Molten fluoride salts are promising heat-transfer media for advanced molten salt reactors (MSRs), where reliable thermophysical property determination is critical for component design and safety. We present an integrated multiscale framework that couples machine-learning-driven atomistic simulations with finite-element (FE) modeling to predict the heat-transfer performance of FLiBe-based salts in a linear heat exchanger. At the atomistic scale, Moment Tensor Potentials (MTPs), actively trained on ab initio data, are developed for pure FLiBe (66-34 and 74-26 LiF-BeF2 mol%), FLiBe-LaF3, and FLiBe-UF4. These potentials are used in molecular dynamics simulations to obtain temperature- and composition-dependent transport properties (density, viscosity, thermal conductivity, and isobaric heat capacity), which are mapped as inputs to a three-dimensional FE model of the experimental thermal loop. The FE model with literature transport properties reproduces the experimental heat-transfer behavior of pure FLiBe to within 10% in the laminar regime and 18% in the transitional and turbulent regimes, validating the end-to-end pipeline for this composition. The same model with MTP-MD-derived transport properties systematically overestimates the heat-transfer coefficient by 25-28%, an offset consistent with the MTP-MD biases on thermal conductivity and viscosity. Applied to the ternary systems FLiBe-LaF3 and FLiBe-UF4 over 0-5 mol%, the MTP-MD-driven FE model predicts a mean reduction in heat-transfer efficiency of 8-11% relative to pure FLiBe, with UF4 exhibiting the strongest effect. The qualitative ordering of the three systems is the more robust result; the absolute value of the 8-11% figure is contingent on the MTP accuracy. The framework is complementary to high-temperature experiments and provides a physics-based pathway for the rapid screening of MSR coolant formulations.
Accurate prediction of non-Newtonian nanofluid flow and heat transfer under melting heat conditions is essential for applications in advanced thermal management systems, polymer extrusion and coating processes, metallurgical melting operations, biomedical fluid transport, micro- and nano-scale heat exchangers, and energy systems. Motivated by these practical applications, the current work systematically examines the Williamson-Casson nanofluid flow over a slippery curved expanding surface while simultaneously incorporating the Cattaneo-Christov heat flux model. The flow is affected by melting heat transfer conditions at boundary, activation energy, chemical reactivity and Soret/Dufour effects. The modeled equations have been solved through the bvp4c approach in dimensionless form. The solution obtained from this approach is then used to provide a dataset for Artificial Neural Network approach. It has discovered in this work that velocity profiles are augmented with growth in thermal buoyancy factor and velocity slip parameter, while declining with augmentation in magnetic parameter, ferrohydrodynamic interaction and Weissenberg number. Thermal profiles escalated with augmentation in heat source/sink parameter, heat dissipation factor, unsteadiness factor, radiation factor and Dufour number, while declining with augmentation in melting heat parameter, thermal buoyancy factor and thermal relaxation factor. The analysis of the histograms across the entire dataset by plotting the frequency of errors within discrete intervals confirms the validity and reliability of the model. With growth in local Weissenberg number, Casson factor and curvature parameter there is augmentation in skin friction. For higher Prandtl number and melting heat parameter there is intensification in Nusselt number. With escalation in Schmidt number and Soret number there is augmentation in Sherwood number while higher factor of activation energy causes reduction in it.
E. Algehyne, I. A. A. Manahill, M. Rabih et al.· Discover Nano· 0 citations
Predicting the properties of multicomponent molten salts using density functional theory (DFT) remains challenging because the spatial and temporal scales required to evaluate transport properties and phase behavior are computationally prohibitive. In this work, we develop a moment tensor potential trained using a a DFT dataset of NaCl, KCl, NaCl-KCl mixtures, and the NaK alloy, enabling large-scale molecular dynamics simulations across wide ranges of temperatures and compositions. We systematically evaluate the effect of D3 dispersion corrections and apply the resulting potential to predict liquid densities, diffusion coefficients, radial distribution functions, heat capacities, thermal conductivities, and the NaCl-KCl phase diagram. The model successfully reproduces many temperature- and composition-dependent trends. However, systematic deviations in several absolute properties persist, highlighting the importance of experimental validation and calibration. These findings support a hybrid modeling framework in which first-principles-informed machine-learning potentials provide transferable predictive capability and mechanistic insight, while experimental data incorporated during model development or subsequent engineering assessments is necessary to improve quantitative accuracy.
K. Zongo, Hao Sun, Zijian Meng et al.· 0 citations
As one of the most promising technological pathways for Generation IV advanced reactors, molten salt reactors (MSRs) rely on the fuel salt LiF-BeF2-UF4 (FLiBeU), whose microstructural characteristics and fundamental physical properties determine the reactor's thermal-hydraulic behavior and safe operating limits. In response to the experimental challenges posed by the high temperature and high radioactivity of this molten salt system, this study adopts the deep potential molecular dynamics (DPMD) method combined with an active learning strategy to construct a high-precision machine learning force field model, whose accuracy is validated by density functional theory (DFT) and experimental results. Based on this force field model, the microstructure (radial distribution function, coordination number, angular distribution function, and network structure), thermophysical properties (density and heat capacity), and transport properties (self-diffusion coefficient, electrical conductivity, and shear viscosity) of the FLiBeU molten salt system were systematically investigated over a wide temperature range (773-1173 K) and a wide composition range (UF4 3-50 mol%). The study reveals the patterns of how temperature and UF4 concentration influence the structural evolution of the molten salt. In particular, it identifies the key mechanisms by which high UF4 concentration leads to a decline in ionic diffusion capacity, a sharp nonlinear increase in viscosity, and reductions in heat capacity and electrical conductivity. This research provides rich physical property data and microscopic mechanistic insights for both fundamental research and engineering applications of FLiBeU fuel salt, while also establishing a robust framework for studying high-temperature molten salt systems using machine learning methods.
Xinyu Li, Yu-Huan Lv, Lei Zhang et al.· Physical Chemistry, Chemical...· 0 citations
Radar‐metric analysis shows that PBE + D3‐based models provide the most balanced overall performance for density, liquid structure, and diffusion, whereas LDA‐based models give the best predictions for Ga and EGaIn.
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
The purpose of this investigation is to assess the outcome of Soret and Dufour effects on viscoelastic hybrid nanofluid flow across a sheet with convective conditions. The Levenberg–Marquardt technique is notable for its novel approach and convergent stability in the field of artificial neural networks. Using regression plots, state transition measures, histogram representations, and mean squared errors, this proposed model generates a numerical approach. The thermal–solutal convective flow of viscoelastic hybrid nanofluid based on AA7072–AA7075-ethylene glycol–water that is appropriate for complex industrial heat transfer systems where simultaneous mass and heat transport is essential. Heat exchanger design and optimization, cooling systems for metallurgical and chemical processing facilities, polymer production, and energy systems needing improved thermal performance under intricate flow circumstances are all areas in which it is especially helpful. The model helps enhance thermal efficiency, regulate concentration gradients, and guarantee stable operation in high-performance industrial applications by taking into consideration Soret-Dufour effects in addition to viscoelastic behavior. This study investigates mass and heat transmission enhancement in a laminar, steady, and incompressible flow of AA7072–AA7075/EG–H₂O Boger hybrid nanofluid across a sheet. Dufour–Soret effects, convective boundary conditions, thermal radiation, magnetic fields, and Darcy–Forchheimer porous resistance all affect the flow.
Muhammad Azhar Iqbal, Saba Liaqat, Munawar Abbas et al.· Discover Mechanical Engineer...· 0 citations
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