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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 19 records

Volumetric Assessment of UPRITE Exercises From Marker-Based Motion Capture

BACKGROUND Lack of volumetric data on full-body movement of exercises presents a challenge to ensuring the fit of crew member’s full range of motion on the International Space Station (ISS). The Upright Proprioception Retention via In-flight Training and Evaluation (UPRITE) is a sensorimotor countermeasure device designed for maintaining crew members’ proprioception in a microgravity environment. A footplate—attached to a static base—rotates in two degrees of freedom (pitch and roll) up to a 20 deg angle. An initial volumetric assessment assuming an upright standing posture produced a cone-like shape with a narrow bottom and wide top. Such general volumetric assessments risk creating an overly conservative volume estimate, taking up more space than is necessary on the already limited interior space of the ISS, and neglecting necessary volume due to oversimplifying assumptions. Rather, higher-fidelity volumetric assessments offer more comprehensive insights in an environment where every area counts. The main objective of this work is to provide the spatial parameters of exercises on the UPRITE such that it is placed on the ISS according to its volumetric demands or that usage is adjusted to fit the available space. METHODS In 2023, a data collection was performed originally to inform loads and dynamics of system use and was recently leveraged for volumetric assessment. Three human subjects representing different body types (~63-76 inches in stature) performed a variety of board manipulations using UPRITE with body weight offload. The test collected the 3D positional data of a modified full-body Plug-in Gait marker set [1] via a 16-camera OptiTrack MoCap system. After processing – filling marker gaps and trimming data – in OptiTrack Motive, the recorded marker location data, which included device markers, was exported to a readable trajectory file. To accurately represent the full volume defining landmarks, additional markers were digitally added to an unscaled Modified Full Body Model [2]. The model was then scaled according to its subject parameters upon which an inverse kinematics analysis was performed. A custom plugin yielded model marker location data files. Volumetric analyses were performed on the recorded trajectory and model trajectory files using a custom Python-built tool that extracted the marker location data and plotted it in a 3D space. Concerned with only the maximum volume of the motion, a 3D convex hull analysis was applied to the plot, extracting the vertices or external points of the eventual 3D CAD output, dubbed aptly as a “volume shell”. This overall approach was based on guidance in a NASA-STD-3001 Technical Brief [3]. RESULTS AND DISCUSSION Batch volumetric assessment on the exercises for each subject was performed, producing high-fidelity volume shells in minimal time. Preliminary results highlighted the value in higher-fidelity volumes based on collected data when possible. For example, revolving a single posture in the cone assessment would not have sufficiently captured a single leg stance; rather, it would need to involve swinging the leg both forward and back. Additional observations and the maximal dimensions of the volumes, including those based on scaled data for ISS anthropometric requirements, will be presented at the Human Research Program Investigator’s Workshop. CONCLUSIONS While this work’s primary objective was for the UPRITE-to-ISS integration, the tool built to conduct this analysis has wide applications for future exercise systems as an informational tool for optimal device placement. The tool and its findings also have implications for exercise device design and spacecraft interior considerations on Gateway, the Lunar Pressurized Rover, and beyond. REFERENCES [1] Bell, C. A., et al. (2023) Recent Improvements and Verification of a Full Body Model in OpenSim. NASA Human Research Program Investigator’s Workshop. https://ntrs.nasa.gov/citations/20230001080 [2] Lostroscio, K., et al (2023) The Digital Astronaut Simulation. AHFE International Conference on Human Factors in Design, Engineering, and Computing for All. [3] Exercise Overview. (2023) NASA-STD-3001 Technical Brief. https://www.nasa.gov/wp-content/uploads/2023/12/ochmo-tb-031-exercise-overview.pdf?emrc=9d454c?emrc=9d454c

L D Quinto↗

Computer-Aided System Engineering and Analysis (CASE/A) Programmer's Manual, Version 5.0

The Computer Aided System Engineering and Analysis (CASE/A) Version 5.0 Programmer's Manual provides the programmer and user with information regarding the internal structure of the CASE/A 5.0 software system. CASE/A 5.0 is a trade study tool that provides modeling/simulation capabilities for analyzing environmental control and life support systems and active thermal control systems. CASE/A has been successfully used in studies such as the evaluation of carbon dioxide removal in the space station. CASE/A modeling provides a graphical and command-driven interface for the user. This interface allows the user to construct a model by placing equipment components in a graphical layout of the system hardware, then connect the components via flow streams and define their operating parameters. Once the equipment is placed, the simulation time and other control parameters can be set to run the simulation based on the model constructed. After completion of the simulation, graphical plots or text files can be obtained for evaluation of the simulation results over time. Additionally, users have the capability to control the simulation and extract information at various times in the simulation (e.g., control equipment operating parameters over the simulation time or extract plot data) by using "User Operations (OPS) Code." This OPS code is written in FORTRAN with a canned set of utility subroutines for performing common tasks. CASE/A version 5.0 software runs under the VAX VMS(Trademark) environment. It utilizes the Tektronics 4014(Trademark) graphics display system and the VTIOO(Trademark) text manipulation/display system.

Knox, J. C.↗

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗

Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis tool, which can simultaneously provide elemental, isotopic, and molecular information with part per million (ppm) sensitivity. However, each spectrum may be composed of hundreds of ion signals, which makes the spectra data complex. Principal component analysis (PCA) is a multivariate analysis technique that has been widely used to figure out the variances among samples in ToF-SIMS spectra data analysis and is showing great success in the explanation of complex ToF-SIMS spectra. So far, several software tools have been developed for PCA of ToF-SIMS spectra; however, none of them are freely available. Such a situation leads to some difficulties in extending applications of PCA to various research fields. More importantly, it has long been challenging for common researchers to understand PCA plots and extract chemical differences among samples. In this work, we developed a new and flexible software tool (named “advanced spectra pca toolbox”) based on python for PCA of complex ToF-SIMS spectra along with an easy-to-read manual. It can generate data analysis reports automatically to explain chemical differences among samples, allowing less experienced researchers to easily understand tricky PCA results. Moreover, it is expandable and compatible with artificial intelligence/machine learning functions. Pure goethite and different lignin adsorbed goethite samples were used as a model system to demonstrate our new software tool, proving that our software tool can be readily used in complex spectra data processing. Our new software tool is open-source, convenient, flexible, and expandable. We expect this open-source tool will benefit the ToF-SIMS community.

47 OTHER INSTRUMENTATION↗

FuelsGen

This toolset generates heterogeneous spatial layouts, aimed at representing forest fuels applicable to wildland fire modeling. Methods are included for calibrating (fitting) this model to data extracted from plot survey information.

Gattiker, James↗

The utility of calibration - Landsat 4 TM simulator data, Patrick Draw, Wyoming

A novel calibration approach was used for Landsat 4 TM simulator data acquired at the Patrick Draw test site, Wyoming, where the 'true' reflectance spectrum for any pixel area cannot be practically determined because of mixing of spectral components. An empirically derived function was first applied to the scanner data to remove the brightness gradient due to directional scattering and surface geometry. Based on the observation that the distribution of scanner brightness values (DN) were Gaussian, the minimum, mean, and maximum DN for each band were extracted and plotted versus the similar percent reflectance values determined from a large representative collection of field reflectance data for the area. These plots provided linear transformations relating DN value to reflectance in each band.

Lang, H. R.↗

Photogrammetry Measurements During a Tanking Test on the Space Shuttle External Tank, ET-137

On November 5, 2010, a significant foam liberation threat was observed as the Space Shuttle STS-133 launch effort was scrubbed because of a hydrogen leak at the ground umbilical carrier plate. Further investigation revealed the presence of multiple cracks at the tops of stringers in the intertank region of the Space Shuttle External Tank. As part of an instrumented tanking test conducted on December 17, 2010, a three dimensional digital image correlation photogrammetry system was used to measure radial deflections and overall deformations of a section of the intertank region. This paper will describe the experimental challenges that were overcome in order to implement the photogrammetry measurements for the tanking test in support of STS-133. The technique consisted of configuring and installing two pairs of custom stereo camera bars containing calibrated cameras on the 215-ft level of the fixed service structure of Launch Pad 39-A. The cameras were remotely operated from the Launch Control Center 3.5 miles away during the 8 hour duration test, which began before sunrise and lasted through sunset. The complete deformation time history was successfully computed from the acquired images and would prove to play a crucial role in the computer modeling validation efforts supporting the successful completion of the root cause analysis of the cracked stringer problem by the Space Shuttle Program. The resulting data generated included full field fringe plots, data extraction time history analysis, section line spatial analyses and differential stringer peak ]valley motion. Some of the sample results are included with discussion. The resulting data showed that new stringer crack formation did not occur for the panel examined, and that large amounts of displacement in the external tank occurred because of the loads derived from its filling. The measurements acquired were also used to validate computer modeling efforts completed by NASA Marshall Space Flight Center (MSFC).

Littell, Justin D.↗

A Comparative Study of Simulated and Measured Gear-Flap Flow Interaction

The ability of two CFD solvers to accurately characterize the transient, complex, interacting flowfield asso-ciated with a realistic gear-flap configuration is assessed via comparison of simulated flow with experimental measurements. The simulated results, obtained with NASA's FUN3D and Exa's PowerFLOW® for a high-fidelity, 18% scale semi-span model of a Gulfstream aircraft in landing configuration (39 deg flap deflection, main landing gear on and off) are compared to two-dimensional and stereo particle image velocimetry measurements taken within the gear-flap flow interaction region during wind tunnel tests of the model. As part of the bench-marking process, direct comparisons of the mean and fluctuating velocity fields are presented in the form of planar contour plots and extracted line profiles at measurement planes in various orientations stationed in the main gear wake. The measurement planes in the vicinity of the flap side edge and downstream of the flap trailing edge are used to highlight the effects of gear presence on tip vortex development and the ability of the computational tools to accurately capture such effects. The present study indicates that both computed datasets contain enough detail to construct a relatively accurate depiction of gear-flap flow interaction. Such a finding increases confidence in using the simulated volumetric flow solutions to examine the behavior of pertinent aer-odynamic mechanisms within the gear-flap interaction zone.

Khorrami, Mehdi R.↗

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗

Ar-Ar Age of Shergottite Dhofar 378: Formation or Early Shock Event?

The Ar-39-Ar40 data for 16 stepwise temperature extractions of mixed mesostasis plus plagioclase show the following major characteristics. Changes in the K/Ca ratio and in the differential rate of Ar-39 release with extraction temperature suggest three distinct, but overlapping Ar diffusion domains: <13%, 13-45%, and >45% cumulative Ar-39 release:. The youngest Ar-Ar age, approx.162-165 Myr is observed at approx.28-40% Ar-39 release, which we attribute primarily to the mesostasis. Extractions releasing >45% Ar-39, probably from plagioclase, suggest older Ar-Ar ages and indicate release of trapped martian Ar-40. An isochron plot for 8 extractions, releasing 3-45% of the Ar-39 and corrected for 36Arcos using directly measured 36Arcos, gives an Ar-Ar age of 143+/-4 Myr (where the +/- ignores the uncertainty in applying a correction for Ar-36cos). Applying a correction assuming only one-half of the measured Ar-36cos gives an age of 159+/-2 Myr. Correcting for cos-Ar-36 using the minimum measured Ar-36/Ar-37 ratio gives a minimum possible age of 138+/-5 Myr. All of these ages are within combined uncertainties of the Sm-Nd age of 157+/-24 Myr [4]. The trapped Ar-40/Ar-36 ratio obtained from the isochron is largely defined by the highest [K] data.

Park, J.↗

Bringing Analysis Closer to Data: Developing a Visualization Tool for L2 Earth Science Satellite Data

Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.

Li, Angela W.↗

Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction

Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.

47 OTHER INSTRUMENTATION↗

DK UMa: A Star on the Ascent

DK UMa (= 24 UMa = HD 82210) is a G4 IV-III star. According to its M(sub v) and B - V color, it is located at the base of the red giant branch, having recently exited from the Hertzsprung Gap. Now poised to start its first ascent along the giant branch, DK UMa is at a significant juncture in its post-main-sequence evolution, offering an important evolutionary comparison for magnetic activity with stars like 31 Comae, which is just entering the Hertzsprung Gap, and older stars like the Hyades giants or P Ceti, which have passed the tip of the giant branch and lie in the so-called 'clump'. As part of a major survey of the ultraviolet and X ray properties of a well-defined sample of evolved giant stars, DK UMa was observed with the Extreme Ultraviolet Explorer (EUVE) spacecraft in March 1997, for a total exposure time of 230 kiloseconds. A plot of the extracted short-wavelength (SW) spectrum of this star is shown, where it is compared with similar EUVE exposures for other yellow and red giant stars in the activity survey. In terms of the spectral lines of different ionization stages present in these spectra, the transition region and coronal temperature of DK UMa appears to be intermediate between those of 31 Com and P Ceti. Combining the relative strengths of the EUVE lines with Hubble Space Telescope (HST) data at near UV wavelengths and with ROSAT X-ray fluxes, the differential emission measure (DEM) distributions of these stars form a sequence in coronal temperature, which peaks at 10(exp 7.2) K for 31 Com, at 10(exp 6.8) K for B Ceti, and at intermediate temperatures for DK UMa - consistent with the evolutionary stages represented by the three stars. The integrated fluxes of the strongest emission lines found in the EUVE spectrum of DK UMa are listed, again compared with similar measurements for other giant stars that were observed in the course of other EUVE Guest Observer programs.

Simon, Theodore↗

Determining experimentally induced variation in coniferous canopy chemistry with Airborne Imaging Spectrometer data

Experimental treatments in a Douglas-fir forest in NE New Mexico were carried out to determine whether differences in forest canopy chemistry could be detected using data from the Airborne Imaging Spectrometer (AIS-2). Experimental treatments consisted of nitrogen fertilizer additions, sawdust additions, and control plots. After AIS-2 data were collected, the digital number of a given pixel was extracted from each channel, yielding 128 values that were used to form a spectrum. Four spectra were extracted from each treatment plot. Multiple stepwise linear regressions between first and second difference transformations of AIS-2 spectra and the canopy characteristics of biomass, nitrogen concentration, and nitrogen content were performed. The results showed a coefficient of multiple determination of 0.71 between first-difference AIS-2 spectra and measured nitrogen concentration in foliage, indicating that it may be possible to predict nitrogen concentration in Douglas fir using AIS-2 spectra.

Swanberg, N. A.↗

Improved NASTRAN plotting

The new 1991 COSMIC/NASTRAN version, compatible with the older versions, tries to remove some old constraints and make it easier to extract information from the plot file. It also includes some useful improvements and new enhancements. New features available in the 1991 version are described. They include a new PLT1 tape with simplified ASCII plot commands and short records, combined hidden and shrunk plot, an x-y-z coordinate system on all structural plots, element offset plot, improved character size control, improved FIND and NOFIND logic, a new NASPLOT post-prosessor to perform screen plotting or generate PostScript files, and a BASIC/NASTPLOT program for PC.

Chan, Gordon C.↗

Impedance-Based State-of-Health Estimation for Lithium-Ion Battery Management Systems

This paper discusses an impedance-based state-of-health (SOH) estimation for lithium-ion battery management systems. Features obtained from the Nyquist plot of the electrochemical impedance spectroscopy (EIS) are utilized to estimate the SOH. Two commercial lithium-ion batteries were aged (cycled) in the laboratory to collect aging and impedance data. An algorithm for SOH estimation is presented and evaluated. The performance evaluation results show that the SOH features extracted from the Nyquist plot can be utilized for SOH estimation. Estimated SOH values can be then utilized in various battery management systems (BMS) functions such as for calibrating the available capacity, adjusting charging/discharging strategies, and protection.

Al-Smadi, Mohammad↗

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗