian nanofluid are investigated numerically. The problem geometry includes a double-fluid parabolic trough solar receiver with high insulation and a non-circular adsorbent tube. The finite volume method, SIMPLE algorithm, and k-ε model were used for numerical solutions. The result showed that the double-fluid solar receiver was equipped with a helical absorber tube filled with nanofluid with a volume fraction of 4% and nanoparticles diameter of 50nm flowing in the absorber tube, and an annulus glass cover is introduced as the optimal model in this paper. For single-flow PTSC, the value of ηmax nanofluid- and water-based PTSCs are 47.5% and 43.1%, respectively, at a flow velocity of Re=5000. In double-flow PTSC, the value of ηmax at 1 and 4 vol.% is 56.5% and 58.2%, respectively, at the flow velocity of Re=5000. Finally, a deep learning method is employed to examine the parameters affecting the efficiency, including the concentration and diameter of nanoparticles and Re number. The results indicate the ANN has acceptable performance in prediction of the efficiency of the double-fluid parabolic trough solar collector occupied by non-Newtonian nanofluid.

An evaluation of the thermal behaviour of a lithium-ion battery pack with a combination of pattern-based artificial neural networks (PBANN) and numerical simulation

Abstract: Thermal management is an important factor in extending the battery's life time and ensuring the quality of the output current. A numerical evaluation of the effect of varying the configuration of the battery cells and liquid-cooled channels was conducted in this case study. For the first time, a physics-informed neural network is coupled with visual tracking and commercial software for prediction of the thermal behaviour of a battery package. Combination of physics-informed neural network and visual tracking present as pattern-based neural networks (PBANNs). This method was used to predict the surface temperature at several cooling rates (Vinlet=0.1, 0.3, and 0.5 m/s) in response to variations in the surface temperature of the battery. Compared with conventional ANN methods, PBANN can significantly reduce the computational cost of transient case studies. Furthermore, PBANN can be directly coupled with commercial software in real-time. The complexity of coding for numerical simulation could be reduced by this coupling algorithm. Based on the results of this coupling, battery configurations may affect temperature profiles. By distributing cooling tubes evenly, the average temperature of the battery and phase change material (PCM) could be reduced by 25.3%. According to the results, the combination of liquid-cooled and PCM could guarantee that the battery temperature would not exceed the limits.