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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Three-dimensional Ising ferrimagnetism of Cr-Fe-Cr trimers in FeCr2Te4

We carried out a comprehensive study of magnetic critical behavior in single crystals of ternary chalcogenide Fe Cr 2 Te 4 that undergoes a ferrimagnetic transition below T c ~ 123 K. Detailed critical behavior analysis and scaled magnetic entropy change indicate a second-order ferrimagnetic transition. Critical exponents β = 0.30 ( 1 ) with T c = 122.4 ( 5 ) K , γ = 1.22 ( 1 ) with T c = 122.8 ( 1 ) K , and δ = 4.24 ( 2 ) at T c ~ 123 K suggest that the spins approach the three-dimensional Ising ( β = 0.325 , γ = 1.24 , and δ = 4.82 ) model coupled with attractive long-range interactions between spins that decay as J ( r ) ≈ r - 4.88 . Our results suggest that the ferrimagnetism in Fe Cr 2 Te 4 is due to itinerant ferromagnetism among the antiferromagnetically coupled Cr-Fe-Cr trimers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Single crystal growth and thermoelectric properties of Nowotny chimney-ladder compound Fe 2 Ge 3

Fe 2 Ge 3 with an incommensurate Nowotny chimney-ladder (NCL) structure is a promising material for thermoelectric applications due to its low thermal conductivity. Previous experimental studies on Fe 2 Ge 3 have mainly focused on polycrystalline samples, resulting in a limited understanding of the material's intrinsic thermoelectric properties and the underlying causes of its low thermal conductivity. Here we report the synthesis and thermoelectric properties of single crystalline Fe 2 Ge 3 . Millimeter-sized Fe 2 Ge 3 single crystals grown by the chemical vapor transport method enable the study of the intrinsic thermoelectric properties. The Seebeck coefficient of Fe 2 Ge 3 is negative and its magnitude increases linearly with temperature, showing a degenerate n-type semiconductor behavior. Analysis of the electrical resistivity and specific heat data indicates the existence of an Einstein mode with a characteristic temperature of about 60 K, suggesting the presence of low-energy optical phonons. Further, the thermal conductivity of Fe 2 Ge 3 along the c axis is as low as 1.9Wm -1 K -1 at 300 K and exhibits a nearly temperature-independent characteristic, which is distinct from the previous theoretical calculations with a stronger temperature dependence. The low thermal conductivity may be attributed to the scattering of acoustic phonons by low-energy optical modes and the presence of non-extended diffuson modes, as reported in another NCL compound, MnSi 1.74 . This study provides valuable insights into the electrical and thermal properties of Fe 2 Ge 3 , which can open up possibilities for future advances in thermoelectric applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identifying Light-Duty Vehicle Travel from Large-Scale Multimodal Wearable GPS Data with Novelty Detection Algorithms

Identifying travel mode within travel survey data sets, especially light-duty vehicle (LDV) travel, is foundational, though nontrivial, to travel behavior analysis and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. They are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data requirements. This paper proposes an LDV travel mode detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique-one-class support vector machines (OCSVMs)-and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Real-time well integrity monitoring in underground gas storage wells using distributed temperature and strain sensing: a field demonstration

Here, this article presents the first successful field demonstration of a combined distributed temperature and strain sensing (DTSS) system installed directly on newly replaced tubing in a 5400-ft-deep operational underground gas storage well. The DTSS system uses a single optical fiber to monitor temperature and strain in real-time, providing a cost-effective solution for long-term well integrity assessment. In this study, the strain–stress correlation of the tubing—representative of material behavior analysis—is investigated as a potential method for monitoring tubing integrity throughout its lifetime. Moreover, the DTSS system’s capability to support both continuous and discrete monitoring is evaluated by comparing future data with historical records, enabling the early detection of issues such as material fatigue, corrosion, or deformation. Overall, the work examines the effectiveness and scalability of the DTSS system for real-time monitoring of well operations and integrity in a newly replaced well.

Distributed Strain Sensing↗
DOE OSTI2022

Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification

Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis and other applications, helping enable continual learning in the field. Conventional methods of OOD detection perform multi-variate analysis on an ensemble of data or features, and usually resort to the supervision with OOD data to improve the accuracy. In reality, such supervision is impractical as one cannot anticipate the anomalous data. In this paper, we propose a novel, self-supervised approach that does not rely on any pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of the gradients between the in-distribution and OOD data. (2) It is assisted by a self-supervised binary classifier to guide the label selection to generate the gradients, and maximize the Mahalanobis distance. In the evaluation with multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and TinyImageNet, the proposed approach consistently outperforms state-of-the-art supervised and unsupervised methods in the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) metrics. We further demonstrate that this detector is able to accurately learn one OOD class in continual learning.

Sun, Jingbo↗
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1970

On coupled systems

Coupled systems stability and oscillatory behavior analysis using Liapunov theory

Aggarwal, J. K.↗
NASA NTRS1972

Investigation of the effect of vacuum environment on the fatigue and fracture behavior of 7075-T6.

Axial-load fatigue-life, fatigue-crack propagation, and fracture-toughness experiments were conducted on sheet specimens made of 7075-T6 aluminum alloy. These experiments were conducted at air pressures ranging from 101 kN/sq m to 7 micronewtons/sq m to determine the effect of air pressure on fatigue behavior. Analysis of the results from the fatigue-life experiments indicated that for a given stress level, the lower the air pressure was the longer the fatigue life. At a pressure of 7 micronewtons/sq m, fatigue lives were 15 to 30 times longer than at 101 kN/sq m. Analysis of the results from the fatigue-crack-growth experiments indicates that at low values of stress-intensity range, the fatigue-crack-growth rates were approximately twice as high at atmospheric pressure as in vacuum. However, at higher values of stress-intensity range, the fatigue-crack-growth rates were nominally the same in vacuum and at atmospheric pressure.

Hudson, C. M.↗
NASA NTRS1974

Comparative magnetic studies of some Apollo 17 rocks and soils and their implications

The lunar rock and soil specimens studied include samples of the orange and gray soil strata from adjancent sites on the South rim of Shorty Crater, two vesicular mare basalts, and a mare basalt of the dense aphanitic type. The magnetic characteristics of the soils are discussed along with a hysteresis behavior analysis of Apollo 17 rocks. The remanent properties are examined, giving attention to viscous remanence, NRM stability, saturation isothermal remanence, and heating experiments.

Brecher, A.↗
NASA NTRS1989

Penning type of ionizing energy transfer collisions in a Hg-Ar discharge detected by the optogalvanic effect

The temporal evolution of the optogalvanic signals has been investigated in detail in a Hg-argon discharge by irradiating it with an excimer pumped dye laser. The signals at 4510.7, 4596 and 4628.4 A exhibited anomalous behavior. Analysis of the data revealed that the excited Ar and Hg atoms respectively in the 1P1 and 3P1 states participated in energy transfer collisions, causing atomic mercury to excite to an ionized energy state, while simultaneously the argon atom relaxes to its ground state.

Reddy, B. R.↗
NASA NTRS1951

A small-deflection theory for curved sandwich plates

A small-deflection theory that takes into account deformations due to transverse shear is presented for the elastic-behavior analysis of orthotropic plates of constant cylindrical curvature, with consideration of buckling included. The theory is applicable primarily to sandwich construction. (author)

Stein, Manuel↗
NASA NTRS2001

Fabrication and Testing of Ceramic Matrix Composite Rocket Propulsion Components

NASA has established goals for Second and Third Generation Reusable Launch Vehicles. Emphasis has been placed on significantly improving safety and decreasing the cost of transporting payloads to orbit. Ceramic matrix composites (CMC) components are being developed by NASA to enable significant increases in safety and engineer performance, while reducing costs. The development of the following CMC components are being pursued by NASA: (1) Simplex CMC Blisk; (2) Cooled CMC Nozzle Ramps; (3) Cooled CMC Thrust Chambers; and (4) CMC Gas Generator. These development efforts are application oriented, but have a strong underpinning of fundamental understanding of processing-microstructure-property relationships relative to structural analyses, nondestructive characterization, and material behavior analysis at the coupon and component and system operation levels. As each effort matures, emphasis will be placed on optimizing and demonstrating material/component durability, ideally using a combined Building Block Approach and Build and Bust Approach.

Effinger, M. R.↗
NASA NTRS2002

Examining the Pilot and Controller Performance Data When in a Free Flight with Weather Phenomenon

The present study investigated effects of weather related factors on the performance of pilots under free flight. A weather scenario was defined by a combination of precipitation factors (light rain, moderate rain, and heavy rain or snow), visibility (1,4,8 miles), wind conditions (light, medium, or heavy), cloud ceiling (800ft. below, 1800ft above, and 4000ft horizontal). The performance of the aircraft self-separation was evaluated in terms of detection accuracy and detection times for student- and commercial (expert) pilots. Overall, the results obtained from a behavioral analysis showed that in general, the ability to recognize intruder aircraft conflict incidents, followed by the ability to acquire the spatial location of the intruder aircraft relative to ownership aircraft were judged to be the major cognitive tasks as perceived by the participants during self-separation. Further, the participants rarely used cockpit display of traffic information (CDTI) during conflict management related to aircraft separation, but used CDTI highly during decision-making tasks. In all weather scenarios, there were remarkable differences between expert and student pilots in detection times. In summary, weather scenarios were observed to affect intruder aircraft detection performance accuracies. There was interaction effects between weather Scenario-1 and Scenario-2 for climbing task data generated by both expert- and student- pilots at high traffic density. Scenario-3 weather condition provided an opportunity for poor detection accuracy as well as detection time increase. This may be attributed to low visibility. The intruder aircraft detection times were not affected by the weather conditions during climbing and descending tasks. The decision of pilots to fly into certain weather condition was dependent in part on the warning distance to the location of the weather. When pilots were warned of the weather conditions, they were more likely to fly their aircraft into it, but mostly when the warning was not close to the weather location.

Nituen, Celestine A.↗
NASA NTRS2015

NASA's Rodent Research Project: Validation of Flight Hardware, Operations and Science Capabilities for Conducting Long Duration Experiments in Space

Research using rodents is an essential tool for advancing biomedical research on Earth and in space. Rodent Research (RR)-1 was conducted to validate flight hardware, operations, and science capabilities that were developed at the NASA Ames Research Center. Twenty C57BL/6J adult female mice were launched on Sept 21, 2014 in a Dragon Capsule (SpaceX-4), then transferred to the ISS for a total time of 21-22 days (10 commercial mice) or 37 (10 validation mice). Tissues collected on-orbit were either rapidly frozen or preserved in RNA later at less than or equal to -80 C (n=2/group) until their return to Earth. Remaining carcasses were rapidly frozen for dissection post-flight. The three controls groups at Kennedy Space Center consisted of: Basal mice euthanized at the time of launch, Vivarium controls, housed in standard cages, and Ground Controls (GC), housed in flight hardware within an environmental chamber. FLT mice appeared more physically active on-orbit than GC, and behavior analysis are in progress. Upon return to Earth, there were no differences in body weights between FLT and GC at the end of the 37 days in space. RNA was of high quality (RIN greater than 8.5). Liver enzyme activity levels of FLT mice and all control mice were similar in magnitude to those of the samples that were optimally processed in the laboratory. Liver samples collected from the intact frozen FLT carcasses had RNA RIN of 7.27 +/- 0.52, which was lower than that of the samples processed on-orbit, but similar to those obtained from the control group intact carcasses. Nonetheless, the RNA samples from the intact carcasses were acceptable for the most demanding transcriptomic analyses. Adrenal glands, thymus and spleen (organs associated with stress response) showed no significant difference in weights between FLT and GC. Enzymatic activity was also not significantly different. Over 3,000 tissues collected from the four groups of mice have become available for the Biospecimen Sharing Program. Together, these validation flight findings demonstrate the capability to support long-duration RR on the ISS to achieve both basic science and biomedical objectives.

rodents↗
NASA NTRS2016

Hemispheric Differences in Tropical Lower Stratospheric Transport and Tracers Annual Cycle

Transport of long-lived tracers (such as O, CO, and N O) in the lower stratosphere largely determines the composition of the entire stratosphere. Stratospheric transport includes the mean residual circulation (with air rising in the tropics and sinking in the polar and middle latitudes), plus two-way isentropic (quasi-horizontal) mixing by eddies. However, the relative importance of two transport components remains uncertain. Previous studies quantified the relative role of these processes based on tropics-wide average characteristics under common assumption of well-mixed tropics. However, multiple instruments provide us with evidence that show significant differences in the seasonal cycle of ozone between the Northern (0-20N) and Southern (0-20S) tropical (NT and ST respectively) lower stratosphere. In this study we investigate these differences in tracer seasonality and quantify transport processes affecting tracers annual cycle amplitude using simulations from Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and Whole Atmosphere Community Climate Model (WACCM) and compare them to observations from the Microwave Limb Sounder (MLS) on the Aura satellite. We detect the observed contrast between the ST and NT in GEOSCCM and WACCM: annual cycle in ozone and other chemical tracers is larger in the NT than in the ST but opposite is true for the annual cycle in vertical advection. Ozone budgets in the models, analyzed based on the Transformed Eulerian Mean (TEM) framework, demonstrate a major role of quasi-horizontal mixing vertical advection in determining the NTST ozone distribution and behavior. Analysis of zonal variations in the NT and ST ozone annual cycles further suggests important role of North American and Asian Summer Monsoons (associated with strong isentropic mixing) on the lower stratospheric ozone in the NT. Furthermore, multi model comparison shows that most CCMs reproduce the observed characteristic of ozone annual cycle quite well. Thus, latitudinal variations within the tropics have to be considered in order to understand the balance between upwelling and quasi- horizontal mixing in the tropical lower stratosphere and the paradigm of well mixed tropics has to be reconsidered.

Chemistry-Climate Models↗