This article presents the application of statistical methods to guide the rotordynamic design of a turbogenerator shaft-line. One of the basic requirements is all shaft components must survive the event of a short circuit at the terminals of the generator. This is typically assessed via a transient response simulation of the complete machine train (including generator's electrical model) to check the calculated response torque against the allowable value. With an increasing demand of a shorter design cycle and competition in performance, cost, footprint, and safety, the probabilistic approach is starting to play an important role in the power train design process. The main challenge arises with the size of the design space and complexity of its mapping onto multiple objective functions and criteria which are defined for different machines. In this paper, the authors give an example demonstrating the use of statistical methods to explore (design of experiment (DoE)) and understand (surface response methods) the design space of the combined cycle power train with respect to the typically most severe constraint (fault torque torsional response), which leads to a quicker definition of a turbogenerator's arrangement. Further statistical analyses are carried out to understand the robustness of the chosen design against future modifications as well as parameters' uncertainties.
Torsional Robustness of the Combined Cycle Power Train Arrangement: Application of Statistical Methods to Accelerate Shaft-Line Design Cycles
Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 20, 2016; final manuscript received December 6, 2016; published online March 28, 2017. Editor: David Wisler.
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Golebiowski, M., Ling, J., Knopf, E., and Niedermeyer, A. (March 28, 2017). "Torsional Robustness of the Combined Cycle Power Train Arrangement: Application of Statistical Methods to Accelerate Shaft-Line Design Cycles." ASME. J. Eng. Gas Turbines Power. August 2017; 139(8): 082502. https://doi.org/10.1115/1.4035893
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