A Practical Axial Compressor Design Optimization Approach Based on Gas Turbine Operation
Bibliographic record
Abstract
In the current study, it is focused on blade optimization of compressor to achieve improved performance characteristics.Due to dependency of mass flow rate on the inlet temperature of the gas turbine, temperature changes influence on compressor performance and efficiency.In order to enhance the working conditions at design and off-design operation, an automated design process is applied.The process has three main steps including parametrization of the geometry, numerical simulation of flow and optimization design approach.Stochastic design approach is utilized for optimization.The objective of this improvement will push the airfoil geometry in a way that minimum loss value, extended acceptable off-design operation in constant exit flow angle can be achieved with being focused on hot day's operation.The considered case in the present study is a compressor of MGT-70 heavy-duty gas turbine and the optimization focuses on the first four stages.Based on numerical simulation of optimized compressor, 1% enhancement in efficiency in all operating conditions is achievable.Moreover, the mass flow rate can be enhanced roughly up to 0.8% and 1% for design and off-design conditions, respectively.After assembling the new developed parts, the first upgraded prototype of the gas turbine has been tested in sixth Unit of Parand power station.More than 600 signals of pressure and temperature in circumferential and radial directions were extracted from compressor section.The results show good agreement predicted in range inlet flow angle between measurements and theoretical targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".