Multiscale modeling of food thermal processing for insight, comprehension, and utilization of heat and mass transfer: A state-of-the-art review

多尺度建模 桥接(联网) 计算 传热 比例(比率) 计算机科学 工业工程 物理 算法 热力学 工程类 程序设计语言 计算机网络 化学 计算化学 量子力学
作者
Jiaheng Li,Yong Deng,Weidong Xu,Runan Zhao,Tingting Chen,Mingzhe Wang,Enbo Xu,Jianwei Zhou,Wenjun Wang,Donghong Liu
出处
期刊:Trends in Food Science and Technology [Elsevier]
卷期号:131: 31-45 被引量:17
标识
DOI:10.1016/j.tifs.2022.11.018
摘要

Food materials have typical heterogeneity of microstructure and physical properties, which brings difficulty in accurately determining the variable heat and mass transfer processes to optimize the food thermal processing. Mathematical modeling is usually utilized to provide predictions of processing results. However, conventional models based on empirical fitting or phenomenology theories cannot explore deep insight into transfer processes and still have certain differences with actual conditions. The multiscale modeling method, which can describe macro/micro phenomena using scale bridging methods, has been widely recognized in many fields and is highly suitable for studying hierarchical transfer variations of food thermal processing between spatial or temporal scales. This review summarized the multiscale phenomena of transfer processes and proposed outstanding characteristics of multiscale modeling compared to conventional modeling, including determining variable physical properties and considering as many real factors as possible. It also detailed scale bridging methods and structured computation, which are both necessary procedures in multiscale modeling. Furthermore, the challenges and prospects in developing multiscale models were also discussed. Due to the incorporations of sub-models and underlying influencing factors, multiscale modeling shows high accuracy in predicting the results of transfer processes and great feasibility in determining scale effects in the micro domain. Both advantages demonstrate its potential to provide foundations for optimizing food processing. More efforts should focus on balancing the complexity and accessibility of multiscale modeling. Moreover, it still needs improvements to obtain broader applications at low consumption of computation resources.
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