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The thermal management of cryogenic storage tanks requires advanced controlstrategies to minimize the boil-off losses produced by heat leakages andsloshing-enhanced heat and mass transfer. This work presents adata-assimilation approach to calibrate a 0D thermodynamic model for cryogenicfuel tanks from data collected in real time from multiple tanks. The modelcombines energy and mass balance between three control volumes (the ullagevapor, the liquid, and the solid tank) with an Artificial Neural Network (ANN)for predicting the heat transfer coefficients from the current tank state. Theproposed approach combines ideas from traditional data assimilation andmulti-environment reinforcement learning, where an agent's training (modelassimilation) is carried out simultaneously on multiple environments (systems).The real-time assimilation uses a mini-batch version of the Limited-memoryBroyden-Fletcher-Goldfarb-Shanno with bounds (L-BFGS-B) and adjoint-basedgradient computation for solving the underlying optimization problem. Theapproach is tested on synthetic datasets simulating multiple tanks undergoingdifferent operation phases (pressurization, hold, long-term storage, andsloshing). The results show that the assimilation is robust against measurementnoise and uses it to explore the parameter space further. Moreover, we showthat sampling from multiple environments simultaneously accelerates theassimilation.

Real-time data assimilation for the thermodynamic modeling of cryogenic storage tanks