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Soft matter approaches as enablers for food macroscale simulation

机译:软质近方法作为食品宏观模拟的推动者

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Macroscopic deformable multiphase porous media models have been successful in describing many complex food processes. However, the properties needed for such detailed physics-based models are scarce and consist of primarily empirical models obtained from experiment. Likewise, driving forces such as swelling pressure have also been approached empirically, without physics-based explanations or prediction capabilities. Soft matter based prediction of properties will provide an additional avenue to obtaining properties and also provide a deeper and critical understanding of how these properties change with composition, temperature and other process variables.
机译:宏观可变形多相多相介质模型已经成功地描述了许多复杂的食物过程。然而,这种基于物理学的模型所需的性质是稀缺的,并且由从实验中获得的主要模型组成。同样地,诸如膨胀压力的驱动力也已经经验地接近,没有基于物理的解释或预测能力。基于软件的属性预测将提供额外的途径来获得属性,并且还提供对这些属性如何变化的更深层次和批判性的理解,这些属性如何变化组成,温度和其他过程变量。

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