Examining rheological behavior of MWCNT-TiO2/5W40 hybrid nanofluid based on experiments and RSM/ANN modeling

Chu, Yu‐Ming; Ibrahim, Muhammad; Saeed, Tareq; Berrouk, Abdallah S.; Algehyne, Ebrahem A.; Kalbasi, Rasool

doi:10.1016/j.molliq.2021.115969

Cited by 90 publications

(11 citation statements)

References 54 publications

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“…The blade height at the inlet and outlet of the nozzle is assumed to be equal to the rotor inlet blade height as given by Equation (35).…”

Section: Nozzle Geometrymentioning

confidence: 99%

“…All the hyperparameters connected with the deep neural network (DNN), i.e., number of hidden layers, neurons in each layer, activation function, and optimization function), were finalized to obtain a minimum value of the root mean square (RSM) error. Further details on the model can be found in the author's previous work [33][34][35]. The current DNN model is finalized with two hidden layers consisting of four and three neurons in the first and second layers, respectively, with Levenberg-Marquardt (LM), as an optimizing algorithm; Rectifier Linear Unit (ReLU) as an activation function for the input and the two hidden layers, and sigmoid as an activation function for the output layer as displayed in Figure 7.…”

Section: The Deep Neural Networkmentioning

confidence: 99%

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Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

et al. 2021

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Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC.

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“…The blade height at the inlet and outlet of the nozzle is assumed to be equal to the rotor inlet blade height as given by Equation (35).…”

Section: Nozzle Geometrymentioning

confidence: 99%

Section: The Deep Neural Networkmentioning

confidence: 99%

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

et al. 2021

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“…Their findings showed that heatsinks could reduce panel temperatures by up to 5.1 °C. One of the major challenges in the field of heat transfer using fluids in various heat exchangers is the low thermal conductivity of fluids [ 41 , 42 , 43 ]. Researchers have tried to increase the thermal conductivity of fluids [ 44 , 45 , 46 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of the Effect of Nanoparticles Size and Shape on the Efficiency of a Micro Heatsink

et al. 2022

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In this paper, two novel micro heat sinks (MHSs) were designed and subjected to thermal analysis using a numerical method. The fluid used was Boehmite alumina–water nanofluid (NFs) with high volume fractions (VOFs). Studies were conducted to determine the influence of a variety of nanoparticle (NP) shapes, such as platelet brick, blade, cylinder, and Os. The heatsink (HS) was made of copper, and the NFs entered it through the middle and exited via four outlets at the side of the HS. The finite element method was used to simulate the NFs flow and heat transfer in the HSs. For this purpose, Multi Physics COMSOL software was used. The maximum and middle values of HS temperature (T-MAX and T-Mid), thermal resistance (TH-R), heat transfer coefficient (h), FOM, etc., were studied for different NP shapes, and with Reynolds numbers (Re) of 300, 1000, and 1700, and VOFs of 0, 3, and 6%. One of the important outcomes of this work was the better thermal efficiency of the HS with rectangular fins. Moreover, it was discovered that a rise in Re increased the heat transfer. In general, adding NPs with high VOFs to MHSs is not appropriate in terms of heat. The Os shape was the best NP shape, and the platelet shape was the worst NP shape for high NPVOF. When NPs were added to an MHS, the temperature of the MHS dropped by an average of 2.8 or 2.19 K, depending on the form of the pin-fins contained inside the MHS (circular or square). The addition of NPs in the MHS with circular and square pin-fins enhanced the pressure drop by 13.5% and 13.3%, respectively, when the Re = 1700.

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“…They gave the concept of modeling the data of considered studied statistically via incorporating statistical distributions. Chu et al 16 , 17 studied ANN modeling of nanofluid examined experimentally and then the results were compared with regression-based methodologies.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling for bioconvective tangent hyperbolic nanofluid towards stretching surface with zero mass flux condition

Shafiq

Lone

Sindhu

et al. 2021

Sci Rep

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This article presents the implementation of a numerical solution of bioconvective nanofluid flow. The boundary layer flow (BLF) towards a vertical exponentially stretching plate with combination of heat and mass transfer rate in tangent hyperbolic nanofluid containing microorganisms. We have introduced zero mass flux condition to achieve physically realistic outcomes. Analysis is conducted with magnetic field phenomenon. By using similarity variables, the partial differential equation which governs the said model was converted into a nonlinear ordinary differential equation, and numerical results are achieved by applying the shooting technique. The paper describes and addresses all numerical outcomes, such as for the Skin friction coefficients (SFC), local density of motile microorganisams (LDMM) and the local number Nusselt (LNN). Furthermore, the effects of the buoyancy force number, bioconvection Lewis parameter, bioconvection Rayleigh number, bioconvection Pecelt parameter, thermophoresis and Brownian motion are discussed. The outcomes of the study ensure that the stretched surface has a unique solution: as Nr (Lb) and Rb (Pe) increase, the drag force (mass transfer rate) increases respectively. Furthermore, for least values of Nb and all the values of Nt under consideration the rate of heat transfer upsurges. The data of SFC, LNN, and LDMM have been tested utilizing various statistical models, and it is noted that data sets for SFC and LDMM fit the Weibull model for different values of Nr and Lb respectively. On the other hand, Frechet distribution fits well for LNN data set for various values of Nt.

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Examining rheological behavior of MWCNT-TiO2/5W40 hybrid nanofluid based on experiments and RSM/ANN modeling

Cited by 90 publications

References 54 publications

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Numerical Analysis of the Effect of Nanoparticles Size and Shape on the Efficiency of a Micro Heatsink

Statistical modeling for bioconvective tangent hyperbolic nanofluid towards stretching surface with zero mass flux condition

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