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Choo, Kyung Hak
(Advisor: Prof. Mavris]
will defend a doctoral thesis entitled,
A Methodology for the Prediction of Non-volatile Particulate Matter from Aircraft Gas Turbine Engine
On
Monday, July 15 at 9:00 a.m.
CoVE
Abstract
There is growing concern about the adverse effects of particulate matter emissions on human health and the environment. It is revealed that particulate emissions are responsible
for cardiovascular and cardiopulmonary diseases resulting in reduced life expectancy as well as climate change. New regulation standards on aviation particulate matters are
expected in the near future. Particulate emissions become one of the important design constraints. They must be evaluated during the conceptual design of an aircraft engine.
Prediction of soot emission from gas turbine combustion is a major subject in this research. Soot is a non-volatile primary particulate matter emitted directly from the combustion chamber. As its size is extremely small, most aviation soot belongs to the PM2.5 category.
Current soot prediction methods utilize engine-specific information. They do not successfully calculate combustor characteristics which highly affects soot formation. As the current methods cannot handle engines with different cycles and sizes, they are not suitable for conceptual design.
Three hypotheses addressing air partitioning, sizing methodology, and statistical distribution are established to develop the prediction environment, capable of a variety of cycles
of engines with different size and thrust.
The prediction environment consists of a Combustor Flow Circuit model, Statistical Distribution Model with the unmixedness curve, Chemical Reactor Networks (CRN), and
Soot Evaluation Model. The Combustor Flow Circuit model taking care of air partitioning and sizing is built on NPSS. The Statistical Distribution Model is to model the imperfectly mixed primary zone over the parallel PSRs in CRN. The CRN, built on CHEMKIN, provides thermodynamic properties of flow and species information to the Soot Evaluation Model. The Soot Evaluation Model computes quantitative non-volatile PM based on the CRN results. These sub-models are integrated on ModelCenter.
The integrated prediction environment developed with the proposed methodology shows good predictability for cycles of different size and thrust engines. As the input of the prediction
environment is a cycle, the proposed methodology is adequate for the prediction of non-volatile PM during the conceptual design of an aircraft engine.
Committee
