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