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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
DOE OSTI2023

MASK4 Test Campaign for Sandia WaveBot Device

This data and report details the findings from a wave tank test focused on production of useful work of a wave energy converter (WEC) device. The experimental system and test were specifically designed to validate models for power transmission throughout the WEC system. Additionally, the validity of co-design informed changes to the power take-off (PTO) were assessed and shown to provide the expected improvements in system performance. These data describe the "MASK4" wave tank test of the Sandia WaveBot device. The WaveBot device has been tested a number of times in different permutations at the US Navy's Maneuvering and Sea Keeping (MASK) basin. Each test in this series is referred to as MASK1, MASK2, etc. The WaveBot device was first tested in one degree of freedom (heave) in 2016. This MASK1 test focused primarily on system identification and modeling. After MASK1, major modifications were performed to improve the overall real-time control and measurement system, improve the heave drive train, and add surge and pitch degrees of freedom. The second set of testing, which was broken up in to two stages: MASK2A and MASK2B, focused on bench testing and closed-loop control performance as well as nonlinear modeling. MASK3 then focused on multi-input, multi-output modeling and control for maximization of electrical power. The attached report presents the results from MASK4, which focuses on detailed modeling of the power conversion chain and validation co-design principles by way of the introduction of a magnetic spring. The test log, report, and data from the MASK4 test of the WaveBot augmented with a tunable magnetic spring. Processing codes can be found at the Github link below.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Abd, Moaed A. (ORCID:0000000284954244)↗
DOE OSTI2022

The COS Legacy Archive Spectroscopy Survey (CLASSY) Treasury Atlas

Far-ultraviolet (FUV; ~1200–2000 Å) spectra are fundamental to our understanding of star-forming galaxies, providing a unique window on massive stellar populations, chemical evolution, feedback processes, and reionization. The launch of the James Webb Space Telescope will soon usher in a new era, pushing the UV spectroscopic frontier to higher redshifts than ever before; however, its success hinges on a comprehensive understanding of the massive star populations and gas conditions that power the observed UV spectral features. This requires a level of detail that is only possible with a combination of ample wavelength coverage, signal-to-noise, spectral-resolution, and sample diversity that has not yet been achieved by any FUV spectral database. We present the Cosmic Origins Spectrograph Legacy Spectroscopic Survey (CLASSY) treasury and its first high-level science product, the CLASSY atlas. CLASSY builds on the Hubble Space Telescope (HST) archive to construct the first high-quality (S/N 1500 Å ≳ 5/resel), high-resolution (R ~ 15,000) FUV spectral database of 45 nearby (0.002 < z < 0.182) star-forming galaxies. The CLASSY atlas, available to the public via the CLASSY website, is the result of optimally extracting and coadding 170 archival+new spectra from 312 orbits of HST observations. The CLASSY sample covers a broad range of properties including stellar mass (6.2 < log M * (M ⊙ ) < 10.1), star formation rate (–2.0 < log SFR (M ⊙ yr –1 ) < +1.6), direct gas-phase metallicity (7.0 < 12+log(O/H) < 8.8), ionization (0.5 < O 32 < 38.0), reddening (0.02 < E(B – V) < 0.67), and nebular density (10 < n e (cm –3 ) < 1120). CLASSY is biased to UV-bright star-forming galaxies, resulting in a sample that is consistent with the z ~ 0 mass–metallicity relationship, but is offset to higher star formation rates by roughly 2 dex, similar to z ≳ 2 galaxies. This unique set of properties makes the CLASSY atlas the benchmark training set for star-forming galaxies across cosmic time.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Shuttle remote manipulator system mission preparation and operations

The preflight planning, analysis, procedures development, and operations support for the Space Transportation System payload deployment and retrieval missions utilizing the Shuttle Remote Manipulator System are summarized. Analysis of the normal operational loads and failure induced loads and motion are factored into all procedures. Both the astronaut flight crews and the Mission Control Center flight control teams receive considerable training for standard and mission specific operations. The real time flight control team activities are described.

Smith, Ernest E., Jr.↗
NASA NTRS1991

A reusable knowledge acquisition shell: KASH

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions.

Westphal, Christopher↗
NASA NTRS1998

MRM Evaluation Research Program

This is an interim report on the current output of the MRM evaluation research program. During 1998 this research program has used new and existing data to create an important tool for the development and improvement of "maintenance resource management" (MRM). Thousands of surveys completed by participants in airline MRM training and/or behavior change programs have, for the first time, been consolidated into a panel of "MRM Attitudes and Opinion Profiles." These profiles can be used to compare the attitudes about decision making and communication in any given company at any stage in its MRM program with attitudes of a large sample of like employees during a similar period in their MRM involvement. This panel of comparison profiles for attitudes and opinions is a tool to help audit the effectiveness of a maintenance human factors program. The profile panel is the first of several tools envisioned for applying the information accumulating in MRM databases produced as one of the program's long range objectives.

Taylor, James C.↗
NASA NTRS1999

Can practice eliminate the psychological refractory period effect?

Can people learn to perform two tasks at the same time without interference? To answer this question, the authors trained 6 participants for 36 sessions in a Psychological Refractory Period (PRP) experiment, where Task 1 required a speeded vocal response to an auditory stimulus and Task 2 required a speeded manual response to a visual stimulus. The large PRP effect found initially (353 ms in Session 1) shrank to only about 40 ms over the course of practice, disappearing entirely for 1 of the 6 participants. This reduction in the PRP effect with practice is considerably larger than has been previously reported. The obtained pattern of factor interactions between stimulus onset asynchrony and each of three task difficulty manipulations (Task 1 judgment difficulty, Task 2 stimulus contrast, and Task 2 mapping compatibility) supports a postponement (bottleneck) account of dual-task interference, both before and after practice.

Pitch Perception/physiology↗
NASA NTRS2008

Absorption in Extended Inhomogeneous Clouds

The launch of several different sensors, including CloudSat, into the A-train constellation of satellites allows us for the first time to compute absorption that can occur in realistic vertically inhomogeneous clouds including multiple cloud decks. CloudSat data show that these situations are common. Therefore, understanding vertically inhomogeneous clouds is important from both climate and satellite atmospheric composition remote sensing perspectives. Satellite passive sensors that operate from the near IR to the UV often rely on radiative cloud pressures derived from absorption in oxygen bands (A, B, gamma, or O2-O2 bands) or from rotational-Raman scattering in order to retrieve information about atmospheric trace gases. The radiative cloud pressure is distinct from the physical cloud top derived from thermal infrared measurements. Therefore, the combination of information from different passive sensors yields some information about the cloud vertical profile. When either or both the clouds or atmospheric absorbers (trace gases and aerosols) are vertically inhomogeneous, the use of an effective cloud pressure derived from these approaches may lead to errors. Here, we focus on several scenarios (deep convective clouds and distinct two layer clouds) based on realistic cloud optical depth vertical profiles derived from the CloudSatfMODIS combination. We focus on implications for trace-gas column amount retrievals (specifically ozone and NO2) and derived surface UV irradiance from the Ozone Monitoring Instrument (OMI) on the Atrain Aura platform.

Joiner, Joanna↗
NASA NTRS2010

Electrostatic Power Generation from Negatively Charged, Simulated Lunar Regolith

Research was conducted to develop an electrostatic power generator for future lunar missions that facilitate the utilization of lunar resources. The lunar surface is known to be negatively charged from the constant bombardment of electrons and protons from the solar wind. The resulting negative electrostatic charge on the dust particles, in the lunar vacuum, causes them to repel each other minimizing the potential. The result is a layer of suspended dust about one meter above the lunar surface. This phenomenon was observed by both Clementine and Surveyor spacecrafts. During the Apollo 17 lunar landing, the charged dust was a major hindrance, as it was attracted to the astronauts' spacesuits, equipment, and the lunar buggies. The dust accumulated on the spacesuits caused reduced visibility for the astronauts, and was unavoidably transported inside the spacecraft where it caused breathing irritation [1]. In the lunar vacuum, the maximum charge on the particles can be extremely high. An article in the journal "Nature", titled "Moon too static for astronauts?" (Feb 2, 2007) estimates that the lunar surface is charged with up to several thousand volts [2]. The electrostatic power generator was devised to alleviate the hazardous effects of negatively charged lunar soil by neutralizing the charged particles through capacitive coupling and thereby simultaneously harnessing power through electric charging [3]. The amount of power generated or collected is dependent on the areal coverage of the device and hovering speed over the lunar soil surface. A thin-film array of capacitors can be continuously charged and sequentially discharged using a time-differentiated trigger discharge process to produce a pulse train of discharge for DC mode output. By controlling the pulse interval, the DC mode power can be modulated for powering devices and equipment. In conjunction with a power storage system, the electrostatic power generator can be a power source for a lunar rover or other systems. The negatively charged lunar soil would also be neutralized mitigating some of the adverse effects resulting from lunar dust.

Choi, Sang H.↗
NASA NTRS2016

Human Error and the International Space Station: Challenges and Triumphs in Science Operations

Any system with a human component is inherently risky. Studies in human factors and psychology have repeatedly shown that human operators will inevitably make errors, regardless of how well they are trained. Onboard the International Space Station (ISS) where crew time is arguably the most valuable resource, errors by the crew or ground operators can be costly to critical science objectives. Operations experts at the ISS Payload Operations Integration Center (POIC), located at NASA's Marshall Space Flight Center in Huntsville, Alabama, have learned that from payload concept development through execution, there are countless opportunities to introduce errors that can potentially result in costly losses of crew time and science. To effectively address this challenge, we must approach the design, testing, and operation processes with two specific goals in mind. First, a systematic approach to error and human centered design methodology should be implemented to minimize opportunities for user error. Second, we must assume that human errors will be made and enable rapid identification and recoverability when they occur. While a systematic approach and human centered development process can go a long way toward eliminating error, the complete exclusion of operator error is not a reasonable expectation. The ISS environment in particular poses challenging conditions, especially for flight controllers and astronauts. Operating a scientific laboratory 250 miles above the Earth is a complicated and dangerous task with high stakes and a steep learning curve. While human error is a reality that may never be fully eliminated, smart implementation of carefully chosen tools and techniques can go a long way toward minimizing risk and increasing the efficiency of NASA's space science operations.

Harris, Samantha S.↗
NASA NTRS2018

Virtual Exercise Training Software System

The purpose of this study was to develop and evaluate a virtual exercise training software system (VETSS) capable of providing real-time instruction and exercise feedback during exploration missions. A resistive exercise instructional system was developed using a Microsoft Kinect depth-camera device, which provides markerless 3-D whole-body motion capture at a small form factor and minimal setup effort. It was hypothesized that subjects using the newly developed instructional software tool would perform the deadlift exercise with more optimal kinematics and consistent technique than those without the instructional software. Following a comprehensive evaluation in the laboratory, the system was deployed for testing and refinement in the NASA Extreme Environment Mission Operations (NEEMO) analog.

Vu, L.↗
NASA NTRSDate not supplied

INFLUENCE OF ACTN3 GENE ON MUSCLE HEALTH AND PHYSICAL FITNESS TO INFORM FUTURE INTERVENTIONS IN SPACEFLIGHT MISSIONS

INTRODUCTION: Exposure to reduced gravity environments leads to diminished muscle size, strength, and endurance. Preservation of physical fitness is critical for mission essential tasks such as extravehicular activities or adaptation to changes in gravity loads. Current countermeasures to maintain ISS astronauts’ muscle mass and fitness include dedicated time for a combination of cardiovascular exercise and resistance training. Countermeasure effectiveness is monitored through VO2 max and isometric mid-thigh pulls. It is important to understand both environmental and genetic contributors to astronaut physical fitness. Multiple genes, including ACTN3, are known to correlate with exercise phenotypes and have the potential to help personalize countermeasures based on an astronaut’s genotypic makeup. TOPIC: The ACTN3 gene encodes a protein expressed only in fast-twitch muscles and correlates with sprint and power phenotypes. A common polymorphism in ACTN3 gene is R577X (rs1815739), produced by a C−to−T base substitution resulting in a nonsense mutation from arginine (R) to a premature stop codon (X) present in approximately 18% of the population. This polymorphism causes an absence of α−actinin−3 in type II muscle fibers but does not lead to a disease phenotype. The RR genotype is associated with elite athletes, especially in sprint and power sports. The XX genotype is believed to be more common in endurance athletes. The ACTN3 gene also has potential associations with training adaptation, post-exercise recovery, and exercise-associated injuries. APPLICATION: Understanding how ACTN3 and other genetic determinates of fitness phenotypes affect astronaut physical performance can inform personalized exercise and recovery prescriptions. For example, individuals with the RR or RX genotype may respond better to high-load and low repetition exercise, while XX may benefit from high repetition with low weight exercise. Since the wild-type protein may confer more resistance to muscle damage, those individuals with the RR or RX genotype may benefit from high intensity interval training for improved VO2 max, whereas those with an XX genotype may benefit from low intensity, high volume endurance activity. Additionally, omics data related to health and performance could be used as biomarkers to monitor astronaut fitness and the effectiveness of training regimens during a mission. Learning objectives: • Learn about the phenotypic effects on muscle function and fitness secondary to R577X mutation in the ACTN3 gene • Learn how mutations in the ACTN3 gene may inform future countermeasures for future spaceflight missions

Lynn K Stanwyck↗
NASA NTRSDate not supplied

Observing Supraglacial Lakes Using Deep Learning and PlanetScope Imagery

Supraglacial lakes (SGL)s result from melt water accumulation in topographic depressions on the surface of glaciers. SGLs primarily affect glacial dynamics through a positive feedback loop in which the albedo-lowering effect of SGLs can escalate surface melt leading to increases in lake extent and depth, amplifying the afore mentioned albedo-lowering effect. The implications of accelerated glacial melt include increased sea level rise and modifications to ocean primary productivity. SGLs are critical indicators of surface melt and its downstream impacts and should be monitored efficiently. In situ observations and measurements of SGLs are time consuming, cost-prohibitive and difficult to scale. Earth observation data and machine learning enable scalable monitoring of SGLs through pattern detection and quantification of lake evolution over time [1]. This work presents a model developed by training a convolutional neural network with imagery and labels from NASA Operation IceBridge and predicting SGLs in high temporal and spatial resolution PlanetScope imagery.

Supraglacial lake↗
DOE OSTI2023

Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures

Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.

deep learning↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗