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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

5-minute Wind Power Data based on WFIP2 WRF Simulation

The second Wind Forecast Improvement Project (WFIP2) was a public-private partnership funded by the U.S. Department of Energy and NOAA, aimed at enhancing the forecast skill of numerical weather prediction models for turbine-height winds in regions with complex terrain. An 18-month Weather Research and Forecasting (WRF) model simulation was conducted over the Pacific Northwest, with model outputs validated against observational data collected during WFIP2. Simulated wind speeds were used to estimate wind power generation using reV (the Renewable Energy Potential Model developed by NREL) at ten wind project sites. Two sets of results were produced: one using wind speeds extracted from the model grid cell at the project centroid, and another using wind speeds from the actual turbine locations. For each dataset, power output was calculated using both actual turbine-specific power curves and nine generic power curves to convert wind speed into power.

17 WIND ENERGY↗
NASA NTRS1993

EnviroNET: On-line information for LDEF

EnviroNET is an on-line, free-form database intended to provide a centralized repository for a wide range of technical information on environmentally induced interactions of use to Space Shuttle customers and spacecraft designers. It provides a user-friendly, menu-driven format on networks that are connected globally and is available twenty-four hours a day - every day. The information, updated regularly, includes expository text, tabular numerical data, charts and graphs, and models. The system pools space data collected over the years by NASA, USAF, other government research facilities, industry, universities, and the European Space Agency. The models accept parameter input from the user, then calculate and display the derived values corresponding to that input. In addition to the archive, interactive graphics programs are also available on space debris, the neutral atmosphere, radiation, magnetic fields, and the ionosphere. A user-friendly, informative interface is standard for all the models and includes a pop-up help window with information on inputs, outputs, and caveats. The system will eventually simplify mission analysis with analytical tools and deliver solutions for computationally intense graphical applications to do 'What if...' scenarios. A proposed plan for developing a repository of information from the Long Duration Exposure Facility (LDEF) for a user group is presented.

Lauriente, Michael↗
NASA NTRS2024

Investigating the Physical Modification of the Bennu Sample During Entry, Descent, and Landing

On September 24, 2023, the OSIRIS-REx Sample Return Capsule (SRC) entered Earth’s atmosphere and landed in the Utah Test and Training Range (UTTR). Preliminary examination of the returned Bennu sample has confirmed that OSIRIS-REx sample mass exceeds the mission requirement of 60 g of material. The sample consists of particles that range from a few centimeters to microscopic fines. During the SRC’s entry, descent, and landing (EDL) sequence, it may have experienced (i) peak decelerations of 10s of g (ii) tumbling, and (iii) touchdown at approximately 10 m/s, which could have induced physical modification of the sample. In addition, the act of sampling may have altered or biased the physical properties of the collected materials. Here, we investigate the likelihood and extent of physical modification of the sample between collection and return using observations and modeling. This work addresses the mission’s hypothesis 12, which concerns, in part, the modification of the sample during collection and Earth entry.

asteroid↗
NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗
DOE OSTI2024

A dynamic volumetric heat source model for laser additive manufacturing

Melt pool scale models of laser powder bed fusion (LPBF) offer insights into the process-structure-property relationships in additive manufacturing (AM). These models often neglect physical phenomena such as vapor cavity formation and fluid mechanics to reduce computational demands. Instead, volumetric heat source models are used to represent the effects that these phenomena have on the predicted melt pool dimensions. Generally, the dimensions and effective absorption of the volumetric heat source are calibrated to reproduce melt pool dimensions observed in metallographic cross sections taken from single-track experiments on bare plate. However, the transient nature of LPBF often deviates the melt pool dimensions from the assumed steady-state conditions of single-track experiments, motivating the need for a volumetric heat source model that more generally considers the dynamic relationship between melt pool shape and laser-material interactions. Here, we introduce a two-parameter volumetric heat source model that integrates several existing models into a generalized mathematical expression, providing independent control over the radial heat distribution via the parameter k and the volumetric shape of the heat source via the parameter m. This parameterization enables the calibration of melt pool shape predictions through simultaneous adjustment of these parameters, while keeping the radial heat source dimensions consistent with the experimental spot size (D4σ) and constraining the heat source depth and absorption to physically derived expressions for cavities. Consequently, the proposed volumetric heat source model adapts to changes in the local melt pool conditions due to scanning strategy and part geometry by dynamically adjusting the heat source depth and absorption. We demonstrate the capabilities of the proposed model through comparisons with a collection of experiments from the Additive Manufacturing Benchmark (AMBench).

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Photon Harvesting and Rocky Planet Biosignatures with 3D Climate Models

On the Earth solar photons are collected, sifted, and harvested by life at a scale that has transformed and defined the planet and continues to do so. That transformation is considered to be a key biosignature to look for in the rocky exoplanet population of our galaxy. However, the availability of photons on planetary surfaces depends on orbital and spin configuration as well as shadowing and scattering by clouds and other atmospheric phenomena. We are using a state-of-the-art 3D climate model (ROCKE-3D, Way et al. 2017) to study rocky planet surface irradiances and the implications for biosignatures and future missions such as the Habitable Worlds Observatory.

exoplanets↗
NASA NTRSDate not supplied

Stochastic Reconstruction of Thermal Protection Material Properties from Arc-Jet Experiments

Material response models are used to assess reliability using variances in the bond-line temperature predictions based on uncertainties in trajectory, aerothermal environment, and material properties. A key deficiency in the current approach is that input uncertainties are too often subjective, empirical, or ad-hoc, and are not rigorously linked to the arc-jet test data used to develop the TPS material model. While materials such as PICA are well understood, future missions may require more novel materials such as HEEET where unknown uncertainties have real consequences on the ability to assess reliability. A quantifiable estimate of reliability requires an iterative methodology where the parameters driving the variance in bond-line temperature (for example) are systematically identified. A test campaign to collect data or develop new models can then be identified to reduce those input uncertainties. A Bayesian inference loop defines these connections mathematically, i.e., prior knowledge about uncertainty is updated based on observation. While these concepts are well known (and often applied intuitively in a non-rigorous approach), only recent advances in reduced-order modelling have made them computationally viable methods for engineering. By replacing deterministic inverse methods with stochastic approaches, the hope is new materials proposed for future missions can more rapidly be developed with a greater understanding of the TPS material reliability. Two additional steps for the analysis of arc jet test data are discussed. The first is ability to construct a reduced-order model using material response simulations (Icarus/US3D) of the arc-jet test articles, and the second is the inclusion of this surrogate model in the Bayesian inversion process. Both capabilities will be demonstrated using prior PICA arc-jet test data. The quality of a surrogate model will be investigated and the variances on the calibrated material properties will be compared to our current understanding of the PICA material model.

Material response↗
NASA NTRS1976

Experimental solar heating-cooling system model tests of a full-scale building system

An experimental solar heating and cooling system model has been built and operated, combining elements that are programmable - heating and/or cooling load of a building, collected solar energy - with experimental equipment. The system model was based on the loads and components used in the Solar Building Test Facility (SBTF) which includes a 1394 square meter solar collector field, at NASA-Langley. Operations covered 5 continuous days under summer conditions. For the system model, up to 55% of the simulated collected solar energy was used for the building load. This amount constituted 35% of the building cooling load. Heat loss was significant. If tank heat loss were eliminated, 75% of the collected solar energy would be used. This amount would supply approximately 50% of the building cooling load.

Namkoong, D.↗
NASA NTRS1983

Implications of complete watershed soil moisture measurements to hydrologic modeling

A series of six microwave data collection flights for measuring soil moisture were made over a small 7.8 square kilometer watershed in southwestern Minnesota. These flights were made to provide 100 percent coverage of the basin at a 400 m resolution. In addition, three flight lines were flown at preselected areas to provide a sample of data at a higher resolution of 60 m. The low level flights provide considerably more information on soil moisture variability. The results are discussed in terms of reproducibility, spatial variability and temporal variability, and their implications for hydrologic modeling.

Engman, E. T.↗
NASA NTRS1988

Space plasma contactor research, 1987

A simple model describing the process of electron collection from a low pressure ambient plasma in the absence of magnetic field and contactor velocity effects is presented. Experimental measurments of the plasma surrounding the contactor are used to demonstrate that a double-sheath generally develops and separates the ambient plasma from a higher density, anode plasma located adjacent to the contactor. Agreement between the predictions of the model and experimental measurements obtained at the electron collection current levels ranging to 1 A suggests the surface area at the ambient plasma boundary of the double-sheath is equal to the electron current being collected divided by the ambient plasma random electron current density; the surface area of the higher density anode plasma boundary of the double-sheath is equal to the ion current being emitted across this boundary divided by the ion current density required to sustain a stable sheath; and the voltage drop across the sheath is determined by the requirement that the ion and electron currents counterflowing across the boundaries be at space-charge limited levels. The efficiency of contactor operation is shown to improve when significant ionization and excitation is induced by electrons that stream from the ambient plasma through the double-sheath and collide with neutral atoms being supplied through the hollow cathode.

Wilbur, Paul J.↗
DOE OSTI2025

FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment (Final Report)

This report documents the FY25 Theory and Simulation Performance Target (TSPT) of developing an integrated modeling framework for fusion reactor design and assessment (FREDA). Over Q1-Q4, new capabilities were developed across both plasma and engineering domains and demonstrated on an example representation of a Compact Advanced Tokamak with a Dual Cooled Lead Lithium blanket. This represents a first-of-a-kind demonstration of coupled core-to-wall-to-engineering for a reactor. Self-consistent CESOL workflows were applied to provide core, pedestal, and SOL prediction; new modules were developed for energetic particle stability (FAR3D) and transport (TGLF-EP) analysis; and boundary plasma modeling (SOLPS-ITER, BOUT++/Hermes-3) was expanded to evaluate wall and divertor heat fluxes and interface with engineering thermal analysis. A parameterized CAD tool, TRACER, was expanded to generate medium-fidelity divertor, blanket, and coil geometries; OpenFOAM and Diablo workflows were applied for first-wall and divertor thermal analyses with helium cooling; and reduced-order models were created for high-mass-flux divertor cooling. Magnet multiphysics capabilities were verified between Elmer, Diablo, and a new MFEM-based solver, and workflows enable stress, thermal, and neutron-fluence analysis of TF coils with neutronics-driven heating. Nuclear and blanket analysis workflows were demonstrated, including tritium breeding, transport, and CFD-informed thermo-mechanical assessment. Preliminary multi-fidelity uncertainty quantification workflows were applied to boundary modeling codes and shown to achieve variance reductions with fewer high-fidelity boundary simulations. Key findings highlight the challenges of resolving the ITEP gap to find suitable balance between wall and divertor loads, neutron heating, and practical limits of PFC cooling. Next step priorities are to develop automated workflows to check boundary code convergence and detachment, implement tighter physics-engineering CAD provenance tracking, and inclusion of plasma-material interface models for SLAG and tungsten cracking behavior. Collectively, these developments establish sophisticated capabilities for predictive, multi-fidelity, whole-device modeling that integrates plasma physics, materials, magnets, and nuclear engineering to guide pathways to viable Fusion Pilot Plant design points.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2003

Cloud Modeling Using Field Project Data for the Study of Precipitation Processes

The use of cloud-resolving models (CRMs) in the study of precipitation process and their relation to the large-scale environment can be generally categorized into two approaches. The first approach is so called "cloud ensemble modeling". In this approach, many clouds of different size in various stages of their lifecycles can be present at any model simulation time. Large-scale effects are derived from observations and imposed into the model as the main forcing. The advantage of this approach is that the modeled convection will be forced to have the same intensity, thermodynamic budget and organization as the obserations.This approach will also allow CRMs to perform multi-day or multi-week time integrations. The second approach usually requires initial temperature and water vapor profiles that have a medium to large CAPE, and open lateral boundary conditions are used. The modeled clouds could be termed "self-forced convection". Model improvements, such as in the microphysics, are achieved using the second approach. In cloud ensemble modeling, accurate large-scale advective tendencies for temperature and water vapor are the main forcing for the CRMs. We found that the large-scale advective terms for temperature and water vapor are not always consistent, For example, large-scale forcing could indicate strong drying which would produce cooling in the model through evaporation but not contain large-scale advective heating to compensate. This discrepancy in forcing would cause differences between the observed and modeled latent heating profiles. Good measurements of other quantities (i.e., surface fluxes and radiation) are also required to perform variational objective analysis that computes and minimizes a "cost function" that constrains the difference between the large-scale advective forcing in temperature and water vapor. With self-forced convection, accurate vertical distributions of temperature, moisture (water vapor), and horizontal winds are required. The timing of the measurements relative to cloud development is crucial (i.e., prior to cloud triggering). Microphysical measurements (i.e., the cloud number concentration and size distribution) can also be used in this second approach but are of secondary importance with cloud ensemble modeling. In this paper, data collected during TRMM field campaigns (FCs; i.e., SCSMEX, LBA and KWAJEX) which were aimed at validating TRMM products (i.e., rainfall and the vertical distribution of latent heating) will be used to examine the impact of errors in the initial conditions (e.g.., soundings an large-scale forcing) on simulated rainfall distributions and brightness. Rainfall and precipitations simulated from a CRM will also be compared with those estimated by a schocastic model.

Tao, W.-K.↗
NASA NTRS2016

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Probability Theory↗
DOE OSTI2026

Decoding THz‐Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks

Ultrafast polarization dynamics in ferroelectrics are of considerable interest for high-speed tunable dielectrics and electro-optics. Extended domain wall networks formed in ferroelectric twin nanodomains can support collective dynamics in the terahertz regime but require techniques that track polarization and strain evolution driven by ultrafast stimulus. Here, we use multi-modal probing of THz-pulse-driven excitations in PbTiO 3 /SrTiO 3 superlattices by combining X-ray free electron laser measurements that directly tracks lattice changes, with optical second harmonic generation that tracks the electronic potential coupled with the lattice potential. Dynamical phase-field modeling enables fingerprinting of these collective modes as superpositions of domain “breathing” through wall oscillations and polarization “rotations” with still walls. Ultrafast domain wall motion at 0.1–0.5 THz is observed at practical fields of 100 kV/cm with wall velocities of >4000 m/s, approaching typical speed of sound in PbTiO 3 . A unique “charging” mode is discovered that can electrically charge and discharge domain walls on ∼4 ps time scale thus dynamically tuning wall conductivity. Integrated experimental and theoretical fingerprinting of the dynamical landscape presented here enables ultrafast control of ferroics for high-speed microelectronics and optical applications.

THz dynamics↗
NASA NTRSDate not supplied

Better Constraining Supercooled Clouds Could Reduce Projected Warming Spread

The increase of climate sensitivity to rising greenhouse gas concentrations in the coupled model intercomparison project phase 6 (CMIP6) Earth system models (ESMs) compared to CMIP5 ESMs is primarily attributed to a larger extratropical cloud response to climate change, referred to as cloud feedback. The ratio of supercooled liquid cloud water relative to all cloud water, termed liquid phase ratio (LPR), which has also notably increased in many recent ESMs, is thought to be a primary driver of the extratropical cloud feedback increase. Unlike the preponderance of previous studies that compare native model LPR directly with observations, here we evaluate LPR over three ESM generations against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using an instrument simulator approach, which mimics instrument limitations and uses consistent cloud definitions and resolutions. We find that current coupled model intercomparison project (CMIP) ESMs collectively simulate greater LPR than previous CMIP generations and overestimate LPR compared to observations, contrary to previous findings, likely driven by past inconsistent comparisons of ESM outputs with CALIPSO observations. We further show that greater LPR in ESM present-day climate is unexpectedly correlated with a smaller extratropical cloud feedback, attributable to a decrease of cloud amount exceeding the increase of cloud optical depth. Finally, our results suggest that improving constraints on model LPR using our evaluation framework would likely reduce the spread in CMIP6 climate sensitivities, owing to its effect on extratropical cloud feedback from supercooled clouds.

greenhouse gas concentrations↗
NASA NTRS1981

Modeling of the aircraft in-trail-following task during profile descent

The cockpit display of traffic information (CDTI) system concepts enable the pilot to observe the surrounding air traffic pattern. The impact of such a system is far reaching in terms of improved safety, pilot and controller workload, and aircraft fuel efficiency. One direct payoff is the ability to distribute the ATC workload to the pilot in such tasks as merging and spacing. The CDTI application of spacing approach aircraft in the terminal area is addressed. In-trail-following/CDTI experiments were performed using realistic cockpit simulators and profile descent approach scenarios. Based on collected experimental simulator data, pilot models were developed which include state estimation, decision making and flight control aspects. These models were coupled with models of aircraft and CDTI equipment to study the dynamic phenomena and stability of strings of aircraft along various approach patterns.

Goka, T.↗
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗