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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Laser Diode Analysis and Verification (Professional Report)

The goal of this project at Lawrence Livermore National Laboratory (LLNL) was focused on analyzing and verifying laser diodes to ensure that the diodes meet requirements for LLNL mission applications. The different stages of this testing were documented in a process flow map which included location color coding so that each step was clearly defined in scope and location of appropriate testing facility. Data were collected and analyzed and compared to minimum acceptable values to see if requirements were met. The process map documents the initial receipt, inspection, and testing of the laser diodes. Initial inspections started with Keyence Microscope imaging and then moved on to High Potential, Ramp, and Burst Testing. Data from the diode testing were processed through MATLAB and Python codes to verify various metrics such as slope efficiency, threshold current, back irradiance, and beam divergence met requirements. These metrics were then recorded in Excel summary reports. Approximately 95% of the laser diodes passed all tests. Presentations were given to Lawrence Livermore’s internal leadership team, an external partner, and to a Lab-wide audience. The data released for this report was constrained by information protection considerations of LLNL’s national security missions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Predicting Thermal Performance of an Enhanced Geothermal System From Tracer Tests in a Data Assimilation Framework

Abstract Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES‐MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field‐scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real‐world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2021

Feature Extraction: Improving Remote Sensor Classification of Non-Proliferation

This research focuses on developing algorithms for nuclear non-proliferation detection using remote sensor modeling. To improve the performance of classification models, we implemented a data pipeline with feature extraction. This pipeline takes raw data and transforms it into smaller data points called features that still describe the model. Improving this classification works towards the departments of energy’s missions of ensuring American’s security and prosperity by creating technology that addresses nuclear challenges. To conduct this analysis, we used the Python programming language and some key packages, including tsfresh and TSFEL. Originally tsfresh was selected because it has the most statistical features out of all the packages. Later TSFEL was incorporated due to the additional features it can extract from data, such as temporal and spectral. However, feature extraction becomes challenging in the presence of missing values. In this case, two additional Python packages were added to our workflow, NumPy and pandas, allowing for the feature extraction process to handle unknown values. Our data pipeline was tested on data collected from a simulation that describes the process state of a physical example. The results show the pipeline’s capability to consume and extract a total 17 features from tabular data. Future work includes producing classifications using decision tree-based models such as XGBoost and improving data collection by analyzing feature importance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A 1-year study on SARS-CoV-2 variant shifts in wastewater using dPCR: comparison with clinical and GISAID data

Wastewater testing can be used to monitor SARS-CoV-2 infections in communities. Data from PCR-based wastewater testing are usually available to public health authorities within 5–7 days after excreta and other body fluids enter the sewer. While PCR-based methods can accurately detect and quantify SARS-CoV-2, sequencing-based methods are usually required to distinguish between variants, delaying the results and adding cost to the process. We developed and assessed a novel, customizable digital PCR (dPCR)-based genotyping method for SARS-CoV-2 variant detection in wastewater, which is more cost-effective, faster, and more accessible than sequencing. This approach was applied to more than 1,400 wastewater samples

Wilton, Rose↗
DOE OSTI2021

Nuclear Data and Cross Section Testing Using ENDF/B-VIII.0

With the release of the Evaluated Nuclear Data File (ENDF)/B-VIII.0 library, nuclear criticality safety practitioners and engineers have access to the latest cross section sets available for their analyses. However, these cross sections must be rigorously tested and validated to ensure that the nuclear data are responsive to the needs of the individuals responsible for developing, implementing, and maintaining computational tools for criticality safety applications. Thus, the ENDF/B-VIII.0 library is tested and validated with a large collection of experiments that were vetted by the International Criticality Safety Benchmark Evaluation Project and made available in the International Handbook of Evaluated Criticality Safety Benchmark Experiments. A selection of benchmark experiments for use within the criticality safety community were prepared and reviewed within the Verified, Archived Library of Inputs and Data (VALID), which is maintained by the Nuclear Energy and Fuel Cycle Division at Oak Ridge National Laboratory. The performance of the ENDF/B-VIII.0 library is assessed by using VALID models of benchmark experiments with the beta 12 version of SCALE 6.3 KENO V.a and KENO-VI Monte Carlo codes. The performance is compared with the results obtained from with the ENDF/B-VII.1 library. This report considers multigroup (MG) and continuous energy (CE) formats of the ENDF/B-VIII.0 and -VII.1 libraries. The benchmark experiments within VALID that validate the ENDF/B-VIII.0 library cover 15 broad system categories by using a range of fissile materials, uranium enrichments, plutonium isotopic vectors, and mixed uranium/plutonium systems. These forms are represented as metals, solutions, or various arrays of rods or plates that cover a variety of neutron energy spectra: thermal, fast, mixed, and intermediate. Over 600 cases were considered for use with the KENO V.a and KENO-VI codes with the ENDF/B-VIII.0 library. The results of the Monte Carlo comparison of ENDF/B-VIII.0 to ENDF/B-VII.1 with both KENO V.a and KENO-VI indicate that there is a less than 0.53% Δk difference between the bias of calculated k eff from the expected values. The CE ENDF/B-VIII.0 library results in smaller magnitude biases than the ENDF/B-VII.1 data for HEU-MET-FAST, HEU-SOL-THERM, IEU-MET-FAST, LEU-SOL-THERM, PU-SOL-THERM, and U233-MET-FAST systems, while the MG results yielded smaller magnitude biases for HEU-MET-FAST, HEU-SOL-THERM, IEU-MET-FAST, LEU-COMP-THERM, LEU-SOL THERM, MIX-COMP-FAST, and U233-MET-FAST systems. Most notable are the adjustments to the plutonium and 233 U cross section data, which has resulted in noticeably lower biases in the ENDF/B VIII.0 results for the mixed, plutonium, and 233 U systems. Results of the sensitivity data file comparison generated from TSUNAMI-3D for selected VALID cases for the ENDF/B-VIII.0 library indicate a very high level of agreement with correlation coefficients of the effect of nuclear data uncertainty on k eff (the c k integral parameter) all above 0.99. This indicates that cases with the ENDF/B-VIII.0 library would see very similar responses to any nuclear data errors or change as those with the ENDF/B-VII.1 library.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Testing Classical Properties from Quantum Data

Many properties of Boolean functions can be tested far more efficiently than the function itself can be learned. However, this dramatic advantage often disappears when testers are limited to random samples of ƒ instead of adaptively chosen queries to f. In this work we investigate the quantum version of this restriction: quantum algorithms that test properties of a Boolean function f solely from copies of either the function state |ƒ⟩ ∝ ∑ x |x, ƒ(x)⟩ or the phase state |(-1) ƒ ⟩ ∝ ∑ x (-1) ƒ(x) |x⟩. For monotonicity, symmetry, and triangle-freeness, we show passive quantum testers are unboundedly or super-polynomially better than their classical passive testing counterparts. They are competitive with classic query -based testers in each case. Our new testers use techniques beyond quantum Fourier sampling, and it turns out this is necessary: we show a certain class of bent functions can be tested from 𝒪(1) function states but has a sample complexity lower bound of 2 Ω(n) for any tester relying exclusively on Fourier and classical samples. Our passive quantum testers are competitive with classical query -based testers, but this isn't universal: we exhibit a testing problem that can be solved from 𝒪(1) classical queries but requires Ω(2 n/2 ) function state copies. The Forrelation problem provides a separation of the same magnitude in the opposite direction, so we conclude that quantum data and classical queries are "maximally incomparable" resources for testing. We also begin the study of lower bounds for testing from quantum data. For quantum monotonicity testing, we prove that the ensembles of [Goldreich et al., 2000; Black, 2024], which give exponential lower bounds for classical sample-based testing, do not yield any nontrivial lower bounds for testing from quantum data. New insights specific to quantum data will be required for proving copy complexity lower bounds for testing in this model.

Boolean Functions↗
DOE OSTI2022

Interim Creep, Fatigue and Creep-Fatigue Data from FY 2022 INL Testing of A709 with Precipitation Treatment for ASME Code Case Data Package

This report provides the status of creep, fatigue, and creep-fatigue testing that transpired in fiscal year 2022 at Idaho National Laboratory (INL). This testing is being conducted to develop the data package to qualify A709 in Section III, Division 5 of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC). This would permit the use of A709 for elevated temperature nuclear construction. Preliminary results continue to demonstrate the improved creep and fatigue resistance of A709 compared to 316H stainless steel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

SMALL UNMANNED MARINE HYDROKINETIC PLATFORMS FOR POWER GENERATION IN COASTAL AND TIDAL WATERS

The feasibility and optimization of small unmanned mobile marine hydrokinetic (MHK) energy platforms for harvesting marine current energy in coastal and tidal waters are examined. A case study of a platform based on the use of a free-surface waterwheel (FSWW) mounted on an autonomous unmanned surface vehicle (USV) was conducted. Such platforms can serve as recharging stations for aerial drones (UAVs), enabling extension of the UAVs’ autonomous operating time. An unmanned MHK platform potentially meets this need with sustainable power harvested from water currents. For the case study, six different waterwheel configurations were field-tested in the Intracoastal Waterway of South Florida in support of determining the configuration that produced the most power. Required technologies for unmanned operations of the MHK platform were developed and tested. The data from the field-testing were analyzed to develop an empirical relation between the wheel’s theoretical hydrokinetic power produced and the mechanical power harnessed by the MHK platform with various waterwheel configurations during field-testing. The field data was also used to determine the electrical power generated by the FSWW configurations during field-testing. The study has led to the development of standardized testing procedures. The empirical relation is used to examine predicted power production through scaling up different physical aspects of the waterwheel.

McKinney, Adriana↗
DOE OSTI2020

Use of high throughput screening methods to study dual-functional crosslinkable latexes

Herein, structure-formulation-property relationships for crosslinkable core-shell latexes were studied initially using high throughput screening methods and then using conventional experimental methods to determine optimum coating formulations. Two approaches that are used within coatings technology to improve sustainability are reduction in volatile organic compounds (VOC) and reduction in energy needed to cure coatings. Hybrid crosslinkable core-shell latexes have shown potential in meeting these challenges where film properties provided by particle coalescence are not sufficient and chemical crosslinking is needed. However, with numerous parameters involved such as latex particle architecture, nature and amount of crosslinker, and curing conditions, the screening of formulations via conventional coating testing methods becomes laborious and time-consuming. The results from high-throughput dye extraction and puncture testing were compared to data from conventional testing methods such as chemical resistance (MEK double rubs), pendulum hardness, and tensile testing to identify correlations. The dye extraction results have shown a strong correlation to solvent resistance and pendulum hardness data whereas toughness from puncture testing data correlated to tensile testing. Both high throughput dye extraction and puncture testing methods can be used for the accelerated screening of crosslinkable coating formulations to optimize curing parameters to achieve the best coating performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven variational multiscale reduced order models

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS–ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS–ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS–ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS–ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient v = 10 -3 ; (ii) a 2D flow past a circular cylinder at Reynolds numbers, Re = 100, Re = 500, and Re= 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS–ROM is significantly more accurate than standard ROMs.

42 ENGINEERING↗
DOE OSTI2024

Development and Experimental Validation of a Heat Transfer Model for Spilled Molten Salt Pools

A spill of radionuclide-bearing molten salt is one of the major postulated events that needs to be analyzed for liquid fluorine salt-cooled high-temperature reactor (FHR) or molten salt reactor licensing purposes. In this postulated event, radioactive source term materials (RSTMs) in the molten salt are discharged from the reactor vessel to the reactor building. The release of RSTMs from the spilled salt pool to the gas space in the reactor building is expected to be controlled by the cooling behavior of the spilled salt, including the growth and shrinkage of the solid crust on the surface of the spilled salt pool. This paper presents a simulation model for spilled salt pool heat transfer and validation efforts. The validation data come from two molten salt spill tests that were performed recently: the PELE2 test by the Rapid Experimental Laboratory of Kairos Power LLC (KP) and the Argonne salt cooling test conducted by Argonne National Laboratory. The former was a large-scale test involving kilograms of molten spilled FLiNaK salt, and the latter was a relatively smaller-scale test targeting various processes associated with a salt spill event. Both tests generated valuable data sets that can be used to assess salt cooling and validate evaluation models. This paper provides a new one-dimensional model that can simulate the cooling process of a spilled salt pool as well as the thermal responses of heat structures, such as the stainless steel liner and the concrete below the salt. The model has been implemented as part of KP-SAM code, which is a branch of the systems code SAM specific to KP FHR. In conclusion, the simulation results of the model are compared with the data of the PELE2 and Argonne tests, and reasonable agreements are observed between the model and test data.

heat transfer model↗
DOE OSTI2022

Foam Fracturing Fluid Half-life Experimental Data

Foam thermal stability was studies at Temple University in collaboration with Oak Ridge National Lab (ORNL). The goal of this project is to explore thermally stable foams as hydrofracking fluid media for potential applications in enhanced geothermal system (EGS). Data generated from this project will allow researchers to explore foam as potential fracturing fluid. More than 800 data points on the half-life of foams are recorded in Excel files in the included archive resource (Half-life of Foams with Different Surfactants and Stabilizing Agents). The Excel file within each surfactant folder contains half-life data of the respective surfactant with different stabilizing agents, pressure, and temperature. The respective folders also contains Word files describing the details of the data included in the respective Excel sheet.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Preliminary Plan for Evaluation of Reactor Pressure Vessel Surveillance Materials from Palisades Nuclear Generating Station

The Palisades Nuclear Generating Station (PNGS), located in Michigan, is owned and operated by Entergy. It is a Combustion Engineering 2-loop pressurized water reactor (PWR) producing 805 MWe (2,565 MWth). The PNGS was built between 1967 and 1970, with approval to operate at full power in 1973; the plant’s original license was due to expire on March 24, 2011. An application for 20-year extension was filed in 2005 with the Nuclear Regulatory Commission and was granted on January 18, 2007. Although the plant was scheduled for decommissioning by 2031, Entergy currently plans to close the PNGS in 2022. The PNGS included in its reactor pressure vessel (RPV) surveillance program a capsule, designated A-60, containing specimens of one of the vessel plates and a weld metal with nickel content of about 1.36 wt% and copper content of about 0.20 wt%. This capsule was irradiated to a fluence of 1.8 x 10 20 n/cm 2 . The capsule was removed from its surveillance position in 1995 and has been resident in the spent fuel pool since that time. The material is also of special interest because of its very high nickel content and because of the potential for development of NiMnSi (nickel-manganese-silicon) precipitates, dubbed “late blooming phases.” The surveillance program also includes a capsule, designated T-150, dedicated specifically for thermal aging, which would provide results for at least 33 effective full power years, which is beyond the current thermal-aging database for such materials. Given that license extensions to 60 years of operation have been approved by the US Nuclear Regulatory Commission for most of the currently operating light water reactors in the United States, and that the first extension to 80 years was recently approved, there exists the probability that some RPVs will reach and possibly exceed a fast neutron fluence (> 1 MeV) of 1 x 10 20 n/cm 2 . This is a fluence regime with no US surveillance data and very little test reactor data, except for the Light Water Reactor Sustainability Program–sponsored University of California Santa Barbara Advanced Test Reactor (ATR) ATR-2 project. Thus, the materials in the A-60 capsule represent a valuable resource for directly exploring the effects of commercial surveillance irradiation on a typical plate and a high-nickel weld with similar materials irradiated in the ATR-2 test reactor project. This report provides background information for the surveillance program, previous results of surveillance materials testing, the preliminary plan for capsule retrieval and disassembly, and the plan for testing and microstructural examination of the mechanical test specimens of these unique materials to assess the features induced by very high irradiation fluence or very long thermal aging time

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Meas. K+ -Ar Total Inelastic Cross Section at ProtoDUNE-SP

ProtoDUNE-SP is a single-phase liquid argon time projection chamber that took hadron test beam data in 2018. The test beam included positively charged kaons with test beam momenta of 6 GeV/c and 7 GeV/c, providing a sample to study kaons to benefit future DUNE proton decay and neutrino interaction studies with kaons in the final state. The total inelastic cross section of a positively charged kaon was measured at these test beam settings using the LArIAT thin-slice method of dividing the wires of the time projection chamber into target slices for calculating the cross section, which leverages the monolithic quality of liquid argon detectors. A Bayesian-like unfolding method using RooUnfold was applied to both the incident and interacting slice distributions to measure the cross sections at both test beam momenta. The talk will discuss the method of unfolding, optimization studies for unfolding, and applying systematic uncertainties using a LArIAT-style hadronic cross section using unfolding to extract a kaon total inelastic cross section.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

RF Digitizer for EIC LLRF

With the shutdown of Relativisitc Heavy Ion Collider (RHIC) in FY26, the former RHIC facility will be upgraded to the new Electron-Ion Collider (EIC). An RF Digitizer for the EIC LLRF system has been developed and tested on a RHIC 197 MHz cavity in Q2 2026 for a baseline per formance. Firmware and software has been developed and validated during the 197 MHz cavity test. Preliminary data from the cavity test shows phase noise performance signifi cantly better than the RHIC LLRF system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗