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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Photovoltaic fleet degradation insights

Abstract In the PV Fleet Performance Data Initiative, high‐frequency data from commercial and utility‐scale photovoltaic (PV) systems have been collected to examine performance loss rates (PLRs) at a fleet scale. To date, performance data from more than 7.2‐gigawatt (GW) capacity, 1700 sites and 19,000 inverters—approximately equivalent to 6% to 7% of the entire US PV market—have been collected. An overall PLR of −0.75%/year was found, which is in line with historical and recent findings. Tracked silicon (Si) and cadmium telluride (CdTe) performed comparably with all fixed‐tilt systems. Higher PLRs were found for hotter temperature zones; cooler climates exhibit a median −0.48%/year loss, which increases to −0.88%/year in hotter climates. High‐efficiency module technologies showed median PLRs in line with conventional Si technologies but demonstrated markedly different PLR behavior when filtered only for low‐light conditions <600 W/m 2 . Causes for this technology‐dependent behavior are under investigation.

14 SOLAR ENERGY↗
DOE OSTI2022

Threat emulation framework

A method for emulating threats in virtual network computing environment is provided. The method comprises creating a number of virtual machines in the virtual network computing environment. A number of threat actors are emulated, wherein each threat actor comprises a number of threat artifacts that form a sequence of attack steps against the virtual network computing environment. The threat actors are then deployed against the virtual network computing environment. Behavioral data about actions of the threat actors in the virtual network computing environment is collected, as is performance data about the virtual network computing environment in response to the threat actors. The collected behavioral and performance data is then presented to a user via an interface.

Urias, Vincent↗
DOE OSTI2022

Scale up, Field testing, and Optimization of Nontoxic, Durable, Economical Coatings for Control of Biofouling and Corrosion (Abstract Only)

The objective of this project is the technical maturation and demonstration of a durable, economical, and nontoxic coating, Superhydrophobic Lubricant Infused Composite (SLIC), that will prevent fouling organisms from growing on marine and hydrokinetic (MHK) structures and prevent invasive mussels from growing on hydropower structures. SLIC technology was developed and patented by PNNL. It combines multiple antifouling mechanisms to provide excellent antifouling performance, durability, and low hydrodynamic drag without using toxic materials. Advanced proof-of-concept testing and demonstrations completed during a Phase 1 TCF project attracted industry interest and partnerships for this Phase 2 effort focused on maturation of a commercial product. Specific tasks and tests will shift the development emphasis to optimizing formulations, validating antifouling performance to address specific application needs, explore compatibility with other paint systems, develop a product with useful shelf life (in can), and determine curing times and effective applications methodology. Phase 2 efforts will focus upon technology transfer and commercialization of the technology with the industrial team. Industrial partners now include a coatings development specialist (Lorama), hydrophobic material manufacturer and paint developer (Dry Surface Technologies), nontoxic biodegradable lubricants manufacturer (BioBlend), and aquatic applications specialists (Prometheus Innovations, River Connectivity Systems). Engagement with the MHK device developers, the PNNL Marine and Coastal Research Laboratory (MCRL) test site in Sequim WA, Bureau of Reclamation, and Taylor Shellfish throughout the project will provide topical expertise and field test sites that will deliver crucial real-world performance data. Through this Phase 2 effort, SLIC will transition from a Technical Readiness Level (TRL) 5 to TRL 6 and long-term performance data will be acquired through demonstrations. The field test data will allow optimization of SLIC formulation to enhance performance (e.g., durability) and packaging, which are key de-risking activities for technology transfer and ultimately to the production of a viable commercial product.

13 HYDRO ENERGY↗
NASA NTRS2011

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Bhaduri, Kanishka↗
NASA NTRS2009

Sea-Level Flight Demonstration and Altitude Characterization of a LO2/LCH4 Based Accent Propulsion Lander

A 1,500 lbf thrust-class liquid oxygen (LO2)/Liquid Methane (LCH4) rocket engine was developed and tested at both sea-level and simulated altitude conditions. The engine was fabricated by Armadillo Aerospace (AA) in collaboration with NASA Johnson Space Center. Sea level testing was conducted at Armadillo Aerospace facilities at Caddo Mills, TX. Sea-level tests were conducted using both a static horizontal test bed and a vertical take-off and landing (VTOL) test bed capable of lift-off and hover-flight in low atmosphere conditions. The vertical test bed configuration is capable of throttling the engine valves to enable liftoff and hover-flight. Simulated altitude vacuum testing was conducted at NASA Johnson Space Center White Sands Test Facility (WSTF), which is capable of providing altitude simulation greater than 120,000 ft equivalent. The engine tests demonstrated ignition using two different methods, a gas-torch and a pyrotechnic igniter. Both gas torch and pyrotechnic ignition were demonstrated at both sea-level and vacuum conditions. The rocket engine was designed to be configured with three different nozzle configurations, including a dual-bell nozzle geometry. Dual-bell nozzle tests were conducted at WSTF and engine performance data was achieved at both ambient pressure and simulated altitude conditions. Dual-bell nozzle performance data was achieved over a range of altitude conditions from 90,000 ft to 50,000 ft altitude. Thrust and propellant mass flow rates were measured in the tests for specific impulse (Isp) and C* calculations.

Collins, Jacob↗
DOE OSTI2021

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Advanced high pressure engine study for mixed-mode vehicle applications

High pressure liquid rocket engine design, performance, weight, envelope, and operational characteristics were evaluated for a variety of candidate engines for use in mixed-mode, single-stage-to-orbit applications. Propellant property and performance data were obtained for candidate Mode 1 fuels which included: RP-1, RJ-5, hydrazine, monomethyl-hydrazine, and methane. The common oxidizer was liquid oxygen. Oxygen, the candidate Mode 1 fuels, and hydrogen were evaluated as thrust chamber coolants. Oxygen, methane, and hydrogen were found to be the most viable cooling candidates. Water, lithium, and sodium-potassium were also evaluated as auxiliary coolant systems. Water proved to be the best of these, but the system was heavier than those systems which cooled with the engine propellants. Engine weight and envelope parametric data were established for candidate Mode 1, Mode 2, and dual-fuel engines. Delivered engine performance data were also calculated for all candidate Mode 1 and dual-fuel engines.

Luscher, W. P.↗
NASA NTRS1989

Experiences with hypercube operating system instrumentation

The difficulties in conceptualizing the interactions among a large number of processors make it difficult both to identify the sources of inefficiencies and to determine how a parallel program could be made more efficient. This paper describes an instrumentation system that can trace the execution of distributed memory parallel programs by recording the occurrence of parallel program events. The resulting event traces can be used to compile summary statistics that provide a global view of program performance. In addition, visualization tools permit the graphic display of event traces. Visual presentation of performance data is particularly useful, indeed, necessary for large-scale parallel computers; the enormous volume of performance data mandates visual display.

Reed, Daniel A.↗
NASA NTRS1993

Performance results from a test of an S-76 rotor in the NASA Ames 80- by 120-foot wind tunnel

A full-scale helicopter rotor wind tunnel test has been conducted which covers a wide range of rotor-shaft angles-of-attack and 0-100 kt thrust conditions. The hover performance data thus obtained were compared with the results of momentum theory calculations; forward flight rotor-performance data were compared with calculations from a comprehensive rotorcraft analysis. These comparisons suggest that hover testing at an outdoor facility in the absence of ground effect is required to make a final determination of the absolute accuracy of the wind tunnel hover data.

Shinoda, Patrick M.↗
NASA NTRS2010

NDARC - NASA Design and Analysis of Rotorcraft Validation and Demonstration

Validation and demonstration results from the development of the conceptual design tool NDARC (NASA Design and Analysis of Rotorcraft) are presented. The principal tasks of NDARC are to design a rotorcraft to satisfy specified design conditions and missions, and then analyze the performance of the aircraft for a set of off-design missions and point operating conditions. The aircraft chosen as NDARC development test cases are the UH-60A single main-rotor and tail-rotor helicopter, the CH-47D tandem helicopter, the XH-59A coaxial lift-offset helicopter, and the XV-15 tiltrotor. These aircraft were selected because flight performance data, a weight statement, detailed geometry information, and a correlated comprehensive analysis model are available for each. Validation consists of developing the NDARC models for these aircraft by using geometry and weight information, airframe wind tunnel test data, engine decks, rotor performance tests, and comprehensive analysis results; and then comparing the NDARC results for aircraft and component performance with flight test data. Based on the calibrated models, the capability of the code to size rotorcraft is explored.

Johnson, Wayne↗
NASA NTRS1976

High data rate performance of a BCCD for rapid scan imaging

A commercially available 1728 element shallow bulk channel CCD line array was clocked at output data rates up to 30 MHz. Temporal noise was near 200 electrons and was largely independent of clock rate. Charge transfer efficiency was in excess of 0.9999 at 30 MHz. Utilization is feasible for visual simulation and aerial mapping.

Schmieder, D. E.↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗
NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗
NASA NTRS2010

Cognitive Assessment During Long-Duration Space Flight

The Space Flight Cognitive Assessment Tool for Windows (WinSCAT) is a computer-based, self-administered battery of five cognitive assessment tests developed for medical operations at NASA's Johnson Space Center in Houston, Texas. WinSCAT is a medical requirement for U.S. long-duration astronauts and has been implemented with U.S. astronauts from one NASA/Mir mission (NASA-7 mission) and all expeditions to date on the International Space Station (ISS). Its purpose is to provide ISS crew surgeons with an objective clinical tool after an unexpected traumatic event, a medical condition, or the cumulative effects of space flight that could negatively affect an astronaut's cognitive status and threaten mission success. WinSCAT was recently updated to add network capability to support a 6-person crew on the station support computers. Additionally, WinSCAT Version 2.0.28 has increased difficulty of items in Mathematics, increased number of items in Match-to-Sample, incorporates a moving rather than a fixed baseline, and implements stricter interpretation rules. ISS performance data were assessed to compare initial to modified interpretation rules for detecting potential changes in cognitive functioning during space flight. WinSCAT tests are routinely taken monthly during an ISS mission. Performance data from these ISS missions do not indicate significant cognitive decrements due to microgravity/space flight alone but have shown decrements. Applying the newly derived rules to ISS data results in a number of off-nominal performances at various times during and after flight.. Correlation to actual events is needed, but possible explanations for off-nominal performances could include actual physical factors such as toxic exposure, medication effects, or fatigue; emotional factors including stress from the mission or life events; or failure to exert adequate effort on the tests.

Seaton, Kimberly↗
NASA NTRS1972

Pulse performance analysis for small hypergolic-propellant rocket engines

Small rocket engine tests were conducted for the purpose of obtaining pulse performance data to aid in preliminary design and evaluation of attitude control systems. Both monopropellant and hypergolic bipropellant engines of thrust levels from 1 to 100 lbs were tested. The performance data for the hypergolic propellant rockets are compared with theoretical performance calculated from idealized chamber filling and evacuation characteristics. Electromechanical delays in valve response and heat transfer characteristics were found to cause substantial deviation between theoretical and test performance. The theoretical analysis is modified to obtain a semi-empirical model for hypergolic propellant rockets which is demonstrated to be reasonably accurate for two different engine configurations over a considerable range of duty cycles.

Smith, G. W.↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
NASA NTRS1991

Performance analysis of LAN bridges and routers

Bridges and routers are used to interconnect Local Area Networks (LANs). The performance of these devices is important since they can become bottlenecks in large multi-segment networks. Performance metrics and test methodology for bridges and routers were not standardized. Performance data reported by vendors is not applicable to the actual scenarios encountered in an operational network. However, vendor-provided data can be used to calibrate models of bridges and routers that, along with other models, yield performance data for a network. Several tools are available for modeling bridges and routers - Network II.5 was used. The results of the analysis of some bridges and routers are presented.

Hajare, Ankur R.↗
NASA NTRS2019

Active Array Measurements using the Portable Laser Guided Robotic Metrology System

In this paper, we will discuss the impact of mounting structures on the installed performance of phased arrays. In particular, performance data for the Conformal, Lightweight Antennas for Aeronautical Communications Technology (CLAS-ACT) antenna will be presented. Performance data from a series of mounting configurations will show that null depth and location is particularly susceptible while the main beam steering angle remain relatively stable. In addition, the Portable Laser Guided Robotic antenna range (PLGR) will be discussed as a suitable instrument for measuring antenna patterns in complex or difficult locations that are challenging for traditional ranges. The PLGR antenna range was recently developed at the National Aeronautics and Space Administration's (NASA) Glenn Research Center (GRC) and deployed to measure in situ antenna patterns.

Piasecki, Marie↗