Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin is a true representation of the monitored system. One way this is achieved is by calibration of simulation models, whether data-derived or physics-based, or a combination of both. Traditional manual calibration is not possible in this context hence new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and applied to an example of an underground farm. The methodology is applied to a synthetic problem with known calibration parameter values prior to being used in conjunction with monitored data. The proposed methodology is compared against static and sequential Bayesian calibration approaches and compares favourably in terms of determination of the distribution of parameter values and analysis run-times, both essential requirements. The methodology is shown to be potentially useful as a means to ensure continuing model fidelity.

中文翻译：

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数字孪生的连续校准：粒子滤波器和贝叶斯校准方法的比较

连续流监控数据的同化是数字孪生的重要组成部分；同化数据用于确保数字孪生是受监控系统的真实代表。实现这一目标的一种方法是校准仿真模型，无论是基于数据的还是基于物理的，或者两者的结合。在这种情况下，传统的手动校准是不可能的，因此需要新的方法来进行连续校准。在本文中，提出了一种用于连续校准基于物理的数字孪生模型元素的粒子滤波器方法，并将其应用于地下农场的示例。在与监测数据结合使用之前，该方法应用于具有已知校准参数值的综合问题。所提出的方法与静态和顺序贝叶斯校准方法进行了比较，并且在确定参数值的分布和分析运行时间这两个基本要求方面具有优势。该方法被证明作为一种确保持续模型保真度的手段可能很有用。