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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Using the ATLAS experiment software on heterogeneous resources

With the large dataset expected from 2030 onwards by the HL-LHC at CERN, the ATLAS experiment is reaching the limits of the current data processing model in terms of traditional CPU resources based on x86_64 architectures and an extensive program for software upgrades towards the HL-LHC has been set up. The ARM CPU architecture is becoming a competitive and energy efficient alternative. Accelerators like GPUs are available in any recent HPC. In the past years ATLAS has successfully ported its full data processing and simulation software framework Athena to ARM and has invested significant effort in porting parts of the reconstruction and simulation algorithms to GPUs. We report on the successful usage of the ATLAS experiment offline and online software framework Athena on ARM and GPUs through the PanDA workflow management system at various WLCG sites. Furthermore we report on performance optimizations of the builds for ARM CPUs and the GPU integration efforts. We will discuss performance comparisons of different ARM and x86_64 architectures on WLCG resources and Cloud compute providers like GCP and AWS using ATLAS productions workflows as used in the Hep-Score23 benchmark suite.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

SU-E-T-184: Clinical VMAT QA Practice Using LINAC Delivery Log Files

Purpose: To evaluate the accuracy of volumetric modulated arc therapy (VMAT) treatment delivery dose clouds by comparing linac log data to doses measured using an ionization chamber and film. Methods: A commercial IMRT quality assurance (QA) process utilizing a DICOM-RT framework was tested for clinical practice using 30 prostate and 30 head and neck VMAT plans. Delivered 3D VMAT dose distributions were independently checked using a PinPoint ionization chamber and radiographic film in a solid water phantom. DICOM RT coordinates were used to extract the corresponding point and planar doses from 3D log file dose distributions. Point doses were evaluated by computing the percent error between log file and chamber measured values. A planar dose evaluation was performed for each plan using a 2D gamma analysis with 3% global dose difference and 3 mm isodose point distance criteria. The same analysis was performed to compare treatment planning system (TPS) doses to measured values to establish a baseline assessment of agreement. Results: The mean percent error between log file and ionization chamber dose was 1.0%±2.1% for prostate VMAT plans and −0.2%±1.4% for head and neck plans. The corresponding TPS calculated and measured ionization chamber values agree within 1.7%±1.6%. The average 2D gamma passing rates for the log file comparison to film are 98.8%±1.0% and 96.2%±4.2% for the prostate and head and neck plans, respectively. The corresponding passing rates for the TPS comparison to film are 99.4%±0.5% and 93.9%±5.1%. Overall, the point dose and film data indicate that log file determined doses are in excellent agreement with measured values. Conclusion: Clinical VMAT QA practice using LINAC treatment log files is a fast and reliable method for patient-specific plan evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Site B - NREL ASSIST (SN11) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site G - NREL ASSIST (SN10) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 10) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site C1a - NREL ASSIST (SN12) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
NASA NTRS1982

Outer satellite atmospheres: Their nature and planetary interactions

Significant progress is reported in early modeling analysis of observed sodium cloud images with our new model which includes the oscillating Io plasma torus ionization sink. Both the general w-D morphology of the region B cloud as well as the large spatial gradient seen between the region A and B clouds are found to be consistent with an isotropic flux of sodium atoms from Io. Model analysis of the spatially extended high velocity directional features provided substantial evidence for a magnetospheric wind driven gas escape mechanism from Io. In our efforts to define the source(s) of hydrogen atoms in the Saturn system, major steps were taken in order to understand the role of Titan. We have completed the comparison of the Voyager UVS data with previous Titan model results, as well as the update of the old model computer code to handle the spatially varying ionization sink for H atoms.

Smyth, W. H.↗
NASA NTRS2009

Exploring Alternative Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
NASA NTRS2017

The Impact of Lidar Detection Sensitivity on Assessing Aerosol Direct Radiative Effects

Spaceborne lidar observations have great potential to provide accurate global estimates of the aerosol direct radiative effect (DRE) in both clear and cloudy conditions. However, comparisons between observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) and multiple years of Atmospheric Radiation Measurement (ARM) program’s ground-based Raman lidars (RL) show that CALIPSO does not detect all radiatively significant aerosol, i.e. aerosol that directly modifies the Earth’s radiation budget. We estimated that using CALIPSO observations results in an underestimate of the magnitude of the global mean aerosol DRE by up to 54%. The ARM RL datasets along with NASA Langley airborne high spectral resolution lidar (HSRL) data from multiple field campaigns are used to compute the detection sensitivity required to accurately resolve the aerosol DRE. This shows that a lidar with a backscatter coefficient detection sensitivity of about 1−2x10(exp -4)km(exp -1)sr(exp -1) at 532nm would resolve all the aerosol needed to derive the DRE to within 1%.

Tyler J. Thorsen↗
NASA NTRSDate not supplied

Experimental and Theoretical Needs for the JWST Early Release Science Program on Radiative Feedback from Massive Stars (PDRs4ALL): III. A Quantum Chemistry Perspective

Massive stars disrupt their natal molecular cloud material by dissociating molecules, ionizing atoms and molecules, and heating the gas and dust. These processes drive the evolution of interstellar matter in our Galaxy and throughout the Universe from the era of vigorous star formation at z=1-3 to the present day. Much of this interaction occurs in Photo- Dissociation Regions (PDRs) where far-ultraviolet photons of these stars create a largely neutral, but warm region of gas and dust. PDR emission dominates the IR spectra of starforming galaxies and provides a unique tool to study the physical and chemical processes that are relevant for inter- and circumstellar media including diffuse clouds, molecular cloud and protoplanetary disk surfaces, and starburst galaxies. The ERS program ID1288 is dedicated to provide template data as well as data processing and analysis tools for PDRs. To this end, it will observe the Orion Bar, the proto-typical PDR situated in the nearby Orion Nebula, using NIRSpec IFU, MIRI IFU, and NIRCAM and MIRI imaging. These observations will, for the first time, spatially resolve and perform a tomography of the PDR, revealing the individual IR spectral signatures from the key zones and sub-regions within the ionized gas, the PDR, and the molecular cloud. Quantum chemistry can be used to determine molecular data such as ionization potentials, chemical reaction rates, electronic spectra, dissociation energies, channels, and branching ratios, etc. These are often used in synergy with experiment to take advantage of the strengths of each approach. An area of active research is modeling the infrared (IR) emission spectra produced by PAH molecules, including the effects of anharmonicity. This will be important in the interpretation of JWST data which will yield higher-resolution spectral data compared to Spitzer or ISO. Quantum chemistry methods can compute accurate anharmonic IR absorption spectra of PAH molecules up to about 25 C atoms, and the results have been benchmarked against high-resolution experimental spectra. These feed into a quantum Monte Carlo (QMC) approach to generate temperature-dependent anharmonic IR emission spectra and then used to generate fully anharmonic cascade emission IR spectra of PAH molecules -- the very type of spectra that JWST will observe. Work continues to extend these methods to larger PAH molecules. We will discuss the current state of this work, as well as

Timothy Lee↗
DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗
DOE OSTI2022

EVSE Cybersecurity and Resilience

Consequence-driven Cybersecurity Analysis for Extreme Fast Charging Electric Vehicle Infrastructure Electric vehicle (EV) development and associated charging infrastructure are expected to advance rapidly. Thirty percent of all global vehicle sales may be EVs and hybrid EVs by 2025, and they will rely on increasingly sophisticated strategies for grid integration. Next-generation EV charging infrastructure is expected to include interconnected renewable resources, such as photovoltaic (PV) arrays and battery storage systems, along with grid-edge devices. Although distributed energy resources (DERs) are useful in several ways, such as peak shaving at high demand times and backup supply for added resilience, the integration of vehicle charging and DERs could create more avenues for cyberattack. Physical and/or remote access to EV charging station components, including charge ports, power electronics, controllers, and local generation (e.g., PV and energy storage) could be paths to cause power fluctuations, leading to altered operations at the charging station, escalated privileges to administrative systems, exfiltration of financial information (including personally identifiable information), and reduced grid stability. One compromised EV supply equipment component can open the door to a variety of exploitable vulnerabilities. Cloud computing and mobile application control have the potential to expand the threat surface to non-repudiation and firmware integrity challenges. Vendor clouds have access to hundreds of chargers, and if compromised, can scale the attack surface exponentially. The high power and voltage levels of xFC infrastructure (e.g., 400 kW at 1000- V DC) increase the hazards and ability to impact the grid and vehicles more than lower-power charging systems. Legacy communications systems and protocols could also put EV infrastructure at risk of cyberattacks requiring a robust patch management process. Communications networks link EVs and chargers to several stakeholders - including charging station operators, grid operators, vendors/manufacturers, and aggregators - who have both physical and network access to share information for control, monitoring, and analytics. Information in these networks that is vulnerable to compromise includes the state of charge, charging duration, payment information, electricity price, and load control. Analyzing and prioritizing these interconnections risks could help address cybersecurity related to data leakage and manipulation.

charging↗
DOE OSTI2019

A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/Battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control (EMPC) approach is used as the benchmark. The residential load and PV data used in the paper are 1-minute data downloaded from the the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four machine learning algorithms (i.e. neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the machine learning approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based Machine Learning engine. Furthermore, the work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.

25 ENERGY STORAGE↗
NASA NTRS1977

Planetary isophotes as a clue to aerosol characteristics

A study was made to see how much information could be extracted from the Mariner 9 Mars isophotes taken at phase angle roughly 60 deg. Minnaert functions and both isotropic and Rayleigh scattering could easily be ruled out, and it was essential to use forward-peaked phase functions computed from Mie theory. Isophotes similar to those observed are plotted assuming a semi-infinite dust cloud with a considerable variation in particle properties and size distribution, so long as the ratio of the multiply to singly scattered light is held within certain limits. These conditions are met by micron-sized, moderately absorbing mineral grains whose mean size should not be much larger than a micron. It was also found that a dust cloud of finite optical thickness bounded from below by a Lambert ground would fit the isophote data.

Kattawar, G. W.↗
NASA NTRS1999

Studies of Radiation and Microphysics in Cirrus and Marine Stratocumulus Clouds

Two tasks were completed during this period. In the first, we examined the polarization of millimeter-wavelength radar beams scattered by ice crystals. Because of their non-spherical shape and size, ice crystals depolarize the incident polarized radar beam. In principle, this depolarization can be used to identify ice from liquid water, as well as provide some information on size. However, the amount of de-polarization is small, producing only a weak signal at the receiver. Our task was to determine the magnitude of such a signal and decide if our radar would be capable of measuring it under typical cirrus conditions. The theoretical study was carried out by Henrietta Lemke, a visiting graduate student from Germany. She had prior experience using a discrete dipole code to compute scattering depolarization. Dr. Kultegin Aydin of the Penn State Electrical Engineering Department, who is also expert in this area, consulted with us on this project at no cost to the project. Our conclusion was that the depolarization signal is too weak to be usefully measured by our system. Therefore we proceeded no further in this study. The second task involved the study of the effect of stratus microphysics on surface cloud forcing. Manajit Sengupta, a graduate student, and the project PI jointly carried out this task. The study used data culled from over a year of continuous radar and radiometer observations at the Atmospheric Radiation Measurement (ARM) site in Oklahoma. The study compared solar radiation calculations made using constant microphysics with calculations made using a retrieved mean particle size. The results showed that on average the constant microphysics produced the correct forcing when compared with the observed forcing. We conclude, therefore, that there is little impetus on radiation grounds alone to include explicit microphysics in climate models. The question of pollutant particle emission impacts on microphysics remains to be resolved. A manuscript is in preparation and will be submitted this year.

Source record↗
NASA NTRS1998

Theoretical Studies of Starburst Infrared Emission: Luminosity Indicators in Dusty Photoionized Environments

The luminosity of the central source in ionizing radiation is an essential parameter in a photoionized environment and is one of the most fundamental physical quantities one can measure. We outline a method of determining the luminosity for any emission-line region using only infrared data. In dusty environments, grains compete with hydrogen in absorbing continuum radiation. Grains produce infrared emission, and hydrogen produces recombination lines. We have computed a very large variety of photoionization models, using ranges of abundances, grain mixtures, ionizing continua, densities, and ionization parameters. The conditions were appropriate for such diverse objects as H(II) regions, planetary nebulae, starburst galaxies, and the narrow- and broad-line regions of active nuclei. The ratio of the total thermal grain emission relative to H-Beta (IR/H-Beta) is the primary indicator of whether the cloud behaves as a classical Stroemgren sphere (a hydrogen-bounded nebula) or whether grains absorb most of the incident continuum (a dust-bounded nebula). We find two global limits: when IR/H-Beta < 100, infrared recombination lines determine the source luminosity in ionizing photons; when IR/H-Beta >> 100, the grains act as a bolometer to measure the luminosity.

Bottorff, Mark↗
NASA NTRS2006

Chemical Observations of a Polar Vortex Intrusion

An intrusion of vortex edge air in D the interior of the Arctic polar vortex was observed on the January 31,2005 flight of the NASA DC-8 aircraft. This intrusion was identified as anomalously high values of ozone by the AROTAL and DIAL lidars. Our analysis shows that this intrusion formed when a blocking feature near Iceland collapsed, allowing edge air to sweep into the vortex interior. along the DC-8 flight track also shows the intrusion in both ozone and HNO3. Polar Stratospheric Clouds (PSCs) were observed by the DIAL lidar on the DC-8. The spatial variability of the PSCs can be explained using MLS HNO3 and H2O observations and meteorological analysis temperatures. We also estimate vortex denitrification using the relationship between N2O and HNO3. Reverse domain fill back trajectory calculations are used to focus on the features in the MLS data. The trajectory results improve the agreement between lidar measured ozone and MLS ozone and also improve the agreement between the HNO3 measurements PSC locations. The back trajectory calculations allow us to compute the local denitrification rate and reduction of HCl within the filament. We estimate a denitrification rate of about lO%/day after exposure to below PSC formation temperature. Analysis of Aura MLS observations made

Schoeberl, M. R.↗
DOE OSTI2022

Incorporating the effects of objects in an approximate model of light transport in scattering media

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

42 ENGINEERING↗
DOE OSTI2025

Enterprise Artificial Intelligence Strategy for Los Alamos National Laboratory

In the 1984 martial arts drama film, The Karate Kid, a young Daniel LaRusso is unexpectedly placed in an adversarial environment unable to eYectively adapt to a series of new threats and limitations. Fortunately for the main character, once placed under the tutelage of a Mr. Miyagi, he finds resiliency not through the adoption of new tools, but a re-focused set of fundamentals. Much in the same way that Daniel learns waxing on and buYing oY car wax by hand has rewards for Karate, LANL is choosing the harder path of self-hosting Large Language Models (LLMs) for enterprise use instead of only relying on buying access to a hosted AI service like Azure’s OpenAI Application Programming Interface (API). We also are not willing to wait for software-as-a-service (SAAS) AI services to meet us where we need to be from a FedRAMP accreditation standpoint. Our operations regularly depend on access at CUI, UCNI, ITAR and other FIPS-199 moderate-impact data levels and hosting our own services gives us the right security and compliance posture to be useful across the broad range of our work at LANL. With the rise in threats to critical infrastructure, cloud service providers (CSPs), and supply chain attacks from both state and non-state actors, we are not placing the bet that SAAS hosted AI services will be available when we need them. Should a major event occur, we do not want our staY and operations left without a pathway for us to fix the problem and resume the use of AI tools.

42 ENGINEERING↗