Energy Minimization Research Articles

Computational study of charge transfer iso-surface in first three excited states, electron-hole transition effects, chemical nature and bond order analysis investigations of chrysogine

This work presents the theoretical DFT (Density Functional Theory) studies and the biological application of chrysogine, a marine alkaloid. Energy minimisation and additional DFT evaluations were performed for vacuum and solvent media. It has been observed that solvation with polar solvents has resulted in a slight variation in the molecule’s properties. The Multiwfn software was employed to carry out various topological analyses. Among these, the charge transfer studies show that the second and third excited states are the most significant. From the reactivity analysis, the least energy gap (4.9624 eV) is obtained in water, indicating that chrysogine is most reactive in aqueous media. Theoretical UV studies show that the trends in λmax values correspond to n -->π* and n -->σ* electronic transitions within the molecule. An increase in medium polarity has demonstrated in the MEP (Molecular Electrostatic Potential) maps an increase in the potential range from −6.619 × 10−2 a.u. to 6.619 × 10−2 a.u. in the gas phase, to a sharp rise to −8.036 × 10−2 a.u. to 8.036 × 10−2 a.u. in ethanol, −8.098 × 10−2 a.u. to 8.098 × 10−2 a.u. in methanol, −8.130 × 10−2 a.u. to 8.130 × 10−2 a.u. in DMSO, and −8.127 × 10-2 a.u. to 8.127 × 10−2 a.u. in water. The most significant transition contributing to molecular stability from NBO (Natural Bond Orbital) analysis is: (O2-C9) π* → π* (C7-C8) with the energy of 258.13 kcal mol−1. The ADMET profile for the molecule was assimilated with the help of online servers. The molecule was docked against lung cancer target proteins (PDB ID: 1NTK, 3QFB) using software such as AutoDock Tools and PyMOL. The respective illustrations and data were visualised using Discovery Studio Visualizer. Good binding affinities (−5.69 kcal mol−1 for 1NTK and −6.64 kcal mol−1 for 3QFB proteins) and interactions were achieved with the selected targets.

ASTER data processing and fusion for alteration minerals and silicification detection: Implications for cupriferous mineralization exploration in the western Anti-Atlas, Morocco

Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration. In this investigation, the Visible Near-Infra-Red (VNIR), Short-Wave Infra-Red (SWIR), and Thermal Infra-Red (TIR) bands of the ASTER satellite sensor derived layers were fused to detect alteration minerals and silicification in east the Kerdous inlier for cupriferous mineralization exploration. Several image processing techniques were executed in the present investigation, namely, Band Ratio (BR), Selective Principal Component Analysis (SPCA) and Constrained Energy Minimization (CEM) techniques. Initially, the BR and SPCA processing results revealed several alteration zones, including argillic, phyllic, dolomitization and silicification as well as iron oxides and hydroxides. Then, these zones were mapped at sub-pixel level using the CEM technique. Pyrophyllite, kaolinite, dolomite, illite, muscovite, montmorillonite, topaz and hematite were revealed displaying a significant distribution in relation with the eastern Amlen region lithological units and previously detected mineral potential zones using HyMap imaging spectroscopy. Mainly, a close spatial association between iron oxides and hydroxide minerals, argillic, and phyllic alteration was detected, as well as a strong silicification was detected around doleritic dykes unit in Jbel Lkest area. A weighted overlay approach was used in the integration of hydrothermal alteration minerals and silicification, which allowed the elaboration of a new mineral alteration map of study area with five alteration intensities. ASTER and the various employed processing techniques allowed a practical and cost effective mapping of alteration features, which corroborates well with field survey and X-ray diffraction analysis. Therefore, ASTER data and the employed processing techniques offers a practical approach for mineral prospection in comparable settings.

Comparative properties of ZnO modified Au/Fe nanocomposite: electronic, dynamic, and locator annealing investigation.

A computational representation was used to model the doping and nanomodification of ZnO nanoparticles incorporated in Au/Fe nanocomposite. Au/Fe nanostructure was geometrically and discussed to investigate its electronic properties such electronic band structure and PDOS spectra. Moreover, the ZnO interacted with Au/Fe system was illustrated concerning the modified properties present on the surface of the nanocomposite as it may behave different attribution of band gap evaluated after ZnO modification included. Molecular dynamic simulation of the whole nano system was studied to predict the system stability concerning temperature and energy parameters during 100 ps simulation time. The most effective models under investigation were evaluated using adsorption annealing computations associated with the adsorption energy surface. A highly stable energetic adsorption system was anticipated by the periodic adsorption-annealing calculation. Au and Fe pure metals nanostructures were studied as a separate molecule with (0 0 1) plane surface for optimum energy minimization. Dmol3 module in/materials studio software was utilized for this protocol. The designed Au/Fe layers for nanostructure building material was computationally optimized, where DFT level was considered involving generalized gradient approximation (GGA) with Perdew-Burke-Ernzerh (PBE) exchange functional. In the computations of the structure matrix simulation, the global orbital cutoff was selected. To address the weak quantification of the standard DFT functionals, Tkatchenko-Schefer (TS) (DFT + D) was utilized to precisely correct the pairwise dispersion of the functionals. The electrical parameters were interpreted using the reciprocal space of the ultrasoft pseudopotential representation. To overcome the issues of self-electron interaction, the nonlocal hybrid functional with PBE0 method was utilized to calculate the electronic properties of the studied systems. The computations generated are predicated on a particular trajectory of the gamma k-point band energy interpolations proposed in this examination. An investigation into the position of adsorption came after geometric optimization. Adsorbed on an optimized Au/Fe surface, ZnO nanostructure was computationally explored using the Dmol3 simulation software.

H2-enriched gaseous fuel production via steam-gasification of biomass by using lime-based sorbents

The main objective of the present study was to compare the catalytic activity of selected limestones (Göynük, Dikmen, Polatlı Yolu, Kınık Tüneli and Köserelik) in terms of H2-rich syngas production from biomass gasification. In order to achieve this goal, an ASPEN plus-based model for biomass gasification integrated with carbonation reaction was developed. Tar formation was also considered and the gasification process was carried out based on Gibbs free energy minimization. A combined effect of temperature (600-710 °C), steam/biomass ratio, S/B (0.58-1.58), and limestone/biomass ratio, L/B (0-2.0) on gas composition, H2 yield (HY) and gas yield (GY) was also studied. An increase in both S/B and L/B was beneficial for H2 enrichment. The CO2 content declined sharply from 16.83 vol% to 10.28 vol% with increasing L/B from 0 to 1.5; however, a marginal reduction in CO2 content was observed with further increase in L/B. The highest HY (51.01 g/kg) was produced with Dikmen followed by Göynük, Polatlı Yolu, Kınık Tüneli and Köserelik. The results also showed that the limestone samples with high CaO content in general exhibits a better H2 production capacity than limestones with low CaO content. To the best of our knowledge, no simulation study has been presented to compare the catalytic activity of different limestones in terms of H2 production.

Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization

BackgroundDental resin-based composites are widely recognized for their aesthetic appeal and adhesive properties, which make them integral to modern restorative dentistry. Despite their advantages, adhesion and biomechanical performance challenges persist, necessitating innovative strategies for improvement. This study addressed the challenges associated with adhesion and biomechanical properties in dental resin-based composites by employing molecular docking and dynamics simulation.MethodsMolecular docking assesses the binding energies and provides valuable insights into the interactions between monomers, fillers, and coupling agents. This investigation prioritizes SiO2 and TRIS, considering their consistent influence. Molecular dynamics simulations, executed with the Forcite module and COMPASS II force field, extend the analysis to the mechanical properties of dental composite complexes. The simulations encompassed energy minimization, controlled NVT and NPT ensemble simulations, and equilibration stages. Notably, the molecular dynamics simulations spanned a duration of 50 ns.ResultsSiO2 and TRIS consistently emerged as influential components, showcasing their versatility in promoting solid interactions. A correlation matrix underscores the significant roles of van der Waals and desolvation energies in determining the overall binding energy. Molecular dynamics simulations provide in-depth insights into the mechanical properties of dental composite complexes. HEMA-SiO2-TRIS excelled in stiffness, BisGMA-SiO2-TRIS prevailed in terms of flexural strength, and EBPADMA-SiO2-TRIS offered a balanced combination of mechanical properties.ConclusionThese findings provide valuable insights into optimizing dental composites tailored to diverse clinical requirements. While EBPADMA-SiO2-TRIS demonstrates distinct strengths, this study emphasizes the need for further research. Future investigations should validate the computational findings experimentally and assess the material's response to dynamic environmental factors.

Multi-agent reinforcement learning based computation offloading and resource allocation for LEO Satellite edge computing networks

Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals’ energy consumption can be effectively reduced by our proposed algorithm.

Unified framework for the separation property in binary phase-segregation processes with singular entropy densities

Abstract This paper investigates the separation property in binary phase-segregation processes modelled by Cahn-Hilliard type equations with constant mobility, singular entropy densities and different particle interactions. Under general assumptions on the entropy potential, we prove the strict separation property in both two and three-space dimensions. Namely, in 2D, we notably extend the minimal assumptions on the potential adopted so far in the literature, by only requiring a mild growth condition of its first derivative near the singular points $\pm 1$ , without any pointwise additional assumption on its second derivative. For all cases, we provide a compact proof using De Giorgi’s iterations. In 3D, we also extend the validity of the asymptotic strict separation property to the case of fractional Cahn-Hilliard equation, as well as show the validity of the separation when the initial datum is close to an ‘energy minimizer’. Our framework offers insights into statistical factors like particle interactions, entropy choices and correlations governing separation, with broad applicability.

Variational manifolds for ground states and scarred dynamics of blockade-constrained spin models on two- and three-dimensional lattices

We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-12 systems as realized by Rydberg atom arrays. Our manifold permits analytical calculation via perturbative expansion of one- and two-point functions in arbitrary spatial dimensions and allows for efficient computation of the matrix elements required for variational energy minimization and variational time evolution in up to three dimensions. We apply this framework to the PXP model on the hypercubic lattice in one, two, and three dimensions and show that, in each case, it exhibits quantum phase transitions breaking the sublattice symmetry in equilibrium, and hosts quantum many-body scars out of equilibrium. We demonstrate that our variational ansatz qualitatively captures all these phenomena and predicts key quantities with an accuracy that increases with the dimensionality of the lattice, and conclude that our method can be interpreted as a generalization of mean-field theory to constrained spin models. Published by the American Physical Society 2024

A hybrid approach combining mechanism-guided data augmentation and machine learning for biomass pyrolysis

Process accuracy modelling requires comprehensive thermodynamics and kinetic knowledge, which is not feasible for complex biomass pyrolysis. Here, we propose a hybrid modelling strategy that combines mechanism-guided modelling (Aspen Plus) with data-driven machine learning (ML) models to accurately predict biomass pyrolysis products. Specifically, we establish the pyrolysis equilibrium model based on the first principles and Gibbs free energy minimization approach; then, the equilibrium model is leveraged to augment data by selecting points with smaller residuals; finally, the augmented data and experimental data serve as input for the ML models training. The results revealed that the hybrid model exhibited superior performance, with average R2 and MAPE values of 0.981 and 0.266, respectively, for predicting biochar yield and heating value. Conclusively, this work introduces new insight and strategy to accelerate the engineered application of bioenergy via modelling biomass pyrolysis and screening biomass.

Local and global changes in cell density induce reorganisation of 3D packing in a proliferating epithelium.

Tissue morphogenesis is intimately linked to the changes in shape and organisation of individual cells. In curved epithelia, cells can intercalate along their own apicobasal axes, adopting a shape named 'scutoid' that allows energy minimization in the tissue. Although several geometric and biophysical factors have been associated with this 3D reorganisation, the dynamic changes underlying scutoid formation in 3D epithelial packing remain poorly understood. Here, we use live imaging of the sea star embryo coupled with deep learning-based segmentation to dissect the relative contributions of cell density, tissue compaction and cell proliferation on epithelial architecture. We find that tissue compaction, which naturally occurs in the embryo, is necessary for the appearance of scutoids. Physical compression experiments identify cell density as the factor promoting scutoid formation at a global level. Finally, the comparison of the developing embryo with computational models indicates that the increase in the proportion of scutoids is directly associated with cell divisions. Our results suggest that apico-basal intercalations appearing immediately after mitosis may help accommodate the new cells within the tissue. We propose that proliferation in a compact epithelium induces 3D cell rearrangements during development.

Joint energy management with integration of renewable energy sources considering energy and reserve minimization

In light of electric vehicles (EVs) and demand-side management, a combined optimization paradigm for microgrid (MG) resource scheduling is proposed in this study. Renewable energy sources, including wind and solar power, fulfill a fraction of the consumption demand, while the MG maintains a connection to the electrical network to facilitate power transactions. By utilizing EVs, we can mitigate the inherent volatility of renewable energy production with manageable loads and level out the system's demand profile. The proposed system employs a bi-level programming strategy, wherein the initial level is dedicated to the minimization of aggregate costs, encompassing those related to energy and reserves. Additionally, costs associated with resource redistribution due to the uncertainty of solar and wind power output are minimized at the second level. Subsequently, the objective function is addressed using the enhanced adapted approach of modified marine predators algorithm (MMPA), chosen for its robustness and effectiveness as an optimization technique. The outcomes obtained for a microgrid underscore the superior performance of the results achieved through the application of the MMPA algorithm when contrasted with alternative established methodologies. The outcomes have demonstrated that integrating EVs and adaptable loads contributes to a decrease in operational expenses and emissions. Furthermore, the uncertainties associated with WT and PV are effectively counterbalanced. Additionally, the results garnered from the simulations indicate that the recommended MMPA technique has the capacity to yield superior solutions compared to several other widely recognized optimization algorithms. Scheduling with both DR and EVs with three cost components are considered, generation cost, reserve cost, and initial unit cost, amounting to $740.11, $9.61, and $6.07, correspondingly and it can lead to decreased emission levels and increased incentives for proprietors of EVs, responsive load participants, and RES.

Hybrid optimization approach for energy minimization in wireless sensor networks leveraging XGBoost and random forest

Wireless Sensor Networks (WSNs) have garnered significant attention from both the academic and industrial communities. However, the limited battery capacity of WSN nodes imposes a set of restrictions on energy dissipations, which has compelled researchers to seek ways to save and minimize energy consumption. This paper presents a hybrid optimization model to minimize energy dissipation in Wireless Sensor Networks (WSNs). Employing linear programming and a combination of XGBoost and Random Forest algorithms, it effectively predicts internode distances and network lifetime. The results demonstrate significant energy savings in WSN deployments, outperforming traditional methods. This approach contributes to the field by offering a practical, energy-efficient strategy for WSN configuration planning, highlighting the model’s applicability in real-world scenarios, where energy conservation is critical.

Smart traffic management of vehicles using faster R-CNN based deep learning method

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses adaptive background modeling. It also addresses shadow and illumination issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this framework with respect to other methods.

Optimizing thermoelectric performance of BiSbPbTe thin films through controlled post-annealing: A comprehensive structural and electronic study

This study investigates the structural and electronic properties of BiSbPbTe thin films, focusing on the impact of post-annealing time and temperature. The X-ray diffraction (XRD) analysis reveals a stable rhombohedral phase formation in all samples, with no secondary phases detected within the detection limit. The high crystallinity of the films is confirmed, and post-annealing leads to an increase in peak intensity, indicating enhanced crystalline structure. Surface morphology analysis using scanning electron microscopy (SEM) demonstrates a transition from inhomogeneous features in as-grown films to homogeneous and dense formations in post-annealed films. The grain size increases with post-annealing time, attributed to diffusion, energy minimization, nucleation, growth, and thermal activation processes. The electronic transport properties, including carrier concentration and conductivity, are influenced by post-annealing temperature and duration. A stable carrier concentration is observed for films post-annealed for up to 2 h, while longer durations decrease due to reduced defect vacancies and grain boundaries. The increase in charge carrier mobility, confirmed by SEM results, contributes to enhanced electrical conductivity. The Seebeck coefficient shows an initial enhancement up to 2 h post-annealing, attributed to defect creation and increased grain boundaries. However, further increases in post-annealing duration result in a decrease in the Seebeck coefficient due to enhanced charge carrier mobility and reduced grain boundaries. The thermoelectric power factor (4.4 μW/cm1K2), determined by the square of the Seebeck coefficient and electrical conductivity, reaches its maximum value in the 2-h post-annealed sample, emphasizing the importance of optimizing post-annealing conditions for improved thermoelectric performance.

MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

VALORIZATION OF WASTE FRUIT PEELS: A COMPUTATIONAL STUDY ON PREDICTION OF GASIFICATION PRODUCT COMPOSITION

In this work, a variety of discarded fruit peels are investigated using a computational technique in order to anticipate the products of their gasification when subjected to higher temperatures, namely 800°C and 900 °C. The proximate and ultimate analyses of eight different fruit peels were collected from a variety of published sources, and the composition of gasification was predicted by applying the Gibbs free energy minimization method to each fruit peel individually and to both temperatures using the Solver Tool feature of the Microsoft Excel Spreadsheet. As input for the simulation, the thermodynamic constants for each gaseous component and the elemental analysis of each fruit peel were needed. The predicted gaseous composition was compared with some of the data obtained in the literature, and it was determined to be in a close match based on the root mean square error for each number. This was done while taking into account the fact that the carbon conversion process during gasification was only partially successful. For citation: Khedkar R.S., Khonde R.D., Meshram P. Valorization of waste fruit peels: a computational study on prediction of gasification product composition. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2024. V. 67. N 6. P. 127-134. DOI: 10.6060/ivkkt.20246706.6959. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Quasiconformal almost parametrizations of metric surfaces

We look for minimal conditions on a two-dimensional metric surface X of locally finite Hausdorff 2-measure under which X admits an (almost) parametrization with good geometric and analytic properties. Only assuming that X is locally geodesic, we show that Jordan domains in X of finite boundary length admit a quasiconformal almost parametrization. If X satisfies some further conditions, then such an almost parametrization can be upgraded to a geometrically quasiconformal homeomorphism or a quasisymmetric homeomorphism. In particular, we recover Rajala’s recent quasiconformal uniformization theorem in the special case that X is locally geodesic as well as Bonk–Kleiner’s quasisymmetric uniformization theorem. On the way, we establish the existence of Sobolev discs spanning a given Jordan curve in X under nearly minimal assumptions on X and prove the continuity of energy minimizers.

Natural language syntax complies with the free-energy principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” criteria from theoretical syntax—adhere to the FEP. This affords a greater degree of explanatory power to the FEP—with respect to higher language functions—and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel–Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing–Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

A bilevel fast-convergent optimizer via high-fidelity convex models: Application on optimal operation of all-parallel heterogeneous chiller-pump systems

To cope with buildings’ growing and diverse cooling demand, heterogeneous multi-chiller systems with non-dedicated pumps are commonly applied in large-scale centralized chiller plants. All-parallel heterogeneous systems offer high operation flexibility but pose challenges to optimal operation planning. For the deficiencies in convexity tractability, the classical neural networks (NNs) achieved great success in system identification yet were restricted in model-based optimization. In this study, an optimization-oriented convex modeling approach based on input convex neural networks (ICNN) to identify energy consumption models for energy units of chiller-pump systems was presented, which provides convex input–output mappings while leveraging the high-fidelity capability of NNs. Using the convex models, the energy minimization issue subjected to multiple constraints was formulated, the optimality of which was mathematically proven. A bilevel deterministic optimizer was developed to determine the global optimal. Comprehensive data experiments were conducted using practical and simulated operation data to evaluate the modeling and optimization performances of the proposed methods compared with conventional candidates. Numerical results suggest that the ICNN-based convex models exhibit superior modeling performances than physics-based models. A better overall energy saving ratio of 8.86 % and faster convergence time of 3.53 s for average achieved by the proposed bilevel optimizer compared with rule-based and meta-heuristic optimizers under identical external conditions.

A Simplified Bubble Size Relation Compatible with the Energy Minimization Multiscale Drag Model for Studying Hydrodynamics in a 2D Gas–Solid Tapered Fluidized Bed

Gas–solid fluidized bed reactors are extensively utilized in direct reduced iron production. In practice, these reactors will have a wide particle size distribution, which is better handled by tapered fluidized beds due to their vertical velocity gradient. Herein, a simplified bubble size relation is proposed to remove implicit interdependency between the bubble size and its drag coefficient in the bubble‐based energy minimization multiscale (EMMS) heterogeneous drag model. Further, the proposed drag model is coupled with the two‐fluid kinetic theory of granular flow model to investigate hydrodynamics. The heterogeneous flow structure predicted by the model is similar to experiments. Further, the bulk parameters such as bed expansion ratio and bubble fraction obtained from the simulations using a simplified EMMS drag model are compared and are found to be in good agreement with the experimental findings, with mean relative deviations of 3.95% and 14.64%, respectively. The time‐averaged bubble fraction and bed expansion ratio are found to increase with air velocity and decrease with taper angle, whereas a reverse trend is observed for the mean particulate fluidized area fraction. Based on the current study, the taper angle between 5° and 10° is found to be most suitable.