A computational toolchain for the automatic generation of multiple Reduced-Order Models from CFD simulations. (1st August 2019) - Marzullo, Thibault; Keane, Marcus M.; Geron, Marco; Monaghan, Rory F.D.
Abstract: This paper describes the development of a systematic tool chain capable of automatically extracting accurate and efficient Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations. These ROMs can then be used to support the design and operation of Near-Zero Energy Buildings (NZEB), with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of these ROMs when solved for appropriate Boundary Conditions (BCs), found in the built environment, in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures (K) and mass flow rates (kg/s). Results demonstrate that the absolute error can be maintained at under 0.5 K for changes in temperature of up to ±15 K, and under 0.25 K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has the potential for applications in the built environment where the ROM accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving the energy efficiency in smart buildings. Highlights: A method for extracting zonal models from CFD simulations is presented. A reduced fluid network model is generated by clustering CFD cells. The ROM can be solved for different boundary conditions. The model retains a high level of accuracy and is solved in real time.