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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

California Trees Seasonally Use Augmented Water Sources: Water Isotope Tracking in a Groundwater‐Dependent Ecosystem

Sustainable groundwater management must account for the needs of groundwater dependent ecosystems. To understand the relationship of ecosystems and seasonal water use, we studied the stable isotope composition (δ 18 O and δ 2 H) of water in streamside trees in a semi-arid streamside environment (Livermore, California, USA). We sampled seven trees at two sites every other month from April 2024 through April 2025 for tree xylem stable water isotope signatures. These data were compared to potential source waters: precipitation, imported surface water, soil water and regional groundwaters. Large daily precipitation events were found to be isotopically similar to regional groundwater and were thus treated as one water source. A Bayesian mixing model using stable water isotopes was used to determine the ratios of these three potential source waters (small daily precipitation events, groundwater/large daily precipitation events and imported water) present in tree xylem water. On average, tree water sources include 32% imported water (SD = 9%), 35% small daily precipitation events (SD = 10%) and 33% groundwater (SD = 3%), with significant seasonal variation (t summer-winter = 30.8, p < 0.01), particularly drawing more imported water (more than 55%) in the summer. While small daily precipitation events contribute only 10% of the total precipitation in our dataset, it represents a third of water used by trees. In addition, while these ecosystems are designated as groundwater dependent ecosystems, the trees use approximately one third imported water and even more during dry summer months. This approach provides water managers with a practical tool for quantifying ecosystem water needs, supporting data-driven decisions and regulatory compliance. While California's Sustainable Groundwater Management Act emphasizes supporting groundwater dependent ecosystems, it allows flexibility in demonstrating benefits. In conclusion, our methodology provides a way to document that management actions (such as managed aquifer recharge with imported water) deliver measurable co-benefits to GDEs.

Geosciences↗
DOE OSTI2020

Geothermal potential of Harrat Rahat, Northern Arabian Shield: geological constraints

Arabian Shield offers a strong potential for geothermal energy. However, only a limited amount of exploration effort has taken place. Harrat Rahat is a recent volcanic area located along the Makkah-Madinah-Nafud volcanic line in western Saudi Arabia. This study is oriented to identify the geothermal potential of Harrat Rahat. The geologic data was collected and processed. Here, we focus on evaluating the potential sources of subsurface heat and groundwater. Subsurface heat is based on the age of erupted volcanoes and the expected volume of subsurface magma chambers. An analogy with an existing geothermal field in a similar setting as Mexico suggests that the faults and the coincident seismicity may be associated with higher permeability. It is concluded that Harrat Rahat is characterized by a good geothermal potential for future geothermal energy source production.

58 GEOSCIENCES↗
DOE OSTI2017

High fluid shear strain causes injury in silver shark: Preliminary implications for Mekong hydropower turbine design

Fluid shear arises when two bodies of water, travelling at different velocities, intersect. Fish entrained at the interface of these two water masses will experience shear stress; which can be harmful. The stress magnitude is dependent on waterbody mass and velocity; with the fish impact largely related to body size. Elevated shear stress occurs where rapidly flowing water passes near spillways, across screens, within turbine draft tubes or other passage routes. A flume was used to determine critical tolerances of silver shark (Balantiocheilos melanopterus) to different shear stress rates generated by a high velocity jet. Fish experienced higher levels of injury and mortality as shear stress was increased. Excessive shear forces had damaging impacts on fish. Mortality occurred at shear levels higher that 600/s. It is important that developers should attempt to model potential shear profiles expected during turbine passage in selected designs. These data will be critical to determine potential impacts on fish. If the likelihood of adverse impact is high, then alternative designs which have lower shear stress could be explored.

hydro power plants↗
DOE OSTI2023

Modeling the Environment-Dependent Kinetics of Oxygen Reduction Reaction – a Continuum Model for Electric Double Layer

Here, for proton-exchange-membrane fuel cells (PEMFCs) to achieve broad commercialization, improved energy-conversion efficiency with minimal Pt-based electrocatalyst is required. Because the sluggish rate of oxygen reduction reaction (ORR) limits the efficiency of PEMFCs, the efficiency improvement requires a better understanding of ORR kinetics and mechanism to design better catalyst. To understand the ORR mechanism, theoretical and experimental analyses have been conducted. While previous studies reasonably explained the catalyst-dependent activity on single crystal catalysts in 0.1 M perchloric acid solution, the explicit effect of electrolyte and related microenvironments is not thoroughly understood. The change in the electrolyte alters the electric-double-layer (EDL) structure and thus the local microenvironment at the electrode/electrolyte interface. Thus, the structure of the EDL should be carefully analyzed to uncover the electrolyte-dependent reaction kinetics. In this talk, we propose a multiscale continuum model to predict the EDL structure and examine the effect of perchloric acid concentration on ORR activity on Pt (111). The model includes Density Potential Functional Theory (DPFT) for electron density and Modified Poisson Boltzmann equation for species’ density and electric potential. Also, the interaction between adsorbents and electric field is taken into account by minimizing the grand potential. After model validation with experimentally measured double-layer capacity data as a function of applied potential and concentration, the effect of the perchloric acid concentration (0.02 M – 0.2 M) on ORR activity is analyzed and discussed. It is shown that the model reproduces the specific activity obtained in the experiments when assuming the oxygen adsorption is limiting the rate, which can be attributed to the large energetic barrier for solvent reorganization. Then, extension of the model to PEMFC ionomer electrolytes will be introduced. Overall, the model framework and findings provide insights into the ORR mechanism and guidance on how to tailor catalyst materials for increased PEMFC performance.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS2003

Precision of a radial basis function neural network tracking method

The precision of a radial basis function (RBF) neural network based tracking method has been assessed against real targets. Precision was assessed against traditionally measured frame-by-frame measurements from the recorded data set. The results show the potential limit for the technique and reveal intricacies associated with empirical data not necessarily observed in simulations.

autonomous tracking neural network target recognit↗
NASA NTRS2014

User Needs and Assessing the Impact of Low Latency NASA Earth Observation Data Availability on Societal Benefit

Since the advent of NASA's Earth Observing System, knowledge of the practical benefits of Earth science data has grown considerably. The community using NASA Earth science observations in applications has grown significantly, with increasing sophistication to serve national interests. Data latency, or how quickly communities receive science observations after acquisition, can have a direct impact on the applications and usability of the information. This study was conducted to determine how users are incorporating NASA data into applications and operational processes to benefit society beyond scientific research, as well as to determine the need for data latency of less than 12 h. The results of the analysis clearly show the significant benefit to society of serving the needs of the agricultural, emergency response, environmental monitoring and weather communities who use rapidly delivered, accurate Earth science data. The study also showed the potential of expanding the communities who use low latency NASA science data products to provide new ways of transforming data into information. These benefits can be achieved with a clear and consistent NASA policy on product latency.

NASA↗
DOE OSTI2017

Drifting oscillations in axion monodromy

In this paper, we study the pattern of oscillations in the primordial power spectrum in axion monodromy inflation, accounting for drifts in the oscillation period that can be important for comparing to cosmological data. In these models the potential energy has a monomial form over a super-Planckian field range, with superimposed modulations whose size is model-dependent. The amplitude and frequency of the modulations are set by the expectation values of moduli fields. We show that during the course of inflation, the diminishing energy density can induce slow adjustments of the moduli, changing the modulations. We provide templates capturing the effects of drifting moduli, as well as drifts arising in effective field theory models based on softly broken discrete shift symmetries, and we estimate the precision required to detect a drifting period. A non-drifting template suffices over a wide range of parameters, but for the highest frequencies of interest, or for sufficiently strong drift, it is necessary to include parameters characterizing the change in frequency over the e-folds visible in the CMB. Finally, we use these templates to perform a preliminary search for drifting oscillations in a part of the parameter space in the Planck nominal mission data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona

Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Multiresolution persistent homology for excessively large biomolecular datasets

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Integrating Light Curve and Atmospheric Modeling of Transiting Exoplanets

Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade; it is therefore paramount to upgrade current models and improve their reliability, their completeness, and the numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the said methodology is more sensitive against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from Hubble Space Telescope/Wide Field Camera 3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to flag potential discrepancies between the data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Heterobarrier Varactors with Nonuniformly Doped Modulation Layers

Optimum shape of the capacitance–voltage (C–V) characteristic is a critical parameter determining the efficiency of frequency multiplication in heterobarrier varactors (HBVs) operating in the millimeter and submillimeter frequency ranges. A numerical model for calculating the C–V characteristics and leakage currents of HBV heterostructures with arbitrary composition and doping profiles has been verified on the basis of published and original experimental data. A specially designed HBV heterostructure with three undoped InAlAs/AlAs/InAlAs barriers surrounded by nonuniformly doped n-InGaAs modulation layers has been grown by molecular beam epitaxy on InP substrate. Prototype HBVs manufactured using the proposed heterostructure demonstrated a nearly cosine shape of the C–V curve at bias voltages up to 2 V, increased overlap capacitance, and low leakage currents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Feasibility Analysis of Taxi Fleet Electrification Using 4.9 Million Miles of Real-World Driving Data: Preprint

Ride hailing activity is rapidly increasing, largely due to the growth of transportation network companies such as Uber and Lyft. However, traditional taxi companies continue to represent an important mobility option for travelers. Columbus Yellow Cab, a taxi company in Columbus, Ohio, offers traditional line-of-sight hailing as well as digital hailing through a mobile app. Data from Columbus Yellow Cab was provided to the National Renewable Energy Laboratory to analyze the potential for taxi electrification. Columbus Yellow Cab data contained information describing both global positioning system trajectories and taxi meter information. The data spanned a period of 13 months, containing approximately 70 million global system positioning system points, 840 thousand trips, and 170 unique vehicles. A variety of scenarios were evaluated using Columbus Yellow Cab data and the Electric Vehicle Infrastructure Projection Tool (EVI-Pro) to understand challenges and opportunities associated with operating an electrified taxi fleet. Two main factors-access to home charging and vehicle specifications-are shown to be major variables affecting successful electric fleet operation. The analysis indicates that 95.7% of taxi travel days can be successfully completed by a 250-mile-range electric vehicle assuming access to overnight and public charging infrastructure. However, when no overnight access is available to fleet vehicles, only 39.9% of taxi travel days are possible with 250-mile range electric vehicles. An additional scenario, reducing the vehicle range from 250 miles to 100 miles (while controlling for infrastructure access and permitting overnight charging) resulted in only 34.4% of taxi travel days being completed.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2008

Assessment and Mission Planning Capability For Quantitative Aerothermodynamic Flight Measurements Using Remote Imaging

High resolution calibrated infrared imagery of vehicles during hypervelocity atmospheric entry or sustained hypersonic cruise has the potential to provide flight data on the distribution of surface temperature and the state of the airflow over the vehicle. In the early 1980 s NASA sought to obtain high spatial resolution infrared imagery of the Shuttle during entry. Despite mission execution with a technically rigorous pre-planning capability, the single airborne optical system for this attempt was considered developmental and the scientific return was marginal. In 2005 the Space Shuttle Program again sponsored an effort to obtain imagery of the Orbiter. Imaging requirements were targeted towards Shuttle ascent; companion requirements for entry did not exist. The engineering community was allowed to define observation goals and incrementally demonstrate key elements of a quantitative spatially resolved measurement capability over a series of flights. These imaging opportunities were extremely beneficial and clearly demonstrated capability to capture infrared imagery with mature and operational assets of the US Navy and the Missile Defense Agency. While successful, the usefulness of the imagery was, from an engineering perspective, limited. These limitations were mainly associated with uncertainties regarding operational aspects of data acquisition. These uncertainties, in turn, came about because of limited pre-flight mission planning capability, a poor understanding of several factors including the infrared signature of the Shuttle, optical hardware limitations, atmospheric effects and detector response characteristics. Operational details of sensor configuration such as detector integration time and tracking system algorithms were carried out ad hoc (best practices) which led to low probability of target acquisition and detector saturation. Leveraging from the qualified success during Return-to-Flight, the NASA Engineering and Safety Center sponsored an assessment study focused on increasing the probability of returning spatially resolved scientific/engineering thermal imagery. This paper provides an overview of the assessment task and the systematic approach designed to establish confidence in the ability of existing assets to reliably acquire, track and return global quantitative surface temperatures of the Shuttle during entry. A discussion of capability demonstration in support of a potential Shuttle boundary layer transition flight test is presented. Successful demonstration of a quantitative, spatially resolved, global temperature measurement on the proposed Shuttle boundary layer transition flight test could lead to potential future applications with hypersonic flight test programs within the USAF and DARPA along with flight test opportunities supporting NASA s project Constellation.

Horvath, Thomas↗
NASA NTRS2020

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗
NASA NTRS2008

Electrical NEP in Hot-Electron Titanium Superconducting Bolometers

We are presenting the current progress on the titanium (Ti) hot-electron transition-edge devices. The ultimate goal of this work is to develop a submillimeter Hot-Electron Direct Detector (HEDD) with the noise equivalent power NEP = 10(sup -1) - 10(sup -20) W/Hz(sup 1/2) for the moderate resolution spectroscopy and Cosmic Microwave Background (CMB) studies on future space telescope (e.g., SPICA, SAFIR, SPECS, CMBPol) with cryogenically cooled (approximately 4-5 K) mirrors. Recently, we have achieved the extremely low thermal conductance (approximately 20 fW/K at 300 mK and approximately 0.1 fW/K at 40 mK) due to the electron-phonon decoupling in Ti nanodevices with niobium (Nb) Andreev contacts. This thermal conductance translates into the "phonon-noise" NEP approximately equal to 3 x 10(sup -21) W/Hz(sup 1/2) at 40 mK and NEP approximately equal to 3 x 10(sup -19) W/Hz(sup 1/2) at 300 mK. These record data indicate the great potential of the hot-electron detector for meeting many application needs. Beside the extremely low phonon-noise NEP, the nanobolometers have a very low electron heat capacitance that makes them promising as detectors of single THz photons. As the next step towards the practical demonstration of the HEDD, we fabricated and tested somewhat larger than in Ref.1 devices (approximately 6 micrometers x 0.35 micrometers x 40 nm) whose critical temperature is well reproduced in the range 300-350 mK. The output electrical noise measured in these devices with a low-noise dc SQUID is dominated by the thermal energy fluctuations (ETF) aka "phonon noise". This indicates the high electrothermal loop gain that effectively suppresses the contributions of the Johnson noise and the amplifier (SQUID) noise. The electrical NEP = 6.7 x 10(sup -18) W/Hz(sup 1/2) derived from these measurements is in good agreement with the predictions based on the thermal conductance data. The very low NEP and the high speed (approximately microns) are a unique combination not found in other detectors.

transition-edge sensor↗
DOE OSTI2019

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗
NASA NTRS1977

An operational, multistate, earth observation data management system

State, local, and regional agencies involved in natural resources management were investigated as potential users of satellite remotely sensed data. This group's needs are assessed and alternative data management systems serving some of those needs are outlined. It is concluded that an operational earth observation data management system will be of most use to these user agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products.

Eastwood, L. F., Jr.↗