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Yang, Wen‐Ching

doi:10.1201/9780203912744.bmatt

Cited by 41 publications

(61 citation statements)

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“…With a hierarchical distribution of radii, however, ϕ k are continuous and we use a Metropolis Hastings with simulated annealing technique [4,5]. The use of an augmented Lagrangian allows mass conservation to be enforced without ill-conditioning the random search [6].…”

Section: Pruning To Increase Taylor Dispersion In Physarum Polycephalummentioning

confidence: 99%

“…Vf, 87.16.Wd Transport due to fluid flowing through tubular networks is of great interest, because it has technological applications to biomimetic microfluidic devices [1][2][3], foams [4], fuel cells [5], and other filtration systems [6] and lies at the heart of extended organisms that rely on transport networks to function: animal vasculature [7,8], fungal mycelia [9], and plant tubes [10][11][12]. A big challenge regarding transport networks is to understand how network architecture changes the efficiency of particle spread throughout a network.…”

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confidence: 99%

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Pruning to Increase Taylor Dispersion inPhysarum polycephalumNetworks

et al. 2016

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How do the topology and geometry of a tubular network affect the spread of particles within fluid flows? We investigate patterns of effective dispersion in the hierarchical, biological transport network formed by Physarum polycephalum. We demonstrate that a change in topology -pruning in the foraging state -causes a large increase in effective dispersion throughout the network. By comparison, changes in the hierarchy of tube radii result in smaller and more localized differences. Pruned networks capitalize on Taylor dispersion to increase the dispersion capability.PACS numbers: 87.18. Vf, 87.16.Wd Transport due to fluid flowing through tubular networks is of great interest, because it has technological applications to biomimetic microfluidic devices [1][2][3], foams [4], fuel cells [5], and other filtration systems [6] and lies at the heart of extended organisms that rely on transport networks to function: animal vasculature [7,8], fungal mycelia [9], and plant tubes [10][11][12]. A big challenge regarding transport networks is to understand how network architecture changes the efficiency of particle spread throughout a network. While it is experimentally tedious to map particle transport in a network, predicting the spread of particles is also a theoretical challenge [13][14][15][16][17][18][19][20]. Attempts to understand how the network topology and geometry affect the transport of particles are scarce [17]. Alternatively, we can study the dynamic changes of tubular network architecture in living beings. Organisms spontaneously reorganize their transport networks, including tube pruning [21][22][23][24]. Examples are vessel development in zebra fish brain development [21], or growth of a large foraging fungal body [22]. Here, we study the slime mold Physarum polycephalum which emerged as an inspiring and yet puzzling model for 'intelligent' living transport networks.P. polycephalum like foraging fungi, actively adapts its network to environmental cues [25][26][27][28][29]. Networks connecting multiple food sources are a good compromise between efficiency, reliability, and cost, comparable to human transport networks [29]. Fluid cytoplasm enclosed in the tubular network exhibits nonstationary shuttle flows [30][31][32] driven by a peristaltic wave of contractions spanning the entire organism [33]. Investigations of transport in these networks are so far limited to estimates based on the minimal distance between tubes [29,34,35]. We tracked a well-reticulated individual trimmed from a larger network (Fig. 1). After several hours, the thin central tubes were abandoned in favor of a few large central tubes and globular structures at the periphery. How does this radical change of topology affect the transport capabilities of the individual? What role do hierarchical tube radii play? We present a method to efficiently map the effective dispersion of particles from any initiation site throughout any network with nonstationary but periodic fluid flows. We use this method to study the change in dispersion patterns ...

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Section: Pruning To Increase Taylor Dispersion In Physarum Polycephalummentioning

confidence: 99%

mentioning

confidence: 99%

Pruning to Increase Taylor Dispersion inPhysarum polycephalumNetworks

et al. 2016

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“…The shaded areas in Fig. 4 illustrate ranges of stable operation in the corresponding hydrodynamic regime (Bubbling Fluidization, Fast Fluidization, Conveying (Yang, 2003), and Spouting in conical reactors (Olazar et al, 2004)). U c * and U se * depict the theoretical upper bound of stable stagnant fluidization and the critical velocity of entrainment, respectively.…”

Section: Fluidized Bed Modelmentioning

confidence: 99%

Overview of Chemical-Looping Reduction in Fixed Bed and Fluidized Bed Reactors Focused on Oxygen Carrier Utilization and Reactor Efficiency

Zhou

Han

Bollas

2014

Aerosol Air Qual. Res.

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A model-assisted comparison of two types of chemical-looping (CL) reactors (fixed bed and fluidized bed), with the same oxygen carrier loading and fuel capacity, is carried out to examine performance and efficiency of CL Reducers, operating with methane as the feedstock and nickel oxide as the oxygen carrier. The study focuses on the reduction step of chemical-looping combustion (CLC), for which the reactor efficiency and fuel utilization are crucial in terms of economics and carbon capture efficiency. Process models (a three phase dynamic model for bubbling fluidized beds and a two dimensional homogeneous model for fixed beds) and reaction kinetics developed and validated in previous studies are used. A fluidized bed chemical-looping combustion Reducer is compared to a fixed bed equivalent reactor, scaled-up from a smaller experimental reactor, constrained to bed height to reactor diameter ratios that prohibit excessive temperature and pressure drops across the bed. Through a detailed comparison, CLC operated in the fluidized bed reactor is shown to deliver superior performance, i.e., uniform temperature and pressure distribution; high methane conversion (> 95%) and carbon dioxide selectivity (> 95%) sustained for longer reduction periods; negligible carbon formation (< 2 mol% C basis); and better efficiency in oxygen carrier utilization.

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“…The principle difference between Geldart B and Geldart D fluidization is that the interstitial gas rises slower than the most bubbles for Geldart B powders, but faster than most bubbles in Geldart D powders [11][12][13]. This transition significantly alters bubble dynamics and therefore offers one possible explanation for the trend change in Figure 3.…”

Section: Hydrodynamic Grid Independencementioning

confidence: 99%