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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 OSTI2021

Low-Cost, Accessible Vaccine Cooler Options

In response to cold chain management requirements for COVID-19 vaccines, Los Alamos National Laboratory has investigated, built, and demonstrated viability for different cryogenic storage options. With ease-of-access and affordability in mind, these cooler units have been built using commercial off-the-shelf (COTS) components. The team was also able to design a testing method for evaluating the performance of the different vaccine cooler options as part of the Technology Evaluation and Demonstration program. The tested cooler units demonstrated compatibility with either dry ice or liquid nitrogen as the cryogen, along with several commercial solvents. This compatibility demonstration would potentially offer sufficient flexibility for temperature control continuity. Next steps are to investigate industry interest in supporting field testing of the cooler units.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Target fabrication fields first NIF shot with new 3D-printed microfluidic target design

Capable of producing ultrafine features, two-photon polymerization (2PP) 3D printing made it possible to field a first-of-its-kind leak-proof liquid-filled diffraction target. These targets are used for shots aimed at using laser pressure instead of low temperatures turn water into ice. The shot is the product of months of design, assembly, and installation work by LSEO’s target fabrication team, in collaboration with 3D printing experts Widi Moestopo (MED) and Xiaoxing Xia (MED) and PLS researcher Neal Bhandarkar.

42 ENGINEERING↗
DOE OSTI2016

Fast and low-cost method to fabricate large-area superhydrophobic surface on steel substrate with anticorrosion and anti-icing properties

A facile and quick fabrication method was proposed to prepare superhydrophobic surfaces on iron substrate by chemical immersion and subsequent stearic acid modification. The association between wettability and surface morphology was studied through altering the copper ion concentration and immersion time. Surface tension instrument, scanning electron microscopy, x-ray diffraction, x-ray photoelectron spectroscopy, and electrochemical workstation were used to characterize the wettability, physical morphology, chemical composition, and corrosion resistance ability of the prepared film. Results showed that both the rough micro/nanostructures and low surface energy material play critical roles in surface wettability. The superhydrophobic film achieved a better anticorrosion property compared to barrier iron by analysis of open circuit potential, potentiodynamic polarization curves, and Nyquist plots. In addition, the superhydrophobic surface showed excellent performance of acid and alkali resistance, anti-icing, and self-cleaning through a series of environmental tests. This study provides a valid method for quick-preparation of the stable superhydrophobic surfaces, which has a promising application in steel buildings and facilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Absorbing Aerosols Workshop, January 20-21, 2016

A workshop was held at DOE Headquarters on January 20-21, 2016 during which experts within and outside DOE were brought together to identify knowledge gaps in modeling and measurement of the contribution of absorbing aerosols (AA) to radiative forcing. Absorbing aerosols refer to those aerosols that absorb light, whereby they both reduce the amount of sunlight reaching the surface (direct effect) and heat their surroundings. By doing so, they modify the vertical distribution of heat in the atmosphere and affect atmospheric thermodynamics and stability, possibly hastening cloud drop evaporation, and thereby affecting cloud amount, formation, dissipation and, ultimately, precipitation. Deposition of AA on snow and ice reduces surface albedo leading to accelerated melt. The most abundant AA type is black carbon (BC), which results from combustion of fossil fuel and biofuel. The other key AA types are brown carbon (BrC), which also results from combustion of fossil fuel and biofuel, and dust (crustal material). Each of these sources may result from, and be strongly influenced by, anthropogenic activities. The properties and amounts of AA depend upon various factors, primarily fuel source and burn conditions (e.g., internal combustion engine, flaming or smoldering wildfire), vegetation type (in the case of BC and BrC), and in the case of dust, soil type and ground cover (i.e., vegetation, snow, etc.). After emission, AA undergo chemical processing in the atmosphere that affects their physical and chemical properties. Thus, attribution of sources of AA, and understanding processes AA undergo during their atmospheric lifetimes, are necessary to understand how they will behave in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

System Study: Auxiliary Feedwater 1998–2018

This report presents an unreliability evaluation of the auxiliary feedwater (AFW) system at 69 U.S. commercial nuclear power plants. Demand, run hours, and failure data from calendar year 1998 through 2018 for selected components were obtained from the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS), formerly the INPO Consolidated Events Database (ICES). The unreliability results are trended for the most recent 10 year period while yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the AFW results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

System Study: Emergency Power System 1998–2018

This report presents an unreliability evaluation of the emergency power system (EPS) at 104 U.S. commercial nuclear power plants. Demand, run hours, and failure data from 1998 through 2018 for selected components were obtained from the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS), formerly the INPO Consolidated Events Database (ICES). The unreliability results are trended for the most recent 10 year period while yearly estimates for system unreliability are provided for the entire active period. No statistically significant increasing or decreasing trends were identified in the EPS results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Understanding asymmetries using integrated simulations of capsule implosions in low gas-fill hohlraums at the National Ignition Facility

Current capsule implosions at the National Ignition Facility (NIF) using high-density-carbon ablators and laser energies close to 2 MJ have shown neutron yields in excess of 50 kJ. Improving on this performance requires understanding of the different degradation mechanisms. For many NIF implosions, nuclear diagnostic signatures have inferred residual hot-spot velocities that correlate with fuel areal density variations consistent with a low-order mode-1 asymmetry. A current working hypothesis attributes these asymmetries to a combination of beam to beam variations in the laser delivery and possibly coupling to target features, such as the diagnostic holes needed for x-ray imaging. Recently, a new source has been identified, thickness variation in the ablator shell. To gain better understanding and eventually mitigate the causes of <ρR> asymmetries, 3D integrated simulations using the actual delivered laser powers are needed. To capture the effect of the diagnostic holes (DHs) using direct numerical simulation would require significantly large computational resources. Instead, our 3D simulations make use of a subgrid model developed using highly-resolved 2D simulations that include several details of the DH engineering complexity. Simulations of NIF shots using hohlraums without DHs, to isolate the effect of beam-to-beam variations, reproduce fairly well the observed nuclear diagnostic signatures. Similarly, reasonable agreement between data and simulations is also obtained in the presence of diagnostic holes. To account for the remaining discrepancies a sensitivity study of ablator thickness variation showed that 1% thickness asymmetries are comparable in effect to 1% peak drive mode-1 asymmetries. Furthermore, this study identified sensitivity to variations in the imbedded doped layer (needed to shield the DT ice from the high energy x-rays generated in the hohlraum) thickness even when the inner and outer surface of the ablator are perfectly spherical.

radiation hydrodynamics↗
DOE OSTI2020

Structural Manipulation of Phase Transitions by Self‐Induced Strain in Geometrically Confined Thin Films

Strain engineering is a well-known method often used to tune material properties in thin films. The most studied sources of strain are lattice mismatch and differential thermal contraction between the substrate and film. However, in materials which undergo a structural phase transition (SPT), a third and often overlooked source of strain may play a very significant role. If the substrate confines the area of the film, the SPT may induce stress which changes the evolution of the transition. This is a 2D analog of the isochoric phase transition between water and ice, where the freezing point drops below 0 °C. To illustrate this, the prototypical Mott insulator V 2 O 3 which has an SPT coupled to a metal–insulator transition is used to show how self-induced strain can drastically alter structural and electronic properties. This effect provides an elegant approach for mapping the phase diagram of the SPT and the transitions coupled to it. Moreover, the magnitude of self-straining is tunable by modifying the substrate morphology. Furthermore, this effect may be important for numerous materials which exhibit an SPT and are subjected to geometrical constraints.

Kalcheim, Yoav↗
DOE OSTI2019

Machine learning coarse grained models for water

An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP dih , and ML-mW) that accurately describe the structure and thermodynamic anomalies of both water and ice at mesoscopic scales, all at two orders of magnitude cheaper computational cost than existing atomistic models. In a significant departure from conventional force-field fitting, we use a multilevel evolutionary strategy that trains CG models against not just energetics from first-principles and experiments but also temperature-dependent properties inferred from on-the-fly molecular dynamics (~ 10’s of milliseconds of overall trajectories). Our ML BOP models predict both the correct experimental melting point of ice and the temperature of maximum density of liquid water that remained elusive to-date. Our ML workflow navigates efficiently through the high-dimensional parameter space to even improve upon existing high-quality CG models (e.g. mW model).

42 ENGINEERING↗
DOE OSTI2020

Columnar Vertical Profile (CVP) Methodology for Validating Polarimetric Radar Retrievals in Ice Using In Situ Aircraft Measurements

Abstract A novel way to process polarimetric radar data collected via plan position indicator (PPI) scans and display those data in a time–height format is introduced. The columnar vertical profile (CVP) methodology uses radar data collected via multiple elevation scans, limited to data within a set region in range and azimuth relative to the radar, to create vertical profiles of polarimetric radar data representative of that limited region in space. This technique is compared to others existing in the literature, and various applications are discussed. Polarimetric ice microphysical retrievals are performed on CVPs created within the stratiform rain region of two mesoscale convective systems sampled during two field campaigns, where CVPs follow the track of research aircraft. Aircraft in situ data are collocated to microphysical retrieval data, and the accuracy of these retrievals is tested against other retrieval techniques in the literature.

Engineering↗
DOE OSTI2022

Adaptive Sampling for In Situ Cloud Probe (Final Report)

Clouds play a leading role in the Earth's global energy and solar radiation balance and hydrological cycle. Improving cloud models requires detailed information on the cloud microphysical properties, such as droplet size distribution and number density, liquid water content and cloud composition (droplets, ice particles), which can only be provided by aerial in situ measurements. However, for many atmospheric measurement instruments, the lack of flexibility in selecting the operational mode during operation can lead to uncertainties in sampling and measurement characteristics under continuously varying atmospheric conditions. This SBIR project is developing an advanced, compact optical imaging technology for in situ characterization of cloud hydrometeors. The development involves a deep modification of the existing Mesa Photonics’ Cloud Droplet Measurement System (CDMS) in order to implement real-time automatic adaptive sampling based on the acquired in situ data and environmental parameters. The new system, CDMS-2, implements two measurement modes: side-scatter imaging for smaller hydrometeors and direct bright-field-illumination imaging for larger hydrometeors in a significantly larger sample volume. The system measures the droplet size distribution (DSD) and number density with an added capability of discriminating between liquid water and ice hydrometeors (based on polarization-resolved side-scatter imaging). The instrument will implement automatic switching or alternating between the regular side-scatter imaging mode and sparse/large hydrometeor mode (based on the acquired data). Other adaptive sampling capabilities include variable sample volume and dynamic range (based on the measured DSD). The preferred deployment platforms are uncrewed aircraft systems (UAS) and tethered balloon/kite systems (TBS). The Phase I project achieved (or exceeded) the goals listed in the Work Plan. A CDMS-2 laboratory prototype implementing the polarization-resolved side-scatter imaging mode and direct bright-field-illumination imaging mode was designed and built. Additional capabilities included the variable illumination pulse energy and sample volume. The smallest detectable droplet diameter was improved to 3–4 μm (from the nominal 10 μm value specified for the original CDMS). Discrimination between water droplets and ice particles was experimentally demonstrated. The Phase I prototype was extensively tested and calibrated in the laboratory and also tested in the Pi Cloud Chamber at Michigan Technological University (MTU). The two intensive experimental campaigns at MTU provided unique opportunities of testing the CDMS-2 laboratory prototype under realistic warm and mixed-phase cloud conditions (stable for long periods of time), testing different sampling modes and intercomparing the CDMS-2 prototype to other co-located cloud characterization instruments. The Phase I project successfully demonstrated the feasibility of the proposed technology and identified the engineering challenges of designing a field deployable prototype instrument in Phase II. The Phase I study provides a solid basis for development, characterization and field-testing of the proposed advanced cloud probe with adaptive sampling in Phase II followed by commercialization of the technology in Phase III.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A Unified Ice Microphysical Parameterization for Numerical Models: Development and Testing with In-situ Data

The primary goal of the project is to develop and test new microphysical parameterizations of ice crystal growth. Over the past 7 years, we have developed a bulk ice microphysical model that evolves a measure of particle shape and density during vapor diffusional growth. The method allows smaller, pristine ice crystals to evolve into larger snow crystals in a more natural fashion than traditional microphysical schemes. In doing so, the new method avoids problems that occur when ice is transferred among pre-defined classes. The new model was developed and tested for vapor grown ice only, and so the goal of this project is to develop bulk parameterizations of the missing physics for the model including the prediction of surface deposition coefficients, riming, and aggregation. The parameterizations are being developed using prior laboratory data as a constraint, and then will be tested in simulations of case studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2014

This report presents an enhanced performance evaluation of emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Consolidated Events Database (ICES) data from 1998 through 2014 and (2) maintenance unavailability (UA) performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2014. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: sub-component, failure cause, detection method, recovery, manufacturer, and EDG rating. Six trends with varying degrees of statistical significance were identified in the data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Enhanced Component Performance Study: Emergency Diesel Generators 1998–2018

This report presents an enhanced performance evaluation of emergency diesel generators (EDGs) at U.S. commercial nuclear power plants. This report evaluates component performance over time using (1) Institute of Nuclear Power Operations (INPO) Consolidated Events Database (ICES) data from 1998 through 2018 and (2) maintenance unavailability (UA) performance data from Mitigating Systems Performance Index (MSPI) Basis Document data from 2002 through 2018. The objective is to show estimates of current failure probabilities and rates related to EDGs, trend these data on an annual basis, determine if the current data are consistent with the probability distributions currently recommended for use in NRC probabilistic risk assessments, show how the reliability data differ for different EDG manufacturers and for EDGs with different ratings; and summarize the subcomponents, causes, detection methods, and recovery associated with each EDG failure mode. Engineering analyses were performed with respect to time period and failure mode without regard to the actual number of EDGs at each plant. The factors analyzed are: sub-component, failure cause, detection method, recovery, manufacturer, and EDG rating. A statistically significant increasing trend was identified in the frequency of FTLR demands for emergency power system (EPS) and high pressure core spray (HPCS) EDGs and a statistically significant decreasing trend was identified in the frequency of run > 1H hours for EPS and HPCS EDGs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Quenching-Induced Defects Liberate the Latent Reversible Capacity of Lithium Titanate Anode

Interest in defect engineering for lithium-ion battery (LIB) materials is sparked by its ability to tailor electrical conductivity and introduce extra active sites for electrochemical reactions. However, harvesting excessive intrinsic defects in the bulk of the electrodes rather than near their surface remains a long-standing challenge. Here, a versatile strategy of quenching is demonstrated, which is exercised in lithium titanate (Li 4 Ti 5 O 12 , LTO), a renowned anode for LIBs, to achieve off-stoichiometry in the interior region. In situ synchrotron analysis and atomic-resolution microscopy reveal the enriched oxygen vacancies and cation redistribution after ice-water quenching, which can facilitate the native unextractable Li ions to participate in reversible cycling. Further, the fabricated LTO anode delivers a sustained capacity of 202 mAh g -1 in the 1.0–2.5 V range with excellent rate capability and overcomes the poor cycling stability seen in conventional defective electrodes. The feasibility of tuning the degree of structural defectiveness via quenching agents is also proven, which can open up an intriguing avenue of research to harness the intrinsic defects for improving the energy density of rechargeable batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗
DOE OSTI2023

Supporting data for "Frozen flume experiments indicate rapid permafrost riverbank erosion depends on bank roughness"

This dataset contains supporting files detailing five frozen flume experiments conducted at the Caltech Earth Surface Dynamics Laboratory to investigate rates of ablation-limited permafrost riverbank erosion under controlled conditions. Water flowed past a bank of saturated, frozen sand and ice and gradually eroded the bank by thawing pore ice and immediately entraining sand and washing it downstream. Experiments were scaled for flow hydraulics and heat transfer allowing comparisons between our results and natural permafrost riverbanks. For each experiment, we measured the initial and final sand bank topography using a Keyence laser scanner, water surface slope at 3-min intervals throughout the experiment using a Massa sonar scanner, bank erosion using 10-sec overhead timelapse imagery taken by an overhead camera, water and bank temperature using thermistors frozen into the sand bank and sampling at 2 Hz, and water discharge using an in-line flow meter. We include calibration data for the carriage (engineered by the Saint Anthony Falls Laboratory) used to make sonar and laser topography measurements. We also include calibration data for temperature sensors, water discharge measurements, and images of a regular grid placed in the flume to align overhead camera images with the carriage datum. Grain size analysis for the channel bed (gravel) was produced using a pebble count and bank sand was measured using a Camsizer X2. In addition to the five frozen experiments, we include sonar scans of water surface slope and Keyence scans of bed and bank topography for calibration experiments ran with an immobile gravel bank and bed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Euler–Lagrange stochastic modeling of droplet breakup and impact in supersonic flight

Blunt bodied aircraft traveling supersonically in weather environments may be damaged by impacts with water droplets and other airborne particles, such as snow and ice. Prior to an impact, these particles will encounter a bow shock that causes a discontinuity in their relative velocity with the gas phase, which can lead droplets to breakup into smaller droplets. These smaller droplets are more easily diverted from colliding with the blunt body due to their significantly reduced inertia relative to the initial rain droplets. One-way coupled Euler–Lagrange simulations are used to study the dynamics of droplets approaching a blunt body in steady two dimensional and axi-symmetric flow fields using a stochastic version of the Taylor analogy breakup model for the breakup dynamics. Ultimately, the dominant mechanism determining engineering quantities of interest was observed to be a competition between breakup time and the time available for a droplet to reach the body after encountering the bow shock. At Mach numbers 2, 3, and 6, the competition between these mechanisms was the dominant factor determining the momentum transfer to the blunt body via droplet collisions, which can be well characterized by a scaling relation.

Briney, Sam (ORCID:0000000179061081)↗