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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluation of Physical Microphysical Property Retrieval Algorithms During the 2020 IMPACTS Field Campaign

The NASA Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W-band) and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships (Brown and Francis (1995, BF95); Heymsfield (2014, H14) and two lidar-radar microphysical retrieval algorithms (Cloudsat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)) to estimate aircraft-retrieved volume extinction coefficient (σ), ice water content (IWC), and effective radius (r e ) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R 2 = 0.98) with in-situ observations of σ. However, only BF95 displays a linear, consistent, and almost temperature-independent low bias for IWC and r e , which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate σ, IWC, and r e . For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (-15°C to -10°C) and near freezing (≥-5°C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Inversion of limb radiance measurements - An operational algorithm

The limb radiance inversion radiometer (LRIR) and limb infrared monitor of the stratosphere (LIMS) experiments aboard the Nimbus 6 and 7 spacecraft have made observations of infrared emission by CO2, O3, H2O, HNO3, and NO2 at the earth's limb. This paper describes a method by which such measurements can be inverted to give vertical distributions of temperature and mixing ratios as functions of pressure. The simple and efficient approach was successfully applied to the LRIR data and subsequently in the initial assessment of the LIMS data. Inversion of synthetic data indicates the size of the errors to be expected as a result of the assumptions and instrumental errors. Retrievals of measured LIMS radiances are shown as examples and compared to in situ observations. The differences are comparable to those obtained with the more complex retrieval scheme used to process the LIMS archival products. Some problems are noted.

Bailey, P. L.↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Deep Learning for Multigroup Cross-Section Representation in Two-Step Core Calculations

Here we investigate using deep learning, a type of machine-learning algorithm employing multiple layers of artificial neurons, for the mathematical representation of multigroup cross sections for use in the Griffin reactor multiphysics code for two-step deterministic neutronics calculations. A three-dimensional fuel element typical of a high-temperature gas reactor as well as a two-dimensional sodium-cooled fast reactor lattice are modeled using the Serpent Monte Carlo code, and multigroup macroscopic cross sections are generated for various state parameters to produce a training data set and a separate validation data set. A fully connected, feedforward neural network is trained using the open-source PyTorch machine-learning framework, and its accuracy is compared against the standard piecewise linear interpolation model. Additionally, we provide in this work a generic technique for propagating the cross-section model errors up to the k eff using sensitivity coefficients with the first-order uncertainty propagation rule. Quantifying the eigenvalue error due to the cross-section regression errors is especially practical for appropriately selecting the mathematical representation of the cross sections. We demonstrate that the artificial neural network model produces lower errors and therefore enables better accuracy relative to the piecewise linear model when the cross sections exhibit nonlinear dependencies; especially when a coarse grid is employed, where the errors can be halved by the artificial neural network. However, for linearly dependent multigroup cross sections as found for the sodium-cooled fast reactor case, a simpler linear regression outperforms deeper networks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Crystalline phase transitions and vibrational spectra of silicon up to multiterapascal pressures

A composite high-pressure phase diagram for silicon has been predicted for up to 4 TPa. Here, this diagram has been built using a combination of evolutionary algorithm-based structure searches, electronic density functional theory, lattice dynamics of perfect crystals, and anharmonic corrections for the solid state in conjunction with molecular dynamics for evaluating the melt curve. The anharmonic corrections to free energy, arising from both finite-temperature three-phonon interactions and temperature dependence of the lattice axial ratios, play a critical role in properly identifying the solid–solid transition boundaries of the orthorhombic structures. A double hexagonal close-packed structure has been found to be thermodynamically and dynamically stable, sandwiched between the experimentally observed base-centered orthorhombic and hexagonal close-packed structures. In addition, beyond 2.8 TPa, there exists a sequence of face-centered cubic-to-body-centered cubic-to-simple cubic transitions that are accompanied by the localization of electrons in the interstitial spaces between the ions. Supplementing the structural calculations, second- and third-order interatomic force constants were evaluated to compute the phonon vibration modes and linewidths, respectively. This allowed an elaborate analysis of the Raman and infrared spectra for all of the structures of silicon identified so far.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Domains and defects in nuclear pasta

Nuclear pasta topology is an essential ingredient to determine transport properties in the inner crust of neutron stars. Herein we perform semiclassical molecular dynamics simulations of nuclear pasta for proton fractions Y p = 0.30 and Y p = 0.40 near one-third of nuclear saturation density, n = 0.05 fm – 3 , at a temperature T = 1.0 MeV . Our simulations are, to our knowledge, the largest nuclear pasta simulations to date and contain up to 3 276 800 nucleons in the Y p = 0.30 and 819 200 nucleons in the Y p = 0.40 case. An algorithm to determine which nucleons are part of a given sub-domain in the system is presented. By comparing runs of different sizes we study finite-size effects, equilibration time, the formation of multiple domains and defects in the pasta structures, as well as the structure factor dependence on simulation size. Although we find qualitative agreement between the topological structure and the structure factors of runs with 51 200 nucleons and those with 819 200 nucleons or more, we show that simulations with hundreds of thousands of nucleons may be necessary to accurately predict pasta transport properties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Evaluation of the first version of the new RFPI system dedicated to PIP-II project accelerator

The Low-Level Radio Frequency (LLRF) control system is one of the most critical superconducting linac infrastructures responsible for the parameters of the beam acceleration. The LLRF system mainly focuses on the electromagnetic field parameters inside the cavity. While it incorporates fast feedback algorithms to optimize energy transfer to the passing particle beam it does not follow other cryomodule or cavity parameter changes. The Radio Frequency Protection Interlock (RFPI) system closely monitors various factors (like cryomodule vacuum, beamline vacuum, field emission probe current level, temperature, RF signal leakage, etc). Its simple but reliable logic has to provide an instant decision about the LLRF system or high-power amplifier output signal blocking in case of safety region excitation. This contribution presents a new version of the RFPI system which logic is implemented in the FPGA chip. The initial work on the prototype of the new system design resulted in the PoC (Proof of Concept) device. The PoC offers the possibility of various protection logic configurations, input signals parameters evaluation, and modularity aspects verification. The structure and test results from the device evaluation are summarized and discussed in this contribution.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Solid-State Mixed-Potential Electrochemical Sensors for Natural Gas Leak Detection and Quality Control (Final Technical Report)

Mitigation of methane emissions are a critical factor to limiting the impact of the natural gas industry on global climate change. Throughout the period of 2020-2024, the University of New Mexico and its commercialization partner and subcontractor, SensorComm Technologies, Inc. (SCT), have worked together to develop a low-cost Artificial Intelligence (AI)-driven Internet of Things (IoT)-based multi-gas sensor platform for methane emissions detection. In the final year of the project, we extended this work to include hydrogen detection in support of a transition to a hydrogen economy where hydrogen could be transported through existing natural gas infrastructure. Mixed potential electrochemical sensors were first prototyped by ceramic additive manufacturing and then transitioned to conventional ceramic manufacturing tape casting and screen-printing technologies in preparation for mass production. Demonstrated limits of detection of 5 ppm of methane in natural gas and 1 ppm of hydrogen were measured. These limits of detection are among the lowest of solid-state electrochemical sensors that have been reported in the literature or available in the industry. Machine learning algorithms were developed to identify natural gas mixtures with > 98% accuracy level and quantify methane concentrations at 97% accuracy. The presence of hydrogen could also be identified, and its concentration quantified at these accuracy levels. These algorithms were optimized for running on portable computing hardware which enabled > 1 Hz processing rates. A portable packaged IoT system was integrated with the electrochemical sensor in collaboration with SCT. The package consists of readout electronics with < 1 mV resolution, sensor temperature control, and data transmission over cellular wireless and/or Wi-Fi networks. Field testing was performed in two rounds at Colorado State University’s Methane Emissions Technology Evaluation Center (CSU METEC). The first round of testing demonstrated successful measurements of methane from an underground natural gas leak of 20 standard liters per minute (SLPM), which agreed with previously published literature using more sophisticated and expensive analytical equipment. The second round of testing showed that an above ground leak of 2 SLPM of hydrogen could be detected at 32 ft. This project has resulted in six published peer reviewed journal articles, over ten presentations at professional conferences, and one full patent application filed in 2023. Future work on this project includes increased sensitivity, higher production yields, and applications in the hydrogen safety and flare emissions monitoring spaces.

03 NATURAL GAS↗
DOE OSTI2024

A Multiphysics Multiscale Simulation Platform for Damage, Environmental Degradation, and Life Prediction of CMCs in Extreme Environments

This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.

03 NATURAL GAS↗
NASA NTRS1982

The GLAS physical inversion method for analysis of HIRS2/MSU sounding data

Goddard Laboratory for Atmospheric Sciences has developed a method to derive atmospheric temperature profiles, sea or land surface temperatures, sea ice extent and snow cover, and cloud heights and fractional cloud, from HIRS2/MSU radiance data. Chapter 1 describes the physics used in the radiative transfer calculations and demonstrates the accuracy of the calculations. Chapter 2 describes the rapid transmittance algorithm used and demonstrates its accuracy. Chapter 3 describes the theory and application of the techniques used to analyze the satellite data. Chapter 4 shows results obtained for January 1979.

Susskind, J.↗
NASA NTRS1991

Discrete random media techniques for microwave modeling of vegetated terrain

Microwave remote sensing models of vegetated terrain are investigated. The problem is to determine canopy characteristics such as biomass, canopy height, and the moisture of the underlying soil. The report describes a discrete scatter model which has been employed to model backscatter in the active (radar) case and to model brightness temperature in the passive (radiometric) case. The acquisition of ground truth data is discussed, as well as the comparison of theory and experiment. The overall conclusion of the work has been that the discrete scatter model in conjunction with efficient scatter algorithms and the distorted Born approximation is a most appropriate methodology to use for modeling purposes in the microwave region.

Lang, Roger H.↗
NASA NTRS1992

Regularizing the satellite temperature-retrieval problem through singular-value decomposition of the radiative transfer physics

A new technique is derived for retrieving atmospheric temperature profiles from satellite-measured spectral radiance that appears, in first tests, to effectively circumvent certain difficulties of other well-known and implemented techniques. This method provides an effective manner to avoid numerical instability without having to force the algorithm toward adherence to a priori statistics not dependent on the radiance measurements. This provides the opportunity to obtain more information from satellite sounding instruments, without encountering instabilities owing to overlapping weighting functions.

Thompson, Owen E.↗
NASA NTRS1996

The EOS CERES Global Cloud Mask

To detect long-term climate trends, it is essential to produce long-term and consistent data sets from a variety of different satellite platforms. With current global cloud climatology data sets, such as the International Satellite Cloud Climatology Experiment (ISCCP) or CLAVR (Clouds from Advanced Very High Resolution Radiometer), one of the first processing steps is to determine whether an imager pixel is obstructed between the satellite and the surface, i.e., determine a cloud 'mask.' A cloud mask is essential to studies monitoring changes over ocean, land, or snow-covered surfaces. As part of the Earth Observing System (EOS) program, a series of platforms will be flown beginning in 1997 with the Tropical Rainfall Measurement Mission (TRMM) and subsequently the EOS-AM and EOS-PM platforms in following years. The cloud imager on TRMM is the Visible/Infrared Sensor (VIRS), while the Moderate Resolution Imaging Spectroradiometer (MODIS) is the imager on the EOS platforms. To be useful for long term studies, a cloud masking algorithm should produce consistent results between existing (AVHRR) data, and future VIRS and MODIS data. The present work outlines both existing and proposed approaches to detecting cloud using multispectral narrowband radiance data. Clouds generally are characterized by higher albedos and lower temperatures than the underlying surface. However, there are numerous conditions when this characterization is inappropriate, most notably over snow and ice of the cloud types, cirrus, stratocumulus and cumulus are the most difficult to detect. Other problems arise when analyzing data from sun-glint areas over oceans or lakes over deserts or over regions containing numerous fires and smoke. The cloud mask effort builds upon operational experience of several groups that will now be discussed.

Berendes, T. A.↗
NASA NTRS2002

Fail Safe, High Temperature Magnetic Bearings

This paper contributes to the magnetic bearing literature in two distinct areas: high temperature and redundant actuation. Design considerations and test results are given for the first published combined 538 C (1000 F) high speed rotating test performance of a magnetic bearing. Secondly, a significant extension of the flux isolation based, redundant actuator control algorithm is proposed to eliminate the prior deficiency of changing position stiffness after failure. The benefit of the novel extension was not experimentally demonstrated due to a high active stiffness requirement. In addition, test results are given for actuator failure tests at 399 C (750 F), 12,500 rpm. Finally, simulation results are presented confirming the experimental data and validating the redundant control algorithm.

Minihan, Thomas↗
NASA NTRS2005

Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads

An analytical methodology is developed to predict the probability of survival (reliability) of ceramic components subjected to harsh thermomechanical loads that can vary with time (transient reliability analysis). This capability enables more accurate prediction of ceramic component integrity against fracture in situations such as turbine startup and shutdown, operational vibrations, atmospheric reentry, or other rapid heating or cooling situations (thermal shock). The transient reliability analysis methodology developed herein incorporates the following features: fast-fracture transient analysis (reliability analysis without slow crack growth, SCG); transient analysis with SCG (reliability analysis with time-dependent damage due to SCG); a computationally efficient algorithm to compute the reliability for components subjected to repeated transient loading (block loading); cyclic fatigue modeling using a combined SCG and Walker fatigue law; proof testing for transient loads; and Weibull and fatigue parameters that are allowed to vary with temperature or time. Component-to-component variation in strength (stochastic strength response) is accounted for with the Weibull distribution, and either the principle of independent action or the Batdorf theory is used to predict the effect of multiaxial stresses on reliability. The reliability analysis can be performed either as a function of the component surface (for surface-distributed flaws) or component volume (for volume-distributed flaws). The transient reliability analysis capability has been added to the NASA CARES/ Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. CARES/Life was also updated to interface with commercially available finite element analysis software, such as ANSYS, when used to model the effects of transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

Nemeth, Noel N.↗
NASA NTRS2009

Data Assimilation and Regional Forecasts Using Atmospheric InfraRed Sounder (AIRS) Profiles

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to optimally assimilate AIRS thermodynamic profiles--obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm-into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses will be used to conduct a month-long series of regional forecasts over the continental U.S. The long-tern1 impact of AIRS profiles on forecast will be assessed against verifying radiosonde and stage IV precipitation data.

Chou, Shih-Hung↗
NASA NTRS2022

Estimating the Migrating Diurnal Tide Component of Mesospheric Water Vapor

This work presents a method for estimating the migrating diurnal tide (DW1) component of mesospheric H2O from observations of the temperature tide and zonal-mean H2O made by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument. This work first shows that a 2D least-squares fit on SABER H2O yields an erroneous DW1 due to retrieval algorithm problems. Consequently, the adiabatic displacement method is used. Applying the method to SABER observations and comparing it with Microwave Limb Sounder (MLS) H2O observations reveals that the method reproduces the MLS-observed H2O DW1 component best in March and June over low-latitudes in the altitude range between 65 and 75 km where errors range between +5% and +20%. Applying the method for simulations using the Specified Dynamics—Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD-WACCM-X) indicates that in the model, it is best only in March and not in June. Model simulations further showed that in March, theoretical errors due to the vertical advection assumption and aliasing are best at less than +13% for the low-latitudes and altitudes between 65 and 75 km. These results, therefore, lead to the conclusion that despite the errors in the vertical advection assumption and aliasing, the adiabatic displacement method performs best in estimating mesospheric H2O DW1 with SABER observations during both March equinox and June solstice over the low latitudes between 65 and 75 km. Results also show that SD-WACCM-X satisfactorily simulates the H2O DW1 magnitude and mechanisms only in the March equinox period.

h20↗
NASA NTRSDate not supplied

Designing the Imaging and Breadboard System for FBCE-TL Project

This presentation outlines the support work done on designing the imaging and breadboard system of the Transfer Line (TL) project, which is the third module for the Flow Boiling and Condensation Experiment (FBCE) operating in the Fluids Integrated Rack on-board the International Space Station (ISS). The goal of the FBCE is to serve as a facility for flow boiling and condensation investigating two-phase flow and heat transfer data in microgravity. The TL module aims use existing FBCE hardware to study cryogenic chilldown, a large component of cryogenic propellant tank transfer research. The TL test module will perform chilldown studies with normal-perfluorohexane (nPFH), a cryogenic simulant, flowing through a super-heated line, which is heated through the process of Joule-heating. SolidWorks and Creo were used to create electrically conductive pipe junctions that connect and seal several Indium Tin Oxide (ITO) coated sapphire tube sections together to allow for low leak probability fluid flow and circumferentially uniform electrical current density. It is necessary to understand and calculate the thermal expansion of the tubing sections such that the junctions would not leak at anticipated operating temperatures and pressures, would maintain electrical contact, and not crack the crystalline tubes. Optical imaging techniques were used to build an experimentally validated image remapping process for flight-like setups of cylindrical tubes. Then MATLAB was used to create an algorithm that distorts an image based on the refraction of light passing through the mediums we were imaging through to accurately visualize the cryogenic chilldown that is of scientific interest. This research will assist NASA to move forward with long-duration spaceflight missions where knowledge of efficient heat transfer processes becomes more critical.

imaging system↗

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