Bernd Noack

Cluster-based reduced-order modelling of a mixing layer

Publisher: Cambridge University Press (CUP)

Date: 06-08-2014

DOI: 10.1017/JFM.2014.355

Abstract: We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger’s group (Burkardt, Gunzburger & Lee, Comput. Meth. Appl. Mech. Engng , vol. 196, 2006 a , pp. 337–355) and transition matrix models introduced in fluid dynamics in Eckhardt’s group (Schneider, Eckhardt & Vollmer, Phys. Rev. E, vol. 75, 2007, art. 066313). CROM constitutes a potential alternative to POD models and generalises the Ulam–Galerkin method classically used in dynamical systems to determine a finite-rank approximation of the Perron–Frobenius operator. The proposed strategy processes a time-resolved sequence of flow snapshots in two steps. First, the snapshot data are clustered into a small number of representative states, called centroids, in the state space. These centroids partition the state space in complementary non-overlapping regions (centroidal Voronoi cells). Departing from the standard algorithm, the probabilities of the clusters are determined, and the states are sorted by analysis of the transition matrix. Second, the transitions between the states are dynamically modelled using a Markov process. Physical mechanisms are then distilled by a refined analysis of the Markov process, e.g. using finite-time Lyapunov exponent (FTLE) and entropic methods. This CROM framework is applied to the Lorenz attractor (as illustrative ex le), to velocity fields of the spatially evolving incompressible mixing layer and the three-dimensional turbulent wake of a bluff body. For these ex les, CROM is shown to identify non-trivial quasi-attractors and transition processes in an unsupervised manner. CROM has numerous potential applications for the systematic identification of physical mechanisms of complex dynamics, for comparison of flow evolution models, for the identification of precursors to desirable and undesirable events, and for flow control applications exploiting nonlinear actuation dynamics.

Microparticle Transport and Sedimentation in a Rhythmically Expanding Alveolar Chip

Publisher: MDPI AG

Date: 20-03-2022

DOI: 10.3390/MI13030485

Abstract: Understanding the mechanism of particle transport and sedimentation in pulmonary alveolus is important for deciphering the causes of respiratory diseases and helping the development of drug delivery. In this study, taking advantage of the microfluidic technique, an experimental platform was developed to study particle behavior in a rhythmically expanding alveolar chip for a sufficient number of cycles. The alveolar flow patterns at different generations were measured for two cases with the gravity direction parallel or vertical to the alveolar duct. Affected by both the vortex flow inside the alveoli and the shear flow in the duct simultaneously, it was observed that particles inside the alveoli either escaped from the inlet of the alveolar duct or stayed in the alveoli, revealing the irreversibility of particle transport in the alveoli. At the earlier acinar generations, particles were inclined to deposit on the distal alveolar wall. The settling rates of particles of different sizes in the alveoli were also compared. This study provides valuable data for understanding particle transport and sedimentation in the alveoli.

Identification strategies for model-based control

Publisher: Springer Science and Business Media LLC

Date: 26-07-2013

DOI: 10.1007/S00348-013-1580-9

A hierarchy of maximum entropy closures for Galerkin systems of incompressible flows

Publisher: Elsevier BV

Date: 05-2013

DOI: 10.1016/J.CAMWA.2013.02.016

Aerodynamic Characterization of a Fan-Array Wind Generator

Publisher: American Institute of Aeronautics and Astronautics (AIAA)

Date: 04-10-2023

DOI: 10.2514/1.J063114

DRLinFluids: An open-source Python platform of coupling deep reinforcement learning and OpenFOAM

Publisher: AIP Publishing

Date: 08-2022

DOI: 10.1063/5.0103113

Abstract: We propose an open-source Python platform for applications of deep reinforcement learning (DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in nonlinear and high-dimensional problems. Here, an agent maximizes a cumulative reward by learning a feedback policy by acting in an environment. In control theory terms, the cumulative reward would correspond to the cost function, the agent to the actuator, the environment to the measured signals, and the learned policy to the feedback law. Thus, DRL assumes an interactive environment or, equivalently, a control plant. The setup of a numerical simulation plant with DRL is challenging and time-consuming. In this work, a novel Python platform, namely DRLinFluids, is developed for this purpose, with DRL for flow control and optimization problems in fluid mechanics. The simulations employ OpenFOAM as a popular, flexible Navier–Stokes solver in industry and academia, and Tensorforce or Tianshou as widely used versatile DRL packages. The reliability and efficiency of DRLinFluids are demonstrated for two wake stabilization benchmark problems. DRLinFluids significantly reduces the application effort of DRL in fluid mechanics, and it is expected to greatly accelerate academic and industrial applications.

Maximum-entropy closure for a Galerkin model of an incompressible periodic wake

Publisher: Cambridge University Press (CUP)

Date: 24-04-2012

DOI: 10.1017/JFM.2012.125

Abstract: A statistical closure is proposed for a Galerkin model of an incompressible periodic cylinder wake. This closure employs Jaynes’ maximum entropy principle to infer the probability distribution for mode litudes using exact statistical balance equations as side constraints. The analysis predicts mean litude values and modal energy levels in good agreement with direct Navier–Stokes simulation. In addition, it provides an analytical equation for the modal energy distribution.

Harbin Institute Of Technology

Location: China

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Harbin Institute Of Technology

Location: No location found

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Technische Universität Berlin

Location: Germany

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Discovery Projects - Grant ID: DP140104402

Start Date: 2014

End Date: 04-2018

Amount: $313,000.00

Funder: Australian Research Council