Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690819
Title: A new stochastic backscatter model for large-eddy simulation of neutral atmospheric flows
Author: O'Neill, James Joseph
ISNI:       0000 0004 5915 5509
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2016
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Abstract:
A stochastic backscatter (SB) approach to subgrid-scale (SGS) modelling for large-eddy simulation (LES) of the neutral atmospheric boundary layer (ABL) has previously been shown to reduce excessive velocity shear, as seen with the popular Smagorinsky SGS model, in the under-resolved surface layer. However, previous SB models exhibit unwanted grid-dependency issues, and the range of atmospheric flows tested remains limited. Here, a new SB model is proposed that uses a grid-adaptive filter to control the length-scale, anisotropy and momentum flux of the backscatter fluctuations, independently of the model grid. Model performance is confirmed to be grid-independent in simulations of the neutral ABL, in which an 80% reduction in excessive near-surface velocity shear is achieved. The model is also applied to street canyon flow, where the shear layer that separates the recirculating vortex within the canyon from the external flow is again typically under-resolved in most LES set-ups. The backscatter acts to increase momentum transfer across the shear layer, bringing the simulated vortex intensity significantly closer towards wind-tunnel observations. A passive tracer is also released to model traffic emissions, and the pollutant exchange velocity between the canyon and the external flow is again found in better agreement with wind-tunnel data. This information can be used to improve operational urban dispersion models.
Supervisor: Not available Sponsor: Natural Environment Research Council ; English Environment Agency
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.690819  DOI: Not available
Keywords: GE Environmental Sciences
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