Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Experimental Assessment of Alternating Magnetic Fields for Subcooled Flow Boiling Enhancement in an Annulus(Pergamon-Elsevier Science Ltd, 2026) Youzbashi-Zade, Saeed; Zonouzi, Sajjad Ahangar; Aminfar, Habib; Mohammadpourfard, MousaThe application of magnetic fields to enhance boiling heat transfer in magnetic nanofluids has emerged as a promising strategy for advanced thermal management, yet the influence of alternating magnetic fields remains largely unexplored compared to their constant counterparts. The effects of alternating and constant (steady) magnetic fields on the subcooled flow boiling of a ferrofluid in a vertically oriented annulus are thoroughly investigated experimentally in this work. The magnetic field generated by face-to-face electromagnets was systematically varied in strength (up to 0.3 T), frequency, and waveform (square, triangular, sinusoidal). The results demonstrate that magnetic fields under constant and alternating conditions substantially enhance local and average convective heat transfer coefficients and critical heat flux compared to the no-field baseline. Due to its ability to effectively disrupt the thermal boundary layer and improve bubble dynamics, the alternating square-wave magnetic field (0.3 T, 2 Hz) notably produces the greatest enhancement. Under this condition, the convective heat transfer coefficient increased by up to 21 %, and the critical heat flux improved by approximately 24 % compared to the no-field baseline. The enhancement strongly depends on mass flux and field frequency, with optimal frequencies shifting higher at increased flow rates due to shortened nanoparticle residence time in the magnetic region. At elevated mass fluxes, the benefit of alternating over constant fields diminishes as inertial effects become dominant.Article A Novel ORC-PEM Integrated System for Sustainable Hydrogen Production from Low-Grade Waste Heat in Oil Refineries(Elsevier, 2025) Nazerifard, Reza; Mohammadpourfard, Mousa; Zarghami, RezaThis study presents an integrated multi-generation system for sustainable hydrogen production by harnessing low-grade waste heat from the overhead stream of the NHT unit's stripper column in an oil refinery. The proposed system integrates an ORC with a PEM electrolyzer, forming a novel energy solution that efficiently converts waste heat into clean hydrogen through electricity generation. A detailed model of the proposed system is developed, enabling a comprehensive assessment of its performance from thermodynamic, economic, and environmental viewpoints. At the same time, key operational parameters are optimized using the RSM-BBD method to minimize the hydrogen production cost, thereby enhancing the system's economic viability and practical implementation. The results demonstrated that the system achieves a yearly hydrogen production of 304.53 tons under optimized conditions, for 2.36 $/kg. The integrated system's overall energy and exergy efficiencies are calculated at 8.62 % and 33.43 %, respectively, demonstrating its high thermodynamic performance. Additionally, the system mitigates 3047 tons of CO2 annually by displacing conventional hydrogen production methods.Article Citation - WoS: 2Citation - Scopus: 2Experimental Optimization of Alternating Magnetic Field Parameters for Convective Heat Transfer Enhancement of Ferrofluid in a Vertical Annulus(Pergamon-Elsevier Science Ltd, 2025) Youzbashi-Zade, Saeed; Aminfar, Habib; Mohammadpourfard, MousaThis study presents a detailed experimental investigation of how applying constant and alternating magnetic fields enhances the convective heat transfer of Fe3O4/water ferrofluid flowing through a vertical annulus. The setup was exposed to both constant (steady) and alternating magnetic fields with different waveforms (square, triangular, and sinusoidal), frequencies, intensities, and axial positions. Results showed that both steady and alternating fields substantially increased heat transfer within the active region, with the alternating field providing the highest enhancement. This improvement comes from stronger fluid movement under the oscillating field, which disrupts the thermal boundary layer more efficiently than the steady field. The maximum local heat transfer enhancement decreased from 54.98 % at Re = 200 to 29.43 % at Re = 1000, highlighting the reduced influence of magnetic forces at higher flow rates. The study also explored the influence of magnetic field initiation location, revealing that downstream activation yields higher peak local enhancement, while earlier activation ensures more uniform improvement along the annulus. Among the tested waveforms, the square wave resulted in the greatest convective enhancement, followed by triangular and sinusoidal forms. Results also revealed that, regardless of waveform, increasing frequency initially enhances the heat transfer coefficient, reaching an optimal value typically at 2-5 Hz depending on Reynolds number and waveform.Article Reversibility and Entropy in Bubbling Fluidized Beds: A Recurrence-Based Analysis(Elsevier, 2026) Zarghami, Reza; Mohammadpourfard, Mousa; Akkurt, Gulden GokcenNonlinear time series analysis techniques were applied to characterize bubbling fluidization. The delay method was used to reconstruct the state space attractor and analyze the reconstructed state space. The experiments were carried out in a laboratory-scale fluidized bed, operated under ambient conditions and with various sizes of particles, settled bed heights, measurement heights, and superficial gas velocities. The reversibility of the gas-solid fluidized bed hydrodynamics was investigated using pressure fluctuations by recurrence plot analysis. The anti-diagonal lines of the recurrence plot (RP) were regarded as a measure of reversibility. It was shown that the reversibility versus gas velocity has a concave shape in the bubbling regime. The highest reversibility occurs at velocities remarkably lower than the turbulent transition velocity. In addition, reversibility increases as the size of the particles increases. The Kolmogorov entropy was also estimated to confirm the reversibility analysis in the state space domain. In addition, the average cycle frequency and wideband energy in the frequency domain were also used to clarify the results in the state domain. It was found that a minimum in average cycle frequency, wideband energy, and entropy with an increase in the velocity corresponds to the transition between macro-structures and finer structures of the fluidization system. This minimum was primarily found in the macro-structures of the bubbling fluidization system. These findings can provide a practical tool for the optimal design and operation of the fluidized bed.Article Comprehensive 4E Analysis, Multi-Objective Optimization, and Feasibility Study of Five Natural Gas Liquefaction Processes With a Case Study for Iran(Elsevier Sci Ltd, 2026) Basmenj, Farhad Rahmdel; Tabriz, Zahra Hajimohammadi; Aghdasinia, Hassan; Mohammadpourfard, MousaNatural gas (NG) is increasingly vital as a cleaner energy source due to its lower carbon emissions compared to other fossil fuels. Liquefaction facilitates efficient long-distance transportation. While numerous studies address NG liquefaction's technical aspects, holistic research remains limited. This study presents a comprehensive evaluation of five conventional natural gas (NG) liquefaction processes (including SMR-Linde, SMR-APCI, C3MRLinde, DMR-APCI, and MFC-Linde) through a 4E framework: energy, exergy, exergoeconomic, and exergoenvironmental analyses. Addressing limitations in prior research, it incorporates environmental considerations and introduces production volume-independent metrics to ensure equitable comparisons. Multi-objective optimization, based on exergoeconomic and exergoenvironmental criteria, is employed to identify Pareto-optimal operating conditions. To accelerate this complex process, neural networks are utilized. The study concludes with a feasibility assessment of large-scale LNG production in Iran, offering practical insights for optimizing process selection and enhancing the economic and environmental viability of LNG technologies. Simulations show that the MFC-Linde cycle as the most efficient regarding specific energy consumption (0.2563 kWh/kgLNG), coefficient of performance (3.184), and exergy efficiency (52.32 %). It also demonstrates the lowest unit exergy cost (3.67$/GJ) and exergy unit environmental impact (5271.86mPts/GJ). Multi-objective optimization, considering both exergetic-economic and exergetic-environmental criteria, using neural networks and genetic algorithms in MATLAB identifies Pareto-optimal conditions for all processes. For the MFCLinde, as the most efficient process, optimal operating conditions in the exergetic-economic trade off scenario are: Exergy efficiency of process = 51.45% and Exergy cost rate of LNG = 82, 162.15$/h; at Pressure of NG feed = 5, 925.32kPa, Pressure drop in valve = 5, 831.99kPa, and NG side temperature in heat exchanger = -168.34 degrees C. Finally, a feasibility study for large-scale LNG (Liquefied Natural Gas) production in Iran shows promising results, with a return on investment of 32.24 %/year and a payback period of 2.34 years, indicating the project's potential success in regions with abundant NG reserves.Article Citation - WoS: 1Citation - Scopus: 1Design and Comprehensive Analysis of a Solar-Biomass Hybrid System With Hydrogen Production and Storage: Towards Self-Sufficient Wastewater Treatment Plants(Pergamon-Elsevier Science Ltd, 2025) Tabriz, Zahra Hajimohammadi; Kasaeian, Alibakhsh; Mohammadpourfard, Mousa; Shariaty-Niassar, MojtabaThis paper comprehensively investigates a novel solar-biomass hybrid system designed to produce power, heating, hydrogen, methane, and digestate. The system's design is grounded in regional weather patterns and site-specific resource availability. A comprehensive thermodynamic and exergoeconomic analysis, based on the first and second laws of thermodynamics, is performed alongside parametric studies to evaluate the influence of key parameters on system performance. Multi-objective optimization employs a genetic algorithm facilitated by an artificial neural network to reduce computational time and balance exergy efficiency and total cost. The Pareto front is generated, and the TOPSIS method is employed to identify the optimal trade-off point. The system integrates an auxiliary boiler powered by stored hydrogen and methane to maintain consistent operation during periods of low solar irradiance. Key findings include a base-case overall energy efficiency of 78.67 % and exergy efficiency of 60.41 %. The base-case unit cost of hydrogen is determined to be $3.174/kg, demonstrating competitive viability. Integrating the biomass subsystem with the solar plant resulted in a 40 % increase in exergy efficiency and a 35 % improvement in the total unit cost of products compared to a stand-alone solar system. Optimized system parameters yielded an exergy efficiency of 55.52 % and a total cost rate of 14.98 M $/year. These results confirm the potential of this hybrid system as a promising sustainable solution for developing self-sufficient wastewater treatment plants.Article Machine Learning in Flow Boiling: Predicting Bubble Lift-Off Diameter Despite Data Limitations(Yildiz Technical University, 2025) Tabrizi, Atta Heydarpour; Mohammadpourfard, Mousa; Mohammadpourfard, MostafaThis study concentrates on applying machine learning techniques to flow boiling in order to predict the bubble lift-off diameter. This prediction is critical because the diameter plays a key role in understanding boiling dynamics and calculating heat transfer rates. Additionally, accurately predicting this diameter is essential for optimizing thermal systems and enhancing energy efficiency. By evaluating the performance of three different machine learning algorithms: M5 tree, multilinear regression, and random forest, we aimed to assess their effectiveness in providing reliable predictions even with limited experimental data. This research is essential as it demonstrates the potential of machine learning to enhance predictive accuracy in scenarios where obtaining extensive datasets is challenging. Our findings show that these machine-learning techniques are effective for accurate predictions. The results show that the coefficient of determination ranged from 0.64 to 0.94, indicating how well the models fit the data. The root mean square error was between 0.009 and 0.02, and the mean absolute error ranged from 0.0004 to 0.02. Based on the findings, it can be inferred that the machine learning algorithms used in this study are reliable for predicting bubble lift-off diameter. This reliability also extends to other experimental parameters, suggesting that these techniques can be effectively applied in various contexts with limited data. This study demonstrates the potential of machine learning to predict experimental parameters and advances previous research by identifying key factors that influence bubble lift-off diameter. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 19Citation - Scopus: 20Experimental Investigation of Zinc Ferrite/Insulation Oil Nanofluid Natural Convection Heat Transfer, Ac Dielectric Breakdown Voltage, and Thermophysical Properties(Nature Portfolio, 2024) Pourpasha, Hadi; Heris, Saeed Zeinali; Javadpour, Reza; Mohammadpourfard, Mousa; Li, YaqingImproving the thermal and dielectric properties of insulation oil (INO) with nanoadditives is an important challenge, and achieving dispersion stability in these nanofluids is quite challenging, necessitating further investigation. The main goal of this study is the synthesis and use of the hydrophobicity of zinc ferrite (ZnFe2O4) nanoparticles, which can improve both the thermal and dielectric properties of the INO. This oil is made from distillate (petroleum), including severely hydrotreated light naphthenic oil (75-85%) and severely hydrotreated light paraffinic oil (15-25%). A comprehensive investigation was carried out, involving the creation of nanofluids with ZnFe2O4 nanoparticles at various concentrations, and employing various characterization methods such as X-ray diffraction (XRD), Fourier-transform infrared (FTIR), scanning electron microscopy, energy dispersive X-ray (EDX), zeta potential analysis, and dynamic light scattering (DLS). The KD2 Pro thermal analyzer was used to investigate the thermal characteristics, including the thermal conductivity coefficient (TCC) and volumetric heat capacity (VHC). Under free convection conditions, the free convection heat transfer coefficient (FCHTC) and Nusselt numbers (Nu) were evaluated, revealing enhancements ranging from 14.15 to 11.7%. Furthermore, the most significant improvement observed in the AC Breakdown voltage (BDV) for nanofluids containing 0.1 wt% of ZnFe2O4 amounted to 17.3%. The most significant finding of this study is the improvement in the heat transfer performance, AC BDV, and stability of the nanofluids.Article Citation - WoS: 10Citation - Scopus: 12Bimetallic Mof@cds Nanorod Composite for Highly Efficient Piezo-Photocatalytic Co2 Methanation Under Visible Light(Elsevier Sci Ltd, 2024) Farshchi, Mahdi Ebrahimi; Asgharizadeh, Kimia; Jalili, Hadi; Nejatbakhsh, Siyamak; Azimi, Babak; Aghdasinia, Hassan; Mohammadpourfard, MousaCO2 methanation is leading progress in both dwindling the emitted greenhouse gas and taking advantage of CO2 conversion to a worthwhile fuel. Various types of catalysts have gained researchers' attention. On the other hand, those catalysts chiefly suffer from being uneconomical, owning laborious processes, and having low efficiency. Particularly in the photocatalytic process, electron-hole recombination, charge separation efficiency, and the photocorrosion are the most remarkable obstacles in the path of gaining high efficiency. To conquer the aforementioned hindrances, Cu/Zr-MOF@CdS had been designed in order to not only do elevate CH4 selectivity but also increase CO2 conversion by altering the electron transfer mechanism. Doping Cu in Zr-MOF structure restrains C-C coupling and ameliorates the viability of protonation of *CO to *HCO during methane production. CdS and Zr-MOF both grant piezoelectricity trait to the catalyst in a way that by merging it with the photocatalytic process the mechanism of process converted from type (II) scheme to Z-scheme, culminating in thwarting recombination and increase of charge separation efficiency. The photocatalytic process achieved 23.6 mu mol. g- 1. h- 1 CH4 reaction rate and 80 % CO2 conversion, hereafter applying the piezo-photocatalytic process, these two factors reached 52.2 mu mol. g- 1. h- 1 and 99 %, respectively. This work unveils the viable reaction routes along with their several quotas in piezo-photocatalytic CO2 methanation process by scrutinizing the intricate mechanisms via in-situ analyses.Article Citation - WoS: 13Citation - Scopus: 13The Influence of Nano Filter Elements on Pressure Drop and Pollutant Elimination Efficiency in Town Border Stations(Nature Portfolio, 2023) Ebadiyan, Hamed; Heris, Saeed Zeinali; Mousavi, Seyed Borhan; Nami, Shamin Hosseini; Mohammadpourfard, MousaNatural gas stands as the most ecologically sustainable fossil fuel, constituting nearly 25% of worldwide primary energy utilization and experiencing rapid expansion. This article offers an extensive comparative analysis of nano filter elements, focusing on pressure drop and pollutant removal efficiency. The primary goal was to assess the superior performance of nano filter elements and their suitability as an alternative for Town Border Station (TBS). The research encompassed a six-month examination period, involving routine pressure assessments, structural examinations, and particle characterization of the filter elements. The results revealed that nano filters showed better performance in adsorbing aluminum than conventional filters, possibly due to their cartridge composition. Nano filters contained phosphorus, sulfur, and copper, while conventional filters lacked these elements. The disparity can be attributed to the finer mesh of the nano filter, capturing smaller pollutants. Although the nano filter had minimal silicon, the conventional filter showed some, posing concerns. Despite having 19 extra pleats, the nano filter maintained gas flow pressure while capturing more particles than the conventional filter.