Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks(ELSEVIER, 2024) Jasim, Dheyaa J.; Rajab, Husam; Alizadeh, As'ad; Sharma, Kamal; Ahmed, Mohsen; Kassim, Murizah; AbdulAmeer, S.; Alwan, Adil A.; Salahshour, Soheil; Maleki, Hamid; Al-Amarah University College; Alasala Colleges; Cihan University-Erbil; GLA University; Imam Abdulrahman Bin Faisal University; Universiti Teknologi MARA; Universiti Teknologi MARA; University of Babylon; University of Ahl al-Bayt; National University of Science & Technology - Iraq; Okan University; Bahcesehir University; Lebanese American UniversityAccurately predicting thermo-physical properties (TPPs) of MXene/graphene-based nanofluids is crucial for photovoltaic/thermal solar systems, driving focused research on developing precise TPP predictive models. This study presents optimized multi-layer perceptron neural network (MLPNN) models, leveraging Bayesian optimization to refine architectural and training hyperparameters, including hidden layers, neurons, activation functions, standardization, and regularization terms. A comparative analysis of Bayesian acquisition functions-the probability of improvement (POI), lower confidence bound (LCB), expected improvement (EI), expected improvement plus (EIP), expected improvement per second plus (EIPSP), and expected improvement per second (EIPS)-demonstrated that the POI-MLPNN achieves the most accurate results, as evidenced by the lowest MAPE of 1.0923 % and exceptional consistency with an R-value of 0.99811. The EI-MLPNN and EIP-MLPNN models recorded the same outputs. The EI/EIP-MLPNN (R = 0.99668) model excels in consistency over LCB-MLPNN (R = 0.99529) and EIPSP-MLPNN (R = 0.99667). The optimized models offer a reliable, cost-efficient alternate for experimental and computational TPP analyses. Leveraging insights from these models enables better control over nanofluid TPPs in solar systems, enhancing energy conversion efficiency.Publication Metadata only Combining neutral scalar and isothermal Shan-Chen lattice Boltzmann method to simulate droplet placement on a wall in isothermal and non-isothermal states(ELSEVIER, 2024) Liu, Yanan; Jasim, Dheyaa J.; Sajadi, S. Mohammad; Nasajpour-Esfahani, Navid; Salahshour, Soheil; Zarringhalm, Majid; Rahmani, Amin; Al-Amarah University College; Cihan University-Erbil; University System of Georgia; Georgia Institute of Technology; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad University; University of ExeterBackground: In the current article, two-phase thermal fluxes are created by combining the thermal model of the neutral scalar model with the two-phase Shan-Chen model of the lattice Boltzmann method (LBM). Methods: The different intermolecular powers for the isothermal Shan-Chen model show how a droplet would be placed on a wall. By raising the droplet intermolecular power parameter, the surface area increases and becomes wet. Next, the isothermal Shan-Chen method and the neutral scalar method are combined to investigate multiphase thermal problems. The droplet placement on the hot wall is therefore done at relatively high Rayleigh numbers. By raising the Rayleigh number, the isothermal lines within the droplet's interior gradually become less ascending and less descending until they eventually achieve a uniform state when it is placed against a hot wall. Additionally, the channel's Rayleigh-Benard convective heat transfer is enhanced by increasing the Rayleigh number. Significant findings: Natural convection in the enclosures can be used in solar collectors. As the Rayleigh number increases, the average Nusselt number (Nuavg) rises as would be expected. The results demonstrate that LBM is a practical method for simulating multi-phase thermal flows.Publication Metadata only Using molecular dynamics approach to investigate the effect of copper nanoparticles on the thermal behavior of the ammonia/copper coolant by focusing on aggregation time(ELSEVIER, 2024) Fan, Zhongmian; Jasim, Dheyaa J.; Sajadi, S. Mohammad; Salahshour, Soheil; Nasajpour-Esfahani, Navid; Toghraie, D.; Shenyang University of Technology; Al-Amarah University College; Cihan University-Erbil; Okan University; Bahcesehir University; Lebanese American University; University System of Georgia; Georgia Institute of Technology; Islamic Azad UniversityNanofluids, fluids containing nanometer-sized particles, have significant properties which make them useful in devices and systems. They boost thermal conductivity and heat transfer better than base fluid. This research studied the atomic behavior, and thermal behavior of simulated ammonia -copper nanofluid using molecular dynamics (MD) simulation method. The effect of increasing Cu nanoparticles' volume fraction (phi) (1-10 %) on the atomic behavior and thermal behavior of nanofluids was studied. The atomic behavior of simulated structure was studied with velocity and temperature profiles. The maximum values of velocity and temperature were 0.00086 angstrom/ps and 240 K, respectively. To study the thermal behavior of simulated structure, heat flux and the aggregation time (AT) of nanoparticles (NPs) were studied. Numerically, the heat flux (HF) and the aggregation time of Ammonia -Cu nanofluid converged to 1411 W/m2 and 3.96 ns, respectively. The study showed that the maximum velocity and temperature decreased by increasing phi. Moreover, by increasing the phi to 5 %, the heat flux and aggregation time increase to 1553 W/m2 and 4.05 ns. By more increase of NPs up to 10 %, the heat flux and AT of samples decrease. By increasing NPs by 10 % in the base fluid, the aggregation process of NPs occurred in a shorter time. It reduces the thermal efficiency of simulated samples.Publication Metadata only Investigating the initial pressure effect on Brownian displacement, thermophoresis, and thermal properties of graphene/ water nanofluid by molecular dynamics simulation(ELSEVIER, 2024) Ren, Jiaxuan; Jasim, Dheyaa J.; Sajadi, S. Mohammad; Nasajpour-Esfahani, Navid; Salahshour, Soheil; Sabetvand, Rozbeh; Al-Amarah University College; Cihan University-Erbil; University System of Georgia; Georgia Institute of Technology; Okan University; Bahcesehir University; Lebanese American University; Amirkabir University of TechnologyThe concept of nanofluid includes suspensions containing nanoparticles, metallic and non-metallic materials. Nanofluids have many potentials in different environments and conditions that make them exist in industries and food industries. Considering their high thermal conductivity, the nanoparticles increased the fluid's thermal conductivity, one of the basic heat transfer parameters, when distributed in the base fluid. The present research investigated the thermal properties, Brownian motion, and thermophoresis of water/ graphene nanofluid affected by different ratios of initial pressure (1, 2, 3 and 5 bar) by molecular dynamics simulation. This study reported the changes in heat flux, thermal conductivity, average Brownian displacement, and thermophoresis. The results depict that by increasing the initial pressure from 1 to 5 bar, average Brownian displacement and thermophoresis values decrease from 06.3 and 23.88 to 2.91 and 23.53 angstrom, respectively. Also, by raising the initial pressure (1 to 5 bar), the heat flux and thermal conductivity after 10 ns decrease from 39.54 and 0.36 to 35.12 W/m2 and 0.28 W/m.K, and the maximum temperature reduces from 1415 K to 1033 K. These results can be useful in different industries, especially for improving the thermal properties of different nanofluids.Publication Metadata only Optimization of thermophysical properties of nanofluids using a hybrid procedure based on machine learning, multi-objective optimization, and multi-criteria decision-making(ELSEVIER SCIENCE SA, 2024) Zhang, Tao; Pasha, Anahita Manafi Khajeh; Sajadi, S. Mohammad; Jasim, Dheyaa J.; Nasajpour-Esfahani, Navid; Maleki, Hamid; Salahshour, Soheil; Baghaei, Sh.; China Agricultural University; Urmia University of Medical Sciences; Cihan University-Erbil; Al-Amarah University College; University System of Georgia; Georgia Institute of Technology; Isfahan University of Technology; Okan University; Bahcesehir University; Lebanese American UniversityThe rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40-50 degrees C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.Publication Metadata only A molecular dynamics study of the effect of initial pressure on the mechanical resilience of aluminum polycrystalline(ELSEVIER, 2024) Ali, Ali B. M.; Jasim, Dheyaa J.; Alizadeh, As'ad; Chan, Choon Kit; Salahshour, Soheil; Hekmatifar, Maboud; University of Warith Alanbiyaa; Al-Amarah University College; Cihan University-Erbil; INTI International University; Okan University; Bahcesehir University; Lebanese American UniversityPolycrystalline materials are essential in engineering due to their ability to withstand various forces, heat, and environmental conditions. The arrangement of atoms within these crystals significantly affects their mechanical properties. This study used molecular dynamics simulations to explore how initial pressure affects the mechanical resilience of aluminum polycrystals. Aluminum composite materials, known for their strength, flexibility, and environmental sustainability, are the focus of this investigation. We particularly investigated stress- strain reactions at 1, 2, and 3 bar initial pressures. Reduced free volume causes atomic migration to be hampered as pressure increases, therefore affecting mean square displacement and diffusion coefficient. The results show that ultimate strength and Young's modulus of the polycrystalline samples were 30 and 6.64 GPa at 1 bar pressure. Moreover, the results demonstrated a notable decrease in mechanical performance by increasing pressure, the ultimate strength and Young's modulus of the polycrystalline samples diminished to 5.66 GPa and 22.43 GPa, respectively, at 3 bar. Furthermore, the heat flux increased by rising initial pressure in the Al- polycrystalline sample due to the compression of material that reduced atomic distances. This improved atomic arrangement facilitated more efficient heat transfer. These insights are essential for engineering applications, as they establish a foundation for the production of aluminum components that maintain structural integrity in the face of extreme conditions.Publication Metadata only Artificial neural network modeling of thermal characteristics of WO3-CuO (50:50)/water hybrid nanofluid with a back-propagation algorithm(ELSEVIER, 2024) Qu, Yiran; Jasim, Dheyaa J.; Sajadi, S. Mohammad; Salahshour, Soheil; Khabaz, Mohamad Khaje; Rahmanian, Alireza; Baghaei, Sh.; Newcastle University - UK; Al-Amarah University College; Cihan University-Erbil; Okan University; Bahcesehir University; Lebanese American University; Islamic Azad University; Isfahan University of TechnologyThermophysical properties such as thermal conductivity (knf) make the use of fluid suitable for heat transfer. Fluids such as water have limited applications due to their low thermal conductivity. One of the new methods to improve the properties of fluids is to add nanoparticles with high thermal conductivity and create a nanofluid. Nanofluids combine the suspension of two or more nanoparticles in a base fluid or the suspension of hybrid nanoparticles in a base fluid. This study investigates the thermal behavior of WO3-CuO (50:50)/water nanofluid using an artificial neural network (ANN) and back -propagation algorithm. The results show that increasing the volume fraction of nanoparticles (phi) (due to increasing the surface -to -volume ratio) increases the knf. In this study, ANN modeling for WO3-CuO/water (50:50) hybrid nanofluid was performed to investigate the effect of nanofluid on knf. These two important parameters are phi and temperature. The results show that increasing the phi increases the knf due to increasing the surface -to -volume ratio and the collision between nanoparticles. Increasing the temperature shows a similar effect and improves the knf by increasing the interaction between the nanoparticles. The effect of temperature on the knf is more significant than the phi, equal to 16.33% and 6.72%, respectively. Function parameters such as correlation and error value for hidden layer 7 and 12 neurons are about 0.982, 0.981, and 10-6, respectively. As a result, ANN models offer acceptable performance in estimating knf, and the correlation coefficients and error values are 0.96 and 10-6, respectively. Given the absolute error value, it can be concluded that the proposed models can predict the knf of WO3-CuO (50:50)/water hybrid nanofluid.Publication Metadata only Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and group method of data handling artificial neural networks(ELSEVIER, 2024) Rostamzadeh-Renani, Reza; Jasim, Dheyaa J.; Baghoolizadeh, Mohammadreza; Rostamzadeh-Renani, Mohammad; Andani, Hamid Taheri; Salahshour, Soheil; Baghaei, Sh.; Polytechnic University of Milan; Al-Amarah University College; Shahrekord University; Isfahan University of Technology; Islamic Azad University; Okan University; Bahcesehir University; Lebanese American UniversityIn this article, the ability of GMDH artificial neural networks (ANNs) to predict the rheological behavior (RB) of nanofluids (NFs) containing CuO NPs is studied. ANNs are a powerful mathematical tool that can identify the relationship among the parameters without the need to extract the relationship among them. The main purpose of this study is to use the GMDH ANN method to generate and predict the viscosity (mu) parameter using several input variables (IPV) such as solid volume fraction (SVF), nanoparticles (NPs), temperature (Temp), and shear rate (SR). By pairing the GMDH ANN with the evolutionary algorithm, this capability is created so that the values predicted by the ANN are more compatible with the laboratory numbers. The evolutionary algorithms (EAs) used in this study include three algorithms: Non-Dominated Sorting Genetic Algorithm II (NSGA II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf Optimizer (MOGWO). These algorithms are selected for optimization, among which the best performance is related to the coupling of GMDH ANN with the MOGWO algorithm. In the next step, the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Grey Wolf Optimizer (GWO) algorithms are used. This process is done to minimize the target function (TF) (mu) and evaluate the optimal points. According to the obtained results, among the EAs used in this study, the best performance belongs to the GA algorithm. Finally, in the last part of this study, the most optimal mode for IPV and output variable (OPV) of TF is determined. Numerically, the values of IPV data, such as SVF, T, and SR, are respectively 0.2242%, 50, and 246.7427, and the most optimal value for the OPV of TF (mu) was estimated as 0.96686 cP.