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DOE OSTI2024

NORTH DAKOTA CARBONSAFE PHASE III: SITE CHARACTERIZATION AND PERMITTING OF GEOLOGIC STORAGE OF CARBON DIOXIDE

The Energy & Environmental Research Center (EERC), in partnership with Minnkota Power Cooperative Inc. (Minnkota), SLB, and Computer Modelling Group Ltd. (CMG), supported wide-scale deployment of carbon capture and storage (CCS) as part of the U.S. Department of Energy (DOE) National Energy Technology Laboratory Carbon Storage Assurance Facility Enterprise (CarbonSAFE) Initiative Phase III. This phase included the acquisition, analysis, and development of information to fully characterize two storage complexes to demonstrate viable storage resources for commercial volumes of CO2 (defined by DOE as a minimum of 50 million tonnes [MMt] of CO2 within a 30-year period) (National Energy Technology Laboratory, 2024). Phase III also involved the preparation, submission, and approval of North Dakota underground injection control (UIC) Class VI storage facility permits (SFPs)—required precursors to applications for Class VI injection well permits. The presumed viability of commercial-scale CCS, situated adjacent to Minnkota’s Milton R. Young Station (MRYS), is validated by Minnkota’s continued pursuit of Project Tundra—an initiative to build the world’s largest lignite-based CCS project in central North Dakota (www.projecttundrand.com). Project Tundra comprises two scopes of work, Tundra Capture (installation of postcombustion CO2 capture at MRYS) and Tundra SGS (secure geologic storage). The efforts of North Dakota CarbonSAFE Phase III, Site Characterization and Permitting, supported Tundra SGS. Extensive site-specific characterization activities included a successful multimeasurement geophysical approach and drilling a stratigraphic test well (J-ROC 1, subsequently renamed Liberty-1) adjacent to MRYS. Core collection and analyses, downhole testing and fluid sampling, and geophysical logging were performed on J-ROC 1 and on a nearby stratigraphic test well (known as J-LOC 1), which was drilled, cored, and tested under a complementary project funded by the North Dakota Lignite Research Program. The injection tests performed on J-LOC 1 positively impacted the CarbonSAFE project, resulting in fewer proposed injection wells and significant construction, operations, and monitoring cost savings. The characterization data collected and analyses performed were integrated into geologic models, and successive numerical simulations were run to determine CO2 plume extent and subsurface pressure buildup associated with the planned CO2 injection rate of nearly 4 MMt per year. The latter doubles the CarbonSAFE Initiative goal with an estimated 100 MMt of CO2 stored in 20 years. Application of the U.S. Environmental Protection Agency’s (EPA’s) method for estimating the Class VI Rule area of review (AOR) to the overpressurized Broom Creek Formation inspired an alternative method of calculation, called risk-based AOR delineation. This peer-reviewed method was applied for the first time during the storage facility-permitting process. The two SFP applications submitted in 2021 successfully resulted in North Dakota Industrial Commission (NDIC) orders in 2022 authorizing the creation of the storage facility areas and amalgamation of pore space as well as establishing financial responsibility requirements. After approval of the SFPs, Minnkota filed in 2022 applications for permits to reenter the J-ROC 1 well and to drill two new wells—all with the intended purpose to become Class VI injection wells. To establish eligibility under the Internal Revenue Code for Section 45Q tax incentives, a monitoring, reporting, and verification (MRV) plan was prepared and submitted by Minnkota to EPA in 2022, resulting in the first such plan approved in North Dakota. Also in 2022, under the National Environmental Policy Act (NEPA), Minnkota prepared and submitted an environmental information volume (EIV) describing the proposed CCS project and associated potential environmental impacts. Based on the EIV, DOE determined that the proposed construction project required an environmental assessment, and Minnkota submitted the first draft in 2023 and a revised draft in 2024. Both submissions were followed by a public comment period. Subsequently, DOE issued a finding of no significant impact (FONSI) on September 13, 2024. A successful outreach program, strongly based in the production, presentation, and dissemination of informational material, fostered an environment to aid stakeholders in making informed decisions regarding the planned project. Opportunities for public input were provided at various steps along the way, including at county planning and zoning meetings, before and during the SFP administrative hearing, and during environmental assessment public comment periods. In addition, land/pore space owners and mineral owners had various points of contact, including granting access rights, securing pore space leasing, and mineral owner notifications. Based upon the successful storage facility permitting issued by NDIC, approval of the MRV plan by EPA, and receipt of a FONSI under the NEPA, Minnkota is continuing its pursuit of Project Tundra. In December 2023, the Office of Clean Energy Demonstrations under its Carbon Capture Demonstrations Projects Program announced funding for the capture system (Office of Clean Energy Demonstrations, 2023) and a proposal for CarbonSAFE Phase IV: Construction funding was submitted in March 2024 for the storage project. A go/no-go decision to proceed with construction and operations in the Broom Creek Formation is anticipated in 2024. References National Energy Technology Laboratory, CarbonSafe Initiative, https://netl.doe.gov/carbon-management/carbon-storage/carbonsafe (accessed August 2024). Office of Clean Energy Demonstrations, 2023, OCED selects three projects in CA, ND, and TX to reduce harmful carbon pollution, create new economic opportunities, and advance carbon reducing technologies, December, www.energy.gov/oced/articles/oced-selects-three-projects-ca-nd-and-tx-reduce-harmful-carbon-pollution-create-new (accessed August 2024).

Peck, Wesley↗
DOE OSTI2024

A multiscale anisotropic polymer network model coupled with phase field fracture

The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local-global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double-edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. In conclusion, the results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber-like materials.

42 ENGINEERING↗
DOE OSTI2025

Spin dynamics in the itinerant antiferromagnet SrCr 2 ⁢As 2

SrCr 2 ⁢As 2 is an itinerant antiferromagnet in the same structural family as the SrFe 2 ⁢As 2 high-temperature superconductors. Here, we report our calculations of exchange-coupling parameters 𝐽 𝑖⁢𝑗 for SrCr 2 ⁢As 2 using a static linear-response method based on first-principles electronic-structure calculations. We find that the dominant nearest-neighbor exchange coupling 𝐽 1 >0 is antiferromagnetic whereas the next-nearest-neighbor exchange coupling 𝐽 2 <0 is ferromagnetic with 𝐽 2 /𝐽 1 = −0.68, reinforcing the checkerboard in-plane magnetic structure. Thus, unlike other transition-metal arsenides based on Mn, Fe, or Co, we find no competing magnetic interactions in SrCr 2 ⁢As 2 , which aligns with experimental findings. Moreover, the orbital resolution of exchange interactions shows that 𝐽 1 and 𝐽 2 are dominated by direct exchange mediated by the Cr 𝑑 orbitals. To validate the calculations we conduct inelastic neutron-scattering measurements on powder samples that show steeply dispersive magnetic excitations arising from the magnetic Γ points and persisting up to energies of at least 175 meV. The spin-wave spectra are then modeled using the Heisenberg Hamiltonian with the theoretically calculated exchange couplings. In conclusion, the calculated neutron-scattering spectra are in good agreement with the experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2021

Testing the DIII-D co/counter off-axis neutral beam injected power and ability to balance injected torque

DIII-D has undergone a major upgrade and successfully injected high power off-axis neutral beams (~4 MW) in both co-current and counter-current directions. This capability of high power co/counter steerable off-axis neutral beams on a major tokamak opens a unique parameter space of broad pressure and current profiles for high beta steady-state advanced tokamak (AT) scenarios, while retaining the ability to balance the injected torque for low rotation studies. This co/counter off-axis neutral beam capability is being used to validate physics-based energetic particle and thermal transport models for designing next-step facilities based on the steady-state AT approach. This paper reports on the critical evaluation of the transmitted power and energetic ion population produced by this heating and current drive system, which is assessed through visible imaging, neutron measurements and rotation profile measurements at balanced torque. Minimal losses of neutral beam power have been achieved by optimizing the strongly focused ion sources required to pass through the aperture. Tilting of the ion source has been guided by fast visible imaging and resulted in neutral beam injection along the design centerline with empirical characterization of each beam's divergence derived from the imaging data and used in the NUBEAM description of the beam injection. Through exclusive power injection of each neutral beam into MHD quiescent plasmas across a range of neutral beam voltage, perveance and plasma current we conclude that a modest reduction (~10%–15%) of transmitted power compared to on-axis, standard focus has been incurred. We report corrections that more accurately represent the injected power. Good ability to balance the neutral beam torque has been demonstrated by injecting the new off-axis counter injecting beam against the existing off-axis co-injected beam in 2.0 T, 1.0 MA, MHD quiescent L-mode plasmas. Furthermore, the torque balance studies verify the ability to operate with balanced injection, which is critical for achieving low torque and low rotation operation for physics studies and in ITER demonstration discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index

Background Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. Methods and findings We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality–no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77–0.81), and in early (AUC = 0.81 95% CI: 0.78–0.83) and late (AUC = 0.84, 95% CI: 0.78–0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60–64 years, overall mortality was estimated at 9% (95% CI: 6–11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3–7%) to 21% (95% CI: 12–31%), depending on sex and comorbid disease. Conclusion Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

Abstract The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Kaste, Joshua A. M.↗
DOE OSTI2023

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct Numerical Simulation of the Flow through a Randomly Packed Pebble Bed

The proposition for molten salt and high-temperature gas-cooled reactors has increased the focus on the dynamics and physics in randomly packed pebble beds. Research is being conducted on the validity of these designs as a possible contestant for the fourth-generation nuclear power systems. A detailed understanding of the coolant flow behavior is required in order to ensure proper cooling of the reactor core during normal and accident conditions. In order to increase the understanding of the flow through these complex geometries and enhance the accuracy of lower-fidelity modeling, high-fidelity approaches such as direct numerical simulation (DNS) can be utilized. Nek5000, a spectral-element computational fluid dynamics (CFD) code, was used to develop DNS fluid flow data. The flow domain consisted of 147 pebbles enclosed by a bounding wall. In the work presented, the Reynolds numbers ranged from 430 to 1050 based on the pebble diameter and inlet velocity. Characteristics of the flow domain such as volume averaged porosity, axial porosity, and radial porosity were studied and compared with correlations available in the literature. Friction factors from the DNS results for all Reynolds numbers were compared with correlations in the literature. The first- and second-order statistics show good agreement with the available experimental data. Turbulence length scales were analyzed in the flow. Reynolds stress anisotropy was characterized by utilizing invariant analysis. Overall, the results of the analysis in this study provide deeper understanding of the flow behavior and the effect of the wall in packed beds.

Yildiz, Mustafa Alper↗
DOE OSTI2022

Advanced Distributed Wind Turbine Controls Series: Part 2-Wind Energy in Isolated Grids – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

In an isolated grid, wind turbines are typically deployed to provide energy to maximize energy production, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) this paper demonstrates, through desktop simulations, advanced wind turbine controls that can be employed to support higher contributions of wind in isolated grids, and to demonstrate ways that wind can play a role in supporting stability of an isolated grid. This isolated grid used in these desktop simulations is comprised of a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a simulated diesel generator (2 MW) and various types of loads (critical, dynamic). We developed a model of the subsystems in MATLAB/Simulink and validated them with available data from real-world components on NREL's Flatirons Campus. These validated models are then configured for various case studies. We compare the output of the desktop simulation with a baseline case with the diesel generator. Active and reactive power control of the wind turbine can help improve frequency and voltage responses in the isolated grid, respectively. By utilizing a small integrated battery energy storage system in the DC-link of the wind turbine, we also demonstrate that wind turbines can help blackstart a critical load comparable to its rated power and support other renewables (e.g. solar PV) come online and pick up an additional load. This report illustrates some of these reliability and resilience services a wind turbine can provide in an isolated grid.

17 WIND ENERGY↗
DOE OSTI2023

Thermal Hydraulic Experimental Test Article - Fiscal Year 2023 (Final Report)

The Thermal Hydraulic Experimental Test Article (THETA) is a facility that is used to develop sodium components and instrumentation as well as acquire experimental data for validation of reactor thermal hydraulic and safety analysis codes. The facility simulates nominal conditions as well as protected/unprotected loss of flow accidents in a sodium-cooled fast reactor (SFR). High fidelity distributed temperature profiles of the developed flow field may be acquired with Rayleigh backscatter based optical fiber temperature sensors. The facility was designed in partnership with systems code experts to tailor the experiment to ensure the most relevant and highest quality data for code validation. THETA is comprised of a traditional primary coolant and secondary coolant system. The primary system is submerged in the pool of sodium and consists of a pump, electrically heated core, intermediate heat exchanger, and connected piping and thermal barriers (redan). The secondary system, located outside of the sodium pool, consists of a pump, sodium to air heat exchanger, and connected piping and valves. To date a test matrix has been completed utilizing the primary system of THETA. These tests, along with computational fluid dynamics and systems code models, determined the heat transfer across the core barrel and intermediate heat exchanger outlet was too great to effectively represent scaled thermal hydraulic phenomena of a liquid metal cooled reactor. Therefore, a significant effort was made to remove the primary system from the METL 28” test vessel #4 and clean the residual sodium from the primary system to facilitate upgrades. Thermal insulation was then incorporated in the core barrel and intermediate heat exchanger outlets. The primary system was then replaced, and a series of tests were performed to assess the performance of the thermal insulation. With primary system testing and upgrades complete, the secondary system could then be brought online. The tube side of the shell-and-tube intermediate heat exchanger was installed onto the primary system flange to begin installing the secondary system. The support structure for the secondary system was then erected on the METL mezzanine alongside the THETA primary system to facilitate installation of the secondary system components (sodium-to-air heat exchanger, flowmeter and pump). The piping and expansion tank were welded into the secondary system. Non-destructive examination of the secondary system welds was completed in order to satisfy ASME B31.3 pipe code for class M process fluids. The heating system and insulation were then added to prepare the system to be filled with sodium. The ancillary electrical equipment was installed which included the pump control box, blower VFD, pipe heater control system, etc. The secondary system will be filled, and a test matrix will be completed in early FY2024.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Score-based deterministic density sampling

We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density π given only its score ∇ log π. Our method approximates the Wasserstein gradient flow on KL($f_t$∥π) by learning the time-varying score ∇ log $f_t$ on the fly using score matching. While having the same marginal distribution as Langevin dynamics, our method produces smooth deterministic trajectories, resulting in monotone noise-free convergence. We prove that our method dissipates relative entropy at the same rate as the exact gradient flow, provided sufficient training. Numerical experiments validate our theoretical findings: our method converges at the optimal rate, has smooth trajectories, and is often more sample efficient than its stochastic counterpart. Experiments on high-dimensional image data show that our method produces high-quality generations in as few as 15 steps and exhibits natural exploratory behavior. The memory and runtime scale linearly in the sample size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An open-access simulated earthquake ground-motion database for an M7 Hayward Fault earthquake in the San Francisco Bay Region

Comprehensive understanding of earthquake ground motions, particularly in the near-fault region of large-magnitude events, is limited by gaps in strong-motion data. This challenge is prominent in areas with high seismic hazard but infrequent large earthquakes where data is sparse and difficult to interpret. These data limitations lead to uncertainties in the development of site-specific ground motions, which are crucial for engineering risk assessments. To address these challenges, physics-based regional-scale ground-motion simulations have been developed. With the emergence of exaflop-scale computing ecosystems, it is now possible to simulate regional earthquake processes at unprecedented fidelity and generate the large number of fault rupture realizations necessary to characterize both intra- and inter-event ground-motion variability. This article introduces a new database of simulated earthquake ground motions, created for applications in earthquake engineering, earthquake planning, and emergency response. The inaugural version of the database features simulated ground motions for a magnitude 7 Hayward Fault earthquake in the San Francisco Bay Region (SFBR), using the EarthQuake SIMulation (EQSIM) simulation framework and the Graves–Pitarka kinematic rupture model. The aim is to provide high-fidelity, spatially dense, three-component motions generated on the Department of Energy’s (DOE) newest generation of graphics processing unit (GPU)-accelerated supercomputers. These motions are being made openly available to the engineering, scientific, and disaster planning communities. In addition, this work develops protocols for the efficient dissemination of these large data sets and emphasizes community engagement to build confidence in their application. This article discusses the methodology behind the data, underlying software verification and validation, scalable data management, and a user interface for data access. The goal is to facilitate widespread use and elicit expert feedback to maximize the utility and exploitation of simulated motions. While the initial focus is on the San Francisco Region, simulations for additional regions will be added as the DOE program progresses.

Simulated ground-motion database↗
DOE OSTI2022

Experimental and kinetic modeling study of the low- temperature and high-pressure combustion chemistry of straight chain pentanol isomers: 1-, 2- and 3-Pentanol

Pentanols have received significant attention as a potential alternative fuel or fuel additive owing to their high energy densities and low vapor pressure. The development of robust chemical kinetic models for alternative fuels which can provide accurate and efficient predictions of combustion performance across a wide range of engine relevant conditions is important in developing cleaner, more efficient combustors. Although the high temperature oxidation kinetics of pentanol isomers has been researched considerably, their low temperature combustion chemistry needs further investigation. While previously proposed low temperature mechanisms for 1-pentanol based on analogy and rate rules need further refinement, the low temperature oxidation kinetics of 2-pentanol and 3-pentanol has not been studied previously by any means, experimentally or theoretically. A newly developed kinetic mechanism is presented in this work for the three straight chain pentanol isomers: 1-, 2- and 3-pentanol. Low temperature kinetics is based on a recent study by Lockwood et al., 2022 [20] involving theoretical calculations at the CCSD(T)/cc-pV∞Z level of theory for the oxidation pathways involving alcohol peroxy radicals. Rate of production analyses performed in this study highlight the importance of the newly added pressure-dependent reactions of the α-alcohol peroxy radical forming an RO 2 adduct. While the α-alcohol fuel radical reacts with O 2 to directly decompose via a chemically activated pathway at low pressures, the formation of the RO 2 adduct is favored at high pressures. Furthermore, the detailed model is comprehensively validated against new ignition experiments at low temperature and high pressure, together with the wide range of data available in the literature. Both qualitative and quantitative predictions of the experimental data using the proposed kinetic model are satisfactory for all three pentanol isomers studied here.

1-pentanol↗
DOE OSTI2023

Direct imaging of shock wave splitting in diamond at Mbar pressure

Understanding the behavior of matter at extreme pressures of the order of a megabar (Mbar) is essential to gain insight into various physical phenomena at macroscales—the formation of planets, young stars, and the cores of super-Earths, and at microscales—damage to ceramic materials and high-pressure plastic transformation and phase transitions in solids. Under dynamic compression of solids up to Mbar pressures, even a solid with high strength exhibits plastic properties, causing the induced shock wave to split in two: an elastic precursor and a plastic shock wave. This phenomenon is described by theoretical models based on indirect measurements of material response. The advent of x-ray free-electron lasers (XFELs) has made it possible to use their ultrashort pulses for direct observations of the propagation of shock waves in solid materials by the method of phase-contrast radiography. However, there is still a lack of comprehensive data for verification of theoretical models of different solids. Here, we present the results of an experiment in which the evolution of the coupled elastic–plastic wave structure in diamond was directly observed and studied with submicrometer spatial resolution, using the unique capabilities of the x-ray free-electron laser (XFEL). The direct measurements allowed, for the first time, the fitting and validation of the 2D failure model for diamond in the range of several Mbar. Our experimental approach opens new possibilities for the direct verification and construction of equations of state of matter in the ultra-high-stress range, which are relevant to solving a variety of problems in high-energy-density physics.

36 MATERIALS SCIENCE↗