Flow Modeling Research Articles

A one-dimensional computational model for blood flow in an elastic blood vessel with a rigid catheter.

Strokes are one of the leading causes of death in the United States. Stroke treatment involves removal or dissolution of the obstruction (usually a clot) in the blocked artery by catheter insertion. A computer simulation to systematically plan such patient-specific treatments needs a network of about 105 blood vessels including collaterals. The existing computational fluid dynamic (CFD) solvers are not employed for stroke treatment planning as they are incapable of providing solutions for such big arterial trees in a reasonable amount of time. This work presents a novel one-dimensional mathematical formulation for blood flow modeling in an elastic blood vessel with a centrally placed rigid catheter. The governing equations are first-order hyperbolic partial differential equations, and the hypergeometric function needs to be computed to obtain the characteristic system of these hyperbolic equations. We employed the Discontinuous Galerkin method to solve the hyperbolic system and validated the implementation by comparing it against a well-established 3D CFD solver using idealized vessels and a realistic truncated arterial network. The results showed clinically insignificant differences in steady flow cases, with overall variations between 1D and 3D models remaining below 10%. Additionally, the solver accurately captured wave reflection phenomena at domain discontinuities in unsteady cases. A primary advantage of this model over 3D solvers is its ease in obtaining a discretized geometry of complex vasculatures with multiple arterial branches. Thus, the 1D computational model offers good accuracy and applicability in simulating complex vasculatures, demonstrating promising potential for investigating patient-specific endovascular interventions in strokes.

Experiments and numerical modelling of secondary flows of blood and shear-thinning blood analogue fluids in rotating domains

AbstractThe transition from concentric primary flow to non-tangential secondary flow of blood was investigated using experimental steady shear rheometry and numerical modelling. The aims were to: assess the difference in secondary flow in a Newtonian versus shear-thinning blood analogue; and measure the secondary flow in the blood. Both experiments and numerical modelling showed that the transition from primary to secondary flow was the same in a Newtonian fluid and a shear-thinning blood analogue. Experiments showed whole blood transitioned to secondary flow at lower modified Reynolds numbers than the Newtonian fluid; and transition was haematocrit dependent with higher RBC concentrations transitioning at lower modified Reynolds numbers. These results indicate that modelling blood as a purely shear-thinning fluid does not predict the correct secondary flow fields in whole blood; non-Newtonian effects beyond shear-thinning behaviour are influential, and incorporating effects such as multiphase contributions and viscoelasticity, yield stress and thixotropy is necessary.

Numerical simulation of neutral gas dynamics in the W7-X sub-divertor

Abstract The present work presents a 2D and 3D modelling of the neutral gas flow in the sub-divertor region of the W7-X. The investigations have been done using the DIVGAS code. The complex 2D and 3D geometries of the divertor components in the sub-divertor region have been considered and the Standard and High-Iota magnetic configurations have been numerically simulated. The main objective of this study is to investigate the dynamics of neutral particles in the sub-divertor region including the effects due to geometry and toroidal and poloidal leakages located at the divertor targets and baffles on the achieved pumping efficiency. A sensitivity analysis has been performed for the effect of various geometrical and flow parameters on the pumping performance, under different plasma scenarios. The considered incoming fluxes in the sub-divertor range between 1020 to 1022 (H2/s). The main conclusions, which can be extracted from the present numerical analysis could be summarized as follows; a large fraction of incoming neutral particle flux i.e. > 70% on the low iota side and > 40% for the high iota side is leaked back to the main divertor region, while higher incoming neutral fluxes facilitate the increase of the pumped flux as well as the decrease of the outflux. It has been estimated that a small fraction ~3-4% of the incoming neutral flux is being pumped via the turbo-molecular pumps. The closure of the toroidal leakages as well as the inclination of the pumping gap panel by 9 o facilitate the increase of the pumped flux, but considering the all the engineering constraints, the latter option seems to be more easy to be implemented. For low incoming neutral fluxes (~1020 H2/s) and for the case of AEH section, free molecular flow conditions are estimated and therefore neutral-neutral collisions could be neglected. For higher incoming neutral fluxes and for both AEH and AEP sections neutral-neutral collisions play a significant role in the flow establishment. A comparison with available experimental measurements of the neutral pressure in the sub-divertor has been performed for Standard and High-Iota plasma discharges. The 3D DIVGAS simulations predict qualitatively the experimental data with relative deviation between 25 and 63%. All the above numerical findings will actively support the optimization of the W7-X particle exhaust, in view of the experimental campaign OP2.

Predictive Modelling of Traffic Flow with Deep Learning Techniques

This project delves into predictive modeling for traffic flow using deep learning techniques, focusing on the Metro Interstate dataset. Traffic Flow Prediction (TFP) is crucial for Intelligent Transport Systems (ITS), optimizing vehicle movement, reducing congestion, and improving route efficiency. Leveraging advancements in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data, our study explores various techniques and models for TFP. We highlight DL models' advantages over traditional ML methods, propelled by the wealth of real-time traffic data fostered by smart cities, presenting opportunities to craft robust predictive models. The core of our project revolves around developing a multi-step Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture. Our model forecasts traffic volume between Minneapolis and St. Paul, Minnesota, predicting volume two hours into the future based on a six-hour historical window. We explore DL algorithms' efficacy, including LSTM and Gated Recurrent Unit (GRU), in mitigating challenges like the vanishing gradient problem common in RNNs. Our analysis compares various NN models, emphasizing the importance of data availability for training and fine-tuning ML/DL models, with the Metro Interstate dataset serving as a crucial asset for comprehensive traffic flow analysis and model development.

The effects of damming and dam regulation on a river–lake-aquifer system: 3D groundwater flow modeling of Poyang Lake (China)

Large numbers of dams are planned or constructed for hydroelectric power production, water supply, and flood control in many rivers around the world. While the social and environmental impacts of dams are now well-understood, there is little knowledge about the effects of dams on groundwater-surface water interactions in a complex lake–river-aquifer system. This study adopts a three-dimensional groundwater numerical model to quantify the potential impacts of dam construction, on regional groundwater flow system, exemplified by the proposed Poyang Lake Hydraulic Dam (PLHD) in China. Once implemented, the PLHD will regulate lake levels, artificially controlling the lake inundation area during the recession season (September-October) and the dry season (November-December). Modeling results indicated that the increase in lake infiltration induced by PLHD regulation would alter the regional water exchange pattern. The PLHD regulation will likely enhance the groundwater storage by 20.44 % in the general dry year (i.e., 2018), alleviating 78.1 % of the groundwater deficit in the extreme dry year (i.e., 2019). Additionally, the response of groundwater dynamics to the PLHD regulation exhibits significant spatial differences between the upstream and downstream areas of the dam, primarily depending on topographical changes. Overall, the PLHD regulation is most likely to impact groundwater levels across an area of approximately 5,700 km2, and the associated groundwater storage is anticipated to reach a stable state, under future long-term regulation. Our findings may guide policy-makers wishing to develop more suitable PLHD scheduling plans and floodplain protection strategies by providing reliable information regarding groundwater storage change and ecosystem succession.

Delineation of a conceptual groundwater flow model of the Kandi basin in Benin (West Africa): Insights from isotopes, piezometric and hydrological investigations

Study regionThe hydrogeological Kandi basin, the Benin’s sector of the transboundary Iullumeden basin, located in northern Benin, West Africa Study focusIn many parts of the world, groundwater resources constitute the main, if not the only, source of potable water available to address a growing demand for numerous uses. And yet, a basic understanding of the hydrogeology of many critical groundwater systems is lacking in many instances. Only when severe groundwater depletion or serious groundwater quality issues arise is the attention paid to the lack of specific groundwater studies. This study targets the Kandi basin, a ∼8700 km2 hydrogeological basin located in northern Benin, which is the primary source of potable water for its inhabitants, but its hydrogeological functioning remains unclear. We conducted isotopic and hydrogeological investigations in this basin from 2013 to 2019 to characterize groundwater recharge processes and delineate flow patterns. The resulting hydrological and geochemical data have been integrated and interpreted using tools including mapping tools. New hydrological insights for the regionThis study has identified the preferential groundwater recharge areas and characterized the main groundwater flow paths. Predominant areas of groundwater dischage into surface water, regardless of the seasons, were detected and areas where the surface-groundwater interflows are season-dependent were discriminated. The main findings presented in this paper are expected to provide a baseline for further in-depth hydrogeological investigations, namely groundwater flow modeling that will help understand the impacts of various groundwater withdrawals and climate changes on the groundwater resources availability of this basin.

A changing river: Long‐term changes of sinuosity and land cover in the Navasota River Watershed, Texas

AbstractThe Navasota River Basin, itself a tributary of the Brazos River in Texas, is a dynamic watershed undergoing many natural and anthropogenic changes. Local stakeholder involvement in this watershed is quite high, and many landowners in the southern portion of the watershed have concerns regarding the increasing frequency and duration of flooding on private property adjacent to the river, often attributing these impacts to the construction of the Lake Limestone dam. In this study, we examine historical flow data, channel morphology, land use/land cover, and precipitation. Our findings indicate that while there appears to be increasing flow in the northern portion of the watershed, temporal data gaps near the watershed outfall prevent the indication of such a trend in the southern portion of the watershed. Nevertheless, other natural and anthropogenic factors are evident in the watershed that may have a significant influence on downstream flooding. Overall river sinuosity (meandering) declined over the study period, with some river segments encountering significant straightening. Total river length declined by 4.3 km from 1972 to 2020. The number and length of offtake channels also decreased substantially during this period. Land use/land cover use shifted dramatically, with a 39.2% increase in impervious cover and a 12.5% decrease in herbaceous cover since 1972. Finally, yearly precipitation increased, with the change point occurring in 1972. Our findings suggest that the shortening and straightening of the river has reduced its volumetric capacity over time. Coupled with increasing impervious surface cover and precipitation, more water is being delivered downstream at a rate exceeding the watershed's ability to discharge it, thereby contributing to flooding issues expressed by stakeholders. We recommend that bathymetric data and supplemental flow monitoring and modeling within the watershed is needed to fully understand how anthropogenic and natural forces may further affect streamflow in the future.

Shear mechanical properties of aggregate cemented materials: a numerical study based on a particle flow modeling strategy

Shear mechanical properties of aggregate cemented materials: a numerical study based on a particle flow modeling strategy

A Critical Review of Multiphase Modelling of Blood Flow in Human Cardiovascular System

A Critical Review of Multiphase Modelling of Blood Flow in Human Cardiovascular System

Impact of modelling assumptions in cavitating flow of simplified injector

Here, three-dimensional Reynolds-averaged Navier–Stokes (RANS) simulations are employed for in-nozzle flow modelling. The aim is to evaluate the capabilities and sensitivities of simulating in-nozzle flow phenomena by using homogeneous phase change models. Two commonly used homogeneous models are employed – (i) Homogeneous Equilibrium Model (HEM) and (ii) Homogeneous Relaxation Model (HRM) A simplified nozzle with reference experimental data is modelled in the developing, super cavitation, and hydraulic flip regimes using OpenFOAM. The influence of the inlet boundary condition and turbulence models are investigated. With HEM, three barotropic compressibility models are tested. The research seeks to understand the strengths and limitations of each approach by comparing different sub-models and their interactions with the flow fields and phase change phenomena. Ultimately, this study contributes to a better understanding of how different modelling choices can influence the accuracy and reliability of cavitation simulations using homogeneous phase change models. While the simulations demonstrated reliable predictions for low-order velocity statistics, substantial discrepancies were identified in the prediction of the cavitating region by both homogeneous models. Additionally, the relaxation model predicted only minor cavitation. In contrast, acceptable flow predictions were achieved using different barotropic compressibility models and inlet boundary condition types, as well as most RANS turbulence models, where the RNG k-ɛ model deviated more from the other turbulence models. Therefore, the added value of the present study is summarised as: (a) Four different cavitation regimes are simulated with HEM and HRM in a single study. (b) The more comprehensive HRM approach does not produce better velocity or cavitation predictions for a low-speed flow using the default relaxation time. (c) The choice of turbulence model can radically affect the cavitation prediction, in addition to velocity fields. (d) The HEM flow simulation has little sensitivity to the barotropic compressibility model.

Numerical modeling of mixed convective nanofluid flow with fractal stochastic heat and mass transfer using finite differences

This study presents the first comprehensive numerical simulation of heat and mass transfer in fractal-like mixed convective nanofluid flows. The flow of non-Newtonian nanofluids over flat and oscillating sheets is modelled mathematically, and a finite difference scheme is used to solve this model. The two-stage scheme can tackle fractal and fractal stochastic mathematical models of partial differential equations. The consistency in the mean square is proved, and Fourier series stability analysis is adopted to find stability conditions for fractal stochastic partial differential equation. The scheme is applied to solve the unsteady Casson nanofluid flow over the flat and oscillatory sheet, which affects thermal radiation, heat source, and chemical reaction. The existence of the solution is also provided for the Navier-Stokes equation of the considered flow model using fractal time derivative. The graph illustrates that the proposed fractal technique achieves faster convergence than the Crank-Nicolson approach. Applications in energy systems, materials science, and environmental engineering are just a few of the domains that could benefit from a better understanding of mixed convective nanofluid flows with fractal features, and that is what this research study hopes to accomplish. Scientists and engineers may better develop efficient and environmentally friendly systems by simulating and analyzing these complicated processes with the suggested finite difference technique.

Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks

Analyzing rock mass seepage using the discrete fracture network (DFN) flow model poses challenges when dealing with complex fracture networks. This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures. Notably, this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry. All geometric analyses, such as identifying connected fractures, dividing the two-dimensional domain into closed loops, triangulating arbitrary loops, and refining triangular elements, are fully automated. The analysis processes are comprehensively introduced, and core algorithms, along with their pseudo-codes, are outlined and explained to assist readers in their programming endeavors. The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures. In practical application, the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project. The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures. Furthermore, following extensive modification and optimization, the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.

Directional saturation of a strongly bimodal pore size distribution carbon interlock fabric: Measurement and multiphase flow modeling

This study focuses on the analysis of the directionality of saturation of materials with bimodal pore size distributions. The material studied is an anisotropic carbon interlock with highly contrasted dual-scale porosity, with heavy warp and light weft tows. Experimentally, 6 configurations are tested using injection combined with dielectric sensors to measure saturation times and unsaturated lengths. The experimental results show that this material has a high contrast in dual-scale porosity and the unsaturation is anisotropic. In parallel, a two-phase flow in porous media continuum model is developed within the OpenFOAM® toolbox of libraries to simulate the transient impregnation. The need to use a tensorial relative permeability and its alignment with the saturated permeability is analyzed by a numerical multiphase mesoscale flow model. The combined experimental and numerical analysis paves the way for a methodology to analyze the saturation of various bimodal pore size distribution materials.

Field inversion machine learning augmented turbulence modeling for time-accurate unsteady flow

Field inversion machine learning (FIML) has the advantages of model consistency and low data dependency and has been used to augment imperfect turbulence models. However, the solver-intrusive field inversion has a high entry bar, and existing FIML studies focused on improving only steady-state or time-averaged periodic flow predictions. To break this limit, this paper develops an open-source FIML framework for time-accurate unsteady flow, where both spatial and temporal variations of flow are of interest. We augment a Reynolds-Averaged Navier–Stokes (RANS) turbulence model's production term with a scalar field. We then integrate a neural network (NN) model into the flow solver to compute the above augmentation scalar field based on local flow features at each time step. Finally, we optimize the weights and biases of the built-in NN model to minimize the regulated spatial-temporal prediction error between the augmented flow solver and reference data. We consider the spatial-temporal evolution of unsteady flow over a 45° ramp and use only the surface pressure as the training data. The unsteady-FIML-trained model accurately predicts the spatial-temporal variations of unsteady flow fields. In addition, the trained model exhibits reasonably good prediction accuracy for various ramp angles, Reynolds numbers, and flow variables (e.g., velocity fields) that are not used in training, highlighting its generalizability. The FIML capability has been integrated into our open-source framework DAFoam. It has the potential to train more accurate RANS turbulence models for other unsteady flow phenomena, such as wind gust response, bubbly flow, and particle dispersion in the atmosphere.

Image-based finite element modeling of air flow and thermal transport in Al-fiber-sintered porous materials

In this study, the pressure drop and heat transfer in a heat-transfer tube filled with a sintered porous medium comprising Al fibers were investigated using computational fluid dynamics (CFD) simulations. We reconstructed the sintered fibrous porous structure and generated a computational mesh using X-ray computed tomography data, and the simulated pressure drop and heat transfer agreed well with those reported by Enoki et al. [2021]. The differences in both the form coefficient and the permeability between two different samples can be explained from the differences in both the specific solid surface area and the porosity. Further, the CFD simulations indicated that thermal conduction of the Al solid phase enhanced the heat exchange between the air and Al fibers. To examine the effect of solid-phase thermal conduction, we generated a model of a heat-transfer tube having an ideal wire mesh structure, in which we introduced the interfacial thermal conductivity between Al fibers and the inner tube wall as additional factors. We also performed CFD simulations with four different shell-region thicknesses and found that even a very narrow gap of 5 to 10 μm heavily affected the heat-exchange performance because of the low thermal conductivity of the shell region.

Inverse modeling of frequency domain-based one-dimensional soil water flow in layered soils

Soils are heterogeneous at multiple scales. Estimates of spatially varying saturated hydraulic conductivity (Ks) of soils dictate the resolution of modeling water flux based on the Richardson-Richards equation (RRE), but direct measurements of Ks everywhere in a field are practically impossible. As a result, this study develops an inverse approach to estimate spatially varying Ks utilizing soil moisture observations driven by periodic weather variability at different frequencies. Using a numerical solution to vertical, one-dimensional RRE based on Gardner-Kozeny (GK) model in a heterogeneous soil with periodic flux, we investigate the spatial cross-correlation between Ks and observations (the amplitude or phase shift) of soil moisture fluctuation at different frequencies without high computational cost in the conventional time-domain model. These correlation patterns vary with the spatial location and frequency, suggesting that soil moisture fluctuations at different frequencies at one monitoring location carry non-redundant information about the heterogeneous distribution of Ks. Guided by cross-correlation maps, several inverse experiments reveal the effectiveness of multi-frequency data of soil moisture fluctuation for mapping the heterogeneous distribution of Ks. Monte Carlo simulations suggest that multi-frequency data can provide non-redundant and useful information about the estimation of heterogeneous Ks. Synthetic data based on a commonly-applied nonlinear van Genuchten-Mualem (VG) model is then tested in our inversion algorithm. The results indicate that the spatial pattern of the inverted GK Ks is very similar to that of the VG Ks, although these two parameters are inherently not identical since they are used in two different models. In addition, the inverted GK Ks can successfully predict the soil moisture fluctuation of frequencies that were not used in the inversion. Despite having no field experiments to validate this model, we believe this is the first attempt to conduct frequency-domain inverse modeling in vadose zone hydrology using soil moisture fluctuation.

Detailed modelling and performance analysis of power flow topology in a hybrid electric vehicle having series-parallel architecture

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle’s actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner.

Investigation of physics-informed deep learning for the prediction of parametric, three-dimensional flow based on boundary data

The placement of temperature sensitive and safety-critical components is crucial in the automotive industry. It is therefore inevitable, even at the design stage of new vehicles, that these components are assessed for potential safety issues. However, with increasing number of design proposals, risk assessment using Computational Fluid Dynamics (CFD) quickly becomes expensive. We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation. The novelty of our method compared to existing works in the field of PINN lies in the extension of parametric flow prediction to three-dimensional space by applying a mini-batch based Quasi-Newton optimization. We contribute a parametric minibatch training algorithm which enables the utilization of the large datasets necessary for the three-dimensional flow modeling. Further, we introduce a continuous resampling algorithm that allows to represent domain variations, while operating on one static dataset of reduced size. In scope of this work, we could show that our nondimensional, multivariate scheme can be efficiently extended to predict the velocity and pressure distribution in three-dimensional space for different design scenarios and geometric scales. Every feature of our methodology is tested individually and verified against conventional CFD simulations. Finally, we apply our proposed method in context of an exemplary real-world automotive application.

Flood Risk Assessment Basing on Flood Flow Modeling in the Oued Martil Region, Western Part of Northern Morocco

Flood Risk Assessment Basing on Flood Flow Modeling in the Oued Martil Region, Western Part of Northern Morocco

Advanced hybrid numerical-machine learning computational study on fluid flow modeling in magnetic nanocarriers for targeted drug delivery

Development of comprehensive models for simulation of blood flow containing magnetic nanoparticles is of fundamental importance due to its application in medical science and cancer treatment. Owing to the complexity of the process, advanced hybrid models are preferred over conventional models. The focus of this study is on the application of machine learning models for predicting velocity in a given dataset to study the fluid flow in vessel containing magnetic nanocarrier for targeted cancer therapy. The numerical model was performed to simulate the blood flow through vessel, while the output data was used in developing machine learning models. The dataset is utilized to train three distinct models: Random Forest, Extra Tree, and AdaBoost Decision Tree. The hyper-parameter optimization is carried out via the Whale Optimization Algorithm (WOA) to improve the models' predictive capabilities. The dataset encompasses more than 17 thousand rows of data. The findings demonstrate the effectiveness of the suggested methodology, as evidenced by the impressive performance metrics exhibited by each model. The Random Forest model achieved a high R-squared value of 0.98768, indicating a robust fit to the data. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for Random Forest were recorded at 6.1223E-04 and 6.09246, respectively. Similarly, the Extra Tree model exhibited superior predictive accuracy with a score of 0.99151 with R-squared metric, showcasing its skill in uncovering the fundamental patterns present in the dataset. The associated RMSE and MAPE for Extra Tree were notably low at 5.1682E-04 and 1.20955E-01, respectively. The AdaBoost Decision Tree model exhibited notable efficacy, as evidenced by its R-squared score of 0.96944, albeit marginally lower than the other models. The RMSE and MAPE for AdaBoost DT were reported at 9.1863E-04 and 8.06333, respectively.