MULTIDISCIPLINARY DESIGN AND OPTIMIZATION OF GAS TURBINE ENGINE LOW PRESSURE TURBINE AT PRELIMINARY DESIGN STAGE
Bibliographic record
Abstract
The gas turbine engine has evolved rapidly during past decades to provide a reliable and efficient business solution for global transportation. The engine design process is clearly a large contributor to this evolution. This process is highly iterative, multidisciplinary and complex in nature. The success of an engine depends on a carefully balanced design that best exploits the interactions between numerous traditional engineering disciplines such as aerodynamics and structures as well as lifecycle analysis of cost, manufacturability, serviceability and supportability. To take into account all of these disciplines and optimization should be used. Currently most of present state-of-art numerical modelling methods, which are used mainly at detailed design stage, are unsuitable for this task due to very high computational time. The solution to this problem can be found in multidisciplinary design and optimization at preliminary design stage with use of simple 1-2D models. This paper presents current aero engine design process and indicates possibilities of future improvements by utilization of proposed methodology, which take into account aerodynamic, thermodynamic and structures (blade, fixing and disc) calculations, connected in one multidisciplinary model, which is suited for optimization. All disciplinary models are presented and described in this paper as well as connection between them, with study over design variable, goal function and constrains that should be used. Moreover, a strategy of optimization is proposed as well as methods for acceleration of optimization process by use of surrogate. The presentation of methodology is followed by example optimization of low-pressure aero engine turbine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".