Molecular regulation and genetic improvement of seed oil content in Brassica napus L.

Hua, Wei; Liu, Jing; Wang, Hanzhong

doi:10.15302/j-fase-2016107

Cited by 19 publications

(20 citation statements)

References 68 publications

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“…The time taken in this process is much longer than our previous simplified study because the geometry of the U‐valve is much more complex, leading to much more complex trajectories of the particles. This analysis obviously shows that the EMMS‐DPM could provide high‐resolution information to obtain the particle‐scale motion in each component of CFBs, which is much more convenient than various residence time distribution models developed for specific CFD simulations of different operating conditions or flow regimes …”

Section: Resultsmentioning

confidence: 99%

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

Liu

et al. 2019

J Adv Manuf & Process

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The combination of (quasi-)real-time simulation on industrial processes and virtual reality technologies may lead to a new paradigm of research and development in chemical engineering, that is, virtual process engineering (VPE). However, as the main engines of VPE, accurate and efficient simulation methods are still in urgent demand. In this paper, with substantial improvements to a discrete particle method (DPM), namely, energy-minimization multiscale (EMMS)-DPM, such an engine was established for a typical multiphase system, the circulating fluid beds (CFBs).To improve the accuracy, the coupling of particle and fluid flow solvers was updated to be more sensitive to local flow structures, and different drag laws were used for different flow regimes. To speed up the computation, heterogeneous central processing unit (CPU)-graphics processing unit (GPU) computing is developed for solving the fluids and particles. A two-layer domain decomposition method is developed to improve the load balance for real applications with complex geometries. Thus, a computer virtual experiment on a three-dimensional (3D) full-loop CFB has been achieved by simulating 1.27 × 10 11 real particles with 1.27 × 10 8 coarse-grained particles at the speed of 1.5 × 10 7 particle updates per GPU per second on 135 NVIDA K80 GPUs. To our knowledge, this is the largest-scale and highest-performance DPM simulation of a 3D full-loop CFB in terms of the computational particles used. It is a strong indication that VPE can be realized for industrial systems in the near future.K E Y W O R D S discrete particle method, energy-minimization multiscale model, three-dimensional circulating fluidized bed, virtual process engineering

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Section: Resultsmentioning

confidence: 99%

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

Liu

et al. 2019

J Adv Manuf & Process

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“…The third moment, skewness ( s 3 ), measures the degree of asymmetry of the probability distribution function:

s^{3} = \int_{0}^{\infty} {(t - \overset{false¯}{t})}^{3} E ((), t) normald t = \frac{\int_{0}^{\infty} {(t - τ)}^{3} E ((), t) normald t}{σ^{3 / 2}}

…”

Section: Theorymentioning

confidence: 99%

“…Another variable applied to compare several RTD experiments together is the coefficient of variation, C v , that measures the relative spreading of the distribution E ( t ):

C_{v} = \frac{σ}{\overset{false¯}{t}}

…”

Section: Theorymentioning

confidence: 99%

“…The third moment, skewness (s 3 ), measures the degree of asymmetry of the probability distribution function [27,39] :…”

Section: Curvesmentioning

confidence: 99%

“…When most of the tracer moves slower than the average, the distribution will show a tail on the left side. [27] Another variable applied to compare several RTD experiments together is the coefficient of variation, C v , that measures the relative spreading of the distribution E(t) [8,39] :…”

Section: Curvesmentioning

confidence: 99%

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Experimental methods in chemical engineering: Residence time distribution—RTD

2020

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Reactor performance, solids‐(gas)‐mixing, flow through porous media, distillation columns, or through granulators improve as the fluid dynamics approach ideal plug flow. The residence time distribution (RTD) is a diagnostic measure of how close fluid flow approaches ideal conditions. The technique introduces a step change to the inlet concentration—a Dirac‐δ function, Heaviside step function, or a rectangular pulse (bolus)—while high frequency detectors monitor the concentration along the vessel and/or at the exit. The effluent concentration profile spreads due to the variance in the process lines leading to the vessel and at the exit, the detector response, and the system. We quantify how much each of these contributes to the overall variance in a fluidized bed with 9 g of fluid cracking catalyst in 8 mm diameter quartz tubes. The injection variance is lowest for a GC sample loop configuration, compared to a 3‐way valve or 4‐way valve geometry. RTD measurements detect bypassing due to dead zones in vessels and the axial‐dispersion model and continuous stirred‐tank model to characterize deviation from plug flow. However, when the contribution to variance from the ancillary lines and detector is large compared to the system, the uncertainty in the model parameters is high. Research on RTD fundamentals concentrate on boundary conditions while, here, we focus on experimental errors: mechanical, physicochemical mathematical, and instrumental.

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CFD simulation of solids residence time distribution for scaling up gas‐solid bubbling fluidized bed reactors based on the modified structure‐based drag model

et al. 2020

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Solids residence time distribution (RTD), which reflects the degree of solids mixing in bubbling fluidized bed (BFB), has become an essential parameter for the evaluation of reactor performances. In this paper, a modified structure‐based drag model was established to investigate the effect of the bed size and the correlation of bubble dimension on the gas‐solids hydrodynamics and solids RTD in three different scales of BFB. The result obtained from the modified structure‐based drag model shows better agreement with the experimental values and it can be used to predict the RTD properly. The deviation of flow field will cause a large difference in the RTD prediction. An accurate flow field is a prerequisite for calculating the RTD. For the cases of Geldart B particles, the Darton correlation is the best choice for low gas velocities. When scaling up a BFB, the RTD calculated by the modified structure‐based drag model is reduced in comparison with the experimental value, which is related to the increase of the bed size and the excessive estimation of the bubble size. On the other hand, the lack of consideration for the friction on both front and back walls in 2D simulation may lead to the over‐prediction of particle velocity and result in the deviation between calculated and experimental RTD value.

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Molecular regulation and genetic improvement of seed oil content in Brassica napus L.

Cited by 19 publications

References 68 publications

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

Virtual process engineering on a three‐dimensional circulating fluidized bed with multiscale parallel computation

Experimental methods in chemical engineering: Residence time distribution—RTD

CFD simulation of solids residence time distribution for scaling up gas‐solid bubbling fluidized bed reactors based on the modified structure‐based drag model

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