@inproceedings{li_kleinstreuer_feng_2012, title={Computational analysis of thermal performance and entropy generation of nanofluid flow in microchannels}, booktitle={Proceedings of the ASME Micro/Nanoscale Heat and Mass Transfer International Conference, 2012}, author={Li, J. and Kleinstreuer, C. and Feng, Y.}, year={2012}, pages={135–144} }
 @article{li_sheeran_kleinstreuer_2011, title={Analysis of Multi-Layer Immiscible Fluid Flow in a Microchannel}, volume={133}, ISSN={["1528-901X"]}, DOI={10.1115/1.4005134}, abstractNote={The development of microfluidics platforms in recent years has led to an increase in the number of applications involving the flow of multiple immiscible layers of viscous electrolyte fluids. In this study, numerical results as well as analytic equations for velocity and shear stress profiles were derived for N layers with known viscosities, assuming steady laminar flow in a microchannel driven by pressure and/or electro-static (Coulomb) forces. Numerical simulation results, using a commercial software package, match analytical results for fully-developed flow. Entrance flow effects with centered fluid-layer shrinking were studied as well. Specifically, cases with larger viscosities in the inner layers show a very good agreement with experimental correlations for the dimensionless entrance length as a function of inlet Reynolds number. However, significant deviations may occur for multilayer flows with smaller viscosities in the inner layers. A correlation was deduced for the two-layer electroosmotic flow and the pressure driven flow, both being more complex when compared with single-layer flows. The impact of using power-law fluids on resulting velocity profiles has also been explored and compared to Newtonian fluid flows. The present model readily allows for an exploration of the impact of design choices on velocity profiles, shear stress, and channel distribution in multilayer microchannel flows as a function of layered viscosity distribution and type of driving force.}, number={11}, journal={JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME}, author={Li, Jie and Sheeran, Paul S. and Kleinstreuer, Clement}, year={2011}, month={Nov} }
 @article{li_kleinstreuer_2009, title={Microfluidics analysis of nanoparticle mixing in a microchannel system}, volume={6}, ISSN={["1613-4990"]}, DOI={10.1007/s10404-008-0341-1}, number={5}, journal={MICROFLUIDICS AND NANOFLUIDICS}, author={Li, Jie and Kleinstreuer, Clement}, year={2009}, month={May}, pages={661–668} }
 @article{kleinstreuer_li_2008, title={Discussion: "Effects of various parameters on nanotluid thermal conductivity" ( Jang, S.P., and Choi, S.D.S., 2007, ASME J. heat transfer, 129, pp. 617-623)}, volume={130}, ISSN={["1528-8943"]}, DOI={10.1115/1.2812307}, abstractNote={In a series of articles, Jang and Choi (123) listed and explained their effective thermal conductivity (keff) model for nanofluids. For example, in the 2004 article (1), they constructed a keff correlation for dilute liquid suspensions interestingly, based on kinetic gas theory as well as nanosize boundary-layer theory, the Kapitza resistance, and nanoparticle-induced convection. Three mechanisms contributing to keff were summed up, i.e., base-fluid and nanoparticle conductions as well as convection due to random motion of the liquid molecules. Thus, after an order-of-magnitude analysis, their effective thermal conductivity model of nanofluids reads1keff=kf(1−φ)+knanoφ+3C1dfdpkfRedp2Prφwhere kf is the thermal conductivity of the base fluid, φ is the particle volume fraction, knano=kpβ is the thermal conductivity of suspended nanoparticles involving the Kapitza resistance, C1=6×106 is a constant (never explained or justified), df and dp are the diameters of the base-fluid molecules and nanoparticles, respectively, Redp is a “random” Reynolds number, and Pr is the Prandtl number. Specifically,2Redp=C¯RMdpνwhere C¯RM is a random motion velocity and ν is the kinematic viscosity of the base fluid.Jang and Choi (3) claimed that they were the first to propose Brownian motion induced nanoconvection as a key nanoscale mechanism governing the thermal behavior of nanofluids. However, they just added a random term to Eq. 1, actually quite small in magnitude for certain base liquids, although enhanced by the large factor 3 C1=18×106, while, independently, in the same year, Koo and Kleinstreuer (4) proposed their effective thermal conductivity model, based on micromixing induced by Brownian motion, followed by Prasher et. al. (5) and others (see review by Jang and Choi (3)).However, it should be noted that the validity of the different origins for the unusual thermal effect of nanofluids has been questioned (see Evans et. al. (6) and Vladkov and Barrat (7), among others) as well as the actual keff increase as reported in experimental papers (see Venerus et. al. (8) and Putnam et. al. (9), among others). Controversies arose from using different experimental techniques (e.g., transient hot wire versus optical methods) and from phenomenological models relying more on empirical correlations rather than sound physics and benchmark experimental data.In 2006, Jang and Choi (2) changed the thermal conductivity correlation slightly to3keff=kf(1−φ)+kpβφ+3C1dfdpkfRedp2Prwhere the volume fraction term φ is now missing in the last term. Most recently, Jang and Choi (3) tried to explain more clearly the modeling terms they had proposed. Their present thermal conductivity model reads4keff=kf(1−φ)+kpβφ+3C1dfdpkfRedp2PrφIn the 2006 article (2), the random motion velocity, which is used to define the Reynolds number (see Eq. 2), was defined as5aC¯RM=2D0lfwhile in the 2007 article (3), the authors changed the random motion velocity to5bC¯RM=D0lfwhere D0=κbT∕3πμdp is the diffusion coefficient given by Einstein (10), and lf is the mean free path of the (liquid) base fluid. The mean free path of the base fluid is calculated from Kittel and Kroemer (11), which deals only with transport properties of ideal gases (see their Chap. 14):6alf=3kfc¯ĈVwhere c¯ is the mean molecular velocity, and ĈV is the heat capacity per unit volume. Although Eq. 6a is certainly not applicable to liquids, the mean free path for (ideal) gases can also be written as6blf=3kfρcvc¯with cv being the thermal capacity at constant volume, where ρcv≡ĈV.According to the parameters Jang and Choi (3) provided and the terms they explained, the effective thermal conductivities of CuO-water and Al2O3-water nanofluids were calculated and compared. Figures12 provide comparisons of Jang and Choi’s 2007 model (3) with the experimental data sets of Lee et. al. (12) for CuO-water and Al2O3-water nanofluids, respectively. Two random motion velocities C¯RM were compared, where the dashed line relates to Eq. 5a while the solid line is based on Eq. 5b. Clearly, these comparisons do not match the results given by Jang and Choi (3) in their Fig. 2, unless new matching coefficients in the third term of Eq. 4 are applied. Specifically, the first two terms contribute very little, i.e., ∑i=12termi∕kf≈0.99. Is the contribution of the particle’s thermal conductivity really that small? Many researchers indicated that the higher thermal conductivity of the nanoparticles is a factor in enhancing the effective thermal conductivity (Hong et. al. (13), Hwang et. al. (14)). It has to be stressed that all the data comparisons are based on the thermal properties provided by Jang and Choi (3) in Table 1. However, thermal conductivity values found in the literature indicated 32.9W∕mK for CuO (Wang et. al. (15)) and, for Al2O3, a range of 18–35W∕mK depending on the purity, i.e., 94–99.5%.1 When using the more reasonable particle thermal conductivity values in the model of Jang and Choi (3), only small differences were observed.Now, in contrast to water, if the base fluid is changed to ethylene glycol (EG), the third term in Eq. 4 is suddenly of the order of 10−6, i.e., it does not contribute to the effective thermal conductivity when compared to the first two terms (10−1 and 10−3). The nondimensionalized effective thermal conductivity of CuO-EG nanofluids is about 0.99 for all volume fraction cases, while for Al2O3-EG nanofluids, keff∕kf is slightly higher at approximately 1.015. Both graphs are well below the experimental data of Lee et. al. (12), as shown in Fig.3. The larger EG viscosity provided a much smaller Reynolds number, which almost eliminates the third term.For the experimental result of Das et. al. (16), Jang and Choi (3) compared their model for Al2O3 particles with a volume fraction of 1% in their Fig. 7. Considering the temperature influence on the thermal characteristics of base fluid (water), Fig.4 provides again an updated comparison. If we consider C¯RM=2D0∕lf, indicated with the dashed curve, the model shows a good agreement in the lower temperature range; however, the model prediction fails when the temperature is higher than 40°C. Figure5 shows the comparison of Jang and Choi’s model with the experimental data of Das et. al. (16) when the volume fraction is 4%. Clearly, their model does not match the experimental results well.On June 14, 2007, Choi responded to the analysis presented so far. Specifically, he provided the following new information: Number-weighted diameters (24.4nm for Al2O3 and 18.6nm for CuO) were replaced with the area-weighted diameters (38.4nm for Al2O3 and 23.6nm for CuO),the random motion velocity C¯RM=D0∕lf was selected, andnew proportionality constants, i.e., C1=7.2×107 for water and C1=3.2×1011 for EG, were recommended.Thus, employing the new information, Figs.67 now replace Figs. 345, respectively. The Jang and Choi (3) model achieved a good match with the new numerical values for CuO-water nanofluids and Al2O3-water nanofluids (not shown). However, when using EG-based nanofluids, the model still cannot provide a good match even for the very large proportionality constant of C1=3.2×1011 (see Fig. 6). When compared with the experimental data of Das et. al. (16), as shown in Fig. 7 for a volume fraction of 1%, the model generates a decent data match, which is not the case when the volume fraction reaches 4%.}, number={2}, journal={JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME}, author={Kleinstreuer, C. and Li, Jie}, year={2008}, month={Feb} }
 @article{kleinstreuer_li_koo_2008, title={Microfluidics of nano-drug delivery}, volume={51}, ISSN={["1879-2189"]}, DOI={10.1016/j.ijheatmasstransfer.2008.04.043}, abstractNote={After a brief review of microfluidics, a bio-MEMS application in terms of nanofluid flow in microchannels is presented. Specifically, the transient 3-D problem of controlled nano-drug delivery in a heated microchannel has been numerically solved to gain new physical insight and to determine suitable geometric and operational system parameters. Computer model accuracy was verified via numerical tests and comparisons with benchmark experimental data sets. The overall design goals of near-uniform nano-drug concentration at the microchannel exit plane and desired mixture fluid temperature were achieved with computer experiments considering different microchannel lengths, nanoparticle diameters, channel flow rates, wall heat flux areas, and nanofluid supply rates. Such micro-systems, featuring controlled transport processes for optimal nano-drug delivery, are important in laboratory-testing of predecessors of implantable smart devices as well as for analyzing pharmaceuticals and performing biomedical precision tasks.}, number={23-24}, journal={INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER}, author={Kleinstreuer, Clement and Li, Jie and Koo, Junemo}, year={2008}, month={Nov}, pages={5590–5597} }
 @article{li_kleinstreuer_2008, title={Thermal performance of nanofluid flow in microchannels}, volume={29}, ISSN={["1879-2278"]}, DOI={10.1016/j.ijheatfluidflow.2008.01.005}, abstractNote={Two effective thermal conductivity models for nanofluids were compared in detail, where the new KKL (Koo–Kleinstreuer–Li) model, based on Brownian motion induced micro-mixing, achieved good agreements with the currently available experimental data sets. Employing the commercial Navier–Stokes solver CFX-10 (Ansys Inc., Canonsburg, PA) and user-supplied pre- and post-processing software, the thermal performance of nanofluid flow in a trapezoidal microchannel was analyzed using pure water as well as a nanofluid, i.e., CuO–water, with volume fractions of 1% and 4% CuO-particles with dp = 28.6 nm. The results show that nanofluids do measurably enhance the thermal performance of microchannel mixture flow with a small increase in pumping power. Specifically, the thermal performance increases with volume fraction; but, the extra pressure drop, or pumping power, will somewhat decrease the beneficial effects. Microchannel heat sinks with nanofluids are expected to be good candidates for the next generation of cooling devices.}, number={4}, journal={INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW}, author={Li, Jie and Kleinstreuer, Clement}, year={2008}, month={Aug}, pages={1221–1232} }
 @article{zheng_basciano_li_kuznetsov_2007, title={Fluid dynamics of cell cytokinesis - Numerical analysis of intracellular flow during cell division}, volume={34}, ISSN={["1879-0178"]}, DOI={10.1016/j.icheatmasstransfer.2006.09.005}, abstractNote={Intracellular flow of cytoplasmic fluid during cell cytokinesis is investigated. The intercellular bridge connecting two daughter cells is modeled as a cylindrical microchannel whose squeezing causes cytoplasmic flow inside the bridge itself and into the daughter cells. An equation from recent experimental measurements by Zhang and Robinson [W. Zhang, D.N. Robinson, Balance of actively generated contractile and resistive forces controls cytokinesis dynamics, Proceedings of the National Academy of Sciences of the United States of America 102 (2005) 7186–7191.] that governs the dynamics of bridge thinning is implemented in this model. The purpose of this research is to compute intracellular flow induced by the bridge thinning process. Two different types of boundary conditions are compared at the membrane–cytoplasm interface; these are a no-slip condition and a no tangential stress condition. Pressure and flow velocity distributions in the daughter cells and the force exerted by this flow on the daughter cell nucleus are computed. It is established that the pressure difference between the daughter cell and the intercellular bridge increases as time progresses. It is also observed that a region of stagnation develops on the downstream side of the nucleus as the bridge thins.}, number={1}, journal={INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER}, author={Zheng, F. and Basciano, C. and Li, J. and Kuznetsov, A. V.}, year={2007}, month={Jan}, pages={1–7} }
 @inbook{kleinstreuer_li_feng, title={Computational analysis of enhanced cooling performance and pressure drop for nanofluid flow
in microchannels}, ISBN={9781439861929}, booktitle={Advanced in numerical heat transfer}, publisher={Boca Raton: CRC Press/Taylor & Francis Group}, author={Kleinstreuer, C. and Li, J. and Feng, Y.}, editor={W.J. Minkowycz, E. M. and Sparrow and Abraham, J. P.Editors}, pages={250–273} }