Optimized SGT-A35 (GT61) for Improved Emissions and Enhanced Efficiency Across the Load Range
Bibliographic record
Abstract
Abstract The RT61 is a three-stage industrial power turbine which couples with the SGT-A35 aeroderivative gas generator (formerly named Industrial RB211). It was designed for improved efficiency and modular construction for ease in maintainability. The aeroderivative SGT-A35 (GT61) product serves both oil & gas and power generation market with a fleet size of greater than 90 units. In recent years, there has been increased emphasis on clean energy, only complemented by the regulatory changes and market conditions. The power generation and oil & gas customers (upstream, midstream, and downstream) are continuously looking for opportunities to decrease their greenhouse gas emissions and reduce fuel consumption by improving the gas turbine cycle efficiency. The SGT-A35 (GT61) power turbine has > 93% isentropic efficiency and industry standard overhaul schedule of 100,000 hours. However the potential for further cycle efficiency improvements and reduction in emissions exist by optimizing the power turbine capacity to a specific load range. This served as the main motivation for this technical work. This paper discusses the engineering efforts taken in implementing the above stated improvement and further optimizing the product for reduced emissions. The improvements are discussed on the product level and on the TransCanada Pipelines fleet level. A new power turbine variant was developed on a demanding timeline driven by the customer project. A detailed development project was undertaken to establish the new operating point, aerodynamic design, and the new geometry. It was optimized to the customer project-specific load range. During the manufacturing phase, novel rapid prototyping methods were used to achieve desired lead times. Flow path change was limited to the first stage vane to minimize the introduction of new risks and uncertainties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".