Analysis and three-dimensional modeling of vanadium flow batteries

This study presents 1.) a multi-dimensional model of vanadium Redox Flow Batteries (RFB); 2.) rigorous explanation of porelevel transport resistance, dilute solution assumption, and pumping power; and 3.) analysis of time constants of heat and mass transfer and dimensionless parameter. The model, describing the dynamic system of a RFB, consists of a set of partial differential equations of mass, momentum, species, charges, and energy conservation, in conjunctionwith the electrode's electrochemical reaction kinetics. The governing equations are successfully implemented into three-dimensional numerical simulation of charging, idling, and discharging operations. The model, validated against experimental data, predicts fluid flow, concentration increase/decrease, temperature contours and local reaction rate. The prediction indicates a large variation in local reaction rate across electrodes and the time constants for reactant variation and temperature evolution, which are consistent with theoretical analysis. © 2014 The Electrochemical Society. All rights reserved

Two-phase flow dynamics in a micro hydrophilic channel: A theoretical and experimental study

In this paper, two-phase flow dynamics in a micro hydrophilic channel are experimentally and theoretically investigated. Flow patterns of annulus, wavy, and slug are observed in the range of operating condition. A set of empirical models based on the Lockhart-Martinelli parameter and a two-fluid model using several correlations of the relative permeability are adopted; and their predictions are compared with experimental data. It shows that for low liquid flow rates most model predictions show acceptable agreement with experimental data, while in the regime of high liquid flow rate only a few of them exhibit a good match. Correlation optimization is conducted for individual flow pattern. Through theoretical analysis of flows in a circular and 2-D channel, respectively, we obtain correlations close to the experimental observation. Real-time pressure measurement shows that different flow patterns yield different pressure evolutions. © 2013 Elsevier Ltd. All rights reserved

Analysis of Air Cathode Perfomance for Lithium-Air Batteries

Lithium-air (Li-air) batteries have a theoretical specific energy comparable to gasolines. The air cathode plays a critical role in battery operation, where oxygen reacts with Li ions and electrons; and discharge products are stored in the pore structure. In major non-aqueous electrolytes, discharge products are insoluble and extremely low in electric conductivity, causing electrode passiviation and raising transport polarization. As discharging proceeds, insoluble materials are deposited at the reaction site and accumulate, increasing voltage loss and eventually shutting down operation. In this work, we present analysis of air cathode performance, taking into account both electrode passivation and transport resistance raised by insoluble products. Both effects are theoretically evaluated and compared. Validation is carried out against experimental data under low currents. The effects of electrode pore structure, such as porosity and tortuosity, on both the influence of insoluble precipitates and discharge capability are investigated. © 2013, The Electrochemical Society, Inc. All rights reserved

Analysis and multi-dimensional modeling of lithium-air batteries

This study contributes to: 1) a multi-dimensional model framework of lithium-air (Li-air) battery, 2) incorporation of mechanisms of insoluble precipitates' impacts, and 3) analysis and discussion on oxygen supply channel for Li-air battery. The model consists of a set of partial differential equations of species and charge conservation, in conjunction with the electrochemical reaction kinetics, and takes into account the two major mechanisms of voltage loss caused by insoluble discharge products: namely, electrode passivation and increased oxygen transport resistance. Two-dimensional (2-D) simulation indicates that the pore space in the cathode electrode is not fully utilized for Li compounds storage, particularly under high discharging current. For selected battery designs, considerable variation of quantities is observed only in the thickness direction. Through analysis, we evaluate the oxygen concentration drop along an oxygen supply channel and relate it to the Damköhler (Da) number, and further explore potential cases that yield oxygen starvation

Preparing effective medical illustrations for publication (Part 2): software processing, drawing and illustration

10.2349/biij.4.2.e12Biomedical Imaging and Intervention Journal4

Quantum Spin Hall and Quantum Anomalous Hall States Realized in Junction Quantum Wells

Both quantum spin Hall and quantum anomalous Hall states are novel states of quantum matter with promising applications. We propose junction quantum wells comprising II-VI, III-V or IV semiconductors as a large class of new materials realizing the quantum spin Hall state. Especially, we find that the bulk band gap for the quantum spin Hall state can be as large as 0.1 eV. Further more, magnetic doping would induce the ferromagnetism in these junction quantum wells due to band edge singularities in the band-inversion regime and to realize the quantum anomalous Hall state.Comment: 5 pages, 4 figure

Influence of convection and biomass burning outflow on tropospheric chemistry over the tropical Pacific

Observations over the tropics from the Pacific Exploratory Mission-Tropics A Experiment are analyzed using a one-dimensional model with an explicit formulation for convective transport. Adopting tropical convective mass fluxes from a general circulation model (GCM) yields a large discrepancy between observed and simulated CH3I concentrations. Observations of CH3I imply the convective mass outflux to be more evenly distributed with altitude over the tropical ocean than suggested by the GCM. We find that using a uniform convective turnover lifetime of 20 days in the upper and middle troposphere enables the model to reproduce CH3I observations. The model reproduces observed concentrations of H2O2 and CH3OOH. Convective transport of CH3OOH from the lower troposphere is estimated to account for 40-80% of CH3OOH concentrations in the upper troposphere. Photolysis of CH3OOH transported by convection more than doubles the primary HOx source and increases OH concentrations and O3 production by 10-50% and 0.4 ppbv d-1, respectively, above 11 km. Its effect on the OH concentration and O3 production integrated over the tropospheric column is, however, small. The effects of pollutant import from biomass burning regions are much more dominant. Using C2H2 as a tracer, we estimate that biomass burning outflow enhances O3 concentrations, O3 production, and concentrations of NOx and OH by 60%, 45%, 75%, and 7%, respectively. The model overestimates HNO3 concentrations by about a factor of 2 above 4 km for the upper one-third quantile of C2H2 data while it generally reproduces HNO3 concentrations for the lower and middle one-third quantiles of C2H2 data. Copyright 2000 by the American Geophysical Union

A new bandwidth adaptive non-local kernel regression algorithm for image/video restoration and its GPU realization

This paper presents a new bandwidth adaptive nonlocal kernel regression (BA-NLKR) algorithm for image and video restoration. NLKR is a recent approach for improving the performance of conventional steering kernel regression (SKR) and local polynomial regression (LPR) in image/video processing. Its bandwidth, which controls the amount of smoothing, however is chosen empirically. The proposed algorithm incorporates the intersecting confidence intervals (ICI) bandwidth selection method into the framework of NLKR to facilitate automatic bandwidth selection so as to achieve better performance. A parallel implementation of the proposed algorithm is also introduced to reduce significantly its computation time. The effectiveness of the proposed algorithm is illustrated by experimental results on both single image and videos super resolution and denoising.published_or_final_versio

Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016