A New Empirical Model Derivation for the Estimation of Turbulent Boundary Layer Power Spectral Density Aircraft Using Machine Learning Regression
Bibliographic record
Abstract
ENGLISHThis paper explores the possibility of using a novel machine learning technique, a nonlinear least squares (NLS) regression algorithm, to produce an original turbulent boundary layer (TBL) wall pressure fluctuations model. An iterative technique was developed in the statistical programming language R that fit the coefficients and exponents of numerous candidate models via NLS techniques. This allowed for 186 unique model forms to be generated. The optimum model was accurate near the conditions of the training data (which had an average airspeed of 10.54 m/s and an average Reynolds number of 849,803), at lower airspeeds, and at both lower and higher Reynolds numbers. Due to limited outside data, conclusions regarding the model performance at higher airspeeds were more limited. Overall, it is concluded that machine learning techniques show strong promise in the search for an accurate, universally applicable TBL noise model.FRANÇAISCet article explore la possibilité d'utiliser une nouvelle technique d'apprentissage automatique, un algorithme de régression des moindres carrés non linéaires pour produire un modèle original de fluctuations de pression de la paroi de la couche limite turbulente (CLT). Une technique itérative a été développée dans le langage de programmation statistique R qui ajuste les coefficients et les exposants de nombreux modèles candidats via des techniques des moindres carrés. Cela a permis de générer 186 formulaires modèles uniques. Le modèle optimal était précis près des conditions des données d'entraînement (qui avaient une vitesse moyenne de 10,54 m / s et un nombre de Reynolds moyen de 849 803), à des vitesses plus faibles et à des nombres de Reynolds inférieurs et supérieurs. En raison de données extérieures limitées, les conclusions concernant les performances du modèle à des vitesses plus élevées étaient plus limitées. Dans l'ensemble, il est conclu que les techniques d'apprentissage automatique sont très prometteuses dans la recherche d'un modèle de bruit CLT précis et universellement applicable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".