Multidisciplinary Design and Optimizations of Swept and Leaned Transonic Rotor
Why this work is in the frame
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Bibliographic record
Abstract
Optimization problems in many engineering applications are usually considered as complex subjects. Researchers are often obliged to solve a multi-objective optimization problem. Several methodologies such as genetic algorithm (GA) and artificial neural network (ANN) are proposed to optimize multi-objective optimization problems. In the present study, various levels of sweep and lean were exerted to blades of an existing transonic rotor, the well-known NASA rotor-67. Afterward, an ANN optimization method was used to find the most appropriate settings to achieve the maximum stage pressure ratio, efficiency, and operating range. At first, the study of the impact of sweep and lean on aerodynamic and performance parameters of the transonic axial flow compressor rotors was undertaken using a systematic step-by-step procedure. This was done by employing a three-dimensional (3D) compressible turbulent model. The results were then used as the input data to the optimization computer code. It was found that the optimized sweep angles can increase the safe operating range up to 30% and simultaneously increase the pressure ratio and subsequently the efficiency by 1% and 2%. Moreover, it was found that the optimized leaned blades, according to their target function, had positive (forward (FW)) or negative (backward (BW)) optimized angles. Leaning the blade at the optimum point can increase the safe operating range up to 12% and simultaneously increase the pressure ratio and subsequently the efficiency by 4% and 5%.
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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 | 0.000 |
| 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 it