Fuzzy model identification and self learning with smooth compositions

This Paper Develops A Smooth Model Identification And Self-Learning Strategy For Dynamic Systems Taking Into Account Possible Parameter Variations And Uncertainties. We Have Tried To Solve The Problem Such That The Model Follows The Changes And Variations In The System On A Continuous And Smooth Surface. Running The Model To Adaptively Gain The Optimum Values Of The Parameters On A Smooth Surface Would Facilitate Further Improvements In The Application Of Other Derivative Based Optimization Control Algorithms Such As Mpc Or Robust Control Algorithms To Achieve A Combined Modeling-Control Scheme. Compared To The Earlier Works On The Smooth Fuzzy Modeling Structures, We Could Reach A Desired Trade-Off Between The Model Optimality And The Computational Load. The Proposed Method Has Been Evaluated On A Test Problem As Well As The Non-Linear Dynamic Of A Chemical Process.This publication was supported in part by project MINECO, TEC2017-88048-C2-2-

Enhancement of heat transfer of nanofluids in the presence of sinusoidal side obstacles between two parallel plates through the lattice Boltzmann method

In the present study, heat transfer by two-dimensional incompressible nanofluids around four sinusoidal side obstacles in a horizontal channel is numerically analyzed via the lattice Boltzmann method (LBM). The liquid in the channel is a water containing copper nano-sized particles. The viscosity and effective thermal conductivity of the nanoliquid are respectively defined through the Brinkman and Patel approaches. Analysis is run for various magnitudes of the Reynolds number (Re = 100, 200, 400) and nano-sized particles concentration ( = 0, 0.03, 0.05) at different non-dimensional amplitudes (A = 0, 10, 20) of the wavy wall of the sinusoidal obstacles. The effects of changing the distance between the obstacles regarding their arrangement inside the channel are also investigated. The obtained data are presented with reference to the streamlines, temperature and Nusselt numbers profiles. The results indicate that, as the magnitude of the nano-sized particles concentration increases and the distance between the obstacles decreases, the mean Nusselt number rises. In addition, decreasing in the wavy border amplitude results to the thermal transmission enhancement

Heat transfer enhancement inside channel by using the Lattice Boltzmann Method

International audienceIn this study, the Lattice Boltzmann Method (LBM) is employed in order to examine the fluid flow and forced convection heat transfer inside a two-dimensional horizontal channel with and without obstacles. In order to enhance the heat and thermal energy transfer within the channel, different obstacle arrangements are posed to the flow field and heat transfer with the purpose of studying their sensitivity to these changes. The results indicate that, when the value of the Reynolds number is maximum, the maximum average Nusselt numbers happens on the lower wall (Case 4). The paper extends the topic to the use of nanofluids to introduce a possibility to enhancement of the heat transfer in the channel with an array of the obstacles with forced convection. For this purpose, the AgMgO/water micropolar hybrid nanofluid is used, and the volume fraction of the nanoparticle (50% Ag and 50% MgO by volume) is set between 0 and 0.02. The results showed that, when the hybrid nanofluid is used instead of a typical nanofluid, the rate of the heat transfer inside the channel increases, especially for the high values of the Reynolds number, and the volume fraction of the nanoparticles. Increasing the volume fraction of the nanoparticles increase the local Nusselt number ( 1.17-fold). It is shown that the type of obstacle arrangement and the specific nanofluid can exerts significant effects on the characteristics of the flow field and heat transfer in the channel. This study provides a platform for using the LBM to examine fluid flow through discrete obstacles in offset positions