Publication Date: 2013
Journal of Shi'a Islamic Studies (17489423)6(1)pp. 53-74
After the fall of the Buyids at the hands of the Seljuq Turks and the Seljuqs' entry into Baghdad, the apparatus of the 'Abbasid caliphs and the Sunnis once again gained control of Baghdad. However, this should not be considered as having been to the downfall or detriment of the Shi'a, because it is in this period that we come across three powerful and influential forces in Baghdad: first, the 'Abbasid caliphs and the Sunnis; second, the Seljuqs; and third, the Shi'a. Despite the fact that the Seljuqs were followers and defenders of the Sunnis, their relationship with the 'Abbasid caliphs had many ups and downs which saw them turn from allies to foes over time. Similarly, the position of the Seljuqs in relation to the Shi'a was not such that they felt the Imami Shi'a were a force that acted in opposition to them; rather, in certain instances, the Shi'a would be treated favourably, peaceably, and respectfully by the Seljuq rulers. In this article we aim to show that in the period when the Seljuqs ruled Baghdad, the Shi'a were recognized as a third effective power broker in the political and social scene, and there was no clear unified stance between the caliphs and the sultanate against the Shi'a.
Publication Date: 2025
Research On History Of Medicine (2251886X)14(2)pp. 155-164
In Persian medicine, the concepts of patient and disease, medicine, and treatment are based on the Iranian-Islamic worldview. In this kind of philosophy, they believed that in the human body, the three members (the heart, the brain, and the liver) are the chief due to the vital actions they perform for the body, and they are called the board members (Major vital organs or Azayeh Reiseh). The remaining bodily organs operate as subordinates to these principal members. Each of the Physicians examined in this study has different opinions about the priority of the board members. The main purpose of the research is to review and compare the Physicians’ opinions about the priority of the board members, relying on historical sources and citations and using the library method. The findings of this research show that the sages of Eastern Islamic civilization do not agree with the members of the body board. © Journal of Research on History of Medicine.
Publication Date: 2025
Research On History Of Medicine (2251886X)14(1)pp. 59-74
Food hygiene is a crucial aspect of public health, directly impacting societal well-being. From the mid-Qajar period onward, mirroring the broader “medicalization” of public health, traditional folk and religious understandings of food safety were gradually replaced by modern medical teachings. This research employs a descriptive-analytical approach, utilizing primary source documents to examine the components and challenges of food hygiene during the Naseri era alongside government interventions designed to improve it. Key focus areas included water, bread, meat, kitchens, cooks, eating practices, food storage, and fruits/dried fruits. The study analyzes health concerns in each area and the government’s corresponding actions, such as issuing advisory and directive decrees, establishing health institutions, and conducting public health awareness campaigns through print media like newspapers and magazines. The findings highlight the gradual infiltration of new scientific knowledge into traditional Qajar society, supplanting long-established folk practices while simultaneously presenting the inherent challenges accompanying this transition. © Journal of Research on History of Medicine.
Publication Date: 2021
Journal Of Medical Ethics And History Of Medicine (20080387)14
Zhang, Q.,
Li, X.,
Ali, A.B.,
Sawaran singh, N.S.,
Yazdekhasti, A.,
Pirmoradian, M.,
Marzouki, R. Publication Date: 2025
Case Studies in Thermal Engineering (2214157X)72
The increase in performance of home heaters, accompanied by energy and environmental crises, becomes noticeable in the construction industry. Hence, this paper has investigated a solution to enhance the system's efficiency. For this purpose, a novel experimental set-up is provided to simulate the heater, exhaust heater, and indoor environment. This setup includes two boxes, a heat source controlled by a Proportional Integral Derivative (PID) controller and an aluminum box with copper tubes. The heat is transferred from the first box as a heater to the aluminum box as a heat exhaust by the air force of the fan to warm the second box. Also, Lauric acid as a phase change material (PCM) and metal foam are used to increase heat absorption and heat transfer inside the heater exhaust regarding the thermal and physical properties of these two materials. Seven temperature sensors are located in different places to evaluate and control the system. Moreover, 6 modes are designed to find the best arrangement of materials for improving the system. The results show that a new arrangement of materials defined as filling the aluminum box with PCM and covering this box with metal foam, acquires remarkable efficiency. According to the achieved data, the mentioned design can decrease the outlet temperature of exhaust up to 2.3 ± 0.1 °C as well as increase the second box temperature in a home environment to 1.6 ± 0.1 °C. Finally, this mode can improve the efficiencies of the system both in outlet temperature and indoor temperature of the second box as much as 4.73 ± 0.1% in the former and 3.72 ± 0.1% in the latter. © 2025 The Authors.
Sawaran singh, N.S.,
Hassan, W.H.,
Ameen ahmed, Z.M.,
Al-zahy, Y.M.A.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2025
Case Studies in Chemical and Environmental Engineering (26660164)11
This study presents an investigation into the vibration resonance of Mindlin piezoelectric polymeric nanoplates under electromechanical loading, particularly in the presence of a rotating nanoparticle. The novelty of this research lies in the application of non-local piezoelasticity, which effectively incorporates the influence of small-scale factors on the resonance behavior of the nanoplate. By employing a variational approach to derive the governing equations, this work advances the understanding of how various parameters such as the non-local parameter, dimensions of the nanoplate, excitation voltage, and mass of the nanoparticle affect resonance frequencies. The Galerkin method is utilized to solve the partial differential equations governing the dynamics of the piezoelectric polymeric nanoplate, marking a significant methodological contribution to the field. The incremental harmonic balance approach is then applied to estimate the system's resonance frequencies, with numerical simulations confirming their existence. This research not only elucidates the complex interactions affecting resonance behavior but also highlights the potential for optimizing the design of nanostructures in various applications, including sensors and energy-harvesting devices. The findings suggest that increasing the non-local parameter softens the nanoplate's rigidity, leading to decreased resonance frequencies, while modifications in dimensions and applied voltages can enhance these frequencies. Overall, this study lays the groundwork for future explorations into the dynamic behavior of piezoelectric materials, emphasizing the importance of small-scale effects in nanotechnology applications. © 2025 The Authors
Graish, M.S.,
Ali, A.B.,
Al-zahiwat, M.M.,
Alardhi, S.M.,
Baghoolizadeh, M.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2025
Case Studies in Chemical and Environmental Engineering (26660164)11
Viscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number; thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R2 and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R2 = 0.992) and lowest error metrics. At the other end, is the Extreme Learning Machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Authors
Sawaran singh, N.S.,
Ali, A.B.,
Abed hussein, M.,
Mohammed, J.K.,
Kharraji, O.,
Pirmoradian, M.,
Hashemian, M.,
Salahshour, S. Publication Date: 2025
Case Studies in Chemical and Environmental Engineering (26660164)11
This study aims to explore the dynamic instability of micro and nano-sized Timoshenko beams as they are traversed by sequentially moving nanoparticles. The beams, characterized by a rectangular cross-section and homogeneity, are situated within a Pasternak foundation, which provides a supportive elastic medium. The research investigation determines nanoparticle inertia effects at velocity while establishing motion equations through Hamilton's principle. The model unites nonlinear von Kàrmàn strain-displacement kinematics with strain gradient theory and Gurtin-Murdoch small-scale accounting. The system's behavior gets analyzed through the implementation of Galerkin method which derives time-periodic motion equations. The incremental harmonic balance approach develops stability boundary maps that separate stable and unstable regions through which analysts can examine parameter spaces containing moving particle mass and velocity values. This study evaluates how different parameters like beam diameters together with small-scale characteristics and elastic medium constants and residual stress and axial compressive forces affect the stability diagram. The analysis demonstrates that stability parameters become substantially modified when researchers include length scale characteristics along with surface effects. The outcome reveals that axial compressive forces reduce stability yet environmental effects strengthen the stability of small-scale beams which leads to transition curve movements towards faster moving particles velocities. This study contributes fundamental knowledge about dynamic instability effects in small-scale beams which will help future advances in nanotechnology and materials science. © 2025 The Authors
Huaguang li, ,
Ali, A.B.,
Hussein, R.A.,
Sawaran singh, N.S.,
Abdullaeva, B.,
Ahmad, Z.,
Salahshour, S.,
Baghoolizadeh, M.,
Pirmoradian, M. Publication Date: 2025
Case Studies in Thermal Engineering (2214157X)69
Background: Because of their enhanced thermophysical characteristics, namely greater thermal conductivity, viscosity control, and long-term stability than traditional nanofluids, hybrid nanofluids drew interest. Such properties make them suitable candidates for many industrial applications such as solar systems and thermal management. However, knowing the thermophysical properties of these materials accurately is difficult because of the complexities of nanoparticles and the interaction with the base fluid. This paper utilizes machine learning methods to predict the thermophysical properties of water/ethylene glycol mixture-based hybrid nanofluids containing reduced silver-graphene oxide.Method: ology: This study aimed to predict Viscosity (DV), Thermal Conductivity (TC) and Density (D) by three machine learning algorithms including multiple linear regression (MLR), Multiple Polynomial Regression (MPR) and Gaussian Process Regression (GPR). A 5 × 28 dataset was used for training and testing the network, with 80 % of the data used for training the network and 20 % for testing the network. Evaluating the performance of algorithms is based on the evaluation indices of Correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Standard Deviation (STD). In addition, optimization is done by the Non- dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm and the impact results of different mutation and combination rates are examined. Results: The MPR algorithm yielded the lowest MoD values (0.07 % and − 0.06 %) and the highest prediction accuracy among the models tested (R = 0.9999, RMSD = 2.726 × 10− 4, STD = 0.0219). Furthermore, NSGA-II optimization results revealed that the temperature and concentration of nanoparticles could effectively increase the thermal conductivity, while too high concentration could also increase viscosity. Finally, through the TOPSIS method, the best point was chosen giving a blend of ideal thermophysical properties. This signifies that machine learning methods can be successfully employed for the prediction and optimization of hybrid nanofluid characteristics. © 2025 The Authors.
Motallebi, M.A.,
Hashemian, M.,
Eftekhari, S.A.,
Toghraie, D.,
Pirmoradian, M. Publication Date: 2025
Propulsion and Power Research (2212540X)14(1)pp. 110-132
In the presented paper, the size-dependent flutter analysis of a nanobeam made of metal-ceramic functionally graded (FG) materials subjected to supersonic fluid flow is examined. The volume fractions of metal and ceramic vary along both longitudinal and thickness directions. The size effects are modeled based on the nonlocal strain gradient theory (NSGT) and the surface effects are included according to the Gurtin-Murdoch surface elasticity theory. The mathematical modeling of nanobeam is performed in the framework of Reddy's third-order shear deformation beam theory (TSDBT), and the aerodynamic pressure is modeled according to the linear approximation of the piston theory. The governing equations and boundary conditions are obtained utilizing Hamilton's principle and are solved approximately via the differential quadrature method (DQM). Convergence and precision of the presented work are proved and the effects of several parameters on the flutter boundaries are inspected such as material gradation indexes, nonlocal and strain gradient parameters, thickness-to-length ratio, and incorporation of surface effects. It is discovered that the incorporation of the surface effects has a remarkable impact on the flutter boundaries of nanobeams and increases both critical aerodynamic pressure and flutter frequency of the nanobeam. The aim of this work is to examine how the aeroelastic stability characteristics of an FG nanobeam can be affected by the nonlocal and strain gradient parameters and the variations in the volume fractions of the metal and ceramic in the longitudinal and thickness directions. © 2025 The Authors
Ali, A.B.,
Hussein, R.A.,
Sawaran singh, N.S.,
Salahshour, S.,
Pirmoradian, M.,
Mohammad sajadi s., S.M.,
Deriszadeh, A. Publication Date: 2025
International Journal of Thermofluids (26662027)26
This work examines the impact of different pressure levels (1 to 5 bar) and magnetic field frequencies (0.01 to 0.05 ps⁻¹) on the thermal behavior of sodium sulfate/magnesium chloride hexahydrate as a phase change material inside iron nanochannels, using molecular dynamics simulation. The system's kinetic and potential energies converge to 39.79 eV and -7204.99 eV, indicating the stability of the nanostructures. The impact of pressure and magnetic field frequency on heat flow, maximum temperature, and charge/discharge times was examined. Increasing the pressure from 1 to 5 bar reduced the heat flux and maximum temperature to 1509 W/m² and 391.18 K, respectively. Simultaneously, the charge duration extendes to 3.99 ns, whilst the discharge duration decreases to 4.30 ns. Moreover, increasing the magnetic field frequency from 0.01 to 0.05 ps⁻¹ results in a decrease in maximum temperature and heat flux, which fell to 415.67 K and 1566 W/m², respectively. The charge time decreases to 3.87 ns and the discharge time to 4.50 ns little owing to the increase in frequency. © 2025 The Author(s)
Omar, I.,
Marhoon, T.,
Babadoust, S.,
Najm, A.S.,
Pirmoradian, M.,
Salahshour, S.,
Mohammad sajadi s., S.M. Publication Date: 2025
Results in Engineering (25901230)25
This work examines the buckling behavior of functionally graded porous nanoplates embedded in elastic media. Size effects are added to the nanoplate constitutive equations using nonlocal strain gradient theory. The four-variable refined plate theory is employed for nanoplate modeling. This theory assures stress-free conditions on both sides of the nanoplate and has less uncertainty than high-order shear deformation theories. It is postulated that the nanoplate experiences in-plane compressive loads, which may have both linear and nonlinear distributions. Additionally, uniform and non-uniform porosity distributions are considered. The governing partial differential equations are extracted using the notion of the minimal total potential energy. Following this, the Galerkin method is employed to solve these equations utilizing trigonometric shape functions. Simple, clamped, and combined boundary conditions for nanoplate edges are studied. Once the governing algebraic equations were extracted, the critical buckling load of the nanoplate is determined. To conduct a validation study, the obtained data are juxtaposed with the findings of previous studies, revealing a notable level of concurrence. After the critical buckling load has been ascertained, an inquiry is undertaken to assess the influence of various parameters including nonlocal and length scale parameters, boundary conditions, porosity distribution type, in-plane loading type, geometric dimensions of the nanoplate, and stiffness of the elastic environment, on the static stability of nanoplates. © 2024
Abdolvand, R.,
Yoosefzadeh, S.,
Jaffar, H.A.,
Abdul-redha, H.K.,
Akbari, O.A.,
Ahmadi, G.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2025
International Journal of Thermofluids (26662027)26
Improving the thermal performance of equipment on large and small scales is one of the most important issues in engineering. In this numerical study, the flow and combined convection heat transfer in a two-dimensional (2D) sinusoidal cavity affected by the movement of indirect hot fluid flow are investigated using the finite volume method. By using water/silver nanofluid in volume fractions (φ) of 0 to 0.06 and using fractal surfaces in a 2D cavity with a lid-driven cap in Richardson numbers (Ri) 0 to10, an attempt is made to increase the heat transfer efficiency of the sinusoidal hot surface. The results of this research show that due to the increase in the convective heat transfer coefficient resulted from the strengthening of the fluid velocity, a significant decrease in the temperature of the hot surface is achieved. At Ri = 10, due to the slower movement of the cap and the full compliance of the fluid with the sinusoidal surface, the heat penetration in the fluid layers increases and the temperature graphs become more uniform. The flow circulation between the two hot and cold sources is affected by the density gradients in the cooling fluid and the movement of the cap can create a different temperature distribution. The fluid temperature distribution is also dependent on moving areas in the cavity. The placement of fluid on fractal surfaces is associated with extreme velocity changes. Due to the presence of viscosity and the formation of the velocity boundary layer, this behavior also affects the movement of the fluid layers to the solid surface areas. The highest value of the Nusselt number (Nu) is gained during fluid contact with a cold lid-driven cap on the left side of the cavity. As the fluid moves further on the surfaces of the moving cavity, the hot fluid gradually exchanges its energy with the cavity cover and the fluid cools down. The presence of solid nanoparticles in a higher φ has a significant effect on reducing the temperature of the hot surface, which is due to the increase in the thermal conductivity of the cooling fluid. Compared to the base fluid, this behavior at φ = 0.06 has created a higher thermal efficiency increase of about 15 %. The lowest shear stress is related to the areas of fluid separation on the curved surface. In all investigated cases, the increase of φ can increase the average shear stress between 35 % and 43 % in different Ri. © 2024
Mottaghi, A.,
Mokhtarian, A.,
Hashemian, M.,
Pirmoradian, M.,
Salahshour, S. Publication Date: 2024
Forces in Mechanics (26663597)17
This research investigates the free vibrational behavior of a functionally graded porous (FGP) nanoplate resting on an elastic Pasternak foundation in a hygrothermal environment. The nanoplate is modeled based on the nonlocal strain gradient theory (NSGT) and considering several plate theories including the CPT (classical plate theory), the FSDT (first-order shear deformation theory), and the TSDT (third-order shear deformation theory). Several patterns are investigated for the dispersion of pores, and the surface effects are incorporated to enhance the precision of the model. The governing equations and boundary conditions are derived via Hamilton's principle and an exact solution is provided via the Navier method. The impacts of several parameters on the natural frequencies are inspected such as length scale and nonlocal parameters, surface effects, porosity parameter, hygrothermal environment, and coefficients of the foundation. The results show that the impact of the porosity parameter on the natural frequencies of nanoplates is significantly dependent on the porosity distribution pattern. It is discovered that by increasing the porosity parameter from 0 to 0.6, the relative changes of natural frequencies vary from a decrease of 30 % to an increase of 6 %. © 2024 The Author(s)
Hussein, S.A.,
Omar, I.,
Saddam, A.B.,
Baghoolizadeh, M.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2024
International Journal of Thermofluids (26662027)24
While machine learning has become the new way of analyzing data, neutral networks form the basis of this revolutionary technology. In this work, we shall employ the power of neural networks to analyze and demystify the processes in nanofluids. By combining the precision of neural networks with the optimization capabilities of genetic algorithms, we aim to create a more accurate and efficient prediction model for MWCNT-alumina/water-ethylene glycol (80:20) hybrid antifreeze. Our approach entails using an MLP neural network and several training functions (LM, GD, BFGS, BN) with an adjustable number of neurons. The inputs of the network are φ (solid volume fraction or ϕ), temperature (T), and shear rate (γ), and the output is μnf of MWCNT-alumina/water-ethylene glycol (80:20) hybrid anti-freeze. To improve the accuracy of the final model, we use genetic optimization to make final adjustments to the parameters of the neural network. Utilizing the detailed analysis of the primary characteristics of these algorithms, we conclude that the BFGS function is the best to obtain neural network training. Steady performance achieved by this function—0.99828 of the R-value and RMSE value significantly equal to 0.213—illustrates good stability and accuracy of the suggested model. This work contributes to progressing the existing knowledge about the behavior of nanofluids and can stimulate further improvement in heat transfer and energy utilization. © 2024 The Author(s)
Gao, X.,
Abbas, W.N.,
Al-zahy, Y.M.A.,
Al-bahrani, M.,
Kumar, N.,
Hanoon, Z.A.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2024
Physica A: Statistical Mechanics and its Applications (03784371)653
Most studies considered metal matrix nanocomposites (NCs) because of their excellent mechanical and electrical properties. In recent years, external electric fields (EEFs) in the aforementioned NCs were identified as a crucial role in modulating mechanical behavior. The EEF may affect strength, hardness, ductility, and fracture toughness. The explanation for these changes is the interaction of EEF with the nanoparticles in the metal matrix. In the present study, the effects of various EEF values on the mechanical properties of Al/Cu/Al three-layer NCs (TLNCs) were assessed using the molecular dynamics (MD) modeling method and LAMMPS software. MD findings predicted that the EEF reduced the physical stability and mechanical strength of modeled samples. Physically, this performance resulted from a decrease in attraction force among distinct particles inside the computing box in the presence of EEF. The proposed samples' ultimate tensile strength (UTS) and Young's modulus (YM) decreased to 2.587 GPa and 20.19 GPa, respectively, when the EEF value increased to 0.05 V/Å. Finally, it was determined that EEF is a crucial parameter in the mechanical development of MMNC structures and should be used in mechanical bacterial design in industrial applications. © 2024 Elsevier B.V.
Ali, A.B.,
Al-zahiwat, M.M.,
Fadhil, D.A.,
Nemah, A.K.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2024
International Journal of Thermofluids (26662027)23
Fossil fuels cause global warming and create greenhouse gases that cause irreparable environmental damage. On the other hand, because the combustion reactions are not completely done, dangerous compounds, such as nitrogen or carbon monoxide are produced which are very toxic and dangerous. As a result, innovative methods were implemented in combustion processes. One such method is to use a catalyst during the combustion process. This study used a molecular dynamics method to examine how the concentration of CuO[sbnd]CeO2 catalyst affected air-methane combustion in a helical microchannel. The results show that the maximum (Max) values of density (Dens), velocity (Velo), and temperature (Temp) in the excess oxygen (EO) state were 0.142 atoms per second, 0.35 Å/ps, and 1089 K, respectively, when the atomic ratio of CuO[sbnd]CeO2 increased from 1 % to 4 %. Subsequently, these values exhibited a declining trend. Also, the values of heat flux (HF), thermal conductivity, and combustion efficiency in 4 % catalyst reached the max values of 2038 W/m2, 1.15 W/m·K and 88 %. The results related to the max values of Dens, Velo, and Temp for the oxygen deficiency state had a similar trend and increased to the max values of 0.103 atom/Å3, 0.41 Å/ps, and 1024 K in 4 % catalyst, and then decreased by increasing the catalyst ratio of CuO[sbnd]CeO2 and reaching 10 %. The thermal behavior of nanostructure was more optimal in the deficient oxygen medium. © 2024 The Author(s)
Song, X.,
Baghoolizadeh, M.,
Alizadeh, A.,
Jasim, D.J.,
Basem, A.,
Sultan, A.J.,
Salahshour, S.,
Pirmoradian, M. Publication Date: 2024
International Communications in Heat and Mass Transfer (07351933)156
This paper aims to explore the utilization of machine learning techniques for the accurate prediction of rheological properties in a specific nanofluid system, ZnO(50 %)-MWCNTs (50 %)/Ethylene glycol (20 %)-water (80 %), designed for nano-refrigeration applications. The effective manipulation of the rheological behavior of nanofluids is pivotal for enhancing their heat transfer efficiency and overall performance. By harnessing the predictive power of machine learning, this study endeavors to unravel the intricate relationships governing the rheological characteristics of the nano-refrigerant, ultimately contributing to the development of advanced cooling solutions. The obtained results show that μnf of ZnO(50%)-MWCNTs (50%)/ Ethylene glycol(20%)-water(80%) nano-refrigerant is little affected by T, and even when T varies, this result does not alter much. Also, the lowest μnf occurs when it has the highest temperature and the lowest γ and φ. Finally, it was concluded that the best algorithm in terms of the Taylor diagram for μnf output is the MPR algorithm and the worst is the ECR algorithm and the pattern of γ changes shows that the ideal value of γ is the biggest when μnf levels fall in tandem with their growth. © 2024 Elsevier Ltd
Baghoolizadeh, M.,
Pirmoradian, M.,
Mohammad sajadi s., S.M.,
Salahshour, S.,
Baghaei, S. Publication Date: 2024
Tribology International (0301679X)195
Genetic algorithms and machine learning methods can accurately anticipate hybrid nanofluids' complicated rheology. Scientists and engineers can understand hybrid materials by using genetic algorithms to optimize and machine learning to discover complicated relationships between input variables and rheological responses. As a continuation of the author's previous research on the rheological properties of a nano-lubricant based on engine oil and hybrid nanoparticles, this study uses machine learning and genetic algorithms to theoretically assess the dynamic viscosity of the MWCNT-MgO/oil SAE 50 hybrid nanofluid and identify optimal properties. MLR, D-Tree, Ridge, PLR, SVM, Lasso, ECR, GPR, and MPR are used for regression analysis. Best multi-objective issue solutions are represented by the Pareto front. The NSGA-II algorithm determines the Pareto front. The MPR and NSGA-II algorithms provide a Pareto front with the most precise optimal spot boundaries. The Weighted Sum Method (WSM) simplifies multi-objective problems into single-objective problems, making optimal solutions easier to find. The results show that the maximum margin of deviation for μnf and τ is − 2.5615 and − 5.239, respectively. According to the Taylor chart, the best μnf mode for R, RMSE and STD is equal to 0.9983, 7.6639, 130.0056. Also, these values for τ are equal to 0.9996, 15.4515, and 516.0219. © 2024 Elsevier Ltd
Hashemian, M.,
Jasim, D.J.,
Mohammad sajadi s., S.M.,
Khanahmadi, R.,
Pirmoradian, M.,
Salahshour, S. Publication Date: 2024
Heliyon (24058440)10(9)
This research studied the dynamic stability of the Euler-Bernoulli nanobeam considering the nonlocal strain gradient theory (NSGT) and surface effects. The nanobeam rests on the Pasternak foundation and a sequence of inertial nanoparticles passes above the nanobeam continuously at a fixed velocity. Surface effects have been utilized using the Gurtin-Murdoch theory. Final governing equations have been gathered implementing the energy method and Hamilton's principle alongside NSGT. Dynamic instability regions (DIRs) are drawn in the plane of mass-velocity coordinates of nanoparticles based on the incremental harmonic balance method (IHBM). A parametric study shows the effects of NSGT parameters and Pasternak foundation constants on the nanobeam's DIRs. In addition, the results exhibit the importance of 2T-period DIRs in comparison to T-period ones. According to the results, the Winkler spring constant is more effective than the Pasternak shear constant on the DIR movement of nanobeam. So, a 4 times increase of Winkler and Pasternak constants results in 102 % and 10 % of DIR movement towards higher velocity regions, respectively. Furthermore, the effect of increasing nonlocal and material length scale parameters on the DIR movement are in the same order regarding the magnitude but opposite considering the motion direction. Unlike nonlocal parameter, an increase in material length scale parameter shifts the DIR to the more stable region. © 2024 The Authors
Rostamzadeh-renani, R.,
Baghoolizadeh, M.,
Mohammad sajadi s., S.M.,
Pirmoradian, M.,
Rostamzadeh-renani, M.,
Baghaei, S.,
Salahshour, S. Publication Date: 2023
Alexandria Engineering Journal (11100168)84pp. 184-203
For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero. © 2023 THE AUTHORS
Publication Date: 2023
Tribology International (0301679X)187
In the present study, using 15 machine learning algorithms (MLP, SVM, RBF, ELM, ANFIS, D-Tree, MLR, MPR, BPNN, BN, LM, GD, BFGS, XGB and GMDH), the rheological behavior of oil SAE40 based nano-lubricant in the presence of MWCNT and MgO nanoparticles was predicted. According to the review of several criteria and data analysis charts, it can be concluded that the best algorithm for predicting fluid properties in this article, which are μnf and torque, is equal to GMDH and MPR, respectively. Also, it can be seen that the data predicted by the machine learning algorithms were able to predict the experimental data very accurately. According to this correlation and the high accuracy of the algorithms, data analysis can be performed on the equations. After determining the range of input variables and specifying the objectives, optimization can be done by the NSGA-Ⅱ algorithm. Considering that the problem is multi-objective, it is not possible to find a point where both functions are at their minimum value. For this purpose, optimization provides a set of points to the user to choose the optimal point among them based on the need. © 2023 Elsevier Ltd
Publication Date: 2023
Heliyon (24058440)9(9)
This paper examines the design and fabrication of a soft robot that can connect to a virtual reality environment. This study's primary objective is to utilize these technologies concurrently and demonstrate their applicability in various applications, particularly rehabilitation. Therefore, the process of designing and modeling the soft robot is carried out, and an applied model is created using a 3D printer and silicon material, which is then installed on gloves. Using Unity software, a virtual reality environment is created in which programs, commands, and Arduino processors control the movements of the soft robot, allowing the user to move and pick up an object in a real environment while wearing gloves, and to adjust the amount of pressure and angle of its motion based on the size of each virtual object. During the system evaluation phase, a delay in the performance and reaction time of the soft robot installed on the gloves is observed. This delay is reduced by modifying the programming structure, resulting in optimal system functionality. This capability is used to create proper mobility conditions and rehabilitation for the majority of patients with wrist injuries resulting from strokes and accidents, and it may be effective in accelerating patients' recoveries. © 2023 The Authors
Salarnia, M.,
Toghraie, D.,
Fazilati, M.A.,
Mehmandoust, B.,
Pirmoradian, M. Publication Date: 2023
Journal of the Taiwan Institute of Chemical Engineers (18761070)143
Background: Oscillating heat pipes (OHP) are equipment for heat transfer (HT) with a high heat transfer capacity which transfer heat from a heat source to a heat sink. One of the most significant factors affecting the performance of the heat pipes is the operating fluid contained inside them. Nanofluids (NFs), the fluids containing nanoparticles (NP), improve the thermal conductivity (TC), and HT over the base fluid. Methods: This study investigated the physical and thermal properties behaviors of water and Fe-Fe2O3-Fe3O4/water NFs in an OHP with copper (Cu) walls. In this approach, the molecular dynamics (MD) simulation was used. The current simulation was performed using LAMMPS software. By solving Newton's equation of motion, the trajectories of particles were simulated over time. Significant findings: After 10 ns, the numerical value of heat flux (HF) in the presence of water converged to 1354 W/m2. The maximum numerical density of simulated Fe-Fe2O3-Fe3O4/water NF in the OHP reached 0.016 atom/Å3, 0.021 atom/Å 3, and 0.022 atom/Å3 values, respectively. The numerical maximum velocity of simulated Fe-Fe2O3-Fe3O4 water NF in the OHP converged to 0.057 Å/ps, 0.051 Å/ps, and 0.044 Å/ps, respectively. The numerical maximum temperature of simulated Fe-Fe2O3-Fe3O4 /water NF in the OHP was 522.68 K, 483.48 K, and 452.77 K, respectively. The numerical values of HF of simulated Fe-Fe2O3-Fe3O4/water NF in the OHP increased by 1462 W/m2, 1505 W/m2, and 1561 W/m2, respectively. Finally, the above studies expect an optimal mechanism for HT in the practical applications to be provided. © 2023 Taiwan Institute of Chemical Engineers
Esfe, M.H.,
Esmaily, R.,
Khabaz, M.K.,
Alizadeh, A.,
Pirmoradian, M.,
Rahmanian, A.,
Toghraie, D. Publication Date: 2023
Tribology International (0301679X)178
In this study, a unique incorporated version is presented to enhance the dynamic viscosity of MWCNT- Al2O3 (40:60)/Oil 5W50 hybrid nanofluid (HNF) the usage of the 3 maximum vast and vital powerful parameters corresponding to temperatures, solid volume fractions (SVFs) and shear rates (SRs). An empirical relationship between energy consumption and these characteristics is presented. Thus, ANNs are used to develop a high-level data analysis model to predict the dynamic viscosity of MWCNT-Al2O3 (40:60)/Oil 5W50 HNF. A sensitivity analysis is employed to assess the importance of various parameters of MWCNT- Al2O3 (40:60)/Oil 5W50 HNF dynamic viscosity and the position of temperature, SVF and SR in simulation. It is found that the highest dynamic viscosity values are observed at temperatures below 5 °C. In addition, the dynamic viscosity is reduced by SR changes from 0 rpm to 800 rpm. Statistical analysis shows that the model performance is nearly equal, ranging between 0.98, 0.978, and 0.925, and that the errors are less than 2.6 % for the training, testing, and validation phases, respectively. Overall, it could be determined that the ANN simulation can generate the connection between the measured dynamic viscosity and anticipated dynamic viscosity of HNF. © 2022 Elsevier Ltd
Esfe, M.H.,
Hajian, M.,
Toghraie, D.,
Khabaz, M.K.,
Rahmanian, A.,
Pirmoradian, M.,
Rostamian, H. Publication Date: 2022
Egyptian Informatics Journal (11108665)23(3)pp. 427-436
In this study, the prediction of dynamic viscosity (µnf) of MWCNT-Al2O3 (30:70)/ Oil 5W50 hybrid nano-lubricant using Artificial Neural Network (ANN) is performed. The objective of the present research is to investigate the effect of temperature and solid volume fraction (SVF) to predict the shear rates (SR) and µnf using ANN. The feed-forward ANN consists of a multilayer perceptron network (MLP), which is capable of predicting µnf in connection with experimental data of temperature, SR and SVF. Sensitivity analysis is used to evaluate the importance and role of temperature, SR, and SVF in experimental µnf variations. ANN is generated and tested with experimental data sets and the results show that there was a good agreement between the actual and predicted ANN values. Moreover, the results of ANN simulation are compared with other data processing methods such as Support Vector Machine (SVM), Partial Least Squares (PLS), Principal Component Regression. In addition, the results of the residual value of ANN with seven neurons for µnf can be very small and close to the expected normal value. From this, it can be concluded that the given model can expect exact values. © 2022
