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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Dynamic Analysis of Reynolds Number Effects on Trailing Edge Transonic Vortex Shedding and Its Impact on Turbine Blade Aerodynamic Performance

In this work, we will discuss observations from images acquired from a time-resolved, high-speed self-aligned focusing Schlieren campaign that was performed at the CW-22 facility at NASA Glenn Research Center to understand the dynamic behavior of thick trailing-edge high-pressure turbine blades simulating a ceramic matrix composite (CMC) construction at high inlet turbulence conditions. For the CMC-9 blade, which has a trailing edge thickness of 9% of the axial chord, we identified a regime where an excessive total pressure loss (loss anomaly) is observed only for a narrow range of Reynolds numbers at a fixed exit Mach number of 0.74. The loss anomaly is qualitatively explained by our images, which were taken at 10 distinct blade Reynolds numbers spanning a factor of 6. The images show a significant increase in energy related to the oscillations due to transonic vortex shedding at the Reynolds numbers related to the high loss conditions. This increased energy leads to the formation of strong acoustic waves that turn into shock waves at the highest loss conditions. From our observations stemming from Spectral POD analysis of the high-speed images, we see the acoustic waves produced by the trailing edge vortex shedding exist in all conditions tested; but the shedding frequency has a very slight trend upwards as the Reynolds number is increased, varying about 6% in the range tested. Considering this variation of shedding frequency as a function of Reynolds number, which is well-established for other bluff-body flows, we stipulate there may be a potential feedback mechanism involving an acoustic information transfer path across neighboring blades that may explain why only a narrow range of Reynolds numbers displays strong, shock-forming vortex shedding. We consider a few feedback paths and examine the timing based on the mean flow field from a high-resolution LES simulation. It appears that all feedback mechanisms are viable, presenting an integer number of delay cycles with respect to disturbances generated at the trailing edge. Most noteworthy, however, is the acoustic/shock-boundary layer interaction mechanism at the blade geometric throat. Based on our analysis, this mechanism likely plays an important role in realistic, curved turbine blade passages.

Aerodynamics↗
NASA NTRS1990

Space Environments and their Effects on Space Automation and Robotics

Automated and robotic systems will be exposed to a variety of environmental anomalies as a result of adverse interactions with the space environment. As an example, the coupling of electrical transients into control systems, due to EMI from plasma interactions and solar array arcing, may cause spurious commands that could be difficult to detect and correct in time to prevent damage during critical operations. Spacecraft glow and space debris could introduce false imaging information into optical sensor systems. The presentation provides a brief overview of the primary environments (plasma, neutral atmosphere, magnetic and electric fields, and solid particulates) that cause such adverse interactions. The descriptions, while brief, are intended to provide a basis for the other papers presented at this conference which detail the key interactions with automated and robotic systems. Given the growing complexity and sensitivity of automated and robotic space systems, an understanding of adverse space environments will be crucial to mitigating their effects.

Henry B Garrett↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Icy Worlds: Moving and Grooving

Reports from the conference session entitled Icy Worlds: Moving and Grooving, include:Mass Anomalies on Ganymede; Europan Chaos and Lenticulae: A Synthesis of Size, Spacing, and Areal Density Analyses; Thermal and Topographic Tests of Europa Chaos Formation Models; Flexure of Europa s Lithosphere Due to Ridge-Loading; Ridges on Europa: Origin by Incremental Ice-Wedging ; Convergent Boundaries on Europa: a Numerical Approach to Euler Pole Analysis and Its' Implications for Plate Reconstruction; Numerical Simulations of Subsolidus Convection in the Ice Shell of Europa: Implications for the Thermal Evolution and Present State; Effects of Plasticity on Convection in an Ice Shell: Implications for Europa; Non-Newtonian Convection and Compositional Buoyancy: Advances in Modeling Convection and Dome Formation on Europa; Convective Instability in Ice I: Application to Callisto and Ganymede; Crater Size Distributions on Callisto: A Galileo SSI Summary; Neutron Diffraction Studies of Planetary Ices; and H2O2 Synthesis Induced by Irradiation of H2O with Energetic H+ and Ar+ Ions at Various Temperatures.

Source record↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
NASA NTRSDate not supplied

Thermal Data-driven Model Reduction for Enhanced Battery Health Monitoring

Electric aviation faces a major challenge of avoiding potentially catastrophic consequences of the battery’s thermal runaway while keeping the weight of the battery low. Detection of early warning signals of battery failures requires accurate monitoring of the battery’s health throughout its lifespan. However, identifying the parameters of the battery from field data is notoriously difficult. We investigate this problem within the framework of modeling the temperature dynamics of a Li-ion cell during tests simulating loading in electric aircraft flights. It is found that the parameters of a higher-fidelity physics-based thermal model cannot be identified from the simulated flight data. To resolve this issue, we reduce the higher-fidelity thermal model to a model with fewer parameters. The resulting reduced-order model can predict temperature dynamics accurately and is identifiable throughout the cell’s lifespan which allows using the model’s parameters to monitor the state-of-health of the aging cell and detect anomalies in thermal behavior.

Li ion batteries↗
DOE OSTI2025

Interaction of magnetic fields with spinons in a fractionalized state

The 4d-electron trimer lattice Ba₄Nb₁₋ₓRu₃₊ₓO₁₂ exhibits either a quantum spin liquid (QSL) or a heavy-fermion strange metal (HFSM) phase, depending on Nb content. In the QSL state, itinerant spinons act as effective heat carriers, enhancing thermal conductivity. Strikingly, applying a magnetic field up to 14 T causes an abrupt, up-to-5000% increase in heat capacity below 150 mK, disrupting the linear temperature dependence typical of both phases. Meanwhile, AC susceptibility and electrical resistivity remain nearly unchanged, while thermal conductivity drops by up to 40% below 4 K. These results suggest spinons, despite being charge-neutral, are highly sensitive to magnetic fields at low temperatures. We propose that the magnetic field could induce Anderson localization of spinons, creating emergent non-magnetic two-level systems responsible for the Schottky-like anomaly in heat capacity. These findings point to a previously unexplored regime of spinon dynamics, potentially governed by field-induced localization and distinct from conventional magnetic or transport signatures.

Condensed-matter physics↗
DOE OSTI2026

Upper ocean mixing, surface heat fluxes, and heat content variability in the upper 150 m during Hurricane Laura (2020)

Observations (from thermistor sensors and a hurricane glider) and model-based temperature were used to answer questions including the following: 1) What physical and environmental conditions explain the rapid intensification of Hurricane Laura? 2) How did the pre-existing warm mixed layer at Stone mooring (StM) influence the degree of cooling in the mixed layer? 3) How did vertical mixing, surface heat fluxes, and advective processes collectively shape the mixed layer heat evolution at StM? Observations measured by thermistor sensors showed that Hurricane Laura induced a cooling of -1.2oC in the mixed layer when Hurricane Laura’s wind speed increased to 47 m/s on 26 August. This contrasts with the -1.04oC mixed layer temperature change estimated from model-based temperature. At StM, the presence of a 31oC warm mixed layer and elevated heat content (60-80kJ/cm2) effectively preconditioned the upper ocean ahead of Hurricane Laura’s passage. After Hurricane Laura passed, turbulent mixing associated with the storm transported the heat anomalies downward into subsurface layers. In addition, this study shows that a surface heat flux of 5.04 kJ/cm2 supported the intensity of Hurricane Laura as it traversed over the StM. Using a mixed layer heat balance model, this study shows that entrainment flux and surface flux mostly contributed to the observed change in the mixed layer temperature.

Tsai, Senam↗
NASA NTRSDate not supplied

On-Orbit Calibration Assessment of NOAA-21 VIIRS Thermal Emissive Bands and Implications for JPSS-4 VIIRS

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the NOAA-21 (N21) satellite has been successfully operating for over three years collecting valuable scientific measurements. A large suite of weather forecasting models and scientific research applications are supported with the VIIRS measurements from N21 combined with those acquired from the VIIRS instrument onboard the S-NPP and NOAA-20 spacecraft. Among the 22 VIIRS spectral bands, there are seven thermal emissive bands (TEB) covering the 3.7 to 12.2 µm spectral range at two different spatial resolutions. The VIIRS TEB detectors are calibrated onboard using a blackbody at a controlled temperature and a space view for background signal measurement. In this paper, we discuss the on-orbit performance of the N21 VIIRS TEB using various parameters such as the detector gain, noise, and offset. This on-orbit assessment provides critical insights into instrument behavior and calibration performance that will inform pre-launch testing and post-launch validation strategies for the upcoming JPSS-4 VIIRS mission scheduled for launch in 2027. The methodologies and lessons learned from characterizing the N21 TEB performance will enable more efficient and comprehensive radiometric assessment of the next VIIRS instrument. Specific focus is placed on identifying performance trends and anomaly signatures observed during the N21 commissioning phase that can enhance the JPSS4 instrument checkout procedures and accelerate the transition to routine on-orbit operations. The established performance baselines and uncertainty estimates will serve as an acceptance criterion for the JPSS-4 commissioning activities, ensuring mission readiness and data continuity for operational users.

Amit Angal↗
DOE OSTI2026

Heat metrics and thresholds reshape population exposure and inequality signals

Extreme heat is intensifying worldwide, yet estimates of heat hazard and exposure inequality depend on both the heat metric and how extreme days are defined. Using summer 2022 across the Mediterranean, we quantify population heat exposure with four metrics—land surface temperature (LST), air temperature (Ta), heat index (HI), and wet-bulb globe temperature (WBGT)—under absolute (fixed-value) and relative (anomaly-based) thresholds. Under absolute thresholds, total heat exposure differs by more than two orders of magnitude across metrics (31.3 billion person-days for Ta vs 0.3 billion for HI). Geographic hotspots also diverge: WBGT concentrates in humid coastal North Africa (e.g. the Nile Delta), whereas Ta and LST are more widespread. Under relative thresholds, exposure totals converge and cross-metric hotspot agreement increases (e.g. Ta–WBGT top-tercile overlap increases from 10.7% to 29.0%), shifting hotspots toward densely populated southern Europe. Crucially, the exposure–deprivation relationship also reverses across threshold frameworks: absolute thresholds concentrate exposure in more deprived North Africa and the Middle East, whereas relative thresholds shift the burden toward less-deprived European cities. This sensitivity is decision-relevant: city rankings based on WBGT exposure duration are almost completely reordered when switching threshold frameworks. Threshold choice therefore systematically reshapes hotspot patterns and inequality signals. Reporting both absolute and relative exposures can reveal hidden hotspots and support more targeted heat-risk monitoring and intervention planning.

Mediterranean↗
DOE OSTI2026

Laser-heated diamond anvil cell synthesis and recovery of metastable MnSb 2 and YbZn 2 for post-synthesis transport studies

The creation and exploration of new materials under extreme pressure–temperature conditions has become increasingly reliant on laser-heated diamond anvil cell (LHDAC) techniques, which provide direct access to previously unexplored regions of multinary phase diagrams. Whereas numerous high-pressure phases have been identified in situ, systematic recovery and post-synthesis physical property characterization of these materials remain significant challenges. In this work, we describe the setup and implementation of an LHDAC-based synthesis and recovery workflow and demonstrate its application to metastable MnSb 2 and YbZn 2 phases. Synchrotron x-ray diffraction and spatial mapping confirm dominant formation of the targeted phases, whereas laboratory-based refinement quantifies phase fractions despite intrinsic microstrain and minor secondary phases. High-pressure transport measurements on recovered samples reveal pressure-tunable electronic instabilities in both systems. In MnSb 2 , pressure suppresses two high-temperature magnetic ordering anomalies, observed in transport, by ∼5 GPa and, for higher pressures, induces a new low-temperature feature that increases with further pressure increase. In hexagonal high-pressure YbZn 2 , an electronic reconstruction emerges at ∼11 GPa, characterized by semiconducting-like behavior from ∼30 to 300 K and a broad low-temperature coherence crossover near 30 K. Our results establish LHDAC synthesis not only as a structural discovery tool but also as an experimental platform for investigating correlated quantum states stabilized far from equilibrium thermodynamic conditions.

Huyan, S. [Iowa State University, Ames, IA (United↗
NASA NTRSDate not supplied

Revisiting the Soyuz-1 Parachute Failure in the Context of Safety in the Modern Era

The Soyuz‑1 accident remains one of the most consequential parachute related failures in human spaceflight history and provides enduring lessons for modern Entry, Descent, and Landing (EDL) system design. Occurring during the height of the Cold War and the Space Race, the mission unfolded under extraordinary political and schedule pressure as the Soviet Union sought to maintain its early leadership in space achievements following the death of chief designer Sergei Korolev. Despite unresolved propulsion, electrical, and parachute system deficiencies, Soyuz‑1 proceeded to launch and immediately encountered critical inflight anomalies, including a failed solar panel deployment, attitude control issues, and communication dropouts. Upon reentry, a malfunction in the parachute system, driven by a primary main canopy that failed to deploy, and subsequent entanglement of the reserve main canopy with the primary drogue parachute, resulted in insufficient deceleration and the fatal crash of cosmonaut Vladimir Komarov. Subsequent investigations revealed deep rooted cultural and organizational issues within the Soviet space program, including inadequate testing, suppression of dissent, undocumented last minute design changes, and the absence of integrated parachute system verification. More than 200 design flaws were identified after the accident, and firsthand accounts, including those from Yuri Gagarin, highlighted widespread concern prior to launch. Over time, the Soviet program implemented substantial reforms: systematic design corrections, rigorous process documentation, and an extensive series of drop tests that ultimately transformed the Soyuz system into one of the world’s most reliable human-rated return vehicles. This paper examines the technical architecture of the Soyuz‑1 parachute system, reconstructs the likely deployment sequence and failure mechanism, and analyzes the cultural contributors that shaped the accident. The study draws parallels to modern spacecraft parachute development, emphasizing the critical importance of integrated system testing, transparent engineering culture, and continuous hardware surveillance. These lessons remain directly relevant to today’s NASA and Commercial Crew Programs (CCP), where the Government continues to refine its understanding of aggregate risk and strengthen overall astronaut safety in the face of increasingly complex parachute systems.

Aaron L Morris↗
DOE OSTI2026

RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.

Ghawaly, James M. [Division of Computer Science an↗
NASA NTRS2026

Timing the Flames: Geostationary Satellite Detection of Diurnally Shifting Stubble Burning in Northwestern India

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts.

post-monsoon stubble burning; northwestern India;↗
NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗