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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 307 records · Page 17

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DOE OSTI2025

Multivariate environmental and trait-based controls of transpiration in the Central Amazon Rainforest

Tropical forest tree mortality is increasing due to more severe droughts, yet our understanding of how tree traits and life strategies are linked to drought stress has been limited by measurement scarcity. The BIONTE (BIOmass and NuTrient Experiment) near Manaus, Brazil hosts one of the world’s largest sap flow installations, with sensors in 90 canopy trees across a wood density gradient monitored since June 2022. The 2023 El Niño drought provided a unique opportunity to evaluate how water availability impacts tree transpiration. An interpretable machine learning framework was used to study the complex interactions between transpiration and multiple environmental variables such as soil water availability and vapor pressure deficit (VPD), and how these interactions vary with wood density and individual trees. We found varying responses of transpiration from different trees during the El Niño drought. Transpiration generally increased with temperature, with stronger effects in wetter areas and in trees with low to medium wood density. However, this response was modulated by stomatal sensitivity to VPD, which constrained transpiration under high atmospheric demand, particularly in intermediate-moisture area. The inflection in transpiration rate at high temperatures (>32°C) underscores the role of stomatal and hydraulic regulation in limiting water loss and protecting trees from excessive evaporative demand. Analysis of soil water contribution to transpiration revealed unimodal patterns in wetter area, with peak contributions near 0.45 cm 3 cm -3 of surface soil water and declining or flat responses beyond that threshold, suggesting a shift from water- to energy-limited transpiration. In contrast, drier areas exhibited limited transpiration sensitivity to soil water conditions and minimal trait-based variation in VPD responses, indicating supply-limited conditions. Despite higher wood density trees being generally more resilient, this study shows diverse tree drought resilience, prompting further investigation into the specific traits and dynamics between environmental variables in regulating transpiration and other physiological processes in trees.

Drought↗

DOE OSTI2025

Equation of state of boron carbide B 4 ⁢C

We present the results of recent experiments conducted on the Sandia Z machine and a new tabular equation of state for B 4 ⁢C. The equation of state was calibrated to a combination of density functional calculations reported here and fits to preexisting data. It was constructed partly to recover the effects of a shock-driven, polymorphic phase transition of unknown character beginning at particle velocities of just under 3 km/s (shock pressures of 95 GPa). Some of the Z experiments included sound speeds determined by the overtaking rarefaction method, from which we calculate the Grüneisen parameter and compare with previous experiments conducted at the OMEGA laser [Fratanduono et al ., Phys. Rev. B 94 , 184107 (2016)], our own first principles calculations, and another recent tabular equation of state [Zhang et al ., Phys. Rev. E 102 , 053203 (2020)]. We also compare our results with previous static compression, thermophysical, and melt studies, finding mixed consistency. We predict the onset and completion of shock melting at 225 and 265 GPa, respectively, and predict a melt curve that is largely flat to pressures of several hundred GPa.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Systems, Methods and Apparatus for Determining Physical Properties of Fluids

In some embodiments, systems and methods and apparatus are provided through which the equation of state is used to control a process through analyses of one or more properties of a fluid through an interactive modeler that models the equation of state for the fluid in the process based on measured signals and for selectively enabling the modeling of control changes to the process. In some embodiments, a device generates an indication of machine health based on variations on the equation of state for a fluid in a machine. In some embodiments, one or more properties for the fluid from at least one unmeasured machine parameter in the interactive modeler are determined for the machine at various operating states. In some embodiments, a difference between an expected one or more properties of the fluid beyond a set point indicates the health of the machine

Butas, John P.↗

DOE OSTI2024

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

DelloStritto, Mark (ORCID:0000000206785860)↗

DOE OSTI2025

Popnet : computer vision based deep learning model for forecasting gridded population

Here, this study introduces Popnet, a deep learning model for forecasting 1 km-gridded populations, integrating U-Net, ConvLSTM, a Spatial Autocorrelation module and deep ensemble methods. Using spatial variables and population data from 2000 to 2020, Popnet predicts South Korea’s population trends by age groups (under 14, 15-64 and over 65) up to 2040. In validation, it outperforms traditional machine learning and state-of-the-art computer vision models. The output of this model discovered significant polarisation: population growth in urban areas, especially the capital region, and severe depopulation in rural areas. Popnet is a robust tool for offering significant insights to policymakers and related stakeholders about the detailed future population, which allows them to establish detailed, localised planning and resource allocations.

computer vision↗

DOE OSTI2026

Estimating Residual Gas Beam Loss in the BNL AGS Booster:Model, Numerical Tools, and Implications for a Polarimeter Chamber

Beamloss from residual gas scattering is calculated for a storage ring of circumference C = 200m, covering a linear magnetic rigidity ramp from B ρ inj = 1.25Tm to B ρ ext = 9.50Tm over tramp = 1.00s. Three loss mechanisms are considered: nuclear inelastic reactions, single Coulomb scattering beyond the acceptance angle of the machine, and charge-state exchange for heavy ions. Calculations are performed for protons and Au 32+ ions, the latter being the charge state delivered by the EBIS pre-injector into the BNL AGS Booster. At a baseline vacuum of P 0 = 5 × 10 −11 mbar the integrated fractional beam loss over the full ramp is negligible for protons (< 0.001%) and 33.1% for Au 32+ . The impact of a 1m polarimeter section at elevated pressure P s = 5 × 10 −9 mbar is evaluated: the proton loss remains negligible, while the Au 32+ loss rises to 45.1%, establishing the vacuum requirement for the polarimeter chamber. In the present compact approximate model, Au 32+ losses are dominated by charge exchange at injection, while proton losses are dominated by single Coulomb scattering. The design, outgassing budget, pumping strategy, and commissioning sequence for a UHV polarimeter chamber housing silicon detectors and ASICs are described in detail.

43 PARTICLE ACCELERATORS↗

NASA NTRS1980

Electric vehicle traction motors - The development of an advanced motor concept

An axial-field permanent magnet traction motor is described, similar to several advanced motors that are being developed in the United States. This type of machine has several advantages over conventional dc motors, particularly in the electric vehicle application. The rapidly changing cost of magnetic materials, particularly cobalt, makes it important to study the utilization of permanent magnet materials in such machines. The impact of different magnets on machine design is evaluated, and the advantages of using iron powder composites in the armature are assessed.

Campbell, P.↗

NASA NTRS1981

Multi-channel chopper system for a total ozone mapping spectrometer

A multi-channel chopper system designed and built to stringent specifications is providing an excellent performance for a total ozone mapping spectrometer (TOMS). State of the art machining technology, suitable material selection, and a way to hold and position the slit plate resulted in the instrument's better than expected performance. A shutter method used for internal calibration allows compensation for the occurrence of an unlikely wavelength shift during testing, launch, or during the orbiting life of the instrument. The TOMS is part of a payload on Nimbus 7 launched on October 24, 1978.

Krueger, A. J.↗

NASA NTRS1992

Manufacture and performance of carbon/epoxy 3-D woven composites

This paper evaluates 3-D orthogonal woven carbon/epoxy composites. Preforms were manufactured on an automatic 3-D weaving machine developed at N.C. State University. Matrix infiltration was conducted at MBB Central Laboratories. Testing was carried out at both locations and the joint results will be reported. The properties investigated include: interlaminar shear strength, compression, compression after impact, bending, tensile and penetration resistance. The 3-D orthogonal woven composites were compared with laminated and other 3-D composites made with preforms having interlock structure. C-scans were used to examine the quality of infiltration and the damage area after impact. The performance of the composites made from the 3-D orthogonal preforms showed superior properties compared to the other composites. The penetration resistance test showed unexpectedly very good performance.

Brandt, J.↗

NASA NTRS1995

Thermal barrier coatings issues in advanced land-based gas turbines

The Department of Energy's Advanced Turbine System (ATS) program is aimed at forecasting the development of a new generation of land-based gas turbine systems with overall efficiencies significantly beyond those of current state-of-the-art machines, as well as greatly increased times between inspection and refurbishment, improved environmental impact, and decreased cost. The proposed duty cycle of ATS turbines will require the use of different criteria in the design of the materials for the critical hot gas path components. In particular, thermal barrier coatings will be an essential feature of the hot gas path components in these machines. While such coatings are routinely used in high-performance aircraft engines and are becoming established in land-based turbines, the requirements of the ATS turbine application are sufficiently different that significant improvements in thermal barrier coating technology will be necessary. In particular, it appears that thermal barrier coatings will have to function on all airfoil sections of the first stage vanes and blades to provide the significant temperature reduction required. In contrast, such coatings applied to the blades and vances of advanced aircraft engines are intended primarily to reduce air cooling requirements and extend component lifetime; failure of those coatings can be tolerated without jeopardizing mechanical or corrosion performance. A major difference is that in ATS turbines these components will be totally reliant on thermal barrier coatings which will, therefore, need to be highly reliable even over the leading edges of first stage blades. Obviously, the ATS program provides a very challenging opportunity for TBC's, and involves some significant opportunities to extend this technology.

Parks, W. P.↗

NASA NTRS1995

Thermal barrier coatings issues in advanced land-based gas turbines

The Department of Energy's Advanced Turbine Systems (ATS) program is aimed at fostering the development of a new generation of land-based gas turbine systems with overall efficiencies significantly beyond those of current state-of-the-art machines, as well as greatly increased times between inspection and refurbishment, improved environmental impact, and decreased cost. The proposed duty cycle of ATS machines will emphasize different criteria in the selection of materials for the critical components. In particular, thermal barrier coatings (TBC's) will be an essential feature of the hot gas path components in these machines. In fact, the goals of the ATS will require significant improvements in TBC technology, since these turbines will be totally reliant on TBC's, which will be required to function on critical components such as the first stage vanes and blades for times considerably in excess of those experienced in current applications. Issues that assume increased importance are the mechanical and chemical stability of the ceramic layer and of the metallic bond coat; the thermal expansion characteristics and compliance of the ceramic layer; and the thermal conductivity across the thickness of the ceramic layer. Obviously, the ATS program provides a very challenging opportunity for TBC's, and involves some significant opportunities to extend this technology. A significant TBC development effort is planned in the ATS program which will address these key issues.

Parks, William P.↗

NASA NTRS2011

Imbalanced Learning for Functional State Assessment

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Li, Feng↗

NASA NTRSDate not supplied

Phenomena Portal: Large- Scale Visual Exploration of Atmospheric Phenomena

The Earth science community is experiencing a high influx of remote sensing data due to recent advancements in sensor technology. This enables the community to extend their research on a larger scale than ever before. Unfortunately, traditional data processing techniques do not scale well to these new, high volume data sources. State-of-the-art machine learning (ML) pipelines have been proven to overcome these burdens in various other fields but are underexploited within the physical sciences community. Moreover, ML is reliant on labeled data, which is currently sparsely available, owing to the fact that ML adoption is still in the early stages within the Earth and atmospheric science communities. To address these issues, we developed the Phenomena Portal, a visual exploration tool that uses ML to detect various atmospheric phenomena on a global scale. This allows the Earth and atmospheric science communities to view trends of occurrences of phenomena, identify potential relationships between them, and analyze spatiotemporal patterns over time. These detections can also serve as initial labeled data for ML research pertaining to the respective phenomena. The tool also incorporates feedback from subject matter experts to further improve the model detection accuracy, thereby facilitating human-in-the-loop. This presentation will provide an overview of the ML model development and cloud deployment. We also discuss the capabilities of the user interface for displaying the detections.

Muthukumaran Ramasubramanian↗

NASA NTRS2020

Object and Gas Source Detection with Robotic Platforms in Perceptually-Degraded Environments

In exploration-oriented robotic missions for disaster relief in unknown subterranean environments, it is of prime importance for a human supervisor to rapidly gain situational awareness of salient objects within the environment. In this paper we present an automated object detection pipeline that is adaptable to heterogeneous robots with arbitrary sensor configurations. It has been deployed in time-critical scenarios with multiple collaborative robots in a variety of demanding underground environments. For visually observable objects, detections are made in both the visible and thermal spectra using a state-of-the-art machine learning framework for object detection and classification. Our pipeline can be rapidly adapted to a specific task by using a small, structured dataset to fine-tune a pre-trained convolutional neural network (CNN). Relative localization is separated from the CNN for speed of operation. A robust architecture for localization is used with outlier rejection and a hierarchy of fall-back distance measurement methods. Point-source objects such as gas and WiFi hotspots can also be detected, by tracking signal strength over time and presenting an intuitive visualization on a map. Observations of each object types are presented to the operator in ranked confidence order for final evaluation.

Agha-mohammadi, Ali-akbar↗

NASA NTRS2022

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

NASA NTRSDate not supplied

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

Natural Language Processing↗

DOE OSTI2024

ML Classifier Fusion for Three Data Streams with Quality Inversely Proportional to Time Resolution

We consider a monitoring scenario of phenomenon using three different streams of measurements whose quality is proportional to their constant inter-arrival times. Each measurement of a stream needs to be binary-classified to reflect the state of interest of the phenomenon. A set of classifiers is separately trained and fused for each stream at its time resolution using measurements collected under known states. We present a machine learning method to fuse the outputs of these fusers to provide a final classification at the finest time resolution. We show that this fused-fusers method provides decisions with likely superior classification probability compared to the best individual classifiers and fused-classifiers. We derive generalization equations that guarantee a superior classification probability of fused-fusers with a confidence probability specified by the classifiers’ generalization equations. We apply these results to study a practical problem of classifying Pu/Np target dissolution events at a radiochemical processing facility using gamma spectral measurements of effluent flows.

Rao, Nageswara↗

DOE OSTI2025

REIMAGINING HEAT EXCHANGERS FOR NEXT GENERATION ENVIRONMENTAL SYSTEMS

Air-to-refrigerant heat exchangers (HXs) are essential components in space conditioning, refrigeration, and power systems, and recent efforts have focused on making these devices more compact, reducing refrigerant charge and lowering manufacturing costs. Historically, HX innovation has been limited by available computational resources, design tools, and manufacturing constraints. The best available technologies utilize tube-fin and micro- or macro-channel tubes with fins, which are not necessarily the optimal designs achievable with current technology. In this paper, we highlight the latest advancements in air-to-refrigerant HXs, specifically emphasizing innovations achieved through shape and topology optimization. A multi-scale design optimization approach is introduced, alongside similar methods in literature, which enable highly sophisticated shape-optimized tube designs with more than 50% reduction in size and 25% reduction in refrigerant charge, essential for A3 refrigerant charge limit compliance. The frameworks integrate traditional heat and mass transfer science with state-of-the-art machine learning, genetic algorithms, and adjoint algorithms to create novel designs. While many of these innovative designs may not be manufacturable using conventional methods, they allow us explore the boundaries of what is possible. These novel air-to-refrigerant HXs are key enablers for ultra-low-refrigerant charge heat pump and refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗