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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Performance Improvement of Friction Stir Welds by Better Surface Finish

The as-welded friction stir weld has a cross section that may act as a stress concentrator. The geometry associated with the stress concentration may reduce the weld strength and it makes the weld challenging to inspect with ultrasound. In some cases, the geometry leads to false positive nondestructive evaluation (NDE) indications and, in many cases, it requires manual blending to facilitate the inspection. This study will measure the stress concentration effect and develop an improved phased array ultrasound testing (PAUT) technique for friction stir welding. Post-welding, the friction stir weld (FSW) tool would be fitted with an end mill that would machine the weld smooth, trimmed shaved. This would eliminate the need for manual weld preparation for ultrasonic inspections. Manual surface preparation is a hand operation that varies widely depending on the person preparing the welds. Shaving is a process that can be automated and tightly controlled.

Russell, Sam↗
NASA NTRS2019

Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture

Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.

Zhang, Hailiang↗
NASA NTRSDate not supplied

Improved performance For Drone and UAM Propellers at Low RPM for Noise Reduction

ODU has a performance test capability for small propellers in the Low-Speed Wind Tunnel. Recent work has been in support of NASA’s LA-8 Urban Air Mobility concept vehicle. Three, four, Five and Six blade folding propellers were characterized – high blade count is used for low-noise applications. A performance loss due to low RPM was identified in studies.

Propeller↗
NASA NTRS2021

Light-concentrating microcone array for improving performance of infrared imaging devices

Light-harvesting low-index (n=1.6) microconical arrays are proposed for increasing the sensitivity and the signal-to-noise ratio (SNR) of mid-wave infrared (MWIR) focal plane arrays (FPAs) used in thermal cameras. Numerical modeling showed a 3-D Power enhancement factor ~ 50 in MWIR for 10° tapered microcones with Dt/Db = 60 μm/8μm and h=150μm, where Dt and Db are the top and bottom diameters of microcones, respectively. Such microconical arrays were fabricated in photoresist by using a nanoscribe tool directly on top of the front-illuminated Ni/Si Schottky-barrier, and three-fold enhancement in the photocurrent response was observed. The proposed approach permits increase of the SNR and the operation temperature of the MWIR imaging devices.

Astratov, Vasily N.↗
NASA NTRSDate not supplied

Evaluation of High-Temperature Fabrics for Improved Performance of Seals and Thermal Barriers

Hypersonic and space flight applications continue to push material demands of component-level technologies such as high-temperature seals and thermal barriers. NASA Glenn Research Center continues to develop and evaluate materials and coatings as well as thermal barrier designs for these applications. Experimental test data is presented for two different sheath fabrics that were evaluated in full thermal barrier assemblies through compression and wear testing at elevated temperatures, as well as flow testing at ambient temperature.

Seals↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2020

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N: One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an ''intelligent'' mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

Loring, Burlen↗
DOE OSTI2021

Method and apparatus for using compression to improve performance of low voltage caches

A method of operating a cache in a computing device includes, in response to receiving a memory access request at the cache, determining compressibility of data specified by the request, selecting in the cache a destination portion for storing the data based on the compressibility of the data and a persistent fault history of the destination portion, and storing a compressed copy of the data in a non-faulted subportion of the destination portion, wherein the persistent fault history indicates that the non-faulted subportion excludes any persistent faults.

Kalamatianos, John↗
DOE OSTI2024

Performance Improvements of Poincaré Analysis for Exascale Fusion Simulations

Understanding the time-varying magnetic field in a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincaré analysis provides a powerful method for the visualization of magnetic fields in fusion devices. However, Poincaré plots can be very computationally expensive making it impractical, for example, to generate these plots in situ during a simulation. In this short paper, we describe a collaboration among computer science and physics researchers to develop a new Poincaré tool that provides a significant reduction in the time to generate analysis results.

Pugmire, Dave↗
DOE OSTI2024

ReEDS Performance Improvement

The Regional Energy Deployment System (ReEDS) is an open-source, spatially explicit, long-term capacity expansion model for the bulk electric power system of the contiguous United States, encompassing multiple scenarios with technological and political assumptions (see https://github.com/NREL/ReEDS-2.0). With the increased needs for capabilities, higher temporal and spatial resolutions to model the evolution of the power system with modern technologies and low-carbon pathways, ReEDS' model solution times have increased significantly from 4-6 hours in 2018 to 18-48+ hours in 2023 . Also, the model size for commonly-run ReEDS scenarios reached 22 and 28 million equations and variables, respectively. These runtimes can be especially challenging under certain scenario settings (e.g., very high temporal or spatial resolution) or with limited computational power. In this presentation, we will discuss several methods we used to improve model runtime, including data preparation, model modification, and solver tuning. The implementation of these methods shrank the model size to 7.2 and 7.3 million equations and variables, respectively. Furthermore, this led to a 77% reduction in the model's run time for commonly-run ReEDS scenarios. We will discuss the process of identifying areas for solve time improvements and how the specific enhancements for the ReEDS model might be applied to other similar large-scale models.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗