In the nuclear power industry, one important class of accidents is the loss of coolant accident (LOCA). This paper presents methods to detect a LOCA that is initiated: (i) while the plant is going through a small transient, and (ii) with a time-varying leak magnitude. The accident is simulated using a generic pressurized water reactor (GPWR) simulator. The fault is detected using a model-based approach with models identi ed using GPWR data. The model-based approach is multiple-model adaptive estimation (MMAE), which uses multiple system models representing both normal and faulted operating conditions. During operation, these models simulate the potential operating conditions, incorporating measurement feedback in a Kalman lter state-estimation structure. Faults are detected by selecting the model that most closely matches the system according to statistical characteristics. For a LOCA, data-driven models of the pressurizer liquid level are derived using rst-principles and system identi cation. In system identi cation, a physics-based model form is derived that contains unknown parameters. System identi cation is then used to estimate the parameter values based on measurement data, providing plant-speci c pressurizer models. For the accident scenario described above, the proposed methods di erentiate between the transient and the accident, and provide real-time estimates of the leak magnitude after it has been initiated.