An Adaptive Grid Algorithm for Air Quality Modeling

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dc.contributor.advisor Dr. D. Scott McRae, Chair en_US
dc.contributor.advisor Dr. F. DeJarnette, Member en_US
dc.contributor.advisor Dr. Robert White, Member en_US
dc.contributor.advisor Dr. M. Talat Odman, Member en_US Srivastava, Ravi K en_US 2010-04-02T18:59:49Z 2010-04-02T18:59:49Z 1998-09-29 en_US
dc.identifier.other etd-19980919-174712 en_US
dc.description.abstract The physical and chemical processes responsible for air pollution span a wide range of spatial scales. For example, there may be point sources, such as power plants that are characterized by relatively small spatial scales compared to the size of the region that may be impacted by such sources. To obtain accurate distributions of pollutants in an air quality simulation, the pertinent spatial scales can be resolved by varying the physical grid node spacing.A new dynamic adaptive grid algorithm, the Dynamic Solution Adaptive Grid Algorithm - PPM (DSAGA-PPM), is developed for use in air quality modeling. Given a fixed number of grid nodes, DSAGA-PPM distributes these nodes in response to spatial resolution requirements of the solution field and then updates the solution field based on the resulting distribution of nodes. DSAGA-PPM is implemented dynamically to resolve any evolving solution features. Tests with model problems demonstrate that DSAGA-PPM calculates advection much more accurately than the corresponding static grid algorithm (SGA-PPM) and, therefore, would assure more accurate starting concentrations for chemistry calculations. For example, after one revolution of four rotating cones, 87% of each of the cone peaks is retained using DSAGA-PPM while only 63% is retained using SGA-PPM. The root-mean-square errors in DSAGA-PPM results are about 4-5 times lower than those in the corresponding SGA-PPM results. Tests with reacting species and sources demonstrate that DSAGA-PPM provides the needed solution resolution. In simulations of a rotating and reacting conical puff, the root-mean-square errors in DSAGA-PPM results are about 4-6 times lower than those in the corresponding SGA-PPM results. In simulations of a power plant plume, the DSAGA-PPM solution reflects the early, the intermediate, and the mature stages of plume development; these stages are not seen in the corresponding SGA-PPM solution. Finally, it is demonstrated that DSAGA-PPM provides an accurate description of the ozone production resulting due to dynamic interactions between emissions from two power plants and an urban area. In general, these results reflect that DSAGA-PPM is able to provide accurate spatial and temporal resolution of rapidly changing and complex concentration fields. Performance achieved by DSAGA-PPM in model problem simulations indicates that it can provide accurate air quality modeling solutions at costs 10 times less than those incurred in obtaining equivalent static grid solutions. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.title An Adaptive Grid Algorithm for Air Quality Modeling en_US PhD en_US PhD Dissertation en_US Aerospace Engineering en_US

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