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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Just the Right Amount of Reinforcement

Lockheed Martin Skunk Works, is taking the next step towards economical low-Earth-orbit (LEO) operations with NASA's X-33 technology demonstrator, that uses composite tanks for liquid hydrogen (LH sub2) fuel storage and structural support, The X-33 is a 53% scale model of the VentureStar single-stage-to-orbit (SSTO) reusable launch vehicle(RLV) projected to orbit payloads at a rate, of $1,000 per pound beginning in 2004 In order to make VentureStar completely reusable and economical engineers are using composite materials throughout the spacecrafts structure. The first test of the design comes in 1999 on the X-33 technology demonstrator. Two of the primary structures that engineers will be evaluating are the carbon fiber/epoxy LH2 fuel tanks. The 29-ft long by 18-ft wide tanks, which fill two-thirds of the X-33's interior, serve a dual purpose carrying fuel and providing structural support to the walls of the spacecraft. Fiber placement makes it possible to build the fuel tanks, large, light and strong enough to satisfy X33's requirements. Lockheed Martin choose the fabrication technology to produce the eight sections of each tank because of fiber placement's ability to handle complex surfaces, speed and repeatability.

Walton, Greg↗
DOE OSTI2022

Applying Waveform Correlation and Waveform Template Metadata to Mining Blasts to Reduce Analyst Workload

Organizations that monitor for underground nuclear explosive tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high - quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. In this study we use waveform template event metadata to seek corroborating detections from multiple stations in the International Monitoring System of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization. We build upon events detected in a prior waveform correlation study of mining blasts in two geographic regions, Wyoming and Scandinavia. Using a set of expert analyst-reviewed waveform correlation events that were declared to be true positive detections, we explore criteria for choosing the waveform correlation detections that are most likely to lead to bulletin-worthy events and reduction of analyst effort.

47 OTHER INSTRUMENTATION↗
NASA NTRS2008

On the Shoulders of Giants - Apollo's Contribution to Ares

It has been over 35 years since NASA developed new human spaceflight capabilities. As NASA builds vehicles to once again venture beyond Earth's orbit, it has the advantage of a powerful legacy of seasoned professionals who have already been there. Apollo-era veterans are lending their knowledge and expertise to nearly every aspect of the new Ares I crew launch vehicle and the Ares V cargo launch vehicle, from management to design and manufacturing techniques. Through group discussions, personal interviews, and consultant relationships, these talented individuals are sharing their "lessons lived" to help a new generation of engineers repeat the successes and avoid some of the pitfalls of America's first journeys to the Moon. In addition to learning from resident and retired experts, Ares will draw on legacy facilities, tooling, and hardware like the J-2 engine from the Apollo era and the Reusable Solid Rocket Boosters from the Space Shuttle Program. NASA needs to re-learn the skills required to send astronauts to the Moon, Mars, and beyond. The new Ares team is training with the best and building on the work of their eminent predecessors. They are standing on the shoulders of giants to see a future that is bright with possibilities on the space frontier.

Cook, Stephen A.↗
DOE OSTI2022

Electronegative metal dopants improve switching variability in Al 2 ⁢O 3 resistive switching devices

Resistive random-access memories are promising for nonvolatile memory and brain-inspired computing applications. High variability and low yield of these devices are key drawbacks hindering reliable training of physical neural networks. In this paper, we show that doping an oxide electrolyte, Al 2 ⁢O 3 , with electronegative metals makes resistive switching significantly more reproducible, surpassing the reproducibility requirements for obtaining reliable hardware neuromorphic circuits. Based on density functional theory calculations, the underlying mechanism is hypothesized to be the ease of creating oxygen vacancies in the vicinity of electronegative dopants due to the capture of the associated electrons by dopant midgap states and the weakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind significantly to the dopant, thereby serving as preferential sites and building blocks in the formation of conducting paths. Throughout this work, we validate this theory experimentally by implanting different dopants over a range of electronegativities in devices made of multiple alternating layers of Al 2 ⁢O 3 and WN and find superior repeatability and yield with highly electronegative metals, Au, Pt, and Pd. These devices also exhibit a gradual SET transition, enabling multibit switching that is desirable for analog computing.

36 MATERIALS SCIENCE↗
NASA NTRS1982

The airborne laser ranging system, its capabilities and applications

The airborne laser ranging system is a multibeam short pulse laser ranging system on board an aircraft. It simultaneously measures the distances between the aircraft and six laser retroreflectors (targets) deployed on the Earth's surface. The system can interrogate over 100 targets distributed over an area of 25,000 sq, kilometers in a matter of hours. Potentially, a total of 1.3 million individual range measurements can be made in a six hour flight. The precision of these range measurements is approximately + or - 1 cm. These measurements are used in procedure which is basically an extension of trilateration techniques to derive the intersite vector between the laser ground targets. By repeating the estimation of the intersite vector, strain and strain rate errors can be estimated. These quantities are essential for crustal dynamic studies which include determination and monitoring of regional strain in the vicinity of active fault zones, land subsidence, and edifice building preceding volcanic eruptions.

Kahn, W. D.↗
NASA NTRS1983

The Airborne Laser Ranging System - Its capabilities and applications

The Airborne Laser Ranging System is a proposed multibeam short pulse laser ranging system on board an aircraft. It simultaneously measures the distances between the aircraft and six laser retroreflectors (targets) deployed on the earth's surface. Depending on the host aircraft and terrain characteristics, the system can interrogate hundreds of targets distributed over an area as large as 60,000 sq. km in a matter of hours. Potentially, a total of 1.3 million individual range measurements can be made in a 6 hr flight. The precision of these range measurements is approximately 1 cm. These measurements are then used in a procedure which is basically an extension of trilateration techniques to derive the intersite vector between the laser ground targets. By repeating the estimation of the intersite vector, strain and strain rate errors can be estimated. These quantities are essential for crustal dynamic studies which include determination and monitoring of regional strain in the vicinity of active fault zones, land subsidence, and edifice building preceding volcanic eruptions.

Kahn, W. D.↗
NASA NTRS1996

Design of Plant Gas Exchange Experiments in a Variable Pressure Growth Chamber

Sustainable human presence in extreme environments such as lunar and martian bases will require bioregenerative components to human life support systems where plants are used for generation of oxygen, food, and water. Reduced atmospheric pressures will be used to minimize mass and engineering requirements. Few studies have assessed the metabolic and developmental responses of plants to reduced pressure and varied oxygen atmospheres. The first tests of hypobaric pressures on plant gas exchange and biomass production at the Johnson Space Center will be initiated in January 1996 in the Variable Pressure Growth Chamber (VPGC), a large, closed plant growth chamber rated for 10.2 psi. Experiments were designed and protocols detailed for two complete growouts each of lettuce and wheat to generate a general database for human life support requirements and to answer questions about plant growth processes in reduced pressure and varied oxygen environments. The central objective of crop growth studies in the VPGC is to determine the influence of reduced pressure and reduced oxygen on the rates of photosynthesis, dark respiration, evapotranspiration and biomass production of lettuce and wheat. Due to the constraint of one experimental unit, internal controls, called pressure transients, will be used to evaluate rates of CO2 uptake, O2 evolution, and H2O generation. Pressure transients will give interpretive power to the results of repeated growouts at both reduced and ambient pressures. Other experiments involve the generation of response functions to partial pressures of O2 and CO2 and to light intensity. Protocol for determining and calculating rates of gas exchange have been detailed. In order to build these databases and implement the necessary treatment combinations in short time periods, specific requirements for gas injections and removals have been defined. A set of system capability checks will include determination of leakage rates conducted prior to the actual crop growouts. Schedules of experimental events for lettuce and wheat are outlined and include replications in time of diurnal routines, pressure transients, variable pO2, pO2/pCO2 ratio, and light intensity responses.

Corey, Kenneth A.↗
NASA NTRS1993

Development of programmable artificial neural networks

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Meade, Andrew J.↗
NASA NTRS1991

Explosive Transient Camera (ETC) Program

Since the inception of the ETC program, a wide range of new technologies was developed to support this astronomical instrument. The prototype unit was installed at ETC Site 1. The first partially automated observations were made and some major renovations were later added to the ETC hardware. The ETC was outfitted with new thermoelectrically-cooled CCD cameras and a sophisticated vacuum manifold, which, together, made the ETC a much more reliable unit than the prototype. The ETC instrumentation and building were placed under full computer control, allowing the ETC to operate as an automated, autonomous instrument with virtually no human intervention necessary. The first fully-automated operation of the ETC was performed, during which the ETC monitored the error region of the repeating soft gamma-ray burster SGR 1806-21.

Ricker, George↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
DOE OSTI2026

Building a High-Throughput MiniFuel PIE Pipeline for HFIR-Based Advanced Fuel Evaluation

MiniFuel irradiation experiments have become a key capability for accelerated evaluation of advanced and high-burnup nuclear fuels in the High Flux Isotope Reactor. As the number of irradiated MiniFuel targets entering postirradiation examination (PIE) has increased, improvements to the PIE workflow were needed to support more repeatable target disassembly, subcapsule processing, specimen recovery, and downstream measurements. This report summarizes FY 2026 improvements developed and implemented at the Irradiated Fuels Examination Laboratory to improve the MiniFuel target-to-specimen (T2S) workflow and related PIE activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Commuting embeddings for parallel strategies in non-local games

Non-local games provide a versatile framework for probing quantum correlations and for benchmarking the power of entanglement. In finite dimensions, the standard method for playing several games in parallel requires a tensor product of the local Hilbert spaces, which scales additively in the number of qubits. In this work, we show that this additive cost can be reduced by exploiting algebraic embeddings. We introduce two forms of compressions. First, when a referee selects one game from a finite collection of games at random, the game quantum strategy can be implemented using a maximally entangled state of dimension equal to the largest individual game, thereby eliminating the need for repeated state preparations. Second, we establish conditions under which several games can be played simultaneously in parallel on fewer qubits than the tensor product baseline. These conditions are expressed in terms of commuting embeddings of the game algebras. Moreover, we provide a constructive framework for building such embeddings. Using tools from Lie theory, we show that aligning the various game algebras into a common Cartan decomposition enables such a qubit reduction. Beyond the theoretical contribution, our framework casts NLGs as algebraic primitives for distributed and resource-constrained quantum computations and suggested NLGs as a comparable device-independent dimension witness.

Commuting embeddings↗
DOE OSTI2021

Medium and Heavy Duty Vehicle Powertrain Electrification and Fleet Demonstration

The Contract Award Grant # EE0007513 dated 2 June, 2016 is for the design, development and demonstration of medium duty package delivery vehicles that will result in a 100% increase in fuel economy as measured in miles per gallon. The project was proposed in 3 phases over two budget periods. The Phases were: 1. Modeling and Simulation – This phase was completed and formed the basis of the specifications for the design and build of the physical systems. The modeling and simulation was conducted by AVL and used real world package delivery routes that AVL mapped. The motors, battery pack, range extender and balance of plant support systems were modeled and iterated until a system with the desired fuel economy improvement was realized. Early design of the major subsystems using the actual vehicle space constraints was also completed. This phase was successfully completed in May of 2017. 2. 1st Build and FE Confirmation - This phase was completed and resulted in the final design, release, procurement and build of all major vehicle systems for commissioning into the first vehicle. The work was a partnered effort between McLaren and AVL. McLaren built the eAxle system while AVL built the range extender and integrated the range extender, battery and balance of plant sub-systems into the first vehicle with a control system to make it operate. Prior to the end of this phase a real world HIL system test gave evidence that the fuel economy improvement would be achieved in the real world. This phase was exited with a 7 month delay due mostly to procurement and supply base issues and ended in May, 2018. 3. Final Build and Demonstration - This phase included the final build and commissioning of the four program vehicles and the operation of a demonstration on a set of routes near San Diego California. During this phase, a multitude of issues arose in different systems in the vehicle. These issue resolution exercises were quite complex and caused repeated delays but were all successfully completed. It should also be noted that an early commercialization analysis resulted in the conclusion that the complexity of the system would not make it cost effective, leading to the start of a follow-on design that used newly available component technologies to specify a more cost effective system. This design will be repurposed and put into demonstration on a separately (non-DOE) funded program. It was decided and agreed with DOE that this phase should be considered completed. The knowledge and know-how gained on this program has been incorporated into and transferred to the new program. This phase and the entire program was considered closed on June 1, 2021.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1995

Numerical solution of differential equations by artificial neural networks

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Meade, Andrew J., Jr.↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Bidirectional Manchester repeater

Bidirectional Manchester repeater is inserted at periodic intervals along single bidirectional twisted pair transmission line to detect, amplify, and transmit bidirectional Manchester 11 code signals. Requiring only 18 TTL 7400 series IC's, some line receivers and drivers, and handful of passive components, circuit is simple and relatively inexpensive to build.

Ferguson, J.↗
NASA NTRS1999

TCA High Lift Preliminary Assessment

This paper presents a TCA (Technology Concept Airplane) High lift Preliminary Assessment. The topics discussed are: 1) Model Description; 2) Data Repeatability; 3) Effect of Inboard L.E. (Leading Edge) Flap Span; 4) Comparison of 14'x22' TCA-1 With NTF (National Transonic Facility) Modified Ref. H; 5) Comparison of 14'x22' and NTF Ref. H Results; 6) Effect of Outboard Sealed Slat on TCA; 7) TCA Full Scale Build-ups; 8) Full Scale L/D Comparisons; 9) TCA Full Scale; and 10) Touchdown Lift Curves. This paper is in viewgraph form.

Wyatt, G. H.↗
NASA NTRS2008

Technology Infusion of CodeSonar into the Space Network Ground Segment (RII07): Software Assurance Symposium Technical Summary

Presents a source code analysis tool (CodeSonar) for use in the Space Network Ground Segment. The Space Network requires 99.9% proficiency and 97.0% availability of systems. Software has historically accounted for an annual average of 28% of the Space Network loss of availability and proficiency. CSCI A and CSCI B account for 42% of the previous eight months of software data loss. The technology infusion of CodeSonar into the Space Network Ground segment is meant to aid in determining the impact of the technology on the project both in the expenditure of effort and the technical results of the technology. Running a CodeSonar analysis and performing a preliminary review of the results averaged 3.5 minutes per finding (approximately 20 hours total). An additional 40 hours is estimated to analyze the 37 findings deemed too complex for the initial review. Using CodeSonar's tools to suppress known non-problems, delta tool runs will not repeat findings that have been marked as non-problems, further reducing the time needed for review. The 'non-interesting' finding rate of 70% is a large number, but filtering, search, and detailed contextual features of CodeSonar reduce the time per finding. Integration of the tool into the build process may also provide further savings by preventing developers from having to configure and operate the tool separately. These preliminary results show the tool to be easy to use and incorporate into the engineering process. These findings also provide significant potential improvements in proficiency and availability on the part of the software. As time-to-fix data become available a better cost trade can be made on person hours saved versus tool cost. Selective factors may be necessary to determine where best to apply CodeSonar to balance cost and benefits.

Benson, Markland J.↗