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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

ExCALiBR: An Instrument for Uncovering the Origin of the Moon’s Organics.

Lipids are organic molecules of great astrobiological interest for future NASA missions to the Moon, Mars and Icy Worlds. Lipids are essential for life as we know it, and likely also required for putative extraterrestrial organisms. These robust organics can be synthesized abiotically and survive for long periods of geological time. Laboratory characterization techniques are well established but are laborious, operator dependent, and require large volumes of consumables, precluding in situ analysis. We are developing an autonomous, miniaturized fluidic system, integrating lab techniques for lipid analysis while minimizing rea-gent volumes and concentrating organics for analysis, thereby increasing signal-to-noise ratios by orders of magnitude. This system, the Extractor for Chemical Analysis of Lipid Biomarkers in Regolith (ExCALiBR), will enable future organic surveys by extracting and concentrating lipids from approximately 50 grams of regolith using a fluidic and microfluidic sample processor made of materials compatible with non-aqueous solvents required for extraction. ExCAL-iBR can be deployed on a lander or used on a crewed mission.

M B Wilhelm↗
NASA NTRS2004

Intensity-based rock detection for acquiring onboard rover science

Missions to Mars already under study call for very long autonomous traverses (on the order of hundreds of meters per sol) during which there will be limited contact with Earth. The onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing science opportunities. By prioritizing data for downlink onboard, it is expected that the set of images selected for downlink by OASIS will have a consistently higher scientific interest then any set of equal number of images of the same traverse obtained using random or periodic sampling. Thus, OASIS can be used to increase the science returned from a long traverse. OASIS prioritizes data and identifies science opportunities by extracting information from image data and then using this information to prioritize the data and identify potential new science opportunities. Currently, the information extracted from the images is the location of the rocks in the image and several properties of these rocks such as size, albedo, texture and shape, In this paper we give an overview of the role of rock detection in the OASIS system and describe the functionality of the rock detector. We then present results on representative images

Judd, Michele↗
NASA NTRSDate not supplied

Smart Crop Farming Systems for Artemis Exploration Missions

Space crop production systems that mitigate risks of crew poor performance or illness due to inadequate food and nutrition are needed during manned Artemis exploration missions beyond LEO. Prototype farms must be designed for deployment on ISS and tested in manned platforms: Gateway, lunar habitats, and Mars trans-hab spacecraft in preparation for human missions to Mars. Food production must be optimal and safe for human consumption. Thus, plant growth facilities (i.e. Veggie and APH) can be enhanced with imaging systems (including hyperspectral, multispectral, lidar, and fluorescence imaging systems) for nondestructive monitoring of plant health, stress and assessing food safety. Databases of crop responses to stress obtained during ground studies can be used to develop novel artificial intelligence (AI) algorithms for optimizing crop production (i.e. environmental settings during growth) and for detecting crop indices that ensure food safety. Future farming systems should be sustainable and smart. Novel adaptive AI algorithms requiring limited data sets for calibration are needed for reducing crew intervention during plant cultivation except for maintenance and harvesting events. Eventually, AI driven control systems that include autonomous planting, growing, and harvesting as well as periodic sanitization need evaluation for supplementing crew diets with fresh produce during future Mars exploration missions.

O Monje↗
NASA NTRS2023

SMD Technology Highlights

Three technology highlights from the Science Mission Directorate (SMD) Astrophysics Division, Biological and Physical Sciences Division, and Planetary Science Division are featured: a) A new class of X-ray detector with unprecedented energy resolution and array size could help transform our understanding of the cosmos through unparalleled vision into the otherwise invisible universe. b) An autonomous microfluidic culturing system with CubeSat heritage is teaming up with two state-of-the-art radiation detectors to measure how biology responds to the radiation and reduced gravity environment on the lunar surface. c) Several core technologies developed via the Hot Operating Temperature Technology (HOTTech) Program were recently tested in the Glenn Extreme Environment Rig (GEER), which simulates the conditions on the surface of Venus.

yeast↗
NASA NTRSDate not supplied

High Density Vertiplex: Scalable Autonomous Operations Flight Test

The NASA High Density Vertiport project has completed a multi-aircraft flight test of a scalable autonomous vertiport prototype system. These tests included end to end system integration testing of hardware and software, operational procedure testing of defined roles and responsibilities within a vertiport environment, and human factors data collection. This paper provides an overview of the flight test setup , scenarios, and summary results.

AAM↗
NASA NTRSDate not supplied

Laser Beam Welding for in-Space Joining Demonstrated Under Vacuum on the Ground and By Parabolic Flight Experiments

High energy density electron beam welding enabled the first and, to date, only American weld performed in space during the M551 experiment on Skylab in 1973. Though welding is critical to 90% of durable goods manufacturing in America, there is not yet regular and reliable application of welding processes to the In-space Servicing, Assembly, and Manufacturing (ISAM) sector. Fundamental studies are needed to develop basic capabilities and to enhance fundamental process knowledge, which will support follow-on efforts to mature in-space welding for use in commercial, defense, and other aerospace applications. The current work seeks to build on past flights and improve understanding and quantification of materials joining in space conditions using laser beam welding (LBW). LBW offers several advantages over the electron beam welding of Skylab, amongst others: reduced electromagnetic interference, less exposure of operators to ionizing radiation, and flexible delivery through optical fibers supporting unique workpieces and joints. Since there is no orbital laboratory to mature laser beam welding for space, the current effort addresses maturation of laser beam welding through parabolic flights augmented with data collection to enable numerical modeling efforts that capture the physical effects of the space environment. This team has retrofitted an LBW experimental apparatus that can simulate the vacuum and, during a parabolic flight, the reduced gravity & microgravity conditions of in-space welding. A team of Capstone students modified the setup, originally developed by NASA Langley Research Center for electron beam free-form fabrication, to replace its electron gun with a 1 kW, 1070 nm Yb fiber laser. The apparatus was further instrumented with temperature sensing and high-speed welding cameras to monitor and record changes in the thermal state of the workpiece, the melt pool, the laser penetration level, and the development and orientation of spatter & plumes. The system will operate autonomously, demonstrating its utility to uncrewed missions. During upcoming parabolic flight campaigns expected summer 2024, this LBW equipment will weld common aerospace alloys of aluminum, stainless steel, and titanium under conditions representative of the space environment. This data will guide future computational modeling efforts of laser welding in space and help to qualify in-space welding as a viable ISAM technique.

laser beam welding↗
NASA NTRS2024

Considerations for Using Autonomous Flight Termination Software in Crewed Launch Vehicles

Autonomous flight termination systems (AFTS) are being progressively employed onboard launch vehicles to replace ground personnel and infrastructure needed to terminate flight or destruct the vehicle should an anomaly occur. This automation uses on-board real-time data and encoded logic to determine if the flight should be self-terminated. For uncrewed launch vehicles, FTS systems are required to protect the public and governed by the United States Space Force (USSF). For crewed missions, NASA must augment range AFTS requirements for crew safety and certify each flight according to human rating standards, thus adding unique requirements for reuse of software originally intended for uncrewed missions. This bulletin summarizes new information relating to AFTS to raise awareness of key distinctions, summarize considerations and outline best practices for incorporating AFTS into human-rated systems.

Avionics↗
NASA NTRS2026

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones↗
DOE OSTI2014

Materials and methods for autonomous restoration of electrical conductivity

An autonomic conductivity restoration system includes a solid conductor and a plurality of particles. The particles include a conductive fluid, a plurality of conductive microparticles, and/or a conductive material forming agent. The solid conductor has a first end, a second end, and a first conductivity between the first and second ends. When a crack forms between the first and second ends of the conductor, the contents of at least a portion of the particles are released into the crack. The cracked conductor and the released contents of the particles form a restored conductor having a second conductivity, which may be at least 90% of the first conductivity.

42 ENGINEERING↗
DOE OSTI2016

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studying the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.

Jha, Sumit Kumar↗
DOE OSTI2022

ESTABLISHING ETHICAL GUIDELINES FOR APPLYING ARTIFICIAL INTELLIGENCE TO IAEA SAFEGUARDS

Drawing upon globally recognized efforts by a variety of institutions, the paper develops a practical and defensible framework for consideration and use by the International Atomic Energy Agency (IAEA) to support the adoption of credible ethical standards for autonomous and intelligent systems (AIS). The analysis is based on a future hypothetical AIS that identifies signatures of misuse, diversion, and other research and development (R&D). The hypothetical system serves as an exemplar for subject matter expert (SME) workshop participants to begin to answer ethical AIS questions around how these systems should be governed and maintained, what the internal and external considerations associated with artificial intelligence (AI) tools are, and where the boundaries between ethical and non-ethical uses are. The ethical AIS workshop explored several important topics: (a) Recommendations that can be currently implemented by the IAEA, (b) Proposed defensible criteria and metrics aligned with an ethical AI for safeguards implementation, (c) Discussion of future needs and capabilities, and (d) Derive appropriate next steps and actions to support filling current technological, operational, and cultural gaps and realizing an effective ethical AI for safeguards framework. The Establishing Ethical Guidelines for Applying AI to IAEA Safeguards project creates awareness in the community of the ethical AIS approach.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Low power and privacy preserving sensor platform for occupancy detection

A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Velipasalar, Senem↗
DOE OSTI2018

Robotic Measurement of Holdup Deposit Volume in Gaseous Diffusion Piping to Quantify U-235 Content - 18375

During D and D of old gaseous diffusion uranium enrichment facilities, radiometric assay of U-235 holdup in pipes is a costly, time consuming, and labor-intensive process. Subject to human interpretation using approximate modeling, radiometric assay introduces significant challenges. Taking advantage of routine demolition activities in which D and D cuts pipes open, robotic in-pipe assay is explored. The novel method introduced here generates models of the internal pipe and deposit surface geometries that are used to derive volumetric quantities. The pipe surface is sensed using a non-contact inductive proximity sensor and the deposit surface is sensed using an optical laser triangulation sensor. These sensors are mounted on a spinning disk and driven down the pipe to construct a helical point cloud of discrete measurements. Surfaces are fit to each of the two point clouds to create a watertight volume that represents the holdup. This provides the location and volume per foot of pipe that is used to compare against criticality incredible (CI) thresholds during evaluation. The robotic system collects data autonomously, deploying and returning to the same pipe opening from which it is launched. This provides redundant measurements of the pipe and deposit surfaces as well as odometry used in localization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

47 OTHER INSTRUMENTATION↗
NASA NTRS2007

The "Biologically-Inspired Computing" Column

Self-managing systems, whether viewed from the perspective of Autonomic Computing, or from that of another initiative, offers a holistic vision for the development and evolution of biologically-inspired computer-based systems. It aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing costs. A case can certainly be made that all computer-based systems should exhibit autonomic properties [6], and we envisage greater interest in, and uptake of, autonomic principles in future system development.

Hinchey, Mike↗
NASA NTRS1987

A systems engineering approach to automated failure cause diagnosis in space power systems

Automatic failure-cause diagnosis is a key element in autonomous operation of space power systems such as Space Station's. A rule-based diagnostic system has been developed for determining the cause of degraded performance. The knowledge required for such diagnosis is elicited from the system engineering process by using traditional failure analysis techniques. Symptoms, failures, causes, and detector information are represented with structured data; and diagnostic procedural knowledge is represented with rules. Detected symptoms instantiate failure modes and possible causes consistent with currently held beliefs about the likelihood of the cause. A diagnosis concludes with an explanation of the observed symptoms in terms of a chain of possible causes and subcauses.

Dolce, James L.↗

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