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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Design of Quasi-Integral Experiments at the Flexible Neutron Source at the University of Tennessee

Nuclear reaction data libraries make use of experimental data that are differential in reaction and neutron energy for evaluation and data that are integral in reaction and energy for validation. In many cases, integral measurements can provide lower experimental uncertainties than differential measurements, but the impact of individual reactions and energies cannot generally be determined from the single experimental observable. “Quasi-integral” or “quasi-differential” measurements, which are integral in one characteristic (energy or reaction) and differential in the other, can often provide a middle ground of lower experimental uncertainties combined with some ability to unpack the impact of individual reactions or energies. Furthermore, the Flexible Neutron Source (FNS) at the University of Tennessee, Knoxville, is a new experimental facility that can perform integral and quasi-integral measurements by removing components of the integral configuration to determine the impact of specific reactions. Possible flux-averaged cross-section measurements, flux perturbation measurements, and neutron downscattering measurements are simulated here to show the value of the FNS to evaluation and validation of nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Computational study of TATB

We have performed a computational study of TATB. The study is composed of two parts, one where we perform static T = 0 K calculations with several different DFT functionals, to investigate structural properties and obtain a cold curve. Even though the functionals used in this study give poor results at ambient pressure, they perform much better at compression, when the dominance of the van der Waals’ forces in the binding is replaced by more normal interactions. In particular AM05 gives good results, mainly because it doesn’t include any “faulty” van der Waals’. Indeed the van der Waals’ corrected functional (PBE+D2) results shows the worst performance on structural properties. For the cold curve the PBE+D2 functional gives the best results compared to experiments (room temperature) but since the structural properties were bad with this functional we cannot for sure say that this is in any way a validation. It is shown that scaling the AM05 results with a common factor reproduces the experimental data. We leave the question on why to a follow up study but speculate that maybe the experiment and our calculations do not represent the same system set-up. From the structural properties we identify a site in the TATB lattice that contains a high concentration of oxygen and hydrogen. The consequences of this finding, if any, are left to a future project. In the second part of the study we perform extensive high-quality DFT-MD (molecular dynamics with DFT forces) calculations to obtain points on the Hugoniot. This part of the study did not get finished within this project and a more extensive summary of this part will be given in a follow up report at a future date. However, we show that obtaining calculated data is essential for equations of state development since the existing experimental Hugoniot data barely deviates from room temperature isotherm data and thus give no information of the high temperature dependency of materials properties. Our conclusion so far in the project is that being able to calculate materials properties is essential for high explosives since experiments are unable to give information in the high temperature part of phase space. While blind use of DFT cannot (yet) give unambiguous answers, we can still explore qualitatively and use such information to guide us in EOS development. However, the main conclusion is that we need better functionals in DFT to easily extract needed properties from calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning the metastable phase diagram of covalently bonded carbon

Abstract Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct “metastable” phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n -diamond as a cubic-analog of diaphite-like lonsdaelite phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Updating Fission Yield Data for Applications (Summary Report)

The First Research Coordination Meeting of the CRP on “Updating Fission Yield Data for Applications” was held by video conference from 31 August to 4 September 2020 with more than 50 international experts from 16 countries attending the meeting. The CRP is devoted to evaluation efforts of cumulative and independent fission yields for incident energies from the thermal point up to 14 MeV on actinide targets. Produced fission yield evaluations should include full uncertainty quantification and are expected to combine available experimental data and state-of-art model information. Four working groups were created within the collaboration: 1) Availability of experimental fission product yield data for evaluations; 2) New fission product yield experimental data; 3) Fission product yield evaluation; and 4) Fission product yield validation. Technical discussions and the resulting work plan of the Coordinated Research Programme are summarized in this report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Summary Report of the 1st RCM of the CRP on the Updating Fission Yield Data for Applications

The First Research Coordination Meeting of the CRP on “Updating Fission Yield Data for Applications” was held by video conference from 31 August to 4 September 2020 with more than 50 international experts from 16 countries attending the meeting. The CRP is devoted to evaluation efforts of cumulative and independent fission yields for incident energies from the thermal point up to 14 MeV on actinide targets. Produced fission yield evaluations should include full uncertainty quantification and are expected to combine available experimental data and state-of-art model information. Four working groups were created within the collaboration: 1) Availability of experimental fission product yield data for evaluations; 2) New fission product yield experimental data; 3) Fission product yield evaluation; and 4) Fission product yield validation. Technical discussions and the resulting work plan of the Coordinated Research Programme are summarized in this report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

TRIPOLI-4 neutronics calculations for IAEA-CRP benchmark of CEFR start-up tests using new libraries JEFF-3.3 and ENDF/B-VIII

Sodium-cooled Fast Reactors (SFR) are one of the promising Generation IV fast reactors. The China Experimental Fast Reactor (CEFR) is a 65 MWth pool-type SFR with a high neutron leakage core using high enriched uranium oxide fuel. The CEFR start-up tests in 2010 consist of series of neutronics experiments. Essential experimental data are available from the 2018 IAEA-CEFR-CRP benchmark document and useful for the validation of neutron transport codes and nuclear data libraries. The TRIPOLI-4 Monte Carlo transport code is a general-purpose neutronics code using continuous-energy nuclear data libraries. It has a rich validation database covering different computational and experimental benchmark data sets to assure the accuracy and credibility of numerical studies. Previous TRIPOLI-4 SFR core physics calculations used mainly MOX fuels. SFR control rod worth studies with TRIPOLI-4 were mainly on big-size cores. To test the modeling capability of TRIPOLI-4 on fuel loading patterns and on variable control rods positions for SFRs and to check recent nuclear data libraries, it is interesting to investigate the CEFR start-up tests in this work, including core states from subcritical to supercritical, control rods and rod group worth, point kinetics parameters, and radial foil activation measurements. TRIPOLI-4 calculation results using new data libraries JEFF-3.3, ENDF/B-VIII, and those of ENDF/B-VII.1 for different CEFR core states are reported here. By means of different options of TRIPOLI-4, control-rod SAs reactivity worth, point kinetics parameters, and radial {sup 237}Np(n,f) fission rate distribution were successfully evaluated. Both ENDF/B-VIII and JEFF-3.3 nuclear data libraries provided reliable results with a difference of 170 +/- 11 pcm in k{sub eff}.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Nuclear Data Sheets for A=190

The evaluated experimental data are presented and evaluated for 15 known nuclides of mass 190 (Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg, Tl, Pb, Bi, Po). This work supersedes earlier full evaluations of A=190 by 2003Si05, 1990Si09, 1982Le22 and 1973Sc42. For 190Hf, only the isotopic identification has been made, with no information about the half-life of the ground state or its decay properties. For 190 Ta, only one excited state as an isomer has been identified, but with no knowledge about its decay properties. For 190 W, 190 Re, 190 Bi and 190 Po, available spectroscopic data are scarce. Here, the decay schemes of 190 Ta, 190 W, 190 Au, 190 Hg, 190 Au isomer decay, 190 Tl (both the activities), 190 Pb, and 190 Bi (both the activities) are considered incomplete by the evaluators. At present, extensive level structures, both the low-spin and high-spin, are known for 190 Os, 190 Ir, 190 Pt, 190 Au, 190 Hg, 190 Tl and 190 Pb, but data for level lifetimes, and γ-ray transition probabilities are generally lacking.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Nuclear Data Sheets for A=100

Here, evaluated experimental data are presented for 15 known nuclides of mass 100 (Kr, Rb, Sr, Y, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, In, Sn). Recommended values are given for level parameters, γ and β radiations, and other spectroscopic information. No excited states are known in 100 Rb and 100 Sn. Except for isotopic identification, no information is available for the decay of the 100 Kr isotope, and limited information is known about the decay properties of the 100 Sn isotope. According to Fig. 2 (A/Q versus counts plot) and as mentioned in the text in 2021Su01, 100 Br isotope was not observed, although, discovery of 101 Br was claimed in this work. In the case of 100 Pd, the agreement in the orderings of the γ-ray cascades and γ-ray inventory between various studies of high-spin structures remains poor at high excitation energies (>9 MeV). For 100 Y, the assignments of the two previously known half-lives to the respective states (g.s. and the 145-keV isomer) remain to be established. Here, this work supersedes earlier full evaluations of A=100 by 2008Si01, 1997Si09, 1990Si05 and 1974Ko37.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Nuclear Data Sheets for A=64

The evaluated experimental data are presented and evaluated for 13 known nuclides of mass 64 (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se). For each nuclide, the best values combining all available data are recommended for spectroscopic properties. No excited states have been identified in 64 Ti, 64 As, and 64 Se. Only one excited state in 64 V as an isomer, and three in 64 Cr have been identified. Data for excited states in 64 Mn, 64 Fe, and 64 Co remain limited. 64 Ni, 64 Cu, and 64 Zn are the most studied nuclides through various reactions and decays, followed by 64 Ga and 64 Ge. In the opinion of the evaluators, there are several incomplete or discrepant aspects of the high-spin portion of the level scheme for 64 Zn above ≈5 MeV excitation which need to be resolved in further experiments using large γ-detector arrays. The decay schemes of 64 Ti β - , 64 As ε and 64 Se ε are unknown, while very little information is available for 64 V β - and 64 Cr β - . The decay schemes of 64 Mn β - , 64 Fe β - , 64 Co β - and 64Ge ε are somewhat better known, but still considered incomplete by evaluators. The decay scheme of 64 Ga ε decay is known in detail, however there is the possibility of additional levels populated above 4713 keV, as the Q(ε) value is 7171 keV. The β - , β + and ε decay modes of 64 Cu are well known, as this radionuclide is of great importance in applications, for example, as a dosimeter for neutron flux determination in nuclear reactors, and as a radiopharmaceutical for positron emission tomography (PET). This work supersedes earlier full evaluations of A=64 by 2007Si04, 1996Si12, 1991Si03, 1979Ha35 and 1974Au04.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Large Language Model for Validation, Optical Calibration, and Learning (VOCAL) Distributed Temperature Sensing Interface

Distributed temperature sensing (DTS) using fiber optic sensors (FOS) offers a promising method for temperature measurements in advanced reactors, such as sodium fast reactors and molten salt cooled reactors. To support the calibration and validation of DTS measurements, Argonne National Laboratory developed the Validation, Optical Calibration, and Learning (VOCAL) software package. This report describes the integration of a local large language model (LLM) with a retrieval-augmented generation (RAG) system into the VOCAL interface to serve as an interactive user assistant. The LLM framework enhances the VOCAL platform’s accessibility to users by explaining interface components, clarifying inputs and outputs, and answering user queries dynamically in real-time. The accuracy of the LLM assistant performance was evaluated with 20 queries regarding the interface and its parameters using experimental data from the Thermal Hydraulic Experimental Test Article (THETA) facility. Results demonstrate that the LLM achieved a 95% accuracy rate, with a BERTScore of 0.8816 and SBERT value of 0.7417. Furthermore, validation of the RAG system within the LLM framework showed optimal accuracy with k-values between 1 and 2 using the k-refinement convergence test. The prompt perturbation analysis demonstrated good initial consistency for the RAG system, exhibiting the highest accuracy under punctuation variations and the greatest sensitivity under query reordering. Notably, the model’s errors were limited to data retrieval failures rather than factual hallucinations, reinforcing its baseline reliability. The integration of LLM provides a highly accurate, userfriendly enhancement to the VOCAL platform without disrupting its core computational capabilities for FOS calibration and validation.

Hong, Evan↗
DOE OSTI2022

Computational determination of a primary diffusion mode in γ U-10Mo under irradiation

Low enriched uranium (< 20 % 235 U)-molybdenum (U-Mo) monolithic fuel is the primary candidate for high performance research and test reactors, and is in the process of being qualified to replace highly enriched uranium (≥ 20 % 23 5U) fuel. As part of the qualification process, it is critical to understand and predict the behavior of fission gas bubbles under irradiation, which affects fuel swelling and fuel failure. Mechanistic fuel models are being developed that can both reproduce the existing experimental data for fuel swelling, and be further applied to irradiation conditions beyond the experimental scope. Diffusion of species under irradiation conditions is an important parameter in the mechanistic fuel models; however, no temperature-relevant experimental diffusion data exists. In the present work, radiation-enhanced diffusion coefficients of U, Mo, and Xe in λU-10wt.%Mo were calculated in the temperature range between 300 K and 1400 K via rate-theory models and molecular dynamics simulations with an embedded-atom method interatomic potential for the U-Mo-Xe system. Accordingly, total diffusion coefficients under relevant irradiation conditions are determined using previously obtained intrinsic thermal diffusion and radiation-driven diffusion coefficients, as well as the newly calculated radiation-enhanced diffusion coefficients presented herein. Radiation-enhanced diffusion of U and Mo was dominant in the intermediate temperature range, whereas radiation-enhanced diffusion of Xe did not significantly contribute to total diffusion of Xe at the relevant fission rate densities. Radiation-enhanced diffusion of Xe became faster than both intrinsic thermal diffusion and radiation-driven diffusion at a fission rate density of 5 x 10 22 fissions/m 3 /s, which is higher than the typical fission rate density range in research reactors. The temperature regime that radiation-enhanced diffusion of each element dominated was dependent on the fission rate density. Finally, the total diffusion coefficients of U, Mo, and Xe, updated in this work, will be utilized as parameters in the mechanistic fuel models to help predict the behavior of fission gas bubbles under irradiation more accurately.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Evaluation of the Prompt Fission Gamma Properties for Neutron Induced Fission of 235,238 U and 239 Pu

Herein we present details of the prompt fission gamma property evaluations included in the released ENDF/B-VIII.0 files. The average prompt gamma multiplicity, the average prompt gamma spectrum, the average total prompt gamma energy released and the prompt gamma multiplicity distributions, included in the ENDF/B-VIII.0 files, are presented for 235 U(n,f), 238 U(n,f), and 239 Pu(n,f) reactions, as a function of incident neutron energy up to 30 MeV. The evaluation is based on available experimental data and model calculations. Notable data include recent measurements by experimental groups at Los Alamos and Lawrence Livermore National Laboratories, at Geel and Budapest, at CEA labs, as well as seminal historical gamma production measurements by Drake at Los Alamos.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. Furthermore, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A machine learning Automated Recommendation Tool for synthetic biology

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

New, improved analysis of correlation ECE data to accurately determine turbulent electron temperature spectra and magnitudes (invited)

Turbulent electron temperature fluctuation measurement using a correlation electron cyclotron emission (CECE) radiometer has become an important diagnostic for studying energy transport in fusion plasmas, and its use is widespread in tokamaks (DIII-D, ASDEX Upgrade, Alcator C-Mod, Tore Supra, EAST, TCV, HL-2A, etc.). The CECE diagnostic typically performs correlation analysis between two closely spaced (within the turbulent correlation length) ECE channels that are dominated by uncorrelated thermal noise emission. This allows electron temperature fluctuations embedded in the thermal noise to be revealed and fluctuation level and spectra determined. We have demonstrated a new, improved CECE coherency-based analysis for calculating the temperature fluctuation frequency spectrum and level, which has been verified both numerically through the simulation of synthetic ECE radiometer data and through analysis of experimental data from the CECE system on DIII-D. The new formulation places coherency-based analysis on a firm foundational footing and corrects some currently published methodologies. This new method accurately accounts for bias error in the coherence function and correctly calculates noise levels for a fixed data record length. It provides excellent accuracy in determining temperature fluctuation level (e.g., <10% error) even for a small realization number in the ensemble average. The method also has a smaller uncertainty (i.e., error bar) in the power spectrum when compared to the more standard cross-power method when evaluated at low coherency. Direct calculation of system noise level using correlation between randomized intermediate frequency signals is recommended.

Wang, G. (ORCID:0000000225739827)↗
DOE OSTI2023

Parallel transport modeling of linear divertor simulators with fundamental ion cyclotron heating *

Abstract The Material Plasma Exposure eXperiment (MPEX) is a steady state linear device with the goal to perform plasma material interaction studies at future fusion reactor relevant conditions. A prototype of MPEX referred as ‘Proto-MPEX’ is designed to carry out research and development related to source, heating and transport concepts on the planned full MPEX device. The auxiliary heating schemes in MPEX are based on cyclotron resonance heating with radio frequency (RF) waves. Ion cyclotron heating (ICH) and electron cyclotron heating in MPEX are used to independently heat the ions and electrons and provide fusion divertor conditions ranging from sheath-limited to fully detached divertor regimes at a material target. A hybrid particle-in-cell code- PICOS++ is developed and applied to understand the plasma parallel transport during ICH in MPEX/Proto-MPEX to the target. With this tool, evolution of the distribution function of MPEX/Proto-MPEX ions is modeled in the presence of (a) Coulomb collisions, (b) volumetric particle sources and (c) quasi-linear RF-based ICH. The code is benchmarked against experimental data from Proto-MPEX and simulation data from B2.5 EIRENE. The experimental observation of ‘density-drop’ near the target in Proto-MPEX and MPEX during ICH is demonstrated and explained via physics-based arguments using PICOS++ modeling. In fact, the density drops at the target during ICH in Proto-MPEX/MPEX to conserve the flux and to compensate for the increased flow during ICH. Furthermore, sensitivity scans of various plasma parameters with respect to ICH power are performed for MPEX to investigate its role on plasma transport and particle and energy fluxes at the target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗