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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Early Career: Constraining the Electromagnetic Shower Energy Scale at LArTPC Neutrino Detectors Near and Far (Final Scientific/Technical Report)

This is the final scientific/technical report associated with the award "Early Career: Constraining the Electromagnetic Shower Energy Scale at LArTPC Neutrino Detectors Near and Far"; the primary research topics associated with the award are neutrino oscillation physics, reconstruction of neutrino interaction events using machine learning techniques, and detector calibration.

43 PARTICLE ACCELERATORS↗
NASA NTRS2004

Multi-Agent Methods for the Configuration of Random Nanocomputers

As computational devices continue to shrink, the cost of manufacturing such devices is expected to grow exponentially. One alternative to the costly, detailed design and assembly of conventional computers is to place the nano-electronic components randomly on a chip. The price for such a trivial assembly process is that the resulting chip would not be programmable by conventional means. In this work, we show that such random nanocomputers can be adaptively programmed using multi-agent methods. This is accomplished through the optimization of an associated high dimensional error function. By representing each of the independent variables as a reinforcement learning agent, we are able to achieve convergence must faster than with other methods, including simulated annealing. Standard combinational logic circuits such as adders and multipliers are implemented in a straightforward manner. In addition, we show that the intrinsic flexibility of these adaptive methods allows the random computers to be reconfigured easily, making them reusable. Recovery from faults is also demonstrated.

Lawson, John W.↗
NASA NTRS2017

Methodology for Assessing Reusability of Spaceflight Hardware

In 2011 the Space Shuttle, the only Reusable Launch Vehicle (RLV) in the world, returned to earth for the final time. Upon retirement of the Space Shuttle, the United States (U.S.) no longer possessed a reusable vehicle or the capability to send American astronauts to space. With the National Aeronautics and Space Administration (NASA) out of the RLV business and now only pursuing Expendable Launch Vehicles (ELV), not only did companies within the U.S. start to actively pursue the development of either RLVs or reusable components, but entities around the world began to venture into the reusable market. For example, SpaceX and Blue Origin are developing reusable vehicles and engines. The Indian Space Research Organization is developing a reusable space plane and Airbus is exploring the possibility of reusing its first stage engines and avionics housed in the flyback propulsion unit referred to as the Advanced Expendable Launcher with Innovative engine Economy (Adeline). Even United Launch Alliance (ULA) has announced plans for eventually replacing the Atlas and Delta expendable rockets with a family of RLVs called Vulcan. Reuse can be categorized as either fully reusable, the situation in which the entire vehicle is recovered, or partially reusable such as the National Space Transportation System (NSTS) where only the Space Shuttle, Space Shuttle Main Engines (SSME), and Solid Rocket Boosters (SRB) are reused. With this influx of renewed interest in reusability for space applications, it is imperative that a systematic approach be developed for assessing the reusability of spaceflight hardware. The partially reusable NSTS offered many opportunities to glean lessons learned; however, when it came to efficient operability for reuse the Space Shuttle and its associated hardware fell short primarily because of its two to four-month turnaround time. Although there have been several attempts at designing RLVs in the past with the X-33, Venture Star and Delta Clipper Experimental (DC-X), reusability within the spaceflight arena is still in its infancy. With unlimited resources (namely, time and money), almost any launch vehicle and its associated hardware can be made reusable. However, an endless supply of funds for space exploration is not the case in today's economy for neither government agencies nor their commercial counterparts. Therefore, any organization wanting to be a leader in space exploration and remain competitive in this unforgiving space faring industry must confront shrinking budgets with more cost conscious and efficient designs. Therefore, standards for developing reusable spaceflight hardware need to be established. By having standards available to existing and emerging companies, some of the potential roadblocks and limitations that plagued previous attempts at reuse may be minimized or completely avoided.

Childress-Thompson, Rhonda↗
NASA NTRS2019

Flow Boiling and Condensation Experiment Flight Hardware Development

The Flow Boiling and Condensation Experiment (FBCE) to be manifested on the International Space Station (ISS) consists of a fluid system and the associated electronics to provide for conditioning the test fluid (normal-PerFluorohexane or nPFH-C6F14) to the proper thermodynamic state prior to entering a test module, which can be interchangeable based on the science objectives. Two separate test modules have been manufactured for the FBCE, the Flow Boiling Module (FBM), which investigates flow boiling for a subcooled liquid, saturated liquid, or two phase mixture, and the Condensation Module Heat Transfer (CM-HT), which investigates condensation of a flowing saturated or superheated vapor. The test fluid heating is accomplished using the Bulk Heater Module (BHM), which heats the fluid to various states based on the demands of the currently installed test module. ISS Internal Thermal Control System (ITCS) water is utilized to cool the test fluid prior to entering the circulation pump, and is also utilized for cooling for condensation in CM-HT, for cooling of a camera in FBM. An adjustable pressure bellows-type accumulator is used to set the pressure at the inlet of the test section, but does not provide active pressure control during testing. The flow of the test fluid is achieved using a gear pump controlled by a coriolis flow meter, which also provides the flow rate measurement. Flow rates for the ITCS water loops are measured and controlled using coriolis flow meters with directly controlled proportional valves. During execution of FBCE operations, the FBM is scheduled to collect data for three months before being exchanged with CM-HT for another three month data collection run. In this work, we present the development of the flight hardware, the associated challenges experienced during the development such as packaging flight system hardware, and the lessons learned in overcoming the encountered challenges.

Guzik, Monica C.↗
NASA NTRS2020

Shape Memory Alloys – Not Your Ordinary Metal

Sixty years after the discovery of shape memory alloys (SMAs), many actuation and structural applications using these materials have been conceived and developed. SMAs are a unique class of multifunctional materials that have the ability to recover large deformations and generate high stresses in response to thermal, mechanical and/or electromagnetic stimuli. These abilities have made them a viable option for actuation/structural systems in aerospace applications, amongst others. However, designing with SMAs is a paradigm shift from the conventional way we look at metals and mechanisms. In this course, you will learn how the unique properties of SMAs can be applied to designing mechanisms and the associated benefits. Basic primer will be provided on what they are and why they work with examples of the most successful applications that have been imagined. Common design tool and properties-database will be discussed.

Othmane Benafan↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

New Technologies for Space Avionics, 1993

The report reviews a 1993 effort that investigated issues associated with the development of requirements, with the practice of concurrent engineering and with rapid prototyping, in the development of a next-generation Reaction Jet Drive Controller. This report details lessons learned, the current status of the prototype, and suggestions for future work. The report concludes with a discussion of the vision of future avionics architectures based on the principles associated with open architectures and integrated vehicle health management.

Aibel, David W.↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Learning from Past Experiences

Space flight is a risky business. This truism has been bandied about since the earliest days of the space program. When asked by the young daughter of a coworker, one of the Mercury astronauts likened launching into space to "riding a Roman candle" -- it was both exciting and dangerous. Even in these more technologically advanced days, the solid rocket boosters and external tanks of the space shuttle provide a no less exciting, or dangerous, ride into space. However much the phrase "risk mitigation" is bandied about within the U.S. space program, there is still the history of the Apollo 1 fire during a ground test at Cape Canaveral, Fla., the loss of the shuttle Challenger during liftoff, and the loss of the shuttle Columbia when returning to Earth to remind us that while we give lip-service to risk management, we have not learned to manage risk as well as we ought. Moreover, there are many more less dramatic, but equally critical, incidents that have occurred in association with the space program that also highlight our inability to accurately gauge and manage risk. Why do we seem caught in a senseless spiral in which we focus most on risk only after a tragedy? Why do we repeat serious mishaps and not learn from our mistakes? This paper reviews some possible explanations for our risk-taking behavior and provides examples of interest to the NASA centers, while also discussing inter center and intra-center opportunities for sharing information to mitigate risk.

Hulet, Michael W.↗
NASA NTRS2007

Passive Endwall Treatments for Enhancing Stability

These lecture notes were presented at the von Karman Institutes lecture series on Advances in Axial Compressor Aerodynamics, May 2006. They provide a fairly extensive overview of what's been learned from numerous investigations of various passive casing endwall technologies that have been proposed for alleviating the stall limiting physics associated with the compressor endwall flow field. The lecture notes are organized to give an appreciation for the inventiveness and understanding of the earliest compressor technologists and to provide a coherent thread of understanding that has arisen out of the early investigations. As such the lecture notes begin with a historical overview of casing treatments from their infancy through the earliest proposed concepts involving blowing, suction and flow recirculation. A summary of lessons learned from these early investigations is provided at the end of this section. The lecture notes then provide a somewhat more in-depth overview of recent advancements in the development of passive casing treatments from the late 1990's through 2006, including advancements in understanding the flow mechanism of circumferential groove casing treatments, and the development of discrete tip injection and self-recirculating casing treatments. At the conclusion of the lecture notes a final summary of lessons learned throughout the history of the development of passive casing treatments is provided. Finally, a list of future needs is given. It is hoped that these lecture notes will be a useful reference for future research endeavors to improve our understanding of the fluid physics of passive casing treatments and how they act to enhance compressor stability, and that they will perhaps provide a springboard for future research activities in this area of interest

Hathaway, Michael D.↗
DOE OSTI2025

Divide and conquer: separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process—the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labelled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as ‘probability’ of the picks. Here, we show that this ‘probability’ represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multiclass classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

58 GEOSCIENCES↗
DOE OSTI2024

4th Big Data for Nuclear Power Plants Workshop 2023

The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Summary of Potential Incidents and Consequences from Carbon Dioxide Pipeline and Storage Systems Construction and Operation

This document provides a high-level summary of the potential human and environmental impacts associated with carbon dioxide (CO 2 ) pipeline transport, injection, and storage activities. It also reviews lessons learned from natural and industrial analogs of CO 2 storage as well as case studies of notable accidental CO 2 releases. The analysis is structured into several key sections, each addressing specific resource impacts and potential consequences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Recursive heuristic classification

The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

Wilkins, David C.↗
NASA NTRS2003

NASA's Bioreactor: Growing Cells in a Simulated Microgravity Environment

National Science Education Standards (NSES), Science for All Americans, the Secretary's Commission on Achieving Necessary Skills (SCANS) as well as the National Aeronautics and Space Administration (NASA) are all making an effort to promote scientific literacy in America. Unfortunately, major evaluation programs such as the National Assessment of Educational Progress (NAEP) and the Third International Mathematics and Science Study (TIMSS) have provided information that suggested our students are not able to compete with peers from comparable countries. Although results indicated that American students are recalling memorized, factual knowledge well enough, the real problem is the ability to apply what they know. Concerned with these reports, the National Science Teacher's Association (NSTA) has developed a mission to support innovation and high quality in science teaching and learning for every student. NSTA recommends less emphasis on factual knowledge (memorization) and information and more understanding of the concepts. Science process skills are considered imperative to prepare America's students for the 21st century. The National Aeronautics and Space Administration (NASA) supports this mission and adds that NASA strives to help prepare and encourage the next generation of researchers and explorers. One method that NASA supports educators and its mission is to publish educational briefs. NASA describes a brief as a publication that ranges from one-to-thirty pages. The focus is on mission discoveries and results. The brief provides curriculum to educators that supports their objectives and NASA's interest. Educational Briefs are specific to the grade level and course so that educators may have choices that fit their methods and students level. Sometimes, the brief includes lessons and activities teachers may use. For example, NASA's Microgravity Division has designed a student bioreactor. Consequently, an Educational Brief is being written that focuses on how to build a student bioreactor and experiments that can be conducted in it. These experiments mimic the experiments done by NASA and other researchers in the real world of work.

Richardson, Denise↗
NASA NTRSDate not supplied

Green Propulsion : A NASA GSFC Assessment

In the ever-changing paradigm of efficient and capable spacecraft design, scientific missions continue pushing spacecraft subsystems to deliver effective solutions to meet challenging new mission/spacecraft applications. From an in-space storable liquid chemical propulsion perspective, monopropellant hydrazine is a dependable propellant. Bi-propellant architectures offer even superior performance, but add the complexity of a hypergolic fuel (hydrazine/ mono-methyl hydrazine) and oxidizer (Nitrogen Tetroxide/ mixed oxides of nitrogen) dual tank combination. These propulsion system designs (mono-propellant and bi-propellant) have high heritage, high propellant throughput qualified engines, widely tested material compatibility, qualified fluid delivery commercial-off-the-shelf components, known handling practices, and repeatable performance in successfully delivering on mission requirements. NASA and the broader propulsion community have historically selected hypergolic propellants for most mission applications. The space propulsion community has learned to successfully handle these highly toxic and hazardous materials, navigate the regulated use and the associated safety protocols, personnel protective equipment, and unique training standards – all requisite for loading spacecraft propulsion systems with hypergolic propellants. The question now arises as to what is next for in-space chemical propulsion? Is there an alternative, or even replacement, to the reliable hypergolic fluids, or propellant alternatives that promise increased mission benefits? With the evolution and proven advancements in innovative in-space green propellant technologies capable of delivering benefits to scientific missions, concern over the reliability and infusibility of this higher performing and safer to handle class of propellants is waning. As NASA science missions move forward with the potential flight infusion of green propulsion, NASA and the broader propulsion community are working to address remaining gaps in hardware development, reliability, performance, unique operational considerations, and risk mitigations for high value scientific assets.

Henry W. Mulkey↗