Averaging principle for two time-scale regime-switching processes

This work studies the averaging principle for a fully coupled two time-scale system, whose slow process is a diffusion process and fast process is a purely jumping process on an infinitely countable state space. The ergodicity of the fast process has important impact on the limit system and the averaging principle. We showed that under strongly ergodic condition, the limit system admits a unique solution, and the slow process converges in the L1-norm to the limit system. However, under certain weaker ergodicity condition, the limit system admits a solution, but not necessarily unique, and the slow process can be proved to converge weakly to a solution of the limit system.Comment: 30 page

Numerical simulation of scale-up effects of methanol-to-olefins fluidized bed reactors

Scale-up of fluidized bed reactors has long been regarded as a big challenge in chemical reaction engineering. While traditional scaling theories are mostly based on hydrodynamics similarity, computational fluid dynamics (CFD) aided approach allows direct coupling between hydrodynamics and reaction factors and is expected to speed up the experiment-based scale-up process with lower cost. In this study, we aim to investigate the scale-up effects through simulations of a series of methanol-to-olefins (MTO) reactors of different sizes. The two-fluid model and energy-minimization multi-scale (EMMS)-based drag models, are combined in simulations. The fluidization characteristics in terms of flow structures, velocity distribution, mass fractions of gaseous product and coke distribution are presented against available experimental data for different-sized reactors. It is found that typical hydrodynamic features can be reasonably predicted, while the prediction of reaction behavior shows growing discrepancy with increasing reactor size. Possible reasons are discussed in the last section along with future work presented for scale-up studies. (C) 2017 Elsevier Ltd. All rights reserved

A CRM1 Inhibitor Alleviates Cardiac Hypertrophy and Increases the Nuclear Distribution of NT-PGC-1α in NRVMs

Chromosomal maintenance 1 (CRM1) inhibitors display antihypertrophic effects and control protein trafficking between the nucleus and the cytoplasm. PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1alpha) is a type of transcriptional coactivator that predominantly resides in the nucleus and is downregulated during heart failure. NT-PGC-1α is an alternative splicing variant of PGC-1α that is primarily distributed in the cytoplasm. We hypothesized that the use of a CRM1 inhibitor could shuttle NT-PGC-1α into the nucleus and activate PGC-1α target genes to potentially improve cardiac function in a mouse model of myocardial infarction (MI). We showed that PGC-1α and NT-PGC-1α were decreased in MI-induced heart failure mice. Phenylephrine and angiotensin II were applied to induce hypertrophy in neonatal rat ventricular myocytes (NRVMs). The antihypertrophic effects of the CRM1-inhibitor Selinexor was verified through profiling the expression of β-MHC and through visualizing the cell cross-sectional area. NRVMs were transfected with adenovirus-NT-PGC-1α or adenovirus-NLS (nucleus localization sequence)-NT-PGC-1α and then exposed to Selinexor. Confocal microscopy was then used to observe the shuttling of NT-PGC-1α. After NT-PGC-1α was shuttled into the nucleus, there was increased expression of its related genes, including PPAR-α, Tfam, ERR-γ, CPT1b, PDK4, and Nrf2. The effects of Selinexor on post-MI C57BL/6j mice were determined by echocardiography and qPCR. We found that Selinexor showed antihypertrophic effects but did not influence the ejection fraction of MI-mice. Interestingly, the antihypertrophic effects of Selinexor might be independent of NT-PGC-1α transportation

Chem. Eng. Sci.

Solids residence time distribution (RTD) in circulating fluidized bed risers is a critical parameter for evaluating reactor performances, however, it is still very difficult to be predicted via computational fluid dynamics (CFD) simulation due to the complexity of particle clustering phenomenon. This paper tries to establish an effective CFD model to reasonably predict solids RTD of gas solids riser flows by means of properly addressing the paramount role of particle clusters in determining solids RTD. The gas solids hydrodynamic characteristics were solved by Eulerian-Eulerian model, where an energy minimization multi scale (EMMS) drag model was applied to modify the gas solids drag force to account for the influence of particle clusters. The motion of tracer particles was calculated using species transport equation, where the diffusion coefficient of particles, a vital parameter indicating particle diffusion capacity, was investigated thoroughly. The established CFD model was validated against the available experimental data in the literature It was shown that axial profiles of solids volume fraction and radial profiles of solids mass flux can be well predicted with EMMS drag model, but not with homogeneous drag model. The proper prediction of bed hydrodynamics is also very crucial to the success of solids RTD simulation. On the other hand, the effect of the diffusion coefficient of particles, the magnitude of which can span a range from 10(-5) m(2)/s to 10 m(2)/s, is minor when compared with the convective transport mechanism, at least for the specific cases we studied. In addition, the importance of the sampling time resolution and tracer injection time for a RTD curve was addressed. The simulation results showed that a low time resolution often results in the loss of some micro-scale information, i.e. drastically smoothing the fluctuations of the RTD curve, and an inappropriate assessment of the tracer injection time can lead to a significant change of the RTD curve. (C) 2014 Elsevier Ltd. All rights reserved.Solids residence time distribution (RTD) in circulating fluidized bed risers is a critical parameter for evaluating reactor performances, however, it is still very difficult to be predicted via computational fluid dynamics (CFD) simulation due to the complexity of particle clustering phenomenon. This paper tries to establish an effective CFD model to reasonably predict solids RTD of gas solids riser flows by means of properly addressing the paramount role of particle clusters in determining solids RTD. The gas solids hydrodynamic characteristics were solved by Eulerian-Eulerian model, where an energy minimization multi scale (EMMS) drag model was applied to modify the gas solids drag force to account for the influence of particle clusters. The motion of tracer particles was calculated using species transport equation, where the diffusion coefficient of particles, a vital parameter indicating particle diffusion capacity, was investigated thoroughly. The established CFD model was validated against the available experimental data in the literature It was shown that axial profiles of solids volume fraction and radial profiles of solids mass flux can be well predicted with EMMS drag model, but not with homogeneous drag model. The proper prediction of bed hydrodynamics is also very crucial to the success of solids RTD simulation. On the other hand, the effect of the diffusion coefficient of particles, the magnitude of which can span a range from 10(-5) m(2)/s to 10 m(2)/s, is minor when compared with the convective transport mechanism, at least for the specific cases we studied. In addition, the importance of the sampling time resolution and tracer injection time for a RTD curve was addressed. The simulation results showed that a low time resolution often results in the loss of some micro-scale information, i.e. drastically smoothing the fluctuations of the RTD curve, and an inappropriate assessment of the tracer injection time can lead to a significant change of the RTD curve. (C) 2014 Elsevier Ltd. All rights reserved

Multi-Stream Representation Learning for Pedestrian Trajectory Prediction

Forecasting the future trajectory of pedestrians is an important task in computer vision with a range of applications, from security cameras to autonomous driving. It is very challenging because pedestrians not only move individually across time but also interact spatially, and the spatial and temporal information is deeply coupled with one another in a multi-agent scenario. Learning such complex spatio-temporal correlation is a fundamental issue in pedestrian trajectory prediction. Inspired by the procedure that the hippocampus processes and integrates spatio-temporal information to form memories, we propose a novel multi-stream representation learning module to learn complex spatio-temporal features of pedestrian trajectory. Specifically, we learn temporal, spatial and cross spatio-temporal correlation features in three respective pathways and then adaptively integrate these features with learnable weights by a gated network. Besides, we leverage the sparse attention gate to select informative interactions and correlations brought by complex spatio-temporal modeling and reduce complexity of our model. We evaluate our proposed method on two commonly used datasets, i.e. ETH-UCY and SDD, and the experimental results demonstrate our method achieves the state-of-the-art performance. Code: https://github.com/YuxuanIAIR/MSRL-maste

Speeding up CFD simulation of fluidized bed reactor for MTO by coupling CRE model

The methanol to olefins (MTO) process opens an economical and important route to produce light olefins. The design of MTO reactor borrows ideas from the reaction-regeneration configuration of the modern fluid catalytic cracking (FCC) units. However, their hydrodynamic behaviors are quite different in the sense that the fluidized bed for MTO reactions operates in different flow regime from that of FCC, calling for new modeling for scale-up. In addition, the coke deposited on catalysts greatly affects the MTO reaction while its generation is very slow. It normally takes tens of minutes or even hours for catalysts to reach the desired level of coke content. Time-dependent computational fluid dynamics (CFD) simulation of such a long process poses a big challenge to reactive multiphase flow modeling. To speed up it, we try to integrate the classic chemical reaction engineering (CRE) model with CFD. In particular, the continuous stirred tank reactor (CSTR) model is established to estimate the steady state distribution of coke content, which is then set as the initial distribution for CFD simulation to shorten the time to reach the steady state of reactive flows. Comparison with experimental data shows good agreement and also great speed-up ratio compared to traditional CFD simulation. (C) 2016 Elsevier Ltd. All rights reserved.</p