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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗
DOE OSTI2019

3-D Modeling of the CFR Engine for the Investigation of Knock on Natural Gas

Abstract: Engine knock and misfire are barriers to pathways leading to higher efficiency natural gas (NG) engines, where the general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG, making the understanding of fuel- engine interactions essential to achieving high efficiencies in spark-ignited NG engines. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior when running on NG. Using a Converge CFD model of the CFR engine fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation, are being investigated. A GT-Power model is used to estimate appropriate initial conditions in the Converge CFD model, including cylinder wall, piston, valves, and port surface temperatures as well as residual gas fraction. In order to validate the model, in-cylinder pressure data taken with a CFR engine is compared to model predictions while varying compression ratio, CA50, and BMEP.

CFR Engine, Converge CFD, knock, Natural Gas↗
DOE OSTI2026

Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds

The planetary boundary layer (PBL) is a critical interface between Earth's surface and atmosphere, influencing atmospheric convection, weather patterns, and air quality. Recognized by the 2017 National Academies of Sciences, Engineering, and Medicine Earth Science Decadal Survey as an Incubation Targeted Observable, high-quality and effective observation of the PBL has become a priority. This paper reviews surface-based and satellite remote sensing techniques for characterizing PBL features, including PBL height (PBLH), boundary layer thermodynamics, turbulence, and boundary layer clouds (BLCs). These elements are treated as interconnected aspects of the PBL system, while recognizing that different instruments retrieve different physical manifestations of the PBL. The review summarizes recent advances and limitations in Micro-Pulse Lidar (MPL), Doppler lidar, Raman lidar, Differential Absorption Lidar (DIAL), ceilometers, wind profilers, GNSS Radio Occultation, radar, and hyperspectral sounders. These observations help trace dynamic processes within the PBL and link PBL structure to broader weather and climate processes. The review also highlights persistent observational gaps over oceans, remote land, and polar regions, where continuous surface-based profiling is sparse. By comparing instrument capabilities, practical limitations, and interpretation issues, this review emphasizes the need for integrated remote sensing approaches and careful definition of the retrieved PBL quantities.

Boundary layer clouds↗
NASA NTRS1996

Development and status of data quality assurance program at NASA Langley research center: Toward national standards

As part of a continuing effort to re-engineer the wind tunnel testing process, a comprehensive data quality assurance program is being established at NASA Langley Research Center (LaRC). The ultimate goal of the program is routing provision of tunnel-to-tunnel reproducibility with total uncertainty levels acceptable for test and evaluation of civilian transports. The operational elements for reaching such levels of reproducibility are: (1) statistical control, which provides long term measurement uncertainty predictability and a base for continuous improvement, (2) measurement uncertainty prediction, which provides test designs that can meet data quality expectations with the system's predictable variation, and (3) national standards, which provide a means for resolving tunnel-to-tunnel differences. The paper presents the LaRC design for the program and discusses the process of implementation.

Hemsch, Michael J.↗
NASA NTRS2010

Performance Analysis of Scientific and Engineering Applications Using MPInside and TAU

In this paper, we present performance analysis of two NASA applications using performance tools like Tuning and Analysis Utilities (TAU) and SGI MPInside. MITgcmUV and OVERFLOW are two production-quality applications used extensively by scientists and engineers at NASA. MITgcmUV is a global ocean simulation model, developed by the Estimating the Circulation and Climate of the Ocean (ECCO) Consortium, for solving the fluid equations of motion using the hydrostatic approximation. OVERFLOW is a general-purpose Navier-Stokes solver for computational fluid dynamics (CFD) problems. Using these tools, we analyze the MPI functions (MPI_Sendrecv, MPI_Bcast, MPI_Reduce, MPI_Allreduce, MPI_Barrier, etc.) with respect to message size of each rank, time consumed by each function, and how ranks communicate. MPI communication is further analyzed by studying the performance of MPI functions used in these two applications as a function of message size and number of cores. Finally, we present the compute time, communication time, and I/O time as a function of the number of cores.

Saini, Subhash↗
NASA NTRS1973

Apollo experience report: Reliability and quality assurance

The reliability of the Apollo spacecraft resulted from the application of proven reliability and quality techniques and from sound management, engineering, and manufacturing practices. Continual assessment of these techniques and practices was made during the program, and, when deficiencies were detected, adjustments were made and the deficiencies were effectively corrected. The most significant practices, deficiencies, adjustments, and experiences during the Apollo Program are described in this report. These experiences can be helpful in establishing an effective base on which to structure an efficient reliability and quality assurance effort for future space-flight programs.

Sperber, K. P.↗
DOE OSTI2019

Pansophy enhancements for SRF through collecting and analyzing inputs/outputs to further project efficiency in reporting and performance

SRF cavity and Cryomodule testing and production requires a consistent means of collecting and analyzing data against quality and production parameters. JLab's engineering data management system, Pansophy, is utilized to assist project leaders and subject matter experts (SMEs) with such tasks, by providing a means to data mine key parameter indicators (KPI) and production planning and status data. Recent enhancements to reporting and trending have been utilized for the LCLS-II and CEBAF 12GeV upgrade projects. Further enhancements are being planned for future projects, like SNS-PPU, such as KPI trending, KPI quality, vendor quality, production timelines and user defined queries. Being able to understand past trends will assist with enhancements to future projects.

Bookwalter, Valerie D.↗
DOE OSTI2023

Efficient and choreographed quality-of- service management in dense 6G verticals with high-speed mobility requirements

Future 6G networks are envisioned to support very heterogeneous and extreme applications (known as verticals). Some examples are further-enhanced mobile broadband communications, where bitrates could go above one terabit per second, or extremely reliable and low-latency communications, whose end-to-end delay must be below one hundred microseconds. To achieve that ultra-high Quality-of-Service, 6G networks are commonly provided with redundant resources and intelligent management mechanisms to ensure that all devices get the expected performance. But this approach is not feasible or scalable for all verticals. Specifically, in 6G scenarios, mobile devices are expected to have speeds greater than 500 kilometers per hour, and device density will exceed ten million devices per square kilometer. In those verticals, resources cannot be redundant as, because of such a huge number of devices, Quality-of-Service requirements are pushing the effective performance of technologies at physical level. And, on the other hand, high-speed mobility prevents intelligent mechanisms to be useful, as devices move around and evolve faster than the usual convergence time of those intelligent solutions. New technologies are needed to fill this unexplored gap. Therefore, in this paper we propose a choreographed Quality-of-Service management solution, where 6G base stations predict the evolution of verticals at real-time, and run a lightweight distributed optimization algorithm in advance, so they can manage the resource consumption and ensure all devices get the required Quality-of-Service. Prediction mechanism includes mobility models (Markov, Bayesian, etc.) and models for time-variant communication channels. Besides, a traffic prediction solution is also considered to explore the achieved Quality-of-Service in advance. The optimization algorithm calculates an efficient resource distribution according to the predicted future vertical situation, so devices achieve the expected Quality-of-Service according to the proposed traffic models. An experimental validation based on simulation tools is also provided. Results show that the proposed approach reduces up to 12% of the network resource consumption for a given Quality-of-Service.

42 ENGINEERING↗
NASA NTRS1993

Software engineering methodologies and tools

Over the years many engineering disciplines have developed, including chemical, electronic, etc. Common to all engineering disciplines is the use of rigor, models, metrics, and predefined methodologies. Recently, a new engineering discipline has appeared on the scene, called software engineering. For over thirty years computer software has been developed and the track record has not been good. Software development projects often miss schedules, are over budget, do not give the user what is wanted, and produce defects. One estimate is there are one to three defects per 1000 lines of deployed code. More and more systems are requiring larger and more complex software for support. As this requirement grows, the software development problems grow exponentially. It is believed that software quality can be improved by applying engineering principles. Another compelling reason to bring the engineering disciplines to software development is productivity. It has been estimated that productivity of producing software has only increased one to two percent a year in the last thirty years. Ironically, the computer and its software have contributed significantly to the industry-wide productivity, but computer professionals have done a poor job of using the computer to do their job. Engineering disciplines and methodologies are now emerging supported by software tools that address the problems of software development. This paper addresses some of the current software engineering methodologies as a backdrop for the general evaluation of computer assisted software engineering (CASE) tools from actual installation of and experimentation with some specific tools.

Wilcox, Lawrence M.↗
NASA NTRS2025

Hilo Bay Water Resources: Monitoring Water Quality in Hilo Bay, Hawaii to Support Future Community Planning

Designated as an impaired body of water by both state and federal water quality standards, Hilo Bay, Hawaiʻi is highly susceptible to brown water, a condition where the water becomes murky and is associated with excess levels of bacteria, contaminants, and nutrients. A breakwater in Hilo Bay, which was established to protect Hilo town from tsunamis, interferes with water circulation and prolongs the presence of brown water in the bay. The State of Hawaiʻi issues brown water advisories (BWAs) following flash flood warnings, sewage spills, and other events to indicate a public health concern for those who use Hilo Bay for recreation, cultural purposes, and fishing. Due to the elevated public health risk and ecosystem disturbance that brown water poses to Hilo Bay, we partnered with the Hawaiʻi County Office of Sustainability, Climate, Equity, and Resilience (OSCER) to examine the feasibility of using Earth observations (EO) to monitor water quality in the Hilo Bay region. We leveraged data from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, and Aqua and Terra Moderate Resolution Imagine Spectroradiometer (MODIS) instruments to identify and assess spatial and temporal patterns of two main water quality parameters, turbidity and chlorophyll-a, during BWAs. We used the Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine to process EO data and generate water quality maps and time series. Our study found that increased turbidity levels can be identified by EO data during BWAs. In addition, our map products indicated the presence of several turbidity plumes along the coast, with the highest concentration of turbidity found within Hilo Bay. While chlorophyll-a levels were relatively flat within our study region during BWAs, we found that regional chlorophyll-a patterns could be derived from MODIS chlorophyll-a data in NASA Worldview. Our study’s multi-sensor approach provided valuable insights for how water quality in the Hilo Bay region can be monitored in the future.

remote sensing↗
NASA NTRS1990

Software technology insertion: A study of success factors

Managing software development in large organizations has become increasingly difficult due to increasing technical complexity, stricter government standards, a shortage of experienced software engineers, competitive pressure for improved productivity and quality, the need to co-develop hardware and software together, and the rapid changes in both hardware and software technology. The 'software factory' approach to software development minimizes risks while maximizing productivity and quality through standardization, automation, and training. However, in practice, this approach is relatively inflexible when adopting new software technologies. The methods that a large multi-project software engineering organization can use to increase the likelihood of successful software technology insertion (STI), especially in a standardized engineering environment, are described.

Lydon, Tom↗
NASA NTRS2015

Software Facilitates Sharing of Water Quality Data Worldwide

John Freighery was an environmental engineer at Johnson Space Center when a new, simplified version of the coliform bacteria test was developed for astronaut use on the International Space Station. Through his New York City-based mWater Foundation, Freighery is using the test to help rural communities monitor their water supplies for contamination. The organization has also developed a mobile phone app to make the information publicly available.

Source record↗
NASA NTRS1972

Simulation study of the lift roll coupling problem for hovering VTOL aircraft

The effects of lift-roll coupling on the handling qualities of hovering VTOL aircraft using differential thrust for roll control were assessed in a piloted simulation study in the Ames six-degrees-of-freedom motion simulator. The configuration tested has three vertical thrust sources, one on the roll axis and two laterally displaced from the roll axis, with a thrust distribution of 25%/50%/25%. The outboard thrust sources were modulated to provide roll control whereas all three provided height control. Maximum thrust-to-weight ratio was varied together with a coupling parameter that combined roll-inertia, weight, and engine location. Results showed that handling qualities are affected not only by the occurrence of lift-roll coupling (dependent on both variables) but also by the severity of the coupling (dependent on the coupling parameter alone). However, the advantages of differential thrust for control can be retained with careful design.

Jacobsen, R. A.↗
NASA NTRS1989

Graphical postprocessing for 3-D mesh quality evaluation

An important objective of three-dimensional graphical finite-element postprocessing is to indicate to the engineer the accuracy of analysis results. The inclusion of mesh quality sensors permits a subjective evaluation of the adequacy of a single analysis being interpreted. For graphical approaches, both strain-energy-density gradients and discontinuities of unsmoothed responses and their gradients have proved to be effective sensors. Interactive graphical tools which can display discontinuity information effectively are described; these are essentially different from the ordinary methods used for the viewing of smoothed results.

Panthaki, M. J.↗
NASA NTRS1990

Prototype automated post-MECO ascent I-load Verification Data Table

A prototype automated processor for quality assurance of Space Shuttle post-Main Engine Cut Off (MECO) ascent initialization parameters (I-loads) is described. The processor incorporates Clips rules adapted from the quality assurance criteria for the post-MECO ascent I-loads. Specifically, the criteria are implemented for nominal and abort targets, as given in the 'I-load Verification Data Table, Part 3, Post-MECO Ascent, Version 2.1, December 1989.' This processor, ivdt, compares a given l-load set with the stated mission design and quality assurance criteria. It determines which I-loads violate the stated criteria, and presents a summary of I-loads that pass or fail the tests.

Lardas, George D.↗
DOE OSTI2017

Artificial neural network based predictions of cetane number for furanic biofuel additives

The next generation of alternative fuels is being explored through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels offers a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Current work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present report improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5'-(furan-2-ylmethylene)bis(2-methylfuran), 5,5'-((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl)methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furan-based molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements presented in this paper, enables chemists to focus on promising molecules by eliminating less favorable candidates in relation to their ignition quality.

09 BIOMASS FUELS↗
NASA NTRS2012

Modeling Programs Increase Aircraft Design Safety

Flutter may sound like a benign word when associated with a flag in a breeze, a butterfly, or seaweed in an ocean current. When used in the context of aerodynamics, however, it describes a highly dangerous, potentially deadly condition. Consider the case of the Lockheed L-188 Electra Turboprop, an airliner that first took to the skies in 1957. Two years later, an Electra plummeted to the ground en route from Houston to Dallas. Within another year, a second Electra crashed. In both cases, all crew and passengers died. Lockheed engineers were at a loss as to why the planes wings were tearing off in midair. For an answer, the company turned to NASA s Transonic Dynamics Tunnel (TDT) at Langley Research Center. At the time, the newly renovated wind tunnel offered engineers the capability of testing aeroelastic qualities in aircraft flying at transonic speeds near or just below the speed of sound. (Aeroelasticity is the interaction between aerodynamic forces and the structural dynamics of an aircraft or other structure.) Through round-the-clock testing in the TDT, NASA and industry researchers discovered the cause: flutter. Flutter occurs when aerodynamic forces acting on a wing cause it to vibrate. As the aircraft moves faster, certain conditions can cause that vibration to multiply and feed off itself, building to greater amplitudes until the flutter causes severe damage or even the destruction of the aircraft. Flutter can impact other structures as well. Famous film footage of the Tacoma Narrows Bridge in Washington in 1940 shows the main span of the bridge collapsing after strong winds generated powerful flutter forces. In the Electra s case, faulty engine mounts allowed a type of flutter known as whirl flutter, generated by the spinning propellers, to transfer to the wings, causing them to vibrate violently enough to tear off. Thanks to the NASA testing, Lockheed was able to correct the Electra s design flaws that led to the flutter conditions and return the aircraft to safe flight. Today, all aircraft must have a flutter boundary 15 percent beyond the aircraft s expected maximum speed to ensure that flutter conditions are not encountered in flight. NASA continues to support research in new aircraft designs to improve knowledge of aeroelasticity and flutter. Through platforms such as Dryden Flight Research Center s Active Aeroelastic Wing (AAW) research aircraft, the Agency researches methods for in-flight validation of predictions and for controlling and taking advantage of aeroelastic conditions to enhance aircraft performance.

Source record↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine

03 NATURAL GAS↗