[3C01-03] Young Researcher

10:00 AM - 10:15 AM JST(1:00 AM - 1:15 AM UTC)

[3C03] Reduced-order Modeling of Heat Transfer in Gas–Solid Flows with Deep Learning

*Michael Castro¹, Shuo Li¹, Kai-en Yang¹, Toshiki Imatani¹, Mikio Sakai¹ (1. UTokyo)

Keywords:

discrete element method,computational fluid dynamics,heat transfer,machine learning,surrogate modeling,proper orthogonal decomposition,long-short term memory,neural network

Reduced-order models (ROM) are essential to Industry 4.0 since they can generate real-time data about equipment. The ROMs have been developed for modeling gas-solid flows in solids handling equipment. Heat transfer must also be modeled in some equipment, but this has yet to be studied extensively. In this study, an ROM of a fluidized bed accounting for gas-solid flows and heat transfer is developed. To this end, a full-order model (FM) of the fluidized bed was formulated using coupled computational fluid dynamics and discrete element method (CFD-DEM) with heat transfer. Next, a Lanczos-based proper orthogonal decomposition (LPOD) is applied to decompose the FM simulation results into its modes and coefficients. Afterwards, the ROMs are developed by employing long-short term memory (LSTM) neural networks to predict the system dynamics within a reduced space spanned by a set of POD modes. It was found that LSTM-based ROMs can accurately predict the flow fields involved in gas-solid flows and heat transfer in bubbling fluidized beds. ROMs for other fluidization regimes will be developed in future work.