Special Issue "Reduced Order Modeling of Fluid Flows"

A special issue of Fluids (ISSN 2311-5521).

Deadline for manuscript submissions: closed (15 August 2018)

Special Issue Editors

Guest Editor
Dr. Omer San

Mechanical and Aerospace Engineering, Oklahoma State University, 218 Engineering North, Stillwater, OK 74078-5016, USA
Website | E-Mail
Interests: fluid dynamics; turbulence modeling and large eddy simulations; geophysical flows; multiphase and multimaterial flows; high performance computing; model order reduction and optimization; computational mathematics and numerical methods
Guest Editor
Prof. Dr. Goodarz Ahmadi

Department of Mechanical and Aeronautical Engineering, Clarkson University, Potsdam, NY 13699–5725, USA
Website | E-Mail
Phone: 315-268-2322
Interests: multiphase flows; indoor air pollution; outdoor air pollution; lung/nose deposition; turbulence modeling; aerosol transport and deposition; electro-hydrodynamic flows; particle and fiber adhesion and removal; chemical-mechanical polishing surface cleaning; gas filtration; sprays; nanoparticle transport; CO2 sequestration; natural gas production form hydrate dissociation; three phase slurry reactors; micro-contamination control; vibration isolation; vibration control of space structures; base isolation of buildings

Special Issue Information

Dear Colleagues,

The ever-increasing need for computational efficiency and improved accuracy of many applications in fluids leads to very large-scale dynamical systems, whose simulations and analyses make excessive and unmanageable demands on computational resources. Since the computational cost of traditional full-order numerical simulations is extremely prohibitive, many successful model order reduction approaches have been introduced. The purpose of such approaches is to reduce this computational burden and serve as surrogate models for efficient computational analysis fluid systems, especially in settings where the traditional methods require repeated model evaluations over a large range of parameter values. Simplifying computational complexity of the underlying mathematical model, these reduced order models offer promises in many prediction, identification, design, optimization, and control applications. However, they are neither robust with respect to the parameter changes nor low-cost to handle nonlinear dependence for complex nonlinear dynamical systems with temporal and stochastic parameters. Therefore, reduced order modeling remains an open challenge and the development of efficient and reliable model order reduction techniques is of paramount importance for both fundamental and applied fluid dynamics research. Topics in this call include, but are not limited to: projection-based approaches, reduced subspace or basis generation methods, regularization algorithms, coarse-grained simulations, data-driven methods, compressive or sparse sampling ideas and their implementations for fast predictive modeling, parameter identification, data assimilation, design, control, optimization and uncertainty quantification problems arising in fluid dynamics applications.

Dr. Omer San
Prof. Dr. Goodarz Ahmadi
Guest Editors

Manuscript Submission Information

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Keywords

  • parametric model reduction methods

  • proper orthogonal decomposition

  • dynamic mode decomposition

  • Koopman operators

  • radial basis functions

  • rational interpolation methods

  • empirical interpolation methods

  • manifold interpolation methods

  • balanced truncation

  • closure modeling

  • regularization algorithms

  • scale-aware basis selection

  • Krylov subspace methods

  • Volterra series

  • nonlinear kernel-based methods

  • data-driven methods

  • spatiotemporal patterns extraction

  • predictability quantification

  • non-projection based model reduction

  • machine learning enabled reduced-order models

  • high-dimensional parameter subspace characterization

  • tensor techniques

  • non-intrusive models

  • compressive sensing

  • greedy sampling algorithms

  • reduced-order adaptive flow controller for fluid flows

Published Papers (3 papers)

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Research

Open AccessArticle A Hybrid Analytics Paradigm Combining Physics-Based Modeling and Data-Driven Modeling to Accelerate Incompressible Flow Solvers
Received: 16 June 2018 / Revised: 13 July 2018 / Accepted: 16 July 2018 / Published: 18 July 2018
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Abstract
Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm,
[...] Read more.
Numerical solution of the incompressible Navier–Stokes equations poses a significant computational challenge due to the solenoidal velocity field constraint. In most computational modeling frameworks, this divergence-free constraint requires the solution of a Poisson equation at every step of the underlying time integration algorithm, which constitutes the major component of the computational expense. In this study, we propose a hybrid analytics procedure combining a data-driven approach with a physics-based simulation technique to accelerate the computation of incompressible flows. In our approach, proper orthogonal basis functions are generated to be used in solving the Poisson equation in a reduced order space. Since the time integration of the advection–diffusion equation part of the physics-based model is computationally inexpensive in a typical incompressible flow solver, it is retained in the full order space to represent the dynamics more accurately. Encoder and decoder interface conditions are provided by incorporating the elliptic constraint along with the data exchange between the full order and reduced order spaces. We investigate the feasibility of the proposed method by solving the Taylor–Green vortex decaying problem, and it is found that a remarkable speed-up can be achieved while retaining a similar accuracy with respect to the full order model. Full article
(This article belongs to the Special Issue Reduced Order Modeling of Fluid Flows)
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Open AccessArticle One Dimensional Model for Droplet Ejection Process in Inkjet Devices
Received: 6 March 2018 / Revised: 11 April 2018 / Accepted: 18 April 2018 / Published: 23 April 2018
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Abstract
In recent years, physics-based computer models have been increasingly applied to design the drop-on-demand (DOD) inkjet devices. The initial design stage for these devices often requires a fast turnaround time of computer models, because it usually involves a massive screening of a large
[...] Read more.
In recent years, physics-based computer models have been increasingly applied to design the drop-on-demand (DOD) inkjet devices. The initial design stage for these devices often requires a fast turnaround time of computer models, because it usually involves a massive screening of a large number of design parameters. Thus, in the present study, a 1D model is developed to achieve the fast prediction of droplet ejection process from DOD devices, including the droplet breakup and coalescence. A popular 1D slender-jet method (Egger, 1994) is adopted in this study. The fluid dynamics in the nozzle region is described by a 2D axisymmetric unsteady Poiseuille flow model. Droplet formation and nozzle fluid dynamics are coupled, and hence solved together, to simulate the inkjet droplet ejection. The arbitrary Lagrangian–Eulerian method is employed to solve the governing equations. Numerical methods have been proposed to handle the breakup and coalescence of droplets. The proposed methods are implemented in an in-house developed MATLAB code. A series of validation examples have been carried out to evaluate the accuracy and the robustness of the proposed 1D model. Finally, a case study of the inkjet droplet ejection with different Ohnesorge number (Oh) is presented to demonstrate the capability of the proposed 1D model for DOD inkjet process. Our study has shown that 1D model can significantly reduce the computational time (usually less than one minute) yet with acceptable accuracy, which makes it very useful to explore the large parameter space of inkjet devices in a short amount of time. Full article
(This article belongs to the Special Issue Reduced Order Modeling of Fluid Flows)
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Open AccessArticle Multiscale Stuart-Landau Emulators: Application to Wind-Driven Ocean Gyres
Received: 13 February 2018 / Revised: 27 February 2018 / Accepted: 28 February 2018 / Published: 6 March 2018
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Abstract
The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inverse stochastic modeling techniques introduced in (Chekroun and Kondrashov, (2017),
[...] Read more.
The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inverse stochastic modeling techniques introduced in (Chekroun and Kondrashov, (2017), Chaos, 27), allow for reproducing with high fidelity the main statistical properties of multiscale variability in a coarse-grained eddy-resolving ocean flow. This fully-data-driven approach relies on extraction of frequency-ranked time-dependent coefficients describing the evolution of spatio-temporal DAH modes (DAHMs) in the oceanic flow data. In turn, the time series of these coefficients are efficiently modeled by a family of low-order stochastic differential equations (SDEs) stacked per frequency, involving a fixed set of predictor functions and a small number of model coefficients. These SDEs take the form of stochastic oscillators, identified as multilayer Stuart–Landau models (MSLMs), and their use is justified by relying on the theory of Ruelle–Pollicott resonances. The good modeling skills shown by the resulting DAH-MSLM emulators demonstrates the feasibility of using a network of stochastic oscillators for the modeling of geophysical turbulence. In a certain sense, the original quasiperiodic Landau view of turbulence, with the amendment of the inclusion of stochasticity, may be well suited to describe turbulence. Full article
(This article belongs to the Special Issue Reduced Order Modeling of Fluid Flows)
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