Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator

We present an approach to sensor placement for turbulent mean flow data assimilation in the context of Reynolds-averaged Navier–Stokes (RANS) simulations. It entails generating a spatial uncertainty map through the eigenspace perturbations (ESPs) of the baseline turbulence model (e.g., the k−ω shear stress transport model) to quantify the epistemic structural errors in the model. A novel greedy search algorithm is proposed to place sensors targeting regions of highest uncertainty in the spatial uncertainty map generated from ESP. The algorithm is computationally efficient (e.g., computational cost negligible compared to a RANS solution) and is both easy to implement and tune. It involves two hyper-parameters (a constraint to avoid sensor clustering and the number of sensors) which we investigate in-depth. A variational (adjoint-based) data assimilation approach is used for flow reconstruction. The proposed strategy was tested on three two-dimensional wall-bounded flows (Reynolds number ranging 5.6×103–9.36×105) involving flow separation and reattachment. For the wall-mounted hump case, we found that data assimilation using 33 sensors with our proposed sensor placement algorithm reduced the average velocity prediction error by 60% vs 38% with a simple uniform placement of sensors. Furthermore, we found that we could achieve 61% error reduction using our algorithm with only three sensors. Notably, in all tested cases, the error reduction using our method for sensor placement was close in accuracy to the instances where the entire flow field data were used for flow reconstruction, which involved two to three orders of magnitude more data points than the placed sensors.

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Role of flow structures on the deposition of low-inertia particles in turbulent pipe flow

Schlander,

Rigopoulos,

Papadakis

2024

Phys. Rev. Fluids

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We analyze the effect of large-scale coherent structures on the deposition of low-inertia particles in a turbulent pipe flow using extended proper orthogonal decomposition (EPOD) and spectral analysis. We perform direct numerical simulations (DNSs) at two the Reynolds numbers 5300 and 10 300 (based on bulk parameters) with the particles released at the pipe inlet. The equilibrium Eulerian model is employed for calculating particle velocity, and the analysis is limited to particles with Stokes number (based on wall units) less than 1. Increasing the Stokes number increases the energy at small streamwise wavelengths (due to inertial clustering), and the spectral energy peak moves from λz+≈1000 to λz+≈150. The spectral peak in the (λz+,y+) plane, where y+ is the wall-normal distance, moves from the buffer layer to the logarithmic region. Gravity has a substantial effect on the POD mode shapes. For the downward flow, a second peak appears closer to the center. A new Fukagata-Iwamoto-Kasagi (FIK) identity is derived for the wall deposition rate coefficient (Sherwood number, Sh) and employed to quantify the contributions of the mean and fluctuating velocity and particle concentration fields for different Stokes, Froude, and Reynolds numbers. Modes with azimuthal wave numbers kθ equal to three or four are found to contribute most to deposition. Application of the developed methodology to higher Reynolds number can elucidate the role of large- and very-large-scale flow structures on particle deposition to the wall. It is well known these structures leave their footprint at the wall but their contribution to deposition is not well understood. Published by the American Physical Society 2024

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Flow Reconstruction Around a Surface-Mounted Prism from Sparse Velocity and/or Scalar Measurements Using a Combination of POD and a Data-Driven Estimator

Cited by 3 publications

References 49 publications

Three-dimensional characterisation of macro-instabilities in a turbulent stirred tank flow and reconstruction from sparse measurements using machine learning methods

Three-dimensional characterisation of macro-instabilities in a turbulent stirred tank flow and reconstruction from sparse measurements using machine learning methods

Sensor placement for data assimilation of turbulence models using eigenspace perturbations

Role of flow structures on the deposition of low-inertia particles in turbulent pipe flow

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