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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Horizontal axis wind turbine post stall airfoil characteristics synthesization

Blade-element/momentum performance prediction codes are routinely used for wind turbine design and analysis. A weakness of these codes is their inability to consistently predict peak power upon which the machine structural design and cost are strongly dependent. The purpose of this study was to compare post-stall airfoil characteristics synthesization theory to a systematically acquired wind tunnel data set in which the effects of aspect ratio, airfoil thickness, and Reynolds number were investigated. The results of this comparison identified discrepancies between current theory and the wind tunnel data which could not be resolved. Other factors not previously investigated may account for these discrepancies and have a significant effect on peak power prediction.

Tangler, James L.↗
DOE OSTI2025

Identification of short-range ordering motifs in semiconductors

Chemical short-range ordering is expected to be a key factor for tuning the electronic structure of semiconductors. However, experimental evidence of short-range ordering is still lacking due to the challenge of characterizing atomic-scale ordering motifs. Here, we determined the presence of short-range order in a ternary GeSiSn semiconductor system using advanced energy-filtered four-dimensional scanning transmission electron microscopy and large-scale atomistic models generated by a machine learning neuroevolution potential of first-principles accuracy. This approach revealed preferred ordering of different atomic species with the dominant occurrence of Si–Ge–Sn triplets. Our findings not only confirmed the presence of short-range order but also directly revealed the actual atomic structure, demonstrating the potential for informed atomic order–based band engineering as a third degree of freedom beyond composition and strain tuning.

Vogl, Lilian M. [University of California Berkeley↗
NASA NTRS1985

Human factors of intelligent computer aided display design

Design concepts for a decision support system being studied at NASA Langley as an aid to visual display unit (VDU) designers are described. Ideally, human factors should be taken into account by VDU designers. In reality, although the human factors database on VDUs is small, such systems must be constantly developed. Human factors are therefore a secondary consideration. An expert system will thus serve mainly in an advisory capacity. Functions can include facilitating the design process by shortening the time to generate and alter drawings, enhancing the capability of breaking design requirements down into simpler functions, and providing visual displays equivalent to the final product. The VDU system could also discriminate, and display the difference, between designer decisions and machine inferences. The system could also aid in analyzing the effects of designer choices on future options and in ennunciating when there are data available on a design selections.

Hunt, R. M.↗
NASA NTRS1937

An analysis of the factors that determine the periodic twist of an autogiro rotor blade, with a comparison of predicted and measured results

Report presents an analysis of the factors that determine the periodic twist of a rotor blade under the action of the air forces on it. The results of the analysis show that the Fourier coefficients of the twist are linear expressions involving only the tip-speed ratio, the pitch setting, the inflow coefficient, the pitching-moment coefficient of the blade airfoil section, and the physical characteristics of the rotor blade and machine. The validity of the analysis was examined by using it to predict the twist of a rotor whose twist characteristics had previously been measured in flight. The agreement between the calculated and experimental results was satisfactory.

Wheatley, John B↗
NASA NTRS1994

Activity Catalog Tool (ACT) user manual, version 2.0

This report comprises the user manual for version 2.0 of the Activity Catalog Tool (ACT) software program, developed by Leon D. Segal and Anthony D. Andre in cooperation with NASA Ames Aerospace Human Factors Research Division, FLR branch. ACT is a software tool for recording and analyzing sequences of activity over time that runs on the Macintosh platform. It was designed as an aid for professionals who are interested in observing and understanding human behavior in field settings, or from video or audio recordings of the same. Specifically, the program is aimed at two primary areas of interest: human-machine interactions and interactions between humans. The program provides a means by which an observer can record an observed sequence of events, logging such parameters as frequency and duration of particular events. The program goes further by providing the user with a quantified description of the observed sequence, through application of a basic set of statistical routines, and enables merging and appending of several files and more extensive analysis of the resultant data.

Segal, Leon D.↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Radiation induced non-linear oscillations in ITER baseline scenario plasmas in DIII-D

Abstract This work shows how the radiation brought about by metals or metal-equivalent radiators such as Kr and Xe produces non-linear dynamics on otherwise stationary β N flattops of DIII-D ITER Baseline Scenario demonstration discharges. The Kr and Xe gases are used to reproduce the radiative loss rates of W in present machines that operate at core temperatures much lower than the expected ITER temperature. Experiments on DIII-D with injection of Kr and Xe, as well as with sources of intrinsic metals reach the range of radiated fraction values expected in the ITER core and experience slow oscillations in temperature and radiated power. In many cases of high radiated fraction, the core temperature decreases enough for the safety factor profile to rise above the 1/1 rational surface, naturally eliminating sawteeth and occasionally producing a persistent helical core. The oscillations can be reproduced by a modified Lotka–Volterra system for temperature and radiated fraction if diffusion and noise are included, which indicates that the interplay between temperature and radiation can be the main cause of the cyclic nature of the system. A new physics based model which includes equations for temperature, density and input power can also reproduce the oscillations observed in the experiments. The present results suggest that the non-linearity of the system can be increased by the inclusion of the inherently non-linear alpha heating term, which is proportional to ∼ n e 2 T i 2 , and obtains oscillations in the model when added to an otherwise more stationary system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗
DOE OSTI2024

Improvement and generalization of ABCD method with Bayesian inference

To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors, such as the capabilities and the performance of the accelerator and detectors, the use and exploitation of the available information, the design of search strategies and observables, as well as the proposal of new models. We focus on the use of the information and pour our effort in re-thinking the usual data-driven ABCD method to improve it and to generalize it using Bayesian Machine Learning techniques and tools. We propose that a dataset consisting of a signal and many backgrounds is well described through a mixture model. Signal, backgrounds and their relative fractions in the sample can be well extracted by exploiting the prior knowledge and the dependence between the different observables at the event-by-event level with Bayesian tools. We show how, in contrast to the ABCD method, one can take advantage of understanding some properties of the different backgrounds and of having more than two independent observables to measure in each event. In addition, instead of regions defined through hard cuts, the Bayesian framework uses the information of continuous distribution to obtain soft-assignments of the events which are statistically more robust. To compare both methods we use a toy problem inspired by pp\to hh\to b\bar b b \bar b p p → h h → b b ‾ b b ‾ , selecting a reduced and simplified number of processes and analysing the flavor of the four jets and the invariant mass of the jet-pairs, modeled with simplified distributions. Taking advantage of all this information, and starting from a combination of biased and agnostic priors, leads us to a very good posterior once we use the Bayesian framework to exploit the data and the mutual information of the observables at the event-by-event level. We show how, in this simplified model, the Bayesian framework outperforms the ABCD method sensitivity in obtaining the signal fraction in scenarios with 1% and 0.5% true signal fractions in the dataset. We also show that the method is robust against the absence of signal. We discuss potential prospects for taking this Bayesian data-driven paradigm into more realistic scenarios.

Alvarez, Ezequiel↗
NASA NTRS2010

Effects of Buckling Knockdown Factor, Internal Pressure and Material on the Design of Stiffened Cylinders

A design study was conducted to investigate the effect shell buckling knockdown factor (SBKF), internal pressure and aluminum alloy material selection on the structural weight of stiffened cylindrical shells. Two structural optimization codes were used for the design study to determine the optimum minimum-weight design for a series of design cases, and included an in-house developed genetic algorithm (GA) code and PANDA2. Each design case specified a unique set of geometry, material, knockdown factor combinations and loads. The resulting designs were examined and compared to determine the effects of SBKF, internal pressure and material selection on the acreage design weight and controlling failure mode. This design study shows that use of less conservative SBKF values, including internal pressure, and proper selection of material alloy can result in significant weight savings for stiffened cylinders. In particular, buckling-critical cylinders with integrally machined stiffener construction can benefit from the use of thicker plate material that enables taller stiffeners, even when the stiffness, strength and density properties of these materials appear to be inferior.

Lovejoy, Andrew E.↗
DOE OSTI2026

Estimators and Fusers for Fiber Delay Estimation Using Environmental Measurements

The properties of deployed network fiber are affected by environmental factors due to their exposure to the elements. Particularly for quantum networks, the resultant delay variations may have significant impacts due to the extreme sensitivity of synchronization, coincidence counting, and other critical operations. In this paper, the delays of 15 km aerial-inground fiber connections are measured, and effects due to temperature, humidity and wind speed are analyzed over multiple periods spanning four seasons of a year. Machine learning methods are first utilized to reveal surprisingly pronounced effects of humidity on the delay, in addition to the expected temperature and its seasonal variations. Estimator and fusion methods are developed to estimate the delay using temperature, humidity and wind speed measurements, by utilizing smooth Gaussian Process Regression (GPR) and nonsmooth Ensemble of Trees (EOT) methods. Measurements from winter and summer periods are temporally fused using twelve different methods, and eight methods provide estimates for the delay throughout the year with median test errors under 1.28%. The results reveal distinct temperature-humidity trends across the seasons, and the ability of estimator and temporal fusion methods to exploit them for estimating the delay. These results constitute a case study of machine learning analytical results, wherein generalization equations explain the performance of various estimator and fuser methods.

Rao, Nageswara [ORNL] (ORCID:0000000234085941)↗
NASA NTRS1989

Pulse-Population Modulation For Induction Machines

Low-frequency synthesizer developed to provide low-frequency waveform by synthesis from high-frequency power system. Waveform assembled by allowing conduction of discrete half cycles of high-frequency carrier. Frequency of synthesized wave controlled by pulse pattern. By controlling relationship between slip and stator frequencies, one operates induction motor either as motor or generator. Such bidirectional energy transducer used as starter/generator for engine, or as servo-mechanism for control of acceleration and deceleration. Other advantages include operation under controlled voltage-to-frequency ratios to maintain high-efficiency and high power factor, and no reflection of low-frequency noise into 20-kHz distribution bus.

Hansen, Irving G.↗
DOE OSTI2025

Self-consistent equilibrium and transport simulations for NSTX-U plasmas enhanced via machine learning surrogate models

The Control-Oriented Transport SIMulator (COTSIM) is an advanced equilibrium and transport code designed for simulating tokamak discharges at computational speeds suitable for control applications. COTSIM’s modular framework enables users to select models that balance accuracy with speed according to specific needs, allowing the code to operate from fast to faster-than-real-time performance levels. This work presents recent enhancements to COTSIM’s predictive accuracy for NSTX-U scenarios, achieved by integrating neural-network-based surrogate models and self-consistent equilibrium calculations. To improve source deposition predictions, a surrogate model for NUBEAM has been incorporated. Additionally, a surrogate model for the Multi-Mode Module (MMM) now supports predictions of anomalous thermal, momentum, and particle diffusivities—key factors for modeling the evolution of temperature and rotation. Each surrogate model was specifically trained for the NSTX-U operational regime to enhance COTSIM’s accuracy while maintaining computational efficiency. Moreover, COTSIM now couples fixed-boundary equilibrium solvers with its transport solvers, enabling self-consistent predictions of plasma profiles and equilibrium evolution over the discharge. Simulation results demonstrate strong agreement between COTSIM and TRANSP predictions for NSTX-U discharges. These substantial advancements expand COTSIM’s utility in model-based control applications for NSTX-U. Potential applications include simultaneous optimization of equilibrium and transport scenarios, integration into digital twins, real-time profile estimation (e.g., temperature and rotation) from limited or noisy measurements, and advanced feedback-based scenario control.

Equilibrium and transport modeling↗
NASA NTRS2019

Assessing the Use of SAR/Optical Data Fusion and TensorFlow for Improved Mangrove Mapping

Mangrove forests are found in intertidal zones of tropical regions around the world and provide important ecological and economic benefits – they are considered carbon sequesters, habitats for flora and fauna, and natural barriers to hurricanes and tsunamis. Wood from mangrove forests are used as fuel and building materials in surrounding coastal communities, therefore promoting local livelihoods. Despite the importance of these ecosystems, mangrove forests have historically been degraded in natural processes such as severe weather, and anthropogenic factors like conversion to agriculture and aquaculture. This study assesses change in mangrove forests in Nigeria and Mozambique from 2015 to 2018 using SAR and optical data fusion. Due to frequent cloud cover over the study area, SAR and optical data is fused to obtain gap-free imagery without clouds. Landsat-8 OLI and Sentinel-1 imagery is fused with TensorFlow, an open source platform used in developing machine learning models. The resulting images are classified to discriminate mangrove forest cover from other land cover types, and change is estimated using image differencing. Understanding the rates and magnitude of mangrove change across space and time can aid in identifying priority areas for forest regeneration, and can help construct sustainable management practices for the future.

Strattman, Katherine↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Advanced ceramic material for high temperature turbine tip seals

Ceramic material systems are being considered for potential use as turbine blade tip gas path seals at temperatures up to 1370 1/4 C. Silicon carbide and silicon nitride structures were selected for study since an initial analysis of the problem gave these materials the greatest potential for development into a successful materials system. Segments of silicon nitride and silicon carbide materials over a range of densities, processed by various methods, a honeycomb structure of silicon nitride and ceramic blade tip inserts fabricated from both materials by hot pressing were tested singly and in combination. The evaluations included wear under simulated engine blade tip rub conditions, thermal stability, impact resistance, machinability, hot gas erosion and feasibility of fabrication into engine components. The silicon nitride honeycomb and low-density silicon carbide using a selected grain size distribution gave the most promising results as rub-tolerant shroud liners. Ceramic blade tip inserts made from hot-pressed silicon nitride gave excellent test results. Their behavior closely simulated metal tips. Wear was similar to that of metals but reduced by a factor of six.

Solomon, N. G.↗
NASA NTRS1980

Incipient Failure Detection (IFD) of antifriction ball bearings

Two methods were investigated for retrieving the ball bearing signals buried by the overall signature of an operating large engine such as the main space shuttle engine. The signal averaging method averages n** signals over a time duration equaling the known period of the ball bearing defect frequency. With this method, the desired ball bearing defect signal will be enhanced over the noise by a factor of the square root of n. A second method is to perform a further frequency analysis on the logarithmic spectrum in order to detect and separate different families of harmonics and sidebands, thereby giving information about modulating frequencies. These two alternate IFD methods were carried out with a Norland 3001 waveform and data system. The obtained results are then compared with the power spectral density (PSD) of the overall machine signals and conclusions are made accordingly.

Wang, D. Y.↗
NASA NTRS1988

Ergonomic Models of Anthropometry, Human Biomechanics and Operator-Equipment Interfaces

The Committee on Human Factors was established in October 1980 by the Commission on Behavioral and Social Sciences and Education of the National Research Council. The committee is sponsored by the Office of Naval Research, the Air Force Office of Scientific Research, the Army Research Institute for the Behavioral and Social Sciences, the National Aeronautics and Space Administration, and the National Science Foundation. The workshop discussed the following: anthropometric models; biomechanical models; human-machine interface models; and research recommendations. A 17-page bibliography is included.

Kroemer, Karl H. E.↗