scholarly journals Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows

2021 ◽  
Vol 912 ◽  
Author(s):  
Mostafa Aghaei Jouybari ◽  
Junlin Yuan ◽  
Giles J. Brereton ◽  
Michael S. Murillo
Keyword(s):  
Turbulent Flows ◽  
Rough Wall ◽  
Data Driven

Abstract

scholarly journals Bubbles in turbulent flows: Data-driven, kinematic models with history terms

2020 ◽  
Vol 129 ◽  
pp. 103286
Author(s):  
Zhong Yi Wan ◽  
Petr Karnakov ◽  
Petros Koumoutsakos ◽  
Themistoklis P. Sapsis
Keyword(s):  
Turbulent Flows ◽  
Data Driven ◽  
Kinematic Models

New Two-Equation Closure for Rough-Wall Turbulent Flows Using the Brinkman Equation

AIAA Journal ◽  
2009 ◽  
Vol 47 (2) ◽  
pp. 386-398 ◽  
Author(s):  
Meng-Huang Lu ◽  
William W. Liou
Keyword(s):  
Turbulent Flows ◽  
Rough Wall ◽  
Brinkman Equation

scholarly journals Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

2022 ◽  
Author(s):  
Ricardo Vinuesa ◽  
Oriol Lehmkuhl ◽  
Adrian Lozano-Duran ◽  
Jean Rabault
Keyword(s):  
Reinforcement Learning ◽  
Flow Control ◽  
Flat Plate ◽  
Turbulent Flows ◽  
Control Strategies ◽  
Data Driven ◽  
Crucial Aspect ◽  
Aerodynamic Efficiency ◽  
Turbulence Simulation ◽  
Novel Approaches

In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.

scholarly journals Data-driven POD-Galerkin reduced order model for turbulent flows

2020 ◽  
Vol 416 ◽  
pp. 109513 ◽  
Author(s):  
Saddam Hijazi ◽  
Giovanni Stabile ◽  
Andrea Mola ◽  
Gianluigi Rozza
Keyword(s):  
Turbulent Flows ◽  
Reduced Order Model ◽  
Data Driven ◽  
Order Model ◽  
Reduced Order

Geomorphological hazards assessment using machine learning and data fusion

2020 ◽  
Author(s):  
Mohammad Ahmed ◽  
Hamed Farhadi ◽  
Panagiotis Michalis ◽  
Manousos Valyrakis
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Turbulent Flow ◽  
Turbulent Flows ◽  
Fuzzy Inference ◽  
Data Driven ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Flume Experiments ◽  
Motion Data

<p>Turbulent flows may destabilise riverbeds and banks, transporting sediment or underscouring hydraulic infrastructure built near water bodies. For example, scour is a significant challenge that can affect the stability of bridge foundations as the transport of sediment around a bridge pier may cause structural instabilities and catastrophic failures. The aim of this study is to use machine learning techniques & data driven algorithms to predict how energetic turbulent flow events can result in the removal of individual sediment grains, resting on the bed surface or on the protective armour layer around built infrastructure. </p><p>The flume experiments involve flow and particle motion data gathering campaigns [1]. Turbulent flow data are collected upstream the exposed target particle using acoustic Doppler velocimetry. Particle's motion data are gathered using novel micro-electro-mechanical sensors embedded within its waterproof casing, for a range of flow conditions. The obtained data are fed into neural networks having distinct algorithmic complexity (inputs, levels and neutrons). A comparison of the performance of the various model architectures, as well as with past ones [2], is conducted to identify the optimal predictive algorithm for the configuration tested. Sensor data fusion combined with artificial intelligence techniques are shown to provide a unique tool for live and robust data-driven predictions to help tackle significant engineering problems, such as geomorphological activity and scouring of infrastructure (eg bridge piers and embankments) due to turbulent flows, which become increasingly more challenging, under the scope of climate change and intensifying extreme weather hazards.</p><p> </p><p>References</p><p>[1] Valyrakis, M., Farhadi, H. 2017. Investigating coarse sediment particles transport using PTV and “smart-pebbles” instrumented with inertial sensors, EGU General Assembly 2017, Vienna, Austria, 23-28 April 2017, id. 9980.</p><p>[2] Valyrakis, M., Diplas, P., Dancey, C.L. 2011b. Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25 (22). pp. 3513-3524. ISSN 0885-6087, doi:10.1002/hyp.8228.</p>

scholarly journals Data-driven reduced modelling of turbulent Rayleigh–Bénard convection using DMD-enhanced fluctuation–dissipation theorem

2018 ◽  
Vol 852 ◽  
Author(s):  
M. A. Khodkar ◽  
Pedram Hassanzadeh
Keyword(s):  
Turbulent Flows ◽  
Data Driven ◽  
High Dimensional ◽  
Dynamic Mode ◽  
Fluctuation Dissipation ◽  
Fluctuation Dissipation Theorem ◽  
Bénard Convection ◽  
Benard Convection ◽  
Rayleigh Benard Convection ◽  
Rayleigh Benard

A data-driven model-free framework is introduced for the calculation of reduced-order models (ROMs) capable of accurately predicting time-mean responses to external forcings, or forcings needed for specified responses, e.g. for control, in fully turbulent flows. The framework is based on using the fluctuation–dissipation theorem (FDT) in the space of a limited number of modes obtained from dynamic mode decomposition (DMD). Use of the DMD modes as the basis functions, rather than the commonly used proper orthogonal decomposition modes, resolves a previously identified problem in applying FDT to high-dimensional non-normal turbulent flows. Employing this DMD-enhanced FDT method ($\text{FDT}_{DMD}$), a linear ROM with horizontally averaged temperature as state vector is calculated for a 3D Rayleigh–Bénard convection system at a Rayleigh number of$10^{6}$using data obtained from direct numerical simulation. The calculated ROM performs well in various tests for this turbulent flow, suggesting$\text{FDT}_{DMD}$as a promising method for developing ROMs for high-dimensional turbulent systems.

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