Physics-Informed Deep Learning for Real-Time Thermal Anomaly Detection in Regenerative Cooling Channels of RP-1 Liquid Rocket Engines

Liquid Rocket Engines (LREs) utilizing RP-1 fuel
are susceptible to “coking,” a thermal degradation phenomenon
where carbon deposits impair heat transfer in regenerative
cooling channels. Traditional Computational Fluid Dynamics
(CFD) methods are computationally prohibitive for flight-time
monitoring. This paper proposes a Physics-Informed Neural
Network (PINN) framework functioning as a virtual sensor to
predict nominal wall temperatures. By integrating a calibrated
Bartz approximation directly into the loss function, the model
establishes a physics-consistent baseline. We demonstrate that
this approach achieves real-time thermal anomaly detection with
a Root Mean Square Error (RMSE) of 5.55 K. The model exhibits
an inference latency of < 10 ms on a standard CPU, offering a
viable pathway for autonomous abort systems in next-generation
reusable launch vehicles.