One-dimensional finite-difference modeling on temperature history and freezing time of individual food

Wang, Zhengfu; Wu, Han; Zhao, Guanghua; Liao, Xiaojun; Chen, Fang; Wu, Jihong; Hu, Xiaosong

doi:10.1016/j.jfoodeng.2006.02.012

Cited by 29 publications

(17 citation statements)

References 26 publications

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“…Spatial and time variations of the local convective heat transfer coefficients caused errors in the range of 5-15% compared to analytical models and with respect to the experimental data [50][51][52]. In industrial practice a simplified fast predictive method is preferred and the development of simple and available software is desired [53]. Wang and Sun [54,55] studied the two-and three-dimensional transient cooling processes of roasted and cooked meat using the FEM with variations in the food physical properties.…”

Section: Nonconjugated Casesmentioning

confidence: 99%

“…The numerical technique used was the FDM with a Crank-Nicolson scheme and results were compared with those obtained by analytical solutions as well as with experimental data. Wang et al [53] carried out a study on the unsteady one-dimensional freezing in spherical and cylindrical foods. The FDM with the CrankNicolson scheme was used in the numerical simulation.…”

Section: Nonconjugated Casesmentioning

confidence: 99%

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Computational Simulation and Developments Applied to Food Thermal Processing

2011

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New challenges to improve food processing have created an incentive in the potential use of computeraided engineering for simulating thermal processes in foods as a viable technique to provide effective and efficient design solutions. Mathematical conjugated and nonconjugated models used along suitable numerical methods such as finite differences, finite elements, and finite volumes in predicting food freezing, dehydration, and sterilization are discussed in this review. The application of computational simulation should be used in combination with conventional techniques such as physical experiments and analytical solutions in order to enhance the knowledge of fluid mechanics, heat and mass transfer in foods.

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Section: Nonconjugated Casesmentioning

confidence: 99%

Section: Nonconjugated Casesmentioning

confidence: 99%

Computational Simulation and Developments Applied to Food Thermal Processing

2011

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“…Numerical approaches have also been adopted by several researchers using finite element methods [18][19][20][21][22][23] as well as finite difference methods [24][25][26][27][28][29][30][31][32][33] for solving the transient heat transfer equation in order to generate cooling curves with reasonable accuracy. However, while combining the generated cooling curves with phase transformation kinetics for the prediction of evolved microstructure or determination of hardenability is concerned, finite element approaches have been used extensively; whereas, finite difference-based approaches are rarely reported in literature.…”

Section: Introductionmentioning

confidence: 99%

A Pure Implicit Finite Difference-Based Modeling Approach for Prediction of the Hardenability of Eutectoid Steel

Singh

Mondal

Mishra

et al. 2014

Metallogr. Microstruct. Anal.

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In this work, an independent pure implicit finite difference-based modeling approach has been adopted for the determination of the hardenability of eutectoid steel. In this model, cooling curves were generated by solving transient heat transfer equations through discretization with pure implicit finite difference scheme in view of constant effective thermophysical properties of AISI-1080 steel. The cooling curves were solved against the 50% transformation nose of the time-temperature-transformation diagram in order to predict hardening behavior of AISI-1080 steel in terms of hardenability parameters (Grossmann critical diameter, D C ; and ideal critical diameter, D I ) and the variation of the ratio of the unhardened core diameter (D u ) to diameter of steel bar (D). Furthermore, a relationship is established between the Grossmann critical diameter and the heat transfer coefficient. The hardenability predicted by the developed model was found to match reasonably with that obtained through the chemical composition method. Therefore, the model developed in the present work can be used for direct determination of D C , D I , and D u without resorting to any rigorous experimentation.

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“…The results obtained indicate its effectiveness to solve the phase change problem with high accuracy. Wang et al (2007) predicted the freezing time and temperature history of foodstuffs with diverse physical properties by means of finite difference method with Crank-Nicolson scheme. The numerical results showed that the predictions were in good agreement with the experimental values and were better than that of the variable grid method.…”

Section: Introductionmentioning

confidence: 99%

“…This model eliminates the need of specifying a convective heat transfer coefficient and replaces the convective boundary condition. From the aforementioned, in industrial practice, a simplified fast predictive method is preferred and the development of simple and available software is desired (Salvadori et al, 1997;Wang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Experimental Freezing Process of Ground Meat Cylinders

Moraga¹,

Vega‐Gálvez²,

Lemus‐Mondaca³

2012

International Journal of Food Engineering

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The efficient prediction of freezing processes is important for the frozen food industry with the purpose of preserving food quality. A comparison is made between the finite difference and finite volume methods to predict the unsteady 2D temperature distribution for the freezing of three different ground meat cylinders, with variable convective boundary conditions. Experiments were carried out to measure the local heat transfer coefficients as a function of freezing time and location. Time-dependent air temperature conditions caused by thermostat action were included in the analysis. The meat thermophysical properties as density, specific heat, and thermal conductivity are assumed to vary non-linearly with temperature. The experimentally measured time-temperature data were compared with the simulation results obtained by both numerical methods. According to statistical test results (RE %), the finite volume method gave the best fit quality. The finite volume method is more accurate for simulating the freezing process, as well as estimating the freezing time of this product

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One-dimensional finite-difference modeling on temperature history and freezing time of individual food

Cited by 29 publications

References 26 publications

Computational Simulation and Developments Applied to Food Thermal Processing

Computational Simulation and Developments Applied to Food Thermal Processing

A Pure Implicit Finite Difference-Based Modeling Approach for Prediction of the Hardenability of Eutectoid Steel

Numerical Simulation of Experimental Freezing Process of Ground Meat Cylinders

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