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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 1,441 records · Page 80
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NASA NTRS1996

Conversion-Integration of MSFC Nonlinear Signal Diagnostic Analysis Algorithms for Realtime Execution of MSFC's MPP Prototype System

NASA's advanced propulsion system Small Scale Magnetic Disturbances/Advanced Technology Development (SSME/ATD) has been undergoing extensive flight certification and developmental testing, which involves large numbers of health monitoring measurements. To enhance engine safety and reliability, detailed analysis and evaluation of the measurement signals are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce the risk of catastrophic system failures and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. During the development of SSME, ASRI participated in the research and development of several advanced non- linear signal diagnostic methods for health monitoring and failure prediction in turbomachinery components. However, due to the intensive computational requirement associated with such advanced analysis tasks, current SSME dynamic data analysis and diagnostic evaluation is performed off-line following flight or ground test with a typical diagnostic turnaround time of one to two days. The objective of MSFC's MPP Prototype System is to eliminate such 'diagnostic lag time' by achieving signal processing and analysis in real-time. Such an on-line diagnostic system can provide sufficient lead time to initiate corrective action and also to enable efficient scheduling of inspection, maintenance and repair activities. The major objective of this project was to convert and implement a number of advanced nonlinear diagnostic DSP algorithms in a format consistent with that required for integration into the Vanderbilt Multigraph Architecture (MGA) Model Based Programming environment. This effort will allow the real-time execution of these algorithms using the MSFC MPP Prototype System. ASRI has completed the software conversion and integration of a sequence of nonlinear signal analysis techniques specified in the SOW for real-time execution on MSFC's MPP Prototype. This report documents and summarizes the results of the contract tasks; provides the complete computer source code; including all FORTRAN/C Utilities; and all other utilities/supporting software libraries that are required for operation.

Jong, Jen-Yi↗
NASA NTRS2012

Autonomous Aerobraking Development Software: Phase One Performance Analysis at Mars, Venus, and Titan

When entering orbit about a planet or moon with an appreciable atmosphere, instead of using only the propulsion system to insert the spacecraft into its desired orbit, aerodynamic drag can be used after the initial orbit insertion to further decelerate the spacecraft. Several past NASA missions have used this aerobraking technique to reduce the fuel required to deliver a spacecraft into a desired orbit. Aerobraking was first demonstrated at Venus with Magellan in 1993 and then was used to achieve the science orbit of three Mars orbiters: Mars Global Surveyor in 1997, Mars Odyssey in 2001, and Mars Reconnaissance Orbiter in 2006. Although aerobraking itself reduces the propellant required to reach a final low period orbit, it does so at the expense of additional mission time to accommodate the aerobraking operations phase (typically 3-6 months), a large mission operations staff, and significant Deep Space Network (DSN) coverage. By automating ground based tasks and analyses associated with aerobraking and moving these onboard the spacecraft, a flight project could save millions of dollars in operations staffing and DSN costs (Ref. 1).

Maddock, Robert W.↗
NASA NTRS2018

Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA's Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can bean abstract task and relies on an orbit analyst to structure a mission based on an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Guzman, Esteban↗
NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses - What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Lucie Poulet↗
NASA NTRSDate not supplied

Crew Time Requirements in Future Space Greenhouses: What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Veggie↗
NASA NTRSDate not supplied

Rapid Response Landslide Detection Using ICEYE

Satellite synthetic aperture radar (SAR) data provides the best opportunity to quickly detect landslides for hazard response because SAR can image the ground regardless of weather or daylight conditions. The recent failure of the Sentinel-1B satellite has significantly reduced our ability to generate landslide maps in a timely manner across certain parts of the world leaving a gap in rapid response support. The ICEYE constellation may fill this gap because it can acquire repeat imagery over the same area every 24 hours and can be tasked to acquire data in specific areas. In addition, ICEYE acquires data with a higher spatial resolution, which is better suited for detecting smaller landslides. The high revisit time and high resolution of ICEYE significantly increases the capability to rapidly detect landslides and can provide key information to first responders. The main goal of this work was to test applicability/suitability of SAR imagery from ICEYE to generate rapid response landslide products.

Pukar Amatya↗
DOE OSTI2025

Reflections on the role of MOOSE in Unleashing a Golden Era of American Energy Innovation

As the use of artificial intelligence spreads across most professions, projections for the expected energy consumption of these virtual assistants/workers dictate a large increase in energy production. Recent published estimates refer to up to a necessary tripling of nuclear energy production~\cite{iaea}. % TODO add citations This is supported by the new administration, calling on February 5th to "unleash Commercial Nuclear Power in the United States" and to launch the "long-awaited American nuclear renaissance"~\cite{doe-order}. The Department of Energy (DOE) is tasked with "enabl[ing] the rapid deployment and export of next generation nuclear technology". In terms of research of development, we must seek to "unleash American Energy Innovation", by "focus[ing] our time and resources on technologies that will advance basic science, grow America’s scientific leadership, reduce costs for American families [...]". To that end, the Department will "prioritize affordable, reliable, and secure energy technologies, including fossil fuels, advanced nuclear, geothermal, and hydropower." Following this call to action, we elect to examine the role of the open-source Multiphysics Object Oriented Simulation Environment (MOOSE)~\cite{moose3} in meeting the stated goals.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗
DOE OSTI2018

A hybrid of fractal image coding and fractal dimension for an efficient retrieval method

Fractal image coding (FIC) based on the inverse problem of an iterated function system plays an essential role in several areas of computer graphics and in many other interesting applications. Through FIC, an image can be transformed to compressed representative parameters and be expressed in a simple geometric way. Dealing with digital images requires storing a large number of images in databases, where searching such databases is time consuming. Therefore, finding a new technique that facilitates this task is a challenge that has received increasing attention from many researchers. In this study, a new method that combines fractal dimension (FD) which is an indicator of image complexity with the FIC scheme is proposed. Classifying images in databases according to their texture by using FD helps reduce the retrieval time of query images. The validity of the proposed method is evaluated using geosciences images. Result shows that the method is computationally attractive.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A General Framework for Process Synthesis, Integration, and Intensification

Process synthesis, integration, and intensification are the three pillars of process design. Current synthesis and integration methods are able to find optimal design targets and process configurations when all the alternatives are known beforehand. Process intensification, on the other hand, combines multiple physicochemical phenomena and exploits their interactions to create innovative designs. Often times, these designs are not known beforehand, and a phenomena-level representation of chemical processes are required to identify them. This disconnection between the three paradigms limits the ability to systematically discover optimal design pathways. We demonstrate that the building block representation, originally proposed in our earlier work on process intensification, has the potential to bridge this gap. Depending on the attributes assigned to the interior and the boundaries of these two-dimensional abstract building blocks, they can represent various intensified or isolated phenomena at the lowest level, various tasks at the equipment level, and various unit operations at the flowsheet level. Here, this common multiscale representation enables an mixed-integer nonlinear optimization-based single framework for the sequential or simultaneous synthesis, integration, and intensification of chemical processes. Such a general framework is critical to reduce the risk of eliminating potential intensification pathways and candidate flowsheets at the conceptual design stage. The framework is demonstrated using a case study on an ethylene glycol process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Accuracy of the time-averaged ponderomotive approximation for laser-plasma accelerator modeling

Reliable modeling of laser-plasma accelerators, where a short and intense laser pulse propagates in an underdense plasma over long distances, is a computationally challenging task. This is due to the great disparity among the scales involved in the modeling, ranging from the micrometer scale of the laser wavelength to, for instance, the meter scale of the laser-plasma interaction length for a multi-GeV-class laser-plasma accelerator. To reduce such imbalance, the time-averaged ponderomotive approximation may be used, where the plasma particle dynamics is analytically averaged over the laser frequency, and only spatiotemporal scales associated with the laser envelope are retained in the calculations, resulting in significant computational savings. Here, we characterize the accuracy and robustness of the time-averaged ponderomotive approximation for a range of laser parameters of interest for present and future laser-plasma accelerators, and we show that the error introduced by the averaging process is small in all relevant cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

FrESCO

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information and summary statistics about cancer prevalence to facilitate population level monitoring of cancer incidence. Extracting cancer phenotype from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating this information extraction process from cancer pathology reports has the potential to improve not only the quality of the data by extracting information in a consistent manner across registries, but to improve the quality of patient outcomes by reducing the time to assimilate new data and enabling time-sensitive applications such as precision medicine. Here we present FrESCO: Framework for Exploring Scalable Computational Oncology, a modular deep-learning natural language processing (NLP) library for extracting pathology information from clinical text documents.

Spannaus, Adam [Oak Ridge National Lab. (ORNL), Oa↗
DOE OSTI2015

Low Cost High-H 2 Syngas Production for Power and Liquid Fuels

This report summarizes the technical progress made of the research project entitled “Low Cost High-H2 Syngas Production for Power and Liquid Fuels,” under DOE Contract No. DE-FE-0011958. The period of performance was October 1, 2013 through July 30, 2015. The overall objectives of this project was to determine the technical and economic feasibility of a systems approach for producing high hydrogen syngas from coal with the potential to reduce significantly the cost of producing power, chemical-grade hydrogen or liquid fuels, with carbon capture to reduce the environmental impact of gasification. The project encompasses several areas of study and the results are summarized here. (1) Experimental work to determine the technical feasibility of a novel hybrid polymer/metal H2-membrane to recover pure H2 from a coal-derived syngas was done. This task was not successful. Membranes were synthesized and show impermeability of any gases at required conditions. The cause of this impermeability was most likely due to the densification of the porous polymer membrane support made from polybenzimidazole (PBI) at test temperatures above 250 °C. (2) Bench-scale experimental work was performed to extend GTI's current database on the University of California Sulfur Recovery Process-High Pressure (UCSRP-HP) and recently renamed Sulfur Removal and Recovery (SR2) process for syngas cleanup including removal of sulfur and other trace contaminants, such as, chlorides and ammonia. The SR2 process tests show >90% H2S conversion with outlet H2S concentrations less than 4 ppmv, and 80-90% ammonia and chloride removal with high mass transfer rates. (3) Techno-economic analyses (TEA) were done for the production of electric power, chemical-grade hydrogen and diesel fuels, from a mixture of coal- plus natural gas-derived syngas using the Aerojet Rocketdyne (AR) Advanced Compact coal gasifier and a natural gas partial oxidation reactor (POX) with SR2 technology. Due to the unsuccessful experimental results with the hybrid polymer/metal H2 membrane, a conventional CO2 capture (single-stage Selexol) and hydrogen purification (PSA) technologies were used in the appropriate cases. In all cases, the integrated system of Advanced Compact coal gasifier, non-catalytic natural gas partial oxidation, and SR2 multicontaminant removal with state-of-the-art auxiliary system provided a 5-25% cost advantage over the base line plants using GEE coal gasifier with conventional Selexol/Claus sulfur removal and recovery. These plants also produce 18-30% less CO2 than with the conventional coal gasification plants.

08 HYDROGEN↗
DOE OSTI2018

Improved Preparedness in Galveston County, Texas Final Report CRADA No. TC02275

LLNS, as manager and operator of Lawrence Livermore National Laboratory (LLNL), and UTMB have a multi-year, ongoing, collaborative effort. This effort aimed to improve preparedness in Galveston County, Texas region and, more generally, identifying improved methods to (a) understand and mitigate the hazards of a large industrial chemical release and (b) implement and adapt national response plans and guidance to meet local planning and response needs. This CRADA was one portion of that effort and was intended to support the Hydrofluoric Acid (HF) Incident Symposium. This annual symposium engages a wide variety of government, private, and non-profit groups to (a) increase the resiliency, and reduce the vulnerability, of communities at risk of being impacted by major releases of toxic industrial chemicals and (b) guide the development of policies and best practices to prevent and manage these events. This CRADA project was originally scheduled for six (6) months and consisted of five major tasks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Pilot decision making in a computer-aided flight management situation

An experimental representation of a computer-aided multi-task flight management situation has been developed. A computer aiding program was implemented to serve as a back-up decision maker. An experiment was conducted with a balanced design of several subject runs for different workload levels. This was achieved using three levels of subsystem event arrival rates, three levels of control task involvement, and three levels of availability of computer aiding. Experimental results compared quite favorably with those from a computer simulation which employed a queueing model. It was shown that the aiding had enhanced system performance as well as subjective ratings, and that the adaptive aiding policy further reduced subsystem delay.

Chu, Y. Y.↗
NASA NTRS1986

Active controllers and the time duration to learn a task

An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

Repperger, D. W.↗