Search Results (278)

Growing efforts to reduce air pollution have accelerated the development of advanced flue gas treatment technologies for coal-fired power plants. This study presents the design, development, and industrial-scale implementation of a microwave-assisted non-thermal plasma reactor, powered by a 75 kW, 915 MHz magnetron, for simultaneous sulfur dioxide (SO₂) removal and fly ash agglomeration. The reactor was installed on the outlet line of the selective catalytic reduction (SCR) system of a 22 MW_e pulverized-coal-fired boiler and evaluated under real flue gas conditions. The flue gas stream, extracted by an induced-draft fan, was supplied to the reactor through two configurations—radial and axial injection—to investigate the influence of gas flow rate and microwave power on SO₂ abatement performance. Under radial injection, the system achieved a maximum SO₂ removal efficiency of 99.0% at 5194 Nm³/h and 75 kW, corresponding to a specific energy consumption of 14.4 Wh/Nm³. Axial injection resulted in a removal efficiency of 97.5% at 4100 Nm³/h. Beyond SO₂ mitigation, exposure of flue gas to the microwave-assisted plasma environment significantly enhanced particle agglomeration, as confirmed by means of SEM imaging and particle size distribution analyses. Notably, the proportion of fine particles smaller than 2.5 µm (PM_2.5) decreased from 70.25% to 18.63% after plasma treatment, indicating improved capture potential in the downstream electrostatic precipitator (ESP). Overall, microwave-assisted plasma provides efficient SO₂ removal and enhanced particulate capture, offering a compact and potentially waste-free alternative to conventional systems. Full article

The global concern regarding the reduction of carbon emissions has led to the development of hydrogen as a clean, carbon-free fuel for combustion systems. The present work combines Particle Image Velocimetry flow field measurements and Reynolds-Averaged Navier–Stokes numerical simulations to investigate the reactive flow downstream of a newly developed flame holder as part of a hydrogen-fueled afterburner system. The obtained numerical results are in reasonable agreement, for a RANS simulation, with the PIV measured data. According to the results presented in this article, it can be seen that ignition occurs, the flame is attached to the flame holder, and vortices develop downstream of the flame holder. These vortices facilitate the mixing of hydrogen with the flue gas coming from the gas generator. The recirculation zone generated by the flame holder in the flow measures approximately 100 mm, with the peak negative velocity reaching around 10 m/s. Downstream of the recirculation zone, the far-field free stream velocity on the centerline reaches 20 m/s. Outside the recirculation region, in the radial direction, the free stream is accelerated to an experimentally measured value of approximately 40 m/s, at 20 mm downstream from the flame holder, and 35 m/s at 100 mm downstream of the flame holder. The information gathered thus far will aid further investigation of the presented hydrogen-fueled afterburner system. Full article

Microfluidics enables precise manipulation of scarce Traditional Chinese Medicine (TCM) samples while accelerating analysis and enhancing sensitivity. Device-level structures explain these gains: staggered herringbone and serpentine mixers overcome low-Reynolds-number constraints to shorten diffusion distances and reduce incubation time; flow-focusing or T-junction droplet generators create one-droplet–one-reaction compartments that suppress cross-talk and support high-throughput screening; “Christmas-tree” gradient generators deliver quantitative dosing landscapes for mechanism-aware assays; micropillar/weir arrays and nanostructured capture surfaces raise surface-to-volume ratios and probe density, improving capture efficiency and limits of detection; porous-membrane, perfused organ-on-a-chip architectures recreate apical–basolateral transport and physiological shear, enabling metabolism-aware pharmacology and predictive toxicology; wax-patterned paper microfluidics (µPADs) use capillary networks for instrument-free metering in field settings; and lab-on-a-disc radial channels/valves exploit centrifugal pumping for parallelised workflows. Framed by key performance indicators—sensitivity (LOD/LOQ), reliability/reproducibility, time-to-result, throughput, sample volume, and sustainability/cost—this review synthesises how such structures translate into value across TCM quality/safety control, toxicology, pharmacology, screening, and delivery. Emphasis on structure–function relationships clarifies where microfluidics most effectively closes gaps between chemical fingerprints and biological potency and indicates practical routes for standardisation and deployment. Full article

The present study investigates the magnetohydrodynamic (MHD) flow characteristics of a blood-based ternary nanofluid (Au/Cu/Al₂O₃-blood) in stenosed arteries, with a focus on symmetry-inspired modeling rooted in the axial symmetry of arterial geometry and the symmetric distribution of external physical fields (magnetic field, thermal radiation). The findings offer significant insights into the realm of hyperthermia therapy and targeted drug delivery within the domain of biomedical engineering. A mathematical model is established under a cylindrical coordinate system (consistent with arterial axial symmetry), integrating key physical effects (thermal radiation, chemical reactions, viscous dissipation, body acceleration) and fractional-order dynamics via Caputo derivatives—while ensuring the symmetry of governing equations in time and space. The numerical solutions for velocity and temperature profiles are obtained using the Laplace transform and Concentrated Matrix-Exponential (CME) method, a technique that preserves symmetric properties during the solution process. The results of the study indicate the following: The Hartmann number, which is increased, has been shown to reduce axial velocity due to the Lorentz force, thereby maintaining radial symmetry. Furthermore, thermal radiation has been demonstrated to raise fluid temperature, a critical factor in heat-based therapies, with the temperature field evolving symmetrically. In addition, it has been observed that ternary nanoparticles outperform single and binary systems in heat and mass transfer via symmetric dispersion. This work contributes to the existing body of knowledge by integrating symmetry principles into the study of fractional dynamics, electromagnetic fields, and body acceleration modeling. It establishes a comprehensive biomedical flow framework. It is imperative that future research explore pulsatile flow under symmetric boundaries and validate the model through experimental means. Full article

Compression wood (CW) negatively affects the industrial quality of Pinus taeda by causing distortion in sawn boards and is visually characterized by a darker reddish colour. Thinning is considered a key factor influencing its formation, but the reported effects have been inconsistent. This study evaluated CW incidence at final rotation under five thinning regimes: 500-200, 500-325, 800-600-400, 700-450, and 1000-650 trees.ha⁻¹. The defect was assessed on log ends, basal discs, and sawn boards. Although overall CW severity was low, regimes differed significantly. The 500-325 trees.ha⁻¹ regime showed the highest stain and board defects, while the 500-200 trees.ha⁻¹ regime of similar intensity had lower values, indicating that intensity alone does not explain the occurrence of defects. After thinning, CW in growth rings increased and was positively associated with ring width and negatively with stand density index, indicating that reduced competition and accelerated radial growth are linked to higher formation levels. Visible CW staining on log ends was moderately correlated with board defects, indicating its potential as a practical, low-cost indicator of log quality. Thinning affects compression wood through its impact on growth and stand structure. In addition to intensity, timing and the effect of the wind must also be considered. Moderately intensive regimes help minimize defects, although their practical adoption may be limited by commercial priorities. Full article

Alzheimer’s disease (AD) affects over 55 million individuals worldwide, yet no transformative disease-modifying therapies exist. Mathematical modelling provides a powerful framework to elucidate complex disease mechanisms, predict therapeutic outcomes, and enable precision medicine—capabilities urgently needed where multiscale spatiotemporal processes defy experimental analysis alone. We developed a mechanistic spatiotemporal model coupling four AD hallmarks: β-amyloid (Aβ) accumulation, T-protein (T-p) aggregation, neuroinflammation and electrical conductivity decline. Formulated as non-linear partial differential equations (p.d.es) on a 3-dimensional biological interpretation of non-linear terms (the ellipsoidal brain domain with biologically grounded parameters), the model was solved using eigenfunction expansion, Fourier analysis and numerical methods. Therapeutic interventions were simulated through mechanistically motivated parameter modifications and validated against longitudinal biomarker data from major cohort studies. Simulations reveal Aβ-initiated spatiotemporal cascades originating in the hippocampus and spreading radially at 0.15–0.20 cm/year, with T-pathology emerging after 2–3 years. Conductivity decline accelerates upon T-onset (year 5–7), reflecting the transition to symptomatic disease. Multimodal intervention at early symptomatic stages reduces peak Aβ by 36% and inflammation by 52% and preserves 41% more conductivity than untreated controls. Sensitivity analysis identifies Aβ production and inflammatory regulation as critical therapeutic targets, with dose–response curves demonstrating linear efficacy relationships. This biologically grounded framework explicitly links molecular pathology to functional decline, enabling patient-specific trajectory prediction through parameter calibration. The model establishes a foundation for precision medicine applications including individualized prognosis, optimal treatment timing and virtual clinical trial design, advancing quantitative systems biology of neurodegeneration. Full article

Effective metabolic waste clearance and maintaining ionic homeostasis are essential for the health and normal function of the central nervous system (CNS). To understand its mechanism and the role of fluid flow, we develop a multidomain electro-osmotic model of optic-nerve microcirculation (as a part of the CNS) that couples hydrostatic and osmotic fluid transport with electro-diffusive solute movement across axons, glia, the extracellular space (ECS), and arterial/venous/capillary perivascular spaces (PVS). Cerebrospinal fluid enters the optic nerve via the arterial parivascular space (PVS-A) and passes both the glial and ECS before exiting through the venous parivascular space (PVS-V). Exchanges across astrocytic endfeet are essential and they occur in two distinct and coupled paths: through AQP4 on glial membranes and gaps between glial endfeet, thus establishing a mechanistic substrate for two modes of glymphatic transport, at rest and during stimulus-evoked perturbations. Parameter sweeps show that lowering AQP4-mediated fluid permeability or PVS permeability elevates pressure, suppresses radial exchange (due mainly to hydrostatic pressure difference at the lateral surface and the center of the optic nerve), and slows clearance, effects most pronounced for solutes reliant on PVS–V export. The model reproduces baseline and stimulus-evoked flow and demonstrates that PVS-mediated export is the primary clearance route for both small and moderate solutes. Small molecules (e.g., A$\beta$) clear faster because rapid ECS diffusion broadens their distribution and enhances ECS–PVS exchange, whereas moderate species (e.g., tau monomers/oligomers) have low ECS diffusivity, depend on trans-endfoot transfer, and clear more slowly via PVS–V convection. Our framework can also be used to explain the sleep–wake effect mechanistically: enlarging ECS volume (as occurs in sleep) or permeability increases trans-interface flux and accelerates waste removal. Together, these results provide a unified physical picture of glymphatic transport in the optic nerve, yield testable predictions for how AQP4 function, PVS patency, and sleep modulate size-dependent clearance, and offer guidance for targeting impaired waste removal in neurological disease. Full article

To investigate the seepage and deformation failure characteristics of deep unloaded rock mass under cyclic loading and unloading disturbance, a series of triaxial cyclic loading and unloading tests were conducted on granite. These tests were performed under varying seepage pressures and unloading conditions to analyze the mechanical properties, seepage behavior, and fracture failure characteristics of the material. The findings indicate the following: (1) An increase in seepage pressure and unloading magnitude results in pronounced radial expansion characteristics in the rock specimens following cyclic loading and unloading. Additionally, the axial, radial, and volumetric residual strains exhibit a nonlinear acceleration in growth as the number of cyclic loading and unloading applications increases. (2) The elastic modulus of rocks exhibits two distinct phases: an initial rapid decline followed by a steady-state decrease. Concurrently, Poisson’s ratio demonstrates an initial decrease, which is subsequently followed by a consistent increase. Furthermore, when considering the effects of unloading, the inflection point of the Poisson’s ratio curve will occur earlier. (3) The interplay between seepage pressure and unloading conditions markedly exacerbates the damage and degradation of the rock. Specifically, under conditions of 70% unloading and a seepage pressure of 4 MPa, the peak stress of the rock specimen is reduced by 21.90%, and the peak intensity permeability increases by 446.70%. (4) Under conditions of high confining pressure and elevated seepage pressure, V-shaped conjugate shear fracture surfaces are likely to develop during the cyclic loading failure of granite, accompanied by a limited number of secondary shear cracks. Concurrently, tensile failure surfaces that are parallel to the maximum principal stress are also observed under the influence of unloading. Full article

A coordinated profile co-optimization strategy for the piston–liner pair was introduced to simultaneously reduce friction losses and dynamic excitation. Based on the main parameters of the engine. Lubrication theory and the finite element method, and explicitly accounting for elastic deformation of flexible bodies, a multibody dynamics simulation model of the piston–connecting rod–crankshaft–cylinder liner system was developed in AVL Excite. This model was used to evaluate the dynamic and tribological performance of five cylinder-liner pre-compensation geometries at rated operating conditions. A bottleneck-shaped liner exhibited the best tribological performance, reducing the average total piston–skirt friction loss by 20.8% and the peak asperity–contact pressure by 19.7%, while leaving piston kinematics essentially unchanged (an increase of 0.001 mm in the maximum radial displacement and 0.009° in the maximum tilt angle). Building on this liner, key piston–skirt profile parameters were optimized via response–surface methodology; with the optimized skirt, the maximum radial displacement decreased from 0.123 mm to 0.113 mm, the maximum tilt angle decreased from 0.463° to 0.462°, the third-order Fourier component of lateral acceleration decreased from 14.53 m/s² to 13.26 m/s², and the cycle-averaged total skirt friction loss decreased from 0.307 kW to 0.250 kW. Full article

Presently, numerous non-conventional power resources have been applied in power system networks. However, these resources are very effective in islanded microgrid (IMG) scenarios for addressing numerous operational challenges. Additionally, it is observed that the power output of most of these resources is environment-dependent and intermittent in nature. This intermittency causes a power imbalance between the overall generated power and the load demand, which results in an undesired frequency oscillation. In order to address this unwanted frequency fluctuation, this research work proposes power–frequency synchronisation considering an islanded microgrid scenario under numerous non-conventional power resources. The major contribution of this work includes implementing a suitable and optimised control scheme that effectively controls diverse power system disturbances and various uncertainties. A Fick’s law optimisation-based proportional–integral–derivative–acceleration controller (PIDA) is implemented under this proposed power scenario. Additionally, an extensive performance assessment is conducted considering different simulation test cases in order to verify the performance of the proposed control topology. Further, the effectiveness of the suggested power network is tested on a 33-bus radial distribution network. Finally, simulation results are shown to show the effectiveness of the proposed control scheme for the efficient operation of the microgrid in achieving the desired performance under the diverse operating conditions. Full article

The symmetry-breaking vibrational response of a gun muzzle, induced by the thermo–mechanical coupling effect under continuous firing, is a critical factor degrading shooting accuracy. This study investigates the nonlinear influence of chamber pressure variation on this asymmetric dynamic response. A thermo–mechanically coupled interaction model between a 5.8 mm bullet and its barrel is established using nonlinear finite element methods, incorporating experimentally measured pressure data. The kinematic state of the muzzle under a heated barrel condition (after 90 rounds) is systematically analyzed across five chamber pressure levels (90% to 110% of standard). The results reveal a highly nonlinear relationship between chamber pressure and muzzle vibration. Surprisingly, the maximum values for comprehensive radial displacement (10.601 × 10⁻³ mm), velocity (0.327 m/s), acceleration (11.083 m/s²), swing angle (0.192 mrad), and swing angular velocity (9.166 rad/s) occurred at the 100% standard pressure, not the highest pressure. Reducing the pressure to 90% of the standard effectively suppressed these asymmetric vibrations, with magnitudes declining by 84.28% to 95.49%. This indicates that the symmetry of the muzzle’s dynamic state is disrupted under thermal effects, and strategically lowering chamber pressure can restore a more symmetric and stable launch attitude, thereby enhancing accuracy. This study elucidates the nonlinear correlation mechanism between pressure and thermally induced asymmetric vibration, providing a novel perspective for optimizing the accuracy of rapid-fire weapons based on symmetry principles. Full article

The Discrete Element Method (DEM) has become a cornerstone for analysing granular flow and mixing phenomena in static mixers. This review provides a comprehensive synthesis that distinguishes it from previous studies by: (i) covering a broad range of static mixer geometries, including Kenics, SMX, and Sulzer designs; (ii) integrating experimental validation methods, such as particle tracking, high-speed imaging, Particle Image Velocimetry (PIV), and X-ray tomography, to assess DEM predictions; and (iii) systematically analyzing computational strategies, including advanced contact models, hybrid DEM-CFD/FEM frameworks, machine learning surrogates, and GPU-accelerated simulations. Recent advances in contact mechanics—such as improved cohesion, rolling resistance, and nonspherical particle modelling—have enhanced simulation realism, while adaptive time-stepping and coarse-graining improve computational efficiency. DEM studies have revealed several non-obvious relationships between mixer geometry and particle dynamics. Variations in blade pitch, helix angle, and element arrangement significantly affect local velocity fields, mixing uniformity, and energy dissipation. Alternating left–right element orientations promote cross-sectional particle exchange and reduce stagnant regions, whereas higher pitch angles enhance axial transport but can weaken radial mixing. Particle–wall friction and surface roughness strongly govern shear layer formation and segregation intensity, demonstrating the need for geometry-specific optimization. Comparative analyses elucidate how particle–wall interactions and channel structure influence segregation, residence time, and energy dissipation. The review also identifies current limitations, highlights validation and scale-up challenges, and outlines key directions for developing faster, more physically grounded DEM models, providing practical guidance for industrial mixer design and optimization. Full article

Efficient coalbed methane (CBM) recovery combined with carbon dioxide (CO₂) sequestration is a promising strategy for sustainable energy production and greenhouse gas mitigation. However, the molecular mechanisms controlling pressure-dependent CH₄ displacement by CO₂ in coal nanopores remain insufficiently understood. In this study, molecular dynamics simulations were conducted to investigate CO₂-driven CH₄ recovery in a slit-pore coal model under driving pressures of 15, 20, and 25 Mpa. The simulations quantitatively captured the competitive adsorption, diffusion, and migration behaviors of CH₄, CO₂, and water, providing insights into how pressure influences enhanced coalbed methane (ECBM) recovery at the nanoscale. The results show that as the pressure increases from 15 to 25 Mpa, the mean residence time of CH₄ on the coal surface decreases from 0.0104 ns to 0.0087 ns (a 16% reduction), reflecting accelerated molecular mobility. The CH₄–CO₂ radial distribution function peak height rises from 2.20 to 3.67, indicating strengthened competitive adsorption and interaction between the two gases. Correspondingly, the number of CO₂ molecules entering the CH₄ region grows from 214 to 268, demonstrating higher invasion efficiency at elevated pressures. These quantitative findings illustrate a clear shift from capillary-controlled desorption at low pressure to pressure-driven convection at higher pressures. The results provide molecular-level evidence for optimizing CO₂ injection pressure to improve CBM recovery efficiency and CO₂ storage capacity. Full article

Three-piece alloy wheels are widely used across the automotive industry, favoured due to their lightweight construction and ease of customisation. Vehicle wheels must withstand forces generated during acceleration, braking, cornering, and impacts, ensuring safety and durability under real-world conditions. Finite element analysis (FEA) plays a crucial role in simulating these loading conditions, thoroughly assessing structural performance prior to manufacturing. This study develops and validates a digital FEA testing framework tailored to low-volume wheel manufacturers, demonstrating that FEA can replace traditional physical wheel fatigue tests where such facilities are unavailable. This research was conducted in collaboration with a UK company specialising in the design and manufacture of bespoke, limited-production three-piece alloy wheels. However, the absence of dedicated structural testing procedures caused many of their existing designs to be overengineered, resulting in excessive material usage, increased weight, and high production costs. In some cases, lack of testing also contributed to wheel failures. This work selected three of the company’s existing wheel designs and subjected them to comprehensive analysis. Using FEA, each wheel was evaluated under industry-standard radial, cornering, biaxial, and impact tests. To verify the simulations, a known case of wheel failure was analysed and compared to real-world values. Once verified, any design issues were addressed. The redesigned wheels achieved substantial weight reduction (up to 25%), while still meeting or exceeding the relevant safety standards and allowing for manufacturability. Ultimately, this work demonstrated that applying digital simulation techniques can significantly improve the performance and safety of custom three-piece alloy wheels. Full article

Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article