Leveraging Cluster Computing for Large-Scale Data Processing and Automated Analysis for Radiation Portal Monitor Data
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Smith, Jacob↗
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Structural analysis and automated design
Structural analysis and computerized design of space shuttle structures
Mc Comb, H. G., Jr.↗
Automated Contextual Analysis of Thematic Structure in Natural Language
Automated analysis of natural language text structure
Misek, L. D.↗
Automating Structural Analysis of Spacecraft Vehicles
A major effort within NASA's vehicle analysis discipline has been to automate structural analysis and sizing optimization during conceptual design studies of advanced spacecraft. Traditional spacecraft structural sizing has involved detailed finite element analysis (FEA) requiring large degree-of-freedom (DOF) finite element models (FEM). Creation and analysis of these models can be time consuming and limit model size during conceptual designs. The goal is to find an optimal design that meets the mission requirements but produces the lightest structure. A structural sizing tool called HyperSizer has been successfully used in the conceptual design phase of a reusable launch vehicle and planetary exploration spacecraft. The program couples with FEA to enable system level performance assessments and weight predictions including design optimization of material selections and sizing of spacecraft members. The software's analysis capabilities are based on established aerospace structural methods for strength, stability and stiffness that produce adequately sized members and reliable structural weight estimates. The software also helps to identify potential structural deficiencies early in the conceptual design so changes can be made without wasted time. HyperSizer's automated analysis and sizing optimization increases productivity and brings standardization to a systems study. These benefits will be illustrated in examining two different types of conceptual spacecraft designed using the software. A hypersonic air breathing, single stage to orbit (SSTO), reusable launch vehicle (RLV) will be highlighted as well as an aeroshell for a planetary exploration vehicle used for aerocapture at Mars. By showing the two different types of vehicles, the software's flexibility will be demonstrated with an emphasis on reducing aeroshell structural weight. Member sizes, concepts and material selections will be discussed as well as analysis methods used in optimizing the structure. Analysis based on the HyperSizer structural sizing software will be discussed. Design trades required to optimize structural weight will be presented.
Hrinda, Glenn A.↗
Space plasma research
Temperature and density analysis in the Automated Analysis Program (for the global empirical model) were modified to use flow velocities produced by the flow velocity analysis. Revisions were started to construct an interactive version of the technique for temperature and density analysis used in the automated analysis program. A sutdy of ion and electron heating at high altitudes in the outer plasmasphere was initiated. Also the analysis of the electron gun experiments on SCATHA were extended to include eclipse operations in order to test a hypothesis that there are interactions between the 50 to 100 eV beam and spacecraft generated photoelectrons. The MASSCOMP software to be used in taking and displaying data in the two-ion plasma experiment was tested and is now working satisfactorily. Papers published during the report period are listed.
Comfort, R. H.↗
Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering
Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Development and Analysis of the Automated Object Reentry Survival Analysis Tool Parametric Study Wrapper
The NASA Orbital Debris Program Office (ODPO) Safety Group at the Johnson Space Center analyzes reentering spacecraft at the end of life. The program primarily used by ODPO in this effort is the Object Reentry Survival Analysis Tool (ORSAT). ORSAT utilizes shape primitives as well as a variety of other parameters (material, size, thickness, aerodynamic mass, orbit inclination, etc.) to simulate the reentry process and ultimately, to determine if a spacecraft could be hazardous to the population on the ground. The NASA ODPO plans to automate the ORSAT process to run multiple ORSAT input files either concurrently or consecutively. This type of automation program will provide several benefits. First, there is a need to run large parametric studies for ORSAT analysts to gain a greater understanding of reentering object’s sensitivity to certain input variables. Secondly, a database of pre-run ORSAT cases will be used to develop a survivability model, which could be made available to spacecraft developers as a design for demise (D4D) tool. The recently completed Automated Object Reentry Survival Analysis Tool (AutoORSAT) Wrapper is currently being used to build a survivability database, the first step in developing a survivability model. Already, the data that AutoORSAT has produced provides a greater understanding of the sensitivity of variables such as the initial temperature of the spacecraft, spacecraft breakup altitude, and the aerodynamic mass of spacecraft.
Smith, Andrew N.↗
Application of automated multispectral analysis to Delaware's coastal vegetation mapping
A baseline mapping project was undertaken in Delaware's coastal wetlands as a prelude to an evaluation of the relative value of different parcels of marsh and the setting of priorities for use of these marshes. A description of Delaware's wetlands is given and a mapping approach is discussed together with details concerning an automated analysis. The precision and resolution of the analysis was limited primarily by the quality of the imagery used.
Klemas, V.↗
Scalable Yet Rigorous Floating-Point Error Analysis
Automated techniques for rigorous floating-point round-off error analysis are a prerequisite to placing important activities in HPC such as precision allocation, verification and code optimization on a formal footing. Yet existing techniques cannot provide tight bounds for expressions beyond a few dozen operators; barely enough for HPC. In this work, we offer an approach embedded in a new tool called SATIRE that scales error analysis by four orders of magnitude compared to today’s best-of-class tools. We explain how three key ideas underlying SATIRE help it attain such scale: path strength reduction, bound optimization and abstraction. SATIRE provides tight bounds and rigorous guarantees on significantly larger expressions with well over a hundred thousand operators, covering important examples including FFT, matrix multiplication and PDE stencils.
Das, Arnab↗
Infrascope: Full-Spectrum Phonocardiography With Automated Signal Analysis Report
Physicians have used the stethoscope to listen to heart sounds for over 150 years. During this time the stethoscope has remained nearly unchanged. While the device is inexpensive, simple and easy to use, it has shortcomings as a diagnostic tool. It is dependent on human hearing to detect the complex sounds made by the heart, and human memory to analyze them. There also has been a significant decline in the use of stethoscope, at least in part because of the availability of technologies such as the echocardiogram, which can provide imaging and blood flow through the heart chambers. However, echocardiography can be complex, costly and time consuming and requires the need for a specialist, all of which delay the diagnosis and increase the cost of patient evaluation. Even with the availability of echocardiogram devices such as the handheld pocket ultrasound, diagnosing heart problems can still be difficult. The Infrascope is a new device that resembles a traditional stethoscope and is simple to use. In addition, the Infrascope offers greater sensitivity and a wider range of frequencies heard by the human ear, <20Hz than a traditional stethoscope can detect. Through automated analysis of the full spectrum of heart sounds, it has the potential to improve diagnostic accuracy while reducing the need for referrals, allowing primary care physicians to practice better medicine at the bedside.
Infrasounds↗
Developing an Automated Science Analysis System for Mars Surface Exploration for MSL and Beyond
We are developing an automated science analysis system that could be utilized by robotic or human explorers on Mars (or even in remote locations on Earth) to improve the quality and quantity of science data returned. Three components of this system (our rock, layer, and horizon detectors) [1] have been incorporated into the JPL CLARITY system for possible use by MSL and future Mars robotic missions. Two other components include a multi-spectral image compression (SPEC) algorithm for pancam-type images with multiple filters and image fusion algorithms that identify the in focus regions of individual images in an image focal series [2]. Recently, we have been working to combine image and spectral data, and other knowledge to identify both rocks and minerals. Here we present our progress on developing an igneous rock detection system.
Gulick, V. C.↗
Automating symbolic analysis with CLIPS
Symbolic Analysis is a methodology first applied as an aid in selecting and generating test cases for 'white box' type testing of computer software programs. The feasibility of automating this analysis process has recently been demonstrated through the development of a CLIPS-based prototype tool. Symbolic analysis is based on separating the logic flow diagram of a computer program into its basic elements, and then systematically examining those elements and their relationships to provide a detailed static analysis of the process that those diagrams represent. The basic logic flow diagram elements are flow structure (connections), predicates (decisions), and computations (actions). The symbolic analysis approach supplies a disciplined step-by-step process to identify all executable program paths and produce a truth table that defines the input and output domains for each path identified. The resulting truth table is the tool that allows software test cases to be generated in a comprehensive manner to achieve total program path, input domain, and output domain coverage. Since the manual application of symbolic analysis is extremely labor intensive and is itself error prone, automation of the process is highly desirable. Earlier attempts at automation, utilizing conventional software approaches, had only limited success. This paper briefly describes the automation problems, the symbolic analysis expert's problem solving heuristics, and the implementation of those heuristics as a CLIPS based prototype, and the manual augmentation required. A simple application example is also provided for illustration purposes. The paper concludes with a discussion of implementation experiences, automation limitations, usage experiences, and future development suggestions.
Morris, Keith E.↗
Evaluation of Machine Learning Models for Automated Data Analysis in In-Service Nuclear Power Plant Inspections
The commercial nuclear power industry is facing a potential shortage of certified nondestructive evaluation (NDE) analysts to meet future in-service inspection demands. Automated data analysis (ADA) currently supports human inspectors in tasks such as eddy current evaluations for steam generator examinations. Machine learning (ML) systems are nearing the capability to pass performance demonstration tests for ultrasonic testing (UT) inspections of reactor pressure vessel upper head penetrations in nuclear power plants (NPPs). Current research and development is focused on assisted analysis (AA) of ADA versus fully automated examinations. This presentation will cover assessment of ML flaw detection on dissimilar metal weld (DMW) piping joints.
36 MATERIALS SCIENCE↗
Development and Analysis of the Automated Object Reentry Survival Analysis Tool Parametric Study Wrapper
The NASA Orbital Debris Program Office (ODPO) studies all aspects of spacecraft end-of-life and orbital debris measurement, modeling, and mitigation. The reentry safety group within the ODPO uses the Object Reentry Survival Analysis Tool (ORSAT) to calculate the casualty risk due to reentry of spacecraft and other types of orbital debris. ORSAT models spacecraft as a collection of fragments that break apart from the parent object at a pre-defined “breakup altitude.” It then calculates the trajectory and aero-heating of these fragments to determine which fragments are completely destroyed and which survive to the ground and pose a risk to human population. Because of the historically high computational cost of these calculations, many simplifying assumptions have been made in the traditional calculation and analysis process used by the ODPO, some of which have been shown by recent research by the ODPO and others to be incorrect. Improvements to the ORSAT code and advancements in computer technology have vastly decreased the program’s processing time, and have allowed the ODPO to develop a capability for large-scale parametric studies and Monte Carlo reentry simulations that can aid in both the initial spacecraft design and provide more detailed and accurate risk analysis to spacecraft operators.
Smith, Andrew N.↗
Automated Particle Analysis of Hanford Tank Wastes 241-AN-106, 241-AN-101, and 241-AW-105
This effort is involved in developing particle size and density distribution (PSDD) for the major phases within Hanford tank waste sludge. The object of this work has been to use automated particle analysis (APA) methods on the Scanning Electron Microscope (SEM) to provide PSDDs. The PSDD can be used to quantify the proportion of gibbsite particles likely to settle quickly and enable blending of wastes that may be prone to pipeline plugging such as the uranium phase, clarkeite with other wastes. The Hanford tank waste solids were analyzed with SEM combined with x-ray Energy Dispersive Spectroscopy (EDS) using APA. This method can allow thousands of individual particles to be characterized and so may provide a more representative view of the samples and information PSDDs. We characterized several as-received sludge samples and developed PSDD with APA. Sample preparation methods were found to impact the collected results, as it was more difficult to collect representative samples of the larger particles. Much of the material was also dried and cemented together that further impacted results.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Automated structural analysis of small angle scattering data from common nanoparticles via machine learning
A data-efficient and trustworthy machine learning approach is developed for automated inverse analysis of small angle scattering data.
Roberts, Graham [School of Computing, University o↗
In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination
Abstract Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on‐site, on‐the‐fly analysis needed for real‐time process monitoring. Here, a weighted lagged cross‐correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X‐ray diffraction metrology to monitor the calcination process of the archetypal nickel‐based cathode, LiNiO 2 . The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO 2 , providing information for agile on‐site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early‐stage phase nucleation and growth. Expanding this data‐powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.
36 MATERIALS SCIENCE↗
Pit depth analysis using automated software for three containers with complete imaging
The 3013 surveillance program is tasked with ensuring the long term safety of plutonium storage in 3013-compliant containers. One component of this task is to understand corrosion features found on the interior sidewall of the inner container closure weld region (ICCWR). To this end, three containers were selected to have laser confocal microscope (LCM) 20X imaging of the entire ICCWR. This report describes the analysis of the LCM pit depth data using the automated corrosion analysis software. The analysis included determining maximum pit depths and distributions of pit depths, volumes, and diameters for all pit features identified on the three containers. Results provided insight into possible corrosion mechanisms and were consistent with previous pit growth model predictions.
36 MATERIALS SCIENCE↗