Pith. sign in

REVIEW 1 cited by

Nonlinear estimation in turbulent channel flows

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.12827 v1 pith:RQZLSTRL submitted 2024-08-23 physics.flu-dyn

classification physics.flu-dyn
keywords estimatornonlinearlinearaugmentedchanneleddyheightmeasurement
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
abstract

We design a nonlinear estimator for channel flows at $Re_{\tau}=180$ and $590$. The nonlinear estimator uses a linear estimator structure based on the linearised Navier-Stokes equations and explicitly calculates the nonlinear forcing from the estimated velocities in physical space. The goal is to use the velocities at one wall-normal height to estimate the velocities at other wall-normal heights. The estimation performance is compared among the nonlinear estimator, the linear estimator and the linear estimator augmented with eddy viscosity. At $Re_{\tau}=180$, the nonlinear estimator and the linear estimator augmented with eddy viscosity outperform the linear estimator in terms of estimating the velocity magnitudes, structures and energy transfer (production and dissipation) across the channel height. The limitations of using measurement data at one wall-normal height are discussed. At $Re_{\tau}=590$, the nonlinear estimator does not work well with only one measurement plane, whereas the linear estimator augmented with eddy viscosity performs well. The performance of the nonlinear estimator at $Re_{\tau}=590$ is significantly enhanced by providing multiple measurement planes.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Short-term wind forecasting via surface pressure measurements: stochastic modeling and sensor placement

    physics.flu-dyn 2024-12 conditional novelty 6.0 of 10

    In a two-turbine LES, Kalman filters using ground pressure and nacelle anemometer data estimate hub-height wind fluctuations ahead of and behind the leading turbine with time-averaged normalized errors around 0.2.

Pith tools