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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 235 records · Page 13

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NASA NTRS1996

Micro-Pressure Sensors for Future Mars Missions

The joint research interchange effort was directed at the following principal areas: u further development of NASA-Ames' Mars Micro-meteorology mission concept as a viable NASA space mission especially with regard to the science and instrument specifications u interaction with the flight team from NASA's New Millennium 'Deep-Space 2' (DS-2) mission with regard to selection and design of micro-pressure sensors for Mars u further development of micro-pressure sensors suitable for Mars The research work undertaken in the course of the Joint Research Interchange should be placed in the context of an ongoing planetary exploration objective to characterize the climate system on Mars. In particular, a network of small probes globally-distributed on the surface of the planet has often been cited as the only way to address this particular science goal. A team from NASA Ames has proposed such a mission called the Micrometeorology mission, or 'Micro-met' for short. Surface pressure data are all that are required, in principle, to calculate the Martian atmospheric circulation, provided that simultaneous orbital measurements of the atmosphere are also obtained. Consequently, in the proposed Micro-met mission a large number of landers would measure barometric pressure at various locations around Mars, each equipped with a micro-pressure sensor. Much of the time on the JRI was therefore spent working with the engineers and scientists concerned with Micro-met to develop this particular mission concept into a more realistic proposition.

Catling, David C.↗

NASA NTRS2011

Feature Acquisition with Imbalanced Training Data

This work considers cost-sensitive feature acquisition that attempts to classify a candidate datapoint from incomplete information. In this task, an agent acquires features of the datapoint using one or more costly diagnostic tests, and eventually ascribes a classification label. A cost function describes both the penalties for feature acquisition, as well as misclassification errors. A common solution is a Cost Sensitive Decision Tree (CSDT), a branching sequence of tests with features acquired at interior decision points and class assignment at the leaves. CSDT's can incorporate a wide range of diagnostic tests and can reflect arbitrary cost structures. They are particularly useful for online applications due to their low computational overhead. In this innovation, CSDT's are applied to cost-sensitive feature acquisition where the goal is to recognize very rare or unique phenomena in real time. Example applications from this domain include four areas. In stream processing, one seeks unique events in a real time data stream that is too large to store. In fault protection, a system must adapt quickly to react to anticipated errors by triggering repair activities or follow- up diagnostics. With real-time sensor networks, one seeks to classify unique, new events as they occur. With observational sciences, a new generation of instrumentation seeks unique events through online analysis of large observational datasets. This work presents a solution based on transfer learning principles that permits principled CSDT learning while exploiting any prior knowledge of the designer to correct both between-class and withinclass imbalance. Training examples are adaptively reweighted based on a decomposition of the data attributes. The result is a new, nonparametric representation that matches the anticipated attribute distribution for the target events.

Thompson, David R.↗

DOE OSTI2020

Invite the Chaos Monkey: Test Effect Payloads for Tuning ICS Incident Response

In 2010, when Netflix moved their operational systems to the cloud, they developed a pseudo-random mechanism to cause deliberate outages and failures of key system elements to ensure that automated systems for detecting and remediating outages were mature and fully functional. This Chaos Monkey spawned an entire Simian Army designed to ensure system resilience and enable failure response before issues became outages. Through the DARPA RADICS program, Provatech, INL, and other researchers have developed a mechanism for creating controllable failures via Test Effect Payloads (TEPs) for Industrial Control Systems in order to enable an asset owner to tune network and process sensor technology, increase the effectiveness of incident response and remediation, and highlight areas where resilient technology could bring benefit to Operational Technology. This talk will explore what a TEP is, how to create a TEP, and suggest what a systematic system resilience plan using TEP’s might look like. We will also discuss how regulated entities might employ TEP’s in a compliant resilience strategy.

42 ENGINEERING↗

DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1990

Simple technique for optical flow estimation

The theoretical principles, design, and implementation of an image-motion detection system with possible robotics and autonomous-navigation applications are discussed. The operation of a simple one-dimensional second-derivative edge-motion detector is explained; its extension to a two-dimensional motion sensor based on the Laplacian of a Gaussian (Marr and Hildreth, 1980) is outlined; a center-surround array of 13 of these sensors (12 in a circle around a central sensor) is proposed; and processing techniques to overcome the aperture problem are examined. Simple video images involving unidirectional motion are then analyzed using a prototype version of this design; the sensors employed are based on a 15 x 15-pixel mask made up of 13 7 x 7-pixel kernels. Accurate measurements of image velocity are obtained, as required for navigation through complex environments. It is pointed out that the procedure is noniterative and well suited to implementation as a parallel network.

Perrone, John A.↗

NASA NTRS1992

Simulation of automatic rotorcraft nap-of-the-earth flight in graphics workstation environment

This paper describes a three-dimensional (3D) helicopter flight simulation system. The simulation is designed to be a readily available tool for concept verification and tuning of automatic obstacle-avoidance guidance algorithms. The system has been implemented on networked workstations capable of interactive 3D graphics simulation. The simulation uses realistic terrain and obstacle models. The dynamics of the rotorcraft and the functional capabilities of the range sensors are simulated to provide all the components required to evaluate the guidance function. Standard graphics hardware available on the workstation is utilized to accelerate the range-data calculations for sensor simulation at the guidance rate. An example is given to demonstrate the performance of the obstacle-avoidance capability.

Lam, T.↗

DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗

DOE OSTI2020

Wireless Sensing and Communication Capability from In-Core to a Monitoring Center

Significant cost savings can be made if electrical cables can be replaced by wireless technology in current Nuclear Power Plants (NPP) and in advance reactor designs. Wireless technology can also provide in-core opportunities by significantly reducing the number of penetrations in the pressure vessel, cost and complexity of sensor installation and by increasing the efficiency of current and advanced reactors. Unlike other deployment scenarios for an industrial environment, operators need to have centralized control over all the networks. Centralized control will reduce implementation costs, provide single point control and enable monitoring of network devices, improve security, and enhance connectivity. Micro-sensors that can simultaneously monitor temperature and pressure within a fuel rod inside nuclear reactors will enable preventative actions during abnormal operating conditions. This ability could avert accidents and enable the expedient development of accident tolerant fuels. A novel micro-sensor suite (~ mm) to simultaneously measure multiple parameters such as temperature, strain, pressure, and neutron/gamma flux inside a fuel rod is being developed for use in reactors. The necessary communication architecture is also being developed to transmit measurement signals from the core to the plant's data cloud or control room. A three three-tier strategy has been developed to support wireless transmission of in-core measurements to the control room or to a secure cloud platform for control, analytics, and decision-making purposes. 1. In-core: data signal from in-core to outside of the pressure vessel within the containment building 2. Containment building: data signal from inside to the outside of the containment building and into the balance of the plant network 3. Balance of the plant network: information transmitted to the data cloud and control room This plan presents a wireless sensing and communication system for use within a reactor core and elsewhere. The communication technology is advantageous to compensate for network equipment failures and adverse data transmission conditions. Wireless technology will significantly increase the resiliency of the plants network system. The wireless system naturally provides multiple transmission path capability and data redundancy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2012

Behavioral Mapless Navigation Using Rings

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot.

Monroe, Randall P.↗

NASA NTRSDate not supplied

Fine pointing of Laser Beams by using Laser Arrays for Applications to CubeSats

A method of fine pointing of laser beams by using laser arrays has been developed. The telescope system combines a lens system and a VCSEL/Photodetector Array. It does not use moving parts. In computer simulations, it is applied to CubeSats that use body pointing. Body pointing was used by the Aerospace Corporation for CubeSats in LEO in NASA’s Optical Communications and Sensors Demonstration (OCSD) program. Computer simulations of this fine pointing capability have been applied previously to CubeSats in the OCSD program. In this paper, computer simulations of laser pointing using this telescope design are applied to CubeSats in LLO, at 100 km. These CubeSats could form part of the LunaNet, the lunar communications and navigation network, part of the NASA ARTEMIS Program. With more accurate pointing, a laser beam with smaller divergence can be used. For the case of the AeroCube-7B vehicle that was used in the OCSD program, computer simulations will show, for example, that the divergence of the output beam can be reduced from approximately 0.06° FWHM to 0.014°. For the proposed electro-optical system, reaction times to pointing changes and vibrations are on a nanosecond time scale, much faster than those for fine pointing systems that use moving parts such as fast steering mirrors, including MEMS, or that use quad-cell photodetectors to improve the body pointing of the CubeSat. Other possible applications are to Optical Multiple Access (OMA)for simultaneously communicating with ground stations at different locations and to Wavelength-Division Multiplexing (WDM)for increasing data rate transmission.

SMD↗

NASA NTRS2021

Fine Pointing of Laser Beams by Using Laser Arrays for Applications to CubeSats

HEC↗

NASA NTRS1993

Clementine: An inexpensive mission to the Moon and Geographos

The Clementine Mission, a joint project of the Strategic Defense Initiative Organization (SDIO) and NASA, has been planned primarily to test and demonstrate a suite of lightweight sensors and other lightweight spacecraft components under extended exposure to the space environment. Although the primary objective of the mission is to space-qualify sensors for Department of Defense applications, it was recognized in 1990 that such a mission might also be designed to acquire scientific observations of the Moon and of Apollo asteroid (1620) Geographos. This possibility was explored jointly by SDIO and NASA, including representatives from NASA's Discovery Program Science Working Group, in early 1991. Besides the direct return of scientific information, one of the benefits envisioned from a joint venture was the development of lightweight components for possible future use in NASA's Discovery-class spacecraft. In Jan. 1992, SDIO informed NASA of its intent to fly a 'Deep Space Program Science Experiment,' now popularly called Clementine; NASA then formed an advisory science working group to assist in the early development of the mission. The Clementine spacecraft is being assembled at the Naval Research Laboratory, which is also in charge of the overall mission design and mission operations. Support for mission design is being provided by GSFC and by JPL. NASA's Deep Space Network will be utilized in tracking and communicating with the spacecraft. Following a recommendation of the COMPLEX committee of the Space Science Board, NASA will issue an NRA and appoint a formal science team in early 1993. Clementine is a 3-axis stabilized, 200 kg (dry weight) spacecraft that will be launched on a refurbished Titan-2G. One of the goals has been to build two spacecraft, including the sensors, for $100M. Total time elapsed from the decision to proceed to the launch will be two years.

Shoemaker, Eugene M.↗

DOE OSTI2020

High precision control and deep learning-based corn stand counting algorithms for agricultural robot

This paper presents high precision control and deep learning-based corn stand counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep network to detect corn plants in images, and a visual tracking model to re-identify detected objects at different time steps. We collected data from 53 corn plots in various fields for corn plants around 14 days after emergence (stage V3 - V4). The robot predictions have agreed well with the ground truth with C robot =1.02×C human -0.86 and a correlation coefficient R=0.96. The mean relative error given by the algorithm is -3.78%, and the standard deviation is 6.76%. These results indicate a first and significant step towards autonomous robot-based real-time phenotyping using low-cost, ultra-compact ground robots for corn and potentially other crops.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Performance of a large area photon detector for rare event search applications

We present the design and characterization of a large-area Cryogenic PhotoDetector designed for active particle identification in rare event searches, such as neutrinoless double beta decay and dark matter experiments. The detector consists of a 45.6 cm 2 surface area by a 1-mm-thick 10.6 g Si wafer. It is instrumented with a distributed network of Quasiparticle-trap-assisted Electrothermal feedback Transition-edge sensors with superconducting critical temperature T c = 41.5 mK to measure a thermal phonons released from interactions with photons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1985

The influence of sampling rate on the dynamics and control of aeronautical systems

It is shown in the design of aeronautical control systems that sampling rate variations give rise to migrations of the discrete eigenvalues for both the plant and the controllers. In general, these migrations tend to degrade simulation fidelity and the overall performance of Ultrareliable Fault Tolerant Control Systems (UFTCS). These systems consist of a redundant asynchronous network of microprocessors which use sensor information to generate the servo commands that drive actuators, thrust linkages, etc. A change in the sampling period of a single microprocessor will, in addition to causing an eigenvalue shift, alter the magnitude of the servo command issued by the controller. The influence of these changes and the effect of initial conditions and skew are considered.

Howard, J. C.↗

DOE OSTI2026

Near-field passive sensor for the monitoring of high-temperature oxidative corrosion of metals

This study reports on the development and testing of a passive wireless device designed to track temperature and corrosion behavior in SS304H stainless steel under elevated temperatures. The ANSYS HFSS software was utilized to model and optimize the design of an inductor-capacitor (LC) resonator passive wireless sensors fabricated using platinum designs printed onto an aluminum oxide support operating at frequencies between (50–190 MHz). The optimal LC wireless sensor designs were then fabricated using screen-printing and sintering methods. Here, the sensors were tested by placing the sensor onto polished SS304H flat substrates and heated to 900–1050 °C in air. Wireless acquisition of sensor data during heating, cooling, and isothermal stages was achieved through a Pt loop antenna connected to an RF signal generator and a network analyzer.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS1992

Applications of inertial-sensor high-inheritance instruments to DSN precision antenna pointing

Laboratory test results of the initialization and tracking performance of an existing inertial-sensor-based instrument are given. The instrument, although not primarily designed for precision antenna pointing applications, demonstrated an on-average 10-hour tracking error of several millidegrees. The system-level instrument performance is shown by analysis to be sensor limited. Simulated instrument improvements show a tracking error of less than 1 mdeg, which would provide acceptable performance, i.e., low pointing loss, for the Deep Space Network 70-m antenna subnetwork, operating at Ka-band (1-cm wavelength).

Goddard, R. E.↗