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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Expert systems applications for space shuttle payload integration automation

Expert systems technologies have been and are continuing to be applied to NASA's Space Shuttle orbiter payload integration problems to provide a level of automation previously unrealizable. NASA's Space Shuttle orbiter was designed to be extremely flexible in its ability to accommodate many different types and combinations of satellites and experiments (payloads) within its payload bay. This flexibility results in differnet and unique engineering resource requirements for each of its payloads, creating recurring payload and cargo integration problems. Expert systems provide a successful solution for these recurring problems. The Orbiter Payload Bay Cabling Expert (EXCABL) was the first expert system, developed to solve the electrical services provisioning problem. A second expert system, EXMATCH, was developed to generate a list of the reusable installation drawings available for each EXCABL solution. These successes have proved the applicability of expert systems technologies to payload integration problems and consequently a third expert system is currently in work. These three expert systems, the manner in which they resolve payload problems and how they will be integrated are described.

Morris, Keith↗
NASA NTRS2004

Light Microscopy Module Imaging Tested and Demonstrated

The Fluids Integrated Rack (FIR), a facility-class payload, and the Light Microscopy Module (LMM), a subrack payload, are integrated research facilities that will fly in the U.S. Laboratory module, Destiny, aboard the International Space Station. Both facilities are being engineered, designed, and developed at the NASA Glenn Research Center by Northrop Grumman Information Technology. The FIR is a modular, multiuser scientific research facility that is one of two racks that make up the Fluids and Combustion Facility (the other being the Combustion Integrated Rack). The FIR has a large volume dedicated for experimental hardware; easily reconfigurable diagnostics, power, and data systems that allow for unique experiment configurations; and customizable software. The FIR will also provide imagers, light sources, power management and control, command and data handling for facility and experiment hardware, and data processing and storage. The first payload in the FIR will be the LMM. The LMM integrated with the FIR is a remotely controllable, automated, on-orbit microscope subrack facility, with key diagnostic capabilities for meeting science requirements--including video microscopy to observe microscopic phenonema and dynamic interactions, interferometry to make thin-film measurements with nanometer resolution, laser tweezers to manipulate micrometer-sized particles, confocal microscopy to provide enhanced three-dimensional visualization of structures, and spectrophotometry to measure the photonic properties of materials. Vibration disturbances were identified early in the LMM development phase as a high risk for contaminating the science microgravity environment. An integrated FIR-LMM test was conducted in Glenn's Acoustics Test Laboratory to assess mechanical sources of vibration and their impact to microscopic imaging. The primary purpose of the test was to characterize the LMM response at the sample location, the x-y stage within the microscope, to vibration emissions from the FIR and LMM support structures.

Gati, Frank↗
DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1990

Test bed experiments for various telerobotic system characteristics and configurations

Dexterous manipulation and grasping in telerobotic systems depends on the integration of high-performance sensors, displays, actuators and controls into systems in which careful consideration has been given to human perception and tolerance. Research underway at the Wisconsin Center for Space Automation and Robotics (WCSAR) has the objective of enhancing the performance of these systems and their components, and quantifying the effects of the many electrical, mechanical, control, and human factors that affect their performance. This will lead to a fundamental understanding of performance issues which will in turn allow designers to evaluate sensor, actuator, display, and control technologies with respect to generic measures of dexterous performance. As part of this effort, an experimental test bed was developed which has telerobotic components with exceptionally high fidelity in master/slave operation. A Telerobotic Performance Analysis System has also been developed which allows performance to be determined for various system configurations and electro-mechanical characteristics. Both this performance analysis system and test bed experiments are described.

Duffie, Neil A.↗
NASA NTRS1991

Science requirements for Heavy Nuclei Collection (HNC) experiment on NASA Long Duration Exposure Facility (LDEF) Mission 2

The Heavy Nuclei Collection (HNC) is a passive array of stacks of a special glass, 14 sheets thick, that record tracks of ultraheavy cosmic rays for later readout by automated systems on Earth. The primary goal is to determine the relative abundances of both the odd- and even-Z cosmic rays with Z equal to or greater than 50 with statistics a factor at least 60 greater than obtained in HEAO-3 and to obtain charge resolution at least as good as 0.25 charge unit. The secondary goal is to search for hypothetical particles such as superheavy elements. The HNC detector array will have a cumulative collection power equivalent to flying 32 sq m of detectors in space for 4 years. The array will be flown as a free-flight spacecraft and/or attached to Space Station Freedom.

Price, P. Buford↗
DOE OSTI2022

AMX – the highly automated macromolecular crystallography (17-ID-1) beamline at the NSLS-II

The highly automated macromolecular crystallography beamline AMX/17-ID-1 is an undulator-based high-intensity (>5 × 10 12 photons s −1 ), micro-focus (7 µm × 5 µm), low-divergence (1 mrad × 0.35 mrad) energy-tunable (5–18 keV) beamline at the NSLS-II, Brookhaven National Laboratory, Upton, NY, USA. It is one of the three life science beamlines constructed by the NIH under the ABBIX project and it shares sector 17-ID with the FMX beamline, the frontier micro-focus macromolecular crystallography beamline. AMX saw first light in March 2016 and started general user operation in February 2017. At AMX, emphasis has been placed on high throughput, high capacity, and automation to enable data collection from the most challenging projects using an intense micro-focus beam. Here, the current state and capabilities of the beamline are reported, and the different macromolecular crystallography experiments that are routinely performed at AMX/17-ID-1 as well as some plans for the near future are presented.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Alternative Fuels Research Laboratory

NASA Glenn has invested over $1.5 million in engineering, and infrastructure upgrades to renovate an existing test facility at the NASA Glenn Research Center (GRC), which is now being used as an Alternative Fuels Laboratory. Facility systems have demonstrated reliability and consistency for continuous and safe operations in Fischer-Tropsch (F-T) synthesis and thermal stability testing. This effort is supported by the NASA Fundamental Aeronautics Subsonic Fixed Wing project. The purpose of this test facility is to conduct bench scale F-T catalyst screening experiments. These experiments require the use of a synthesis gas feedstock, which will enable the investigation of F-T reaction kinetics, product yields and hydrocarbon distributions. Currently the facility has the capability of performing three simultaneous reactor screening tests, along with a fourth fixed-bed reactor for catalyst activation studies. Product gas composition and performance data can be continuously obtained with an automated gas sampling system, which directly connects the reactors to a micro-gas chromatograph (micro GC). Liquid and molten product samples are collected intermittently and are analyzed by injecting as a diluted sample into designated gas chromatograph units. The test facility also has the capability of performing thermal stability experiments of alternative aviation fuels with the use of a Hot Liquid Process Simulator (HLPS) (Ref. 1) in accordance to ASTM D 3241 "Thermal Oxidation Stability of Aviation Fuels" (JFTOT method) (Ref. 2). An Ellipsometer will be used to study fuel fouling thicknesses on heated tubes from the HLPS experiments. A detailed overview of the test facility systems and capabilities are described in this paper.

Surgenor, Angela D.↗
DOE OSTI2025

Inertial Confinement Fusion Design Search Using Bayesian Optimization

Inertial confinement fusion (ICF) experiments rely on complex multi-physics simulation codes such as the Lawrence Livermore National Laboratory-developed HYDRA to guide design work. However, these simulations have several dozen tunable parameters and can be computationally expensive. This makes searching the parameter space challenging and time-consuming. Recently developed automated tools utilize Bayesian optimization to search these high-dimensional parameter spaces for optimal designs. The optimization tools run 2D integrated simulations in HYDRA to converge on a design that produces specified scalar or vector outputs. In this paper, we apply the Bayesian optimization tools to two common tuning scenarios. First, we tune simulation inputs to match measurements of a well-characterized experiment at the National Ignition Facility. This type of tuning is commonly performed to compensate for the use of simplified simulation settings (e.g. reduced resolution) or to account for missing physics in the simulations. Second, we search for an ICF simulation design that has a particular radiation drive profile. These optimizations replicate the kinds of tuning researchers routinely perform, but do so with significantly reduced manual effort. This approach demonstrates a powerful and efficient pathway toward autonomous, high-fidelity design optimization for future ICF experiments.

Bayesian optimization↗
DOE OSTI2020

Energy Impact of Connected and Automated Vehicle Technologies. Final report

The overarching goal of this project is to understand the potential impact of connected and automated vehicles. The goal was achieved through data collection, model development, algorithm designs, simulations, and limited field tests. The main outcomes from this project include: (1) we collected energy consumption and GPS data from 500 vehicles over one year, with a total mileage of 8 million miles; (2) Based on the collected data and other datasets collected at the University of Michigan, we developed a calibrated Ann Arbor model in Polaris (model developed by ANL), and the fuel economy accuracy was found to be around 3.9% by comparison with field collected data; (3) An open-source SUMO model of Ann Arbor was developed; (4) Eco-Routing algorithms in Ann Arbor using the SUMO model shows 6% fuel saving potential; (5) Experiments conducted at the Mcity test facility shows that human drivers roughly follow the Eco-driving suggestions roughly 70% of the time; (6) Based in the Ann Arbor travel patterns, we found that each shared automated vehicle can replace around 4 individually owned vehicles; and (7) Adaptive Traffic Signal Control Algorithm developed through this project has been validated both in simulations and preliminary test results. For connected and automated vehicles, on average the performance is 13% delay reduction, and 10% fuel reduction. While connected and automated vehicles are in their early stage of deployment, the results from this project confirm that there is significant potential for energy saving if the technologies are developed and used properly. The three main technologies studied in this project include eco-routing, shared autonomous rides, and adaptive traffic signal controls. The data collected and model developed through the project can be used to study many other connected and automated vehicle technologies.

02 PETROLEUM↗
DOE OSTI2026

Integrating DOE ASCR Computing into HEPCloud through GlideinWMS

Fermilab's HEPCloud facility expands the laboratory's computing capacity by provisioning resources beyond the local grid, using GlideinWMS to deliver pilots to where experiments such as CMS and DUNE run. The High-Performance Computing (HPC) facilities of the DOE Office of Advanced Scientific Computing Research (ASCR) are a growing part of that pool. HEPCloud currently provisions NERSC over SSH, but NERSC is moving away from that path as it adopts multi-factor authentication and directs automated access to its Superfacility API and the DOE Integrated Research Infrastructure (IRI) APIs. Maintaining and extending access across the ASCR ecosystem now requires provisioning through these interfaces. This work adds new pilot submission paths to GlideinWMS for the NERSC Superfacility API, IRI, and Globus Compute. Each uses the provisioning model GlideinWMS already applies to batch resources, so experiments can run on ASCR computing resources without changes to their existing workflows. This work finally presents a comparison of the paths to guide which interfaces are best suited for different workflows.

Majumder, Meghanto [U. Houston (main)]↗
DOE OSTI2020

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Substitution Reactions in the Pyrolysis of Acetone Revealed through a Modeling, Experiment, Theory Paradigm

The development of high-fidelity mechanisms for chemically reactive systems is a challenging process that requires the compilation of rate descriptions for a large and somewhat ill-defined set of reactions. The present unified combination of modeling, experiment, and theory provides a paradigm for improving such mechanism development efforts. Here we combine broadband rotational spectroscopy with detailed chemical modeling based on rate constants obtained from automated ab initio transition state theory-based master equation calculations and high-level thermochemical parametrizations. Broadband rotational spectroscopy offers quantitative and isomer-specific detection by which branching ratios of polar reaction products may be obtained. Using this technique, we observe and characterize products arising from H atom substitution reactions in the flash pyrolysis of acetone (CH 3 C(O)CH 3 ) at a nominal temperature of 1800 K. The major product observed is ketene (CH 2 CO). Minor products identified include acetaldehyde (CH 3 CHO), propyne (CH 3 CCH), propene (CH 2 CHCH 3 ), and water (HDO). Literature mechanisms for the pyrolysis of acetone do not adequately describe the minor products. The inclusion of a variety of substitution reactions, with rate constants and thermochemistry obtained from automated ab initio kinetics predictions and Active Thermochemical Tables analyses, demonstrates an important role for such processes. Furthermore, the pathway to acetaldehyde is shown to be a direct result of substitution of acetone's methyl group by a free H atom, while propene formation arises from OH substitution in the enol form of acetone by a free H atom.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

Quantum materials research requires co-design of theory with experiments and involves demanding simulations and the analysis of vast quantities of data, usually including pattern recognition and clustering. Artificial intelligence is a natural route to optimise these processes and bring theory and experiments together. Here, we propose a scheme that integrates machine learning with high-performance simulations and scattering measurements, covering the pipeline of typical neutron experiments. Our approach uses nonlinear autoencoders trained on realistic simulations along with a fast surrogate for the calculation of scattering in the form of a generative model. We demonstrate this approach in a highly frustrated magnet, Dy 2 Ti 2 O 7 , using machine learning predictions to guide the neutron scattering experiment under hydrostatic pressure, extract material parameters and construct a phase diagram. Our scheme provides a comprehensive set of capabilities that allows direct integration of theory along with automated data processing and provides on a rapid timescale direct insight into a challenging condensed matter system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

TriGORank: A Gene Ontology Enriched Learning-to-Rank Framework for Trigenic Fitness Prediction

Machine learning (ML) has been gaining interest in the metabolic engineering community as a means to automate prediction tasks. In this work, we introduce and study the task of using ML to recommend high-fitness triplet mutants as candidates for wet-lab experiments. We first utilize individual fitness and digenic fitness scores as features and train machine learning models that produce a ranked list, from high to low fitness scores, for triplet gene mutants of S. cerevisiae. Then, we incorporate prior metabolic knowledge from an existing gene ontology, by designing a novel graph representation and deducing features that can capture gene similarity and gene interactions. Lastly, experimental results show that our proposed gene ontology enriched model, termed TriGORank, improves both performance and explainability.

Labhishetty, Sahiti↗
NASA NTRS1972

Automated Microbial Metabolism Laboratory

The Automated Microbial Metabolism Laboratory (AMML) 1971-1972 program involved the investigation of three separate life detection schemes. The first was a continued further development of the labeled release experiment. The possibility of chamber reuse without inbetween sterilization, to provide comparative biochemical information was tested. Findings show that individual substrates or concentrations of antimetabolites may be sequentially added to a single test chamber. The second detection system which was investigated for possible inclusion in the AMML package of assays, was nitrogen fixation as detected by acetylene reduction. Thirdly, a series of preliminary steps were taken to investigate the feasibility of detecting biopolymers in soil. A strategy for the safe return to Earth of a Mars sample prior to manned landings on Mars is outlined. The program assumes that the probability of indigenous life on Mars is unity and then broadly presents the procedures for acquisition and analysis of the Mars sample in a manner to satisfy the scientific community and the public that adequate safeguards are being taken.

Source record↗
NASA NTRS1976

Issues in advanced automation for manipulator control

This paper provides a brief description and analysis of the main issues in advanced autonomous control of manipulators as seen from a system point of view. The nature of manipulation is analyzed at some depth. A general multilevel structure is outlined for manipulator control organization which includes the human operator at the top level of the control structure. Different approaches to the development of advanced automation of mechanical arms are summarized. Recent work in the JPL teleoperator project is described, including control system, force/torque sensor, and control software development. Some results from control experiments are summarized.

Bejczy, A. K.↗
NASA NTRS1979

Methodology for error analysis and simulation of lidar aerosol measurements

A methodology is presented for objective and automated determination of the uncertainty in lidar aerosol measurements. This methodology is based on standard error-propagation procedures, a large data base on atmospheric behavior, and long experience in lidar data processing. Algebraic expressions for probable error are derived as a function of the relevant parameters. The validity of these expressions is then tested by making simulated measurements and analyses in which random errors of appropriate size are injected at proper steps of the measurement and analysis process. An illustrative example is given where the methodology is applied to a new lidar system now being used for airborne measurements of the stratospheric aerosol.

Russell, P. B.↗