To enhance the accuracy of 3D flow simulations in fuel assembly subchannels, a data assimilation framework (DA-DRM) is proposed by integrating the Ensemble Kalman Filter (EnKF) into the fine-mesh subchannel thermal-hydraulic code CUNLUN. In this framework, DA-DRM serves as the overall data assimilation scheme, while the EnKF functions as the core algorithm to iteratively update model parameters and state variables. This approach dynamically calibrates key momentum source parameters and updates the state variables based on the covariance of simulation–observation residuals, while maintaining physical consistency.As a result, both local adaptability and global consistency of flow predictions are improved.The method is validated against the MATiS-H international thermal-hydraulic benchmark. Multiple observation configurations are designed to systematically assess the impact of sensor placement on optimization performance. Results show that the EnKF-based DA-DRM framework significantly improves the spatial agreement of both axial and lateral velocities across representative cross-sections. In regions with steep velocity gradients downstream of the mixing spacer grid (region ), the root mean square error (RMSE) of velocity predictions is reduced from 0.127 to 0.039, corresponding to a 69.6 % reduction.Further convergence analysis reveals that well-designed observation layouts not only enhance prediction accuracy but also accelerate and stabilize the data assimilation process. Simplified configurations yield faster convergence, while more complex setups offer improved robustness. Overall, the proposed framework provides an effective and generalizable strategy for calibrating subchannel models and improving the fidelity of thermal-hydraulic simulations in complex reactor components.
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