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Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
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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.