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NASA NTRS1985

Expert systems for Space Station automation

The expert systems required for automating key functions of the Manned Space Station (MSS) are explored. It is necessary that the expert systems developed be flexible, degrade gracefully in the case of a failure, and be able to work with incomplete data. The AI systems will have to perform interpretation and diagnosis, design, prediction and induction, and monitoring and control functions. Both quantitative and qualitative reasoning capabilities need improvements, as do automatic verification techniques, explanation and learning capabilities, and the use of metaknowledge, i. e., knowledge about the knowledge contained in the knowledge base. Information retrieval, fault isolation and manufacturing process control demonstrations are needed to validate expert systems for the MSS.

Georgeff, M. P.↗

NASA NTRS1988

A generic expert system for materials processing in space

A generic expert system is described for inspecting materials processed in space (MPS). The system may be applied, with the appropriate knowledge base, to any of the nondestructive testing methods (NDT) which are appropriate to MPS. Regardless of the method being used, the inspection process consists of three tasks: (1) signal or image processing of the NDT output and feature extraction, (2) interpretation of features in terms of MPS discontinuities, and (3) evaluation of the quality of the MPS based upon industry standards. In contrast to rule based systems, this system represents its knowledge as multidimensional vectors and appropriate functions on them. Currently, the expert system accepts manual input of observed features. Once the expert system has been tested and compared to human expert inspectors, a vision front-end will be developed to complete automation of the expert MPS inspection system, based on visual discontuities. Then the data base will be extended to include a variety of other NDT methods. In addition to functional performance, ease of use was established through menu window driven input as well as flexibility in building, using and modifying data bases for different applications.

Andersen, Kristinn↗

NASA NTRS1998

Flight Evaluation of Center-TRACON Automation System Trajectory Prediction Process

Two flight experiments (Phase 1 in October 1992 and Phase 2 in September 1994) were conducted to evaluate the accuracy of the Center-TRACON Automation System (CTAS) trajectory prediction process. The Transport Systems Research Vehicle (TSRV) Boeing 737 based at Langley Research Center flew 57 arrival trajectories that included cruise and descent segments; at the same time, descent clearance advisories from CTAS were followed. Actual trajectories of the airplane were compared with the trajectories predicted by the CTAS trajectory synthesis algorithms and airplane Flight Management System (FMS). Trajectory prediction accuracy was evaluated over several levels of cockpit automation that ranged from a conventional cockpit to performance-based FMS vertical navigation (VNAV). Error sources and their magnitudes were identified and measured from the flight data. The major source of error during these tests was found to be the predicted winds aloft used by CTAS. The most significant effect related to flight guidance was the cross-track and turn-overshoot errors associated with conventional VOR guidance. FMS lateral navigation (LNAV) guidance significantly reduced both the cross-track and turn-overshoot error. Pilot procedures and VNAV guidance were found to significantly reduce the vertical profile errors associated with atmospheric and airplane performance model errors.

Williams, David H.↗

NASA NTRS2002

Thermophysical Property Measurements in the MSFC ESL

Electrostatic Levitation (ESL) is an advanced technique for containerless processing of metals, ceramics, and semiconductors. Because no container is required, there is no contamination from reaction with a crucible, allowing processing of high temperature, highly reactive melts. The high vacuum processing environment further reduces possible contamination of the samples. Finally, there is no container to provide heterogeneous nucleation sites, so the undercooled range is also accessible for many materials. For these reasons, ESL provides a unique environment for measuring thermophysical properties of liquid materials. The properties that can be measured in ESL include density, surface tension, viscosity, electrical and thermal conductivity, specific heat, phase diagram, TTT- and CCT- curves, and other thermodynamic properties. In this paper, we present data on surface tension and viscosity, measured by the oscillating drop technique, and density, measured by an automated photographic technique, measured in the ESL at NASA Marshall Space Flight Center.

Hyers, R. W.↗

NASA NTRS2011

Guidance, Navigation, and Control Program

The Rendezvous and Proximity Operations Program (RPOP) is real-time guidance, navigation, and control (GN&C) domain piloting-aid software that provides 3D Orbiter graphics and runs on the Space Shuttle's Criticality-3 Payload and General Support Computer (PGSC) in the crew cockpit. This software provides the crew with Situational Awareness during the rendezvous and proximity operations phases of flight. RPOP can be configured from flight to flight, accounting for mission-specific flight scenarios and target vehicles, via initialization load (I-load) data files. The software provides real-time, automated, closed-loop guidance recommendations and the capability to integrate the crew s manual backup techniques. The software can bring all relative navigation sensor data, including the Orbiter's GPC (general purpose computer) data, into one central application to provide comprehensive situational awareness of the rendezvous and proximity operations trajectory. RPOP also can separately maintain trajectory estimates (past, current, and predicted) based on certain data types and co-plot them, in order to show how the various navigation solutions compare. RPOP s best estimate of the relative trajectory is determined by a relative Kalman filter processing data provided by the sensor suite s most accurate sensor, the trajectory control sensor (TCS). Integrated with the Kalman filter is an algorithm that identifies the reflector that the TCS is tracking. Because RPOP runs on PC laptop computers, the development and certification lifecycles are more agile, flexible, and cheaper than those that govern the Orbiter FSW (flight software) that runs in the GPC. New releases of RPOP can be turned around on a 3- to 6-month template, from new Change Request (CR) to certification, depending on the complexity of the changes.

Hinkel, Heather↗

DOE OSTI2024

Dose Consequence and Probabilistic Risk Assessment Integration into Digital Documented Safety Analysis

This is an intern poster presentation. The current reactor authorization process is complex and error prone. The development of a digital Documented Safety Analysis has been proposed to provide an automated and integrated solution to enhance the design and authorization process. The digital DSA will consist of interlinked models, analyses, and reports, all of which will be updated using automated workflows when design changes are made. This poster examines the integration of the probabilistic risk assessment (PRA) with the transient and dose consequence analyses. A PRA for a generic high temperature gas-cooled reactor (HTGR) has been constructed which will drive the input parameters for a transient analysis model currently being constructed. Dose consequence will be determined using the results of the transient analysis and the Radiation Safety Analysis Computer (RSAC) code. Dose consequence data will then be input back into the PRA to drive design parameters.

42 ENGINEERING↗

NASA NTRS2000

Video Based Sensor for Tracking 3-Dimensional Targets

Video-Based Sensor for Tracking 3-Dimensional Targets The National Aeronautics and Space Administration's (NASAs) Marshall Space Flight Center (MSFC) has been developing and testing video-based sensors for automated spacecraft guidance for several years, and the next generation of video sensor will have tracking rates up to 100 Hz and will be able to track multiple reflectors and targets. The Video Guidance Sensor (VGS) developed over the past several years has performed well in testing and met the objective of being used as the terminal guidance sensor for an automated rendezvous and capture system. The first VGS was successfully tested in closed-loop 3-degree-of-freedom (3- DOF) tests in 1989 and then in 6-DOF open-loop tests in 1992 and closed-loop tests in 1993-4. Development and testing continued, and in 1995 approval was given to test the VGS in an experiment on the Space Shuttle. The VGS flew in 1997 and in 1998, performing well for both flights. During the development and testing before, during, and after the flight experiments, numerous areas for improvement were found. The VGS was developed with a sensor head and an electronics box, connected by cables. The VGS was used in conjunction with a target that had wavelength-filtered retro-reflectors in a specific pattern, The sensor head contained the laser diodes, video camera, and heaters and coolers. The electronics box contained a frame grabber, image processor, the electronics to control the components in the sensor head, the communications electronics, and the power supply. The system works by sequentially firing two different wavelengths of laser diodes at the target and processing the two images. Since the target only reflects one wavelength, it shows up well in one image and not at all in the other. Because the target's dimensions are known, the relative positions and attitudes of the target and the sensor can be computed from the spots reflected from the target. The system was designed to work from I meter out to I 10 meters. The VGS was mounted on the Space Shuttle while its target was mounted on the Spartan free-flyer carried on the same Shuttle flight. The VGS tracked the Spartan at ranges up to 170m, and the VGS range data very closely matched the range data from the Hand-Held Laser- Rangefinder used by the astronauts on board the Shuttle. While the VGS was designed primarily as a terminal guidance sensor for an automated spacecraft, it could be applied to other uses. It could be used as an alignment aid for an operator of a remote system (giving position and attitude feedback data, as well as a camera view of the target), as a feedback system for a robotic arm, or for automated vehicle guidance. The next generation VGS, with its higher tracking rates, smaller size, and lower power could be used in more places than the original VGS, and by using LED's instead of laser diodes, the system would be eye-safe at any range. Other possible uses include tracking 3-dimensional objects with retro-reflectors mounted at various locations or motion analysis by placing several retro-reflectors on the moving object and tracking them at high speeds. There are few sensors capable of performing tasks similar to those the VGS can perform, and the next generation VGS will be even more capable than the original. Some of this work is previously presented in the papers.

Howard, R. T.↗

DOE OSTI2021

Automated Classification of Plasma Regions Using 3D Particle Energy Distributions

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is urn:x-wiley:21699380:media:jgra56742:jgra56742-math-0001%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗

NASA NTRSDate not supplied

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗

NASA NTRS1974

Automated land-use mapping from spacecraft data

The author has identified the following significant results. In response to the need for a faster, more economical means of producing land use maps, this study evaluated the suitability of using ERTS-1 computer compatible tape (CCT) data as a basis for automatic mapping. Significant findings are: (1) automatic classification accuracy greater than 90% is achieved on categories of deep and shallow water, tended grass, rangeland, extractive (bare earth), urban, forest land, and nonforested wet lands; (2) computer-generated printouts by target class provide a quantitative measure of land use; and (3) the generation of map overlays showing land use from ERTS-1 CCTs offers a significant breakthrough in the rate at which land use maps are generated. Rather than uncorrected classified imagery or computer line printer outputs, the processing results in geometrically-corrected computer-driven pen drawing of land categories, drawn on a transparent material at a scale specified by the operator. These map overlays are economically produced and provide an efficient means of rapidly updating maps showing land use.

Chase, P. E.↗

NASA NTRS2020

Automated Cardiovascular Pathology Assessment using Semantic Segmentation and Ensemble Learning

Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, 0.849 for structure segmentation of the left ventricle (LV), myocardium and right ventricle (RV) respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate potential for advanced machine learning methods to deliver accurate, efficient and reproducible cardiac pathological assessment.

Semantic Segmentation↗

NASA NTRS1987

Automating the parallel processing of fluid and structural dynamics calculations

The NASA Lewis Research Center is actively involved in the development of expert system technology to assist users in applying parallel processing to computational fluid and structural dynamic analysis. The goal of this effort is to eliminate the necessity for the physical scientist to become a computer scientist in order to effectively use the computer as a research tool. Programming and operating software utilities have previously been developed to solve systems of ordinary nonlinear differential equations on parallel scalar processors. Current efforts are aimed at extending these capabilities to systems of partial differential equations, that describe the complex behavior of fluids and structures within aerospace propulsion systems. This paper presents some important considerations in the redesign, in particular, the need for algorithms and software utilities that can automatically identify data flow patterns in the application program and partition and allocate calculations to the parallel processors. A library-oriented multiprocessing concept for integrating the hardware and software functions is described.

Arpasi, Dale J.↗

NASA NTRS1987

Automating the parallel processing of fluid and structural dynamics calculations

The NASA Lewis Research Center is actively involved in the development of expert system technology to assist users in applying parallel processing to computational fluid and structural dynamic analysis. The goal of this effort is to eliminate the necessity for the physical scientist to become a computer scientist in order to effectively use the computer as a research tool. Programming and operating software utilities have previously been developed to solve systems of ordinary nonlinear differential equations on parallel scalar processors. Current efforts are aimed at extending these capabilties to systems of partial differential equations, that describe the complex behavior of fluids and structures within aerospace propulsion systems. This paper presents some important considerations in the redesign, in particular, the need for algorithms and software utilities that can automatically identify data flow patterns in the application program and partition and allocate calculations to the parallel processors. A library-oriented multiprocessing concept for integrating the hardware and software functions is described.

Arpasi, Dale J.↗

NASA NTRSDate not supplied

Automated classification of scientific publications linked to GES DISC datasets

The data collections archived and distributedby the GES DISC NASA data center arewidely utilized for various Earth Science studies.As these collections are created, many researchworks are published regarding the collections, algorithms,validations and applications. SinceGES DISC collects these publications and providestheir citations for the users, it is helpful tocategorize them based on how they relate to the datasetsthey are associated with. Specifically,whether the publication that is linked to GES DISCdataset is using it for applicational research,or if it describes the algorithm for dataset creation,or the validation of the dataset, or providesthe general overview of the data collection. Currently,this process requires simple manuallabelling, and as such, may be possible to solve viaautomation. To approach this problem, wedeveloped machine learning classifiers to predictthe category a publication belongs to. We usedmanually labeled publications as training data forsupervised machine learning algorithms:Random Forest and Naive Bayes. We achieved classificationaccuracy that is substantially betterthan the baseline accuracy, thus greatly improvingthe efficiency of the publication internalanalysis.

Rohan Dayal↗

DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Kim, Yoon G.↗

DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗

NASA NTRS1987

Automated rectification and geocoding of SAR imagery

An automated post-processing system has been developed for rectification and geocoding of SAR (Synthetic Aperture Radar) imagery. The system uses as input a raw uncorrected image from the operational SAR correlator, and produces as a standard output a rectified and geocoded product. The accurate geolocation of SAR image pixels is provided by a spatial transformation model which maps the slant range-azimuth SAR image pixels into their location on a prespecified map grid. This model predicts the geodetic location of each pixel by utilizing: the sensor platform position; a geoid model; the parameters of the data collection system and the processing parameters used in the SAR correlator. Based on their geodetic locations, the pixels are mapped by using the desired cartographic projection equations. This rectification and geocoding technique has been tested with Seasat and SIR-B images. The test results demonstrate absolute location uncertainty of less than 50 m and relative distortion (scale factor and skew) of less than 0.1 percent relative to local variations from the assumed geoid.

Kwok, R.↗