Aero Derivative Mechanical Drive Gas Turbines: The Design of Intermediate Pressure Turbines

Av, Greco; Michelassi, Vittorio; Francini, Stefano; Benedetto, Daniele Di; Manoharan, Mahendran

doi:10.1115/gt2018-76036

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…For example, engine data may include one single combustor discharge temperature, rather than rake data in different streamwise and spanwise positions, or a single point exit discharge pressure, rather than a full pressure map. These data include many engines in a wide range of operating and environmental conditions and can be used to validate and fine tune engine performance models and determine critical components, as illustrated by (Roumeliotis et al, 2016) for industrial gas turbines, and by (Scotti Del Greco et al, 2019) for aeroderivative turbines.…”

Section: Validation -Validation Data Base (Vd)mentioning

confidence: 99%

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

Michelassi¹,

Ling

2021

J. Glob. Power Propuls. Soc.

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Turbomachines are still required for the efficient conversion of renewable, chemical or potential energy into propulsion, mechanical power, or electricity. The increasing demand for efficiency, availability, reduced footprint and cost of ownership poses fundamental challenges to design methods the accuracy of which needs to be constantly upgraded to keep the pace with the availability of new materials, type of fluids and fuels, manufacturing methods and technologies. This paper discusses recent trends in design methods that take advantage of both artificial intelligence and high-fidelity simulations techniques that guide the design process by harvesting design data from multiple sources and improve the accuracy of design verification respectively. Such approach has been successfully used in aero and thermal design as well as in the critical area of materials and fluids engineering. In the future the concerted use of machine learning and high-fidelity methods may allow researchers to conduct reliable virtual tests in the framework of design loops to cut down design time and risk.

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Section: Validation -Validation Data Base (Vd)mentioning

confidence: 99%

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

Michelassi¹,

Ling

2021

J. Glob. Power Propuls. Soc.

Self Cite

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Design, Testing, and Performance Impact of Exhaust Diffusers in Aero-Derivative Gas Turbines for Mechanical Drive Applications

Av¹,

Jurek²,

Benedetto³

et al. 2019

Volume 2B: Turbomachinery

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The demand for gas-turbine (GT) based flexible power generation and mechanical drive is increasing due to the growing penetration of renewables and due to the need to quickly adjust production and operate at part load respectively. As efficiency operability low emissions, small footprint, availability and maintainability are of paramount importance, engine designers are leaning towards aircraft engine architectures that, with appropriate modifications mostly to the combustion system and turbine, can meet market needs. To leverage the large experience from aircraft propulsion, aero-derivative engines maintain the same architecture, with a high-speed shaft core, and a low-speed shaft driven by a multi-stage low-pressure turbine. While in aircraft engines power is adjusted by changing fuel rate and shaft speed, that go hand in hand, mechanical drive engines have more stringent needs that require changing the delivered power by keeping the shaft speed under control to guarantee the operation of the driven equipment (an LNG compressor or an electric generator). Therefore, the power turbine may deliver exit flow profiles and angles that put the turbine exhaust diffuser under severe off-design conditions, with the onset of large scale separations, large kinetic losses, and ultimately a significant drop on cycle performance. This paper describes Baker Hughes, a GE company experience in the CFD assisted design and scale-down testing of aero-derivative exhaust diffusers. The design incorporates the requirements of hot-end mechanical drive in multiple the power turbine operating conditions to determine the best compromise between peak design performance and off-design operability. The test in similitude conditions considered four relevant operating points. The inlet conditions matched with the power turbine exit profiles by the concerted action of swirl vanes and perforated plates, the design of which was heavily CFD assisted. Predictions matched measurements in terms of pressure recovery, kinetic losses, and exhaust velocity profiles. Different data post-processing and averaging were considered to properly factor in the diffuser losses into the overall turbine performance.

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Aero Derivative Mechanical Drive Gas Turbines: The Design of Intermediate Pressure Turbines

Cited by 2 publications

References 11 publications

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

Design, Testing, and Performance Impact of Exhaust Diffusers in Aero-Derivative Gas Turbines for Mechanical Drive Applications

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