Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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 Citation - WoS: 3Citation - Scopus: 1Enhancing Biomass Pyrolysis via Microwave Heating: A CFD-DEM Study on Intensification in Fluidized Beds(Elsevier Sci Ltd, 2026) Hamidani, Golnaz; Kazemi, Saman; Eslami, Ali; Zarghami, Reza; Sotudeh-Gharebagh, Rahmat; Mostoufi, NavidBiomass conversion into high-value products in fluidized beds can be significantly improved by utilizing microwave irradiation as the heating source. The present work studied microwave-assisted biomass pyrolysis using a coupled CFD-DEM model in a fluidized bed. The effect of key operating parameters, including inlet gas velocity (1.5, 2, and 2.5 times the minimum fluidization velocity), mean particle diameter (1.2, 1.3, and 1.5 mm), and microwave power input (200, 400, and 600 W), was evaluated on the performance of the reactor. The results revealed that higher microwave power increased the mean particle temperature and chemical conversion rate due to greater internal energy generation within the biomass particles. Increasing the gas velocity led to lower particle temperature because of enhanced convective heat transfer to the gas phase, and improved the uniformity of temperature and conversion distributions. Furthermore, decreasing the mean particle diameter from 1.5 to 1.2 mm increased the average temperature, from 890 to 987 K, and raised biomass conversion from 14.8 to 18.1 %, mainly by reducing convective heat losses. The validated model developed in this study enables accurate predictions of process behavior and provides valuable insights for optimizing microwave-assisted biomass pyrolysis in fluidized beds. These findings highlight the potential of microwave-assisted fluidized bed pyrolysis as an efficient technique for process intensification in producing valuable bio-based products.Article Citation - WoS: 1Citation - Scopus: 1A Novel Ml-Dem Algorithm for Predicting Particle Motion in Rotary Drums(Elsevier Sci Ltd, 2025) Kazemi, Saman; Zarghami, Reza; Mostoufi, Navid; Sotudeh-Gharebagh, Rahmat; Al-Raoush, Riyadh I.The discrete element method (DEM) is a widely used approach for studying the behavior of particles in industrial equipment, including rotary drums. Although DEM is highly accurate and efficient, it suffers from the computational cost in simulations. The primary objective of this research is to reduce the computational costs of DEM by introducing a novel machine learning (ML) approach based on a deep neural network for predicting particle behavior in rotary drums. The proposed approach utilizes a continuous convolution operator in a neural network. To evaluate its effectiveness, the results of the proposed ML-DEM approach were compared quantitatively and qualitatively with the experimental data and the conventional DEM results. It was shown that in addition to its high accuracy, the proposed approach reduces the computational costs by approximately 35 % and 65 % compared to the conventional DEM simulations on GPU and CPU (with 8 processors), respectively. Furthermore, to ensure the comprehensive and independent validation of the proposed algorithm, the study investigated the effects of various parameters such as drum rotational speed and fill ratio on lateral entropy-based mixing, circulation time, and velocity profile in the active layer. The results were then compared with those obtained using the conventional DEM and found to be in good agreement. This new algorithm can serve as a starting point for reducing computational costs in simulating particle motion in granular systems.