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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Implementing Concurrent Non-Event Transitionsin CMS

Available on CMS information serverCMS CR -2018/277The Compact Muon Solenoid ExperimentMailing address: CMS CERN, CH-1211 GENEVA 23, SwitzerlandConference Report15 October 2018 (v2, 21 November 2018)Implementing Concurrent Non-Event Transitionsin CMSChristopher Jones for the CMS CollaborationAbstractSince the beginning of the LHC Run 2 in 2016 the CMS data processing framework, CMSSW, hasbeen running with multiple threads during production of data and simulation via the use of IntelsThread Building Blocks (TBB) library. The TBB library utilizes tasks as concurrent units of work.CMS used these tasks to allow both concurrent processing of events as well as concurrent running ofmodules processing the same event. This design has served CMS well and has allowed jobs to utilizeless memory per core as well as reduce the number of jobs that must be tracked by CMSs worflowmanagement system. As CMS has begun to utilize greater number of threads in a job, the effect ofserialization points in the framework has decreased jobs CPU efficiency. One major serialization pointoccurs when the framework processes a non-Event transition. These transitions occur when a new Runor LuminosityBlock is to be processed. In this talk we will discuss how the different transitions definethe processing model for CMSSW and how we were able to successfully allow concurrent process-ing of those transitions using TBB via task queues. We will also show CPU efficiency comparisonsbetween the same work being done with and without the additional concurrency.

Jones, Christopher↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of $10^5$, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Innovative PLC Design for Optimized Potable Water Managment

My project here at Idaho National Laboratory (INL) was to work on Potable Water PLC Design (Programmable Logic Controllers). I was tasked in making a new panel design for the PLC system that will be moved from TRA BLDG 696 to TRA BLDG 608. This design needed to be updated since the old PLC system being about 20 years old. The manufacturer of the MicroLogix 1200 life expectancy showed it to be 10-20 years, and the system was discontinued in 2017. The new system we implemented was called CompactLogix Controller 5380, along with a new 24 VDC/10 Amp Power Supply. I made a design using Microsoft Visio, with the new material. But we needed to add it onto an Aluminum Panel that the system will be mounted onto, we need Unistrut’s for the aluminum panel to be mounted to. Some of the material we will still be using, like the R1 Alarm Relay, will be moving with the PLC system. The rest of the material will be new supplies that need to be updated systems, which the site is waiting for to complete the PLC system.

47 - OTHER INSTRUMENTATION↗
NASA NTRS2016

Modifying a Commercial Centrifuge to Reduce Electromagnetic Interference and Evaluating Functionality of Ultrasound Equipment

The Project Management and Engineering Branch (SF4) supports the Human Health and Performance Directorate (HH&P) and is responsible for developing and supporting human systems hardware for the International Space Station (ISS). When a principal investigator's (PI) medical research project on the ISS is accepted, SF4 develops the necessary hardware and software to transport to the ISS. The two projects I primarily worked on were the centrifuge and ultrasound projects. Centrifuge: One concern with spacecraft such as the ISS is electromagnetic interference (EMI) from onboard equipment, typically from radio waves (frequencies of ~3 kHz to ~300 GHz), which can negatively affect nearby circuitry. Standard commercial centrifuges produce EMI above safety limits, so my task was to help reduce EMI production from this equipment. Two centrifuges were tested: one unmodified as a control and one modified. To reduce EMI below safety limits, one centrifuge was modified to become a Faraday shield, in which significant electrical contact was made between all regions of the centrifuge housing. This included removing non-conductive paint, applying conductive fabric to the lid and foam sealer, adding a 10,000 μF decoupling capacitor across the power supply, and adding copper adhesive-mount gaskets to the housing interior. EMI testing of both centrifuges was performed in the EMI/EMC Control Test and Measurement Facility. EMI for both centrifuges was below safety limits for frequencies between 10 MHz and 15 GHz (pass); however, between 14 kHz and 10 MHz, EMI for the unmodified centrifuge exceeded safety limits (fail) as expected. Alternatively, for the modified centrifuge with the Faraday shield, EMI was below the safely limit of 55 dBμV/m for electromagnetic frequencies between 14 kHz and 10 MHz. This result indicates our modifications were successful. The successful EMI test allowed us to communicate with the vendor what modifications they needed to make to their commercial unit to meet our specifications and to understand what needs to be done in lab to the new centrifuge. Our modifications will provide a standard for readying centrifuges for future missions. Once the new modified centrifuge arrives by the vendor, it will need to undergo EMI testing again for validation. The centrifuge is also in the process of compatibility testing with a custom stowage drawer, which is an ongoing project in SF4. Both of these items will be payloads on future missions to the ISS for various research purposes. Ultrasound: ISS currently has an onboard ultrasound (Ultrasound 2 system) for research and medical purposes. Every piece of medical flight hardware has an equivalent ground-unit so instrumentation can be routinely evaluated and transported to the ISS if necessary. The ground-unit ultrasound equipment must be evaluated every six months using a task performance sheet (TPS). A TPS is a document, written by the appropriate scientists and engineers, which describes how to run equipment and is written in such a way that astronauts with unspecialized training can follow the tasks. I was responsible for performing six TPSs on a combination of three ultrasounds and two video power converters (VPCs). Performing a TPS involves checking out and computationally documenting each piece of equipment removed from storage locations, setting up hardware and software, performing tasks to verify functionality, returning equipment, and logging items back into the computerized system. My work revealed all ground-unit ultrasounds were functioning properly. Because of proper function, a discrepancy report (DR) did not have to be opened. The TPS was then passed along to the Quality Engineering (QE) for review and ultimately given to Quality Assurance (QA). Other projects: In addition to my main projects, I participated in other tasks including troubleshooting an EEG headband, volunteering for an ultrasound training research study, and conformal coating printed circuit boards. My internship at SF4 has helped me understand how space systems hardware development for the ISS fits into NASA's mission and vision.

Greening, Gage J.↗
DOE OSTI2026

Phenomena Identification and Ranking Tables (PIRT) analysis of wind turbine blade leading edge erosion

Leading edge erosion (LEE) of wind turbine blades has been identified as a major factor in decreased wind turbine blade lifetimes and energy output over time. Accordingly, the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) has created the Task 46 Phase 2 to undertake cooperative research in the key topic of blade erosion. The purpose of Task 46 Erosion Phase 2 is to further improve understanding of erosion driving factors, develop datasets and model tools to enhance prediction of leading-edge erosion likelihood, identify damage at the earliest possible stage and advance potential solutions. The scope of work covers several technical areas, reflecting the multidisciplinary nature of the challenge. Participants in the task are given in Table 1.

17 WIND ENERGY↗
NASA NTRS2008

A Temperature Sensor using a Silicon-on-Insulator (SOI) Timer for Very Wide Temperature Measurement

A temperature sensor based on a commercial-off-the-shelf (COTS) Silicon-on-Insulator (SOI) Timer was designed for extreme temperature applications. The sensor can operate under a wide temperature range from hot jet engine compartments to cryogenic space exploration missions. For example, in Jet Engine Distributed Control Architecture, the sensor must be able to operate at temperatures exceeding 150 C. For space missions, extremely low cryogenic temperatures need to be measured. The output of the sensor, which consisted of a stream of digitized pulses whose period was proportional to the sensed temperature, can be interfaced with a controller or a computer. The data acquisition system would then give a direct readout of the temperature through the use of a look-up table, a built-in algorithm, or a mathematical model. Because of the wide range of temperature measurement and because the sensor is made of carefully selected COTS parts, this work is directly applicable to the NASA Fundamental Aeronautics/Subsonic Fixed Wing Program--Jet Engine Distributed Engine Control Task and to the NASA Electronic Parts and Packaging (NEPP) Program. In the past, a temperature sensor was designed and built using an SOI operational amplifier, and a report was issued. This work used an SOI 555 timer as its core and is completely new work.

Patterson, Richard L.↗
NASA NTRS2023

Creating a Fully-Electronic GFE Work Authorization Document Process for JSC

JSC’s Electronic Document System (EDS) was published in 2013 by SMA to store and process Task Performance Sheets (TPS), Discrepancy Reports (DR), Hardware Movement Documents (EZT), etc. The 2013 transition turned a fully paper process into an electronic routing one with document level electronic approvals but maintained an in-line paper process . - Today, all work and buy-offs continue to be captured on paper, walked between buildings, and is susceptible to approval errors, a rapidly slowing processing time, and led to projects requesting waivers to use alternate WAD systems, damaging JSC’s QMS. - NT Data Systems team collaborated with multiple platform providers, test user groups, and stakeholders, to find a solution for this growing need in a way that exceeds JSC’s current process, is adaptable to future innovation, and within our limited resource constraints

Nicholas Holaday↗
DOE OSTI2019

Analysis of the Impact of Radiation on Cameras and Robotic Perception - 19242

Robotic systems are increasingly used for inspection, maintenance, and disaster assistance for nuclear power plants. These robots utilize increasingly high levels of autonomy to operate in environments that are unsafe for humans to operate the robot in or require more precise control over the robot than a human can offer. In these cases, the robot must include perception and computation components to autonomously sense and navigate through the environment to fulfill its mission. This difficult task is complicated by radiation interfering with sensors' operation. The impact of this interference is largely unexplored, as existing works on radiation interference on sensors focus on estimating the incident radiation, rather than how it impacts a robotic system using the sensor. In this work, we seek to quantify and describe the impact of radiation on cameras, and how the radiation affects robotic performance. In this paper we study cameras that are suitable for small, lightweight unmanned aerial vehicles (UAVs) that are increasingly deployed to fly in confined areas in emergencies, where the surrounding environment is unsafe for workers to operate. These small UAVs lack the payload capacity to carry significant radiation shielding, so any onboard sensors will be affected by radiation. In the case of cameras, this radiation appears as noise in the camera imagery. This noise will interfere with the UAV's operation, by obscuring obstacles and interfering with localization and optical flow algorithms that detect detailed features in the environment. To date, there is a lack of information on how the radiation noise in the camera image impacts the overall robot system performance Existing work focuses on estimating the amount of incident radiation by measuring the corruption in the images. However, these works suffer several shortcomings for robotic systems. First, a robot must be able to successfully navigate its environment during a successful mission, and merely quantifying the amount of radiation noise in an image does not adequately explain how it may interfere with higher level navigation and perception tasks. Second, radiation estimation procedures rely on focusing the camera on a known and static background. This is unsuitable for robotic exploration tasks, where the robot frequently moves around an unknown environment that may be dynamically changing. Our work contains two contributions that address these shortcomings. First, we conduct a detailed analysis of the noise radiation causes in cameras by exposing cameras that are commonly used on UAVs [e.g., small form factor complementary metal-oxide-semiconductor (CMOS) cameras] to varying levels of radiation that may be present in emergency scenarios. From this exposure, we quantify not only how much noise is present, but quantify its appearance. By quantifying the appearance and amount of noise in a camera image, researchers can design mitigation strategies for the corruption, so that their robotic systems can operate in these challenging environments. Second, we demonstrate how to leverage this experimental data in robotic simulations. Using data from the first set of experiments allows us to test and verify the robots in simulation. Using simulators allows us to verify the robots will perform as expected during critical times without needing expensive test equipment or damaging valuable robot prototypes. Other researchers using robots for exploration of irradiated environments will benefit from our research by incorporating similar noise models in their research. 1 Southwest Research Institute{sup R} is a registered trademark of Southwest Research Institute in the United States and/or other countries. This will increase the reliability of robotic systems prior to deployment in emergency situations, which will in turn increase their effectiveness and speed the development of robots that would otherwise need expensive and time-consuming test procedures to validate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Continuum Model Development for Electrochemical Systems (Statement of Work: Ilenia Battiato Subcontract)

The subcontractor shall provide the services of qualified multiscale modelers to perform tasks that contribute to reaching the objectives of the LDRD project entitled Automated and Accelerated Continuum Model Development for Electrochemical Systems (Tracking number 24-ERD-051). These tasks relate to the development, deployment, and validation of multiscale models relevant to electrochemical systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Hardware accelerated dynamic work creation on a graphics processing unit

A processor core is configured to execute a parent task that is described by a data structure stored in a memory. A coprocessor is configured to dispatch a child task to the at least one processor core in response to the coprocessor receiving a request from the parent task concurrently with the parent task executing on the at least one processor core. In some cases, the parent task registers the child task in a task pool and the child task is a future task that is configured to monitor a completion object and enqueue another task associated with the future task in response to detecting the completion object. The future task is configured to self-enqueue by adding a continuation future task to a continuation queue for subsequent execution in response to the future task failing to detect the completion object.

Gutierrez, Anthony↗
DOE OSTI2023

Hardware accelerated dynamic work creation on a graphics processing unit

A processor core is configured to execute a parent task that is described by a data structure stored in a memory. A coprocessor is configured to dispatch a child task to the at least one processor core in response to the coprocessor receiving a request from the parent task concurrently with the parent task executing on the at least one processor core. In some cases, the parent task registers the child task in a task pool and the child task is a future task that is configured to monitor a completion object and enqueue another task associated with the future task in response to detecting the completion object. The future task is configured to self-enqueue by adding a continuation future task to a continuation queue for subsequent execution in response to the future task failing to detect the completion object.

Gutierrez, Anthony T.↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Feature selection with distance correlation

Choosing which properties of the data to use as input to multivariate decision algorithms—also known as feature selection—is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on distance correlation, and demonstrate its effectiveness on the tasks of boosted top- and W -tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
NASA NTRS2016

Holodeck Testbed Project

The main objective of the Holodeck Testbed is to create a cost effective, realistic, and highly immersive environment that can be used to train astronauts, carry out engineering analysis, develop procedures, and support various operations tasks. Currently, the Holodeck testbed allows to step into a simulated ISS (International Space Station) and interact with objects; as well as, perform Extra Vehicular Activities (EVA) on the surface of the Moon or Mars. The Holodeck Testbed is using the products being developed in the Hybrid Reality Lab (HRL). The HRL is combining technologies related to merging physical models with photo-realistic visuals to create a realistic and highly immersive environment. The lab also investigates technologies and concepts that are needed to allow it to be integrated with other testbeds; such as, the gravity offload capability provided by the Active Response Gravity Offload System (ARGOS). My main two duties were to develop and animate models for use in the HRL environments and work on a new way to interface with computers using Brain Computer Interface (BCI) technology. On my first task, I was able to create precise computer virtual tool models (accurate down to the thousandths or hundredths of an inch). To make these tools even more realistic, I produced animations for these tools so they would have the same mechanical features as the tools in real life. The computer models were also used to create 3D printed replicas that will be outfitted with tracking sensors. The sensor will allow the 3D printed models to align precisely with the computer models in the physical world and provide people with haptic/tactile feedback while wearing a VR (Virtual Reality) headset and interacting with the tools. Getting close to the end of my internship the lab bought a professional grade 3D Scanner. With this, I was able to replicate more intricate tools at a much more time-effective rate. The second task was to investigate the use of BCI to control objects inside the hybrid reality ISS environment. This task looked at using an Electroencephalogram (EEG) headset to collect brain state data that could be mapped to commands that a computer could execute. On this Task, I had a setback with the hardware, which stopped working and was returned to the vendor for repair. However, I was still able to collect some data, was able to process it, and started to create correlation algorithms between the electrical patterns in the brain and the commands we wanted the computer to carry out. I also carried out a test to investigate the comfort of the headset if it is worn for a long time. The knowledge gained will benefit me in my future career. I learned how to use various modeling and programming tools that included Blender, Maya, Substance Painter, Artec Studio, Github, and Unreal Engine 4. I learned how to use a professional grade 3D scanner and 3D printer. On the BCI Project I learned about data mining and how to create correlation algorithms. I also supported various demos including a live demo of the hybrid reality lab capabilities at ComicPalooza. This internship has given me a good look into engineering at NASA. I developed a more thorough understanding of engineering and my overall confidence has grown. I have also realized that any problem can be fixed, if you try hard enough, and as an engineer it is your job to not only fix problems but to embrace coming up with solutions to those problems.

Arias, Adriel↗
NASA NTRS2002

STS-110 Flight Day 7 Highlights

On the seventh day of STS-110, Mission Specialists Steven Smith and Rex Walheim are shown preparing for their second extravehicular activity (EVA) and the third of the mission, with the assistance of Mission Specialist Jerry Ross. Following this mission, Ross and Smith will be the first and second most experienced spacewalkers, respectively, in history. A camera mounted on the Space Shuttle Atlantis shows footage of the International Space Station. While Smith and Walheim are on EVA, Mission Specialists Ross and Lee Morin will provide choreography from inside the Atlantis crew cabin, Pilot Stephen Frick will operate the robotic arm, and Commander Mike Bloom will control the cameras on the robotic arm. Smith and Walheim are shown exiting the airlock hatch and then installing an PFR (Portable Foot Restraint) on the end of the robotic arm, from which much of the work will be done. Following the switching off of a circuit breaker, the main EVA task of rewiring umbilical cables that provide power, commands, and video is begun. These cables will be connected to the Mobile Transporter's (MT) robotic arm. The MT, an external transportation system which runs on rails, is seen with a helmet mounted camera as the astronauts work to release bolts that had served to secure the MT during launch. After a final check of the umbilical connections mated, the Umbilical Reconfiguration Panel is fitted over the cables, and the astronauts make their way to the airlock hatch.

Source record↗
DOE OSTI2023

Use of machine learning to analyze chemistry card sort tasks

Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student’s organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student’s strategy. Here in this work, we propose new machine learning strategies that leverage a potentially richer source of student thinking: free-form written language justifications associated with student sorts. Using data from a university chemistry card sort task, we use vectorized representations of language and unsupervised learning techniques to generate qualitatively interpretable clusters, which can provide unique insight in how students organize their knowledge. We compared these to machine learning analysis of the students’ sorts themselves. Machine learning-generated clusters revealed different organizational strategies than those built into the task; for example, sorts by difficulty or even discipline. There were also many more categories generated by machine learning for what we would identify as more novice-like sorts and justifications than originally built into the task, suggesting students’ organizational strategies converge when they become more expert-like. Finally, we learned that categories generated by machine learning for students’ justifications did not always match the categories for their sorts, and these cases highlight the need for future research on students’ organizational strategies, both manually and aided by machine learning. In sum, the use of machine learning to analyze results from a card sort task has helped us gain a more nuanced understanding of students’ expertise, and demonstrates a promising tool to add to existing analytic methods for card sorts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Computer support for cooperative tasks in Mission Operations Centers

Traditionally, spacecraft management has been performed by fixed teams of operators in Mission Operations Centers. The team cooperatively: (1) ensures that payload(s) on spacecraft perform their work; and (2) maintains the health and safety of the spacecraft through commanding and monitoring the spacecraft's subsystems. In the future, the task demands will increase and overload the operators. This paper describes the traditional spacecraft management environment and describes a new concept in which groupware will be used to create a Virtual Mission Operations Center. Groupware tools will be used to better utilize available resources through increased automation and dynamic sharing of personnel among missions.

Fox, Jeffrey↗