Temporal prediction of parameters in nuclear thermal propulsion reactors via latent space nonlinear operator learning

BackgroundIn nuclear thermal propulsion reactor engineering, the real-time prediction of multi-physics temperature fields under transient operating conditions has long been hindered by the inherent computational inefficiency and high dimensionality of coupled neutronics-thermal-hydraulic simulations. Traditional computational fluid dynamics (CFD) methods for required accurate, struggle to meet real-time decision-making requirements due to their prohibitive computational costs.PurposeThis study aims to develop a lightweight digital twin framework for achieving real-time, high-fidelity temperature field predictions in nuclear reactors, thereby enabling rapid safety assessment and control strategy optimization.MethodsFirst of all, a lightweight architecture of "encoding–operator learning–decoding" was constructed, and temperature field data of 230 000~1.8 million dimensions were compressed into a 100-dimensional latent space. Then an improved Deep Operator Network (DeepONet) was used to learn the dynamic evolution within this latent space, and a physics-constrained decoder was employed for high-precision reconstruction. Finally, a nuclear thermal coupling benchmark dataset generated by the computational fluid dynamics software OpenFOAM was applied to verifying prediction data.ResultsValidation results show that, in 40-s iterative predictions, the average relative error of the fuel region temperature field is below 1%, and the coolant temperature field error is below 0.5%; in 100-s long-step predictions, the maximum relative error remains under 0.5%. Each iteration of the predictive model takes less than 200 s, achieving over 500 times acceleration compared to traditional CFD methods; a single long-step prediction takes 79.23 s, which is shorter than the prediction interval, thus meeting real-time prediction requirements.ConclusionsThis study provides a lightweight digital twin solution for transient condition simulation and safety control of nuclear thermal propulsion reactors.