Endeavouring Intelligent Process Self-Control by Employing Digital Twin Methodology: Proof-of-Concept Study for Cooking Applications
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This work demonstrates the use of the Digital Twin methodology to predict the water concentration and temperature of chicken meat. It marks a milestone on the path to autonomous cooking devices that do not only control device temperatures but culinary food quality markers as well. A custom water transport equation is coupled to the energy equation. The transport equations are implemented in ANSYS Fluent 2019R2 via User Defined Function (UDF) code written in C. The model is in good agreement with experiments provided by project partners. Thermal fluid-structure interaction simulations of pan-frying are performed to obtain realistic heat transfer coefficients. They indicate that the coupling of food transport equations to the surrounding heat transfer mechanisms, such as radiation and natural convection, seems promising for future research. Co-simulation of the process is not feasible during operation in the field, so reduced-order models (ROM) are introduced. An evaluation of ROM toolkits on the ANSYS platform reveals that linear time-invariant (LTI) models are unsuitable for cooking applications. In contrast, the recently launched Dynamic ROM Builder predicts the core temperatures with significantly low errors (factor ten below the model and discretization errors of the full-order model). Two examples demonstrate the usage of a Digital Twin controlling the core temperature of chicken fillets. The PI closed-loop control system remains insensitive to errors induced by the Dynamic ROM evaluation.
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