Estimation of Skin Friction on the NASA BeVERLI Hill using Oil Film Interferometry
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0988.vid Viscous drag reduction plays a vital role in increasing the performance of vehicles. However, there are only so many measurement techniques that can quickly and accurately measure this when compared to pressure drag measurement techniques. The current study makes use of one of the direct and robust measurement techniques that exist, called the Oil Film Interferometry (OFI) to estimate skin friction on the NASA/Virginia Tech BeVERLI (Benchmark Validation Experiment for RANS and LES Investigations) hill. This project aims to develop a detailed database of non-equilibrium, separated turbulent boundary layer flows obtained through wind tunnel experiments for CFD validation. Skin friction measurements are obtained at specific critical locations on the hill and in its close proximity. The challenges involved in obtaining skin friction data from these locations are discussed in detail. Detailed discussions on the experimental setup and data processing methodology are presented. Qualitative and quantitative results from each measurement location are discussed along with uncertainties to explain certain key flow physics. Additionally, skin friction coefficients from selected overlapping measurement locations from another experimental flow measurement technique called Laser Doppler Velocimetry (LDV) are compared with OFI, and a cross-instrument study is performed. Finally, results from well-refined RANS CFD simulations are assessed with the experimental results, and critical improvement areas are identified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".