Unscented Kalman filter (UKF)–based nonlinear parameter estimation for a turbulent boundary layer: a data assimilation framework
Bibliographic record
Abstract
Abstract A turbulent boundary layer is a ubiquitous element of fundamental and applied fluid mechanics. Unfortunately, accurate measurements of turbulent boundary layer parameters (e.g. friction velocity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>τ</mml:mi> </mml:msub> </mml:math> and wall shear τ w ) are challenging, especially for high-speed flows (Smits et al 2011). Many direct and/or indirect diagnostic techniques have been developed to measure wall shear stress (Vinuesa et al 2017). However, based on various principles, these techniques generally give different results with varying uncertainties. The current study introduces a nonlinear data assimilation framework based on the unscented Kalman filter (UKF) that can fuse information from (i) noisy and discretized measurements from stereo particle image velocimetry (SPIV), a Preston tube, and a MEMS shear stress sensor, as well as (ii) the uncertainties of the measurements to estimate the parameters of a turbulent boundary layer. A direct numerical simulation of a fully developed turbulent channel flow is used first to validate the data assimilation algorithm. The algorithm is then applied to experimental boundary layer data at Mach 0.3 obtained in a blowdown wind tunnel facility. Drag coefficients from control volume analysis of the SPIV and wall pressure data and laser interferometer skin friction measurements are used for independent cross-validation. The UKF-based data assimilation algorithm is robust to the uncertain and discretized experimental data and is able to provide accurate estimates of turbulent boundary layer parameters with quantified uncertainty.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".