Complex Hydrodynamics Research Articles

Comprehensive Assessment of the Jebel Zaghouan Karst Aquifer (Northeastern Tunisia): Availability, Quality, and Vulnerability, in the Context of Overexploitation and Global Change

Karst aquifers in the Mediterranean region are crucial for water supply and agriculture but are increasingly threatened by climate change and overexploitation. The Jebel Zaghouan aquifer, historically significant for supplying Carthage and Tunis, serves as the focus of this study, which aims to evaluate its availability, quality, and vulnerability to ensure its long-term sustainability. To achieve this, various methods were employed, including APLIS and COP for recharge assessment and vulnerability mapping, SPEI and SGI drought indices, and stable and radioactive isotope analysis. The findings revealed severe groundwater depletion, primarily caused by overexploitation linked to urban expansion. Minimal recharge was observed, even during wet periods. APLIS analysis indicated moderate infiltration rates, consistent with prior reservoir models and the MEDKAM map. Isotopic analysis highlighted recharge from the Atlantic and mixed rainfall, while Tritium and Carbon-14 dating showed a mix of ancient and recent water, emphasizing the aquifer’s complex hydrodynamics. COP mapping classified 80% of the area as moderately vulnerable. Monitoring of nitrate levels indicated fluctuations, with peaks during wet years at Sidi Medien Spring, necessitating control measures to safeguard water quality amid agricultural activities. This study provides valuable insights into the aquifer’s dynamics, guiding sustainable management and preservation efforts.

Influence of magnetic convection on separation efficiency in magnetophoretic microfluidic processes: a combined simulation and experimental study.

This work explores the complex hydrodynamics in magnetophoretic microfluidic processes, focusing on the interplay of forces and particle concentrations. The study employs a combined simulation and experimental approach to investigate the impact of magnetophoresis on magneto-responsive nanoparticles (MNPs) and their environment, including non-magneto-responsive nanoparticles (non-MNPs) in a microfluidic system. Our findings reveal that the motion of MNPs induces a hydrodynamic convective motion of non-MNPs, significantly affecting the separation efficiency and purity of the particles. The separation efficiency of MNPs increases with the Péclet number, reflecting the increase in the magnetophoretic force, but decreases with lower concentrations. Conversely, non-MNPs exhibit high and constant separation efficiency with increasing Péclet number, independent of the magnetophoretic force. In a mixture, the separation efficiency of non-MNPs decreases, suggesting that non-MNPs drag along MNPs. The Mason number, representing the ratio between shear and magnetophoretic force, also plays a crucial role in the separation process. The results underscore the need for careful control and optimization of the Péclet and Mason numbers, as well as particle concentrations, for efficient magnetophoretic microfluidic processes. This study provides valuable information on the underlying principles of magnetophoresis in microfluidic applications, with implications for biochemistry, biomedicine, and biotechnology.

Unsteady Secondary Flow Structure at a Large River Confluence

AbstractRiver confluences, which are characterized by complex hydrodynamics, are key nodes for flood control and environmental protection. Two field surveys were carried out at the confluence of the Yangtze River and Poyang Lake to investigate the transient character of flow structures, which are often assumed steady. Repeat‐transect acoustic Doppler current profile measurements were processed and analyzed, adopting a new method to separate mean flow from turbulence and measurement error based on physics‐informed generalized Tikhonov regularization. The two field surveys were characterized by two distinct mixing interface modes: A so‐called Kelvin–Helmholtz (KH) mode, and a wake mode. In KH mode, large‐scale flow fluctuations were observed. These flow fluctuations exert a substantial influence on the alternation of secondary flows by changing the water surface pressure gradient, affecting the intensity of secondary flows rather than their spatial structure. We infer that temperature‐induced stratification is the main cause of this. In the wake mode, multiple vortices in the wake region at the confluence apex also produced flow fluctuations, directly related to the primary velocity gradient. We argue that even for constant incoming flows, secondary flow at river confluences can exhibit channel‐scale unsteadiness related to migration of the turbulent mixing interface. Our findings highlight the crucial role of density effects in regulating secondary flow unsteadiness, which is essential for understanding contaminant and sediment dispersal in river systems.

Peridotite Weathering and Ni Redistribution in New Caledonian Laterite Profiles: Influence of Climate, Hydrology, and Structure

The peridotite massifs of New Caledonia are characterised by complex hydrodynamics influenced by intense inherited fracturing, uplift, and erosion. Following the formation of the erosion surfaces and alteration processes, these processes drive chemical redistribution during weathering; particularly lateritisation and saprolitisation. Magnesium, silica, and trace elements such as nickel and cobalt—released as the dissolution front advances—are redistributed through the system. New observations and interpretations reveal how lateritic paleo-land surfaces evolved, and their temporal relationship with alteration processes since the Oligocene. Considering the geometry of discontinuity networks ranging from micro-fractures to faults, the transfers occur in dual-permeability environments. Olivine dissolution rates are heterogeneously due to differential solution renewal caused by erosion and valley deepening. Differential mass transfer occurs between mobile regions of highly transmissive faults, while immobile areas correspond to the rock matrix and the secondary fracture network. The progression of alteration fronts controls the formation of boulders and the distribution of nickel across multiple scales. In the saprolite, nickel reprecipitates mostly in talc-like phases, as well as minor nontronite and goethite with partial diffusion in inherited serpentine. The current nickel distribution results from a complex interplay of climatic, hydrological and structural factors integrated into a model across different scales and times.

Rapid simulation of wave runup on morphologically diverse, reef-lined coasts with the BEWARE-2 (Broad-range Estimator of Wave Attack in Reef Environments) meta-process model

Abstract. Low-lying, tropical, coral-reef-lined coastlines are becoming increasingly vulnerable to wave-driven flooding due to population growth, coral reef degradation, and sea-level rise. Early-warning systems (EWSs) are needed to enable coastal authorities to issue timely alerts and coordinate preparedness and evacuation measures for their coastal communities. At longer timescales, risk management and adaptation planning require robust assessments of future flooding hazard considering uncertainties. However, due to diversity in reef morphologies and complex reef hydrodynamics compared to sandy shorelines, there have been no robust analytical solutions for wave runup to allow for the development of large-scale coastal wave-driven flooding EWSs and risk assessment frameworks for reef-lined coasts. To address the need for fast, robust predictions of runup that account for the natural variability in coral reef morphologies, we constructed the BEWARE-2 (Broad-range Estimator of Wave Attack in Reef Environments) meta-process modeling system. We developed this meta-process model using a training dataset of hydrodynamics and wave runup computed by the XBeach Non-Hydrostatic process-based hydrodynamic model for 440 combinations of water level, wave height, and wave period with 195 representative reef profiles that encompass the natural diversity in real-world fringing coral reef systems. Through this innovation, BEWARE-2 can be applied in a larger range of coastal settings than meta-models that rely on a parametric description of the coral reef geometry. In the validation stage, the BEWARE-2 modeling system produced runup results that had a relative root mean square error of 13 % and relative bias of 5 % relative to runup simulated by XBeach Non-Hydrostatic for a large range of oceanographic forcing conditions and for diverse reef morphologies (root mean square error and bias 0.63 and 0.26 m, respectively, relative to mean simulated wave runup of 4.85 m). Incorporating parametric modifications in the modeling system to account for variations in reef roughness and beach slope allows for systematic errors (relative bias) in BEWARE-2 predictions to be reduced by a factor of 1.5–6.5 for relatively coarse or smooth reefs and mild or steep beach slopes. This prediction provided by the BEWARE-2 modeling system is faster by 4–5 orders of magnitude than the full, process-based hydrodynamic model and could therefore be integrated into large-scale EWSs for tropical, reef-lined coasts and used for large-scale flood risk assessments.

Bubble deformation characteristics and the effect on particle removal efficiency in wet scrubbers

Single-bubble formation and rise are fundamental to the prediction of decontamination factors (DF) under the complex hydrodynamics of bubble flow and the gas–liquid flow pattern. However, bubbles in the water for particle control are not always perfectly spherical or ellipsoidal. To date, studies of bubble washing for particle removal have mainly focused on models with simple spherical and ellipsoidal shapes or the impact of operating parameters on removal efficiency. No attempt has been reported in the literature to determine the effect of bubble shape on particle removal efficiency. An investigation of the influence of bubble deformation during the bubble rising period was carried out using a high-speed camera and image analysis techniques to study the bubble shape. The experimental results show that the deformation of a bubble is directly correlated with its rising velocity, rising tail, equivalent radius and residence time. These parameters are generally estimated using ideal values, which are crucial for the accuracy of efficiency calculation model results. SPARC-90 code is an important scrubbing modelling tool developed for prediction of bubble hydrodynamics and the corresponding aerosol retention behavior. The potential for particle removal with a single rising deformed bubble was recently determined using a prototype of the SPARC-90 code with the experimental results. The results of SPARC-90 calculations with various equivalent diameters obtained in this experiment have provided more accurate decontamination factors compared to those obtained with a round bubble and a default bubble diameter.

Estimation of tidal energy potential in the Vietnam East Sea: A comprehensive analysis using semi-empirical tide models

This study assesses the tidal energy potential in the Vietnam East Sea (VES), a region with complex hydrodynamics and significant interactions with the Western Pacific Ocean. A rigorous validation of six prominent semi-empirical global ocean tide models (FES2014, DTU16, EOT20, GOT4.10c, HAMTIDE12, OSU12.V1) was conducted against an extensive network of 34 tide gauge stations in the VES region. The validation identified FES2014 as the superior model, exhibiting the closest agreement with observations due to its advanced data assimilation techniques, high-resolution grids, and robust hydrodynamic representation influenced by the complex VES bathymetry. Leveraging the FES2014 model, the influential role of the semi-diurnal component (M2) in dictating energy distribution is prominently evident. Evaluation results of tidal amplitudes revealed three emerging hotspots: the Batang Lupar estuary (Malaysia), Anpu Gang (China), and Bac Lieu (Vietnam), with tidal amplitudes up to 326.2 cm. The Anpu Gang exhibited the highest theoretical potential tidal energy of 14.90 Wh/m2, making it the most promising site for localized tidal power development. Although moderate compared to globally renowned sites, the identified VES hotspots merit consideration for small-to-medium scale projects tailored to local conditions. While limited to potential energy assessments, this study provides a crucial baseline for the VES region, highlighting opportunities for sustainable tidal energy exploitation.

Deep learning assisted characterization of bubble behavior in a gas-solid fluidized bed with binary particle mixtures

Gas-solid fluidized beds are widely applied as chemical reactors, and the fluidization quality in the bed is closely related to bubble behavior. Digital image processing is a commonly used non-invasive method for bubble behavior analysis, but it is usually constrained by experimental conditions such as lighting, making identification of bubble and emulsion phases still challenging. Herein, deep learning is applied in this study to optimize traditional digital image processing techniques. By evaluating different deep learning models (FCN, DeepLab V3, U-Net), rapid and accurate identification and segmentation of bubble images can be achieved, and the U-Net model performs best, achieving an identification accuracy of 99.05 %. Further application of U-Net to analyze bubble behavior demonstrates that deep learning methods enable efficient and accurate identification of bubbles and real-time analysis of bubble behavior, highlighting the significant potential application of deep learning in the field of complex hydrodynamics in fluidized beds.

Density effects on the hydrodynamics and mixing at a large confluence with a compound channel tributary

Abstract Confluences are marked by converging streamlines, complex hydrodynamics features and mixing processes. Understanding such intricate flow patterns and their effects on the mixing at natural river junctions is difficult, particularly for large confluences with a compound channel tributary, due to the scarcity of field data. This research aims to assess the effects of density differences on hydrodynamics and mixing at the large river confluence between the Yangtze River and its tributary, the Poyang Lake, whose outflow channel has a large inner-side floodplain. Field data were collected during three surveys under various flow conditions to analyse how the alternate flow mechanism (compound channel vs single channel) of the Poyang Lake outflow channel influences the confluence mixing process. In high flow and relatively high flow conditions, the Poyang Lake outflow channel functioned as a compound channel and switched to a single channel in low flow. Acoustic Doppler current profiling (ADCP) was used in all three surveys to explore the features of hydrodynamics and the mixing process at the confluence, supplemented by water quality sampling to characterise the mixing patterns. Secondary flows observed during the field surveys were found to be affected by alterations in the flow mechanism (compound channel vs single channel) of the Poyang Lake outflow channel and the density effects, which were characterised using the densimetric Froude number Frd. Ultimately, the findings obtained in these field surveys confirm the role of density differences between the tributaries in significantly affecting hydrodynamics features and the mixing process at river confluences.

Computational fluid dynamics (CFD)- deep neural network (DNN) model to predict hydrodynamic parameters in rectangular and cylindrical bubble columns

Bubble columns are omnipresent in the chemical, bio-chemical, petrochemicals, petroleum industries, but their design and scale-up is complex owing to its complex hydrodynamics. Liquid velocity and gas holdup is one of the critical hydrodynamic parameters which effects the mixing, heat and mass transfer in bubble columns. CFD is widely recognized as a powerful tool for estimating critical hydrodynamic parameters but requires significant computational resources, time and expertise. These limitations restrict its practical use in hydrodynamic simulations that need real-time processing involving large-scale simulations of bubble columns. To overcome these limitations, CFD-DNN model is developed to predict the time averaged gas holdup and axial liquid velocity at various operating conditions. The DNN model was trained using CFD data that was produced for rectangular (with dimensions L=0.2 m, W=0.05 m, H=1.2 m) and cylindrical (with a diameter of 0.19 m) bubble columns. The data covers a range of operating conditions and various flow regimes. The superficial gas velocity for the rectangle column was selected at 1.33 and 7.3 mm/s, whereas for the cylindrical bubble column, it was fixed at 0.02 and 0.12 m/s. The CFD-DNN model was validated against the experimental and the CFD data from the literature. Further, the model was tested for new data that the CFD-DNN model has not seen with existing literature and showed good agreement with their data and it reflects the excellent generalization ability of the model. The proposed CFD-DNN approach improves current CFD models by providing shorter computing time, decreasing computational expenses, and reducing the expertise in CFD simulations. The accuracy of the developed CFD-DNN model was evaluated using different metrics for gas holdup and axial liquid velocity. For rectangular bubble columns, the model achieved MSE of 0.0001 for gas holdup and 0.0007 for axial liquid velocity. Similarly, for cylindrical bubble columns, the MSE values were 0.0009 for gas holdup and 0.0006 for axial liquid velocity.

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Computational study on complex wave hydrodynamics of multidirectional extreme waves at fringing reef

Due to the special topography of coral reefs, most of the energy of incoming waves can be reduced through the wave breakings at the reef edge, thus playing a vital role in protecting the coastal regions behind coral reefs. In severe weather conditions, wind-generated waves tend to propagate in multiple directions in real ocean environment. The abrupt multidirectional wave-focusing mechanisms may result in the formation of huge waves in both deep and shallow waters, threatening the safety of coastal infrastructures and well-being of coastal communities at the reef islands. Therefore, it is crucial to thoroughly examine the complex hydrodynamics of multidirectional extreme waves at the fringing reefs. Previous studies on reef wave hydrodynamics predominantly focus on unidirectional waves. Only a small number of studies have considered the wave hydrodynamics of fringing reefs under multidirectional waves, let alone multidirectional extreme waves. To address the knowledge gap in previous studies, this study analyzes the complex hydrodynamic processes of multidirectional extreme waves at the fringing reef by applying a nonhydrostatic numerical wave model (NHWAVE). Influences of several key factors are analyzed, such as significant wave height, peak wave period, water depth, incident wave angle, and reef topography. It is expected that the research findings of this study will contribute to a deeper understanding of the hydrodynamics of multidirectional extreme waves at the fringing reef.

Does polymixis complicate prediction of high‐frequency dissolved oxygen in lakes and reservoirs?

AbstractAs lake and reservoir ecosystems transition across major environmental regimes (e.g., mixing regime) resulting from anthropogenic change, setting predictive expectations is imperative. We tested the hypothesis that (dissolved) oxygen is more predictable in monomictic reservoirs that thermally stratify throughout the summer (warm) season compared to polymictic reservoirs that stratify intermittently. Using two‐hourly vertical profiles of oxygen, we compared daily‐aggregated errors of oxygen predictions from random forests across and within two monomictic and two polymictic reservoirs in the south‐central (subtropical) USA. Although one monomictic reservoir was typically more predictable than the polymictic reservoirs, the hypereutrophic, small monomictic reservoir had less predictable oxygen patterns potentially related to rapid oxygen cycling and intrusions of oxygenated waters in the hypolimnion without mixing. Daily mixing did not relate strongly to model errors. Water temperature, depth, and wind were the most important predictors, but were not clearly related to season or mixing. Lastly, we compared multiple model types (regression, neural network, and process‐based) in one polymictic reservoir to test how our interpretations of oxygen predictability were sensitive to model type, finding that the models generally agreed; however, the process‐based model poorly predicted oxygen in the middle of the vertical profiles (5 m) where most models performed poorly due to a temporally unstable, vacillating metalimnion. Our results suggest predicting reservoir oxygen dynamics may be easier in stratified reservoirs, but eutrophication and complex hydrodynamics may cause forecasting surprises especially for those who use or manage water resources in mono‐ or dimictic reservoirs.

Body length determines flow refuging for rainbow trout (Oncorhynchus mykiss) behind wing dams.

Complex hydrodynamics abound in natural streams, yet the selective pressures these impose upon different size classes of fish are not well understood. Attached vortices are produced by relatively large objects that block freestream flow, which fish routinely utilize for flow refuging. To test how flow refuging and the potential harvesting of energy (as seen in Kármán gaiting) vary across size classes in rainbow trout (Oncorhynchus mykiss; fingerling, 8 cm; parr, 14 cm; adult, 22 cm; n=4 per size class), we used a water flume (4100 l; freestream flow at 65 cm s-1) and created vortices using 45deg wing dams of varying size (small, 15 cm; medium, 31 cm; large, 48 cm). We monitored microhabitat selection and swimming kinematics of individual trout and measured the flow field in the wake of wing dams using time-resolved particle image velocimetry (PIV). Trout of each size class preferentially swam in vortices rather than the freestream, but the capacity to flow refuge varied according to the ratio of vortex width to fish length (WV:LF). Consistent refuging behavior was exhibited when WV:LF≥1.5. All size classes exhibited increased wavelength and Strouhal number and decreased tailbeat frequency within vortices compared with freestream, suggesting that swimming in vortices requires less power output. In 17% of the trials, fish preferentially swam in a manner that suggests energy harvesting from the shear layer. Our results can inform efforts toward riparian restoration and fishway design to improve salmonid conservation.

Unsteady flow in a compound channel during flooding: Insights from a combined two-dimensional and three-dimensional numerical simulation

Natural channels often take the form of compound channels during flooding events. The unsteadiness of floods is crucial for flood management and river-floodplain ecosystems. However, the impact of unsteady flow on three-dimensional hydrodynamic processes within compound channels remains poorly understood. This study employed a validated two-dimensional/three-dimensional numerical model to examine how unsteady features influence key hydrodynamic indicators of compound channels, such as velocity distribution, turbulence characteristics, and discharge partitioning between the main channel and floodplain. A definitive positive correlation was found between water level fluctuation amplitudes and integrated discharge during unsteady events, as well as between mean water levels and mean discharges. The proportion of discharge conveyed by the main channel relative to the total was directly linked to the water depth ratio. The exchange at the main channel–floodplain interface exhibits a dual-layer flow pattern: surface waters spill into the floodplain, while deeper waters are siphoned into the main channel. Unsteady conditions principally modulate the intensity of these exchanges, altering hydraulic interactions and water transfer between areas. Pronounced wall shear stresses were detected at the main channel–floodplain interface due to swift channel flows and momentum exchange at the floodplain margin, with maxima adjacent to the floodplain edge correlating with peak flow velocities. Under deep conditions, cross-sectional flow across the compound channel's mixing layer was essentially two-dimensional. These findings elucidate the complex hydrodynamics inherent to unsteady overbank flows while holding implications for enhancing floodplain management and advancing the understanding of unsteady fluvial hydraulics.

A dynamic compartmental model of a sequencing batch reactor (SBR) for biological phosphorus removal

ABSTRACT Bioreactors are usually modelled as continuous stirred tank reactors (CSTRs) or CSTRs connected in series (Tanks-In-Series configuration). In large systems with non-ideal mixing, such approaches do not sufficiently capture the complex hydrodynamics, leading to model inaccuracies due to the lumping of spatial gradients. Highly detailed computational fluid dynamics (CFD) models provide insight into complex hydrodynamics but are computationally too expensive for flow-sheet models and digital twin applications. A compartmental model (CM) can be a middle-ground by providing a more realistic representation of the hydrodynamics and still being computationally affordable. However, the hydrodynamics of a plant can be very different under varying flow conditions. Dynamic CMs can capture these changes in an elegant way. So far, the application of CMs has been limited mostly to continuous flow systems. In this study, a dynamic CM of a sequencing batch reactor (SBR) is developed for a bio-P removal process. The SBR comes with challenges for CM development due to its distinct operational stages. The dynamic CM shows significant improvements over the CSTR model (using the same biokinetic parameters) for dissolved oxygen and phosphate predictions reducing the need for model recalibration that can lead to over-fitting and limited extrapolation capability of the model.

Elucidating the mechanism of overcuring in microchannels fabricated via vat photopolymerization (VPP) for precise microfluidic chip printing

Vat Photopolymerization (VPP) technology is highly regarded for precision micromachining applications like microfluidic chip fabrication, owing to its fine resolution, adaptability, and rapid prototyping capabilities. However, the challenge of resin over-curing resulting from UV energy penetration poses a significant obstacle in microchannel printing. This study addresses this challenge by investigating the intricate relationship between resin properties and over-curing. Diverse microfluidic chips were printed utilizing VPP technology, and the overall cross-sectional over-curing characteristics of the channels were systematically examined. A novel combination of the classical UV light curing model and computational Fluid-Structure Interaction (FSI) model with the VPP printing process was proposed to examine the complex hydrodynamics and exfoliation dynamics within VPP-printed microchannels. The simulation results suggest that resins with higher viscosities and surface roughness are better suited for microchannel printing. Subsequent experiments using five different resins verified these findings and showed that the microchannels obtained with resins of lower viscosity and surface roughness resins have an irregular trapezoidal cross-section, which is uncontrollable and unpredictable and seriously affects the microchannel printing accuracy. Conversely, resins with higher viscosity and surface roughness have an ideal rectangular cross-section, which can be compensated by the model further refined printing to print microchannel. Finally, microfluidic chips were printed using "high precision 8 K resin" and "8 K-clay resin" with higher viscosity and surface roughness. The results proved that the microchannels printed by these resins had the desired rectangular cross-section, which was consistent with the "DLP light-curing" model. This study elucidates, for the first time, the intricate interplay between resin properties, printing structure, and microchannel over-curing, and this provides a solid groundwork for advancing high-precision microfluidic chip fabrication via DLP printing.

A comparative assessment of CFD based LSTM and GRU for hydrodynamic predictions of gas-solid fluidized bed

Numerical simulation of large-scale fluidized beds presents significant challenges for both scaling up the simulations and evaluating their performance, as full-scale computational fluid dynamics (CFD) simulations incur substantial computational time and cost. To overcome these challenges, we propose a novel approach that combines CFD simulations with data-driven deep-learning models to predict complex hydrodynamics. In this study, we trained two machine learning (ML) algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), using spatiotemporal data obtained from CFD simulations. Impressively, the trained models accurately predicted future events for successive time steps, exhibiting excellent agreement for mean voidage, gas velocity, and particle velocity. Among the models utilized, LSTM outperformed the other. This demonstrated its superior ability to capture spatiotemporal CFD data while requiring less computational time and power. The trained models provide a reliable means to extrapolate desirable mean particle velocity and voidage values within fluidized beds. These findings hold promising opportunities for enhancing the design and operation of gas-solid fluidized bed reactors, potentially leading to a significant reduction of up to 57–68% in simulation time.

An overtopping formula for shallow water vertical seawalls by SWASH

There is now wide evidence that phase-resolving numerical models can capture the general physics of the overtopping process even under complex hydrodynamics. Based on this outcome, an extensive parametric study has been carried out with the model SWASH, to develop a design formula that uniformly predicts the overtopping rate at vertical seawalls for both breaking and non-breaking wave conditions. The use of the numerical model has allowed to vary the experimental conditions smoothly, avoiding the typical limitation of laboratory experiments. Furthermore, particular tests have been performed to assess whether, and up to which extent, the mean overtopping discharge is affected by the low frequency components of the incident wave spectrum.The formula relates the overtopping rate to a new water level statistic, which represents the average of the highest one-fourth wave displacements at the toe of the wall; it has initially been derived for planar beaches and then extended to the case of slope varying foreshores. A comparison with an array of 200 laboratory experiments confirms that the new parametrization can reduce the scatter of data compared to the EurOtop equation.

Bubble Interaction and Heat Transfer Characteristics of Microchannel Flow Boiling With Single and Multiple Cavities

Abstract Flow boiling in microchannels can effectively address the challenges of high power density heat dissipation in electronic devices. However, the intricate bubble dynamics during the two-phase flow in microchannel necessitates understanding the characteristics of complex bubble hydrodynamics. In this study, we perform 2D numerical simulations of flow boiling using the Cahn-Hilliard phase-field method for a 200-µm width microchannel with single and multiple cavities in COMSOL Multiphysics (V5.3). The numerical model successfully captures bubble dynamics, encompassing vapor embryo generation, bubble growth, departure, coalescence, sliding, and stable vapor plug formation. The heat transfer mechanism inside the microchannel is dominated by bubble nucleation and thin-film evaporation. Elevated wall superheats in a single nucleation cavity, and increased mass flux facilitates higher bubble departure frequency and heat transfer performance. Temporal pressure fluctuations are observed inside microchannels in multiple cavities due to bubble coalescence, departure, and subsequent nucleation. Increasing the nucleating cavities from 2 to 5 within the microchannel while maintaining consistent cavity spacing of 100 µm has resulted in nearly 32% enhancement in heat transfer performance. This study offers valuable findings that can help improve the thermal management of electronic devices.