Flow Modeling Research Articles

Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. Case series. Tertiary Academic Center. Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

Modeling of multi-phase flow in plasma transferred arc cladding of NiCrBSi/WC metal matrix composite

In this paper, a 3D multiphase flow model has been established for the plasma transferred arc (PTA) cladding of composite powder (NiCrBSi/50 wt% WC) on a 40CrNi2MoA substrate. The multiphase flow model simulates both the discrete phase (WC particles) and continuous phase in the melt pool, including the behaviors of heat transfer and particle distribution. The buoyancy, arc pressure, surface tension, and electromagnetic force are calculated. The model is then used to predict the influences of welding currents and preheating temperatures on the depth of heat-affected zone (HAZ), the temperature field, and the dimensions of the cladding layers. To validate this model, the dimensions and the HAZ depth are analyzed and compared to experimental data. The results show that the dimensions and HAZ depth obtained by the model simulation are in good agreement with the experiment. The HAZ depth, the melt pool length, and the dimensions of cladding layer would increase with the increasing welding current or preheating temperature. Driven by the inward convection in the melt pool, WC particles tend to distribute around the center line of cladding layers. This is the first work of modeling 3D multiphase flow in PTA cladding process of particle-reinforced metal matrix composite (MMCs). The work can provide theoretical guidance for adjusting process parameters to control HAZ depth and cladding quality in actual production.

Physics‐Informed Convolutional Decoder (PICD): A Novel Approach for Direct Inversion of Heterogeneous Subsurface Flow

AbstractWe propose a physics‐informed convolutional decoder (PICD) framework for inverse modeling of heterogenous groundwater flow. PICD stands out as a direct inversion method, eliminating the need for repeated forward model simulations. The framework combines data‐driven and physics‐driven approaches by integrating monitoring data and domain knowledge into the inversion process. PICD utilizes a convolutional decoder to effectively approximate the spatial distribution of hydraulic heads, while Karhunen–Loève expansion (KLE) is employed to parameterize hydraulic conductivities. During the training process, the stochastic vector in KLE and the parameters of the convolutional decoder are adjusted simultaneously to minimize the data‐mismatch and the physical violation. The final optimized stochastic vectors correspond to the estimation of hydraulic conductivities, and the trained convolutional decoder can predict the evolution and distribution of hydraulic heads. Various scenarios of groundwater flow are examined and results demonstrate the framework's capability to accurately estimate heterogeneous hydraulic conductivities and to deliver satisfactory predictions of hydraulic heads, even with sparse measurements.

Numerical investigation of the erosion reduction in elbows using separate and helical inner ring

This study presents a comprehensive numerical investigation of erosion in bends equipped with inner rings as a novel geometric concept for erosion control. The research aims to assess the effectiveness of these innovative configurations in reducing erosion through rigorous computational fluid dynamics (CFD) simulations. Numerical modeling for the gas-solid two-phase flow has been carried out based on one-way coupling using an Eulerian-Lagrangian approach. The CFD model employed in this work incorporates the RNG k-ε turbulence model for flow solution, the Oka model for simulating surface erosion, and the Grant and Tabakoff model to account for particle-wall collisions. The primary focus of the investigation is to evaluate the erosion reduction capabilities of different inner ring configurations. By systematically varying the number of inner rings, the study elucidates the influence of this parameter on erosion mitigation. The obtained numerical results are analyzed in terms of erosion patterns, erosion reduction percentages, and the integral erosion surface criterion. The findings reveal the elbow with two separate inner rings as the most effective configuration, exhibiting a remarkable decrease in erosion of up to 43 % compared to the standard elbow. Moreover, based on the integral erosion surface criterion, the elbow with five separate inner rings emerges as a more desirable alternative to the standard elbow. In addition, the study investigates the impact of an elbow with helical inner rings configuration on erosion. It is observed that this configuration intensifies flow velocity, thereby accelerating particle motion and leading to increased erosion along the outer wall.

Optimizing small-scale power generation: Exergetic and exergoeconomic evaluation of an integrated gasification system for sacha inchi residues

Biomass is the primary non-fossil energy source, especially in regions such as Central and South America, and holds immense potential for non-fossil energy. Utilizing waste biomass, mainly through gasification for syngas production, offers environmental advantages over direct combustion for electricity and heating. The sacha inchi seed shell (SIS), currently discarded, presents an untapped biomass resource. Hence, the significance of this work lies in assessing the gasification potential of SIS for decentralized power generation, targeting rural off-grid areas. Detailed modeling of mass, energy, and exergy flows was used to establish a self-sustaining 35 kW sacha inchi processing plant using Aspen Plus software. The best operational condition was identified at an equivalence ratio of 0.25, achieving 100 % carbon conversion efficiency and 73.5 % cold gas efficiency with a lower heating value of 6.126 MJ/kg. Exergetic analysis highlights heat exchangers and the power generator as the least efficient components (0.214 % to 27.85 %), contrasting with superior efficiencies in the gasifier, compressor, and cyclone (82.85 %, 85.5 %, and 96.13 %, respectively). Exergoeconomic assessment reveals an energy cost of 10.25 USD/GJ, notably lower than Colombian energy costs for equivalent power needs.

Upper‐flow‐regime deposits related to glacio‐volcanic interactions in Patagonia: Insights from the Pleistocene record in Southern Andes

AbstractSupercritical flows and their associated upper‐flow‐regime bedforms have been increasingly recognized in several contrasting depositional environments around the world, including volcano‐sedimentary settings. In recent years, there have been significant advances in flow modelling, architecture and depositional dynamics in upper‐flow‐regime bedforms related to glacio‐volcanic interaction, however most of these works come from specific volcanic landscapes. Examples of other regions with different tectonic and volcanic settings are needed in order to gain a more global perspective regarding the generation and preservation of upper‐flow‐regime glacio‐volcanic bedforms. This study presents a detailed analysis of Pleistocene volcaniclastic deposits accumulated in alluvial to lacustrine deltaic environments in the Southern Andes within the Southern Volcanic Zone. Six facies were defined and related to distinctive bedforms by examining sedimentary features, including textural characteristics, sedimentary structures, architecture and composition. Among these facies, five are distinguished by deposition from supercritical flows and upper‐flow‐regime bedforms, such as cyclic steps, chutes and pools, and antidunes. The stratigraphic succession reveals a predominance of repeated supercritical flows, which appear to be influenced by the availability of detritus for removal along with sudden water discharges. Compositional analyses indicate that explosive volcanism was the primary source of detritus, occurring synchronously with sedimentation. Additionally, the widespread occurrence of Pleistocene glacial conditions in this region suggests that volcanic‐induced melting ice or snow may have been an important source of water supply. Regarding the Southern Andes, the presence of local depressions, such as volcano‐tectonic calderas plays an essential role in the generation and preservation of the upper‐flow‐regime bedforms related to glacio‐volcanism. Furthermore, the importance of carrying out detailed and multidisciplinary studies seems to be critical for the recognition of these deposits in the record of the Andes.

Fluid-Dynamic Modeling of Flow in Embryonic Tissue Indicates That Lymphatic Valve Location Is Not Consistently Determined by the Local Fluid Shear or Its Gradient.

Intravascular lymphatic valves often occur in proximity to vessel junctions. It is commonly held that disturbed flow at junctions is responsible for accumulation of valve-forming cells (VFCs) at these locations as the initial step in valve creation, and the one which explains the association with these sites. However, evidence in favor is largely limited to cell culture experiments. We acquired images of embryonic lymphatic vascular networks from day E16.5, when VFC accumulation has started but the developing valve has not yet altered the local vessel geometry, stained for Prox1, which co-localizes with Foxc2. Using finite-element computational fluid mechanics, we simulated the flow through the networks, under conditions appropriate to this early development stage. Then we correlated the Prox1 distributions with the distributions of simulated fluid shear and shear stress gradient. Across a total of 16 image sets, no consistent correlation was found between Prox1 distribution and the local magnitude of fluid shear, or its positive or negative gradient. This, the first direct semi-empirical test of the localization hypothesis to interrogate the tissue from invivo at the critical moment of development, does not support the idea that a feature of the local flow determines valve localization.

Investigating the effect of using aluminum oxide nanofluid and twisted tapes with different twisted pitches on the heat transfer efficiency and using perceptron artificial neural networks in data prediction

This study investigates the effect of aluminum oxide nanofluid with 1% and 2% volume fractions (VFs) in four different tubes with identical bending and the uniform, low-high-low, low-high, and high-low-high longitudinal states with twist ratios of 0, 2 and 4. These tubes are affected by a constant thermal flux of 10000 W/m2 and Reynolds numbers of 2000-10000. For the numerical solution of these equations, the finite difference method on the basis of the control volume and the SIMPLEC algorithm and the K-ε-RNG method were applied for the turbulent flow modeling. The determined Nusselt number from the numerical methods were evaluated applying the perceptron neural network. The most optimal increase in the Nusselt number was in the twisted tube with twist ratio of 4 in the Reynolds number of 10,000 and VF of 1% and 2% nanofluid with increasing trend of length from low to high. The most optimal increase in Nusselt number compared to base fluid was about 25%. The sum of squared error and the relative error data obtained from the hyperbolic tangent function for the training and test data were 0.080-1.331 and 0.113-0.675, and from the sigmoid function for the training and test data were 0.093-0.392 and 0.097-0.147.

Reduced-order modeling of unsteady fluid flow using neural network ensembles

The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data that are spatially distributed, including solutions to partial differential equations. When applied to unsteady physics problems, ROMs also require a model for time-series prediction of the low-dimensional latent variables. Long short-term memory (LSTM) networks, a type of recurrent neural network useful for modeling sequential data, are frequently employed in data-driven ROMs for autoregressive time-series prediction. When making predictions at unseen design points over long time horizons, error propagation is a frequently encountered issue, where errors made early on can compound over time and lead to large inaccuracies. In this work, we propose using bagging, a commonly used ensemble learning technique, to develop a fully data-driven ROM framework referred to as the CAE-eLSTM ROM that uses CAEs for spatial reconstruction of the full-order model and LSTM ensembles for time-series prediction. When applied to two unsteady fluid dynamics problems, our results show that the presented framework effectively reduces error propagation and leads to more accurate time-series prediction of latent variables at unseen points.

Modeling of Pore-Scale Capillary-Dominated Flow and Bubble Detachment in PEM Water Electrolyzer Anodes Using the Volume of Fluid Method

Fluid dynamics models complement expensive experiments with limited measurement accuracy that investigate the mass transport in PEM water electrolysis. Here, a first-principle microscale model for oxygen transport is successfully validated that accounts for (1) uncertain transport processes in catalyst layers, (2) numerically challenging capillary-dominated two-phase flow and (3) bubble detachments in channels. We developed algorithms for the stochastic generation of geometries and for the coupling of flow and transport processes. The flow model is based on the volume of fluid method and reproduces experimentally measured pressure drops and bubble velocities within minichannels with a 30% and 20% accuracy, respectively, provided that the capillary number is above 2.1 × 10−7. At lower capillary numbers, excessive spurious currents occur. Correspondingly, two-phase flow simulations within the porous transport layers are stable at current densities above 0.5 A cm−2 and match operando gas saturation measurements within a 20% margin at relevant locations. The simulated bubble detachments occur at pore throats that agree with porosimetry and microfluidic experiments. The presented model allows explaining and optimizing mass transport processes in channels and porous transport layers. These were found to be negligibly sensitive to transport resistances within the catalyst layer, providing information on boundary conditions for future catalyst layer models.

Phase field modeling and numerical algorithm for two-phase dielectric fluid flows

We develop a method for modeling and simulating a class of two-phase flows consisting of two immiscible incompressible dielectric fluids and their interactions with imposed external electric fields in two and three dimensions. We first present a thermodynamically-consistent and reduction-consistent phase field model for two-phase dielectric fluids. The model honors the conservation laws and thermodynamic principles, and has the property that, if only one fluid component is present in the system, the two-phase formulation will exactly reduce to that of the corresponding single-phase system. In particular, this model accommodates an equilibrium solution that is compatible with the zero-velocity requirement based on physics. This property leads to a simpler method for simulating the equilibrium state of two-phase dielectric systems. We further present an efficient numerical algorithm, together with a spectral-element (for two dimensions) or a hybrid Fourier-spectral/spectral-element (for three dimensions) discretization in space, for simulating this class of problems. This algorithm computes different dynamic variables successively in an un-coupled fashion, and involves only coefficient matrices that are time-independent in the resultant linear algebraic systems upon discretization, even when the physical properties (e.g. permittivity, density, viscosity) of the two dielectric fluids are different. This property is crucial and enables us to employ fast Fourier transforms for three-dimensional problems. Ample numerical simulations of two-phase dielectric flows under imposed voltage are presented to demonstrate the performance of the method herein and to compare the simulation results with theoretical models and experimental data.

Computational Fluid Dynamics Modeling of Multiphase Flows in a Side-Blown Furnace: Effects of Air Injection and Nozzle Submerged Depth

The side-blown smelting process is becoming popular in the modern metallurgical industry due to its large potential for dealing with complex materials. To further enhance its efficiency, it is essential to comprehensively understand the complex gas–liquid flow behavior in the smelting bath. In this study, the volume-of-fluid method is employed to establish computational fluid dynamics modeling on a 1:5 scaled model of a side-blown furnace. The simulation was validated against the experimental results. Notably, the influences of the nozzle’s submerged depth, injection velocity, and angle were systematically investigated. The results show that increasing the injection velocity from 29.44 to 58.88 m/s resulted in 52.97%, 116.67%, 500.00%, and 5.88% increases in the interface area, liquid velocity, liquid turbulent kinetic energy, and gas penetration depth, respectively. The maximum gas–liquid interface area, gas penetration depth, velocity, and turbulence of the liquid were found at an injection angle of 30°. Furthermore, increasing the submerged depth increased the interface area and the velocity of the liquid but decreased the turbulent kinetic energy of the liquid. Overall, increasing the injection velocity is considered a more effective measure to strengthen the smelting intensity.

Adaptive reconstruction of ship trajectory considering motion states

Abstract In response to the presence of noise and missing data in the records of ship trajectories from the Automatic Identification System (AIS), which affects the accuracy of ship traffic flow modeling and knowledge discovery, an adaptive reconstruction method for ship trajectories is proposed, taking into account the state of motion of the ship. This method comprehensively considers latitude, longitude, speed, course, and turn rate in the ship trajectory data and enables the identification and removal of position, speed, and course noise. In addition, the ship’s trajectory is divided into straight segments and turning segments based on the rate of change of the ship’s course. Various interpolation methods are adaptively applied to repair the trajectory. The validation with real ship trajectories shows that the proposed method has higher accuracy than other benchmark methods and provides a solid basis for evaluating ship trajectories.

Analisis Kapasitas Efektif Tampungan Kali Pancir

Kali Pancir is one of the rivers in Jombang Regency which often experiences flooding. The cause was heavy rain and the critical condition of the embankments. In this research, flood discharge analysis was carried out using synthetic hydrographs and hydraulic modeling for river flow. The data needed is rain data from rain stations around the Pancir River and river profiles. The method used to calculate flood discharge is the Nakayasu Synthetic Unit Hydrograph for 6 different return periods. Simulation using HEC-RAS software is used to simulate flow profiles to determine river cross-sections that are critical to flooding. Based on the results of the calculation analysis, it was found that the peak flood discharge occurred at 3.5 hours. The greater the return period, the greater the river cross section that experiences overflow. At a return period of 1.01 years, the critical cross section starts at STA 45, while at a return period of 50 years, the critical cross section occurs starting at STA 6.

Enhancing Offshore Drilling Efficiency through Multilevel Flow Modeling of MPD Operations

Exploiting all forms of energy must be coupled with the preservation of human safety and the environment. Despite the energy reserves beneath the ocean depths, challenging obstacles confront offshore drilling operations, notably the high pressure beneath the well. To comprehend this process better, we turned to modeling the managed pressure drilling (MPD) using multilevel flow modeling (MFM). These models provide a robust framework enabling us to understand and analyze the intricacies of this complex process. Our use of these models allows us to examine the causes and effects associated with various aspects of this process. All of this enables us to drill safely for the well, achieving our primary goal in this context.

Determination of conditions for combustion front development near injection well following air injection into oil reservoir using fluid flow modeling

Determination of conditions for combustion front development near injection well following air injection into oil reservoir using fluid flow modeling

Anisotropic turbulence modeling for natural channel flow: A numerical approach with finite element method

In this study, we propose a numerical model aimed at improving the understanding and prediction of flow velocities in natural channels. This model specifically focuses on the challenges presented by anisotropic turbulence and bottom roughness. By incorporating the mixing length concept into an algebraic turbulence model and employing the finite element method with Streamline-Upwind/Petrov–Galerkin (SUPG) stabilization, our model seeks to refine the simulation of axial velocity distribution and secondary motions. Validation was achieved through Acoustic Doppler Current Profiler (ADCP) measurements in three sections of the Canal do Rodeador, showing our model’s predictions of discharge and average velocity to have a deviation of approximately 9.34% from experimental data. The results underline the significance of secondary currents and turbulence anisotropy in shaping channel flow behaviors, offering new insights into the interactions between flow characteristics and channel bed features. This model stands out as a robust tool for hydraulic structure design and hydrokinetic potential evaluation, providing a non-intrusive, cost-effective alternative to traditional methods.

On the efficient non-linear solver for hydraulic fracturing and well cementing simulations based on Anderson acceleration

The aim of this study is to create a fast and stable iterative technique for numerical solution of a quasi-linear elliptic pressure equation. We developed a modified version of the Anderson acceleration (AA) algorithm to fixed-point (FP) iteration method. It computes the approximation to the solutions at each iteration based on the history of vectors in extended space, which includes the vector of unknowns, the discrete form of the operator, and the equation's right-hand side. Several constraints are applied to AA algorithm, including a limitation of the time step variation during the iteration process, which allows switching to the base FP iterations to maintain convergence. Compared to the base FP algorithm, the improved version of the AA algorithm enables a reliable and rapid convergence of the iterative solution for the quasi-linear elliptic pressure equation describing the flow of particle-laden yield-stress fluids in a narrow channel during hydraulic fracturing, a key technology for stimulating hydrocarbon-bearing reservoirs. In particular, the proposed AA algorithm allows for faster computations and resolution of unyielding zones in hydraulic fractures that cannot be calculated using the FP algorithm. The quasi-linear elliptic pressure equation under consideration describes various physical processes, such as the displacement of fluids with viscoplastic rheology in a narrow cylindrical annulus during well cementing, the displacement of cross-linked gel in a proppant pack filling hydraulic fractures during the early stage of well production (fracture flowback), and multiphase filtration in a rock formation. We estimate computational complexity of the developed algorithm as compared to Jacobian-based algorithms and show that the performance of the former one is higher in modelling of flows of viscoplastic fluids. We believe that the developed algorithm is a useful numerical tool that can be implemented in commercial simulators to obtain fast and converged solutions to the non-linear problems described above.

Designing Effective Low-Impact Developments for a Changing Climate: A HYDRUS-Based Vadose Zone Modeling Approach

Low-Impact Developments (LIDs), like green roofs and bioretention cells, are vital for managing stormwater and reducing pollution. Amidst climate change, assessing both current and future LID systems is crucial. This study utilizes variably saturated flow modeling with the HYDRUS software (version 4.17) to analyze ten locations in Ontario, Canada, focusing on Toronto. Historical and projected climate data are used in flow modeling to assess long-term impacts. Future predicted storms, representing extreme precipitation events, derived from a regional climate model, were also used in the flow modeling. This enabled a comprehensive evaluation of LID performance under an evolving climate. A robust methodology is developed to analyze LID designs, exploring parameters like water inflow volumes, peak intensity, time delays, runoff dynamics, and ponding patterns. The findings indicate potential declines in LID performance attributed to rising water volumes, resulting in notable changes in infiltration for green roofs (100%) and bioretention facilities (50%) compared to historical conditions. Future climate predicted storms indicate reduced peak reductions and shorter time delays for green roofs, posing risks of flooding and erosion. Anticipated extreme precipitation is projected to increase ponding depths in bioretention facilities, resulting in untreated stormwater overflow and prolonged ponding times exceeding baseline conditions by up to 13 h at numerous Ontario locations.

Turbulent Channel Flow Simulation Using LES Dynamic Model

Turbulent flow modelling has increasingly becomeone of the main scientific research occupations, seeking todevelop a universal modelling approach that can describeaccurately the dynamics of all turbulent flow scales. Modernattempts try to bring together two main objectives; theapplicability of the turbulent flow models to complex flowconfigurations of industrial interest as well as reducing thecomputational costs needed for their resolution. Large EddySimulation (LES) modelling approach has many promisingfeatures among all other turbulent flow modellingapproaches such as RANS or DNS. In this study, LESmodelling approach has been demonstrated and applied toturbulent channel flow at moderate Reynolds number using Fluent solver. The obtained turbulent flowstatistics were compared to their corresponding values of a detailed, well resolved DNS simulation. The obtained LESturbulent flow statistics have an acceptable trend to those ofthe reference DNS results, which reflects the validity of theLES approach to model turbulent flows accurately andencourages its use in predicting turbulent motion forindustrial and engineering applications.