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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Numerical challenges for energy conservation in N -body simulations of collapsing self-interacting dark matter halos

Dark matter (DM) halos can be subject to gravothermal collapse if the DM is not collisionless, but engaged in strong self-interactions instead. When the scattering is able to efficiently transfer heat from the centre to the outskirts, the central region of the halo collapses and reaches densities much higher than those for collisionless DM. This phenomenon is potentially observable in studies of strong lensing. Current theoretical efforts are motivated by observations of surprisingly dense substructures. However, a comparison with observations requires accurate predictions. One method to obtain such predictions is to use N-body simulations. Collapsed halos are extreme systems that pose severe challenges when applying state-of-the-art codes to model self-interacting dark matter (SIDM). In this work, we investigate the root of such problems, with a focus on energy non-conservation. Moreover, we discuss possible strategies to avoid them. We ran N-body simulations, both with and without SIDM, of an isolated DM-only halo and we adjusted the numerical parameters to check the accuracy of the simulation. We find that not only the numerical scheme for SIDM can lead to energy non-conservation, but also the modelling of gravitational interaction and the time integration are problematic. The main issues we find are: (a) particles changing their time step in a non-time-reversible manner; (b) the asymmetry in the tree-based gravitational force evaluation; and (c) SIDM velocity kicks breaking the time symmetry. Tuning the parameters of the simulation to achieve a high level of accuracy allows us to conserve energy not only at early stages of the evolution, but also later on. However, the cost of the simulations becomes prohibitively large as a result. Some of the problems that make the simulations of the gravothermal collapse phase inaccurate can be overcome by choosing appropriate numerical schemes. However, other issues still pose a challenge. Our findings motivate further works on addressing the challenges in simulating strong DM self-interactions.

dark matter↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Comparing ICME simulations with scaled laboratory experiment

In stellar physics and astrophysics, numerical simulations and laboratory experiments are often compared to observational data to support their representation of the real world. However, there is also merit in comparing numerical simulations to properly scaled experiments, especially when the experiment and the simulation are both emulating the solar phenomena. Confirming the credibility of scaled experiments and their scaling with well-validated models is important to expand our knowledge of the associated physical phenomena. This is significant because experiments and simulations can be performed frequently, whereas observations may be limited by location, field of view, and missing data. In this work, we use the Alfvén Wave Solar atmosphere Model, a well-validated magnetohydrodynamic model, to simulate an interplanetary coronal mass ejection (ICME) and compare it to an experiment which provides a scaled analog to a physical ICME. The experiment was performed on the Big Red Ball facility and scaled using dimensionless parameters such as plasma β and magnetosonic Mach number to reproduce the main structure of an ICME. We compare the model-simulated temperature, density, and magnetic field to those from the experiment, as well as the scaling parameters used in the experiment, to those calculated from the simulation. This comparison is performed to further justify the scaling arguments made by the experiment. Additionally, the comparison would lead to the development of stronger scaling arguments for future experiments.

Bryant, K. [University of Michigan, Ann Arbor, MI ↗
DOE OSTI2024

Agora : Multicomponent simulation for cross-survey science

ABSTRACT Next-generation cosmological surveys will observe large portions of the sky, with significant overlap between them. Multi-wavelength observations will enable us to analyse the same large-scale structure from different angles using a variety of tracers and astrophysical effects. The complex interplay between these observables calls for a model that can accurately and coherently describe their collective behaviour, posing a challenge that can only be met through the use of simulations. In this work, a suite of simulated extragalactic skies is presented, including maps and/or catalogues of cosmic microwave background (CMB) lensing, thermal and kinetic Sunyaev–Zel’dovich (tSZ/kSZ) effects, cosmic infrared background (CIB), radio sources, galaxy overdensity and galaxy weak lensing. Each of these probes is implemented in the lightcone using halo catalogues and/or particles from the Multidark-Planck2 ($\small {MDPL2}$) N-body simulation, and the modelling is calibrated using hydrodynamic simulations and publicly available data. The auto and cross-spectra of the individual probes, as well as the cross-spectra between the observables, are shown to be consistent with theoretical models and measurements from data. The simulation is shown to have a wide range of applications, including forecasting, pipeline testing, and evaluating astrophysical biases in cross-correlation measurements. It is further demonstrated that the simulation products produced in this work have sufficient accuracy to recover the input cosmology when subjected to a full cosmological analysis and are ready for application in real-world analyses for ongoing and future surveys. The simulation products presented in this work can be accessed at: https://yomori.github.io/agora/index.html.

Astronomy & Astrophysics↗
DOE OSTI2025

Quantum dynamics simulation of the advection-diffusion equation

The advection-diffusion equation is simulated via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and a superconducting quantum hardware (IBM Fez). The former emulates the latter without the noise. The actual hardware implementation experiences significant noise. The results of the quantum simulator are compared with data from direct numerical simulation (DNS) with infidelities of the order 10 −5 . In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelmingly large number of operations. Meanwhile, AVQDS and VarQTE can be executed at the hardware level. These algorithms present a new paradigm for computational transport phenomena on quantum computers.

Alipanah, Hirad [Univ. of Pittsburgh, PA (United S↗
DOE OSTI2024

HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network

Understanding, analyzing, and predicting human mobility and dynamics are valuable to solving pressing problems, developing effective plans, and prescribing timely remedies. As a computational approach, realistic human mobility simulations allow us to understand, analyze, and predict complex systems, including human societies. Accurate simulations rely on (1) the model that captures interactions and behaviors of myriad entities in our society and (2) the mapping of model instances to real-world entities. Taking this into account, this paper introduces the Human Mobility Network simulation framework (HumoNet), an integrated patterns of life (POL) simulation framework that leverages real-world data layers including transportation networks, points of interest, populations, popularity, and human trajectories. HumoNet is a data informed model in which agents are equipped with activities, locomotion, and planning capabilities. To simulate realistic kinematic maneuvers of individuals in transportation networks, HumoNet harnesses a microscopic traffic simulator that provides interaction among vehicles and traffic objects. In this paper, we describe the framework, outline our methodologies, and discuss the data processing and challenges of each data layer. Through experiments, we demonstrate that our simulations capture key features of human mobility by comparing them to the literature and real data using standard measures of human mobility (i.e., the radius of gyration, number of locations visited, level of exploration) and metrics scoring (i.e., Jensen-Shannon divergence). We envision that the synthetic data produced by HumoNet will serve as a benchmark for analyzing epidemics, deploying EV charging networks, and validating AI/ML tasks such as location prediction.

Kim, Joon-Seok↗
DOE OSTI2024

A Framework for Integrating Quantum Simulation and High Performance Computing

Scientific applications are starting to explore the viability of quantum computing. This exploration typically begins with quantum simulations that can run on existing classical platforms, albeit without the performance advantages of real quantum resources. In the context of high-performance computing (HPC), the incorporation of simulation software can often take advantage of the powerful resources to help scale-up the simulation size. The configuration, installation and operation of these quantum simulation packages on HPC resources can often be rather daunting and increases friction for experimentation by scientific application developers. We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure to make effective use of the underlying HPC resources. The primary contributions of this work include a classification of different usage models for quantum simulation in an HPC context, a review of the software architecture for our approach and a detailed description of the prototype implementation to experiment with these ideas using two different simulators (TNQVM & NWQ-Sim). We include initial experimental results running on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) using a synthetic workload generated via the SupermarQ quantum benchmarking framework.

Shehata, Amir [ORNL] (ORCID:0000000224531426)↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The reactivity of experimentally reduced lunar regolith simulants: Health implications for future crewed missions to the lunar surface

Crewed missions to the Moon may resume as early as 2026 with NASA's Artemis III mission, and lunar dust exposure/inhalation is a potentially serious health hazard that requires detailed study. Current dust exposure limits are based on Apollo-era samples that spent decades in long-term storage on Earth; their diminished reactivity may lead to underestimation of potential harm that could be caused by lunar dust exposure. In particular, lunar dust contains nanophase metallic iron grains, produced by “space weathering”; the reactivity of this unique component of lunar dust is not well understood. Herein, we employ a chemical reduction technique that exposes lunar simulants to heat and hydrogen gas to produce metallic iron particles on grain surfaces. We assess the capacity of these reduced lunar simulants to generate hydroxyl radical (OH*) when immersed in deionized (DI) water, simulated lung fluid (SLF), and artificial lysosomal fluid (ALF). Lunar simulant reduction produces surface-adhered metallic iron “blebs” that resemble nanophase metallic iron particles found in lunar dust grains. Reduced samples generate ~5–100× greater concentrations of the oxidative OH* in DI water versus non-reduced simulants, which we attribute to metallic iron. SLF and ALF appear to reduce measured OH*. The increase in observed OH* generation for reduced simulants implies high oxidative damage upon exposure to lunar dust. Low levels of OH* measured in SLF and ALF imply potential damage to proteins or quenching of OH* generation, respectively. Reduction of lunar dust simulants provides a quick cost-effective approach to study dusty materials analogous to authentic lunar dust.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Determining Levels of Detail for Simulators of Parallel and Distributed Computing Systems via Automated Calibration

There are two sources of inaccuracy when simulating parallel and distributed computing systems: (i) a simulator implemented at an insufficient level of detail; and (ii) incorrectly calibrated simulation parameter values. Increasing the simulator’s level of detail can improve accuracy, but at the cost of higher space, time, and/or software complexity. Furthermore, evaluating the intrinsic accuracy of a simulator requires that its parameters be well-calibrated. Making decisions regarding the level of detail is thus challenging. We propose a methodology for instantiating the simulation calibration process and a framework for automating this process, which makes it possible to pick appropriate levels of detail for any simulator. We demonstrate the usefulness of our approach via two case studies for two different domains.

McDonald, Jessie [University of Hawaii at Manoa, H↗
DOE OSTI2024

Understanding the Biases in Global Monsoon Simulations from the Perspective of Atmospheric Energy Transport

Understanding global monsoon (GM) variability and projecting its future changes rely heavily on climate models. However, climate models generally show pronounced biases in GM simulations, and the reasons for this remain unclear. Here, in this study, we evaluate the performance of 20 pairs of climate models that participated in both phase 5 of the Coupled Model Intercomparison Project (CMIP5) and phase 6 of CMIP (CMIP6) and identify the sources of their GM simulation biases from an energy transport perspective. The multimodel mean improvement in CMIP6 compared to CMIP5 is demonstrated by the increasing skill scores for various GM metrics from 0.20–0.79 to 0.48–0.83. More specifically, the dry biases in the Northern Hemisphere Summer Monsoon (NHSM) precipitation in CMIP5 [root-mean-square error (RMSE): 1.85 mm day −1 ] are reduced in CMIP6 (RMSE: 1.66 mm day −1 ). This higher simulation skill is associated with higher skill in simulating the precipitation-solstitial mode, monsoon intensity, and monsoon domains. The improvement in the NHSM precipitation simulation results from that in the meridional transport of atmospheric energy. Atmospheric energy budget analysis shows that the negative biases in downward surface longwave radiation and northward energy transport are smaller in CMIP6 than in CMIP5 in the boreal summer, resulting in a more realistic interhemispheric thermal contrast and meridional gradient of moist static energy. However, a major weakness of the CMIP6 models is found in the Southern Hemisphere Summer Monsoon precipitation simulation due to the positive bias in the top-of-the-atmosphere downward longwave radiation. This study shows that reasonably reproducing the meridional global atmospheric energy transportation is necessary for skillful GM simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

New Muon Campus Simulations for the Muon $g\textrm{-}2$ Experiment at Fermilab

The Muon $g\textrm{-}2$ Experiment (E989) at Fermilab conducted high-precision measurements of the muon anomalous magnetic moment $a_{\mu}$ using a storage ring from 2018 to 2023, achieving a remarkable precision of $200\:\mathrm{ppb}$ over Runs 1-3, with analyses for Runs 4-6 ongoing. A comprehensive understanding of the storage ring's beam dynamics and its accurate simulations are crucial for achieving the experiment's ambitious goals. One of the requirements for this effort is a very detailed knowledge of the phase space distribution of the beam. To address this requirement, we performed high-statistics simulations of the Muon $g\textrm{-}2$ Target Station (AP0) and the Muon Campus beamlines: M2 and M3, followed by the Delivery Ring, and then M4 and M5. The resulting muon distribution at the end of the M5 beamline from our previous $3\times10^{12}$ protons-on-target (PoT) simulation serves as an essential input for the storage ring simulations. In 2024, to facilitate the analyses of Runs 4-6, we have updated our Muon Campus models and re-optimised certain parameters to reflect the operational currents and wire chamber measurements of the beam. For these optimisations, we employed the heterogeneous island method, implemented in our evolutionary optimisation tool, glyfada. This key update addresses the need to use the best possible beam for the storage ring simulations and thus supports the experiment's overall precision. The Muon $g\textrm{-}2$ Experiment at Fermilab has successfully achieved its $70\:\mathrm{ppb}$ systematic uncertainty goal and collected 21 times more data than its predecessor at BNL. The updated and improved Muon Campus models and simulations not only facilitate the experiment's efforts to potentially resolve the current tension between experimental measurements and theoretical predictions of $a_{\mu}$, but also provide a basis for future simulations for the Mu2e Experiment (E-973), which utilises shared Muon Campus beamlines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Simulating the CMS High Granularity Calorimeter with ML

Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the existing CMS calorimeters. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation. We study the application of state-of-the-art diffusion models to simulate particle showers in the CMS HGCal. We will discuss methods to overcome the challenges posed by the high-dimensional, irregular geometry of the HGCal. The quality of the showers produced by the diffusion model will be assessed by comparison to the full GEANT4-based simulation. The increase in simulation throughput will be quantified and methods to accelerate the diffusion model inference will also be discussed.

Amram, Oz↗
DOE OSTI2025

Simulation Center for Runaway Electron Avoidance and Mitigation (SCREAM SciDAC) (Technical Final Report)

Runaway electrons can severely damage the plasma facing components on ITER during a major disruption and pose a major risk for tokamak fusion. It has been recognized that an adequate disruption mitigation system (DMS) is essential for the safe operation of ITER. The United States is responsible for the design and implementation of the disruption mitigation system on ITER, and in July 2016 the Simulation Center for Runaway Electron Avoidance and Mitigation (SCREAM) was launched by DOE, in a joint Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) collaboration. SCREAM was a comprehensive theory and simulation SciDAC center that provided physics guidance in the avoidance and mitigation of runaway electrons, and in tandem with domestic and international experiments, helped establish the qualitative and quantitative bases for safe operational scenarios and viable mitigation techniques. The SCREAM center assembled a national team of experts in runaway electron physics, tokamak disruptions, magnetohydrodynamic (MHD) simulation, and advanced algorithms and computing. The team combined advanced simulation and analysis capability facilitated by direct participation of ASCR SciDAC institutes with theoretical models and code development by FES scientists to focus on the runaway risk for ITER and tokamaks in general. The research scope was focussed on integrated simulations of kinetic runaway electrons, including MHD and fluid models of impurity transport, within a research plan guided by theory. The specific research tasks were (1) establish the fundamental physics of runaway generation, saturation, and dynamical evolution in a tokamak; (2) examine the critical path toward runaway avoidance; and (3) investigate the viability and effectiveness of the leading candidate schemes for runaway mitigation. In all three areas, members of the team carried out scoping studies that established the readiness for rapid and critical advances, especially in the deployment and further development of large-to extreme-scale simulation tools. Our multi-pronged computational approach included (1) relativistic Fokker-Planck solvers with discretization in phase space, (2) self-consistent particle-in-cell techniques, (3) particle-based Monte-Carlo, and (4) MHD-particle hybrid simulations. Cross-check between these different methods provided an additional means for verification and further bolstered the fidelity of our physics prediction. Validation against experimental results brings confidence to the predictive capability for ITER and frequently leads to new ideas for understanding and mitigating the thermal quench driven runaway electron phenomenon.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Partnership Center for High-Fidelity Boundary Plasma Simulation (Final Report)

Within the Partnership Center for High-Fidelity Boundary Plasma Simulation (HBPS), work at UT-Austin was aimed at improved verification, validation, and uncertainty quantification (VVUQ) for edge plasma simulations and on performing gyrokinetics simulations of pedestal instabilities and turbulence in order to expand foundational understanding of pedestal transport. Regarding VVUQ, the accomplishments can be summarized as follows. First, it was shown that the Moment Preserving Constrained Resampling technique, when applied periodically in particle-in-cell simulations in the XGC code, can dramatically improve the accuracy of the simulation at essentially equivalent computational cost. Second, a technique for estimating model correlations, which are required to solve the model selection and sample allocation problem in multifidelity UQ techniques, without sampling the highest fidelity, most computationally expensive model, was developed and demonstrated. Third, previously developed methods for estimating statistical and discretization errors were applied to numerical methods relevant to edge plasma simulations, namely in particle-in-cell-based approaches, and shown to work. Finally, benchmark studies for comparing gyrokinetic codes were developed and performed, leading to reasonable agreement between four commonly used codes. Regarding physics studies, gyrokinetic simulations to investigate microtearing modes in the DIII-D pedestal were performed using the GENE code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Simulation of Particulate Transport for Delivery of Solid Amendments into the Subsurface: FY24 Status Report

For particulate-based amendments to be viable for field-scale remediation at the Hanford Site (e.g., 200 DV-1 Operable Unit), particles need to be delivered a sufficient radial distance from an injection well and retained at concentrations high enough for effective treatment. An accurate description of the particle radius of influence (ROI) is critical for developing an overall remediation strategy. However, field-scale particle simulations are currently limited due to insufficient simulation capabilities and a lack of experimental data to validate and parameterize particle transport models. To help build toward field-scale deployment, this fiscal year (FY) we have (1) developed a pre screening tool to estimate particle transport, (2) implemented particle transport models within PFLOTRAN, and (3) conducted preliminary estimations of particle ROI. While field-scale numerical simulations will ultimately be necessary before remedy design and field implementation, we have developed a pre-screening tool that offers valuable estimations of expected particle injectability and ROI in a 1-D system. The advantage of the tool is that it does not require extensive laboratory experiments and instead makes predictions based solely on routine laboratory measurements. This tool can assist in down-selection and decision-making by identifying which particle amendment systems are worth pursuing in future laboratory experiments, such as 1-D column tests and beyond. With any system, scaling up from the lab to the field presents challenges. Currently, there is no field data available for model calibration or validation. However, the theoretical particle models being developed herein are the best tools available to guide progress toward field deployment. To help bridge this gap and verify model predictions, larger-scale lab experiments are being proposed. To advance simulation capabilities, six particle transport models are being integrated into the reactive transport simulator PFLOTRAN. These include colloid filtration theory (CFT) and five additional particle transport models (M1-M5). Each model, from M1 to M5, progressively incorporates additional particle transport and retention processes. Ultimately, the simplest model capable of accurately describing 1-D column data will be selected and parameterized. During FY24, the CFT and M1 model have been fully implemented within PFLOTRAN. Using an existing 1 D column experiment, the two currently implemented particle transport models (CFT and M1), and associated parameters, were fit to this experiment. While simpler model formulations are helpful for estimations, these formulations could not fully describe particle transport and retention behavior in the previous 1-D column experiment. Thus, additional complexities will need to be considered, which will be accounted for in the M2-M5 model formulations. Additionally, because a viscous, shear thinning fluid was required to keep particles in suspension, considerations for flow will also need to also be accounted for. Therefore, a new immiscible two-phase flow mode is currently being implemented in PFLOTRAN. With some modifications, this new flow module could also support simulation of non-Newtonian liquid amendments, foams, and emulsions. We also estimated the expected ROI of solid amendments using 1-D simulations. The average predicted ROI was approximately 15 ft for micron-sized zero valent iron (mZVI) suspended in xanthan gum (XG). Using the pre screening tool and ROI estimates, additional amendment-delivery laboratory characterization and experiments are proposed. The results from additional experiments can be used to validate and parametrize particulate transport model formulations, which will ultimately provide predictive capabilities for field amendment-delivery systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗