  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.
