Search Results (93)

Gas-assisted discharge of granular materials plays a critical role in shaft furnaces, moving-bed reactors, and other industrial multiphase systems, where interstitial gas flow strongly influences arch stability and may induce progressive flow blockage. Existing analytical models generally neglect aerodynamic gas–particle interactions, whereas CFD–DEM simulations provide high predictive accuracy at the expense of substantial computational cost. To bridge this gap, the present study develops and validates a physically based Torque-Balance Model for predicting gas-assisted granular discharge, arch stability, and flow blockage. A comprehensive experimental investigation was performed using a quasi-two-dimensional transparent apparatus and a thermally stabilized shaft model operated under controlled conditions. Gas-assisted discharge was examined for different gas-flow directions, gas properties, outlet geometries, and particulate materials using hydrogen, helium, and air. High-speed imaging together with gravimetric measurements enabled detailed characterization of discharge regimes and arch evolution. The proposed analytical framework explicitly incorporates interparticle mechanical interactions, aerodynamic drag, outlet geometry, and gas-pressure effects within a unified torque-balance formulation. The model describes successive stages of the discharge process, including stable discharge, transition to blockage, and complete flow suppression, while maintaining computational efficiency suitable for engineering calculations. Experimental results demonstrated that gas-flow direction governs arch stability and discharge behavior. Co-current gas flow promoted repeated arch collapse and enhanced discharge, whereas counter-current flow progressively stabilized the granular arch and ultimately produced complete flow blockage. Validation against the complete experimental database demonstrated excellent agreement between theoretical predictions and experimental observations, yielding an average prediction error below 10%, a maximum deviation within ±20%, and a coefficient of determination of R² = 0.96. The proposed Torque-Balance Model provides a computationally efficient and physically interpretable engineering framework that bridges the gap between simplified empirical correlations and computationally intensive CFD–DEM simulations and can be applied to the prediction and optimization of gas-assisted granular discharge in industrial multiphase systems. Full article

High-speed Fused Filament Fabrication (FFF) printers are prone to nozzle vibration caused by moving-part inertia, frame flexibility, and modal coupling during high-acceleration motion, which can reduce deposition-trajectory stability. This study evaluates the dynamic adaptability of electronics layout and X-axis beam configurations for a CoreXY FFF printer under complete-machine boundary conditions. A finite element model including the frame, XY motion mechanism, print head, heated bed, and electronics was established. Modal and Y-direction harmonic response analyses were performed by first comparing rear-mounted and bottom-mounted electronics layouts and then by comparing three beam designs. With the baseline beam, both layouts had a first natural frequency of 83 Hz, whereas the rear-mounted layout increased the second- to sixth-order frequencies by 6.8%, 24.2%, 32.0%, 29.2%, and 15.3%. Under the rear-mounted layout, the three beams showed similar first six modal frequencies, but the perforated beam produced the lowest nozzle peak, with a full-band Y-direction response of 0.402 mm, which was 16.1% and 20.4% lower than those of the baseline and hollow square beams, respectively. This beam also had a mass of 41.98 g, which was 45.1% lower than that of the baseline beam. Therefore, rear-mounted electronics combined with a perforated beam was preferred within the current simulation. Full article

Catalyst deactivation shifts the optimal operating region of exothermic fixed-bed reactors, yet most reactor digital twins focus on monitoring rather than catalyst-state-aware operating decisions. This work presents a simulation-based self-optimizing digital-twin prototype integrating a physics-based reactor model, a moving-window constrained activity estimator, and a target-optimization layer for o-xylene oxidation to phthalic anhydride in a vanadia–titania heat-exchanged fixed-bed reactor. Sparse axial temperature and conversion measurements are reconciled to estimate an axial catalyst activity profile; gas and coolant inlet temperatures are then updated subject to a hot-spot safety constraint. The estimator achieved an activity-profile root mean square error (RMSE) of 0.075, an outlet-conversion RMSE of 0.99 percentage points, and an outlet-temperature RMSE of 1.85 K. Under the baseline noisy-measurement scenario, estimated activity optimization raised the mean phthalic anhydride yield from 46.3% under fixed targets to 61.9%, within 0.14 percentage points of the true-activity optimum, while maintaining the maximum reactor temperature below 730 K. In this matched-model simulation study, this corresponds to recovering approximately 99.1% of the yield improvement available with perfect catalyst-state knowledge. The policy remained superior to fixed-target operation across all tested noise levels, sensor configurations, and kinetic pre-exponential perturbations. All results are obtained from synthetic-measurement simulations rather than experimental or plant data, and plant validation is still required to quantify structural model error. The findings demonstrate the value of linking catalyst-state estimation to operating-target adaptation in a reproducible catalytic-reactor digital-twin workflow. Full article

Computational Fluid Dynamics (CFD) was applied to an intermittent anoxic/aerobic Moving Bed Biofilm Reactor (MBBR) operated under six different aeration intermittency cycles and dissolved oxygen concentration levels. Experimental results showed that most aeration cycles did not provide a sufficiently long anoxic phase to sustain effective denitrification, thereby limiting NOx removal efficiency. This behavior was not adequately captured by simulations performed using conventional biological models (BioWin), which rely on the assumption of complete mixing. In contrast, the CFD model implemented in ANSYS Fluent 2024 R2 enabled a detailed characterization of reactor hydrodynamics and the identification of several inefficiencies, including short-circuiting, back-mixing, and the presence of dead zones. Notably, the simulations revealed a pronounced asymmetric distribution of carriers within the reactor, with the majority accumulating along one side, leaving a significant fraction of the reactor volume largely unoccupied. Further analysis indicated that this phenomenon was caused by a design flaw—specifically, the asymmetric placement of the aerators—combined with an excessively high air injection flow rate. Full article

Lack of fusion (LOF) is a dominant defect in Laser Powder-Bed Fusion (PBF-LB/M) caused by insufficient overlapping between adjacent melt pools. This study introduces a rapid, first-principles model based on Rosenthal’s analytical solution for a moving point heat source to predict melt pool geometry. Using geometric criteria, the model evaluates whether the melt pool width exceeds the hatching distance and whether the melt pool depth exceeds the layer thickness. Based on these conditions, LOF-based process windows are constructed by plotting laser power against scanning speed and classifying each parameter combination as either LOF or no LOF. The process developed here for constructing LOF process windows can be applied to metallic PBF-LB/M systems. As PBF-LB/M of copper is commonly associated with LOF defects, the approach is examined for pure copper by evaluating a range of laser powers and scanning speeds for both near-infrared (NIR) (1064 nm) and green (515 nm) lasers using copper-specific absorptivity values. The resulting process windows are validated against literature-reported relative density data for pure copper, using high relative density values as indicators of full fusion and lower relative density values reported with LOF characteristics as indicators of lack of fusion. For a 30 µm layer thickness, the predicted LOF boundary agreed with 43 of 46 literature-reported copper PBF-LB/M data points when the data were classified using relative density and reported defect morphology. Sensitivity analysis showed that the agreement changed modestly when the relative-density threshold was reduced from 99% to 98.5% and 98% and that near-boundary classifications were sensitive to the selected absorptivity within the reported NIR range. The agreement supports the use of the framework as a preliminary screening tool for identifying LOF-prone parameter regions. By providing a fast, physics-based screening tool for LOF-limited process windows, this framework offers a computationally efficient alternative to high-fidelity numerical simulations commonly used in PBF-LB/M process development. Full article

Efficient and accurate simulation methods are essential for analyzing and optimizing chromatographic processes, which are governed by partial differential equations (PDEs) and characterized by the propagation of steep concentration fronts. These fronts often cause numerical dispersion and high computational costs in conventional finite-volume or finite-difference schemes. In this paper, a fast and accurate simulation method for highly efficient chromatographic columns with negligible axial dispersion, linear and nonlinear non-competitive adsorption isotherms is proposed. The simulation approach is based on the propagation of discrete concentration values using characteristic velocities. In the linear case, the method is exact, and only the graphical representation of the solution depends on the discretization of the concentration coordinate. In the nonlinear case, an approximation is proposed to capture the possible formation of discontinuities efficiently. Nevertheless, it is shown that good agreement with reference solutions is achieved even for a relatively low number of discrete concentration values. Applications of the proposed methods are demonstrated for different multi-column simulated moving bed processes. The results show that the computational effort can be significantly reduced compared to the popular cell model, which represents a first-order finite-volume approximation of the underlying PDEs. The proposed approach thus enables rapid process design and parameter exploration for both linear and nonlinear non competitive adsorption isotherms for SMB chromatography separations with highly efficient columns. Full article

The dynamic behavior within the melt pool governs the final quality of components fabricated by laser powder bed fusion (LPBF). To address key technical challenges—rapid keyhole evolution, low absorption contrast from metal vapor, and difficulties in quantifying internal flow fields—this study introduces move contrast X-ray imaging (MCXI), a technique leveraging time-series frequency characteristics. Combined with a multi-scale Horn–Schunck global optical flow method, MCXI enables full-field quantitative extraction of the melt-pool velocity field. Experimental validation across feature points shows a relative deviation of less than 2% compared to independent manual feature-point tracking, confirming consistency with the best available experimental ground truth. Analysis reveals the keyhole tail evolution cycle comprises three distinct dynamic stages: expansion, stratification, and contraction, with its area increasing from 1329 μm² to 6508 μm² before stabilizing. For the first time, pore pinch-off events were quantitatively measured, revealing front and rear wall collision velocities of 7.98 m/s and 8.04 m/s, respectively, consistent with available high-fidelity simulations. Furthermore, analysis of the overall melt-pool momentum field demonstrates a near-equal distribution of positive and negative momentum, providing an internal self-consistency check confirming the absence of systematic directional bias in the extracted velocity field. This study enables quantitative analysis of LPBF melt-pool dynamics, providing a novel tool for process optimization and defect control. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

This paper reports the chiral separation of menthol enantiomers using the VARICOL process to improve productivity. Amylose 3,5-dimethylphenylcarbamate coated on silica gel was employed as the chiral stationary phase, and n-hexane/2-propanol (95/5, v/v) was used as the eluent. To design and optimize the VARICOL process, a linear driving-force model was developed to predict the separation performance. Separation regions of the conventional simulated moving bed (SMB) and VARICOL processes were evaluated and compared. It was found that, under an outlet purity requirement of 95.0%, the five-column VARICOL process has a separation region comparable to that of the six-column conventional SMB process. As an illustrative example, a five-column VARICOL unit and a six-column conventional SMB unit, both operating under the same conditions, were employed to resolve the menthol racemate. Purities for both the extract and raffinate were above 95.0%, and a productivity of 0.400 g_racemate/(L_CSP∙min) and a solvent consumption of 0.355 L/g_racemate were achieved in the VARICOL process. Productivity increased by 20% while solvent consumption maintained relative to the conventional SMB process, though product purities decreased slightly. Full article

This paper builds a one-dimensional transient numerical model of mixed fuel of woody and non-woody biomass to simulate the multistage conversion process of biomass in a moving grate-fired bed, including drying, pyrolysis, gasification, and char combustion. Based on time and space discretization, the model comprehensively considers the conservation of mass, momentum, and energy. It also introduces reaction kinetics and freeboard radiation coupling effects to more accurately describe the bed temperature distribution and reaction process. The analysis focuses on the effects of different non-woody biomass mixing ratios and moisture content. This provides references for optimization of the design of future furnaces and operating parameters and mixed fuel composition. The simulation results show that, for pure woody biomass, the surface temperature reaches approximately 200 °C in the first zone, followed by char reactions with peak temperatures up to 592 °C. The whole conversion process takes about 62% of the grate length. Increasing the pepper mixing ratio leads to lower bed temperatures due to the higher moisture content. The maximum bed temperature in the first zone decreases from 592 °C for pure wood to 551 °C at 30 wt.% pepper, with delayed pyrolysis and a thinner char reaction zone. When the pepper mixing ratio is below 20 wt.%, the combustion process maintains a stable temperature gradient and a continuous reaction front, compared to the mixing ratio of 30% pepper case. This confirms the feasibility of non-woody biomass application to combustion technology. Although a higher pepper mixing ratio leads to a slight temperature decrease, the reaction remains stable along the grate, indicating reliable combustion performance. Full article

l-menthol is one of the most popular flavors in the world. The separation of menthol enantiomers is crucial because of the unpleasant taste of d-menthol. This work presents the chiral separation of racemic menthol by simulated moving bed chromatography for the first time. Six preparative columns packed with amylose 3,5-dimethylphenylcarbamate coated on silica gel were used for separation, and a mixture of n-hexane/isopropanol was selected as the mobile phase. The hydrodynamic properties of the SMB columns were studied to minimize the packing asymmetry in the SMB experiment. The binary adsorption isotherm of menthol enantiomers was measured by the adsorption–desorption method. Fixed-bed batch chromatography was carried out to evaluate the adsorption kinetic behavior. Mathematical models, considering the mass transfer resistance and axial dispersion, were applied to describe the dynamics of the chromatographic separation process. The SMB process for chiral separation of racemic menthol was designed by evaluating the separation region using simulations. Reasonable agreements were achieved between the predicted results and the experimental results. Purities for both the extract and raffinate were above 99.0%, and a productivity of 0.267 g_racemate/(L_CSP∙min) and a solvent consumption of 0.431 L/g_racemate were achieved. Full article

Subsea pipelines are critical lifelines for marine resource development, yet they face severe threats from accidental ship anchor impacts. This study addresses the scientific challenge of quantifying the “protection margin” of artificial rock-dumping layers, moving beyond traditional passive structural response to a “Critical Failure Intervention” logic. Based on the energy criteria of DNV-RP-F107, a critical velocity required to trigger Concrete Weight Coating (CWC) failure for a bare pipe was derived and established as the Safety Factor baseline (S = 1). Two groups of scaled model tests (1:15) were conducted using a Hall anchor to simulate impact scenarios, where impact forces were measured via force sensors beneath the pipeline under varying backfill thicknesses and configurations. Results show that artificial backfill provides a significant protective redundancy; a 10 cm coarse rock layer increases the safety factor to 3.69 relative to the H₀ baseline, while a multi-layer configuration (sand bedding plus coarse rock) elevates S to 27. Analysis reveals a non-linear relationship between backfill thickness and cushioning efficiency, characterized by diminishing marginal utility once a specific thickness threshold is reached. These findings indicate that while thickness is critical for extreme impacts, the protection efficiency optimizes at specific depths, providing a quantifiable framework to reduce small-particle layers in engineering projects without compromising safety. Full article

This study presents the development and evaluation of a novel solid–gas reactor designed to enhance the hydrogen reduction kinetics of tellurium oxide (TeO₂) under atmospheric pressure. Such gas–solid reactions can be processed in several types of reactors, including but not limited to fixed-bed reactors, moving-bed reactors, and fluidised-bed reactors. A combination of computational fluid dynamics (CFD) and experimental validation was employed to design and optimise a reactor’s geometry and gas-flow distribution. Single-phase CFD simulations were performed using the k–ω SST turbulence model to examine gas-flow behaviour, temperature uniformity, and gas-flow dead zones for two lance designs. The modified lance produced a stable swirling flow that improved gas distribution and eliminated stagnation regions. Experimental trials confirmed the simulation outcome in optimised gas-flow: the redesigned reactor achieved up to 65% conversion after 1 h and 70% after 2 h, a marked improvement over the rotary kiln, which required 5–6 h to reach similar levels. However, excessive gas flow led to cooling effects that reduced conversion efficiency. These results demonstrate the effectiveness of integrated CFD-guided reactor design for accelerating hydrogen-based oxide reduction and advancing sustainable metallurgical processes. Full article

Dam-break flows involve strong non-linearity and complex fluid–solid interactions, often causing severe flooding and structural damage. Particle-based CFD methods, such as the Moving Particle Semi-implicit (MPS) method, are effective in modeling such flows due to their mesh-free, Lagrangian nature. This study presents an improved MPS method with a novel friction model and enhanced fluid–solid interaction scheme to simulate dam-break-induced flows over fixed and mobile beds. The model is validated using experimental and analytical benchmarks, demonstrating improved accuracy and stability. Simulation results show that mobile beds significantly influence wave attenuation, energy dissipation, and sediment transport. In particular, step-down bed conditions promote sediment motion and modify wave behavior. These findings emphasize the importance of accounting for mobile seabed dynamics in numerical modeling of coastal and dam-break scenarios. The proposed MPS model offers a reliable and efficient tool for capturing key phenomena associated with fluid–solid interactions in naval and ocean engineering applications. Full article

Background and Objective: Health services management faces increasing complexity, particularly in developing countries such as Brazil. Digital tools play a central role in optimizing health service operations, yet synthesized evidence on manager-focused applications remains limited. This study aimed to survey digital innovations for management within the Brazilian context. Methods: We systematically reviewed the complete proceedings of the Brazilian Symposium on Computing Applied to Health (SBCAS) from 2001 to 2024, identifying 26 studies that met eligibility criteria based on managerial relevance. Results: Applications identified predominantly addressed hospital management (e.g., resource scheduling and process optimization) and public health surveillance (e.g., disease prediction and monitoring), employing technologies such as machine learning and simulation. These tools primarily leveraged structured administrative data from national health information systems, reflecting existing data infrastructure capabilities. The reported implications suggest improvements in decision-making through optimized resource allocation (e.g., ICU beds and staffing), streamlined operational processes (e.g., bottleneck identification), enhanced planning and monitoring capabilities (e.g., endemic disease control and telemonitoring programs), and more timely, targeted public health surveillance (e.g., georeferenced analysis). Conclusions: The identified research aligns with global digital health trends but is also tailored to the complex realities of the healthcare system. Despite significant technical advancements, these digital solutions predominantly remain at the prototype stage, highlighting a gap between academic innovation and real-world deployment. Realizing the benefits of these tools will require a concerted effort to move beyond technical validation, focusing on implementation science, supportive policies, and strategic partnerships to integrate these solutions into managerial practice. Full article