PyPlume: Python Library for Analysis of WRF-bPlume Computational Turbulent Plume Analysis and Visualization

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dc.contributor.advisorBhaganagar, Kiran
dc.contributor.authorTran, Thanh
dc.contributor.committeeMemberPineda, Daniel I.
dc.contributor.committeeMemberAlaeddini, Adel
dc.creator.orcidhttps://orcid.org/0000-0003-2707-5072
dc.date.accessioned2024-03-08T16:00:09Z
dc.date.available2024-03-08T16:00:09Z
dc.date.issued2022
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractA generalized python-based library (PyPlume) was developed to analyze the plume characteristics, mean state, and turbulence characteristics of the plume, using the data generated from the WRF-bPlume model. WRF-bPlume model is a Weather Research Forecast model coupled with Large-eddy simulation buoyancy plume (WRF-bLES) with two-way feedback between the buoyant plume and the atmosphere, developed by the UTSA team was used to study turbulent heated and buoyant plumes. The PyPlume library automates the processes of using the instantaneous data generated from the model to calculate the plume mean and turbulence characteristics, flow structures, and flow fields. PyPlume was used to study the development of turbulent heated and buoyant plumes and identified the stages of development of the plume in the neutral environment to characterize realistic plumes and quantify the extent of mixing at each stage. Finally, the plume height retrieval method on realistic plume data was developed for comparison with the WRF-bPlume data. The height retrieval method used the Airborne Multiangle Spectro-Polarimetric Imager (AirMSPI) data of the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. The method was developed in Python based on a passive information retrievable technique of a scene observed from several viewpoints including the image's feature matching algorithm (StereoSGBM, OpenCV) and distance measurement by stereo vision method. Overall, in addition to improving our understanding of the basic fluid dynamics of plumes, this work helps characterize the conditions relating to forecasting the plume trajectory of smoke, wild-land fire, or volcanic plumes.
dc.description.departmentMechanical Engineering
dc.format.extent66 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798358490420
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5959
dc.languageen
dc.subjectPyPlume
dc.subjectWRF-bPlume
dc.subjectWRF-bLES
dc.subject.classificationMechanical engineering
dc.titlePyPlume: Python Library for Analysis of WRF-bPlume Computational Turbulent Plume Analysis and Visualization
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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