Heat transfer in material having random thermal conductivity using Monte Carlo simulation and deep neural network

Kumar, Rakesh

doi:10.1007/s41939-024-00388-5

Cited by 3 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This parameter is very much crucial for a thermal process where heat transfer takes place. For example, numerous studies were done based heat transfer coefficient like optimization of finned tube heat exchangers (Alavi and Shirbani 2023), investigation of concentric cylinders (Kumar and Sinha 2024), analysis of various materials having random thermal conductivity (Kumar 2024), CFD analysis of vertical cylinders (Konijeti et al 2023), thermal modelling of power ball grid array (Bocca and Macii 2022), optimization and simulation of latent heat thermal storage systems (Nasehi and Jamekhorshid 2023) etc.…”

mentioning

confidence: 99%

Framework for Accurate Estimation of Freezing Time and Convective Heat Transfer Coefficient for Freezing of Food product in Domestic Refrigerator: A Numerical and Simulation Modeling approach

Ray,

Minz,

Sinha

2024

Preprint

View full text Add to dashboard Cite

Freezing time estimation is essential for quality assurance, process optimization, and innovation in a variety of industries. The total freezing time is greatly influenced by the heat transfer parameters like convective heat transfer coefficient (CHTC), which in turn affects the product's quality characteristics. The correct calculation of CHTC, especially in domestic freezers, remains largely unexplored despite the development of several mathematical models for freezing time prediction. To fill this gap, this research presents a framework-driven analysis of CHTC for estimating the freezing time of frozen sweetened yoghurt in a domestic refrigerator. This research not only compares the performance of several freezing time prediction models, but also seeks to determine the optimal CHTC range for accurate freezing time forecasts, determining the most accurate freezing time prediction model and to replicate the freezing process by simulation accurately for this specific scenario. The results showed great accuracy for the determined CHTC range (RMSE = 27.24, CV = 0.16, MSE = 741.85, MAE = 22.4, MAPE = 12.24 and MBE = 8.14) and lowest average residual (3.95 min) for the fitted prediction for the freezing time calculation of frozen yoghurt. The simulation analysis further backed the results by showcasing marginal temperature difference between the simulated and actual temperature (0.232℃ to 0.684℃) of the frozen yoghurt by using the determined CHTC range. This showed the developed framework’s reliability in freezing time prediction and CHTC range determination, which will eventually help in modeling the freezing process of different food products with high accuracy.

show abstract