In this study, a two-phase interleaved boost converter has been analyzed. The performance of this converter in terms of output voltage regulation has been compared with the proportional-integral (PI), active disturbance rejection control (ADRC), and artificial neural network (ANN) techniques. The ANN controller achieves faster output voltage tracking under load transient conditions and provides less steady-state error in the output voltage as compared to the ADRC and PI controller. The comparative analysis of the three control techniques is performed in the MATLAB/Simulink domain. The ANN controller achieved the lowest steady-state error of 0.021
In a solar photovoltaic (PV)-fed grid-tied two-stage power converter system consisting of a DC–DC boost converter at the front end followed by a three-phase voltage source inverter (VSI) at the rear (grid) end, the boost converter control scheme ensures maximum power point tracking by controlling the duty ratio of its controlled switch, while the VSI is controlled to maintain a regulated and boosted DC bus voltage along with controlling reactive power exchange with the grid. This paper presents development of a state-space averaged linearized mathematical model (SSAM) of the boost converter, operating under continuous conduction mode, considering presence of the solar PV module at its input and the grid-side VSI at its output. The derivation of this model is presented first, followed by comparative study of its predicted behaviours with a more accurate detailed numerical model (DNM) of the system, where active and passive devices and the high-speed PWM switching effects are more rigorously modelled. Finally, experimental verification on a hardware prototype of such a system is made with its results discussed. A comparative study of the results obtained out of the SSAM, DNM and the experiments is finally presented, showing satisfactory agreement. The developed SSAM model can be utilized by researchers and practical system designers for analytical design of the maximum power point tracking (MPPT) controller. It could also use towards scientific rational-based decision-making about the sampling interval to be chosen while sensing the PV panel output voltage and current that are extremely critical for implementing any perturb and observe (P O)-based or hill climbing-based MPPT algorithm.
Effective cooling of small enclosures with complicated shapes is essential in contemporary energy and electronics cooling. Nevertheless, the mixed convection of shear forces, transport of nanofluids, and generation of entropy in crown-shaped enclosures with internal heaters are not well understood. This study fills this gap by performing a systematic analysis of the mixed convection (MC), heat transfer (HT), and entropy generation (EG) properties of TiO_2 –water nanofluid in a shear-driven crown-shaped enclosure that has a centrally located cone heater. The steady, laminar, and incompressible governing equations with the Boussinesq approximation are solved by a Galerkin weighted residual finite element method (GWRFEM). The impact of the nanoparticle volume fraction ( ϕ = 0.00 - 0.05) , Richardson number ( Ri≈ 0.1 - 10) , and Reynolds number ( Re≈ 100 - 400) on fixed Ra = 10^6 and Pr = 6.8 is studied using streamlines, isotherms, and the average Nusselt number ( Nu_avg ), EG, and Bejan number (Be) distributions. The findings demonstrate that the enhancement of Re and Ri causes a significant amplification of circulation strength and heat transfer, and further loading of nanoparticles elevates thermal performance because of the increased effective conductivity. Nu_avg rises with Re, Ri, ϕ , and Be are much less than 0.5, meaning that frictional irreversibility is prevailing throughout the range of study. The Be analysis proves irreversibility between fluids and friction control in all the studied regimes. The results offer effective design guidance to maximize heat transfer and reduce irreversibility in sophisticated thermal systems, such as solar thermal systems, compact heat exchangers, and electronic cooling equipment.
The rationally designed Ni-promoted Cu–Zn/Cr2O3 mixed oxide catalysts were utilized and the thioxopyrimidine derivatives were synthesized in the presence of multiple components by using the condensation reaction. Catalyst was prepared by co-precipitation method with the following load of Cu, Zn, and different amounts (0.3–1.0 wt) of Ni. A comprehensive physiochemical characterization results of the catalyst conducted using FT-IR, XRD, BET, XPS, SEM, and CO2-TPD successfully demonstrated well-dispersed mixed oxide phases having mesoporosity, stable oxidation states (Cu2+, Ni2+, Cr3+), and a well-balanced Lewis acidic and basic sites distribution. This Ni-promoted Cu–Zn/Cr2O3 catalyst with the 0.8 wt
Although several studies have examined the potential of synthetic viscoelastic polymer solutions to improve residual oil recovery beyond that of water flooding, no attempts have examined their recovery potential when the preceding water flooding is conducted at different flux rates. In this manuscript, we report the results of two multi-rate viscoelastic polymer flooding experiments. In the first experiment, the limestone core was preflooded with water at 2ft/day, whereas in the second experiment, the limestone core was subjected to bump water flooding up to 16 ft/day, followed by glycerin injection to attain higher water saturation and well-swept residual oil saturation (Sor). 2500 ppm high saline viscoelastic polymer solutions were injected at 75 °C in both the water flooded cores saturated with 3.6 cP oil. The results showed that polymer injection at shear-thinning fluxes of 0.2 ft/day induced additional oil recovery in both experiments. However, the magnitude of the increase in oil recovery was much lower in the second experiment, at 5.2
In recent years, sentiment analysis has become a prominent research area in natural language processing, with advances in deep learning significantly improving multiclass sentiment classification through enhanced modeling of contextual and semantic relationships. In this study, a deep learning–based sentiment analysis framework is evaluated on multisource English user reviews. Sequential representations derived from FastText word embeddings are used to assess baseline recurrent models (RNN, LSTM, Bi-LSTM, and GRU), attention enhanced hybrid architectures, and the proposed Adaptive Fine-Grained Feature Aggregation (AFFA) model. To improve data quality and label consistency, potentially mislabeled instances are identified and removed using a pretrained CardiffNLP Twitter-RoBERTa–based sentiment classifier. The AFFA architecture adaptively fuses representations from multiple encoders by learning encoder-specific contribution weights, which are normalized through a Softmax function to dynamically emphasize complementary feature representations. The experimental results indicate competitive class-wise discrimination performance, with modest improvements observed in selected metrics for the neutral sentiment class. The AFFA–LSTM (3 Layer)–Attention model achieved the best overall performance, obtaining 94.60
Phthalates (PAEs) are a group of chemicals widely used as plasticizers in various industrial and consumer products. Due to their extensive use and potentially harmful effects on human health, particularly as endocrine-disrupting compounds, the rapid and efficient detection of PAEs is crucial. Recent developments in sensor technology have significantly enhanced the monitoring of environmental and food contaminants. This study aimed to develop a surface plasmon resonance (SPR) sensor utilizing diethyl phthalate-imprinted nanoparticles (DEP-MINPs) for the sensitive and selective detection of DEP. DEP-MINPs were synthesized via microemulsion polymerization and immobilized onto a bare gold SPR chip as a recognition layer. The DEP-MINPs were characterized using FTIR, TEM, and zeta potential analyses. The kinetic performance was investigated within a concentration range of 1.0–135.0 μM, observing a strong linear correlation between DEP concentration and SPR sensor response. The calculated limit of detection was 0.30 μM. The Langmuir model best described the interaction, suggesting monolayer adsorption on a homogeneous surface. Selectivity studies using structurally similar compounds, including dimethyl phthalate, styrene, and vanillic acid, revealed that the sensor was 2.64, 5.00, and 4.83 times more selective for DEP, confirming the success of the molecular imprinting process. The developed SPR sensor was successfully used for DEP analysis in bottled water and sanitary pad samples. The DEP-MINPs SPR sensor demonstrated excellent reproducibility and stability over multiple measurements and days, supporting its potential for practical applications in environmental monitoring.
The inherent stochasticity of wind resources poses fundamental challenges to the reliable integration of wind power plants (WPPs) into modern grid infrastructures. This study introduces a novel, integrated framework that synthesizes high-fidelity, empirically calibrated digital twin modeling—built upon the National Renewable Energy Laboratory’s System Advisor Model (NREL SAM) using validated operational data from existing WPPs—with statistical process control (SPC) methodology to establish a robust, multi-scale early warning system for wind energy systems. A comprehensive, empirically calibrated digital twin model of a 48 MW WPP, comprising 32 GE 1.5 SLE turbines situated in Colorado, USA, was constructed using the NREL SAM platform, which is extensively validated against real-world operational data from existing wind power plants. The model incorporated site-specific meteorological parameters, detailed turbine specifications, rectangular farm configuration with eight rotor diameter spacing, and an extensive loss taxonomy yielding an 11.02
To characterize the deformation irreversibility and loading–unloading path dependence in aeolian sand–loess mixtures (ASLMs), one-dimensional confined loading–unloading tests were conducted on specimens with sand mixing ratios of 0
Vibration-based structural health monitoring (SHM) has become an important tool for damage detection, condition assessment, and integrity evaluation of engineering structures. Consequently, the variational iteration method (VIM) has attracted significant attention due to its accuracy and efficiency in the free vibration analysis of beam structures. However, its application to complex configurations, such as stepped, multi-segment, or multi-cracked beams, has remained limited, primarily because of the high computational cost and memory requirements associated with extensive symbolic integrations. To overcome these limitations, the present study employs an enhanced numeric–symbolic hybrid variational iteration method (VIM), originally developed in earlier foundational studies and recently extended by the authors. This hybrid formulation significantly reduces computational time and memory requirements when applied to complex vibration-based damage assessment problems. In this work, the proposed methodology is further extended to analyze the dynamic behavior of generalized multi-span cracked Timoshenko beam systems, including beams with cracks repaired using bonding (filling) materials. To the authors’ knowledge, this represents the first application of the hybrid VIM framework to the vibration analysis of beams containing filled cracks. The proposed model is validated by comparison with existing analytical solutions for uncracked and unfilled cracked beams. To capture the vibration characteristics of the filled crack, an independent finite element model is developed in ANSYS. The results demonstrate an excellent agreement between the present formulation and the reference solutions, confirming the accuracy and robustness of the proposed approach. A parametric study is subsequently conducted to examine the crack-healing phenomenon via crack filling, with particular emphasis on the effect of the filler material’s shear modulus relative to that of the host beam. The results reveal that, under the assumption of complete filling and perfect bonding between the filler and host materials, even a filler material with a relatively low elastic modulus can effectively restore the stiffness and vibration characteristics of the damaged region, closely approximating the dynamic response of a structurally healed beam.
This paper presents a fracture-mechanics-based analytical framework for evaluating the virtual crack closure integral (VCCI) of structures containing filled cracks. Building on the formulation of Fowkes et al., closed-form expressions for the stress and displacement fields in the vicinity of a filled crack are derived by explicitly accounting for the presence of a wedge-shaped filling material. Using these fields, the virtual crack closure integral is analytically evaluated for the filled-crack problem. Finite element simulations are also conducted using ANSYS to evaluate the J-integral at the crack tip, which is equivalent to the VCCI. Comparisons between the analytical and numerical results for different filling material properties and crack depths show excellent agreement, validating the proposed formulation. The influence of the wedge angle on VCCI is examined. The applicability of the stress intensity factor to filled cracks is also discussed, and its limitations and relationship to energy-based fracture parameters are clarified. In addition, the effects of Poisson’s ratio of both the filling material and the surrounding matrix are investigated. The proposed model provides an efficient tool for evaluating fracture parameters in filled-crack systems and can be readily incorporated into simplified static and dynamic structural analyses, thereby supporting the development of more reliable and resilient structures and contributing to infrastructure sustainability.
Solar-driven photothermal interface evaporation technology offers an attractive route to mitigate water scarcity with low environmental burden and favorable economics, yet it remains nontrivial to engineer evaporators that simultaneously deliver high flux, operational stability and apply across different real-world conditions. Herein, a three-dimensional (3D) hierarchical porous cement-based architecture was constructed, with carbon dots incorporated into interfacial regions to form a carbon-dot-enhanced cement-based evaporator (CDs-CBE). The uniformly distributed carbon dots serve as reinforcing constituents, significantly enhancing broadband light absorption and photothermal conversion performance. The optimized CDs-CBE−2 achieved excellent evaporation rate of 2.16 kg m−2 h−1 and evaporation efficiency of 91.6
Metal additive manufacturing (MAM) has transformed the creation of complex geometries for the aerospace, energy, and biomedical industries, but widespread industrial use has been limited by inherent process limitations such as poor surface finish, dimensional error, and critical defects such as porosity and residual stresses. This review thus presents a thorough analysis of in situ hybrid additive manufacturing (HAM) systems as a practical pathway to address these problems. This review first categorizes and analyzes the main types of defects associated with fusion-based processes, i.e., powder bed fusion (PBF) and directed energy deposition (DED), such as lack of fusion, keyholing, and solidification cracking. Building on this basis, the paper critically explores emerging in situ hybrid architectures combining additive deposition with secondary processes such as subtractive machining, plastic deformation (e.g., interlayer rolling, peening), and laser-based finishing within a single-chucking environment. The review focuses on the active mitigation of defects, the microstructure refinement, and surface integrity enhancement in real time through synergistic combinations, as opposed to sequential post-processing approaches. Furthermore, the critical role of digital integration is discussed and includes the development of advances in hybrid process planning and multi-modal in situ monitoring and the implementation of artificial intelligence (AI) and machine learning (ML) for closed-loop control. Finally, the paper evaluates the techno-economic feasibility of hybrid routes and finds key challenges that still remain in qualification, standardization, and data infrastructure needed for full industrial deployment.
Experimental investigations were carried out to characterize the flame stability and combustion dynamics of non-premixed LPG–air flames in a swirl-stabilized combustor addressing a notable gap in the literature. Experimental data for non-premixed LPG–air flames, across gas turbine-relevant firing rates and varying burner geometries remain limited. The experiments were conducted over a range of equivalence ratios, from the lean blowout (LBO) up to stoichiometric conditions (Φ ≤ 1), and combustor firing rates between 4 and 10 MW/m3-bar. Two annular air ducts with diameters of 12 mm and 16 mm were employed to vary the oxidizer Reynolds number. The novel contributions of this work are: (1) quantification of LBO equivalence ratios for non-premixed LPG–air flames over firing rates representative of micro-gas turbine operation; (2) demonstration that increasing the annular air duct diameter from 12 to 16 mm extends the lean stability limit by reducing the oxidizer Reynolds number and the associated flame strain rate; and (3) provision of axial temperature profile data suitable for validating numerical models of LPG–air combustion, for which benchmark experimental datasets are scarce. The results indicate that the LBO equivalence ratio is consistently lower for the 16 mm burner compared to the 12 mm configuration across all firing rates investigated. Additionally, the LBO equivalence ratio increases with increasing firing rate, primarily due to elevated turbulent strain rates. Froude number analysis reveals that the flames are buoyancy-dominated at blowout conditions for all firing rates considered. Higher axial temperatures are recorded in the 12 mm burner configuration.
Chemical enhanced oil recovery using nanoparticles and surfactants has attracted the attention of researchers because of its efficacy in improving oil displacement efficiency. This research aims to investigate the impact of Al2O3 nanoparticles on cetyltrimethylammonium bromide (CTAB) cationic surfactant for improving oil recovery in low-permeability carbonate reservoirs. The presence of different mineralogy and functional groups in carbonate rock was analyzed by XRD and FTIR, respectively. A conductivity test and the surface tension method detected the critical micelle concentration (CMC) of CTAB to be around 0.9 mM. The nanofluids (Al2O3-CTAB) were found to be stable for more than a week based on visual observation and the zeta potential values of above + 30 mV. The interfacial tension (IFT) between crude oil and nanofluids was reduced to 5.4–6.7 mN/m. The solid–liquid contact angles were reduced to 28–39°, indicating a favorable water-wet condition. The emulsification of crude oil using nanofluids was observed at 30 °C and at an elevated temperature of 70 °C, highlighting the potential of Al2O3-based nanofluids. Core flooding experiments, representing dynamic behavior, resulted in cumulative oil recoveries of 23.4
Grouting is widely used to mitigate geotechnical hazards in underground engineering. However, conventional cement-based grouts often suffer from shrinkage-induced debonding at the grout–rock interface, thereby compromising reinforcement performance. Expansive grouts can effectively alleviate this problem; however, their expansion effects on grout diffusion and reinforcement have not been adequately investigated. This paper develops a numerical method for simulating expansive grout that incorporates both hydro-mechanical (HM) coupling effects and grout expansion effects. The proposed method is validated against laboratory tests. A three-dimensional numerical model of expansive grouting in a single-fracture rock mass is established to investigate the HM coupling and expansion effects on grout diffusion and rock deformation. The method is further applied to grouting reinforcement for a practical underground petroleum storage cavern project. Results indicate that the effective expansive agent contents above 3
This paper presents a Multi-Objective Differential Evolution framework (MODEA) and an enhanced Multi-Objective Particle Swarm Optimization (MOPSO) technique for power transformer design optimization. The proposed approaches integrate multi-objective optimization mechanisms to improve solution quality, convergence, and diversity of the Pareto-optimal front. A comprehensive comparison is conducted against three widely used Multi-Objective Evolutionary Algorithms (MOEAs), namely the Strength Pareto Evolutionary Algorithm (SPEA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm III (NSGA-III). The study simultaneously minimizes active-part cost and maximizes transformer efficiency while evaluating convergence, diversity, and Pareto-front quality. Unlike conventional Differential Evolution approaches, MODEA integrates differential mutation with Pareto-based ranking, crowding-distance diversity preservation, and elitist selection mechanisms within a multi-objective optimization framework tailored for constrained transformer design problems. MATLAB-based simulations demonstrate that MODEA consistently produces high-quality and well-distributed Pareto-optimal solutions. Furthermore, the best compromise solution obtained by MODEA achieved an 11.17
Joining dissimilar austenitic stainless steels is difficult because the two alloys respond differently to heat input and plastic deformation, and fusion welding of these grades often produces solidification defects and wide heat-affected regions. The challenge is to select solid-state welding parameters that give a sound joint with balanced strength, ductility, and toughness. This study investigates the effects of rotational speed and feed rate on the thermal response, microstructure, and mechanical performance of rotary friction-welded dissimilar AISI 304/AISI 316 L stainless-steel rods. Joints were produced at rotational speeds of 1000, 1400, and 2000 rpm and feed rates of 40 and 63 mm/min. The welds were characterized by temperature monitoring, optical microscopy, SEM/EDS, microhardness mapping, tensile testing, Charpy impact testing, and fracture surface analysis. Higher rotational speed increased the peak temperature and produced a more uniform weld region with fewer visible interfacial defects. Tensile strength increased with rotational speed for both feed rates, reaching 550 MPa at 2000 rpm and 63 mm/min. Impact toughness also increased with rotational speed, reaching 111 J/cm2 at 2000 rpm and 40 mm/min. Hardness profiles showed clear zone-dependent variations across the joint and slightly higher hardness on the rotating AISI 304 side. Fractography showed a transition from flat, void-containing fracture surfaces at low rotational speed to dimpled, ductile tearing features at high rotational speed. Within the tested parameter window, rotational speed mainly controls joint consolidation, while feed rate influences the balance between strength and toughness, especially at low and intermediate speeds.
This study presents a novel five-level converter (NFLC) architecture for integrating electric vehicle (EV) and photovoltaic (PV)-based micro-grid. The NFLC converter is designed with fewer switches, has reduced converter cost, and can be applied to systems in industries and power plants employing high power and medium-voltage ranges. There are two distinct DC-link connections on the NFLC. A new Bell polynomial-based controller is proposed in this paper for the control of NFLC. The proposed control method regulates almost identical values across the two DC-link voltages of the NFLC. Further, this new converter is connected to a single-phase power distribution system and used as a compensator. The grid, load, solar panel, compensator, and EV are modeled and integrated into the proposed system. The designed system is modeled in MATLAB/SIMULINK. The controller is designed to support the grid, and both charging and discharging the EV battery are feasible. Experimental validation is carried out using a hardware prototype created in the laboratory. The extraction of fundamental currents through the proposed Bell polynomials is compared with least mean square (LMS) and Bernoulli polynomial control algorithms. The NFLC seamlessly carried out grid-to-vehicle (G2V), vehicle–to-grid (V2G) operations in conjunction with a bidirectional DC-DC converter. The dynamic analysis using the BELL polynomial controller is also investigated during load variations.
Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. Its diagnosis is particularly challenging due to the asymptomatic nature of the disease in its early stages. Although early detection is essential, current automated diagnostic methods often exhibit limited performance due to subtle early-stage lesions and inter-expert variability, with reported accuracies typically ranging from 85 to 93
