NASA Spinoff Article: Data Mining Tools Make Flights Safer, More Efficient
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Southwest Airlines↗
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Development and Operations of the Astrophysics Data System
SAO TASKS ACCOMPLISHED: Abstract Service: (1) Continued regular updates of abstracts in the databases, both at SAO and at all mirror sites; (2) Established a new naming convention of QB books in preparation for adding physics books from Hollis or Library of Congress; (3) Modified handling of object tag so as not to interfere with XHTML definition; (4) Worked on moving 'what's new' announcements to a majordomo email list so as not to interfere with divisional mail handling; (5) Implemented and tested new first author feature following suggestions from users at the AAS meeting; (6) Added SSRv entries back to volume 1 in preparation for scanning of the journal; (7) Assisted in the re-configuration of the ADS mirror site at the CDS and sent a new set of tapes containing article data to allow re-creation of the ADS article data lost during the move; (8) Created scripts to automatically download Astrobiology.
Murray, Stephen S.↗
The Data Acquisition and Controls Systems (DACS) of the E-Complex at the John C. Stennis Space Center, MS: A General Overview
The John C. Stennis Space Center (SSC) provides test operations services to a variety of customers including NASA, DoD, commercial enterprises, and others for the development of current next-generation rocket propulsion systems. Many of these test operations services are provided in the E-Complex series of test facilities. The E-Complex is composed of three active test stands (E1, E2, & E3), each with two or more test positions. Each test position is comprised of unique sets of data acquisition and controls hardware and software that record both facility and test article data and safely operate the test facility. The E-Complex data acquisition system (DAS) is actually composed of two separate systems, one for static data and the other for dynamic. The static DAS, otherwise known as the Low-Speed DAS (LSDAS), samples 16 bit data at 250 samples-per-second (SPS), although an aggregate sample rate of 200,000 SPS is possible. The dynamic data acquisition system, otherwise known as the high-speed DAS (HSDAS), samples 16 bit data at 100K SPS with a 45 KHz bandwidth.
Hughes, Mark S.↗
A General Overview of the Data Acquisition and Controls Systems (DACS) of the E-Complex at NASA's Stennis Space Center
The John C. Stennis Space Center (SSC) provides test operations services to a variety of customers, including NASA, DoD, and commercial enterprises for the development of current and next-generation rocket propulsion systems. Many of these testing services are provided in the E-Complex test facilities composed of three active test stands (E1, E2, & E3) and 7 total test positions. Each test position is outfitted with unique sets of data acquisition and controls hardware and software that record both facility and test article data and enable safe operation of the test facility. This paper addresses each system in more detail including efforts to upgrade hardware and software.
Hughes, Mark S.↗
PLUSWIND Derived Data
This dataset consists of annual CSV files containing multiple sources of modeled, hourly wind speeds and generation. For complete information about this dataset, including validation of modeled generation versus recorded generation, please see the Scientific Data article: Millstein, D., Jeong, S., Ancell, A., & Wiser, R. (2023). A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models. Scientific Data, 10(1), 883. https://doi.org/10.1038/s41597-023-02804-w
17 WIND ENERGY↗
Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance
Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.
42 ENGINEERING↗
An Intelligent Strain Gauge with Debond Detection and Temperature Compensation
The harsh rocket propulsion test environment will expose any inadequacies associated with preexisting instrumentation technologies, and the criticality for collecting reliable test data justifies investigating any encountered data anomalies. Novel concepts for improved systems are often conceived during the high scrutiny investigations by individuals with an in-depth knowledge from maintaining critical test operations. The Intelligent Strain Gauge concept was conceived while performing these kinds of activities. However, the novel concepts are often unexplored even if it has the potential for advancing the current state of the art. Maturing these kinds of concepts is often considered to be a tangential development or a research project which are both normally abandoned within the propulsion-oriented environment. It is also difficult to justify these kinds of projects as a facility enhancement because facility developments are only accepted for mature and proven technologies. Fortunately, the CIF program has provided an avenue for bringing the Intelligent Strain Gauge to fruition. Two types of fully functional smart strain gauges capable of performing reliable and sensitive debond detection have been successfully produced. Ordinary gauges are designed to provide test article data and they lack the ability to supply information concerning the gauge itself. A gauge is considered to be a smart gauge when it provides supplementary data relating other relevant attributes for performing diagnostic function or producing enhanced data. The developed strain gauges provide supplementary signals by measuring strain and temperature through embedded Karma and nickel chromium (NiCr) alloy elements. Intelligently interpreting the supplementary data into valuable information can be performed manually, however, integrating this functionality into an automatic system is considered to be an intelligent gauge. This was achieved while maintaining a very low mass. The low mass enables debond detection and temperature compensation to be performed when the gauge is utilized on small test articles. It was also found that the element's mass must be relatively small to avoid overbearing the desired thermal dissipation characteristics. Detecting the degradation of a gauge s bond was reliably achieved by correlating thermal dissipation with the bond s integrity. This was accomplished by precisely coupling a NiCr element with a Karma element for accurately interjecting and quantifying thermal energy. A finite amount of thermal energy is consistently placed in the gauge by electrically powering the NiCr element. The energy will only be temporarily stored before it begins to dissipate into the surrounding structure through the gauge bond. The ability to transmit the energy into the structure becomes greatly inhibited by any discontinuity in the bond s substrate. Therefore, the way the thermal dissipation occurs will reveal even the slightest change in the integrity of the bond.
Jensen, Scott L.↗
Human Factors Process Task Analysis Liquid Oxygen Pump Acceptance Test Procedure for the Advanced Technology Development Center
A process task analysis effort was undertaken by Dynacs Inc. commencing in June 2002 under contract from NASA YA-D6. Funding was provided through NASA's Ames Research Center (ARC), Code M/HQ, and Industrial Engineering and Safety (IES). The John F. Kennedy Space Center (KSC) Engineering Development Contract (EDC) Task Order was 5SMA768. The scope of the effort was to conduct a Human Factors Process Failure Modes and Effects Analysis (HF PFMEA) of a hazardous activity and provide recommendations to eliminate or reduce the effects of errors caused by human factors. The Liquid Oxygen (LOX) Pump Acceptance Test Procedure (ATP) was selected for this analysis. The HF PFMEA table (see appendix A) provides an analysis of six major categories evaluated for this study. These categories include Personnel Certification, Test Procedure Format, Test Procedure Safety Controls, Test Article Data, Instrumentation, and Voice Communication. For each specific requirement listed in appendix A, the following topics were addressed: Requirement, Potential Human Error, Performance-Shaping Factors, Potential Effects of the Error, Barriers and Controls, Risk Priority Numbers, and Recommended Actions. This report summarizes findings and gives recommendations as determined by the data contained in appendix A. It also includes a discussion of technology barriers and challenges to performing task analyses, as well as lessons learned. The HF PFMEA table in appendix A recommends the use of accepted and required safety criteria in order to reduce the risk of human error. The items with the highest risk priority numbers should receive the greatest amount of consideration. Implementation of the recommendations will result in a safer operation for all personnel.
Diorio, Kimberly A.↗
Greenhouse gas and dissolved oxygen production and consumption rates associated with Regier et al. (2023)
Tidal inundation along the coastal terrestrial-aquatic interface controls soil and sediment biogeochemistry and gas dynamics. Although a rich literature exist on studies of the influence of tidal waters on the biogeochemistry of coastal ecosystem soils, few studies have experimentally addressed the reverse question: How do soils (or sediments) from different coastal ecosystems influence the biogeochemistry of the tidal waters that inundate them? We conducted short-term microcosm laboratory experiments where seawater was amended with sediments and soils collected across regional gradients of inundation exposure (i.e., frequently to rarely inundated) and measured changes in dissolved oxygen and greenhouse gas concentrations to calculate gas consumption or production rates occurring during seawater exposure to terrestrial materials. This data package contains dissolved oxygen and greenhouse gas data collected during incubation of soils and sediments collected at 18 sites, which were used in the publication Regier et al. (2023) entitled “Coastal inundation regime moderates the short-term effects of sediment and soil additions on seawater oxygen and greenhouse gas dynamics: a microcosm experiment” which is published in Frontiers in Marine Science (DOI: https://doi.org/10.3389/fmars.2023.1308590).---Acknowledging EXCHANGE: General Support and Data Product UseWe ask that users of EXCHANGE data add the following acknowledgement when publishing data in scholarly articles and data repositories:"This research is based on work supported by COMPASS-FME, a multi-institutional project supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research as part of the Environmental System Science Program."
54 ENVIRONMENTAL SCIENCES↗
Recovery, Restoration and Archiving of Previously Lost Data and Metadata from the Apollo Lunar Surface Experiments Package (ALSEP)
The Apollo Lunar Surface Experiments Package (ALSEP) is the name used to collectively represent the geophysical instruments deployed on the lunar surface by the astronauts on Apollo 12, 14, 15, 16, and 17. These instruments were active from the times of their deployment (November 1969 – December 1972) to September 1977. During that time, fourteen types of experiments were conducted, and their data were transmitted to Earth. The experiment PIs processed them. At the conclusion of the experiments, some of these data were submitted to the NASA Space Science Data Coordinated Archive (NSSDCA) for archiving, while others were not. The raw instrument data received from the Moon prior to March 1976 were not archived, either. The unarchived data, resided on open-reel magnetic tapes, became lost in the decades since, along with much of the metadata (the information necessary/useful in properly processing/analyzing the data). This article retraces the history of the ALSEP data archiving efforts in the 1970s, the subsequent loss of the data tapes, and the search, recovery, and restoration of the lost data by contemporary researchers in the 21st century. In 2006, NSSDCA began reformatting some of the ALSEP data archived in the 1970s to conform with the current Planetary Data System (PDS). In 2010, 440 of the previously lost magnetic tapes containing the raw ALSEP data were recovered. From these tapes, the data were extracted, re-packaged for individual experiments, and, for those with sufficient metadata, processed into higher order data readily usable by researchers. All of these data products have been recently archived with either PDS or NSSDCA. These newly restored data fill a number of gaps in the previously existing archive of the ALSEP data. In addition, tens of thousands of pages of Apollo era documents have been optically scanned and compiled into an online searchable catalog. This article also describes the content, organization, and usage of the restored raw ALSEP data and metadata.
S Nagihara↗
Overview of the Planetary Data System
This article describes why there is a PDS (Planetary Data System), what the PDS has accompished, how it is organized, what innovations it has added, and what plans for the future. Terms are defined which are used in this article and in the related articles.
planetary data planetary data system↗
TPSAS-NF1676L-12829-DND
Collaborative research between NASA and U.S. rotorcraft companies through the Vertical Lift Consortium, to design, test, and analyze representative validation test articles. - Representative problems and designs developed with partners - Validation articles and characterization specimens manufactured by partners - Develop analytical models to predict delamination behavior using commercial codes, characterization data, and test article data - NASA Space Act Agreement (SAA1-818) with CRI, August 2007 August 2012; funded through Subsonic Rotary Wing program
Gretchen B Murri↗
Grid resolution requirement of chemical explosive mode analysis for large eddy simulations of premixed turbulent combustion
Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article Abstract The grid resolution requirement for trustworthy Chemical Explosive Mode Analysis (CEMA) in Large Eddy Simulation (LES) of premixed turbulent combustion is proposed. Explicit filtering, to emulate the effect of the LES filter, is applied to one-dimensional laminar flame and three-dimensional planar turbulent flames across a wide range of Karlovitz numbers (5 - 239). The identification of the flame front by CEMA is found relatively insensitive to the cell size (Δ), while the combustion mode identification shows more significant sensitivity. Specifically, increasing Δ falsely enhances the auto-ignition and local extinction modes and suppresses the diffusion-assisted mode. Limited dependence of the CEMA performance on the turbulent combustion regime (Karlovitz number) is observed. A simple grid size criterion for reliable CEMA mode identification in LES is proposed as Δ ≲ δ L /2; The criterion can be relaxed to Δ ≲ δ L in the laminar flame limit. Furthermore, theoretical analysis is conducted on an idealised chemistry-diffusion system. The effects of the filtering process and turbulence on the local combustion mode are demonstrated, which is consistent with the numerical observations. Further, by incorporating turbulent combustion models in CEMA, potential improvement in identifying local combustion modes can be expected.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Supporting data for the journal article titled "Multi-parameter constraints on empirical infrasound period–yield relations for bolides and implications for planetary defense"
This supporting dataset comprises input files, regression output files, and associated plots that document the analytical framework used in the study.
Herrera, Natalie [Sandia National Laboratories (SN↗
Communication satellite technology, volume 4. Citations from the International Aerospace Abstracts data base
The cited articles from the international literature concern all aspects of communication satellite technology. Included are articles on satellite networks, data transmission efficiency, time division multiple access, data links, and phase shift keying. This bibliography contains 239 citations.
Zollars, G. F.↗
Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning
An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.
42 ENGINEERING↗
Data-Conforming Data-Driven Control: Avoiding Premature Generalizations Beyond Data
Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This article seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slowing down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude this article with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.
97 MATHEMATICS AND COMPUTING↗
Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling
Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.
13 HYDRO ENERGY↗