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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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This project is a benchmark suite of Open-MP parallel codes that have been checked for data races. The programs are marked to show which do and do not have races. This allows them to be leveraged while testing and developing race detection tools.
This paper presents the development and validation of a high-fidelity thermal-hydraulic model of a molten salt natural circulation flow loop, designed for integration within a digital twin framework. The study evaluates the performance of Idaho National Laboratory’s Pronghorn against experimental data from Texas A&M University Molten Salt Flow Loop (MSFL) four Hitec-salt test benchmark data. Natural circulation of high-Prandtl-number fluids exhibits complex, counter-intuitive flow patterns that make pointwise thermocouple readings unreliable. Experimental work at TAMU’s MSFL provides benchmark data, including flow visualization at a test-section and centerline steady-state temperature measurements along the loop. Validation includes four single-phase natural circulation test cases with Hitec salt. Key metrics include flow profile agreement and steady-state temperature accuracy. Pronghorn results for two-dimensional single-phase agree qualitatively with the experimental flow profile. This paper illustrates the importance of Computational Fluid Dynamics (CFD) in elucidating the behavior of high-Prandtl-number thermal-hydraulics, along with how misleading centerline temperature measurements can be. Pronghorn reproduces the axial and radial stratification that makes single thermocouple readings unreliable. Future research will focus on reduced-order modeling techniques to enable rapid simulation suitable for real-time digital twin applications. The validated cases provide a basis for developing reduced-order surrogates aimed at real-time digital-twin applications.
Fusion energy systems are currently being designed and optimized using radiation transport codes. To deal with the unique environment inside a fusion-based system, many of these designs incorporate novel materials able to withstand the high radiation fields, ensure adequate cooling and thermal protection, and produce tritium. Validation plays a vital role in building trust in the predictive power of these models and computational methods. Validation of a code consists of modeling documented real-world experiments and comparing the code-predicted response to the measured response. Adequate validation requires measured responses from real-world experiments, also known as integral data, that mimic the system being designed, including materials, impinging radiation, and temperature, among other variables. The most trusted integral data are experimental responses that have been through a rigorous benchmarking process that develops a recommended computational model and evaluates all experimental uncertainties. Finally, there are a few research groups around the world that have been producing integral data for fusion applications, but a substantial investment is needed to address the unique validation needs of the fusion community.
The reliability of predictions of the system codes is closely related to the validation of their physical models. For example, the accuracy of void fraction prediction in a Boiling Water Reactor (BWR) using TRACE code depends on the uncertainties of closure relations developed for the two-phase flow models. This work focus on quantifying the uncertainties of two physical models, interfacial drag model and subcooled boiling model, using Markov Chain Monte Carlo (MCMC) method and BFBT experimental benchmark data. We applied the MCMC and MLE algorithms to BFBT benchmark data and estimated the uncertainty distribution of two model parameters: subcooled boiling heat transfer coefficient and interfacial drag (bubbly/slug rod bundle) coefficient. These algorithms tend to give various estimation results. Despite the differences in these algorithms, we find that the estimation results are consistent. After obtaining the uncertainty estimates, we validated the uncertainty estimates by applying the estimated distributions of model parameters to TRACE prediction. The new predictions succeed in reducing the void fraction prediction error, but it is not possible to eliminate all prediction error. We are only adjusting two model parameters, and there exists uncertainties in all model parameters. Application of the algorithms to other physical models and experimental data will be helpful for future work. (authors)
Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as reinforcement learning, have recently been explored to accelerate scheduling. However, there do not yet exist benchmark data sets or user-friendly software frameworks for testing and comparing these methods. We present DeepSurveySim -- a high-fidelity and flexible simulation tool for use in telescope scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration. We envision this tool being used to produce benchmark data sets and for evaluating the efficacy of ground-based telescope scheduling algorithms, particularly for machine learning algorithms that would suffer in efficacy if limited to real data for training.We introduce three example survey configurations and related code implementations as benchmark problems that can be simulated with DeepSurveySim.
Insensitive high explosives based on TATB (1,3,5-triamino-2,4,6-trinitrobenzene) are needed in applications when safety is of paramount importance, but the basic material properties that give rise to its insensitivity are not fully understood. Molecular dynamics modeling using empirical force fields (FFs) has been the main route to characterize many complicated dynamical properties of TATB single crystal, but these FFs have not been comprehensively tested at extreme conditions typical of detonation. We collect a benchmark data set of (quasi)static TATB physical properties as determined by experiments and electronic structure calculations and apply this data set to validate four existing TATB FFs along with a new TATB FF that we develop here and denote as the CEA-LLNL-Missouri (CLM) FF. Benchmark data include vibrational spectra, the TATB crystal temperature–pressure–volume equation of state and lattice parameters, properties of TATB crystal polymorphs and transitions to the gaseous and liquid states, dimer energy landscapes, the pressure-dependent elastic tensor, and the energy landscape for inelastic deformation via sliding of TATB crystal layers. As a general assessment, we find that the two existing nonreactive FFs are more accurate in describing TATB’s physical properties compared to the two variants of the ReaxFF reactive FF considered. The new CLM FF is found to consistently yield similar or better agreement with experiments and electronic structure theory than any of the existing FF models, and it presents a distinct improvement in accurately modeling TATB elasticity and equation of state. So this work is expected to help improve the accuracy of FF-based modeling of complicated dynamic responses that ultimately govern the safety and performance characteristics of this material.
his report details the development of transformational tools and techniques, allowing the accurate characterization of matter at the most extreme conditions yet studied in the high energy density (HED) domain. Before this work, most experiments quantitatively mapping the nature of matter in the HED realm were performed using planar geometry, which allows for the isolation and measurement of variables in a single thermodynamic state. Such measurements include equation of state variables, optical conductivity, heat transport and more. However, due to a variety of plasma processes, such experiments are limited to below ~10 TPa (100 Mbar) pressures. To achieve higher pressures, convergent experiments are needed. Historically, convergent experiments have not been used for benchmark data because they are integral measurements. That is each part of a convergent target undergoes a time dependent wide range of states, making it difficult to accurately isolate any particular quantity for a given thermodynamic state. This effort developed innovative convergent HED platforms and techniques enabling the exploration of matter from atomic to nuclear scale pressures. This effort also performed pioneering experiments that yielded the first rigorous benchmark data at these extreme conditions. This funding award began with the overarching goal to understand the behavior of matter at extreme (atomic-to-nuclear scale) pressures. The motivation for these goals lie in the fact that every time scientists explore matter beyond the threshold of an atomic unit, there is a fundamental shift in science. The atomic unit for energy, mass, charge, length, and time have each been explored, and each time such a threshold was crossed, a new sequence of discoveries was made resulting in significant awards such as the Nobel Prize. The only unexplored atomic unit is pressure, and this effort set the course for exploring matter at and beyond such pressures. That most of the recently discovered extrasolar planets and stars, as well as matter in the late implosion stages of inertial fusion targets, have deep internal pressures at and beyond atomic pressures amplifies the importance of this effort. Much of the initial work in developing techniques to create these atomic-to-nuclear pressures already existed within the HED community, predominantly at large scale laser facilities such as the National Ignition Facility (NIF) and the Omega60 laser the University of Rochester and although these experiments were routinely performed the ability to extract information about the underlying physical states and processes remained elusive due to the complexity of the experiments, extreme scales in both time and space, and the integrated nature of all the measurements. This work built a rigorous framework so such measurements can routinely be made. This was done in part through the introduction of Bayesian inference techniques into the field of HED science. These techniques allow for the self-consistent extraction of the relevant variables and their explicit and implicit correlations, so as to make use of integrated experimental data to constrain physical models and provide accurate uncertainty bounds for the data. The techniques developed here are fully transparent and proved immediately useful providing quantitative rigorous benchmarks for physical states and processes at some of the most extreme conditions yet explored on Earth. This funding is directly or partly responsible for 7 publications in major peer-reviewed scientific journals, a doctoral thesis, 3 invited talks at major conferences, and the training of a post-doc, 2 graduate students, and 1 undergraduate student. Beyond the effort supported by this award launched the introduction of Bayesian inference into the HED physics community and helped push the usage if modern data-science techniques within the physics community at large.
SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.
Herein we present numerical results of ground-state energies of 9 molecules in the well-established G2 molecule set given by the Gutzwiller conjugate gradient minimization (GCGM) method. The method, beyond the commonly used Gutzwiller approximation, was recently developed based on Gutzwiller variational wave functions. We find that compared to benchmark data given by full configuration interaction, GCGM total energies are reasonably well reproduced with the minimum basis set. To include the dynamical correlation beyond the minimal basis calculations, we adopt the local density approximation for the dynamical correlation energy $E_c$. By comparing the results with benchmark data given by experiments and large-basis configuration interaction, the GCGM total energies with $E_c$ are in general better reproduced, but discrepancies are still observed for some dimers.
Abstract. For more accurate projections of both the global carbon (C) cycle and the changing climate, a critical current need is to improve the representation of tropical forests in Earth system models. Tropical forests exchange more C, energy, and water with the atmosphere than any other class of land ecosystems. Further, tropical-forest C cycling is likely responding to the rapid global warming, intensifying water stress, and increasing atmospheric CO2 levels. Projections of the future C balance of the tropics vary widely among global models. A current effort of the modeling community, the ILAMB (International Land Model Benchmarking) project, is to compile robust observations that can be used to improve the accuracy and realism of the land models for all major biomes. Our goal with this paper is to identify field observations of tropical-forest ecosystem C stocks and fluxes, and of their long-term trends and climatic and CO2 sensitivities, that can serve this effort. We propose criteria for reference-level field data from this biome and present a set of documented examples from old-growth lowland tropical forests. We offer these as a starting point towards the goal of a regularly updated consensus set of benchmark field observations of C cycling in tropical forests.
This report supports Critical/Subcritical Experiment Decision 1 (CED-1) for Integral Experiment Request (IER) 498, a shielding benchmark produced using the Godiva IV critical assembly at the National Critical Experiments Research Center (NCERC). Shielding benchmarks are datasets collected to provide a comparison for computer models that simulate the effect of shielding on radiation transmission from a source to a detector. The purpose of a CED-1 report is to produce a conceptual design for an integral experiment and to identify the long lead-time items. The design presented in this report favors certain detailed design options such as material selections. However, alternative options can be selected as the project evolves. Dimensions are specified only as needed to evaluate feasibility and performance. Development of Criticality Accident Alarm System (CAAS) shielding benchmarks supports two main programmatic needs. The first need is to improve confidence in models of facilities that house nuclear material handling operations. Better models can support effective CAAS detector placement when criticality may result in dose, and it enables more accurate analysis of criticality accident impacts at various locations in a facility. A key quantity that can be verified is the secondary gamma production caused by neutron interactions with various materials. The second programmatic need supported by shielding benchmarks is the ongoing development of methods being integrated into nuclear simulation codes such as the Monte Carlo N-Particle (MCNP) transport code and the SCALE code system used in efforts funded by the Nuclear Criticality Safety Program (NCSP). Previous work performed under the International Criticality Safety Benchmark Experimental Program (ICSBEP) to produce shielding benchmark data is primarily limited to a set of measurements at the SILENE critical assembly at the Valduc facility in France. The experiment used an open configuration and a burst mode operation, which limited detector options to thermoluminescent dosimeters (TLDs) and activation foils. Because TLDs do not record gamma spectra, it was difficult to verify the gamma energy spectrum from the experiment. In this work, the Godiva IV assembly will be used because it is a fast system capable of producing a wide range of source strengths, including super-prompt critical bursts. The room return for the facility is expected to be substantial and challenging to characterize, so a more closed, shielded experimental configuration is considered. Furthermore, because the physics of radiation transport outside the assembly does not change between the burst mode and the delayed critical mode, it is anticipated that most measurements will be made with the Godiva IV assembly in delayed critical mode, operating within a range that will allow electronic counters to collect accurate data. Scoping calculations concluded that a workable room return shield is comprised of 40 cm of 5% borated polyethylene backed by 7 cm of lead. This configuration was shown to produce a ratio of signal–to–room return of over 100 for up to 2 mean free paths of shield sample material, and over 10 for up to 4 mean free paths of shield sample material, ensuring that useful results can be obtained. With no shielding sample in the room return shield, it was shown that the neutron and gamma spectra do not appreciably change with a change in room size. This scoping calculation does not preclude the use of tungsten powder in a binder, some other effective gamma shielding material, or a hybrid design. Dose rates for various source intensities at an unshielded detector are listed herein to inform detector selection during CED-2, and calibration chains are identified. Preliminary estimates of costs and schedules are outlined but remain to be verified. Values could double or triple as estimates become more accurate and are principally included here to demonstrate feasibility. Preliminary estimates indicate that the apparatus can be installed within a feasible schedule and budget. Two measurements per day are assumed. Producing a shielding benchmark at NCERC using the Godiva IV assembly has been shown to be feasible, with manageable uncertainties and risks. A clear, continuing need for shielding benchmark data remains present, and this report identifies and evaluates a method for creating integrated shielding benchmark datasets.
We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.
Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.
Benchmark data are presented for the zeroth- through third-order many-body perturbation corrections to the electronic Helmholtz energy, internal energy, and entropy in the canonical ensemble in a wide range of temperature. They are determined as numerical λ-derivatives of the respective quantities computed by thermal full configuration interaction with a perturbation-scaled Hamiltonian, $\hat{H} = \hat{H}_0 + λ\hat{V}$. Sum-over-states analytical formulas for up to the third-order corrections to these properties are also derived as analytical λ-derivatives. These formulas, which are verified by exact numerical agreement with the benchmark data, are given in terms of the Hirschfelder–Certain degenerate perturbation energies and should be valid for both degenerate and nondegenerate reference states at any temperature down to zero. Further, the results in the canonical ensemble are compared with the same in the grand canonical ensemble.
Sum-of-Fractions is a method intended to assure a subcritical margin for aqueous solutions and slurries of fissionable isotopes. The method indicates that a system is subcritical if the sum of the ratios of the mass of each isotope in a mixture to its individual minimum subcritical mass limit is less than or equal to one. The basis of the Sum-of Fractions has historically been derived from allowances given in ANSI/ANS-8.15-1981. However, the allowance was removed in ANSI/ANS-8.15-2014 due to a lack of technical basis. A methodology was developed to assess the validity of using the Sum-of-Fractions for water or polyethylene moderated systems for the following nuclides: 232 U, 233 U, 234 U, 235 U, 237 Np, 236 Pu, 238 Pu, 239 Pu, 240 Pu, 241 Pu, 242 Pu, 241 Am, 242 mAm, 243 Am, 242 Cm, 243 Cm, 244 Cm, 245 Cm, 246 Cm, 247 Cm, 249 Cf, and 251 Cf. The methodology uses available benchmark data for mixtures of 233 U, 235 U, and 239 Pu to establish the calculational margin, and a mass limit reduction to establish the margin of subcriticality. Water or polyethylene moderated and reflected mixtures containing the nuclides are evaluated with SCALE 6.2.4. Including the calculational margin, subcritical mass limits for each nuclide were computed for optimally water or polyethylene moderated and fully reflected systems. These masses were used to create nuclide mixtures in which the sum of the mass to subcritical mass limit ratios is one. The various nuclide mixtures were modeled over a range of moderation and demonstrate the k eff does not exceed the calculational margin. For additional assurance of subcriticality, a significant mass reduction is applied to each computed minimum critical mass of the nuclides without adequate benchmark data consistent with the method in ANSI/ANS-8.15-2014.
Sum-of-fractions is a method intended to make sure a subcritical margin for aqueous solutions and slurries of fissionable isotopes exists. The method indicates that a system is subcritical if the sum of the ratios of the mass of each isotope (in a mixture) to its individual minimum subcritical mass limit is less than or equal to one. Historically, the basis of the sum-of-fractions has been derived from allowances given in the American National Standards Institute (ANSI)/ American Nuclear Society (ANS)-8.15-1981. However, the allowance was removed in ANSI/ANS-8.15-2014 due to a lack of technical basis. A methodology was developed to assess the validity of using the sum-of-fractions for water- or polyethylene-moderated systems for the following nuclides: 232U, 233U, 234U, 235U, 237Np, 236Pu, 238Pu, 239Pu, 240Pu, 241Pu, 242Pu, 241Am, 242mAm, 243Am, 242Cm, 243Cm, 244Cm, 245Cm, 246Cm, 247Cm, 249Cf, and 251Cf. The methodology uses available benchmark data for mixtures of 233U, 235U, and 239Pu to establish the calculational margin, and a mass limit reduction to establish the margin of subcriticality. Water- or polyethylene-moderated and -reflected mixtures containing the nuclides are evaluated with the code system, SCALE 6.2.4. Including the calculational margin, subcritical mass limits for each nuclide were computed for optimally water- or polyethylene-moderated and fully reflected systems. These masses were used to create nuclide mixtures in which the sum of the mass to subcritical mass limit ratios is one. The various nuclide mixtures were modeled over a range of moderation and demonstrate the keff does not exceed the calculational margin. For additional assurance of subcriticality, a significant mass reduction is applied to each computed minimum critical mass of the nuclides without adequate benchmark data consistent with the method in ANSI/ANS-8.15-2014.
Sum-of-fractions is a method intended to make sure a subcritical margin for aqueous solutions and slurries of fissionable isotopes exists. The method indicates that a system is subcritical if the sum of the ratios of the mass of each isotope (in a mixture) to its individual minimum subcritical mass limit is less than or equal to one. Historically, the basis of the sum-of-fractions has been derived from allowances given in the American National Standards Institute (ANSI)/ American Nuclear Society (ANS)-8.15-1981. However, the allowance was removed in ANSI/ANS-8.15-2014 due to a lack of technical basis. A methodology was developed to assess the validity of using the sum-of-fractions for water- or polyethylene-moderated systems for the following nuclides: 232 U, 233 U, 234 U, 235 U, 237 Np, 236 Pu, 238 Pu, 239 Pu, 240 Pu, 241 Pu, 242 Pu, 241 Am, 242 m Am, 243 Am, 242 Cm, 243 Cm, 244 Cm, 245 Cm, 246 Cm, 247 Cm, 249 Cf, and 251 Cf. The methodology uses available benchmark data for mixtures of 233 U, 235 U, and 239 Pu to establish the calculational margin, and a mass limit reduction to establish the margin of subcriticality. Water- or polyethylene-moderated and -reflected mixtures containing the nuclides are evaluated with the code system, SCALE 6.2.4. Including the calculational margin, subcritical mass limits for each nuclide were computed for optimally water- or polyethylene-moderated and fully reflected systems. These masses were used to create nuclide mixtures in which the sum of the mass to subcritical mass limit ratios is one. The various nuclide mixtures were modeled over a range of moderation and demonstrate the keff does not exceed the calculational margin. For additional assurance of subcriticality, a significant mass reduction is applied to each computed minimum critical mass of the nuclides without adequate benchmark data consistent with the method in ANSI/ANS-8.15-2014.
A perennial challenge in buildings research is the lack of high-quality datasets that can be relied upon for a wide array of tasks, including model calibration and improving energy efficiency and load flexibility. Instrumenting a building for data collection is resource intensive, so it is important to be methodical in the approach and ensure that resulting data are flexible and useful for a broad range of analyses. This study aims to fill the gaps in characterizing potential use cases for buildings datasets and mapping them to dataset needs using a well-defined data infrastructure. Here, we have developed a systematic mapping strategy between buildings dataset needs and use cases to help streamline the processes of efficiently targeting datasets, designing building sensing systems, and determining buildings research use cases. We selected 14 prospective use cases and 11 refined buildings data categories for developing the preliminary dataset-needs-to-use-cases mapping matrix (‘DN-UC mapping matrix’) with generic ‘Tags’—a detailed sub-level of data categories extracted by justifying the needs of an aspect of the datasets to use cases. We present two example applications of the developed mapping matrix to demonstrate use of the mapping matrix and its effectiveness.