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Physics-Based, Data-Driven Modeling ...

Yang, Bo .

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Physics-Based, Data-Driven Modeling of Micro-Environmental Air Quality Impact from Stationary and Mobile Sources.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Physics-Based, Data-Driven Modeling of Micro-Environmental Air Quality Impact from Stationary and Mobile Sources./
Author:	Yang, Bo .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	186 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
Contained By:	Dissertations Abstracts International81-08B.
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27542720
ISBN:	9781392413784

Physics-Based, Data-Driven Modeling of Micro-Environmental Air Quality Impact from Stationary and Mobile Sources.
Yang, Bo .

Physics-Based, Data-Driven Modeling of Micro-Environmental Air Quality Impact from Stationary and Mobile Sources. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 186 p.

Source: Dissertations Abstracts International, Volume: 81-08, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

This item must not be sold to any third party vendors.

Public health studies have indicated strong correlations between air pollution and adverse respiratory and cardiovascular effects, premature death, and mortality. Widely used in regulatory applications for permitting new sources and in health studies to estimate exposure, Gaussian-based atmospheric dispersion models play a crucial role in air quality management but have several well-documented limitations. The main challenge of improving dispersion models is how to represent the complex turbulent flow field from the source(s) to the receptor(s) at a local length scale (~1 km) with high resolutions (~1 m). At the local scale, built environment and/or moving vehicle induced turbulence have significant effects on the dispersion process. This dissertation presents the Computational Fluid Dynamics (CFD) modeling of the turbulent, reactive flow fields, the dispersion, and transformation of the air pollutants from two major air pollution sources, the power generation facilities and the highway vehicles. These two major sources could be close to communities and correspondingly raise local air quality concerns. For example, the hydrocarbon fueled distributed generation (DG) facilities are usually located near end-users and have shorter stacks than those of centralized power plants. Communities near major highways are exposed to elevated air pollutant concentrations than those far away from highways.The methods proposed in this dissertation would improve dispersion models and thereby public health research. For the power generators, a CFD-aided air pollutant dispersion parameterization method was developed, and it showed better performance than a regulatory dispersion model. This work was based on studies of a centralized power plant (1,235 MW), a simple cycled gas turbine (47 MW) with a heat recovery system, and a combined heat and power gas turbine (CHP, two units, 15 MW each). For the highway vehicles, NO2/NOx ratios at different plume evolution stages were clearly defined. Curbside measurement, on-road chasing measurement, and CFD simulations demonstrated on-road NO2 formation could be a large contributor of the total NO2 at the curbside, which was neglected in the current dispersion models. A CFD-aided on-road and near-road NO2/NOx ratio parameterization method was proposed and evaluated using curbside measurement data collected at an interstate highway.

ISBN: 9781392413784Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Micro-environmental air quality

Physics-Based, Data-Driven Modeling of Micro-Environmental Air Quality Impact from Stationary and Mobile Sources.
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