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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Effects of magnetometer calibration and maneuvers on accuracies of magnetometer-only attitude-and-rate determination

Two different algorithms - a deterministic magnetic-field-only algorithm and a Kalman filter for gyroless spacecraft - are used to estimate the attitude and rates of the Rossi X-Ray Timing Explorer (RXTE) using only measurements from a three-axis magnetometer. The performance of these algorithms is examined using in-flight data from various scenarios. In particular, significant enhancements in accuracies are observed when' the telemetered magnetometer data are accurately calibrated using a recently developed calibration algorithm. Interesting features observed in these studies of the inertial-pointing RXTE include a remarkable sensitivity of the filter to the numerical values of the noise parameters and relatively long convergence time spans. By analogy, the accuracy of the deterministic scheme is noticeably lower as a result of reduced rates of change of the body-fixed geomagnetic field. Preliminary results show the filter-per-axis attitude accuracies ranging between 0.1 and 0.5 deg and rate accuracies between 0.001 deg/sec and 0.005 deg./sec, whereas the deterministic method needs a more sophisticated techniques for smoothing time derivatives of the measured geomagnetic field to clearly distinguish both attitude and rate solutions from the numerical noise. Also included is a new theoretical development in the deterministic algorithm: the transformation of a transcendental equation in the original theory into an 8th-order polynomial equation. It is shown that this 8th-order polynomial reduces to quadratic equations in the two limiting cases-infinitely high wheel momentum, and constant rates-discussed in previous publications.

Challa, M.↗
NASA NTRS1986

Synoptic impacts of NOAA 6/Tiros N data on numerical circulation forecasts

Profiles of retrieved satellite observed temperatures provided to the Israel Meteorological Service (IMS) by the National Environmental Satellite Data and Information Service were used to enhance the specification of atmospheric conditions during three winter periods. Introduction of the satellite data was via three alternative schemes, and the enhanced specifications were used to initialize 48-hour numerical forecasts with the IMS five-layer primitive equation model; control forecasts were run in parallel from initial conditions without the satellite data. The study selects one forecast from each period for which discernible impacts on forecast synoptic fields can be attributed to the different initializations. The first two case studies examine the results of only one assimilation scheme and the third relates to all three schemes. Verification of the prognostics show the positive impact of including satellite temperatures in the initialization, but there were also examples of negative impact.

Druyan, L. M.↗
NASA NTRS2017

Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Enhanced↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2022

SERS-based ssDNA composition analysis with inhomogeneous peak broadening and reservoir computing

Surface-enhanced Raman spectroscopy employed in conjunction with post-processing machine learning methods is a promising technique for effective data analysis, allowing one to enhance the molecular and chemical composition analysis of information rich DNA molecules. In this work, we report on a room temperature inhomogeneous broadening as a function of the increased adenine concentration and employ this feature to develop one-dimensional and two dimensional chemical composition classification models of 200 long single stranded DNA sequences. Afterwards, we develop a reservoir computing chemical composition classification scheme of the same molecules and demonstrate enhanced performance that does not rely on manual feature identification.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

Observed Coupling Between the International Space Station PCU Plasma and a FPMU Langmuir Probe Facilitated by the Geomagnetic Field

Electrical charging of the International Space Station (ISS) is a matter of serious concern resulting from the possibility of vehicle arcing and electrical shock hazard to crew during extravehicular activity (EVA). A Plasma Contactor Unit (PCU) was developed and integrated into ISS in order to control the ISS floating potential, thereby, minimize vehicle charging and associated hazards. One of the principle factors affecting ISS electrical charging is the ionosphere plasma state (i.e., electron temperature and density). To support ISS electrical charging studies a Floating Potential Monitoring Unit (FPMU) is also integrated into ISS in order to measure the ionosphere properties using Langmuir probes (LP). The FPMU was located on the Starboard side of ISS. The PCU is located near the center of ISS with its plasma exhaust pointed to port. From its integration on ISS in 2006 through November of 2009, the FPMU data exhibited nominal characteristics during PCU operation. On November 21, 2009 the FPMU was relocated from the Starboard location to a new Port location. After relocation significant enhanced noise was observed in both the LP current-voltage sweeps and the derived electron temperature data. The enhanced noise only occurred when the PCU was in discharge and at unique and repeatable locations of the ISS orbit. The cause of this enhanced noise was investigated. It was found that there is coupling occurring between the PCU plasma and the FPMU LP. In this paper we shall 1) present the on-orbit data and the presence of enhanced noise, 2) demonstrate that the coupling of the PCU plasma and the FPMU measurements is geomagnetically organized, 3) show that coupling of the PCU plasma and the FPMU is primarily due to and driven by particle-wave interaction and 4) show that the ionosphere conditions are adequate for Alfven waves to be generated by the PCU plasma.

Hartman, William↗
DOE OSTI2023

Ionothermally Synthesized Nanoporous Ti 0.95 W 0.05 Nb 2 O 7 : a Novel Anode Material for High-Performance Lithium-Ion Batteries

Although TiNb 2 O 7 is regarded as a fast-rechargeable lithium-ion battery (LIB) anode material, the intrinsic poor electrochemical kinetics of TiNb 2 O 7 still dramatically impedes its development. Herein, an ionothermal synthesis-assisted doping strategy is proposed for the preparation of a new W 6+ -doped TiNb 2 O 7 material (Ti 0.95 W 0.05 Nb 2 O 7 ) with nanoporous structure (denoted as NPTWNO). The improved Li + diffusion coefficient of NPTWNO suggests that the ionic-liquid-templated nanoporous architecture improves the Li+ diffusion kinetics. The density functional theory computational study reveals that the doped W 6+ successfully boosts the electronic conductivity due to the narrowed conduction-valance bandgap resulted from charge redistribution, which is reflected by the electrochemical impedance spectroscopy data. With the simultaneously enhanced Li+ diffusivity and electronic conductivity, NPTWNO achieves fast-rechargeability in LIBs. So this work indicates the potential of ionothermal synthesis-assisted doping strategy on energy storage materials and offers NPTWNO material with promising electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion

The biogas upgrading process through bioconversion of CO 2 to CH 4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH 4 % and H 2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R 2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH 4 % and H 2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Biogas upgrading↗
DOE OSTI2021

Impact of KOH on the interfacial precipitation rates of C-S-H during the early hydration of C3S

In this work we investigated the influence of alkalis on the hydration of two triclinic samples of C{sub 3}S. One pure C{sub 3}S and one Al-doped C{sub 3}S (0.5 wt.‐% Al{sub 2}O{sub 3}). We added differently concentrated KOH solutions representing alkalis in the pore solution, which lead to an increase of the interfacial precipitation rates of C-S-H during the hydration of C{sub 3}S compared to the hydration without KOH. Isothermal calorimetry was conducted for the determination of the rates of reaction during the hydration process and ICP-OES measurements for the determination of the pore solution composition at certain times of hydration. The increasing effect of KOH solution on the calculated interfacial precipitation rates from pore solution data can explain the enhancement of the rates of reaction recorded by heat flow calorimetry during the early hydration. Therefore, the interfacial precipitation rates of C-S-H can accurately describe the acceleration effect of alkalis on the early hydration of C{sub 3}S.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Opportunities and challenges in thermochemical conversion of municipal solid waste: A comprehensive review

Recent advancements in thermochemical conversion processes have elucidated new pathways for converting municipal solid waste into valuable resources. This review explores the primary thermochemical conversion methods, including combustion, gasification, pyrolysis, torrefaction, hydrothermal carbonization, and hydrothermal liquefaction, emphasizing their potential roles in waste management and energy recovery. Key challenges including feedstock variability, ash behavior, and scale-up limitations are discussed alongside opportunities for hybrid systems and circular economy integration. A comparative analysis of research publications indicates a significant focus on thermochemical pathways within the broader context of municipal solid waste research, underscoring the growing interest in these technologies. Recent advancements in each thermochemical process, alongside their operational, technical, and economic challenges, are discussed. Comparative data reveal that torrefaction enhances the hydrophobicity and grindability of municipal solid waste components, though its energy densification benefits are more modest than those observed in biomass. Hydrothermal carbonization and liquefaction are highlighted for their ability to process high-moisture and heterogeneous waste streams. The review also synthesizes recent findings on reactor configurations, emissions control, and synergistic effects in co-processing municipal solid waste fractions. The findings underscore the importance of developing standardized protocols for municipal solid waste characterization and the need for innovative hybrid systems to improve efficiency.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

Satellite-based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear-sky conditions for estimating surface urban heat island intensity (SUHII). Clear-sky SUHII may not accurately represent all-sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear-sky and all-sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear-sky SUHII is mostly higher than all-sky SUHII, particularly in summer, daytime, and precipitation-rich regions. Besides, clear-sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all-sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear-sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks

Despite advancements in Artificial Intelligence (AI) methods for climate downscaling, significant challenges remain for their practicality in climate research. Current AI-methods exhibit notable limitations, such as limited application in downscaling Global Climate Models (GCMs), and accurately representing extremes. To address these challenges, we implement an AI-based methodology using super-resolution convolutional neural networks (SRCNN), trained and evaluated on 40 years of daily precipitation data from a reanalysis and a high-resolution dynamically downscaled counterpart. The dynamical downscaled simulations, constrained using spectral nudging, enable the replication of historical events at a higher resolution. This allows the SRCNN to emulate dynamical downscaling effectively. Modifications, such as incorporating elevation data and data pre-processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. Future work will expand this methodology to downscale additional variables for future climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The critical role of soil moisture in compound hazards

Soil moisture regulates the exchange of energy, water, and carbon across land–vegetation–atmosphere interfaces. Extremes in soil moisture can amplify natural hazards through interactions with diverse Earth system processes. Despite its mechanistic importance, soil moisture remains underrepresented in hazard research and predictive frameworks. Here, in this study, we review our current understanding of the role of soil moisture in the evolution and onset of diverse compound hazards by synthesizing the latest findings from observational and modelling studies. We highlight key soil moisture mechanisms, including atmospheric feedbacks that amplify drought–heatwave–wildfire events, precipitation couplings that promote clustered storms, and threshold responses that drive vegetation die-offs, trigger landslides, and induce flooding. Persistent challenges in observational data, model representation and operational implementation have limited the integration of soil moisture into hazard early-warning systems. Addressing these gaps through advances in observations, data assimilation, and physics-based and data-driven modelling will enhance hazard prediction and preparedness in a rapidly changing world.

Li, Chuxuan [University of California, Los Angeles↗
DOE OSTI2025

Oral microbiome and mycobiome dynamics in cancer therapy-induced oral mucositis

Cancer therapy-induced oral mucositis is a frequent major oncological problem, secondary to cytotoxicity of chemo-radiation treatment. Oral mucositis commonly occurs 7–10 days after initiation of therapy; it is a dose-limiting side effect causing significant pain, eating difficulty, need for parenteral nutrition and a rise of infections. The pathobiology derives from complex interactions between the epithelial component, inflammation, and the oral microbiome. Our longitudinal study analysed the dynamics of the oral microbiome (bacteria and fungi) in nineteen patients undergoing chemo-radiation therapy for oral and oropharyngeal squamous cell carcinoma as compared to healthy volunteers. The microbiome was characterized in multiple oral sample types using rRNA and ITS sequence amplicons and followed the treatment regimens. Microbial taxonomic diversity and relative abundance may be correlated with disease state, type of treatment and responses. Identification of microbial-host interactions could lead to further therapeutic interventions of mucositis to re-establish normal flora and promote patients’ health. Data presented here could enhance, complement and diversify other studies that link microbiomes to oral disease, prophylactics, treatments, and outcome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion

Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.

Shan, Siming [Yale University, New Haven, CT (Unit↗
DOE OSTI2023

Laboratory electron screening in nuclear resonance reactions

Both nonresonant and resonance reaction data are subject to laboratory electron screening effects. For non resonant reactions, such effects are well documented and the measured cross sections can be corrected to find the unscreened ones. Frequently, the procedure and expression to calculate laboratory electron screening factors for nonresonant reactions are also applied to isolated narrow resonances, without much theoretical support or experimental evidence. Here, a simple model is applied to estimate electron screening factors, lengths, and potentials for narrow resonances. The corrections to the measured data result in an enhancement of the unscreened resonance strengths by less than 0.2%, contrary to published narrow-resonance screening correction factors, which predict a reduction of the unscreened strengths by up to 25%. Unless it can be proven otherwise, it is recommended that measured strengths of isolated narrow resonances not be corrected for laboratory electron screening. The prospects of investigating laboratory electron screening effects by measuring almost negligible differences in resonance strengths are not promising. Instead, the difference of the resonance energy for the unscreened and screened situation may be measurable. As an example, the case of the E r = 956-keV resonance in the 27 Al(p,γ) 28 Si reaction is discussed. It is also demonstrated that the claim of a previously reported detection of a resonance near 800 keV in the 176 Lu(p,n) 176 Hf reaction is incorrect.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

New methods and simulations for cosmogenic induced spallation removal in Super-Kamiokande-IV

Radioactivity induced by cosmic muon spallation is a dominant source of backgrounds for 𝒪⁡(10 MeV) neutrino interactions in water Cherenkov detectors. In particular, it is crucial to reduce backgrounds to measure the solar neutrino spectrum and find neutrino interactions from distant supernovae. In this paper we introduce new techniques to locate muon-induced hadronic showers and efficiently reject spallation backgrounds. Applying these techniques to the solar neutrino analysis with an exposure of 2790 × 22.5 kton · day increases the signal efficiency by 12.6%, approximately corresponding to an additional year of detector running. Furthermore, we present the first spallation simulation at Super-Kamiokande, where we model hadronic interactions using fluka. The agreement between the isotope yields and shower pattern in this simulation and in the data gives confidence in the accuracy of this simulation, and thus opens the door to use it to optimize muon spallation removal in new data with gadolinium-enhanced neutron capture detection.

muons↗