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DTIC ADA305620: Decision Support Model to Evaluate Methods for Redu...
by Defense Technical Information Center
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The United States Air Force operates more than 6,800
aircraft that use more than 15,700 turbine engines.
Whenever these engines are in operation they generate
pollution. The majority of the pollution is composed of
five air toxics: Particulate Matter, Carbon Monoxide,
oxides of Nitrogen, Oxides of SulAir, and Unburned
Hydrocarbons. Currently, the emissions from these engines
are not regulated while the engines are in use in
military aircraft. However, during the periodic
maintenance and repair of aircraft turbine engines,
maintainers must test the engines' operation at each
power setting. Emissions during these tests are permitted
under Title V of the Clean Air Act Amendments of 1990.
Because the Air Force has a large number of both engines
and engine test facilities, future regulations based on
current law have the potential to severely affect the Air
Force engine testing program. This research uses decision
analysis to clarify issues surrounding the question: How
can Air Combat Command effectively test its jet engines
and still comply with the environmental requirements of
the Clean Air Act as amended in 1990? To answer this
general question, the research objectives of this study
were: (1) Perform a review of modern jet engines and the
emissions that result from jet engine testing. This
review focused on the underlying causes of emissions and
potential methods to reduce these emissions; (2)
Construct a decision model to compare the various
existing and potential methods for meeting Clean Air Act
regulatory requirements during jet engine testing in ACC;
(3) Use this model to compare the costs and benefits of
various notional methods for meeting regulatory
requirements by reducing emissions during the testing of
Air Combat Command's jet engines. These notional methods
were based on the technologies uncovered during the
literature review.
Date Published: 2018-03-29 15:08:49
Identifier: DTIC_ADA305620
Item Size: 65472405
Language: english
Media Type: texts
# Topics
DTIC Archive; Ullmann, Jeffrey R ; AI...
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