Prediction of Nanofluid Temperature Inside the Cavity by Integration of Grid Partition Clustering Categorization of a Learning Structure with the Fuzzy System.
ABSTRACT: In this study, a quadratic cavity is simulated using computational fluid dynamics (CFD). The simulated cavity includes nanofluids containing copper (Cu) nanoparticles. The L-shaped thermal element exists in this cavity to produce heat distribution along with the domain. Results such as fluid velocity distribution in two dimensions and the fluid temperature field were generated as CFD simulation results. These outputs were evaluated using an adaptive neuro-fuzzy inference system (ANFIS) for learning and then the prediction process. In the training process related to the ANFIS method, x coordinates, y coordinates, and fluid temperature are three inputs, and the fluid velocity in line with Y is the output. During the learning process, the data have been classified using a clustering method called grid clustering. In line with the attempt to rise ANFIS intelligence, the alterations in the number of input parameters and of membership structure have been analyzed. After reaching the highest level of intelligence, the fluid velocity nodes were predicted to be in line with y, especially cavity nodes, which were absent in CFD simulations. The simulation findings indicated that there is a good agreement between CFD and clustering approach, while the total simulation time for learning and prediction is shorter than the time needed for calculation using the CFD method.
Project description:This investigation is conducted to study the integration of the artificial intelligence (AI) method with computational fluid dynamics (CFD). The case study is hydrodynamic and heat-transfer analyses of water flow in a metal foam tube under a constant wall heat flux (i.e., 55 kW/m<sup>2</sup>). The adaptive network-based fuzzy inference system (ANFIS) is an AI method. A 3D CFD model is established in ANSYS-FLUENT software. The velocity of the fluid in the x-direction (Ux) is considered as an output of the ANFIS. The x, y, and z coordinates of the node's location are added to the ANFIS step-by-step to achieve the best intelligence. The number and type of membership functions (MFs) are changed in each step. The training process is done by the CFD results on the tube cross-sections at different lengths (i.e., <i>z</i> = 0.1, 0.2, 0.3, 0.4, 0.6, 0.7, 0.8, and 0.9), while all data (including <i>z</i> = 0.5) are selected for the testing process. The results showed that the ANFIS reaches the best intelligence with all three inputs, five MFs, and "<i>gbellmf</i>"-type MF. At this condition, the regression number is close to 1.
Project description:The insertion of porous metal media inside the pipes and channels has already shown a significant heat transfer enhancement by experimental and numerical studies. Porous media could make a mixing flow and small-scale eddies. Therefore, the turbulence parameters are attractive in such cases. The computational fluid dynamics (CFD) approach can predict the turbulence parameters using the turbulence models. However, the CFD is unable to find the relation of the turbulence parameters to the boundary conditions. The artificial intelligence (AI) has shown potential in combination with the CFD to build high-performance predictive models. This study is aimed to establish a new AI algorithm to capture the patterns of the CFD results by changing the system's boundary conditions. The ant colony optimization-based fuzzy inference system (ACOFIS) method is used for the first time to reduce time and computational effort needed in the CFD simulation. This investigation is done on turbulent forced convection of water through an aluminum metal foam tube under constant wall heat flux. The ANSYS-FLUENT CFD software is used for the simulations. The x and y of the fluid nodal locations, inlet temperature, velocity, and turbulent kinetic energy (TKE) are the inputs of the ACOFIS to predict turbulence eddy dissipation (TED) as the output. The results revealed that for the best intelligence of the ACOFIS, the number of inputs, the number of ants, the number of membership functions (MFs) and the rule are 5, 10, 93 and 93, respectively. Further comparison is made with the adaptive network-based fuzzy inference system (ANFIS). The coefficient of determination for both methods was close to 1. The ANFIS showed more learning and prediction times (785 s and 10 s, respectively) than the ACOFIS (556 s and 3 s, respectively). Finding the member function versus the inputs, the value of TED is calculated without the CFD modeling. So, solving the complicated equations by the CFD is replaced with a simple correlation.
Project description:A combination of a fuzzy inference system (FIS) and a differential evolution (DE) algorithm, known as the differential evolution-based fuzzy inference system (DEFIS), is developed for the prediction of natural heat transfer in Cu-water nanofluid within a cavity. In the development of the hybrid model, the DE algorithm is used for the training process of FIS. For this purpose, first, the case study is simulated using the computational fluid dynamic (CFD) method. The CFD outputs, including velocity in the y-direction, the temperature of the nanofluid, and the nanoparticle content (Ø), are employed for the learning process of the DEFIS model. By choosing the optimum number of inputs and the number of population, the underlying DEFIS variable parameters are studied. After reaching the high value of DEFIS intelligence, in the learning step, a variety of Ø values (e.g., 0.5, 1, and 2) are reviewed. For the full intelligence of DEFIS, the velocity of the nanofluid is predicted in further nodes of the cavity domain. Finally, the velocity of the nanofluid is predicted by using the data at Ø = 0.15, which are absent in the DEFIS process.
Project description:To use computational fluid dynamics (CFD) technology to help providers understand (1) how septal perforations may alter nasal physiology and (2) how these alterations are influenced by perforation size and location.Computer simulation study.Facial plastic and reconstructive surgery clinic.With the aid of medical imaging and modeling software, septal perforations of 1 and 2 cm in anterior, posterior, and superior locations were virtually created in a nasal cavity digital model. The CFD techniques were used to analyze airflow, nasal resistance, air conditioning, and wall shear stress.Bilateral nasal resistance was not significantly altered by a septal perforation. Airflow allocation changed, with more air flowing through the lower-resistance nasal cavity. This effect was greater for anterior and posterior perforations than for the superior location. At the perforation sites, there was less localized heat and moisture flux and wall shear stress in superior perforations compared with those in anterior or posterior locations. For anterior perforations, a larger size produced higher wall shear and velocity, whereas in posterior perforations, a smaller size produced higher wall shear and velocity.Septal perforations may alter nasal physiology. In the subject studied, airflow allocation to each side was changed as air was shunted through the perforation to the lower-resistance nasal cavity. Anterior and posterior perforations caused larger effects than those in a superior location. Increasing the size of anterior perforations and decreasing the size of posterior perforations enhanced alterations in wall shear and velocity at the perforation.
Project description:The hydrodynamics and heat transfer of cylindrical gas-solid fluidized beds for polyolefin production was investigated with the two-fluid model (TFM) based on the kinetic theory of granular flow (KTGF). It was found that the fluidized bed becomes more isothermal with increasing superficial gas velocity. This is mainly due to the increase of solids circulation and improvement in gas solid contact. It was also found that the average Nusselt number weakly depends on the gas velocity. The TFM results were qualitatively compared with simulation results of computational fluid dynamics combined with the discrete element model (CFD-DEM). The TFM results were in very good agreement with the CFD-DEM outcomes, so the TFM can be a reliable source for further investigations of fluidized beds especially large lab-scale reactors.
Project description:Abnormal fluid dynamics at the ascending aorta may be at the origin of aortic aneurysms. This study was aimed at comparing the performance of computational fluid dynamics (CFD) and fluid-structure interaction (FSI) simulations against four-dimensional (4D) flow magnetic resonance imaging (MRI) data; and to assess the capacity of advanced fluid dynamics markers to stratify aneurysm progression risk. Eight Marfan syndrome (MFS) patients, four with stable and four with dilating aneurysms of the proximal aorta, and four healthy controls were studied. FSI and CFD simulations were performed with MRI-derived geometry, inlet velocity field and Young's modulus. Flow displacement, jet angle and maximum velocity evaluated from FSI and CFD simulations were compared to 4D flow MRI data. A dimensionless parameter, the shear stress ratio (SSR), was evaluated from FSI and CFD simulations and assessed as potential correlate of aneurysm progression. FSI simulations successfully matched MRI data regarding descending to ascending aorta flow rates (R 2 = 0.92) and pulse wave velocity (R 2 = 0.99). Compared to CFD, FSI simulations showed significantly lower percentage errors in ascending and descending aorta in flow displacement (-46% ascending, -41% descending), jet angle (-28% ascending, -50% descending) and maximum velocity (-37% ascending, -34% descending) with respect to 4D flow MRI. FSI- but not CFD-derived SSR differentiated between stable and dilating MFS patients. Fluid dynamic simulations of the thoracic aorta require fluid-solid interaction to properly reproduce complex haemodynamics. FSI- but not CFD-derived SSR could help stratifying MFS patients.
Project description:Apartment complexes in various forms are built in downtown areas. The arrangement of an apartment complex has great influence on the wind flow inside it. There are issues of residents' walking due to gust occurrence within apartment complexes, problems with pollutant emission due to airflow congestion, and heat island and cool island phenomena in apartment complexes. Currently, the forms of internal arrangements of apartment complexes are divided into the flat type and the tower type. In the present study, a wind tunnel experiment and computational fluid dynamics (CFD) simulation were performed with respect to internal wind flows in different apartment arrangement forms. Findings of the wind tunnel experiment showed that the internal form and arrangement of an apartment complex had significant influence on its internal airflow. The wind velocity of the buildings increased by 80% at maximum due to the proximity effects between the buildings. The CFD simulation for relaxing such wind flows indicated that the wind velocity reduced by 40% or more at maximum when the paths between the lateral sides of the buildings were extended.
Project description:Computational fluid dynamic (CFD) simulation is a powerful tool in the design and implementation of microfluidic systems, especially for systems that involve hydrodynamic behavior of objects such as functionalized microspheres, biological cells, or biopolymers in complex structures. In this work, we investigate hydrodynamic trapping of microspheres in a novel microfluidic particle-trap array device by finite element simulations. The accuracy of the time-dependent simulation of a microsphere's motion towards the traps is validated by our experimental results. Based on the simulation, we study the fluid velocity field, pressure field, and force and stress on the microsphere in the device. We further explore the trap array's geometric parameters and critical fluid velocity, which affect the microsphere's hydrodynamic trapping. The information is valuable for designing microfluidic devices and guiding experimental operation. Besides, we provide guidelines on the simulation set-up and release an openly available implementation of our simulation in one of the popular FEM softwares, COMSOL Multiphysics. Researchers may tailor the model to simulate similar microfluidic systems that may accommodate a variety of structured particles. Therefore, the simulation will be of particular interest to biomedical research involving cell or bead transport and migration, blood flow within microvessels, and drug delivery.
Project description:Here, we report on computational fluid dynamics (CFD) simulations conducted to develop a chemical sample collection device inspired by crayfish. The sensitivity of chemical sensors can be improved when used with a sniffing device. By collecting fluid samples from the surroundings, all solute species are also collected for the sensor. Crayfish generate jet-like water currents for this purpose. Compared to simply sucking water, food smells dissolved in the surrounding water can be more efficiently collected using the inflow induced by the jet discharge because of the smaller decay of the inflow velocity with the distance. Moreover, the angular range of water sample collection can be adjusted by changing the directions of the jet discharge. In our previous work, a chemical sample collection device that mimics the jet discharge of crayfish has been proposed. Here, we report CFD simulations of the flow fields generated by the device. By carefully configuring the simulation setups, we have obtained simulation results in which the angular ranges of the chemical sample collection in real experiments is well reproduced. Although there are still some discrepancies between the simulation and experimental results, such simulations will facilitate the process of designing such devices.
Project description:We report a computational fluid dynamics-discrete element method (CFD-DEM) simulation study on the interplay between mass transfer and a heterogeneous catalyzed chemical reaction in cocurrent gas-particle flows as encountered in risers. Slip velocity, axial gas dispersion, gas bypassing, and particle mixing phenomena have been evaluated under riser flow conditions to study the complex system behavior in detail. The most important factors are found to be directly related to particle cluster formation. Low air-to-solids flux ratios lead to more heterogeneous systems, where the cluster formation is more pronounced and mass transfer more influenced. Falling clusters can be partially circumvented by the gas phase, which therefore does not fully interact with the cluster particles, leading to poor gas-solid contact efficiencies. Cluster gas-solid contact efficiencies are quantified at several gas superficial velocities, reaction rates, and dilution factors in order to gain more insight regarding the influence of clustering phenomena on the performance of riser reactors.