arXiv subjects

Logan A. Burnett

Publications and source records attributed to Logan A. Burnett.

4 recordsLinked to original sources

arXiv2026

High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications

This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water reactor (PWR). A full lower-plenum and core-inlet domain was constructed using publicly available geometry and operating conditions, enabling transient simulations with pump-induced swirl boundary conditions. The results show that cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region, while axial resistance and mixing progressively homogenize the flow at higher elevations. These physics-informed datasets were subsequently used to evaluate machine learning (ML) applications for partial field reconstruction and short-term autoregressive prediction. A 3D convolutional-based inpainting model successfully recon-structed missing assembly-level mass flow rates from partial observations, with errors concentrated in the highly turbulent base (bottom) layer and diminishing significantly in upper layers. Comparative analysis across multiple ML models demon-strates that spatially aware architectures, particularly ConvLSTM, significantly outperform sequence-based (LSTM) and operator-learning (DeepONet) approaches by effectively capturing coupled spatio-temporal dynamics. The study also high-lights key challenges, including the sensitivity of inlet flow predictions to turbulence and mesh resolution, as well as the absence of full-scale experimental validation data. Despite these limitations, the results remain consistent with expected physical behavior. Overall, this work establishes high-fidelity CFD as a critical foundation for developing data-driven surrogates, sparse sensing strategies, and future multiphysics coupling frameworks.

cs.LG↗

arXiv2025

Variational Digital Twins

While digital twins (DT) hold promise for providing real-time insights into complex energy assets, much of the current literature either does not offer a clear framework for information exchange between the model and the asset, lacks key features needed for real-time implementation, or gives limited attention to model uncertainty. Here, we aim to solve these gaps by proposing a variational digital twin (VDT) framework that augments standard neural architectures with a single Bayesian output layer. This lightweight addition, along with a novel VDT updating algorithm, lets a twin update in seconds on commodity GPUs while producing calibrated uncertainty bounds that can inform experiment design, control algorithms, and model reliability. The VDT is evaluated on four energy-sector problems. For critical-heat-flux prediction, uncertainty-driven active learning reaches R2 = 0.98 using 47 % fewer experiments and one-third the training time of random sampling. A three-year renewable-generation twin maintains R2 > 0.95 for solar output and curbs error growth for volatile wind forecasts via monthly updates that process only one month of data at a time. A nuclear reactor transient cooldown twin reconstructs thermocouple signals with R2 > 0.99 and preserves accuracy after 50 % sensor loss, demonstrating robustness to degraded instrumentation. Finally, a physics-informed Li-ion battery twin, retrained after every ten discharges, lowers voltage mean-squared error by an order of magnitude relative to the best static model while adapting its credible intervals as the cell approaches end-of-life. These results demonstrate that combining modest Bayesian augmentation with efficient update schemes turns conventional surrogates into uncertainty-aware, data-efficient, and computationally tractable DTs, paving the way for dependable models across industrial and scientific energy systems.

cs.LG↗

arXiv2024

First-Principles Calculation of Hubbard U for Terbium Metal under High Pressure

Using density functional theory (DFT) and linear response approaches, we compute the on-site Hubbard interaction $U$ of elemental Terbium (Tb) metal in the pressure range $\sim 0-65$ GPa. The resulting first-principles $U$ values with experimental crystal structures enable us to examine the magnetic properties of Tb using a self-consistent DFT+U method. The lowest-energy magnetic states in our calculations for different high-pressure Tb phases -- including hcp, $α$-Sm, and dhcp -- are found to be compatible with the corresponding magnetic ordering vectors reported in experiments. The result shows that the inclusion of Hubbard $U$ substantially improves the accuracy and efficiency in modeling correlated rare-earth materials. Our study also provides the necessary $U$ information for other quantum many-body techniques to study Tb under extreme pressure conditions.

cond-mat.str-el↗

arXiv2024

Absence of Weyl nodes in EuCd$_2$As$_2$ revealed by the carrier density dependence of the anomalous Hall effect

The antiferromagnetic layered compound EuCd$_2$As$_2$ is widely considered as a leading candidate of ideal Weyl semimetal, featuring a single pair of Weyl nodes in its field-induced ferromagnetic (FM) state. Nevertheless, this view has recently been challenged by an optical spectroscopy study, which suggests that it is a magnetic semiconductor. In this study, we have successfully synthesized highly insulating EuCd$_2$As$_2$ crystals with carrier density reaching as low as $2\times 10^{15}$ $\text{cm}^{-3}$. The magneto-transport measurements revealed a progressive decrease of the anomalous Hall conductivity (AHC) by several orders of magnitude as the carrier density decreases. This behavior contradicts with what is expected from the intrinsic AHC generated by the Weyl points, which is independent of carrier density as the Fermi level approaches the charge neutrality point. In contrast, the scaling relationship between AHC and longitudinal conductivity aligns with the characteristics of variable range hopping insulators. Our results suggest that EuCd$_2$As$_2$ is a magnetic semiconductor rather than a topological Weyl semimetal.

cond-mat.mtrl-sci↗