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Estimating the temperature evolution of foodstuffs during freezing with a 3D meshless numerical method

机译:使用3D无网格数值方法估算冷冻过程中食品的温度变化

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Freezing processes are characterised by sharp changes in specific heat capacity and thermal conductivity for temperatures close to the freezing point. This leads to strong nonlinearities in the governing PDE that may be difficult to resolve using traditional numerical methods. In this work we present a meshless numerical method, based on a local Hermite radial basis function collocation approach in finite differencing mode, to allow the solution of freezing problems. By introducing a Kirchhoff transformation and solving the governing equations in Kirchhoff space, the strength of nonlinearity is reduced while preserving the structure of the heat equation. In combination with the high-resolution meshless numerical method, this allows efficient and stable numerical solutions to be obtained for freezing problems using 3D unstructured datasets. We demonstrate the proposed numerical formulation for the freezing of foodstuffs. By approximating the shape of the thermal conductivity and heat capacity curves in a piecewise linear fashion the temperature-dependent material curves may be described using eight independent parameters. We consider the optimisation of these parameters to match experimental data for the freezing of a hemispherical sample of mashed potato by using a simple manual procedure that requires a minimal number of simulations to be performed. Working in this way, a good approximation is obtained to the temperature profile throughout the sample without introducing instability into the numerical results.
机译:冷冻过程的特征在于,在接近冰点的温度下,比热容和热导率会发生急剧变化。这导致控制PDE中存在强烈的非线性,使用传统的数值方法可能难以解决。在这项工作中,我们提出了一种基于有限差分模式的局部Hermite径向基函数配置方法的无网格数值方法,以解决冻结问题。通过引入Kirchhoff变换并求解Kirchhoff空间中的控制方程,非线性强度降低了,同时保留了热方程的结构。结合高分辨率无网格数值方法,可以使用3D非结构化数据集获得冻结问题的有效且稳定的数值解。我们演示了食品冷冻的建议数值公式。通过以分段线性方式近似热导率曲线和热容曲线的形状,可以使用八个独立的参数来描述与温度相关的材料曲线。我们考虑通过使用简单的手动程序(这些程序需要进行最少的模拟)来优化这些参数,以匹配用于冷冻马铃薯泥半球形样品的实验数据。以这种方式工作,可以获得整个样品温度曲线的良好近似值,而不会在数值结果中引入不稳定性。

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