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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1952

Performance of the Components of the XJ34-WE-32 Turbojet Engine over a Range of Engine and Flight Conditions

Performance of the compressor, combustor, and turbine operating as integral parts of the XJ34-WE-32 turbojet engine was determined in the Lewis altitude wind tunnel over a range of altitudes from 5000 to 55,000 feet and flight Mach numbers from 0.28 to 1.05. Data were obtained for each of four exhaust-nozzle areas and are presented in graphical and tabular form.

ENGINES, TURBO-JET↗
NASA NTRS1994

Engineering Analysis Subsystem Environment for Spacecraft Engineering Subsystem Mission Operations

The Engineering Analysis Subsystem Environment (EASE) prototype is a collection of computer programs on networked workstations providing a multimission, multisubsystem environment that enables the operation of several spacecraft simultaneously with fewer analysts. Through the use of automated tools, graphical data visualization and information management, EASE has achieved an increase in mission operations productivity. Recently, a database, a trending tool, and a power sequence expansion tool have been added to EASE. This paper discusses these enhancements and provides an update of the operation experience with the realtime Galileo telemetry data.

EASE↗
DOE OSTI2021

Engineering Graphene-Ceramic 3D Composite Foams by Freeze Drying

A 3D graphene foam produced by the chemical vapor deposition (CVD) technique is recognized as an effective nanofiller material, as it does not agglomerate in the matrix. Though CVD facilitates pristine graphene foam production, the method poses limitations in developing large-scale 3D graphene composite foams as advanced engineering materials. In this paper, a freeze-drying (FD) process is used to produce a composite 3D foam, which is a mixture of graphene nanoplatelet (GNP) and a low-temperature co-fired ceramic (LTCC). The freeze-dried GNP-LTCC reticulated 3D composite foam has an average pore size ranging from 70 to 100 μm. Pore size is varied by regulating the heat transfer rate during the freezing process using a thermally conductive aluminum (Al) mold and a thermally insulating acrylonitrile butadiene styrene (ABS) mold. Computational thermal modeling is used to visualize the heat transfer and its effect on foam pore size. Subsequently, a freeze-dried GNP-LTCC composite foam is embedded into the LTCC matrix to form a hierarchical assembly by a spark plasma technique without compromising the 3D structure of the FD foam. This study established that a simple, eco-friendly, and scalable processing methodology can produce advanced graphene-based 3D composite foams as the future tailorable nanofillers for designing multimatrix materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Engineering catalyst supports to stabilize PdO x two-dimensional rafts for water-tolerant methane oxidation

The treatment of emissions from natural gas engines is an important area of research since methane is a potent greenhouse gas. The benchmark catalysts, based on Pd, still face challenges such as water poisoning and long-term stability. In this work, we report an approach for catalyst synthesis that relies on the trapping of metal single atoms on the support surface, in thermally stable form, to modify the nature of further deposited metal/metal oxide. By anchoring Pt ions on a catalyst support we can tailor the morphology of the deposited phase. In particular, two-dimensional (2D) rafts of PdO x are formed, resulting in higher reaction rates and improved water tolerance during methane oxidation. The results show that modifying the support by trapping single atoms could provide an important addition to the toolkit of catalyst designers for controlling the nucleation and growth of metal and metal oxide clusters in heterogeneous catalysts.

2D raft↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗