Aerodynamic Optimization Strategies in Gas Path Systems: A Turbine Design Exploration
Bibliographic record
Abstract
One of the steps in the complex design of aircraft propulsion system consists of designing an efficient turbine gas-path. Gas-path geometry affects how the stators, rotors and duct will be designed. For this reason, the optimization of the gas-path needs to be done early in the development process of an engine. This is the concept of Preliminary Multi-Disciplinary Optimization (PMDO). At this stage of the design process, as many configurations as possible should be analysed. Due to the size of the design space to be explored thousands of configurations have to be considered. Consequently, each of these configurations needs to be simulated rapidly. To meet this requirement, an in-house 1D meanline code based on a correlation loss model is used. The optimization is done with a combination of direct optimization and design exploration. A simple direct optimization is used to generate a first version of the gaspath. Design exploration is achieved with an in-house Framework for Design Exploration (FDE). This framework includes Design of Experiment (DOE) and Surrogate Assisted Optimization (SAO) workflow. SAO is executed to find a global optimum configuration within user defined limitations. These design limits are defined according to a set of predefined limitations on factors (input parameters) and constraints on responses (output parameters). Furthermore, any responses can be set as an objective to be minimized, maximized or to be targeted for a specific value. To obtain such an optimized gas-path, a robust parametrization has to be developed. An efficient parametrization will limit the number of nonphysical gas-path configurations in the design space without excluding any optimal configurations. Longueuil, Quebec, Canada Hany Moustapha This work focuses on optimizing the turbine gas-path to achieve one of three possible objectives. For most of the cases, the objective is maximizing efficiency. Another possible objective consists of minimizing the total length of the turbine while achieving a specific efficiency. Finally, this optimization tool can be used by turbine aerodynamicists to quickly analyse different stage configurations such as two versus three Power Turbines (PT). Iterating on the number of PT stages becomes much faster when the entire optimization process is automated.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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; both teacher heads agree on what is shown here.
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".