Search

The energy-minimization multiscale (EMMS) principle of compromise in competition is believed to be generally applicable for all mesoscale problems at different levels in the real world, spanning from elementary particles to the universe. This stimulated a fundamental proposition of the concept of mesoscience. This article discusses a potential universality of the regime-specific multi-objective variational feature of this underlying principle through case studies in chemical engineering. It is also elucidated why the currently available variational principles are not applicable to the mesoscale problems. The paper concludes with prospects on future study., Comment: Modified from the version submitted to Chemical Engineering Journal on April 03, 2017

Both discrete and continuum models have been widely used to study rapid granular flow, discrete model is accurate but computationally expensive, whereas continuum model is computationally efficient but its accuracy is doubtful in many situations. Here we propose a hybrid discrete-continuum method to profit from the merits but discard the drawbacks of both discrete and continuum models. Continuum model is used in the regions where it is valid and discrete model is used in the regions where continuum description fails, they are coupled via dynamical exchange of parameters in the overlap regions. Simulation of granular channel flow demonstrates that the proposed hybrid discrete-continuum method is nearly as accurate as discrete model, with much less computational cost.

The closure problem of turbulence is still a challenging issue in turbulence modeling. In this work, a stability condition is used to close turbulence. Specifically, we regard single-phase flow as a mixture of turbulent and non-turbulent fluids, separating the structure of turbulence. Subsequently, according to the picture of the turbulent eddy cascade, the energy contained in turbulent flow is decomposed into different parts and then quantified. A turbulence stability condition, similar to the principle of the energy-minimization multi-scale (EMMS) model for gas-solid systems, is formulated to close the dynamic constraint equations of turbulence, allowing the heterogeneous structural parameters of turbulence to be optimized. We call this model the `EMMS-based turbulence model', and use it to construct the corresponding turbulent viscosity coefficient. To validate the EMMS-based turbulence model, it is used to simulate two classical benchmark problems, lid-driven cavity flow and turbulent flow with forced convection in an empty room. The numerical results show that the EMMS-based turbulence model improves the accuracy of turbulence modeling due to it considers the principle of compromise in competition between viscosity and inertia., Comment: 26 pages, 13 figures, 2 tables

Neither of the two prevalent theories, namely thermodynamic stability and kinetic stability, provides a comprehensive understanding of protein folding. The thermodynamic theory is misleading because it assumes that free energy is the exclusive dominant mechanism of protein folding, and attributes the structural transition from one characteristic state to another to energy barriers. Conversely, the concept of kinetic stability overemphasizes dominant mechanisms that are related to kinetic factors. This article explores the stability condition of protein structures from the viewpoint of meso-science, paying attention to the compromise in the competition between minimum free energy and other dominant mechanisms. Based on our study of complex systems, we propose that protein folding is a meso-scale, dissipative, nonlinear and non-equilibrium process that is dominated by the compromise between free energy and other dominant mechanisms such as environmental factors. Consequently, a protein shows dynamic structures, featuring characteristic states that appear alternately and dynamically, only one of which is the state with minimum free energy. To provide evidence for this concept, we analyzed the time series of energetic and structural changes of three simulations of protein folding/unfolding. Our results indicate that thorough consideration of the multiple dynamic characteristic structures generated by multiple mechanisms may be the key to understanding protein folding., Comment: 19 pages, 9 figures

Discrete particle simulation, a combined approach of computational fluid dynamics and discrete methods such as DEM (Discrete Element Method), DSMC (Direct Simulation Monte Carlo), SPH (Smoothed Particle Hydrodynamics), PIC (Particle-In-Cell), etc., is becoming a practical tool for exploring lab-scale gas-solid systems owing to the fast development of parallel computation. However, gas-solid coupling and the corresponding fluid flow solver remain immature. In this work, we propose a modified lattice Boltzmann approach to consider the effect of both the local solid volume fraction and the local relative velocity between particles and fluid, which is different from the traditional volume-averaged Navier-Stokes equations. A time-driven hard sphere algorithm is combined to simulate the motion of individual particles, in which particles interact with each other via hard-sphere collisions, the collision detection and motion of particles are performed at constant time intervals. The EMMS (energy minimization multi-scale) drag is coupled with the lattice Boltzmann based discrete particle simulation to improve the accuracy. Two typical fluidization processes, namely, a single bubble injection at incipient fluidization and particle clustering in a fast fluidized bed riser, are simulated with this approach, with the results showing a good agreement with published correlations and experimental data. The capability of the approach to capture more detailed and intrinsic characteristics of particle-fluid systems is demonstrated. The method can also be used straightforward with other solid phase solvers., Comment: 15 pages, 11 figures, 2 tables. In Chemical Engineering Science, 2013

The universality of mesoscales, ranging between elemental particles and the universe, is discussed here by reviewing widely disparate fields and presenting four cases, at differing hierarchical levels, from chemistry, chemical engineering, meteorology, through to astronomy. An underpinning concept, "Compromise in competition", is highlighted between various dominant, but competing mechanisms, and is identified here to be the universal origin of complexity and diversity in such examples. We therefore advance this as a key underlying principle of an emerging science -- Mesoscience., Comment: 8 pages, 1 figure

Direct numerical simulation (DNS) for gas-solid flow is implemented on a multi-scale supercomputing system, Mole-8.5, featuring massive parallel GPU-CPU hybrid computing, for which the lattice Boltzmann method (LBM) is deployed together with the immersed moving boundary (IMB) method and discrete element method (DEM). A two-dimensional suspension with about 1,166,400 75-micron solid particles distributed in an area of 11.5cm x46cm, and a three-dimensional suspension with 129,024 solid particles in a domain of 0.384cm x1.512cm x0.384cm are fully-resolved below particle scale and distinct multi-scale heterogeneity are observed. Almost 20-fold speedup is achieved on one Nvidia C2050 GPU over one core of Intel E5520 CPU in double precision, and nearly ideal scalability is maintained when using up to 672 GPUs. The simulations demonstrate that LB-IMB-DEM modeling with parallel GPU computing may suggest a promising approach for exploring the fundamental mechanisms and constitutive laws of complex gas-solid flow, which are, so far, poorly understood in both experiments and theoretical studies., Comment: 12 pages, 7 figures; submitted to Chemical Engineering Science

Molecular dynamics (MD) simulation is a powerful computational tool to study the behavior of macromolecular systems. But many simulations of this field are limited in spatial or temporal scale by the available computational resource. In recent years, graphics processing unit (GPU) provides unprecedented computational power for scientific applications. Many MD algorithms suit with the multithread nature of GPU. In this paper, MD algorithms for macromolecular systems that run entirely on GPU are presented. Compared to the MD simulation with free software GROMACS on a single CPU core, our codes achieve about 10 times speed-up on a single GPU. For validation, we have performed MD simulations of polymer crystallization on GPU, and the results observed perfectly agree with computations on CPU. Therefore, our single GPU codes have already provided an inexpensive alternative for macromolecular simulations on traditional CPU clusters and they can also be used as a basis to develop parallel GPU programs to further speedup the computations., Comment: 21 pages, 16 figures

This short communication analyzes the importance of resolving meso-scale structure in a complex system with multi-scale structure. Similar mechanisms for the formation of meso-scale structures in different systems are found and a general approach to established multi-scale models for such systems is discussed., Comment: 7 pages, 2 figures. Corrected some spelling errors in 3rd version