Flow Modeling Research Articles

Slump-flow channel test for evaluating the relations between spreading and rheological parameters of sediment mixtures

Understanding debris flow phenomena and modeling them effectively requires accurate estimation of their rheological parameters. This study introduces a simple approach defined as the “slump-flow channel test”, which employs a horizontal rectangular channel to evaluate these parameters. The results of slump-flow channel tests indicate that the spreading parameters (spread length, angle of reach, and time to final spread) of sediment mixtures are strongly influenced by their sediment fractions and rheological properties. An empirical analysis of the temporal evolution of the spread length and spread velocity was carried out employing both experimental and simulation results. The numerical simulation of the slump-flow channel tests was performed using the Flow-3D software. Both experimental and simulation results demonstrate that the spreading and the rheological parameters of sediment mixtures exhibit strong correlations and this implies that the rheological parameters of sediment mixtures can be evaluated indirectly using a simple slump-flow channel test.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

How photovoltaics make energy refurbishment more affordable in apartment buildings

The costs of energy efficiency renovation of Germany’s postwar apartment buildings have sharply increased since pre-covid years, as have interest rates and hence the costs of servicing loans that finance these renovations. Despite increases in energy costs, it has become virtually impossible to recoup energy-efficiency renovation costs through energy cost savings, with many projects bringing substantial losses. The common method of third parties offering rotating finance to ease the initial costs, and being repaid after short payback periods, therefore no longer works. On the other hand, rooftop photovoltaics have decreased sharply in price, and continue to do so. This study explores the economics of installing photovoltaics, with or without heat pumps, along with energy-efficiency renovation, based on two typical case study apartment buildings in North Rhine-Westphalia. Using fine-grained energy flow modelling and net-present value cost-benefit analysis, it finds that energy-efficiency renovation brings losses of over 80% but that these can be substantially reduced by the gains from rooftop PV, especially when a heat pump is included. The larger the PV system, the larger the gains, but roof size limits this. The study recommends that shared rooftop PV systems be promoted as the norm with comprehensive energy-efficiency renovation in Germany.

Lagrangian modeling of a nonhomogeneous turbulent shear flow: Molding homogeneous and isotropic trajectories into a jet

Lagrangian modeling of a nonhomogeneous turbulent shear flow: Molding homogeneous and isotropic trajectories into a jet

A computationally efficient modeling of flow in complex porous media by coupling multiscale digital rock physics and deep learning: Improving the tradeoff between resolution and field-of-view

Digital rock physics is at the forefront of characterizing porous media, leveraging advanced tomographic imaging and numerical simulations to extract key rock properties like permeability. However, fully capturing the heterogeneity of natural rocks necessitates imaging increasingly larger sample volumes, presenting a significant challenge. Direct numerical simulations at these scales become either prohibitively expensive or computationally unfeasible due to limitations in resolution and field of view (FOV). This issue is particularly pronounced in carbonate rocks, known for their complex, multiscale pore structures, which exacerbate the resolution-FOV tradeoff. To address this, we introduce a machine learning strategy that merges multiscale imaging data from various resolutions with a 3D convolutional neural network (CNN) model. This approach is innovative in its ability to identify cross-scale correlations, thereby enabling the estimation of transport properties in larger volumes—properties that are difficult to simulate directly—using trainable proxies. The integration of multiscale imaging with deep learning allows for accurate permeability predictions at scales beyond those feasible with traditional direct simulation methods. By employing transfer learning across different scales during the training phase, our multiscale machine learning model achieves robust performance, with an R² exceeding 0.96 when evaluated on diverse lower-resolution domains with larger FOVs. Notably, this method significantly enhances computational efficiency, reducing the computational time by orders of magnitude. Originally developed for the intricate pore structures of carbonate rocks, our approach shows promise for application to a wide range of multiscale porous media, offering a viable solution to the longstanding tradeoff between imaging resolution and FOV in digital rock physics.

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

Formulation of Generalized Hillslope Leakage-Dependent Model for Groundwater Flow in N-Aquifer System within a Sedimentary Basin

In this study, the recently derived Hillslope Leakage-dependent (HL-D) models for groundwater flow in leaky three-aquifer system have been generalized to obtain Generalized Hillslope Leakage-dependent, GHL-D equation that model groundwater flow in leaky n-aquifer system. This generalization extends the capability of Hillslope Leakage-dependent (HL-D) equations to modeling groundwater flow in any number of aquifers (n-aquifer system) of all configurations within a sedimentary basin. The formulated GHL-D Model was tested on some aquifer types and the results have shown that it can be used to obtain groundwater flow models for both hillslope (sloping) and horizontal single aquifer (unconfined and confined) as well as for multi-aquifer system containing any number of aquifers within a sedimentary basin. This unique feature of GHL-D equation to model most aquifers types and configurations makes it a more elegant groundwater flow modeling tool with potentials for wider applications than most of the existing groundwater flow models. The GHL-D Model, when use for simulations of groundwater flow in single or multi-aquifer system, will have the capability of giving better understanding of the groundwater flow dynamics in such systems. This is important because most of the sedimentary basins of the world are multi-aquifer in nature and accurate knowledge of groundwater flow within them is important to geoscientists in determining long time yield and water sustainability in such geological structures

Analysis of Mesh Resolution Effect to Numerical Result of CFD-ROM: Turbulent Flow in Stationary Parallel Plate

Computational fluid dynamics (CFD) is extensively utilized to predict flow behaviour in various industries and applications. The Full Order Model (FOM) is a high-accuracy approach to flow modelling, but it requires significant computational resources due to its high order and thousands of variables. To address this problem, the Reduced Order Model (ROM) was developed. Despite the advancement brought by ROM, there is a notable gap in research concerning the impact of mesh configuration on CFD-ROM results. While the number of modes has been extensively studied for its influence on CFD-ROM, the mesh configuration, a critical aspect of the simulation process, has received relatively limited attention. This study investigates the effect of mesh resolution on numerical results in CFD-ROM concerning turbulent flow within stationary parallel plates. Employing rigorous methods, including Richardson Extrapolation, verification, validation, and error percentage. The results explicitly confirm that mesh resolution directly impacts the numerical results of the velocity field in CFD-ROM. It is found that there is a notable reduction in Convergence Grid Index (CGI) values for different mesh ratios: 6.401% for medium-to-coarse and 2.031% for fine-to-medium ratio. Thus, with the same mode number, mesh resolution selection can enhance the numerical result of the velocity field in CFD-ROM.

GPU-accelerated CFD-DEM modeling of gas-solid flow with complex geometry and an application to raceway dynamics in industry-scale blast furnaces

The coupling of Computational Fluid Dynamics (CFD) and Discrete Element Model (DEM) is a powerful tool for simulating dense particulate systems, yet the conventional CFD-DEM has limits for systems with large particle numbers and complex geometry. This paper reports a novel GPU-based CFD-DEM model to simulate the gas–solid flow with large particle numbers and complex geometry. A novel coupling strategy between the CFD solver and DEM solver is developed, featuring high efficiency and stability. The developed model is validated against the experimental measurements, and its efficiency is compared to the previous CFD-DEM simulations. Then, for demonstration, the model is employed to simulate the dynamic behavior of gas–solid flow in the raceway in ironmaking blast furnaces by considering complex tuyere structure details and the huge particle numbers involved. This model allows to study the effect of the tuyere angle in terms of raceway formulation and tuyere erosion. The results show that the largest and most stable raceway volume can be reached at 5° downward tuyere, although the −5° tuyere nose experiences more wear than 10° downward tuyere. The model provides a cost-effective tool to overcome the longstanding challenge of simulating dense fluid-particle systems with huge particle numbers and complex geometry.

Microcontinuum approach to multiscale modeling of multiphase reactive flow during mineral dissolution

Microcontinuum approach to multiscale modeling of multiphase reactive flow during mineral dissolution

An integrated system theoretic process analysis with multilevel flow modeling for the identification of cyber‐physical hazards in a process industry

AbstractThe deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the “5 flows” concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems.

Computational Analysis of the Dissipative Casson Fluid Flow Originating from a Slippery Sheet in Porous Media

This research paper examines the characteristics of a two-dimensional steady flow involving an incompressible viscous Casson fluid past an elastic surface that is both permeable and convectively heated, with the added feature of slip velocity. In contrast to Darcy’s Law, the current model incorporates the use of Forchheimer’s Law, which accounts for the non-linear resistance that becomes significant at higher flow velocities. The accomplishments of this study hold significant relevance, both in terms of theoretical advancements in mathematical modeling of Casson fluid flow with heat mass transfer in engineering systems, as well as in the context of practical engineering cooling applications. The study takes into account the collective influences of magnetic field, suction mechanism, convective heating, heat generation, viscous dissipation, and chemical reactions. The research incorporates the consideration of fluid properties that vary with respect to temperature or concentration, and solves the governing equations by employing similarity transformations and the shooting approach. The heat transfer process is significantly affected by the presence of heat generation and viscous dissipation. Furthermore, the study illustrates and presents the impact of various physical factors on the dimensionless temperature, velocity, and concentration. From an engineering perspective, the local Nusselt number, the skin friction, and local Sherwood number are also depicted and provided in graphical and tabular formats. In the domains of energy engineering and thermal management in particular, these results have practical relevance in improving our understanding of heat transmission in similar settings. Finally, the thorough comparison analysis reveals a significant level of alignment with the outcomes of the earlier investigations, thus validating the reliability and effectiveness of our obtained results.

Incorporation of Sub‐Resolution Porosity Into Two‐Phase Flow Models With a Multiscale Pore Network for Complex Microporous Rocks

AbstractPorous materials, such as carbonate rocks, frequently have pore sizes which span many orders of magnitude. This is a challenge for models that rely on an image of the pore space, since much of the pore space may be unresolved. In this work, sub‐resolution porosity in X‐ray images is characterized using differential imaging which quantifies the difference between a dry scan and 30 wt% potassium iodide brine saturated images. Once characterized, we develop a robust workflow to incorporate the sub‐resolution pore space into a network model using Darcy‐type elements called microlinks. Each grain voxel with sub‐resolution porosity is assigned to the two nearest resolved pores using an automatic dilation algorithm. By including these microlinks with empirical models in flow modeling, we simulate single‐phase and multiphase flow. By fine‐tuning the microlink empirical models, we match permeability, formation factor (the ratio of the resistivity of a rock filled with brine to the resistivity of that brine), and drainage capillary pressure to experimental results. We then show that our model can successfully predict steady‐state relative permeability measurements on a water‐wet Estaillades carbonate sample within the uncertainty of the experiments and modeling. Our approach of incorporating sub‐resolution porosity in two‐phase flow modeling using image‐based multiscale pore network techniques can capture complex pore structures and accurately predict flow behavior in porous materials with a wide range of pore size.

Modeling and analysis method for carbon emission flow in integrated energy systems considering energy quality

AbstractWhile proposing dual carbon strategy goals, research on carbon reduction in integrated energy systems (IESs) has become a focus of attention. Traditional IES carbon emission flow is mostly based on energy flow, without considering the differences in energy quality. Exergy can be used as a reasonable measure of energy quality. Combining the theory of exergy flow and carbon emission flow, a modeling and analysis method for IES carbon emission flow considering energy quality is proposed. First, exergy flow models for electric and natural gas systems, equivalent exergy flow models for heat systems, and exergy hub models are introduced. Afterward, the exergy branch losses are allocated to the nodes at both ends. A networked modeling method for the carbon emission flow of energy stations is proposed. A unified matrix calculation model for IES carbon emission flow considering energy quality is established. On this basis, an IES carbon emission flow calculation method considering energy quality is proposed. Finally, this method is applied to the test IES, and the results show that the carbon emission flow calculation results reflect the distribution of carbon emissions borne by the effective energy supplied by the source and consumed by the load in the system, taking into account both the quantity and quality of energy. The reasonable allocation of new energy is conducive to synchronously improving the energy efficiency, exergy efficiency, and cleanliness of the system.

Numerical modeling of self-aerated flows: Turbulence modeling and the onset of air entrainment

Numerical modeling of self-aerated flows is essential for understanding water systems and designing hydraulic structures. This work discusses and extends a theoretical/numerical model to represent air entrainment in self-aerated flows, which includes a criterion to define the occurrence of air entrainment, based on a balance between disturbing and stabilizing energies. The impact of the turbulence closure on the modeling of the onset of air entrainment and the distribution of bubble concentration is studied with particular emphasis. This work shows that uniform-density formulations of Reynolds-averaged Navier–Stokes closures lead to severe overprediction of air entrainment due to unphysical transport of turbulent kinetic energy generated in the air phase to the water phase. In contrast, combining variable-density turbulence closures with the energy balance criterion enables accurate prediction of the regions where air is entrained for stepped spillways and plunging jets. The choice of turbulence closure significantly influences the proposed criterion, emphasizing the importance of selecting an appropriate closure for an accurate description of the air entrainment process. Based on the conducted tests, the standard k−ε and buoyancy modified k−ε models better predict the onset of air entrainment and bubble distribution in stepped spillways, while the k−ω SST model proves to be more effective in capturing air entrainment at the impingement point in plunging jets. This study expands the capabilities of numerical models in predicting air entrainment and provides valuable insights into the effects and interrelations of the turbulence modeling, the air entrainment occurrence criteria, and the bubble transport equations.

Flow Modeling in the Wake of a Francis Turbine with Hydraulic Charge: Case of Songloulou-Cameroon

Flow Modeling in the Wake of a Francis Turbine with Hydraulic Charge: Case of Songloulou-Cameroon

Pore-Scale Modeling of Pressure-Driven Flow and Spontaneous Imbibition in Fracturing-Shut-In-Flowback Process of Tight Oil Reservoirs

Tight oil reservoirs are characterized by multiple pore spaces where nano-micropores and multiscale fractures coexist, and each type of medium varies in scale, implying a tight coupling of multiscale fractures with matrix and giving rise to extremely complicated flow patterns. To further investigate its flow mechanism, we first construct three two-dimensional (2-D) fracture-pore geometry models based on microfocus computed tomography (CT) imaging of a typical tight rock. A pore-scale modeling workflow is thereafter developed using the Shan-Chen lattice Boltzmann model (SC-LBM) to simulate the pressure-driven flow and spontaneous imbibition. The influence of fracture-pore geometry on the pore-scale fluid exchange dynamics in the fracturing-shut-in-flowback process is clearly clarified. Results show that, for the porous medium model without fracture, the fracturing fluid can displace and replace some crude oil by spontaneous imbibition while a large amount of crude oil droplets remains unexploited away from the oil/water contact line, resulting in a low oil imbibition recovery. The injected fracturing fluid migrates into the deep position along the fractures, only a few entering the matrix pore space near the fractures. Small pores are the main channel for fracturing fluid to imbibe into the matrix pores, and the replaced crude oil droplets flow into fractures through large pores as intermittent or continuous pipe flow. The complexity of fracture network typically exerts a significant impact on the fluid exchange dynamics during pressure-driven flow and spontaneous imbibition. The more complex the fracture network, the larger the volume of fracturing fluid injected, the easier for oil droplets replaced from the matrix pores, and the more difficult for the flowback of fracturing fluid in the fracture-pore geometry model. Our understanding will provide a basis for explaining the underlying mechanisms of oil replacement by pressure-driven flow and spontaneous imbibition in the fracturing-shut-in-flowback process.

Modeling and analysis of the triple diffusion unsteady flow of couple stress nanofluid with variable viscosity and distinct thermal sources

Thanks to their optimal thermal characteristics, nanomaterials stand out for their varied applications in heat transfer systems, energy storage, industrial processes, and biomedical research. Recently, scientists explored various dynamic properties in nanofluid flow to develop an even better thermal model. In this context, the phenomenon of triple diffusion in nanofluids constitutes an active area of research, offering promising applications in nanotechnology, metallurgical processes, chemical reactors, and thermo-diffusion processes. This paper analyzes the triple diffusion flow of a torque-constrained nanofluid, induced by a periodically oscillating porous surface, taking into account the importance of variations in thermal consequences. The viscosity of the torque-constrained nanofluid is assumed to be temperature-dependent. The analysis takes into account the variable role of thermal conductivity, mass diffusivity, and solute volume fraction. The modeling of the problem is expressed by coupled nonlinear partial differential equations. The semi-analytic technique, known as the homotopic analysis scheme, is used for resolution. The solution is validated and confirms the convergence region. The physical aspects of the parameters are examined with regard to the parameters involved. The simulated observations reveal that with the Dufour–Lewis factor and varying mass diffusivity, an increase in solute concentration is seen. The concentration of nanoparticles decreases with the nano-Lewis number.

Spatiotemporal Analysis of Particle Spread to Assess the Hybrid Particle-Flow CFD Model of Radioembolization of HCC Tumors.

Computational fluid dynamics (CFD) models can potentially aid in pre-operative planning of transarterial radioactive microparticle injections to treat hepatocellular carcinoma, but these models are computationally very costly. Previously, we introduced the hybrid particle-flow model as a surrogate, less costly modelling approach for the full particle distribution in truncated hepatic arterial trees. We hypothesized that higher cross-sectional particle spread could increase the match between flow and particle distribution. Here, we investigate whether truncation is still reliable for selective injection scenarios, and if spread is an important factor to consider for reliable truncation. Moderate and severe up- and downstream truncation for selective injection served as input for the hybrid model to compare downstream particle distributions with non-truncated models. In each simulation, particle cross-sectional spread was quantified for 5-6 planes. Severe truncation gave maximum differences in particle distribution of ∼4-11% and ∼8-9% for down- and upstream truncation, respectively. For moderate truncation, these differences were only ∼1-1.5% and ∼0.5-2%. Considering all particles, spread increased downstream of the tip to 80-90%. However, spread was found to be much lower at specific timepoints, indicating high time-dependency. Combining domain truncation with hybrid particle-flow modelling is an effective method to reduce computational complexity, but moderate truncation is more reliable than severe truncation. Time-dependent spread measures show where differences might arise between flow and particle modelling. The hybrid particle-flow model cuts down computational time significantly by reducing the physical domain, paving the way towards future clinical applications.