High-fidelity neutronic–thermal simulations provide detailed field information, but remain expensive for repeated evaluation. This study develops a physics-regularized heat-source-conditioned Fourier neural operator with linear attention (HSC-FNO-LA) for predicting the volumetric heat-source field Q and temperature field T in a steady-state pressurized-water-reactor single-rod benchmark. Unlike parallel multi-output surrogates, the proposed model first reconstructs Q and uses Q-derived features to condition the thermal decoder. Global heat-source energy regularization and a soft thermal-consistency term are included during three-stage training. With complete physical cases given equal statistical weight, the frozen reference model achieved a validation temperature mean absolute error of 16.26 K (95% bootstrap confidence interval: 15.35–17.22 K), a temperature relative error of 1.997%, a Q-field relative error of 2.366%, and a Q-energy error of 0.291%. Across five independently trained initializations, temperature mean absolute error was 12.46 ± 3.31 K, indicating non-negligible initialization sensitivity. Post-training interventions showed that shuffled and training-set-mean Q inputs increased temperature error by 25.64 and 16.40 K on average, respectively, across all five initializations, whereas replacing predicted Q with reference Q did not reduce mean temperature error. An additional 48 previously unused OpenFOAM cases yielded a temperature mean absolute error of 13.41 K. These results support explicit Q-conditioned thermal decoding for the tested benchmark, while broader geometric, transient, and experimental validation remains necessary.
Jia-Hao Ji, Yuanfeng Wang, Chunbing Wu et al.· Applied Sciences· 0 citations
Source identification in confined underground ventilation systems is essential for hazardous-gas monitoring, and uranium-mine radon provides a representative case in which release locations and strengths must be inferred from limited concentration measurements. This presents an underdetermined, ill-posed inverse problem whose solvability under different sparse-regression strategies and roadway configurations remains poorly understood. In this study, a computational fluid dynamics (CFD) forward model is coupled with sparse regression. The ventilation flow field and radon advection–diffusion process are solved in OpenFOAM to construct a source–sensor contribution matrix, and source recovery is formulated as a sparse linear inverse problem. Four methods—LASSO, LASSO with non-negative least-squares (NNLS) refitting, Elastic Net, and Elastic Net with NNLS refitting—are compared, and the contribution matrix is characterized by its mutual coherence, condition number, and singular-value spectrum. Numerical tests were conducted for single- and multiple-source scenarios in single-main and main–branch roadway models. The results indicate that inversion performance depends on the spatial information and local identifiability provided by the sensor configuration rather than on sensor number alone. LASSO and Elastic Net exhibited varying degrees of source-strength shrinkage or dispersion, whereas NNLS refitting reduced these effects when the first-stage support contained the dominant source candidates. In the prescribed three-source case, denser sensor coverage improved dominant-source localization and reduced the post hoc condition number of the prescribed-source submatrix, although the full-matrix condition number increased. This finding indicates improved local identifiability for the tested source combination rather than a general sensor-count effect. Because the synthetic observations and the inversion operator were derived from the same CFD response matrix, the results represent a controlled model-consistent proof of concept rather than an estimate of field-level performance.
Yuanfeng Wang, Jia-Hao Ji, Chun-Bin Wu et al.· Applied Sciences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
