General

We would like to draw your attention to this workshop that is scheduled just before the Lisbon Symposium 2020. Recently several data assimilation (DA) methods have been developed at the junction between experimental and numerical fluid mechanics and aerodynamics. DA allows increasing the spatial and temporal resolution of sparse measurement data and calculating and extracting physical meaningful content like pressure fields, coherent structures or periodic flow features for a better insight into the flow dynamics. We assume you might be interested in participating, sharing your views, experience and of course latest research results within a relatively small and focused community.

Scope

Many procedures are nowadays available that increase or enhance the information measured with Particle Image Velocimetry (PIV) or Lagrangian Particle Tracking (LPT) using techniques imported from the CFD and applied mathematics community. The advent of time-resolved and volumetric measurements have multiplied the possibilities with much excitement of PIV and LPT development researchers as well as from the applied fluid mechanics community. The methods range from regularization strategies using the (simplified) Navier-Stokes-equation or the use of the momentum equation to obtain pressure from velocity and acceleration measurements, machine learning, to variational data-assimilation frameworks using adjoint CFD.

Challenge on 3D LPT and Data Assimilation

In February 2020 a synthetic test case based on a cylinder wake in an incompressible turbulent boundary layer flow with dynamic wall deformation will be provided via a download link to the participants of two challenges:

  1. A time-series of synthetic particle images created by four virtual camera views of tracer particles in the TBL flow and random dots at the deforming wall will be provided together with the calibration data in order to challenge the latest LPT code developments.
  2. A large number of randomly distributed 3D particle tracks over many time-steps representing the flow and dynamic surface deformations are provided as starting points of a data assimilation challenge.

Both results will be compared with and assessed by physical measures (position, velocity, pressure, etc.) of the full LES input data. The presentation of the challenge results will cover one-half day of the workshop.