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

• URL: http://arxiv.org/abs/2605.24763v1
• Date: Sat, 23 May 2026 22:50:23 GMT
• Title: High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
• Abstract summary: This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a pressurized water reactor.<n>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.<n>Results show that cold-leg swirl and lower-plenum transport generate strongly heterogeneous assembly-wise inlet flow distributions, particularly near the lower core region.<n>A 3D convolutional-based inpainting model successfully recon-structed missing assembly-level mass flow rates from partial observations
• Score: 3.8347851078346427
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation [47.3675633392999]
  We introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation)<n>Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction.<n>Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC.
  arXiv  Detail & Related papers  (2026-07-06T15:59:18Z)
• Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids [0.0]
  High-fidelity simulations of free-surface flows using Lagrangian methods such as the Particle Finite Element Method (PFEM) are computationally demanding.<n>This challenge is further amplified by non-Newtonian rheologies, where material nonlinearities increase computational cost.<n>We propose a self-attention-based neural surrogate for PFEM simulations of free-surface flows.
  arXiv  Detail & Related papers  (2026-06-22T12:33:55Z)
• Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport [0.0]
  This chapter reviews advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena.<n>The chapter surveys state-of-the-art SciML methods for building efficient surrogate models.<n>It shows how SciML enables fast, accurate approximations of complex coupled systems.
  arXiv  Detail & Related papers  (2026-06-17T20:02:13Z)
• Generative modeling of granular flow on inclined planes using conditional flow matching [4.880426970438706]
  This study presents the first conditional flow matching framework for granular-flow reconstruction from sparse boundary observations.<n>A physics decoder maps the reconstructed velocity fields to stress states and energy fluctuation quantities.<n>Results demonstrate that conditional generative modeling offers a practical route for non-invasive inference of hidden bulk mechanics in granular media.
  arXiv  Detail & Related papers  (2026-04-06T05:59:54Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction [53.26903617819014]
  Flow-Matching-guided Unfolding network (FMU) is first to integrate flow matching into HSI reconstruction.<n>To further strengthen the learned dynamics, we introduce a mean velocity loss.<n>Experiments on both simulated and real datasets show that FMU significantly outperforms existing approaches in reconstruction quality.
  arXiv  Detail & Related papers  (2025-10-02T11:32:00Z)
• Multi-Stage Graph Neural Networks for Data-Driven Prediction of Natural Convection in Enclosed Cavities [0.0]
  Graph Neural Networks (GNNs) offer new alternatives for learning thermal-fluid behavior directly from simulation data.<n>We propose a novel multi-stage GNN architecture that leverages hierarchical pooling and unpooling operations to progressively model global-to-local interactions.<n> Experimental results demonstrate that the proposed model achieves higher predictive accuracy, improved training efficiency, and reduced long-term error accumulation.
  arXiv  Detail & Related papers  (2025-09-07T13:05:39Z)
• Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management [44.41703936689344]
  We introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator.<n>The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations.<n>We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations.
  arXiv  Detail & Related papers  (2025-08-26T20:38:02Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models [3.780691701083858]
  This study presents an innovative integration of High-Order Singular Value Decomposition with Long Short-Term Memory (LSTM) architectures to address the complexities of reduced-order modeling (ROM) in fluid dynamics.<n>The methodology is tested across numerical and experimental data sets, including two- and three-dimensional (2D and 3D) cylinder wake flows, spanning both laminar and turbulent regimes.<n>The results demonstrate that HOSVD outperforms SVD in all tested scenarios, as evidenced by using different error metrics.
  arXiv  Detail & Related papers  (2025-04-09T10:56:03Z)
• An Efficient Hierarchical Preconditioner-Learner Architecture for Reconstructing Multi-scale Basis Functions of High-dimensional Subsurface Fluid Flow [4.303037819686676]
  We present an efficient hierarchical preconditioner-learner architecture that reconstructs multi-scale basis functions of high-dimensional subsurface fluid flow. FP-HMsNet achieved an MSE of 0.0036, an MAE of 0.0375, and an R2 of 0.9716 on the testing set, significantly outperforming existing models. This model offers a novel method for efficient and accurate subsurface fluid flow modeling, with promising potential for more complex real-world applications.
  arXiv  Detail & Related papers  (2024-11-01T09:17:08Z)
• Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models [2.8686437689115363]
  This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications.<n>Three autoregressive models were compared: a hybrid model (POD-DL) that combines proper decomposition (POD) with a long-short term memory (LSTM) layer, a convolutional autoencoder combined with a convolutional LSTM layer, and a variational autoencoder (VAE) combined with a ConvLSTM layer.<n>While the VAE and ConvLSTM models accurately predicted laminar flow, the hybrid POD-DL model outperformed the others
  arXiv  Detail & Related papers  (2024-04-27T12:43:02Z)
• A Neural PDE Solver with Temporal Stencil Modeling [44.97241931708181]
  Recent Machine Learning (ML) models have shown new promises in capturing important dynamics in high-resolution signals. This study shows that significant information is often lost in the low-resolution down-sampled features. We propose a new approach, which combines the strengths of advanced time-series sequence modeling and state-of-the-art neural PDE solvers.
  arXiv  Detail & Related papers  (2023-02-16T06:13:01Z)
• Normalizing Flows with Multi-Scale Autoregressive Priors [131.895570212956]
  We introduce channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR) Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. We show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
  arXiv  Detail & Related papers  (2020-04-08T09:07:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.