用于自主热食品加工的基于物理的数字孪生：高效、非侵入式降阶建模
Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modeling
摘要：
使热处理可控的一种可能方法是收集产品当前状态的实时信息。通常，感官设备无法轻松或根本无法捕捉所有相关信息。数字孪生通过与过程同步的实时模拟中的虚拟探头缩小了这一差距。本文提出了一个基于物理的，数据驱动的数字孪生框架的自主食品加工。我们提出了一个精益的数字孪生概念，可在设备级执行，需要最小的计算负载，数据存储和传感器数据的要求。本研究的重点是一个简约的实验设计训练非侵入性的降阶模型（ROM）的热过程。在训练数据中的表面温度的高标准偏差与ROM测试中的低均方根误差之间的相关性（$R=-0.76$）使得能够有效地选择训练数据。在代表性测试集上，最佳ROM的平均测试均方根误差小于1开尔文（0.2%平均平均百分比误差）。Sp $\approx$ 1.8 E4的仿真加速允许在设备上进行模型预测控制。拟议的数字孪生框架旨在适用于该行业。通常，一旦在软件中执行过程的建模，就需要非侵入式降阶建模，其中不提供对求解器的根级访问，例如商业仿真软件。数据驱动的降阶模型的训练仅用一个数据集来实现，因为相关性被用于先验地预测训练成功。
英文摘要：
One possible way of making thermal processing controllable is to gather real-time information on the product's current state. Often, sensory equipment cannot capture all relevant information easily or at all. Digital Twins close this gap with virtual probes in real-time simulations, synchronized with the process. This paper proposes a physics-based, data-driven Digital Twin framework for autonomous food processing. We suggest a lean Digital Twin concept that is executable at the device level, entailing minimal computational load, data storage, and sensor data requirements. This study focuses on a parsimonious experimental design for training non-intrusive reduced-order models (ROMs) of a thermal process. A correlation ($R=-0.76$) between a high standard deviation of the surface temperatures in the training data and a low root mean square error in ROM testing enables efficient selection of training data. The mean test root mean square error of the best ROM is less than 1 Kelvin (0.2 % mean average percentage error) on representative test sets. Simulation speed-ups of Sp $\approx$ 1.8E4 allow on-device model predictive control. The proposed Digital Twin framework is designed to be applicable within the industry. Typically, non-intrusive reduced-order modeling is required as soon as the modeling of the process is performed in software, where root-level access to the solver is not provided, such as commercial simulation software. The data-driven training of the reduced-order model is achieved with only one data set, as correlations are utilized to predict the training success a priori.
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