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An overview is presented of the Automated Reasoning CICT Program/Intelligent Systems project. Automated reasoning technology will help NASA missions by increasing the amount of science achieved, ensuring safety of spacecraft and surface explorers, and by enabling more robust mission operations.
A diamond turning process has made possible a significant advance in the art of whispering-gallery-mode (WGM) optical resonators. By use of this process, it is possible to fashion crystalline materials into WGM resonators that have ultrahigh resonance quality factors (high Q values), are compact (ranging in size from millimeters down to tens of microns), and support single electromagnetic modes. This development combines and extends the developments reported in "Few- Mode Whispering-Gallery-Mode Resonators" (NPO-41256), NASA Tech Briefs, Vol. 30, No. 1 (January 2006), page 16a and "Fabrication of Submillimeter Axisymmetric Optical Components" (NPO-42056), NASA Tech Briefs, Vol. 31, No. 5 (May 2007), page 10a. To recapitulate from the first cited prior article: A WGM resonator of this special type consists of a rod, made of a suitable transparent material, from which protrudes a thin circumferential belt of the same material. The belt is integral with the rest of the rod and acts as a circumferential waveguide. If the depth and width of the belt are made appropriately small, then the belt acts as though it were the core of a single-mode optical fiber: the belt and the rod material adjacent to it support a single, circumferentially propagating mode or family of modes. To recapitulate from the second cited prior article: A major step in the fabrication of a WGM resonator of this special type is diamond turning or computer numerically controlled machining of a rod of a suitable transparent crystalline material on an ultrahigh-precision lathe. During the rotation of a spindle in which the rod is mounted, a diamond tool is used to cut the rod. A computer program is used to control stepping motors that move the diamond tool, thereby controlling the shape cut by the tool. Because the shape can be controlled via software, it is possible to choose a shape designed to optimize a resonator spectrum, including, if desired, to limit the resonator to supporting a single mode. After diamond turning, a resonator can be polished to increase its Q. By virtue of its largely automated, computer-controlled nature, the process is suitable for mass production of nominally identical single-mode WGM resonators. In a demonstration of the capabilities afforded by this development, a number of WGM resonators of various designs were fabricated side by side on the surface of a single CaF2 rod (see figure).
In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.
On-demand mobility (ODM) through aviation refers to the ability to quickly and easily move people or equivalent cargo without delays introduced by lack of, or infrequently, scheduled service. A necessary attribute of ODM is that it be easy to use, requiring a minimum of special training, skills, or workload. Fully-autonomous vehicles would provide the ultimate in ease-of-use (EU) but are currently unproven for safety-critical applications outside of a few, situationally constrained applications (e.g. automated trains operating in segregated systems). Applied to aviation, the current and near-future state of the art of full-autonomy, may entail undesirable trade-offs such as very conservative operational margins resulting in reduced trip reliability and transportation utility. Furthermore, acceptance by potential users and regulatory authorities will be challenging without confidence in autonomous systems in developed in less critical, but still challenging applications. A question for the aviation community is how we can best develop practical ease-of-use for aircraft that are sized to carry a small number of passengers (e.g. 1-9) or equivalent cargo. Such development is unlikely to be a single event, but rather a managed, evolutionary process where responsibility and authority transitions from human to automation agents as operational experience is gained with increasingly intelligent systems. This talk presents a technology road map being developed at NASA Langley, as part of an overall strategy to foster ODM, for the development of ease-of-use for ODM aviation.
In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of man-made radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short “transient” RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation
A manufacturing process to produce 80 single aisle sized thermoplastic composite aircraft a month is presented. Within the Hi-Rate Composite Aircraft Manufacturing (HiCAM) program a baseline aircraft fuselage was designed and modeled based on the Boeing 787 one-piece barrel build, but scaled down to a single aisle aircraft. Key Performance Parameters (KPPs) of recurring cost, non-recurring cost, and weight are calculated from the model. The outcome of this simulation indicated a prohibitively high non-recurring cost, so the thermoplastic team modeled the manufacturing flow for a panelized fuselage concept. The minimum success criteria is a 30% cost reduction and less than two percent increase in weight compared to the baseline aircraft. Automated Fiber Placement (AFP) is used to layup skins, stamp forming is used to make body frames, stringers, and passenger door surround parts. In the model, Co-Fusion was used to consolidate the skin while simultaneously welding on stringers to make stiffened panels. Additional welding technologies were considered to minimize fastener use and a vertical build concept was used for final assembly to reduce factory size. This manufacturing concept for a thermoplastic aircraft indicated a 39% cost and 12% weight savings compared to the baseline. Additional trade studies were conducted using Northrop Grumman’s Automated Stiffener Forming technology to make stringers and stamp forming instead of AFP to make skins.
A manufacturing process to produce 80 single aisle sized thermoplastic composite aircraft a month is presented. Within the Hi-Rate Composite Aircraft Manufacturing (HiCAM) program a baseline aircraft fuselage was designed and modeled based on the Boeing 787 one-piece barrel build, but scaled down to a single aisle aircraft. Key Performance Parameters (KPPs) of recurring cost, non-recurring cost, and weight are calculated from the model. The outcome of this simulation indicated a prohibitively high non-recurring cost, so the thermoplastic team modeled the manufacturing flow for a panelized fuselage concept. The minimum success criteria is a 30% cost reduction and less than two percent increase in weight compared to the baseline aircraft. Automated Fiber Placement (AFP) is used to layup skins, stamp forming is used to make body frames, stringers, and passenger door surround parts. In the model, Co-Fusion was used to consolidate the skin while simultaneously welding on stringers to make stiffened panels. Additional welding technologies were considered to minimize fastener use and a vertical build concept was used for final assembly to reduce factory size. This manufacturing concept for a thermoplastic aircraft indicated a 39% cost and 12% weight savings compared to the baseline. Additional trade studies were conducted using Northrop Grumman’s Automated Stiffener Forming technology to make stringers and stamp forming instead of AFP to make skins.
Dynamic wireless charging of electric vehicles is a flexible and state of the art charging technology with the potential capability of enabling fully automated in-motion charging. With charging power increasing to more than 100 kW for light duty vehicles, electromagnetic field (EMF) emission becomes a critical challenge. Due to the high costs of ferrite materials, this paper proposes an active shielding solution with multiple canceling coils installed on the ground side to supplement ferrite passive shielding to ensure electromagnetic safety. Two canceling coils are designed on two sides of the ground side coil. The canceling coils are small in size and 180 degree opposite in phase to the ground coil. Simulation and modeling shows that the canceling coils can reduce EMF emission from 37.2 µT to 18.2 µT at 0.8 m during 100 kW operation with only 2.5% of ground side current flowing in the canceling coils. These results have been preliminarily verified by inductance measurements and magnetic field measurement at 1.1 m distance without canceling coils. By further increasing the canceling coils’ currents to 4%, EMF emission can also be mitigated at 200 kW, although the canceling coils’ shape, position, and phase angle can be further optimized to improve the three-dimensional field distribution.
In order to meet the stringent time-critical requirements for real-time man-in-the-loop flight simulation, computer processing operations must be consistent in processing time and be completed in as short a time as possible. These operations include simulation mathematical model computation and data input/output to the simulators. In 1986, in response to increased demands for flight simulation performance, NASA's Langley Research Center (LaRC), working with the contractor, developed extensions to the Computer Automated Measurement and Control (CAMAC) technology which resulted in a factor of ten increase in the effective bandwidth and reduced latency of modules necessary for simulator communication. This technology extension is being used by more than 80 leading technological developers in the United States, Canada, and Europe. Included among the commercial applications are nuclear process control, power grid analysis, process monitoring, real-time simulation, and radar data acquisition. Personnel at LaRC are completing the development of the use of supercomputers for mathematical model computation to support real-time flight simulation. This includes the development of a real-time operating system and development of specialized software and hardware for the simulator network. This paper describes the data acquisition technology and the development of supercomputing for flight simulation.
Launch automation programs for Saturn IB SYSTEM utilizing digital computer techniques to increase vehicle reliability from manufacturing through prelaunch checkout
In fiscal year 1988 a study was begun to define the platform management system (PMS) functions required for the mature platform operations era. The objectives of the task include: (1) defining how to increase the operational productivity of the platform by providing enhanced capability for responding to changing events, (2) influencing the initial PMS design by identifying required 'hooks and scars', and (3) evaluation potential automation techniques that are appropriate given predicted onboard computing resources. Initial platform operations scenarios were defined. The focus was on PMS-related functions where operations enhancements are likely to occur. Operations productivity was defined in terms of scientific productivity of the platform as well as the level of automation of the ground system. The Platform Operations Productivity Enhancement Report was completed earlier this year documenting system enhancements to increase science productivity and ground system automation. Using the baseline PMS defined in the PMS Definition Document as a starting point, the resulting PMS-specific enhancements were molded into a sequence of progressively more sophisticated operations management capabilities. This sequence of upgrades to the PMS has been documented in a PMS Evolution Plan. The plan includes enhancements in the areas of resources scheduling, resource modeling, system and payload anomaly management, and transaction sequence interpretation. A plan for migration of functions from the ground portion of the PMS to the flight portion is also included. The impacts of this plan on the platform are now being documented to ensure that the required 'hooks and scars' are included in the baseline system. Future plans include a prototype of some of the PMS enhancements to address the feasibility of and techniques for implementing these enhancements in the onboard computing environment.
GPS-based tracking is increasingly becoming the tracking system of choice for low-Earth orbiters
The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.
The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented data standards and automated data pipelines for the following data types: 1) drilling data, 2) geospatial datasets, and 3) DAS data. An additional data pipeline is proposed for stimulation data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how we can improve this process.
The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has implemented or is currently implementing data standards and automated data pipelines for the following geothermal data types: 1) drilling data, 2) geospatial datasets, and 3) Distributed Acoustic Sensing (DAS) data. These data standards and pipelines are intended to improve the real-world applicability of geothermal machine learning outputs through improving the quality of data. More specifically, through standardizing high-value datasets, the GDR is reducing project-specific data curation requirements, allowing more time to be spent on actual research. By automating this process, the burden of standardization is taken off of the user, overall increasing the availability of standardized data. This paper provides an update on the GDR's transition toward data standardization through automated data pipelines and calls for feedback from the community on how the GDR team can improve this process.
A general functional description of totally automatic photometry of emission spectra is not available for an operating environment in which the sample compositions and analysis procedures are low-volume and non-routine. The advantages of using an interactive approach to computer control in such an operating environment are demonstrated. This approach includes modular subroutines selected at multiple-option, menu-style decision points. This style of programming is used to trace elemental determinations, including the automated reading of spectrographic plates produced by a 3.4 m Ebert mount spectrograph using a dc-arc in an argon atmosphere. The simplified control logic and modular subroutine approach facilitates innovative research and program development, yet is easily adapted to routine tasks. Operator confidence and control are increased by the built-in options including degree of automation, amount of intermediate data printed out, amount of user prompting, and multidirectional decision points.
The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a surrogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention.
Advanced automation has been accompanied, particularly in aircraft, with a proliferation of modes, where modes define mutually exclusive sets of system behavior. The new mode-rich systems provide flexibility and enhanced capabilities, but they also increase the need for and difficulty of maintaining mode awareness. While automation has eliminated some types of operator mode-awareness errors, it has also created the potential for new types of mode-related problems.