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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Design, Fabrication and Concept for the Surface Treatment of the SRF Cavity Prototype for the CLIC Damping Rings

The Compact Linear Collider (CLIC) Damping Rings (DRs) need to generate ultra-low emittance bunches to achieve high luminosity in CLIC. This requires many wiggler magnets with big energy loss which is compensated by the Radio Frequency (RF) system. The resulting strong beam loading transients lead to a challenging design for the RF system. A novel SRF cavity at 2 GHz with an ultra-low R/Q parameter of below 1 Ω is proposed to minimize the transient beam loading effects below acceptable level. The design and fabrication of the bulk Nb prototype based on turning from a single piece of Nb and EB welding is presented. Moreover, conceptual study of the system for cavity surface treatment to achieve the highest surface magnetic field which is the main goal of the prototype cold test is described as well. To enable excellent performance in this cavity, we plan to apply the 75/120C modified low temperature bake in combination with the cold electropolishing process. This surface treatment approach has been shown to consistently deliver high accelerating gradients and improved quality factors in TESLA-shaped 1.3 GHz SRF cavities. By adapting and implementing this process for the 2 GHz ultra-low R/Q design, we aim to maximize the achievable surface magnetic field while minimizing residual resistance and field emission. This treatment strategy will be critical for demonstrating that the cavity can meet the demanding performance requirements of the CLIC damping ring RF system under high beam loading conditions.

Bafia, Daniel P. [Fermilab]↗
DOE OSTI2022

Sensitivity of Modal Parameters of an Offshore Wind Turbine to Operational and Environmental Factors: A Numerical Study: Preprint

The dynamics and performance of offshore wind turbines are sensitive to their structural properties, and in particular their modal parameters, i.e., natural frequencies, damping ratios and mode shapes. In this work, the sensitivity of the modal parameters of a 6 MW fixed-bottom offshore wind turbine to different operational and environmental conditions including wind speed and rotor speed is investigated. Effect of blade flexibility on the modal parameters of the system is also studied.

aerodynamic damping↗
DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2024

The Effects of Material-Filled Voids on Detonation Wave Shape in Rubberized RDX Explosives

The sensitivity of explosives is affected by inhomogeneities within the material. This is evident in the increased shock sensitivity of explosives with slightly lower densities resulting from an increased number of hotspots. The influence of hotspots on explosive initiation has been well studied; however, few studies have been conducted on the effect of intermediate-sized voids (0.1–10 mm) on a propagating detonation wave. Cylindrical voids filled with air have been studied for diameters ranging from 0.3 mm to 0.8 mm for both 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) and 1,3,5-trinitro-1,3,5-triazinane (RDX)-based rubberized explosives. Continuing the investigation into single cylindrical voids, this study examined the effects of 0.5 mm diameter voids filled with different inert cylindrical metals on the detonation wave shape for an RDX-based rubberized explosive. The metals selected for experiments were 1066 aluminum, brass, copper, and tungsten. The propagation of the detonation wave was captured using a digital streak camera. Experimental results showed that the extent of detonation wave shaping was closely tied to the density differential between the bulk explosive and metal insert. Forty-four different filler materials, including non-metals, were simulated using a hydrodynamic code to further analyze material inclusion effects. The main factors hypothesized to be of interest were bulk sound speed, shock impedance, and filler material density. We found that the local detonation delay could be correlated fairly well to a ratio of bulk sound speed and density. Understanding the influence of material inclusions on detonation performance and wave shape allows for tailoring of detonations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

An Enhanced Virtual Crack Closure Technique for Stress Intensity Factor Calculation along Arbitrary Crack Fronts and the Application in Hydraulic Fracturing Simulation

The virtual crack closure technique (VCCT) is widely used for calculating energy release rates along crack fronts and modeling the propagation of cracks in solid materials. Although the VCCT formulation for smooth crack fronts has been sufficiently addressed in the literature, the application of VCCT to a nonsmoothed crack front with sharp corners warrants further investigation. In this study, we describe an enhanced VCCT to calculate energy release rates and stress intensity factors for cracks with arbitrary shapes in 3D domains discretized on structured grids. The formulations of the enhanced VCCT were developed and implemented into a multiphysics simulation environment capable of simulating crack propagation in the framework of linear elastic fracture mechanics. Comparisons with existing analytical/numerical solutions and other VCCT approaches were performed to verify the enhanced VCCT in terms of SIF calculation along nonsmoothed crack fronts. We then applied the enhanced VCCT to a hydraulically driven penny-shaped fracture problem to further demonstrate its capability to simulate nonsmoothed fracture propagation.

02 PETROLEUM↗
DOE OSTI2024

UNCERTAINTIES FOR IN-REACTOR TESTING OF TRITIUM LOADED GETTERS WITH TEMPERATURE CONTROL

The fourth experiment in TPBAR Materials Irradiation Separate-Effects Test (TMIST) series (TMIST-4) is planned to experimentally determine the effect of neutron irradiation on the equilibrium vapor pressure of tritium over a tritium loaded Nickel-Plated Zirconium (NPZ) getter in a simulated TPBAR service environment. This pressure will be used to estimate the tritium permeation rate. In the thermal design in the TMIST-4, two evaluations are required. 1) The cladding temperature is calculated to estimate the feasibility of detecting potential irradiation enhancement of the partial pressure for an NPZ getter. This work is required to obtain a precise cladding temperature for measurement of a low tritium partial pressure. This work has been completed and indicated that the required/target cladding temperatures based on the TMIST-4 test plan can be satisfied under the test condition range by modifying the capsule component shapes and inserting a thermally conductive metal sleeve. 2) The evaluation of uncertainties associated with the test design and operation is performed. This includes fabrication tolerances, and power and nuclear operating conditions. These factors will impact the component temperatures inside capsules and resulting the tritium partial pressure. The tritium permeation rate is a function of temperature and tritium partial pressure. The prediction of tritium permeation rates affects the feasibility of obtaining measurement resolutions required to reach reasonable conclusion for potential irradiation enhanced performance. This work will investigate the uncertainties.

TTP, TPBAR, TMIST-4, TPBAR Materials Irradiation S↗
DOE OSTI2020

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Heat Transfer and Pressure Loss of Additively Manufactured Internal Cooling Channels With Various Shapes

Abstract Additive manufacturing (AM) provides the ability to fabricate highly customized internal cooling passages that are relevant to gas turbine components. This experimental study examines the pressure loss and heat transfer performance of a range of fundamental channel shapes that were produced using direct metal laser sintering. Circular, hexagonal, pentagonal, elliptical, diamond, square, rectangular, trapezoidal, and triangular channel cross sections were investigated. To maintain the same convective surface area between shapes, the wetted perimeters of the channel cross sections were kept constant. Parallel computational fluid dynamic simulations were performed to understand the relationships in cooling performance between several channel shapes. Several characteristic length scales were evaluated to scale the pressure loss and heat transfer measurements. Among the channel shapes investigated, the diamond channel showed the lowest Nusselt number and friction factor. The pentagon exhibited a similar Nusselt number as the circular channel despite having a lower friction factor. There was no difference in scaling the friction factor or Nusselt number results of the different channel shapes between using the square root of cross-sectional area compared to hydraulic diameter as the characteristic length scale

Engineering↗
DOE OSTI2022

Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes

Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm –2 and underestimates the upwelling shortwave flux by –15.70 Wm –2 and downwelling longwave fluxes by –12.58 Wm –2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm –2 ) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm –2 ). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Support of the Development of EPP Plus SMT Design Method (FY2020 Test Results)

Experiments in support of the development of the integrated Elastic–Perfectly Plastic (EPP) analysis and Simplified Model Test (SMT) design methodology continued in FY 2020. The previously developed single-bar SMT (SBSMT) technique was successfully extended to tubular shaped Alloy 617 specimens for evaluation of the effect of primary pressure load on SMT creep-fatigue (CF) properties at 950°C. A series of pressurized SMT tests were performed with various elastic follow-up factors and strain ranges. The results show that the primary pressure load reduces the SMT CF cycles. The reduction of SMT CF life due to primary load was found to be dependent on strain ranges, elastic follow up and test temperatures. These results are crucial for verification of the EPP strain range analysis in the EPP+SMT evaluation procedure. Additionally, hold time effect on the SMT CF life cycles is a determining factor for generating the EPP+SMT CF design curves. Lack of test data in the high cycle and low strain range region results in major uncertainty in developing the design curves. In FY2020, experiments were designed, and testing started on Alloy 617 at 950°C to fill the gap in experimental data. The on-going CF and additional SMT CF with elastic follow-up at low strain ranges will generate the information needed to verify the methods for extrapolation to long hold time and finalize the EPP+SMT design method.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2022

Effect of duration of 120 oC baking on the performance on superconducting radio frequency niobium cavities

Over the last decade much attention was given in increasing the quality factor of superconducting radio frequency (SRF) cavities by impurity doping. Prior to the era of doping, the final cavity processing technique to achieve the high accelerating gradient includes the ?in situ? low temperature baking of SRF cavities at temperature ~ 120 oC for several hours. Here, we present the results of a series of measurements on 1.3 GHz TESLA shape single-cell cavities with successive low temperature baking at 120 oC up to 96 hours. The experimental data were analyzed with available theory of superconductivity to elucidate the effect of the duration of low temperature baking on the superconducting properties of cavity materials as well as the rf performance. In addition, the rf loss related to the trapping of residual magnetic field refereed as flux trapping sensitivity was measured with respect to the duration of 120 oC bake.

Khanal, B. D.↗
DOE OSTI2023

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Energy Storage to Enable Electricity as a Commodity

A lack of large-scale economical storage, demand-side flexibility, and the need to instantaneously balance supply and demand cause challenges for electricity as a commodity. With typical commodities, stock from periods of high production can be stored and sold during low production, smoothing demand and supply shocks. In electricity markets, high storage costs have long outweighed benefits, negating convenience yield; no-arbitrage models do not apply, and typical hedging strategies cannot be performed due to infinite carry costs. However, the mechanics of energy trading are changing due to advancing storage technologies, more price-responsive demands, and virtual bidding in electricity markets, shaping a system where electricity storage can be valued on factors other than diurnal arbitrage. We examine the valuation of electricity storage through the lens of commodity markets to assess the impacts of large-scale storage deployment.

energy storage↗
DOE OSTI2022

The impact of infill percentage and layer height in small-scale material extrusion on porosity and tensile properties

We report material extrusion additive manufacturing is prone to introducing porosity within the structure due to the layer-by-layer construction using elliptical beads of material. This open porosity ultimately plays a role in determining the mechanical properties of printed parts. The shape, size, and amount of porosity within a printed part is influenced by a variety of factors, including nozzle diameter, infill percentage, layer height, raster orientation, and print speed. While several studies have investigated these and other parameters’ effects on mechanical performance and porosity, better understanding the interconnected relationships is crucial in balancing the various input parameters to achieve maximum strength. This work initially examined the influence of key print parameters (infill percentage and layer height) on the internal porosity of a printed Acrylonitrile Butadiene Styrene (ABS) part. Then, the print parameters and internal porosity were statistically correlated to final mechanical properties. Porosity was further classified as either open or closed to differentiate between connected voids in the mesostructure from isolated voids within the material itself. Mechanical performance increased with an increasing density and infill percentage, displaying a 224 % increase in elastic modulus and a 150 % increase in ultimate tensile strength. The contribution of layer height was found to be conditional upon the infill percentage.

3D Printing↗
DOE OSTI2021

Screening of Lost Circulation Materials for Geothermal Applications: Experimental Study at High Temperature

Abstract This study presents a laboratory experimental research to determine the characteristics of lost circulation materials (LCMs) capable of addressing thermal degradation, providing bridging and sealing in geothermal conditions. Eleven different materials were tested: Walnut Fine, Walnut Medium, Sawdust, Altavert, Graphite Blend, Bentonite Chips, Micronized Cellulose (MICRO-C), Magma Fiber Fine, diatomaceous earth/amorphous silica powder (DEASP), Cotton Seed Hulls, and a Calcium Carbonate Blend. The filtration and sealing pressure of the LCMs were measured with HPHT equipment up to 149 °C (300 °F). Besides, the particle size distribution (PSD) of fine granular materials was measured. The results show that the performance of some LCM materials commonly used in geothermal operations is affected by high temperature. Characteristics such as shape and size made some materials more prone to thermal degradation. Also, it was found that the PSD of LCMs is a key factor in the effectiveness of bridging and sealing fractures. The results suggest that granular materials with a wide particle size distribution PSD are suitable for geothermal applications.

Energy & Fuels↗