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Gas Turbine Cycle Design Methodology: A Comparison of Parameter Variation With Numerical Optimization FREE

[+] Author Affiliations
Joachim Kurzke

MTU München GmbH, München, Germany

Paper No. 98-GT-343, pp. V002T02A008; 7 pages
doi:10.1115/98-GT-343
From:
  • ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition
  • Volume 2: Aircraft Engine; Marine; Microturbines and Small Turbomachinery
  • Stockholm, Sweden, June 2–5, 1998
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7863-7
  • Copyright © 1998 by ASME

abstract

In gas turbine performance simulations often the question arises: What is the best thermodynamic cycle design point? This is an optimization task which can be attacked in two ways: One can do a series of parameter variations and pick from the resulting graphs the best solution or one can employ numerical optimization algorithms that produce a single cycle which fulfills all constraints.

The conventional parameter study builds strongly on the engineering judgement and gives useful information over a range of parameter selections. However, when values for more than a few variables have to be determined while several constraints are existing, then numerical optimization routines can help to find the mathematical optimum faster and more accurately. Sometimes even an outstanding solution is found which was overlooked while doing a preliminary parameter study.

For any simulation task a sophisticated graphical user interface is of great benefit. This is especially true for automated numerical optimizations. It is quite helpful to see on the screen of a PC how the variables are changing and which constraints are limiting the design. A quick and clear graphical representation of trade studies is also of great advantage. The paper describes how numerical optimization and parameter studies are implemented in a Windows-based PC program.

As an example, the cycle selection of a derivative turbofan engine with a given core shows the merits of numerical optimization. The parameter variation is best suited for presenting the sensitivity of the result in the neighborhood of the optimum cycle design point.

Copyright © 1998 by ASME
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