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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
DOE OSTI2025

TrojAI Alternate Analysis

In this portion of the TrojAI evaluation, we focus on the cyber-network-c2-mar2024 dataset. Recall that in this round ResNet18 and ResNet34 neural networks (NN) were trained on the USTC-TFC2016 dataset with the aim of distinguishing between benign versus botnet command and control (c2) packets. A range of bytes from each packet was reformatted into a 28x28 pixel image, and the collection of reformatted packets served as the training (and testing) data for the two ResNet models. For some of the data a trigger watermark was strategically placed to affect various inputs to the NNs. This watermarked, or poisoned, data in turn created a poisoned, or trojaned NN. The data were poisoned in different ways ultimately creating different trojaned NNs. This collection of trojaned NNs was combined with various versions of not trojaned NNs and served as the training and testing data for the performers. The performers’ task was to construct a classifier to distinguish between the trojaned and not trojaned models. It was previously noted that the performers struggled with the cyber-network-c2-mar2024 dataset, motivating this investigation of potential reasons the performers experienced challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Stage 2 Full-Scale Rotary Magnetic Gear for a Marine Hydrokinetic Generator

The goal of this project is to design, fabricate, and test a hermetically sealed 50 kilowatt (kW) multistage magnetically geared generator (MGG). This project will benefit MHK device developers by providing an MHK PTO that overcomes the reliability concerns of the mechanical gears and the sizing constraints of the direct-drive generators. Experimental Testing Data for a 6.66:1 gear ratio dual-stack magnetic gear with a measured peak torque of 1391 N-m

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

EXPERIMENTAL VALIDATION OF THEORETICAL BURST STRENGTH SOLUTION FOR DEFECT-FREE THICK-WALLED PIPES

The burst pressure of line pipes is an important strength property required in pipeline design and integrity management. Historically, the Barlow formula in conjunction with the ultimate tensile stress (UTS) of pipeline steels were utilized to estimate the burst strength of line pipes. However, the Barlow formula did not consider the plastic flow effect for ductile steels and is applicable only to thin-walled pipes. In 2006, the present author proposed a new multiaxial plastic yield theory and obtained a theoretical Zhu-Leis solution of burst strength for defect-free thin-walled pipes in term of UTS and strain hardening exponent n of pipeline steels. The Zhu-Leis solution has been validated by various burst test data for thin-walled pipelines for a wide range of steel grades from Grade B to X120. Recently, the present author extended the Zhu-Leis theory of plasticity to thick-walled pipes and obtained the Zhu-Leis solution of burst pressure for thick-walled pipes. The proposed burst pressure solution is applicable to both thin and thick-walled pipes. To experimentally validate the proposed theoretical burst pressure solution, this paper obtains a set of burst test data for three thick-walled pipes in Grade B carbon steel with a nominal diameter of 2.375 inches and three nominal wall thicknesses, resulting in D/t = 15.4, 10.9, 6.9. Through comparisons, these burst data validate the theoretical burst pressure solution for thick-walled pipes. Moreover, two additional burst test datasets collected from literature for thin and thick-walled pipes further validate the proposed burst pressure solution for both thin and thick-walled pipes.

Zhu, Xiankui↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine Learning Travel Time emulator

This notebook makes seismic phase travel time predictions using machine learning. There are 3 different machine learning models corresponding to the three regimes: local, regional, and teleseismic. After reading in the test data and splitting into the 3 regimes, we use precomputed scalers to scale the input features used to make the travel time predictions. We then load the machine learning models and use them to predict travel times for test data.

Anderson, Gemma↗
DOE OSTI2022

EGS Collab Experiment 3: 4100 Tensile Stimulation and Thermal Circulation Testing

These data and test descriptions are from a set of primarily tensile hydraulic-fracture stimulations in wells E2-TC and E2-TU and a subsequent chilled water circulation test conducted by injecting in well E2-TU on the 4100 level of the Sandford Underground Research Facility (SURF). Stimulations were carried out between April and May of 2022. The thermal circulation test ran semi-continuously from May 19 through August 26, 2022, though chilled water injection began on June 3. More information about the test, rationale, and processing of data is available on the EGS Collab project page, which is linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Horizontal Split Table Conceptual Design for Validation of Nuclear Data used in Advanced Reactors [Slides]

This presentation discusses a methodology that was developed to create conceptual designs of benchmark critical experiments for advanced reactors and nuclear data testing. A first concept that was explored was a pebble-bed high-temperature gas cooled reactor, based on the HTR-10 reactor. The very high correlation is a proof of concept that the design is similar to the application, and performing such critical experiments would help nuclear data testing and validation. Other concepts could be explored if needed, such as a molten-salt reactor, a sodium-cooled fast reactor, or heat pipe reactors/microreactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Motorette Thermal Performance Testing and Modeling for an Electric Motor with Additively Manufactured Hollow Conductors with Integrated Heat Pipes

This paper discusses the design, build and test of a motorette to characterize the thermal performance of an additively manufactured coil integrated with heat pipes. The motorette is powered using a variable frequency AC power supply and cooled through two independent cooling systems. The first cooling system cools the stator core using forced convection of air flowing over a finned heat sink mounted on the stator outer diameter. The second cooling system cools the heat pipes using forced convection of 50-50 water ethylene glycol (WEG) mixture flowing through condenser chambers. A 3D thermal FEA model of the setup is built and heat transfer coefficients (HTC) of convective boundaries are computed using coolant flow rates from test data and empirical equations. Temperature at locations on the coil and heat sink are recorded and compared to thermal model predicted values. A maximum temperature error of 22.7% occurs at 180ARMS and 800 Hz operating point for the AM dual coil. The thermal model provides higher temperatures compared to test results and hence it is on the conservative side but in general, there is good correlation between test and model results. Lastly, opportunities for improvement to both test data measurement and 3D thermal FEA modeling are discussed.

additive manufacturing↗
DOE OSTI2023

TEAMER: Water Tunnel Data from Testing the Pterofin Skimmer Concept

Pterofin's Skimmer concept relies on a flapping and pitching hydrofoil to extract hydrokinetic energy from water flows. The concept aims to utilize unsteady fluid dynamics phenomena (added mass, shed vorticity, and unsteady boundary layer development) to achieve higher lift coefficients, enabling increased power density of the hydrokinetic device and a fundamental shift in the rpm/torque scaling of the power take off compared with turbines. The Applied Research Laboratory at Penn State, in collaboration with Pterofin, designed and built a proof-of-concept flapping/pitching mechanism which was subsequently tested in ARL's 12-inch water tunnel facility. The mechanical power supplied to or extracted from the mechanism was measured for a range of hydrofoils provided by Pterofin over operating conditions including reduced frequency, Reynolds number, and the ratio between pitching and flapping amplitudes. The power lost to friction in the mechanism was removed from the net power measurement by means of a bare hub tare, with the resultant hydrodynamic power being used to calculate a mechanism-independent and non-dimensional power coefficient. The product of this effort is a dataset describing the power coefficient of a hydrofoil having simultaneous pitching and flapping motions, both of which are approximately sinusoidal. Power coefficients were collected for a range of primary design variables including: - Reduced frequency: 0.01 to 0.95 - Pitching/flapping peak angle ratio: 1.5 to 3.0 - Chord-based Reynolds number: 60,000 to 560,000 Secondary design variables relating to the hydrofoil geometry were explored including: - Aspect ratio - Planform shape - Section thickness distribution - Hydrofoil position relative to the pitching axis - Hydrofoil sweep angle relative to the pitching axis Measured data are provided in mean and time series formats. MATLAB scripts are provided which can be used to generate figures of time-averaged and phase-averaged hydrodynamic power coefficients calculated from the measured data. A complete description of the experiment and data reduction can be found in the Post Access Report for the Pterofin Skimmer test effort which will be available on the TEAMER website. This work was supported by the Pacific Energy Ocean Trust via a TEAMER award.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Horizontal Split Table Conceptual Design for Validation of Nuclear Data used in Advanced Reactors [Slides]

A methodology to create conceptual designs of benchmark critical experiments for advanced reactors nuclear data testing and validation was developed. A first concept was explored, pebble-bed high-temperature gas cooled reactor, based on the HTR-10 reactor. The very high correlation is a proof of concept that the design is similar to the application, and performing such critical experiments would help nuclear data testing and validation. This proof-of-concept was included in the IER-539 CED-1:Preliminary Design of a New Horizontal Split Table report. Other concepts could be explored if needed, such as Molten-salt reactor, Sodium-cooled fast reactor or Heat pipe reactors/Microreactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Design Guidelines for Predicting Stress in Cemented Doublets Undergoing Temperature Change, Part 2

This work builds on prior work that developed a methodology for evaluating thermal stress in cemented doublets. The prior studies were limited by a paucity of actual test data, particularly from thermally tested cemented doublets. This follow-on study uses the existing methodology to estimate stresses in cemented doublets that have been tested for a temperature range from –40°C to 85°C and establishes safe stress levels using actual data. We also discuss limitations and concerns that we need to address to improve our understanding of cemented doublet failure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗
DOE OSTI2022

Integral Experiment Final Design for Thermal/Epithermal eXperiments (TEX) using Highly Enriched Uranium with Polyethylene at Low Temperature (IER-479 CED-2 Report)

The goal of IER-479 is to design uranium critical experiments that can be used to validate low temperature cross sections and criticality safety analyses over multiple neutron energy regimes. Currently, there are no benchmarks in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook at temperatures lower than room temperature (International Criticality Safety Benchmark Evaluation Project Handbook, 2019). However, there are many needs for validation of criticality safety analysis at lower temperatures, including meeting transportation requirements and operations conducted outside or in unheated facilities. Additionally, NCSP has funded North Carolina State (NCSU) to generate new thermal scattering laws, including at lower temperatures, and the lack of integral benchmarks impedes data testing of these new cross sections. To address these needs, this report will present a critical experiment design covering various fission energy regimes with a goal temperature of -40°C (-40°F), which is based on the lower bound of expected non-cryogenic operational temperatures. The goal of the U.S. Nuclear Criticality Safety Program’s (NCSP) Thermal/Epithermal eXperiments (TEX) is to design and conduct new critical experiments to address high priority nuclear data needs from the nuclear criticality safety and nuclear data communities. The TEX program includes two series of baseline experimental configurations, one based on plutonium fuel (plutonium-aluminum Zero Power Physics Reactor (ZPPR) plates) and the other based on uranium fuel (highly enriched uranium (HEU) plates), that are moderated with varying thickness of polyethylene to create assemblies which span the thermal, intermediate, and fast fission energy regimes. The configurations are designed to be easily modified (for example, to add diluent materials of interest) to allow for efficient generation of additional benchmark configurations and allow for added nuclear data testing utility when comparing modified configurations to baseline configurations. The goal of IER-479 is to use the TEX-HEU concept (stack of HEU plates and polyethylene moderators) to design a critical experiment that can be used to validate low temperature cross sections and criticality safety analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multiscale Modeling of the Mechanical Response of Silicon Carbide Composite Within the Accelerated Fuel Qualification Framework

The accelerated fuel qualification (AFQ) framework has been used for the initial development of multiscale modeling of silicon carbide (SiC) fiber reinforced composite (SiC-SiC). The AFQ framework provides a methodology to leverage physics-informed multiscale modeling along with a reduced set of empirical test data to reduce the time and cost of licensing and qualification of new nuclear fuel systems while maintaining the overall nuclear power plant safety case. SiC-SiC is being proposed for in-core applications, most notably fuel cladding, for current and next-generation nuclear reactors because of its high temperature stability, irradiation tolerance, and ability to withstand many accident conditions. As these composites exhibit multiscale architectures and complex microstructure-based fracture mechanics, it is an appealing use case for the AFQ methodology. While the end goal of this work is a single multiscale model that can be used for predictive in-core performance, current focus is on the individual various length scale models. Four individual models have been initially developed from microscale to engineering system level to capture key physics-based effects across different length scales. These models include a microscale homogenized tow model, a mesoscale fast Fourier transform–based weave model that integrates the homogenized tow model, a mesoscale finite element–based weave model, and a system-level BISON fuel performance model. Results of these models have undergone an initial comparison with separate-effects test data showing a good match to experimental results. By using the AFQ framework during model development, several near-term benefits have been secured including a reduction in development time for the SiC-SiC cladding, more targeted irradiation testing, and a better understanding of uncertainty.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Preliminary description of a new creep-fatigue design method that reduces over conservatism and simplifies the high temperature design process

The report provides the initial description of a new creep-fatigue design method for structural components in high temperature nuclear service. The new method is based on an integrated elastic-perfectly plastic (EPP) analysis and Simplified Model Test (SMT) approach that reduces over conservatism, improves the treatment of elastic follow up, and simplifies the design procedure, when compared with the current creep-fatigue design methods in ASME Boiler and Pressure Vessel Code. Developing the design charts for the EPP-SMT design method requires extrapolating SMT test data as a function of hold time and follow up factor. The report develops the preliminary design charts for Alloy 617 at temperatures between 800°C and 950°C by combining two extrapolation approaches developed in a previous work. The report also presents a comparative analysis between the EPP-SMT design method and the current ASME creep-fatigue design methods by evaluating design life of two sample geometries under different loading conditions. Results from the comparative analysis verify the EPP-SMT design charts but suggest the requirement of additional test data in the low strain range regime for improving the extrapolation procedure that will further reduce the over conservatism in the creep-fatigue damage evaluation. The report also concludes that the EPP-SMT design procedure can account for effect of primary load on creep-fatigue life by using a fixed, bounding value of follow up in constructing the design charts. The conclusions to this report describe the future work required to complete this new design method so it can be codified through a nuclear Code Case.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Cummins R-SOFC System Development

The overall purpose of this project was to reduce the Reversible-Solid Oxide Fuel Cell (R-SOFC) system cost by developing two technologies, an improved cell design and the incorporation of an ejector in the fuel recycle loop instead of a blower. A Simulink model of the baseline SOFC system was developed and calibrated with experimental test data. The R-SOFC system model was built by integrating GT Suite developed models of the steam generation components into the baseline Simulink SOFC system model. The ability to run the stack in SOEC operating mode was also added to the model. The system model was used to explore the ability of the R-SOFC system to meet operational constraints on Steam/Carbon ratio and H2 concentration on the fuel side electrode. A CFD ejector model was developed and used to explore a range of ejector design parameters, leading to the final ejector design that was prototyped for testing. A prototype steam ejector was first tested in a laboratory environment using room temperature air. The steam ejector was subsequently tested using the full hot recycle loop with all relevant heat exchangers and steam generation components. The test conditions utilized temperatures, pressures, and flow rates expected in an R-SOFC application. Throughout the experimental testing work, ejector performance test data was used to improve and then validate the CFD ejector model. A CFD cell model was developed and used to optimize thermal gradients, voltage, and cost of a new cell substrate design. A CFD comparison of co-flow and cross-flow cell designs informed the decision to use a co-flow design for the new cell substrate. Multiple rounds of CFD simulation were used to improve the cell design to minimize the variation in air and fuel distribution across cell channels and to minimize the variation in air and fuel distribution across different cells in the stack. A few prototypes of the new cell substrate design were produced and validated in a laboratory environment by thermally spraying and verifying that they met established manufacturing specifications. The cell manufacturing process was adjusted in order to bring these metrics within acceptable tolerances. Cummins’ internal calculations show that the new cell design reduces cost ~50% compared to the baseline cell, while the ejector + superheater/boiler concept reduces cost of the recycle loop by ~40%. The impact of these cost reductions on the cost of producing H2 will depend on the specific system where they are applied. Therefore, a Techno-Economic Analysis was completed using system cost as a variable, and showing how the NREL Current and Future system costs translate into H2 production cost.

Henrichsen, Lars↗