Results for “machining processes” — Page 48

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗

NASA NTRSDate not supplied

A Census of Severe Weather as Observed From Aqua: Visible/IR and Passive-Microwave Perspectives of Severe Convection

Severe weather phenomena represent the extreme upper end of the spectrum of convection and precipitation and tend to be highly localized and relatively rare compared to the rest of the distribution, but they can cause damage and loss disproportionate to their scale and frequency. Fortunately, severe convection exhibits distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Leveraging these signatures individually has become a long-established practice to detect, analyze and establish climatologies of severe thunderstorms, especially in instances where traditional ground-based data may be unavailable. Spaceborne visible/IR and passive-microwave approaches are not without their pitfalls, however: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis in six different geographical regimes throughout the Aqua domain. We present a census of potentially severe convective storms and their environments as seen by multiple instruments simultaneously, investigating how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically. Using statistical machine learning analysis, we aim to quantify the optimal MODIS and AMSR-E parameter sets for discriminating severe from non-severe storm cells and assess what improvement (if any) in detection results from combining the IR, visible, and microwave datasets.

Sarah Bang↗

NASA NTRS2024

Accelerating Continuous Variable Coherent Ising Machines Via Momentum

The Coherent Ising Machine (CIM) is a non-conventional architecture that takes inspiration from physical annealing processes to solve Ising problems heuristically. Its dynamics are naturally continuous and described by a set of ordinary differential equations that have been proven to be useful for the optimization of continuous variables nonconvex quadratic optimization problems. The dynamics of such Continuous Variable CIMs (CV-CIM) encourage optimization via optical pulses whose amplitudes are determined by the negative gradient of the objective; however, standard gradient descent is known to be trapped by local minima and hampered by poor problem conditioning. In this work, we propose to modify the CV-CIM dynamics using more sophisticated pulse injections based on tried-and-true optimization techniques such as momentum and Adam. Through numerical experiments, we show that the momentum and Adam updates can significantly speed up the CV-CIM’s convergence and improve sample diversity over the original CV-CIM dynamics. We also find that the Adam-CV-CIM’s performance is more stable as a function of feedback strength, especially on poorly conditioned instances, resulting in an algorithm that is more robust, reliable, and easily tunable. More broadly, we identify the CIM dynamical framework as a fertile opportunity for exploring the intersection of classical optimization and modern analog computing.

Ising Model↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗

NASA NTRS2015

Model Generation to Support Model-Based Testing Applied on NASA DAT - An Experience Report

Model-based Testing (MBT), where a model of the system under tests (SUT) behavior is used to automatically generate executable test cases, is a promising and versatile testing technology. Nevertheless, adoption of MBT technologies in industry is slow and many testing tasks are performed via manually created executable test cases (i.e. test programs such as JUnit). In order to adopt MBT, testers must learn how to construct models and use these models to generate test cases, which might be a hurdle. An interesting observation in our previous work is that the existing manually created test cases often provided invaluable insights for the manual creation of the testing models of the system. In this paper we present an approach that allows the tester to first create and debug a set of test cases. When the tester is happy with the test cases, the next step is to automatically generate a model from the test cases. The generated model is derived from the test cases, which are actions that the system can perform (e.g. a button clicks) and their expected outputs in form of assert statements (e.g. assert data entered). The model is a Finite State Machine (FSM) model that can be employed with little or no manual changes to generate additional test cases for the SUT. We successfully applied the approach in a feasibility study to the NASA Data Access Toolkit (DAT), which is a web-based GUI. One compelling finding is that the test cases that were generated from the automatically generated models were able to detect issues that were not detected by the original set of manually created test cases. We present the findings from the case study and discuss best practices for incorporating model generation techniques into an existing testing process.

State Machines↗

NASA NTRS2020

Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

Antarctic↗

NASA NTRS1985

Processing study of injection molding of silicon nitride for engine applications

The high hardness of silicon nitride, which is currently under consideration as a structural material for such hot engine components as turbine blades, renders machining of the material prohibitively costly; the near net shape forming technique of injection molding is accordingly favored as a means for component fabrication. Attention is presently given to the relationships between injection molding processing parameters and the resulting microstructural and mechanical properties of the resulting engine parts. An experimental program has been conducted under NASA sponsorship which tests the quality of injection molded bars of silicon nitride at various stages of processing.

Rorabaugh, M. E.↗

NASA NTRS2000

Development of Individually Addressable Micro-Mirror-Arrays for Space Applications

We have been developing a 32 x 32 prototype array of individually addressable Micro-Mirrors capable of operating at cryogenic temperature for Earth and Space Science applications. Micro-Mirror-Array technology has the potential to revolutionize imaging and spectroscopy systems for NASA's missions of the 21st century. They can be used as programmable slits for the Next Generation Space Telescope, as smart sensors for a steerable spectrometer, as neutral density filters for bright scene attenuation etc. The, entire fabrication process is carried out in the Detector Development Laboratory at NASA, GSFC. The fabrication process is low temperature compatible and involves integration of conventional CMOS technology and surface micro-machining used in MEMS. Aluminum is used as the mirror material and is built on a silicon substrate containing the CMOS address circuit. The mirrors are 100 microns x l00 microns in area and deflect by +/- 10 deg induced by electrostatic actuation between two parallel plate capacitors. A pair of thin aluminum torsion straps allow the mirrors to tilt. Finite-element-analysis and closed form solutions using electrostatic and mechanical torque for mirror operation were developed and the results were compared with laboratory performance. The results agree well both at room temperature and at cryogenic temperature. The development demonstrates the first cryogenic operation of two-dimensional Micro-Mirrors with bi-state operation. Larger arrays will be developed meeting requirements for different science applications. Theoretical analysis, fabrication process, laboratory test results and different science applications will be described in detail.

Dutta, Sanghamitra B.↗

NASA NTRS2005

Multilayer Composite Pressure Vessels

A method has been devised to enable the fabrication of lightweight pressure vessels from multilayer composite materials. This method is related to, but not the same as, the method described in gMaking a Metal- Lined Composite-Overwrapped Pressure Vessel h (MFS-31814), NASA Tech Briefs, Vol. 29, No. 3 (March 2005), page 59. The method is flexible in that it poses no major impediment to changes in tank design and is applicable to a wide range of tank sizes. The figure depicts a finished tank fabricated by this method, showing layers added at various stages of the fabrication process. In the first step of the process, a mandrel that defines the size and shape of the interior of the tank is machined from a polyurethane foam or other suitable lightweight tooling material. The mandrel is outfitted with metallic end fittings on a shaft. Each end fitting includes an outer flange that has a small step to accommodate a thin layer of graphite/epoxy or other suitable composite material. The outer surface of the mandrel (but not the fittings) is covered with a suitable release material. The composite material is filament- wound so as to cover the entire surface of the mandrel from the step on one end fitting to the step on the other end fitting. The composite material is then cured in place. The entire workpiece is cut in half in a plane perpendicular to the axis of symmetry at its mid-length point, yielding two composite-material half shells, each containing half of the foam mandrel. The halves of the mandrel are removed from within the composite shells, then the shells are reassembled and bonded together with a belly band of cured composite material. The resulting composite shell becomes a mandrel for the subsequent steps of the fabrication process and remains inside the final tank. The outer surface of the composite shell is covered with a layer of material designed to be impermeable by the pressurized fluid to be contained in the tank. A second step on the outer flange of each end fitting accommodates this layer. Depending on the application, this layer could be, for example, a layer of rubber, a polymer film, or an electrodeposited layer of metal. If the fluid to be contained in the tank is a gas, then the best permeation barrier is electrodeposited metal (typically copper or nickel), which can be effective at a thickness of as little as 0.005 in (.0.13 mm). The electrodeposited metal becomes molecularly bonded to the second step on each metallic end fitting. The permeation-barrier layer is covered with many layers of filament-wound composite material, which could be the same as, or different from, the composite material of the inner shell. Finally, the filament-wound composite material is cured in an ov

DeLay, Tom↗

NASA NTRS1982

Cell and module formation research area

Metallization is discussed. The influence of hydrogen on the firing of base-metal pastes in reducing atmospheres is reported. A method for optimization of metallization patterns is presented. A process sequence involving an AR coating and thick-film metallization system capable of penetrating the AR coating during firing is reported. Design and construction of the NMA implantation machine is reported. Implanted back-surface fields and NMA primary (front) junctions are discussed. The use of glass beads, a wave-soldering device, and ion milling is reported. Processing through the module fabrication and environmental testing of its design are reported. Metallization patterns by mathematical optimization are assessed.

Bickler, D. B.↗

NASA NTRS1989

Task-oriented display design: Concept and example

The general topic was in the area of display design alternatives for improved man-machine performance. The intent was to define and assess a display design concept oriented toward providing this task-oriented information. The major focus of this concept deals with the processing of data into parameters that are more relevant to the task of the human operator. Closely coupled to this concept of relevant information is the form or manner in which this information is actually presented. Conventional forms of presentation are normally a direct representation of the underlying data. By providing information in a form that is more easily assimilated and understood, a reduction in human error and cognitive workload may be obtained. A description of this proposed concept with a design example is provided. The application for the example was an engine display for a generic, twin-engine civil transport aircraft. The product of this concept was evaluated against a functionally similar, traditional display. The results of this evaluation showed that a task-oriented approach to design is a viable concept with regard to reducing user error and cognitive workload. The goal of this design process, providing task-oriented information to the user, both in content and form, appears to be a feasible mechanism for increasing the overall performance of a man-machine system.

Abbott, Terence S.↗

NASA NTRS1989

Task-oriented display design - Concept and example

The general topic was in the area of display design alternatives for improved man-machine performance. The intent was to define and assess a display design concept oriented toward providing this task-oriented information. The major focus of this concept deals with the processing of data into parameters that are more relevant to the task of the human operator. Closely coupled to this concept of relevant information is the form or manner in which this information is actually presented. Conventional forms of presentation are normally a direct representation of the underlying data. By providing information in a form that is more easily assimilated and understood, a reduction in human error and cognitive workload may be obtained. A description of this proposed concept with a design example is provided. The application for the example was an engine display for a generic, twin-engine civil transport aircraft. The product of this concept was evaluated against a functionally similar, traditional display. The results of this evaluation showed that a task-oriented approach to design is a viable concept with regard to reducing user error and cognitive workload. The goal of this design process, providing task-oriented information to the user, both in content and form, appears to be a feasible mechanism for increasing the overall performance of a man-machine system.

Abbott, Terence S.↗

NASA NTRS1994

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

Sheu, Bing J.↗

NASA NTRS1987

Fluid dynamics parallel computer development at NASA Langley Research Center

To accomplish more detailed simulations of highly complex flows, such as the transition to turbulence, fluid dynamics research requires computers much more powerful than any available today. Only parallel processing on multiple-processor computers offers hope for achieving the required effective speeds. Looking ahead to the use of these machines, the fluid dynamicist faces three issues: algorithm development for near-term parallel computers, architecture development for future computer power increases, and assessment of possible advantages of special purpose designs. Two projects at NASA Langley address these issues. Software development and algorithm exploration is being done on the FLEX/32 Parallel Processing Research Computer. New architecture features are being explored in the special purpose hardware design of the Navier-Stokes Computer. These projects are complementary and are producing promising results.

Townsend, James C.↗

NASA NTRS1988

Machine vision for real time orbital operations

Machine vision for automation and robotic operation of Space Station era systems has the potential for increasing the efficiency of orbital servicing, repair, assembly and docking tasks. A machine vision research project is described in which a TV camera is used for inputing visual data to a computer so that image processing may be achieved for real time control of these orbital operations. A technique has resulted from this research which reduces computer memory requirements and greatly increases typical computational speed such that it has the potential for development into a real time orbital machine vision system. This technique is called AI BOSS (Analysis of Images by Box Scan and Syntax).

Vinz, Frank L.↗

NASA NTRS1967

Continuous internal channels formed in aluminum fusion welds

Process produces continuous internal channel systems on a repeatable basis in 2014-T6 aluminum. Standard machining forms the initial channel, which is filled with tungsten carbide powder. TIG machine fusion welding completes formation of the channel. Chem-mill techniques enlarge it to the desired size.

Gault, J.↗