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A novel procedure for optimizing twin-shaft gas turbine performance at various ambient temperatures is presented, focusing on off-design conditions. The procedure uses genetic algorithm optimization and considers compressor surge margin and turbine blade life cycle as constraints. Results show improved net power output and thermal efficiency compared to traditional control methods.

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Abstract

A novel procedure for finding the optimum values of design parameters of industrial twin-shaft gas turbines at various ambient temperatures is presented here. This paper focuses on being off design due to various ambient temperatures. The gas turbine modeling is performed by applying compressor and turbine characteristic maps and using thermodynamic matching method. The gas turbine power output is selected as an objective function in optimization procedure with genetic algorithm. Design parameters are compressor inlet guide vane angle, turbine exit temperature, and power turbine inlet nozzle guide vane angle. The novel constrains in optimization are compressor surge margin and turbine blade life cycle. A trained neural network is used for life cycle estimation of high pressure (gas generator) turbine blades. Results for optimum values for nozzle guide vane/inlet guide vane (23°/27°–27°/6°) in ambient temperature range of 25–45 ℃ provided higher net power output (3–4.3%) and more secured compressor surge margin in comparison with that for gas turbines control by turbine exit temperature. Gas turbines thermal efficiency also increased from 0.09 to 0.34% (while the gas generator turbine first rotor blade creep life cycle was kept almost constant about 40,000 h). Meanwhile, the averaged values for turbine exit temperature/turbine inlet temperature changed from 831.2/1475 to 823/1471°K, respectively, which shows about 1% decrease in turbine exit temperature and 0.3% decrease in turbine inlet temperature.

Key findings

  • Optimum values for nozzle guide vane/inlet guide vane angles (23°/27°–27°/6°) provided higher net power output (3–4.3%) and more secured compressor surge margin.
  • Compressor surge margin and turbine blade life cycle were considered as novel constraints in the optimization procedure.
  • A trained neural network was used for life cycle estimation of high pressure turbine blades.

Keywords

TurbineGas compressorNozzleCombined cycleTurbine bladeMechanical engineering

Identifiers

DOI
10.1177/0957650919887888
Journal
Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy
Year
2019