Exploration of Frameworks, SysML, LLMs and Data Analytics to Optimize Reliability and Maintainability (R&M) Planning, Execution, and Evaluation for Safe and Successful Missions
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Data Analytics Methods to Measure Plant Outage Resilience
Every 18 or 24 months nuclear power plants (depending on plant configuration, pressurized or boiling water reactor respectively) undergo a period of outage where the plant is taken offline and a large number of maintenance and surveillance activities (that cannot be performed while plant is running) are performed in typically 2–3 weeks. Planning of a plant outage is very challenging since all the activities are required to be performed in the shortest amount of time given available resources (typically contractor crews hired for the duration of the outage). Consequently, plant outages can be costly due the actual loss of power generation and crew costs and, because of it, there is a need to maximize resource usage in the outage planning phase and reduce the risk of outage delays. This paper is addressing these needs by providing a set of analytical methods designed to analyze plant outage schedule and identify critical elements based on available resources (time and crews). These methods are based on natural language processing and optimization algorithms. In this respect, two classes of methods have been developed: one that focuses on the time resource and how variability in the time to complete outage tasks may impact outage delays, and one that minimizes the risk of outage delays by integrating available resources to assess when daily activities should be performed.
97 - MATHEMATICS AND COMPUTING↗
Components Refurbishment and Chemical Analysis Facility, SWMU #041 - Per- and Polyfluoroalkyl Substances Site Assessment Report Kennedy Space Center, Florida
This PFAS Site Assessment (SA) Report presents the activities and results associated with PFAS investigations at the Components Refurbishment and Chemical Analysis (CRCA) facility located at Kennedy Space Center (KSC), Florida. In 2022, CRCA was identified as an Area of Potential Concern because the facility stores several potential PFAS-containing chemicals. A groundwater sample collected from an onsite monitoring well detected PFOA and PFOS at concentrations greater than State of Florida provisional Groundwater Cleanup Target Levels (pGCTLs). PFAS SA activities were conducted between March 2022 and February 2024. During this timeframe, a total of 16 direct-push technology (DPT) locations and 82 discrete samples were collected from these locations, along with 90 monitoring well samples. The analytical data screening process focused on State of Florida pGCTLs and United States Environmental Protection Agency (USEPA) Regional Screening Level (RSLs) from November 2023 to evaluate the data. Analytical results identified PFOS, PFOA, and PFBA at concentrations exceeding their respective RSLs at each depth interval (shallow, intermediate, and deep). PFOS and PFOA had the largest footprint of RSL exceedances in each depth interval, but pGCTL exceedances were only observed at limited locations in the shallow and intermediate intervals. PFBA had the highest detections of any PFAS compound, with concentrations exceeding 180,000 nanograms per liter (ng/L), which is 100-times the RSL of 1,800 ng/L in the shallow and intermediate intervals and 10-times the RSL in the deep interval. The maximum PFBA detection was 841,000 ng/L at shallow monitoring well, MW0006. The PFAS SA also included samples collected from the onsite hydraulic containment system (HCS) which was installed to control and treat the onsite chlorinated volatile organic compound (CVOC) plume. Influent and effluent aqueous samples were collected monthly from the system. PFAS concentrations were relatively the same for both influent and effluent samples, indicating that while the HCS has been effective for CVOC treatment, it does not provide any additional treatment for PFAS compounds. Since the HCS has achieved its objectives, the system was shut down in December 2024. PFAS data gaps still exist, to include surface water and soil, which were not sampled during this SA. Soil sampling near the Chemical Process Area is recommended. Surface water and soil sampling at select stormwater outfalls is also recommended. Additionally, further groundwater sampling is recommended (DPT and monitoring well) in all depth intervals to delineate the extent of PFAS impacts at CRCA.
K Alex Murphy↗
Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute
As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.
97 MATHEMATICS AND COMPUTING↗
Analytical drafting curves provide exact equations for plotted data
Analytical drafting curves provide explicit mathematical expressions for any numerical data that appears in the form of graphical plots. The curves each have a reference coordinate axis system indicated on the curve as well as the mathematical equation from which the curve was generated.
Stewart, R. B.↗
Analytical techniques for the analysis of stall/spin flight test data
Analytical techniques for the analysis of stall/spin flight test data are reviewed by discussing (1) certain special flight instrumentation issues, (2) the mathematical modeling techniques, and (3) the analysis of post stall and spinning flight of general aviation airplanes. The angles of attack, sideslip, roll, pitch, and yaw are derived from measurements of angular velocity and linear acceleration. The key to the success of this approach is to simultaneously estimate both the biases in the instrumentation and the initial conditions. Techniques for determining stability derivatives from flight data are applied to angles of attack too high for stabilized flight. This practice greatly expands the range over which aerodynamic characteristics can be determined from flight test. Nonlinear terms in certain aerodynamic functions are shown to be valid by comparing them with the trends of results at different angles of attack. A very old technique of studying spins is extended and applied to some modern light airplanes. Airplanes for which the wing provides the dominant moments during spins, offer the possibility of linking spin characteristics to longitudinal data.
Taylor, L. W., Jr.↗
Analysis of structural dynamic data from Skylab. Volume 2: Skylab analytical and test modal data
A compendium is presented of orbital configuration test modal data, analytical test modal data, analytical test correlation modal data and analytical flight configuration 1.2 modal data. Section A presents tables showing the generalized mass contributions for each of the thirty test modes. Section B presents the two dimensional mode shape plots for the thirty test modes. Tables of GMC's for the test correlated analytical modes are presented in Section C. These analytical modes were generated from a model that was adjusted to match test results by use of the methodology discussed in Sections 2.3 and 5.4 of Volume I of this report. Section D presents the two dimensional mode shape plots for the analytical modes. Sections E and F contain the uncoupled and coupled modes of the orbital flight configuration 1.2 at three development phases of the model.
Demchak, L.↗
Preparing Earth Data Scientists for 'The Sexiest Job of the 21st Century'
What Exactly do Earth Data Scientists do, and What do They Need to Know, to do It? There is not one simple answer, but there are many complex answers. Data Science, and data analytics, are new and nebulas, and takes on different characteristics depending on: The subject matter being analyzed, the maturity of the research, and whether the employed subject specific analytics is descriptive, diagnostic, discoveritive, predictive, or prescriptive, in nature. In addition, in a, thus far, business driven paradigm shift, university curriculums teaching data analytics pertaining to Earth science have, as a whole, lagged behind, andor have varied in approach.This presentation attempts to breakdown and identify the many activities that Earth Data Scientists, as a profession, encounter, as well as provide case studies of specific Earth Data Scientist and data analytics efforts. I will also address the educational preparation, that best equips future Earth Data Scientists, needed to further Earth science heterogeneous data research and applications analysis. The goal of this presentation is to describe the actual need for Earth Data Scientists and the practical skills to perform Earth science data analytics, thus hoping to initiate discussion addressing a baseline set of needed expertise for educating future Earth Data Scientists.
data analytics↗
Adaptive processing for LANDSAT data
Analytical and test results on the use of adaptive processing on LANDSAT data are presented. The Kalman filter was used as a framework to contain different adapting techniques. When LANDSAT MSS data were used all of the modifications made to the Kalman filter performed the functions for which they were designed. It was found that adaptive processing could provide compensation for incorrect signature means, within limits. However, if the data were such that poor classification accuracy would be obtained when the correct means were used, then adaptive processing would not improve the accuracy and might well lower it even further.
Crane, R. B.↗
Peregrine Software Development: Report on the Code Conversion From Python to C++
This work package seeks to convert the Peregrine software tool from its original Python implementation to a production version based on the C++ language. Peregrine is a powerful research platform with a multitude of advanced data analytics and data visualization functionalities. Developed by scientists to explore multimodal and multidimensional data related to the production of components using powder bed additive manufacturing processes, the tool implements state-of-the-art algorithms to assist machine users in making build or part quality determinations. Given that Peregrine is data-intensive, the goal of this conversion is to enhance the tool’s flexibility and interactivity and reduce the number of code dependencies to facilitate its deployment as part of the ongoing technology transfer campaign. This brief document provides an overview of Peregrine’s functionalities and capabilities, along with a detailed description of the core functionalities that have been implemented to date in the new C++ version. This document serves as a development update at the end of the first year of the ongoing conversion and will be regularly updated as progress continues.
97 MATHEMATICS AND COMPUTING↗
Extravehicular Activity Operations Concepts Under Communication Latency and Bandwidth Constraints
The Biologic Analog Science Associated with Lava Terrains (BASALT) project is a multi-year program dedicated to iteratively develop, implement, and evaluate concepts of operations (ConOps) and supporting capabilities intended to enable and enhance human scientific exploration of Mars. This pa-per describes the planning, execution, and initial results from the first field deployment, referred to as BASALT-1, which consisted of a series of 10 simulated extravehicular activities (EVAs) on volcanic flows in Idaho's Craters of the Moon (COTM) National Monument. The ConOps and capabilities deployed and tested during BASALT-1 were based on previous NASA trade studies and analog testing. Our primary research question was whether those ConOps and capabilities work acceptably when performing real (non-simulated) biological and geological scientific exploration under 4 different Mars-to-Earth communication conditions: 5 and 15 min one-way light time (OWLT) communication latencies and low (0.512 Mb/s uplink, 1.54 Mb/s downlink) and high (5.0 Mb/s uplink, 10.0 Mb/s downlink) bandwidth conditions representing the lower and higher limits of technical communication capabilities currently proposed for future human exploration missions. The synthesized results of BASALT-1 with respect to the ConOps and capabilities assessment were derived from a variety of sources, including EVA task timing data, network analytic data, and subjective ratings and comments regarding the scientific and operational acceptability of the ConOp and the extent to which specific capabilities were enabling and enhancing, and are presented here. BASALT-1 established preliminary findings that baseline ConOp, software systems, and communication protocols were scientifically and operationally acceptable with minor improvements desired by the "Mars" extravehicular (EV) and intravehicular (IV) crewmembers, but unacceptable with improvements required by the "Earth" Mission Support Center. These data will provide a basis for guiding and prioritizing capability development for future BASALT deployments and, ultimately, future human exploration missions.
Beaton, Kara H.↗
Analysis of fatigue, fatique-crack propagation, and fracture data
Analytical methods have been developed for consolidation of fatigue, fatigue-crack propagation, and fracture data for use in design of metallic aerospace structural components. To evaluate these methods, a comprehensive file of data on 2024 and 7075 aluminums, Ti-6A1-4V, and 300M and D6Ac steels was established. Data were obtained from both published literature and unpublished reports furnished by aerospace companies. Fatigue and fatigue-crack-propagation analyses were restricted to information obtained from constant-amplitude load or strain cycling of specimens in air at room temperature. Fracture toughness data were from tests of center-cracked tension panels, part-through crack specimens, and compact-tension specimens.
Jaske, C. E.↗
Assessment of Some IASMS-relevant Data Sources for Aviation Safety
An In-time Aviation Safety Management System (IASMS) [1,2] is a set of services, functions, and capabilities (SFCs) necessary for monitoring known hazards and emergent risks, assessing safety data for anomalies, precursors, and trends, mitigating hazards that reach safety thresholds, and assuring efficacy of controls in mitigating hazards. An IASMS will continually monitor the NAS to collect data on the status of aircraft, air traffic management systems, weather, and airports. Within the NASA Aeronautics Research Mission Directorate (ARMD) System-Wide Safety (SWS) project’s technical challenge called In-time Aviation Safety Management Systems (IASMS) for Commercial Aviation Operations, which we often refer to as Technical Challenge 6 (TC-6), we have performed an assessment of several aviation data sources we have found that are relevant to assessing the safety of the National Airspace System (NAS) in the context of an IASMS. This assessment includes understanding the nature of the data themselves and using some data analytics tools on these data to show how they can be used to identify potential safety issues. We also describe how the data and analytics are part of a system that can allow for other data and analytics to be performed and for the results to be visualized for use by appropriate operators to identify potential safety issues and develop mitigations. This report is a step toward the ultimate goal of TC-6, which is to develop a prototype IASMS system that demonstrates the potential of an IASMS and inspire operators to build analogous systems to make the best possible use of the significant investments that they make in collecting, storing, and managingdata related to their operations.
aviation safety↗
A simple method of calculating power-law velocity profile exponents from experimental data
Analytical expressions for the effects of compressibility and heat transfer on laminar and turbulent shape factors H have been developed. Solving the turbulent equation for the power law velocity profile exponent N has resulted in a simple technique by which the N values of experimental turbulent profiles can be calculated directly from the integral parameters. Thus the data plotting, curve fitting, and slope measuring, which is the normal technique of obtaining experimental N values, is eliminated. The N values obtained by this method should be within the accuracy with which they could be measured.
Allen, J. M.↗
Analysis of structural dynamic data from Skylab. Volume 2: Skylab analytical and test model data
The orbital configuration test modal data, analytical test correlation modal data, and analytical flight configuration modal data are presented. Tables showing the generalized mass contributions (GMCs) for each of the thirty tests modes are given along with the two dimensional mode shape plots and tables of GMCs for the test correlated analytical modes. The two dimensional mode shape plots for the analytical modes and uncoupled and coupled modes of the orbital flight configuration at three development phases of the model are included.
Demchak, L.↗
Methodology for matching experimental and analytical aerodynamic data
Correction factor methodologies have been developed which use steady experimental or analytical pressure or force data to correct steady and unsteady aerodynamic calculations. Three methods of calculating correction factors have been developed to match steady surface pressure distributions, to match airfoil section forces and moments, and to match total forces and moments. Data for a rectangular supercritical wing that was previously tested in the NASA Langley Research Center Transonic Dynamics Tunnel have been used to determine correction factors to match surface pressure distributions for a range of Mach numbers.
Wieseman, Carol D.↗
Relative humidity from psychrometric data
Analytical equation for computing relative humidity as function of wet bulb temperature, dry bulb temperature, and atmospheric pressure is suitable for use with calculator or computer. Analytical expressions may be useful for chemical process control systems and building environmental control systems.
Putnam, T. W.↗
IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX
We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.
visualization↗