Browsing by Author "Hammoodi, Karrar A."

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Citation Count: 0
Artificial neural network hyperparameters optimization for predicting the thermal conductivity of MXene/graphene nanofluids
(Elsevier, 2024) Shang, Yunyan; Salahshour, Soheıl; Alizadeh, As'ad; Sharma, Kamal; Jasim, Dheyaa J.; Rajab, Husam; Salahshour, Soheil
Background: The critical role of thermal conductivity (TC) as a significant thermo-physical property in MXene/ graphene-based nanofluids for photovoltaic/thermal systems has motivated recent research into developing precision predictive models. The multilayer perceptron neural network (MLPNN) has emerged as an eminent AI algorithm for this task. Methods: This study employs Bayesian optimization, random search (RS), and grid search (GS) to fine-tune MLPNN hyperparameters-hidden layers, neurons, activation functions, standardization, and regularization-to elevate TC modeling efficiency. The proposed methodology unfolds in sequential phases: data analysis, data pre-processing, and introduction of MLPNN, GS, RS, Bayesian approach, and their integration algorithm. The next phase entails developing predictive models and presenting optimal cases. Lastly, the final models undergo statistical evaluation and graphical comparison for a thorough analysis. Findings: Results manifest that the GS-MLPNN model excels, achieving the lowest testing data error (MAPE = 0.5261%) and high conformity with empirical data (R = 0.99941). Meanwhile, the RS method adjusts hyperparameters with negligible precision loss (MAPE = 0.6046%, R = 0.99887). Contrarily, Bayesian optimization lags, increasing errors (MAPE = 3.1981%) and lower correlation (R = 0.98099), suggesting its relative inefficacy for this specific application. The optimized models provide efficient predictions, significantly reducing the financial/computing costs associated with experimental/numerical analysis.
Citation Count: 1
The effect of initial conditions (temperature and pressure) on combustion of Fe-coated-aluminum hydride nanoparticles using the molecular dynamics approach
(Elsevier, 2024) Yuanlei, Si; Salahshour, Soheıl; Sajadi, S. Mohammad; Rashid, Farhan Lafta; Li, Z.; Jasim, Dheyaa J.; Sabetvand, Rozbeh
Highly combustible elements like beryllium, lithium, Al, Mg, and Zn have the highest combustion, increasing the heat in explosives and propellants. Al can be used because of its greater avail-ability. Reducing the size of Al nanoparticle (NP) increases the combustion rate and decreases the combustion time. This paper studied the effect of initial conditions on the phase transition (PT) and atomic stability times of Fe-coated-aluminium hydride (AlH3) NPs. The molecular dynamics (MD) technique was used in this research. The microscopic behavior of structures was studied by density (Den.), velocity (Vel.), and temperature (Tem.) profiles. Heat flux (HF), PT, and the atomic stability of the structure were examined at different initial pressures (IP) and initial temperatures (IT). According to the achieved results, Den., Vel., and Tem. values had a maximum value of 0.025 atoms/angstrom 3, 0.026 angstrom/ps, and 603 K. By increasing IT in the simulation box to 350 K, HF in the samples increases to 75.31 W/m2. Moreover, the PT time and atomic stability time by increasing IP reach to 5.93 ns and 8.96 ns, respectively. Regarding the importance of the phe-nomenon of heat transfer and PT of nanofluids (NFs), the findings of this study are predicted to be useful in various industries, including medicine, agriculture, and others.
Citation Count: 0
Effects of variable electric field on crack growth of aluminum nanoplate: A molecular dynamics approach
(Pergamon-elsevier Science Ltd, 2024) Salahshour, Soheıl; Hammoodi, Karrar A.; Fadhil, Dalal Abbas; Hanoon, Zahraa A.; Nayyef, Dhuha Radhi; Salahshour, Soheil; Emami, Nafiseh
Studying cracks in aluminum (Al) nanosheets is crucial because it enhances our understanding of their mechanical properties and failure mechanisms, which are vital for applications in lightweight structures, electronics, and nanotechnology. In this study, different levels of an external electric field (EF) (1, 2, 3, and 5 V/& Aring;) were used to see how they affected the growth of nanocracks in Al nanoplates. This investigation was carried out utilizing molecular dynamics simulation and LAMMPS software. Increasing EFA to 2 V/& Aring; increased to maximum (Max) stress from 230.567 to 242.032 GPa. Furthermore, increasing the voltage to 5 V/& Aring; reduced Max stress to 230.567 GPa. Max (Vel) occurred in the presence of 2 V/& Aring; which reached 14.2192 & Aring;/ps. The increase in atomic Vel in Al nanoplates can be attributed to enhanced atomic collisions and energy transfer among atoms as the EFA increases to 5 V/& Aring;, the Vel declined to 11.9908 & Aring;/ps. On the other hand, the outputs predicted the atomic evolution of designed Al nanoplates can manipulate the EF value changes. Numerically, by changing the EF parameter from 1 to 5 V/& Aring;, the nano-crack length value varied from 27.87 to 30.16 & Aring;. Physically, this structural evolution occurred through changes in interaction energy (mean attraction energy) within various regions of Al nanoplates. In industrial cases, this nano-crack length manipulation by EF amplitude parameter can be used to prepare atomic nanoplates with different resistances to the crack growth process.
Citation Count: 0
Investigating the effect of constant heat flux on the adsorption of doxorubicin by bio-MOF-11 biocarrier using molecular dynamics simulation
(Pergamon-elsevier Science Ltd, 2024) Liu, Zhiming; Salahshour, Soheıl; Mostafa, Loghman; Jasim, Dheyaa J.; Hammoodi, Karrar A.; Salahshour, Soheil; Sabetvand, Rozbeh
This study aimed to investigate the effect of constant heat flux on the adsorption of doxorubicin by bio-MOF-11 biocarrier using molecular dynamics simulation. The research explores the behavior of drug molecule and carrier under different thermal conditions to understand the underlying mechanisms of adsorption. The modeled samples were made of bio-MOF-11 structure, trisodium phosphate buffer (as a drug), and aqueous environment in the presence of NaCl. Technically, the atomic interaction among various atoms inside a computational box was described using a Universal Force Field. The findings of this study could contribute to the development of more effective drug delivery systems and advance the understanding of the adsorption process in carriers. The present outputs predicted the external heat flux was an important parameter in the atomic evolution of the drug-MOF system. The 0.3 W/m2 value of heat flux was optimum for drug diffusion into the MOF sample. Numerically, the number of diffused drug particles and diffusion coefficient converged to 335 and 73.19 nm2/ns (respectively) in the optimum value of heat flux. So, it was concluded that heat flux implementation to the drug-MOF system and changing this external parameter manipulated the drug adsorption (drug delivery) procedure in the designed system for various clinical applications.
Citation Count: 2
Numerical examination of exergy performance of a hybrid solar system equipped with a sheet-and-sinusoidal tube collector: Developing a predictive function using artificial neural network
(Elsevier, 2024) Sun, Chuan; Salahshour, Soheıl; Sajadi, S. Mohammad; Li, Z.; Jasim, Dheyaa J.; Hammoodi, Karrar A.; Alizadeh, As'ad
Integrating cooling systems with photovoltaic-thermal (PVT) collectors has the potential to mitigate the exergy consumption in the building sector due to their capability for simultaneous power and thermal energy generation. The simultaneous utilization of nanofluid and geometry modification resulted in a synergetic enhancement in the performance of PVTs and thereby reducing their sizes and costs. In addition, there is still a lack of high accurate predictive model for the estimation of the performance of PVTs at a given Re number and nanofluid concentration ratio to be used in engineering design for the further product commercialization. To this end, the current numerical study investigates the exergy electricity, thermal, and overall exergies of a building-integrated photovoltaic thermal (BIPVT) solar collector with Al2O3/water coolant. The increase in nanoparticle concentration (omega) from 0 % to 1 % increased the useful thermal exergy and overall exergy efficiency (Exu,t/ Yov) by 0.3999 %/0.0497 %, 1.3959 %/0.2598 %, and 0.7489 %/0.1771 % at Re numbers of 500, 1000, and 1500, respectively, while Exu,t/ Yov exhibited a reducing trend at Re = 2000; 0.3928 %/0.1056 % decrease. In addition, the increase in omega from 0 % to 1 % caused the useful electricity and electrical exergy (Exu,e/ Ye) to be diminished by 0.0060 %/0.0025 % at Res 500 and 1000, and to be escalated by 0.0113 %/0.0055 % at Res of 1500 and 2000. Meanwhile, the Re augmentation, from 500 to 2000, improved the Exu,t, Exe, Ye, and Yov by 60 %, 1.26 %, 1.26 %, and 17.50 %, respectively, at different omega s. In addition, two functions were developed and proposed by applying a group method of data handling-type neural network (GMDH-ANN) to forecast the value of Υov based on two input values (Re and omega). The results showed high accuracy of the proposed model with MSE, EMSE, and R2 of 0.0138, 0.1143, and 0.99785, respectively.
Citation Count: 3
Obtaining an accurate prediction model for viscosity of a new nano-lubricant containing multi-walled carbon nanotube-titanium dioxide nanoparticles with oil SAE50
(Elsevier Sci Ltd, 2024) Zhang, Yuelei; Salahshour, Soheıl; Sajadi, S. Mohammad; Li, Z.; Jasim, Dheyaa J.; Nasajpour-Esfahani, Navid; Khabaz, Mohamad Khaje
This study aims to investigate the viscosity behavior of multi-walled carbon nanotube (MWCNT) - titanium dioxide (TiO2) (40-60) - SAE50 oil nanofluid using an Artificial Neural Network (ANN) modeling approach. The main objective is to develop a highly accurate predictive model for viscosity by considering three input parameters: temperature, solid volume fraction (SVF), and shear rate (SR). Rheological measurements provide experimental data used to train and validate the ANN model. The ANN model's architecture, activation functions, and training algorithms are carefully chosen. Data are divided to three subsets including train, validation and test. ANN is trained using trainlm algorithm for 50 times to vanish the effect of random nature of ANN weight initialization. The trained ANN model is then utilized to predict the viscosity of the nanofluid under varying conditions. The results demonstrate the efficacy of the proposed ANN model in capturing the complex relationship between viscosity and the input parameters, providing accurate viscosity predictions for the MWCNT-TiO2-oil SAE50 nanofluid. Furthermore, the influence of temperature, SVF, and SR on viscosity is analyzed, offering valuable insights into the flow behavior of the nanofluid. According to the obtained results, the developed ANN model presents a reliable and efficient approach to estimate the viscosity of the MWCNTTiO2-SAE50 oil nanofluid, eliminating the need for costly and extensive experimental measurements within the analyzed range. ANN could model the nanofluid viscosity with R2 = 0.9998 and MSE= 0.000189 that is quite acceptable. Also, the experimental data revealed that for the investigated nanofluid, temperature and shear rate have impressive effect on the viscosity (changing viscosity more than 100% for the analyzed margin), on the other hand, the nanoparticle volume fraction effect is much lower, to be more precise, increasing the nanoparticle percentage will increase the viscosity mean value around 30%.