Abstract: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 methodolo… Show more
“…Similarly to our recent work in Dabbagh et al, 33 the short‐term non‐reactive simulation (here using the cgTFM), is employed to investigate, a priori the recurrence properties of the system. Namely, the recurrence time step Δ t rec is decided basing on the pseudo‐periodicity of the system (bubbles evolution) at which the flow can almost be repeated.…”
“…Namely, the recurrence time step Δ t rec is decided basing on the pseudo‐periodicity of the system (bubbles evolution) at which the flow can almost be repeated. Following the time histories of active fields such as, and , taken inside the bed at the probe { T 0 } (see Figure 1a), and shown in Figure 2a, the proper determination of Δ t rec reads 0.03 s (≡ 60Δ t ), with, 33 …”
“…This novel methodology has shown a feasible capability to simulate long‐duration processes such as chemical species conversion and heat transfer, for single 30,32 and multiphase flows, 29,33 in a fast and cheap way. For the case of a gas–solid fluidized bed, lab‐scale reactors were successfully investigated in a fast stable and transient heating process 33,34 . Recently, the authors proposed an enhanced version of transport‐based rCFD 33 and employed it for the fast prediction of species and heat propagation on both gas and solid phase.…”
We present a novel approach for the fast modeling of exothermic chemical reactions in industrial‐scale fluidized bed reactors. It implicates a fast olefin polymerization process, accounting for the catalyst activity, the solubility of the reaction gases in polymer, the particles crystallinity, and the reaction masses and heat transfer. We principally apply the transport‐based recurrence computational fluid dynamics (rCFD) model upon the base of a short‐term non‐reactive simulation performed by a coarse‐grained two‐fluid model (cgTFM). Following the captured recurrent flows, the methodology propagates rapidly passive scalars far beyond the recorded simulation. The reaction kinetics of production/consumption rates due to polymerization are locally embedded into the individual solid/gas species concentrations. These in turn are considered in transporting the enthalpy and the generated heat by reaction. By doing so, the significant computational effort required to couple the thermodynamic effects of polymerization with the cgTFM (hybrid model), is drastically reduced using rCFD with very reliable agreement.
“…Similarly to our recent work in Dabbagh et al, 33 the short‐term non‐reactive simulation (here using the cgTFM), is employed to investigate, a priori the recurrence properties of the system. Namely, the recurrence time step Δ t rec is decided basing on the pseudo‐periodicity of the system (bubbles evolution) at which the flow can almost be repeated.…”
“…Namely, the recurrence time step Δ t rec is decided basing on the pseudo‐periodicity of the system (bubbles evolution) at which the flow can almost be repeated. Following the time histories of active fields such as, and , taken inside the bed at the probe { T 0 } (see Figure 1a), and shown in Figure 2a, the proper determination of Δ t rec reads 0.03 s (≡ 60Δ t ), with, 33 …”
“…This novel methodology has shown a feasible capability to simulate long‐duration processes such as chemical species conversion and heat transfer, for single 30,32 and multiphase flows, 29,33 in a fast and cheap way. For the case of a gas–solid fluidized bed, lab‐scale reactors were successfully investigated in a fast stable and transient heating process 33,34 . Recently, the authors proposed an enhanced version of transport‐based rCFD 33 and employed it for the fast prediction of species and heat propagation on both gas and solid phase.…”
We present a novel approach for the fast modeling of exothermic chemical reactions in industrial‐scale fluidized bed reactors. It implicates a fast olefin polymerization process, accounting for the catalyst activity, the solubility of the reaction gases in polymer, the particles crystallinity, and the reaction masses and heat transfer. We principally apply the transport‐based recurrence computational fluid dynamics (rCFD) model upon the base of a short‐term non‐reactive simulation performed by a coarse‐grained two‐fluid model (cgTFM). Following the captured recurrent flows, the methodology propagates rapidly passive scalars far beyond the recorded simulation. The reaction kinetics of production/consumption rates due to polymerization are locally embedded into the individual solid/gas species concentrations. These in turn are considered in transporting the enthalpy and the generated heat by reaction. By doing so, the significant computational effort required to couple the thermodynamic effects of polymerization with the cgTFM (hybrid model), is drastically reduced using rCFD with very reliable agreement.
“…[ 15 ] Further details with regard to the modeling of physical diffusion are given in the literature. [ 18,19 ] At this place, we therefore just present the main modeling features and abstain from a detailed description of the rCFD methodology.…”
Section: Modeling: Rcfd Simulationmentioning
confidence: 99%
“…Although rCFD is still in development, different versions of rCFD have already been applied successfully to turbulent single‐phase flow, [ 15–18 ] bubble columns, [ 12 ] and fluidized beds. [ 14,19,20,21 ]…”
Computational fluid dynamics (CFD) simulations of steel flow in an Rheinsahl–Heraeus (RH) process are realized by a discrete phase model (DPM) for the driving bubble plumes, a volume of fluid (VoF) method for the free surface in the vacuum chamber (VC), and a large eddy simulations (LES) model for the transport and mixing of steel alloys. CFD simulations are opposed to particle image velocimetry (PIV) analyses of flow pattern at the bath surface in the VC. While simple Reynolds averaged turbulence models fail to reproduce these plant observations, LES agrees fairly well. Furthermore, the steel recirculation rate is compared with empirical correlations from the literature, yielding good agreement with respect to the dependency of the recirculation rate on the gas injection rate. The absolute value of the recirculation rate increases by 15%, in case (realistic) eroded edges are considered instead of a (unrealistic) sharp‐edged geometry. Data‐assisted recurrence CFD (rCFD) is applied to accelerate conventional CFD. The rCFD simulations yield a computational speed‐up of four orders of magnitude, enabling real‐time LES at full grid resolution of three million cells. Titanium homogenization in the steel ladle is addressed by means of rCFD and compared with corresponding plant trials yielding good agreement.
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