Advancing Airfoil Design: A Physics-Inspired Neural Network Model
Bibliographic record
Abstract
Abstract Turbomachines are an integral part of the energy and industrial landscapes, and improvements to their efficiency benefit the environment, profitability of operation, and in turn, society at large. Therefore, the application of advanced methods for rapid design and development of high-performance turbomachinery components is of significant interest. In the past decade, the use of optimization methods has made inroads in improving turbomachinery aerodynamics. Recent advances in machine learning (ML) methods have the potential to augment design systems by providing the ability to explore larger design spaces and generate high-quality initial designs. Physics Informed Neural Networks (PINNs), based on the Navier-Stokes equations, are used to incorporate physical laws into the design process. This approach leverages the power of deep learning while ensuring that the designs conform to fundamental principles of fluid dynamics. The use of Physics Informed Neural Networks (PINNs) not only accelerates the design process by reducing the need for extensive simulations but also improves the accuracy of the designs by ensuring physical consistency as opposed to designs made using Generative Artificial Intelligence (AI) models. However, combining PINNs with Generative AI for airfoil optimization could provide a fruitful avenue in improving compressor blade designs.
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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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".