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An Example of Thermo-Economic Optimization of a CCGT by Means of the Proper Orthogonal Decomposition Method

[+] Author Affiliations
Roberto Melli, Enrico Sciubba, Claudia Toro, Alessandro Zoli-Porroni

University of Rome “Sapienza”, Rome, Italy

Paper No. IMECE2012-87317, pp. 1571-1578; 8 pages
  • ASME 2012 International Mechanical Engineering Congress and Exposition
  • Volume 6: Energy, Parts A and B
  • Houston, Texas, USA, November 9–15, 2012
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-4522-6
  • Copyright © 2012 by ASME


This paper presents an application of the Proper Orthogonal Decomposition (POD) technique (also called Karhunen-Loève Decomposition, Principal Component Analysis, or Singular Value Decomposition) to the thermo-economic optimization of a realistic Combined-Cycle Gas Turbine (CCGT) process. The novel inverse-design approach proposed here employs the thermo-economic cost of the two products as objective function.

The proposed procedure does not require the generation of a complete simulated set of results at each iteration step of the optimization, because POD constructs a very accurate approximation to the function described by a certain number of initial simulations, and thus “new” points in design space can be extrapolated without recurring to repeated process simulations. Thus, the often taxing computational effort needed to iteratively generate numerical process simulations of incrementally different configurations is substantially reduced by replacing much of it by easy-to-perform matrix operations: a non-negligible but quite small number N of initial process simulations is used to calculate the basis of the POD interpolation and to validate (i.e., extend) the results.

Since the accuracy of a POD expansion depends of course on the number N of initial simulations (the “snapshots”), the computational intensity of the method is certainly not negligible: but, as successfully demonstrated in the paper for a realistic CCGT inverse process design problem, the idea that additional full simulations are performed only in the “right direction” indicated by the gradient of the objective function in the solution space leads to a successful strategy at a substantially reduced computational intensity. This “economy” with respect to other classical “optimization” methods is basically due to the capability of the POD procedure to identify the most important “modes” in the functional expansion of the vector basis consisting of a subset of the design parameters used in the evaluation of the objective function.

Copyright © 2012 by ASME



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