Research Papers: Gas Turbines: Turbomachinery

Enhanced Robust Design Simulation and Application to Engine Cycle and Technology Design

[+] Author and Article Information
Jonathan Sands

Aerospace Systems Design Lab,
School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: j.sands@gatech.edu

Christopher Perullo

Aerospace Systems Design Lab,
School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: chris.perullo@ae.gatech.edu

Brian Kestner

Aerospace Systems Design Lab,
School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: bkkestner@yahoo.com

Dimitri Mavris

Aerospace Systems Design Lab,
School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dimitri.mavris@aerospace.gatech.edu

1Corresponding author.

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 23, 2016; final manuscript received November 26, 2016; published online February 23, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(7), 072604 (Feb 23, 2017) (13 pages) Paper No: GTP-16-1462; doi: 10.1115/1.4035599 History: Received September 23, 2016; Revised November 26, 2016

Increased computing power has enabled designers to efficiently perform robust design analyses of engine systems. Traditional, filtered Monte Carlo methods involve creating surrogate model representations of a physics-based model in order to rapidly generate tens of thousands of model responses as design and technology input parameters are randomly varied within user-defined distributions. The downside to this approach is that the designer is often faced with a large design space, requiring significant postprocessing to arrive at probabilities of meeting design requirements. This research enhances the traditional, filtered Monte Carlo robust design approach by regressing surrogate responses of joint confidence intervals for metric responses of interest. Fitting surrogate responses of probabilistic confidence intervals rather than the raw response data changes the problem the engineer is able to answer. Using the new approach, the question can be better phrased in terms of the probability of meeting certain requirements. A more traditional approach does not have the ability to include confidence in the process without significant postprocessing. The process is demonstrated using a turboshaft engine modeled using the numerical propulsion system simulation (NPSS) program. The new robust design process enables the designer to account for probabilistic impacts of both technology and design variables, resulting in the selection of an engine cycle that is robust to requirements and technology uncertainty.

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Fig. 1

Technology impact probability density function changes due to technology maturation

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Fig. 2

RDS methodology [5] modified for robust engine and technology design

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Fig. 3

Cumulative distribution function for likely performance of a candidate design

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Fig. 4

Monte Carlo probabilistic analysis process

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Fig. 5

Two-spool axi-centrifugal turboshaft engine architecture

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Centrifugal compressor tip speed constraints due to material stress limits

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Fig. 7

Axi-centrif engine performance limited by material stress limits

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Fig. 8

Artificial neural network surrogate model fit quality, cruise ESFC

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Fig. 9

Probabilistic surrogate model fit quality, likely cruise ESFC

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Fig. 10

Feasible design centrif compressor pressure ratio settings for various max turbine inlet temperatures

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Fig. 11

Feasible centrif compressor design pressure ratio settings for various power levels

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Fig. 12

Feasible centrif compressor specific speed settings for various design centrif compressor pressure ratios

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Fig. 13

Feasible centrif compressor specific speed settings for various design overall pressure ratios

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Fig. 14

Feasible design overall pressure ratio settings for various design centrif compressor pressure ratios

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Fig. 15

Ninety-five percent likely ESFC levels for various centrif compressor pressure ratio designs

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Fig. 16

Ninety-five percent likely weight levels for various centrif compressor pressure ratio designs

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Fig. 17

Evaluation of robust and deterministic cycle selections under two realized technology scenarios



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