PhD Defense by Kyung Hak Choo

Event Details
  • Date/Time:
    • Monday July 15, 2019
      9:00 am - 11:00 am
  • Location: CoVE
  • Phone:
  • URL:
  • Email:
  • Fee(s):
  • Extras:
No contact information submitted.

Summary Sentence: A Methodology for the Prediction of Non-volatile Particulate Matter from Aircraft Gas Turbine Engine

Full Summary: No summary paragraph submitted.

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


Monday, July 15 at 9:00 a.m.


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.



  • Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
  • Prof. Jechiel Jagoda – School of Aerospace Engineering
  • Prof. Daniel Schrage – School of Aerospace Engineering
  • Dr. Jimmy Tai – School of Aerospace Engineering
  • Mr. Russell Denney - School of Aerospace Engineering
  • Dr. Reza Rezvani – Senior Engineer, Pratt & Whitney


Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jul 8, 2019 - 12:02pm
  • Last Updated: Jul 8, 2019 - 12:02pm