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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Benchmark Performance Metrics of a Vane Propellant Management Device for a 0.15 m3 Liquid Hydrogen Tank

The use of cryogenic propellants has and will continue to play an integral role in manned-space exploration due the high specific impulses offered and its ubiquity through in-situ resource utilization. But guaranteeing vapor-free transfer of such low-surface tension liquids is difficult for traditional capillary-action propellant management devices (PMDs). Screen-channel liquid acquisition devices and compliant origami bladders are potential solutions, but to quantify the benefits these technologies offer, this paper present a cast study analyzing the performance metrics of an orthodox vane PMD as a benchmark for comparison. A 0.15 m3 liquid hydrogen tank at 20.3 K and 103 kPa was selected for study. Then assuming no body forces and no heat transfer (for simplicity), a steady-state 1-D differential equation was numerically solved in tandem with four possible wetted area configurations to yield an expulsion efficiency for a theoretically maximum inputted expulsion flow rate. Maximization of the flow rate was constrained by the onset of choked flow. Additional inputs, including vane height and vane number, were parametrically varied between 0.1 – 10 cm and 4 – 28 vanes, respectively.

Liquid Acquisition Device↗
DOE OSTI2021

Cross Section Generation Capability in Griffin

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code reads multigroup cross sections in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. The implementation of the cross section generation capability in Griffin was initiated last year by plugging in the cross section application programming interface (CSAPI) and reviewing the methodologies for treating particulate fuels. The focus this year was on improving the CSAPI integration and implementing advanced self-shielding methods for applications to advanced reactor problems with TRISO fuels. First, the process for cross section library generation was updated to accurately and rigorously produce isotopic cross section data. Second, the on-the-fly slowing down method for the resonance treatment was implemented in CSAPI to improve the accuracy of effective multigroup cross sections in the resonance energy range. Among various on-the-fly slowing down methods, the equivalent Dancoff factor cell method was employed. Third, the iterative local spatial self-shielding method was implemented under the calculation framework of the equivalent Dancoff factor cell method to accurately deal with the double heterogeneity effect of particulate fuel. The updated CSAPI with the advanced self-shielding methods, together with the cross section libraries generated based on the improved process, were tested for the very high temperature reactor (VHTR), high temperature test reactor (HTTR), and Empire benchmark problems with various resonance self-shielding conditions, indicating that the updated CSAPI in Griffin is able to produce multigroup cross sections accurately and efficiently. We also show that the methodology works well for pebble bed fuel from HTR-10, but the capability still needs to be fully integrated into CSAPI. In the future, further benchmark tests will be performed for various thermal reactor core problems, including particulate fuel-based pebble bed reactors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Radio frequency mixing modules for superconducting qubit room temperature control systems

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost, and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing boards to address this challenge by integrating I/Q quadrature mixing, intermediate frequency/LO (local oscillator)/RF power level adjustments, and direct current bias fine tuning on a 40 × 80 mm 2 four-layer printed circuit board with electromagnetic interference shielding. The RF mixing module is designed to work with RF and LO frequencies between 2.5 and 8.5 GHz. The typical image rejection and adjacent channel isolation are measured to be ~27 dBc and ~50 dB. By scanning the drive phase in a loopback test, the module short-term amplitude and phase linearity are typically measured to be 5 ×10 -4 (V pp /V mean ) and 1 ×10 -3 radian (pk-pk). The operation of the RF mixing board was validated by integrating it into the room temperature control system of a superconducting quantum processor and executing randomized benchmarking characterization of single and two qubit gates. We measured a single-qubit process infidelity of 9.3(3) × 10 -4 and a two-qubit process infidelity of 2.7(1) × 10 -2 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

GRinding Automated Classification Engine

This work is an ML-driven framework for automated surface analysis of microscopy images. We create a training dataset by imaging stainless steel samples to benchmark four developed deep neural network architectures. These models, based on a YOLOv8n-cls backend, integrate image features and process metadata using various fusion methods to distinguish between acceptable and unacceptable surface finishes. This code is associated with publication "Classifying Alloy Surface Preparation Quality with Metadata-Infused Machine Learning for Rapid Alloy Discovery" for project APEX LDRD-ER (25-ERD-039)

Gongora, AldairE [Lawrence Livermore National Labo↗
DOE OSTI2019

Theoretical Determination of Size Effects in Zeolite-Catalyzed Alcohol Dehydration

In the upgrading of biomass pyrolysis vapors to hydrocarbons, dehydration accomplishes a primary objective of removing oxygen, and acidic zeolites represent promising catalysts for the dehydration reaction. Here, we utilized density functional theory calculations to estimate adsorption energetics and intrinsic kinetics of alcohol dehydration over H-ZSM-5, H-BEA, and H-AEL zeolites. The ONIOM (our Own N-layered Integrated molecular Orbital and molecular Mechanics) calculations of adsorption energies were observed to be inconsistent when benchmarked against QM (Quantum Mechanical)/Hartree–Fock and periodic boundary condition calculations. However, reaction coordinate calculations of adsorbed species and transition states were consistent across all levels considered. Comparison of ethanol, isopropanol (IPA), and tert-amyl alcohol (TAA) over these three zeolites allowed for a detailed examination of how confinement impacts on reaction mechanisms and kinetics. The TAA, seen to proceed via a carbocationic mechanism, was found to have the lowest activation barrier, followed by IPA and then ethanol, both of which dehydrate via a concerted mechanism. Barriers in H-BEA were consistently found to be lower than in H-ZSM-5 and H-AEL, attributed to late transition states and either elevated strain or inaccurately estimating long-range electrostatic interactions in H-AEL, respectively. Molecular dynamics simulations revealed that the diffusivity of these three alcohols in H-ZSM-5 were significantly overestimated by Knudsen diffusion, which will complicate experimental efforts to develop a kinetic model for catalytic fast pyrolysis.

09 BIOMASS FUELS↗
NASA NTRS1990

Multiprocessors and runtime compilation

Runtime preprocessing plays a major role in many efficient algorithms in computer science, as well as playing an important role in exploiting multiprocessor architectures. Examples are given that elucidate the importance of runtime preprocessing and show how these optimizations can be integrated into compilers. To support the arguments, transformations implemented in prototype multiprocessor compilers are described and benchmarks from the iPSC2/860, the CM-2, and the Encore Multimax/320 are presented.

Saltz, Joel↗
NASA NTRS2001

Worldwide Historical Estimates of Leaf Area Index, 1932-2000

Approximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LA1 is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, and other materials. It also plays an integral role in determining the energy balance of the land surface. This data set provides a benchmark of typical values and ranges of LA1 for a variety of biomes and land cover types, in support of model development and validation of satellite-derived remote sensing estimates of LA1 and other vegetation parameters. The LA1 data are linked to a bibliography of over 300 originalsource references.This report documents the development of this data set, its contents, and its availability on the Internet from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. Caution is advised in using these data, which were collected using a wide range of methodologies and assumptions that may not allow comparisons among sites.

Scurlock, J. M. O.↗
NASA NTRS2017

SpaceCubeX: A Framework for Evaluating Hybrid Multi-Core CPU FPGA DSP Architectures

The SpaceCubeX project is motivated by the need for high performance, modular, and scalable on-board processing to help scientists answer critical 21st century questions about global climate change, air quality, ocean health, and ecosystem dynamics, while adding new capabilities such as low-latency data products for extreme event warnings. These goals translate into on-board processing throughput requirements that are on the order of 100-1,000 more than those of previous Earth Science missions for standard processing, compression, storage, and downlink operations. To study possible future architectures to achieve these performance requirements, the SpaceCubeX project provides an evolvable testbed and framework that enables a focused design space exploration of candidate hybrid CPU/FPGA/DSP processing architectures. The framework includes ArchGen, an architecture generator tool populated with candidate architecture components, performance models, and IP cores, that allows an end user to specify the type, number, and connectivity of a hybrid architecture. The framework requires minimal extensions to integrate new processors, such as the anticipated High Performance Spaceflight Computer (HPSC), reducing time to initiate benchmarking by months. To evaluate the framework, we leverage a wide suite of high performance embedded computing benchmarks and Earth science scenarios to ensure robust architecture characterization. We report on our projects Year 1 efforts and demonstrate the capabilities across four simulation testbed models, a baseline SpaceCube 2.0 system, a dual ARM A9 processor system, a hybrid quad ARM A53 and FPGA system, and a hybrid quad ARM A53 and DSP system.

Hybrid Flight Architectures↗
NASA NTRS2018

Inverse Determination of Aeroheating and Charring Ablator Response

The Mars Science Laboratory (MSL) was protected during its Mars atmospheric entry by an instrumented heatshield that used NASA's Phenolic Impregnated Carbon Ablator (PICA). PICA is a lightweight carbon fiber/polymeric resin material that offers excellent performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of atmospheric entry and material response. MEDLI recorded, among others, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP). These measurements have been widely used in the literature as a validation benchmark for state-of-the-art ablation codes. The objective of this work is to perform an inverse estimate of the MSL heatshield material properties and aerothermal environment during Mars entry from the MISP flight data.

Aeroheating↗
DOE OSTI2013

The Evolution of LTS at DOE's Hanford Site

Hanford's Long-Term Stewardship (LTS) Program has evolved from a small, informal process, with minimal support, to a robust program that provides comprehensive transitions from cleanup contractors to long-term stewardship for post-cleanup requirements specified in the associated cleanup decision documents. The LTS Program has the responsibility for almost 100,000 acres of land, along with over 200 waste sites and will soon have six cocooned reactors. Close to 2,600 documents have been identified and tagged for storage in the LTS document library. The program has successfully completed six consecutive transitions over the last two years in support of the U.S. DOE Richland Operations Office's (DOE-RL) near-term cleanup objectives of significantly reducing the footprint of active cleanup operations for the River Corridor. The program has evolved from one that was initially responsible for defining and measuring Institutional Controls for the Hanford Site, to a comprehensive, post remediation surveillance and maintenance program that begins early in the transition process. In 2013, the first reactor area -- the cocooned 105-F Reactor and its surrounding 1,100 acres, called the F Area was transitioned. In another first, the program is expected to transition the five remaining cocooned reactors into the program through using a Transition and Turnover Package (TTP). As Hanford's LTS Program moves into the next few years, it will continue to build on a collaborative approach. The program has built strong relationships between contractors, regulators, tribes and stakeholders and with the U.S. Department of Energy's Office of Legacy Management (LM). The LTS Program has been working with LM since its inception. The transition process utilized LM's Site Transition Framework as one of the initial requirement documents and the Hanford Program continues to collaborate with LM today. One example of this collaboration is the development of the LTS Program's records management system in which, LM has been instrumental. The development of a rigorous data collection and records management systems has been influenced and built off of LMs success, which also ensures compatibility between what Hanford's LTS Program develops and LM. In another example, we are exploring a pilot project to ship records from the Hanford Site directly to LM for long-term storage. This pilot would gain program efficiencies so that records would be handled only once. Rather than storage on-site, then shipment to an interim Federal Records Center in Seattle, records would be shipped directly to LM. The Hanford LTS Program is working to best align programmatic processes, find efficiencies, and to benchmark site transition requirements. Involving the Hanford LTS Program early in the transition process with an integrated contractor and DOE team is helping to ensure that there is time to work through details on the completed remediation of transitioning areas. It also will allow for record documentation and storage for the future, and is an opportunity for the program to mature through the experiences that will be gained by implementing LTS Program activities over time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Policy, Permit, Perform: Using City Benchmarking Data and Building Construction Permit History to Identify Energy Performance Improvements

The City of Portland (City) and Multnomah County 2015 Climate Action Plan targets a 40 percent reduction in carbon emissions below 1990 levels by 2030. To reach this goal, the plan includes a key objective to reduce the total energy use of existing buildings by 25 percent. Buildings are responsible for one-half of carbon emissions in Portland, Oregon, U.S., and improving their performance is critical to achieving the City’s climate goals. As part of its climate action planning, the City passed an ordinance on April 22, 2015, requiring commercial buildings 20,000 ft2 and larger to benchmark and disclose annual energy performance metrics through ENERGY STAR® Portfolio Manager® (ESPM), including greenhouse gas emissions, building energy use intensity (EUI) and ENERGY STAR scores. Twenty-five cities in the U.S. have adopted similar policies requiring energy benchmarking. These policies are generating a treasure trove of data about the drivers of building performance, but practitioners are only beginning to link benchmarking data to other datasets to produce actionable insight into improving building performance. In 2016, the City received a financial assistance award from the U.S. Department of Energy (DOE) through the Cities Leading through Energy Analysis and Planning project (Award Number DE-0007737). Portland’s project evaluated the application of the DOE Building Energy Asset Score Tool and rating system developed by Pacific Northwest National Laboratory (PNNL) to commercial buildings that report to the City. The City used building construction permit history to develop Asset Scores for a set of office buildings that reported energy performance for 2015 – the first year the City mandated energy benchmarking and reporting for commercial buildings 50,000 ft2 and larger. The primary project objective was to link energy benchmarking data and commercial building permit data to analyze commercial buildings systems that present the best, specific opportunities to improve energy performance. Integrating these datasets from two City departments, the Bureau of Development Services and the Bureau of Planning and Sustainability, could expand Portland’s use of analytics to inform climate policy decisions and strategic targeting of financial incentives for building owners and managers who are best positioned to improve the performance of their buildings. The project began with the commercial benchmarking data received for 340 commercial buildings that submitted calendar year 2015 energy reports. The City retained Research Into Action and Energy 350 (RIA team) to evaluate this self-reported benchmarking data. Based on a survey of 53 building managers, the RIA team found that most respondents used the City’s compliance tools, including a How-to Guide and Help Desk, and found access to utility data to be straightforward. However, almost one-third of the respondents encountered challenges when estimating the gross floor area (GFA), and some experienced confusion regarding the input of property use types and details into ESPM. Based on the RIA team’s recommendations, the City refined its How-to Guide, created online answers to frequently asked questions, and hosted office hour sessions to help building managers accurately characterize their building’s physical characteristics and operations in ESPM. Concurrently with the RIA team’s evaluation, the City worked with PNNL to estimate DOE Asset Score Preview (Preview) scores for 181 of the 340 commercial buildings that submitted ESPM reports to the City. Asset Score Tool is a web-based tool developed by PNNL for DOE to evaluate and rate the as-built energy efficiency of a building. Asset Score Tool’s full analysis capability runs a detailed energy model based on building geometry, age, envelope, lighting and mechanical systems using standard operating assumptions to give the building a score, similar to a miles per gallon rating for a car. Preview, a simplified deployment of the full Asset Score Tool, provides a preliminary score range using only seven data points, six of which are readily available in the ESPM benchmarking data. Analysis of the combined Preview scores and the operational ESPM scores identified buildings with a high potential for energy improvements through either retro-commissioning and retuning of existing operations or capital investments into building systems. The City contracted Whole Building Solutions (WBS) to mine building permit history and develop full Asset Scores for a subset of 26 office buildings that were eligible for both ENERGY STAR scores and Asset Scores. WBS researched permits and building plans to identify mechanical, lighting and envelope systems and other building characteristics necessary to complete Asset Scores. This historical data was recorded using the web-based Asset Score Tool, which evaluates the as designed energy efficiency of existing buildings. Much of this City information was available online after 2010; however, WBS had to search through microfiche files and the City’s database to obtain permit and plan history prior to 2010. Although building geometry, mechanical system type and basic envelope information was consistently identifiable, WBS found permit data gaps that prevented complete and accurate Asset Scores for most of the office buildings. If the City decides to develop Asset Scores based on building permit information in the future, WBS recommended requiring lighting plans, control diagrams and as-built drawings that reflect how the buildings are actually constructed. In addition, WBS recommended uploading plans and permits from microfiche storage into a more comprehensive, searchable database consistent with the City’s current online permit information. To identify the actual lighting types, WBS and City staff conducted site visits of all 26 office buildings. As part of managing the flow of ESPM and Asset Score data for the project, Earth Advantage, a sub-recipient of the Cities-LEAP award, created a DOE Standard Energy Efficiency Data (SEED) (USDOE 2018) instance for the City and used Asset Score’s Application Programming Interface (API) (Asset Score 2018) capabilities to develop two new features in the SEED Platform™: (1) automated Asset Score Preview ranges calculated based on benchmarking data and the City’s estimate of number of building floors; and (2) storage of full Asset Score information. Leveraging the construction permit history and site visit information for the 26 office buildings, The Cadmus Group (Cadmus) analyzed benchmarking data and Asset Score information to identify opportunities and drivers to improve performance. Correlation and regression analysis of the combined dataset was completed to determine whether building operations or specific systems – mechanical, lighting or envelope – present the best opportunities to improve energy performance. The central outcome of this project was Cadmus’ detailed analysis of the richer dataset that was created by combining the City of Portland’s commercial building permit activity and building-specific energy performance information based on metrics from ESPM and Asset Score Tool. Some of their findings collaborate with well-known facts, such as buildings with less efficient lighting systems are good targets for efficiency improvements through lighting upgrades. This analysis also helped identify that individual heating, ventilation, and air conditioning (HVAC) equipment efficiency does not necessarily correlate with building performance. However, overall HVAC system performance, as measured with the Total System Performance Ratio (TSPR) metric, is a better indicator of building performance (Goel et al. 2014). As a metric, TSPR considers whole system performance that includes equipment efficiency and system controls, such as resets for static pressure and chilled water setpoints, which can be more effective and financially feasible options for improving building performance. Given the high cost associated with envelope retrofits, opportunities to upgrade envelope insulation are likely to be limited. However, buildings with single-pane and non-thermal break windows are good candidates for window replacements, such as upgrading to double-pane windows with thermal breaks. This research project determined that developing Asset Scores based on site visits is preferable to mining City building permit data. In addition, the use of benchmarking data to develop Asset Score Preview Scores is recommended as a first step in engaging building managers to develop full Asset Scores. Asset Score Tool outputs can help building managers quickly identify opportunities for improvement and then work with the utility incentive administrator and energy service providers to develop their retrofit plan. Where full Asset Scores are available, analysis using a quadrant matrix with ENERGY STAR scores is recommended to identify buildings that present the best opportunities for operational-behavioral improvements versus ones with opportunities for upgrades to physical building systems. This workflow and the tools will help cities, utility incentive administrators, building portfolio managers and energy service providers develop energy programs and strategies for screening and analysis of buildings with the greatest energy savings potential. This report describes the project approach and research results to inform future coordination of ESPM and Asset Score data, including the use of benchmarking data to generate Preview scores. Given better awareness of energy performance, building managers can make more informed decisions to reduce energy consumption and carbon emissions. With cities, states and jurisdictions adopting benchmarking and auditing ordinances, there are substantial amounts of data submitted to cities that could be used to inform investments in energy efficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Supporting hierarchical soil biogeochemical modeling: version 2 of the Biogeochemical Transport and Reaction model (BeTR-v2)

Abstract. Reliable soil biogeochemical modeling is a prerequisite for credible projections of climate change and associated ecosystem feedbacks. This recognition has called for frameworks that can support flexible and efficient development and application of new or alternative soil biogeochemical modules in Earth system models (ESMs). The the Biogeochemical Transport and Reaction model version 1 (BeTR-v1) code (i.e., CLM4-BeTR) is one such framework designed to accelerate the development and integration of new soil biogeochemistry formulations into ESMs and to analyze structural uncertainty in ESM simulations. With a generic reactive transport capability, BeTR-v1 can represent multiphase (e.g., gaseous, aqueous, and solid), multi-tracer (e.g., nitrate and organic carbon), and multi-organism (e.g., plants, bacteria, and fungi) dynamics. Here, we describe the new version, Biogeochemical Transport and Reaction model version 2 (BeTR-v2), which adopts more robust numerical solvers for multiphase diffusion and advection and coupling between biogeochemical reactions and improves code modularization over BeTR-v1. BeTR-v2 better supports different mathematical formulations in a hierarchical manner by allowing the resultant model be run for a single topsoil layer or a vertically resolved soil column, and it allows the model to be fully coupled with the land component of the Energy Exascale Earth System Model (E3SM). We demonstrate the capability of BeTR-v2 with benchmark cases and example soil biogeochemical (BGC) implementations. By taking advantage of BeTR-v2's generic structure integrated in E3SM, we then found that calibration could not resolve biases introduced by different numerical coupling strategies of plant–soil biogeochemistry. These results highlight the importance of numerically robust implementation of soil biogeochemistry and coupling with hydrology, thermal dynamics, and plants – capabilities that the open-source BeTR-v2 provides. We contend that Earth system models should strive to minimize this uncertainty by applying better numerical solvers.

58 GEOSCIENCES↗
DOE OSTI2024

Summary Report of the FY24 DOE Contributions to the GIF VHTR CMVB

The Generation-IV Forum (GIF) Very-High-Temperature Reactor-Computational Methods Validation and Benchmark (VHTR-CMVB) initiative, involving organizations from Korea Atomic Energy Research Institute (KAERI) (South Korea), Institute of Nuclear and New Energy Technology of Tsinghua University (INET) (China), U.S. Department of Energy (DOE) (U.S.), Joint Research Centre (JRC) (Europe), and Japan Atomic Energy Agency (JAEA) (Japan), is dedicated to the verification and validation of tools for High-Temperature Gas-Cooled Reactors (HTGRs) analysis, using data shared by Computational Methods Validation and Benchmark (CMVB) signatories. For FY24, the US DOE CMVB has committed to several critical activities. Under WP1, led by the US, the integration of the High Temperature Gas Cooled Reactor - Pebble-Bed Module (HTR-PM) Phenomena Identification and Ranking Table (PIRT) into the comparative PIRT is progressing, with a new draft of the comparison tables issued earlier this year and currently being utilized by INET for their contribution. Neutronic validation efforts under WP3 include the preparation of the burnup analysis benchmark, preliminary calculations, and the development of reference models and results. In WP2, a validation exercise for hot gas mixing in the lower plenum of HTR-PM is in progress, using experimental data from INET (China) to validate modeling approaches. A model of the experimental facility has been developed using StarCCM+, with initial calculations slated for presentation at the GIF CMVB meeting this fall. Another WP2 activity focuses on validating numerical models for air-cooled Reactor Cavity Cooling System (RCCS) with experimental data from the Wisconsin Madison RCCS facility. A high-fidelity model, developed using NEK-RS, is currently being validated with available data from a low power forced convection test. These efforts are aimed at enhancing and confirming the accuracy of HTGR analysis tools, ensuring their alignment with experimental data and regulatory requirements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

High-accuracy Geant4 simulation and semi-analytical modeling of nuclear resonance fluorescence

Nuclear resonance fluorescence (NRF) is a photonuclear interaction that enables highly isotope-specific measurements in both pure and applied physics scenarios. High-accuracy design and analysis of NRF measurements in complex geometries is aided by Monte Carlo simulations of photon physics and transport, motivating Jordan and Warren (2007) to develop the G4NRF codebase for NRF simulation in Geant4. In this work, we enhance the physics accuracy of the G4NRF code and perform improved benchmarking simulations. The NRF cross section calculation in G4NRF, previously a Gaussian approximation, has been replaced with a full numerical integration for improved accuracy in thick-target scenarios. A high-accuracy semi-analytical model of expected NRF count rates in a typical NRF measurement is then constructed and compared against G4NRF simulations for both simple homogeneous and more complex heterogeneous geometries. Furthermore, agreement between rates predicted by the semi-analytical model and G4NRF simulation is found at a level of ~1% in simple test cases and ~3% in more realistic scenarios, improving upon the ~20% level of the initial benchmarking study and establishing a highly-accurate NRF framework for Geant4.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Thermomechanics coupling to Monte Carlo particle transport on unstructured mesh geometries using Cardinal

Geometry deformation due to thermal expansion influences neutron transport in many systems. Studying this phenomenon involves coupling models for neutronics, thermal hydraulics, and solid mechanics. To enable high fidelity modeling of these coupled physics, new capabilities were introduced in Cardinal, coupling OpenMC Monte Carlo particle transport models with MOOSE thermomechanical physics on unstructured moving-mesh geometries. In this work, we present a fully open-source capability leveraging on-the-fly mesh skinning to automatically regenerate OpenMC geometry, which allows multiphysics feedback from temperature, density, and geometry changes. The new capability is verified using an analytic benchmark slab problem, which couples S 2 neutron transport with thermal conduction, convective boundary conditions, Doppler-broadened cross sections, and nonlinear thermal expansion effects along the heated slab. Cardinal reproduces the analytic solutions for the neutron flux, heating, k eff , and temperature with demonstrated convergence in various error terms including mesh resolution and cross section temperature library spacing. For the nominal benchmark conditions and with a fine mesh, maximum relative errors for neutron flux, temperature, and heating are lower than 1%, while errors in integral quantities such as k eff and slab length are within 1 pcm and 48 µm, respectively. This work (i) presents a new numerical approach to thermomechanics coupling with OpenMC models, (ii) is the first (to our knowledge) to utilize a mechanical partial differential equation (PDE) solution to solve the (Griesheimer and Kooreman, 2022) analytic benchmark, and (iii) develops this verified capability within an open-source package.

97 - MATHEMATICS AND COMPUTING↗