Efficiency Enhancement in Pressure Gain Combustion Combined Cycle Gas Turbine by Blade Cooling Integration With Bottoming Cycle
Bibliographic record
Abstract
Abstract This article investigates the potential advantages of integrating turbine blade cooling with bottoming cycle in combined cycle gas turbine (CCGT) with pressure gain combustion (PGC) for land-based power generation application. PGCs have recently emerged as a promising solution to achieve significant performance gains in current gas turbines (GTs) and CCGTs in terms of efficiency and power output. However, GTs with PGC combustors require higher cooling flow compared to conventional GTs due to the increased temperature of the cooling flow from its secondary compression that is necessary for admission in the turbine. The present work aims to address this issue by utilizing the working fluid from the steam cycle for cooling stator and rotor vanes or to decrease the cooling air temperature. PGC is represented by a steady-state zero-dimensional constant volume combustion (CVC) model based on the Humphrey cycle. The PGC combustor model is simulated with different injection pressure losses to investigate the impact of pressure gain on steam cooling integrated CCGT. Different approaches of turbine blade cooling through the bottoming cycle are investigated in this work, such as (i) cooling of compressor-bleed air through steam/water, (ii) open-loop steam cooling (OLSC) for stator and vanes, (iii) closed-loop steam cooling (CLSC) for stator and vanes, and (iv) mixed-loop steam cooling (MLSC) where stator is steam cooled while rotor is air cooled. A heavy-duty industrial H-class CCGT with a PGC combustor and a three-pressure level heat recovery steam generator (HRSG) was modeled in wtemp (web-based thermo-economic modular program) software, an original modular cycle analysis tool developed at the University of Genova. Thermodynamic analysis of the CCGT cycle was performed with realistic component efficiencies at a wide range of operating conditions, with methane as the fuel. The impact of different cooling approaches on the cycle performance was analyzed in terms of efficiency, specific work, and practical feasibility of the solution. Results showed that implementing both PGC technology and steam cooling together in a CCGT can significantly enhance efficiency and work output due to the synergistic effect of both technologies. The efficiency of an H-class CCGT can be increased from 62.6% to 67.2% (4.54 percentage points increment) and specific work by 194.5 kJkgair by using CLSC and PGC combustor with a pressure gain of 0.40. MLSC was identified as the most practical solution, which, when augmented with a PGC combustor with 0.26 pressure gain, can improve the overall CCGT efficiency by 2.9 percentage points and specific work by 104.6 kJ/kgair. Overall, the study demonstrates the theoretical potential of integrating turbine cooling in the PGC combined cycle with a steam bottoming cycle as a potential pathway toward CCGTs with more than 65% efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".