Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Image Processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Hydrodynamic Characterization of a Shallow Rectangular Spouted Bed Using Bed Pressure Signals and Image Processing Methods

To support the design of spouted bed reactors, experiments are performed in a rectangular spouted bed to measure the peak pressure drop (Δ P max ) and the internal and external spouting velocities ( U is and U es ). Four particle types spanning under Geldart D and B are used. U is and U es are measured from bed pressure drop and expansion ratio profiles, respectively, with the U es / U is ratio of each particle type obtained. Additionally, correlations predicting the Δ P max of Geldart D and B particles are developed by reviewing published correlations and investigating the nozzle effect. Good prediction is achieved with a slope of 0.997 and an adjusted R 2 of 0.998 in a 95% confidence interval. U es is predicted through correlations developed by adding effects of d p / D i and H 0 in the Mathur–Gishler equation. The calculated U es agrees well with the experimental data, achieving a slope of 0.998 and an adjusted R 2 of 0.998 in a 95% confidence interval.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗