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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Long Duration Exposure Facility (LDEF) optical systems SIG summary and database

The main objectives of the Long Duration Exposure Facility (LDEF) Optical Systems Special Investigative Group (SIG) Discipline are to develop a database of experimental findings on LDEF optical systems and elements hardware, and provide an optical system overview. Unlike the electrical and mechanical disciplines, the optics effort relies primarily on the testing of hardware at the various principal investigator's laboratories, since minimal testing of optical hardware was done at Boeing. This is because all space-exposed optics hardware are part of other individual experiments. At this time, all optical systems and elements testing by experiment investigator teams is not complete, and in some cases has hardly begun. Most experiment results to date, document observations and measurements that 'show what happened'. Still to come from many principal investigators is a critical analysis to explain 'why it happened' and future design implications. The original optical system related concerns and the lessons learned at a preliminary stage in the Optical Systems Investigations are summarized. The design of the Optical Experiments Database and how to acquire and use the database to review the LDEF results are described.

Bohnhoff-Hlavacek, Gail↗
DOE OSTI2017

Method for Hot Real-Time Sampling of Pyrolysis Vapors

Biomass Pyrolysis has been an increasing topic of research, in particular as a replacement for crude oil. This process utilizes moderate temperatures to thermally deconstruct the biomass which is then condensed into a mixture of liquid oxygenates to be used as fuel precursors. Pyrolysis oils contain more than 400 compounds, up to 60 percent of which do not re-volatilize for subsequent chemical analysis. Vapor chemical composition is also complicated as additional condensation reactions occur during the condensation and collection of the product. Due to the complexity of the pyrolysis oil, and a desire to catalytically upgrade the vapor composition before condensation, online real-time analytical techniques such as Molecular Beam Mass Spectrometry (MBMS) are of great use. However, in order to properly sample hot pyrolysis vapors, many challenges must be overcome. Sampling must occur within a narrow range of temperatures to reduce product composition changes from overheating or partial condensation or plugging of lines from condensed products. Residence times must be kept at a minimum to reduce further reaction chemistries. Pyrolysis vapors also form aerosols that are carried far downstream and can pass through filters resulting in build-up in downstream locations. The co-produced bio-char and ash from the pyrolysis process can lead to plugging of the sample lines, and must be filtered out at temperature, even with the use of cyclonic separators. A practical approach for considerations and sampling system design, as well as lessons learned are integrated into the hot analytical sampling system of the National Renewable Energy Laboratory's (NREL) Thermochemical Process Development Unit (TCPDU) to provide industrially relevant demonstrations of thermochemical transformations of biomass feedstocks at the pilot scale.

09 BIOMASS FUELS↗
DOE OSTI2023

Lessons Learned From Ventilation and Glovebox Flooding Via Overfilling of the Wet Vacuum System in a Plutonium Facility

In the summer of 2021, operations personnel requested maintenance personnel to fill a wet vacuum (Wetvac) seal water tank in a plutonium facility at Los Alamos National Laboratory (LANL). After the filling was finished, the fill valve of the Wetvac seal water tank was left open and a ‘dead-man’ valve on an isolation line failed to close properly. After a series of other failures, the water exited the glovebox and contaminated three rooms of the first floor of the facility and an underlying area in the basement. A retrospective analysis of this event determined that there was no risk of a criticality due to the inherent design features of the ventilation system. This paper will explain the driving factors behind how this event was able to happen, as well as some of the major lessons learned that can be universally applied to other nuclear facilities. One of the main causes of the event was the delegation of work to maintenance personnel that were not qualified for the work being asked of them. Additionally, there was a lack of coordination between criticality safety engineers and facility operations equipment owners regarding equipment and connections present on the seal water tanks for the Wetvac, specifically the presence of overflow drain lines. The respective criticality safety evaluation credited an overflow drain line being present on the seal water tanks that was later found to have not been present. Lastly, there are lessons to be learned regarding equipment configuration and designing systems with safety in mind.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1994

Fiber optic (flight quality) sensors for advanced aircraft propulsion

Development of flight prototype, fiber-optic sensing system components for measuring nine sensed parameters (three temperatures, two speeds, three positions, and one flame) on an F404-400 aircraft engine is described. Details of each sensor's design, functionality, and environmental testing, and the electro-optics architecture for sensor signal conditioning are presented. Eight different optical sensing techniques were utilized. Design, assembly, and environmental testing of an engine-mounted, electro-optics chassis unit (EOU), providing MIL-C-1553 data output, are related. Interconnection cables and connectors between the EOU and the sensors are identified. Results of sensor/cable/circuitry integrated testing, and installation and ground testing of the sensor system on an engine in October 1993 and April 1994 are given, including comparisons with the engine control system's electrical sensors. Lessons learned about the design, fabrication, testing, and integration of the sensor system components are included.

Poppel, Gary L.↗
DOE OSTI2021

Design and calibration of an optically segmented single volume scatter camera for neutron imaging

The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. Our prototype system consists of 64 EJ-204 organic plastic scintillator bars, each measuring 5mm × 5mm × 200mm and individually wrapped in Teflon tape. The scintillator array is optically coupled to two silicon photomultiplier ArrayJ-60035 64P-PCB arrays, each comprised of 64 individual 6x6mm J-Series sensors arranged in an 8 × 8 array. We report on the design details, including component selections, mechanical design and assembly, and the electronics system. The described design leveraged existing off-the-shelf solutions to support the rapid development of a phase 1 prototype. Several valuable lessons were learned from component and system testing, including those related to the detector's mechanical structure and electrical crosstalk that we conclude originates in the commercial photodetector arrays and the associated custom breakout cards. We detail our calibration efforts, beginning with calibrations for the electronics, based on the IRS3D application-specific integrated circuits, and their associated timing resolutions, ranging from 3090ps. With electronics calibrations applied, energy and position calibrations were performed for a set of edge bars using 22 Na and 90 Sr, respectively, reporting an average resolution of 12.07±0.03mm for energy depositions between 900keVee and 1000keVee. We further demonstrate a position calibration method for the internal bars of the matrix using cosmic-ray muons as an alternative to emission sources that cannot easily access these bars, with an average measured resolution of 14.86+-0.29mm for depositions between 900keVee and 1000keVee. The coincident time resolution reported between pairs of bars measured up to 400ps from muon acquisitions. Energy and position calibration values measured with muons are consistent with those obtained using particle emission sources.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗
DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Post-Disaster Microgrid Formation for Enhanced Distribution System Resilience

This paper proposes a deep reinforcement learning (DRL) based approach for post-disaster critical load restoration in active distribution systems to form microgrids through network reconfiguration to minimize critical load curtailments. Distribution networks are represented as graph networks, and optimal network configurations with microgrids are obtained by searching for the optimal spanning forest. The constraints to the research question being explored are the radial topology and power balance. Unlike existing analytical and population-based approaches, which necessitate the repetition of entire analyses and computation for each outage scenario to find the optimal spanning forest, the proposed approach, once properly trained, can quickly determine the optimal, or near-optimal, spanning forest even when outage scenarios change. When multiple lines fail in the system, the proposed approach forms microgrids with distributed energy resources in active distribution systems to reduce critical load curtailment. The proposed DRL-based model learns the action-value function using the REINFORCE algorithm, which is a model-free reinforcement learning technique based on stochastic policy gradients. A case study was conducted on a 33-node distribution test system, demonstrating the effectiveness of the proposed approach for post-disaster critical load restoration.

active distribution systems↗
NASA NTRS2010

Launch Vehicle Propulsion Life Cycle Cost Lessons Learned

This paper will review lessons learned for space transportation systems from the viewpoint of the NASA, Industry and academia Space Propulsion Synergy Team (SPST). The paper provides the basic idea and history of "lessons learned". Recommendations that are extremely relevant to NASA's future investments in research, program development and operations are"'provided. Lastly, a novel and useful approach to documenting lessons learned is recommended, so as to most effectively guide future NASA investments. Applying lessons learned can significantly improve access to space for cargo or people by focusing limited funds on the right areas and needs for improvement. Many NASA human space flight initiatives have faltered, been re-directed or been outright canceled since the birth of the Space Shuttle program. The reasons given at the time have been seemingly unique. It will be shown that there are common threads as lessons learned in many a past initiative.

Zapata, Edgar↗
NASA NTRS2004

Lessons Learned During Cryogenic Optical Testing of the Advanced Mirror System Demonstrators (AMSDs)

Optical testing in a cryogenic environment presents a host of challenges above and beyond those encountered during room temperature testing. The Advanced Mirror System Demonstrators (AMSDs) are 1.4 m diameter, ultra light-weight (<20 kg/mA2), off-axis parabolic segments. They are required to have 250 nm PV & 50 nm RMS surface figure error or less at 35 K. An optical testing system, consisting of an Instantaneous Phase Interferometer (PI), a diffractive null corrector (DNC), and an Absolute Distance Meter (ADM), was used to measure the surface figure & radius-of-curvature of these mirrors at the operational temperature within the X-Ray Calibration Facility (XRCF) at Marshall Space Flight Center (MSFC). The Ah4SD program was designed to improve the technology related to the design, fabrication, & testing of such mirrors in support of NASA s James Webb Space Telescope (JWST). This paper will describe the lessons learned during preparation & cryogenic testing of the AMSDs.

Hadaway, James↗
DOE OSTI2018

CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research

BackgroundCurrent multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines.ResultsThis paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks.ConclusionsInitial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

Jani, Yashvant↗
NASA NTRS1992

Closed ecological systems: From test tubes to Earth's biosphere

Artificially constructed closed ecological systems (CES) have been researched both experimentally and theoretically for over 25 years. The size of these systems have varied from less than one liter to many thousands of cubic meters in volume. The diversity of the included components has a similarly wide range from purely aquatic systems to soil based systems that incorporate many aspects of Earth's biosphere. While much has been learned about the functioning of these closed systems, much remains to be learned. In this paper, we compare and contrast the behavior of closed ecological systems of widely different sizes through an analysis of their atmospheric composition. In addition, we will compare the performance of relatively small CES with the behavior of Earth's biosphere. We address the applicability of small CES as replicable analogs for planetary biospheres and discuss the use of small CES as an experimental milieu for an examination of the evolution of extra-terrestrial colonies.

Frye, Robert J.↗
DOE OSTI2024

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗
NASA NTRS2011

Habitat Demonstration Unit (HDU) Pressurized Excursion Module (PEM) Systems Integration Strategy

The Habitat Demonstration Unit (HDU) project team constructed an analog prototype lunar surface laboratory called the Pressurized Excursion Module (PEM). The prototype unit subsystems were integrated in a short amount of time, utilizing a rapid prototyping approach that brought together over 20 habitation-related technologies from a variety of NASA centers. This paper describes the system integration strategies and lessons learned, that allowed the PEM to be brought from paper design to working field prototype using a multi-center team. The system integration process was based on a rapid prototyping approach. Tailored design review and test and integration processes facilitated that approach. The use of collaboration tools including electronic tools as well as documentation enabled a geographically distributed team take a paper concept to an operational prototype in approximately one year. One of the major tools used in the integration strategy was a coordinated effort to accurately model all the subsystems using computer aided design (CAD), so conflicts were identified before physical components came together. A deliberate effort was made following the deployment of the HDU PEM for field operations to collect lessons learned to facilitate process improvement and inform the design of future flight or analog versions of habitat systems. Significant items within those lessons learned were limitations with the CAD integration approach and the impact of shell design on flexibility of placing systems within the HDU shell.

Gill, Tracy↗
NASA NTRS2003

Standards Advisor-Advanced Information Technology for Advanced Information Delivery

Developers of space systems must deal with an increasing amount of information in responding to extensive requirements and standards from numerous sources. Accessing these requirements and standards, understanding them, comparing them, negotiating them and responding to them is often an overwhelming task. There are resources to aid the space systems developer, such as lessons learned and best practices. Again, though, accessing, understanding, and using this information is often more difficult than helpful. This results in space systems that: 1. Do not meet all their requirements. 2. Do not incorporate prior engineering experience. 3. Cost more to develop. 4. Take longer to develop. The NASA Technical Standards Program (NTSP) web site at http://standards.nasa.gov has made significant improvements in making standards, lessons learned, and related material available to space systems developers agency-wide. The Standards Advisor was conceived to take the next steps beyond the current product, continuing to apply evolving information technology that continues to improve information delivery to space systems developers. This report describes the features of the Standards Advisor and suggests a technical approach to its development.

Hawker, J. Scott↗