Efficient time-extrapolation of single- and multiphase simulations by transport based recurrence CFD (rCFD)

Pirker, Stefan; Lichtenegger, T.

doi:10.1016/j.ces.2018.04.059

Cited by 27 publications

(42 citation statements)

References 12 publications

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“…In different than the previous transport‐based rCFD version reported in Reference, the aforementioned methodology implicates physically consistent enhancements regarding the inflow and outflow modelings. Namely, the inflow modeling before was by setting a concentration source next to the inlet and convey it following the internal tracers.…”

Section: Theorymentioning

confidence: 81%

“…In the so‐called transport based rCFD model, the online recurrent flow patterns evolving during τ rec (for each Δ t rec ), and which are needed to generate the flow candidature of the passive process, are considered in a Lagrangian sense. Namely, we describe the methodology as a tracer‐based method that depends on tracking the dynamics, of the gas or solid phase or both, following inertia‐less particles/tracers trajectories produced each Δ t rec .…”

Section: Theorymentioning

confidence: 99%

“…Meaning that, the concentration in the cells where the outlet tracers were located at the beginning of Δ t rec , and before leaving the domain. 5. One step correcting diffusion controlled by the physical global balance between the coming‐in, accumulated, and going‐out mass concentration in the total domain, that is,

\frac{{italicDm}_{γ}}{dt} = \frac{{italicdm}_{γ}}{dt} + \nabla \cdot ((), u_{normals / normalg} γ) = 0 \int \int \Rightarrow m_{γ}^{t + normalΔ t_{rec}} = ((), {\dot{m}}_{γ_{in}} - {\dot{m}}_{γ_{out}}) normalΔ t_{rec} + m_{γ}^{t},

and which can be written as

\sum_{i = 1}^{cells} {(ɛ_{s / g}^{i} γ_{c_{i}} V_{cell})}^{t + normalΔ t_{rec}} = \sum_{i = 1}^{inlet} v_{inlet}^{i} γ_{c_{i}}^{inlet} - \sum_{i = 1}^{outlet} v_{outlet}^{i} γ_{c_{i}}^{outlet} + \sum_{i = 1}^{cells} {(ɛ_{s / g}^{i} γ_{c_{i}} V_{cell})}^{t} .

…”

Section: Theorymentioning

confidence: 99%

“…The time scale simulation remains of small rates in order to capture mathematically all relevant dynamics of collisions; and therefore, the slow processes, which take hours in large fluidized beds, are still inaccessible. Seeking a remedy, some of us have introduced the idea of recurrence CFD (rCFD) for pseudoperiodic flows . Namely, the continuous reappearing (recurrent) structures of such dynamics, for instance, the gas bubbles evolution in bubbling fluidized beds, is considered and analyzed on the base of a short‐term full CFD simulation, that is, CFD‐DEM or TFM.…”

Section: Introductionmentioning

confidence: 99%

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On the fast modeling of species transport in fluidized beds using recurrence computational fluid dynamics

2020

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Due to variety of scale dynamics evolved in gas–solid flows, most of its numerical description is limited to expensive short durations. This has made the slow processes therein, such as the chemical species conversion, to be out of an appropriate reach. In this work, an application of the transport‐based recurrence computational fluid dynamics (CFD) has been introduced for the fast modeling of passive scalar transport, which is considered as species conversion and heat transfer in fluidized beds. The methodology discloses the recurrent dynamics during a short‐term full CFD simulation as Lagrangian shift operations upon which a passive scalar can infinitely be traced. Apart from convecting, a proper approach based on the turbulent kinetic energy of tracked dynamics is introduced for modeling the physical diffusion of the scalar transported. Our outcomes have revealed a subtle chasing to the full CFD species simulation with a speed‐up up to 1,600.

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Section: Theorymentioning

confidence: 81%

Section: Theorymentioning

confidence: 99%

\frac{{italicDm}_{γ}}{dt} = \frac{{italicdm}_{γ}}{dt} + \nabla \cdot ((), u_{normals / normalg} γ) = 0 \int \int \Rightarrow m_{γ}^{t + normalΔ t_{rec}} = ((), {\dot{m}}_{γ_{in}} - {\dot{m}}_{γ_{out}}) normalΔ t_{rec} + m_{γ}^{t},

and which can be written as

\sum_{i = 1}^{cells} {(ɛ_{s / g}^{i} γ_{c_{i}} V_{cell})}^{t + normalΔ t_{rec}} = \sum_{i = 1}^{inlet} v_{inlet}^{i} γ_{c_{i}}^{inlet} - \sum_{i = 1}^{outlet} v_{outlet}^{i} γ_{c_{i}}^{outlet} + \sum_{i = 1}^{cells} {(ɛ_{s / g}^{i} γ_{c_{i}} V_{cell})}^{t} .

…”

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the fast modeling of species transport in fluidized beds using recurrence computational fluid dynamics

2020

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“…CFD‐based compartment models (Delafosse et al, ; Delafosse et al, ) offer a balance between speed and accuracy but require steady flow fields. Recently developed recurrence‐CFD (Lichtenegger & Pirker, ; Pirker & Lichtenegger, ) reconstructs the dynamic evolution of the flow based on recurring patterns. Volume/flow pattern changes in fed‐batch processes cannot (yet) be accounted for; however.…”

Section: Future Prospectmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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Summary and Outlook

Ranade¹,

Utikar²

2022

Multiphase Flows for Process Industries

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Efficient time-extrapolation of single- and multiphase simulations by transport based recurrence CFD (rCFD)

Cited by 27 publications

References 12 publications

On the fast modeling of species transport in fluidized beds using recurrence computational fluid dynamics

On the fast modeling of species transport in fluidized beds using recurrence computational fluid dynamics

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Summary and Outlook

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