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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Lidar Observations of Raman Scattering from S02 in a Power Plant Stack Plume

LIDAR techniques have been successfully applied to the detection of the Raman backscatter from S02 in the plume of a 200 megawatt coal-burning electrical- generating plant from a distance of 210 meters. The LIDAR system used consists of a 61-centimeter-diameter, f /4 Newtonian telescope and a 1.0-1.5-joules per pulse, 1-pulse-per-second ruby laser. Narrow band interference filters are used to select the 7546-angstrom v (sub 1) vibrational line of S02. The signal from a photomultiplier tube was sequentially applied to each 254-nanosecond-wide channel of a 15-cihannel photon counting system, resulting in a direct correlation between channel number and range increment. Photon counts were accumulated from the backscatter of a number of laser pulses (typically 50 or 100), and the accumulated counts per channel printed on paper tape. One sequence of measurements was made during a two-hour period while the plant electrical output was being reduced by approximately 50 percent. Although the Raman system had not been quantitatively calibrated, the LIDAR data correlated well with the varying plant electrical output. N2 scattering observations were also made and an approximate quantitative S02 concentration obtained by ratioing the S02 data to N2 data. This ratio compared well to the in-situ measurements made during the same period by Environmental Protection Agency sampling instruments.

Brumfield, M. L.↗
NASA NTRS2018

Lessons Learned and Cost Analysis of Hosting a Full Stack Open Data Cube (ODC) Application on the Amazon Web Services (AWS)

The Open Data Cube (ODC) initiative, with support from the Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has developed a state-of-the-art suite of software tools and products to facilitate the analysis of Earth Observation data. This paper presents a short summary and cost analysis of our experience using Amazon Web Services (AWS) to host one such software product, the CEOS Data Cube (CDC) web-based User Interface (UI). In order to provide adaptability, flexibility, scalability, and robustness, we leverage widely-adopted and well-supported technologies such as the Django web framework and the AWS Cloud platform. The UI has empowered users by providing features that assist with streamlining data preparation, data processing, data visualization, and the sub-setting of Analysis Ready Data (ARD) products in order to achieve a wide variety of Earth imaging objectives.

Rizvi, Syed R.↗
NASA NTRSDate not supplied

Error Localization Examples: Looking for a Needle in a Hay-stack

Finite element models (FEM) are routinely developed and used during fabrication of high dollar-value hardware. NASA as part of the pre-flight certification of launch vehicles routinely conducts vibration and static tests to calibrate models used for flight-risk assessments. As part of the calibration process, certain areas in the model are modified, using engineering judgment and sensitivity analysis, to match the test results. Unfortunately, tools to identify problem areas in the FEM using test data directly are scarce. Over the years, Error Localization Algorithms (ELA) have been proposed with very limited success. Recently, the Analytical Dynamics Model Improvement (ADMI) algorithm, which computes closed-form mass and stiffness corrections to match the test data exactly, have been shown to be effective for error localization. The paper will present several FEM example problems where ELA is used with simulated test data to determine FEM problem areas. For each example, the correct answer is shown along with ELA results. It is shown that the ELA process is able to identify general problem areas in the FEM, which are consistent with known model perturbations. However, in most cases the ELA identified area of improvement is larger than the true answer. Nonetheless, with proper optimization tools, calibration results using the ELA identified areas provide excellent results.

error localization↗