arXiv subjects

André Fuchs

Publications and source records attributed to André Fuchs.

5 recordsLinked to original sources

arXiv2022

Experimental and Computational Investigation of a Fractal Grid Wake

Fractal grids generate turbulence by exciting many length scales of different sizes simultaneously rather than using the nonlinear cascade mechanism to obtain multi-scale structures, as it is the case for regular grids. The interest in these grids has been further building up since the surprising findings stemming from the experimental and computational studies conducted on these grids. This work presents experimental wind tunnel and computational fluid dynamics (CFD) studies of the turbulent flow generated by a space-filling fractal square grid. The experimental work includes Particle Image Velocimetry (PIV) and hot-wire measurements. In addition, Delayed Detached Eddy Simulations (DDES) with a Spalart-Allmaras background turbulence model are conducted using the open-source package OpenFOAM. This is the first time DDES simulations are used to simulate and characterize the turbulent flow generated by a fractal grid. Finally, this article reports on the extensive statistical study and the direct comparison between the experimentally and numerically acquired time series to investigate and compare one-point- and two-point statistics. Our goal is to validate our computational results and provide enhanced insight into the complexity of the multi-scale generation of turbulence using a fractal grid with a low number of fractal iterations. In particular, we investigate the different turbulent structures and their complex interaction in the near-grid region or the production regime of the fractal grid flow.

physics.flu-dyn↗

arXiv2022

An open source MATLAB package to perform basic and advanced statistical analysis of turbulence data and other complex systems

We present a user-friendly open-source MATLAB\textsuperscript{\textregistered} package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg. Firstly, this package helps the user to perform a very basic statistical analysis of a given turbulent data set which we believe to be useful to the entire turbulence community. It can be used to estimate the statistical quantities of turbulence such as the spectrum density, turbulent intensity, integral length scale, Taylor microscale, Kolmogorov scale and dissipation rate. Different well-known methods available in the literature were selected so that they can be compared. Secondly, this package also performs an advanced analysis which includes the scale-dependent statistical description of turbulent cascade using the Fokker-Planck equation which consequently leads to the assessment of integral fluctuation theorem. This is utilized to estimate velocity increments, structure functions and their scaling exponents, drift and diffusion coefficients of the Fokker-Planck equation and consequently the total entropy production of the turbulent cascade. As a precondition for the stochastic process approach, Markovian properties of the turbulent cascade in scale are tested. The knowledge of a Fokker-Planck equation allows to determine for each independent cascade trajectories a total entropy production. The estimation of total entropy production allows to verify a rigorous law of non-equilibrium stochastic thermodynamics, namely the integral fluctuation theorem, which must be valid if Markov properties hold and the Fokker-Planck equation is correct. This approach to the turbulent cascade process has the potential for a new way to link the statistical description of turbulence, non-equilibrium stochastic thermodynamics and local turbulent flow structures.

physics.flu-dyn↗

arXiv2022

Instantons and the path to intermittency in turbulent flows

Processes leading to anomalous fluctuations in turbulent flows, referred to as intermittency, are still challenging. We consider cascade trajectories through scales as realizations of a stochastic Langevin process for which multiplicative noise is an intrinsic feature of the turbulent state. The trajectories are conditioned on their entropy exchange. Such selected trajectories concentrate around an optimal path, called instanton, which is the minimum of an effective action. The action is derived from the Langevin equation, estimated from measured data. In particular instantons with negative entropy pinpoint the trajectories responsible for the emergence of non-Gaussian statistics at small-scales.

physics.flu-dyn↗

arXiv2021

Hybrid time series from PIV for characterization of turbulent flow fields: ASTRA -- Approach using Spatially and Temporally Resolved Advection

Particle Image Velocimetry (PIV) has become increasingly popular to study structures in turbulent flows. PIV allows direct extraction and investigation of spatial structures in the given flow field. Increasing temporal resolution of PIV systems allows a more accurate capture of the flow evolution. Despite the very good spatial resolution of PIV, current systems can only match the multiple $kHz$ sampling rates of hot-wire or Laser Doppler Anemometer (LDA) measurements for a very short period in temporal analyses of flow. In this study, an advection-based approach is presented which uses Taylor's hypothesis of "frozen turbulence" for small scale turbulent patterns. Compared to the underlying raw data a major increase of the temporal resolution for extracted time series is shown. The quality of the presented approach is shown for two-point analyses, which would not be possible with presently known methods. To demonstrate this, different turbulent flow cases behind a fractal grid are studied. For the validation of the results corresponding hot-wire measurements at various positions along the centerline were used.

physics.flu-dyn↗

arXiv2015

The Langevin Approach: a simple stochastic method for complex phenomena

We describe a simple stochastic method, so-called Langevin approach, which enables one to extract evolution equations of stochastic variables from a set of measurements. Our method is parameter-free and it is based on the nonlinear Langevin equation. Moreover, it can be applied not only to processes in time, but also to processes in scale, given that the data available shows ergodicity. This chapter introduces the mathematical foundations of the Langevin approach and describes how to implement it numerically. A specific application of the method is presented, namely to a turbulent velocity field measured in the laboratory, retrieving the corresponding energy cascade and comparing with the results from a computational simulation of that experiment. In addition, we describe a physical interpretation bridging between processes in time and in scale. Finally, we describe extensions of the method for time series reconstruction and applications to other fields such as finance, medicine, geophysics and renewable energies.

physics.data-an↗