Mouse Anti-Human EMD Monoclonal Antibody

Both equilibrium and nonequilibrium molecular dynamics (EMD and NEMD, respectively) methods have been used to predict the thermal conductivity of nanofluids. However, there are considerable discrepancies among the results of these two methods. In this study, by estimating the effects of different mechanisms including the Brownian motion of nanoparticles, the micro-convection in the base fluid, the nanolayers around the nanoparticles, and the thermal boundary resistance at the surface of nanoparticle, we determine upper and lower physical limits for the thermal conductivity of a nanofluid with spherical nanoparticles. The prediction of the NEMD simulations is in the acceptable range, while the result of the EMD simulations is higher than the upper bound. Since the prediction of the EMD method is not physically justifiable, we conclude the inadequacy of the traditional EMD method in calculating the thermal conductivity of nanofluids. Consequently, we recommend the researchers to use a modified version of the EMD method or the NEMD method for new studies in this field. We also apply the NEMD method to investigate the effects of the shape of nanoparticles and the formation of percolation networks in enhancing the thermal conductivity of nanofluids. The interference of the effects of nanolayer and thermal boundary resistance on the thermal conductivity of nanofluids is a new phenomenon we introduce in this study. [GRAPHICS] .

This work presents a hybrid model that combines support vector regression (SVR), empirical mode decomposition (EMD), the krill herd (KH) algorithm and a chaotic mapping function. EMD is used to decompose input time series data into components with several intrinsic mode functions (IMFs) and one residual, to capture the trends in the input data. SVR is used to forecast separately IMFs and the residual owing to its effectiveness in solving nonlinear regression and time series problems. The KH algorithm is used to select the parameters in the SVR models. The Tent chaotic mapping function is hybridized with the KH algorithm to prevent premature convergence and increase the accuracy of the whole model. Two real-world datasets from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are used to demonstrate the performance of the proposed EMD-SVRCKH model. The experimental results reveal that the proposed model provides competitive advantages over other models and offers greater forecasting accuracy. (C) 2020 Elsevier B.V. All rights reserved.