A methodology for the prediction of non-volatile particulate matter from aircraft gas turbine engine

Abstract

There is growing concern about the adverse effects of particulate matter emissions on human health and the environment. New regulation standards on aviation particulate matters are expected in the near future. As 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. Current soot prediction methods utilize engine-specific information. 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 integrated prediction environment developed with the proposed methodology demonstrates 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.