Browsing by Author "Kamoon, Saeed S."

Now showing 1 - 3 of 3

Citation Count: 0
The effect of initial pressure and atomic concentration of iron nanoparticles on thermal behavior of sodium sulfate/magnesium chloride hexahydrate nanostructure by molecular dynamics simulation
(Elsevier, 2024) Huang, Yijin; Salahshour, Soheıl; Kaur, Mandeep; Basem, Ali; Khaddour, Mohammad H.; Al-Bahrani, Mohammed; Emami, Nafiseh
Thermal energy storage (TES) is one of the uses of phase change material (PCM). The primary factor contributing to this capability is the elevated latent heat of melting present in these materials. The current study investigates the effect of initial pressure (IP) (ranging from 1 to 5 bar), and atomic ratio (AR) of Iron nanoparticles (NPs) (Fe = 1, 2, 3, and 5 %) on the thermal behavior (TB) and phase transition process of sodium sulfate/Magnesium chloride hexahydrate (Na 2 SO 4 /MgCl 2 & sdot; 6H 2 O) nanostructures as PCMs using molecular dynamics (MD) simulation. The simulated PCM was positioned inside a spherical atomic channel composed of iron. The TB of simulated nanostructures was examined by reporting changes in viscosity (Vis), thermal conductivity (TC), and phase transition time (PTT). The results reveal that by increasing IP from 1 to 5 bar, the PTT reaches from 3.50 to 3.61 ns, and the TC decreases from 1.03 to 0.94 W/m.K. The results show that adding 3 % of Fe NPs was the optimal ratio to improve the TB of the Na 2 SO 4 /MgCl 2 & sdot; 6H 2 O-Fe NP. By raising the ratio of Fe NPs from 1 to 3 %, Vis slightly decreased from 4.31 to 4.22 mPa.s. In comparison, adding more Fe NPs with 5 % ratio raised the Vis to 4.30 mPa.s. According to the results, increasing the IP decreased the distance among the particles. So, the attraction among particles increased, leading to greater adhesion and Vis. By increasing the IP, the distance among atoms decreases, and the space between NPs and atoms in the simulation box decreases. Consequently, NP movement and fluctuations decrease, and collisions decrease. The results of this simulation will be effective in heating - cooling and ventilation systems, automotive industries, textile industries, and so on.
Citation Count: 0
The pool boiling heat transfer of ammonia/Fe 3 O 4 nano-refrigerant in the presence of external magnetic field and heat flux: A molecular dynamics approach
(Pergamon-elsevier Science Ltd, 2024) An, Qing; Salahshour, Soheıl; Alizadeh, As 'ad; Kamoon, Saeed S.; AL-Yasiri, Mortatha; Zhang, Mengyan; Hekmatifar, M.
Pool boiling is distinguished by its capacity to eliminate excessive heat fluxes (HFs) at low temperatures. In recent decades, the optimal design of flooded evaporators elevated the significance of pool boiling HT with refrigerant to conserve natural resources and energy. The industry highly regards this process on account of its superior heat transfer (HT) coefficient in comparison to other HT mechanisms. Among the types of boiling, pool boiling has a special place due to its ability to remove HFs at low temperatures. This study was the first to investigate the boiling characteristics of the ammonia/Fe 3 O 4 nano -refrigerant in a copper (Cu) nanochannel (NC) through molecular dynamics (MD) simulations. The primary goal was to investigate the effect of external HF (EHF) and external magnetic field amplitude (EMFA) on nanostructures ' atomic behavior (AB) and thermal behavior (TB). The research findings indicate that increasing the applied EHF led to increased particle movement and the HT rate. By changing the EHF, boiling behavior in the nano -refrigerant may also be seen. Maximum (Max) velocity (Vel.) increased to 8.970 & Aring;/ps when the EHF increases to 0.5 W/m 2 . Atomic collisions and particle mobility both increase when the EHF increases. Therefore, the maximum temperature value increases to 359.46 K. When the EMFA applied to the nano -refrigerant reaches to 0.5 T, the maximum values of the parameters, such as the Temp. and the velocity, reach to 410.07 K, and 11.802 & Aring;/ps, respectively.
Citation Count: 0
Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms
(Pergamon-elsevier Science Ltd, 2024) Jin, Weihong; Salahshour, Soheıl; Baghoolizadeh, Mohammadreza; Kamoon, Saeed S.; Al-Yasiri, Mortatha; Salahshour, Soheil; Hekmatifar, Maboud
The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid nonNewtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) mu nf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for mu nf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for 9, T, and gamma obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.