Detailed Flow Research Articles

A solar-powered, internet of things (IoT)-controlled water irrigation system supported by rainfall forecasts utilizing aerosols: a review

Abstract Efficient water management is crucial in modern agriculture, especially in regions facing water scarcity. Traditional irrigation systems often result in water wastage, which challenges sustainability goals. This paper presents a comprehensive review of a novel Internet of Things (IoT)-based smart irrigation system with rainfall prediction based on pollutant concentration designed to optimize water usage through real-time environmental monitoring and promote sustainable agriculture through the integration of photovoltaic (PV) power. The system's components, including 160 Wp semi-crystalline PV panels, ESP8266 Wi-Fi module, Arduino Uno, Raspberry Pi, Partial Maximum Power Point Tracking (MPPT) control methods, and a reversible pump, were selected based on a comprehensive literature review, establishing the foundation for the proposed system’s design. The review extensively covers previous PV-irrigation integration systems, their performance in varied environments, and the cost–benefit analysis with special reference to Indian government subsidies for solar adoption in agriculture. In addition, various IoT-based irrigation systems and communication technologies such as Wi-Fi, LoRa, and Zigbee were reviewed, ensuring that the selected components represent the most efficient and secure combination for field deployment. The key novelty of this system is the rainfall prediction methodology, which focuses on pollutant concentration (PM10, PM2.5, SO₂, NO₂, CO, NH3, Cr and Pb) rather than the traditional aerosol size used in previous studies. This approach leverages the unique environmental characteristics of the chosen location, providing more accurate predictions of rainfall, which can be used to dynamically adjust irrigation schedules, reducing water waste. Sample data of pollutant levels and weather conditions from the area are provided as a demonstration. The paper also reviews security concerns in IoT systems, highlighting potential vulnerabilities and mitigation strategies to ensure robust system operation. By combining IoT, PV, and predictive weather analytics, the proposed system offers significant advantages in water and energy management, with the MPPT controller maximizing solar energy efficiency. The review presents a detailed flow diagram of the system, showcasing components chosen through a thorough literature survey. It also examines portable PV-pumping systems, recommending the proposed design for effective use in both small- and medium-scale farms. This paper provides a comprehensive analysis of sustainable irrigation technologies, highlighting their potential to significantly improve agricultural water management practices.

Process-level emission analysis and decarbonization pathway for BF-BOF route in Indian iron and steel industry

The blast furnace-basic oxygen furnace (BF-BOF) route contributes 46% to the production of iron and steel in India and is highly energy and emission-intensive. Its decarbonization needs to be prioritized for reducing long-term climate-related impacts. So far, studies have assessed the macro-level carbon emission and energy consumption of the entire industry (encompassing all production routes i.e., BF-BOF, EAF, DRI, SR-BOF, etc.), and lacks the specific plan for decarbonization of plants based on BF-BOF route. Secondly, these studies have focused on just a single input (raw material, energy, fuel, etc.) and the respective output (steel) and lack the analysis of detailed process-wise material and energy flow and consideration of fugitive emissions, which is needed for emission mitigation of entire BF-BOF based plants, which produces 57.37 million tons of crude steel in India. The current study addresses this gap through a case study of a BF-BOF-based plant by comprehensively analyzing the material and energy flow for each production step followed by assessing the respective carbon emissions. Further, a marginal abatement cost curve (MACC) is developed to identify the process-wise cost-effective mitigation measures. The current study shows that the implementation of proposed measures can significantly reduce carbon emissions from 3.3 to 2.43 tCO2/tcs, making it equal to the global counterparts.

The effects of jet Lorentz factor on a relativistic astrophysical jet

In this Letter, the numerical simulation of axisymmetric hydrodynamic relativistic jet propagation was performed by solving the hydrodynamic relativistic Euler equation using the computer code PLUTO [Mignone et al., Astrophys. J. Suppl. Ser. 170, 228 (2007)]. The detailed flow features involved in this relativistic jet propagation has been thoroughly discussed in this present numerical study. The effect of the jet Lorentz factor (Γj) on the shock–turbulence interaction has been studied by analyzing the divergence of the Lamb vector (L=ω×U). The strong coexistence of two layers ∇·L<0 and ∇·L>0 enhances the momentum transfer due to energy difference, causing turbulence amplification.

Changes in Traffic Density and Vehicle Usage Habits During and After the Pandemic in Balıkesir Province

The COVID-19 pandemic profoundly impacted social dynamics and individuals' lifestyles worldwide, leading to significant changes in traffic density and vehicle usage habits. This study analyzes the changes in urban traffic density and individual vehicle usage habits in Balıkesir province during the pandemic period (2021-2022) and the post-pandemic normalization period (2023-2024). Within the scope of the research, vehicle passage densities at 19 signalized intersections in the city center of Balıkesir were examined, and predictive models were created using machine learning methods such as linear regression and the Random Forest algorithm. The findings reveal that traffic flows in economically active areas, such as industrial zones, were less affected by the pandemic, whereas traffic density significantly decreased in commercial and social centers. Additionally, an increase in individual vehicle usage and a decline in public transportation preferences during the pandemic period were observed. The study also explores recovery trends in post-pandemic traffic flow and intersection-based differences in detail. This study underscores the importance of traffic data obtained during the pandemic for sustainable traffic management and transportation planning. At the end of the study, solutions such as the integration of intelligent traffic systems, the promotion of environmentally friendly transportation modes, and increasing the appeal of public transportation systems were proposed. These findings are expected to guide decision-makers in improving urban traffic dynamics and preparing for similar crises in the future.

Fluorescent imaging agents for mapping temperature field and capillary flow on the surface of volatile solvent

AbstractThe understanding of thermocapillary convection is important in both fundamental and industrial aspects. However, efficient tools that can provide dynamic details of the convective flows are still lacking. Here, we discovered a unique phenomenon of photoinduced fluorogenic shift of HDPI derivatives in chloroform and utilized this trait to map the temperature field and capillary flow on the surface of or inside volatile chloroform with a high spatial resolution and a long observation window. By inducing a proper co‐imaging agent that enhanced the fluorescence contrast via generating more distinguishable chromaticity, the fluorescence‐based method exhibited further enhanced imaging resolution and elongated observation time, facilitating the continuous monitoring of temperature field and capillary flow. This work presents a powerful tool to study the behaviors of fluid (thermo‐)dynamics.

Mathematical sizing and design analysis of permanent magnet vernier machine for contra‐rotating wind power applications

AbstractOver the years, wind energy technology has vividly made progress, as it is economical and cleaner against fossil‐fuel alternatives. With the rapid upsurge in turbine ratings up to the MW‐power generation level, an unceasing effort is crucial to increase the torque/power density and power factor of the wind‐powered machines. Amongst wind‐powered machines, the permanent magnet vernier machine (PMVM) is renowned for its high torque density. This paper discusses the detailed design flow of a novel contra‐rotating PMVM including sizing equation, geometric relationships, and FEA‐based electromagnetic performance analysis compared to the conventional PMVM. The proposed contra‐rotating PMVM is analysed with two different winding configurations that is, first with crossed‐toroidal and later with concentrated winding configuration, and a comprehensive comparative analysis is made between proposed and conventional PMVM topologies. The contra‐rotation of rotors provides more torque/power than the co‐rotation within the given machine's size. This comparative analysis provides noteworthy insights into the superior performance of the proposed contra‐rotating PMVM topologies regarding torque/power, power factor, and efficiency. A superior torque/power of value 538.23 Nm/1.6 kW is achieved in the case of the design with crossed‐toroidal winding configuration with an efficiency of 97.82% whereas, a high‐power factor of 0.98 with an efficiency of 91.36% is achieved in the case of the design with concentrated winding configuration.

Numerical Study on the Effect of Back Cavity on the Aerodynamic Behavior of a Non-Axisymmetric Casing Treatment for Transonic Axial Compressors

Abstract For decades, aeronautic engineers have studied the potential of Casing Treatments (CT) to improve the aerodynamic operability of tip-limited compressors. Axial Slot Casing Treatment (or ASCT) has shown very good results on modern transonic compressor. One possible variant of ASCT uses a circumferential back cavity that links the slots outside the compressor main flow path. This study does a back-to-back comparison of two CT designs that differ only by the presence or the absence of the back cavity. The influence of both CT designs on a transonic rotor performances and aerodynamics are presented. CT flows are detailed and analyzed. The analysis focuses on fluid exchanges between the CT and the main flow path. This study is based on CFD simulation; CFD results are, at first, confronted with available experimental data and then analyzed. Detailed CT flows analyses show that the presence of the cavity reduces the amplitude of the velocity fluctuations and allows for a more uniform redistribution of the recirculated mass flow. The presence of the back cavity reduces significantly the flow mass transfer between the main flow and the front part of the slots. In addition, the synchronization between the rotor blade passage and the extraction cycle, by the rear part of the slots, differs when back cavity is present or not. Because of these differences, the back cavity reduces the influence of the CT on rotor efficiency at the cost of a reduction in its capability to improve stall margin.

Ice flow line maps of three large freshwater calving glaciers in the Southern Patagonian Icefield

Abstract. The Southern Patagonian Icefield is experiencing rapid retreat and thinning in its calving glaciers. To better understand the dynamics of these changes, we generated ice flow line maps for the Viedma, Upsala, and Pio XI glaciers during 2017–2018. An evenly spaced streamline placement algorithm, integrating topographic and flow curvature criteria, was employed to calculate over 2,700 streamlines per glacier at a 50-meter resolution. The algorithm's performance was assessed by comparing the generated flow lines with manually digitized reference lines, resulting in a mean error, standard deviation, and root mean square error of 57.35, 33.62, and 66.46 meters, respectively. The resulting maps reveal detailed flow structures, highlighting flow lines from accumulation to ablation zones, increased velocities in central areas, tributary flows merging with main channels, and regions of flow convergence and divergence. Additionally, the glaciers' 3D lengths were estimated by identifying the longest ice flow lines, with Pio XI measuring 62.27 km, Viedma 54.49 km, and Upsala 51.24 km. We consider that the methodology used, along with the generated maps, provides excellent visual and analytical tools for identifying glacier areas, lengths, and shapes, defining ice origins and glacier catchment boundaries, and analysing zones of flow convergence and divergence—parameters that are critically important for understanding the dynamics, geometry, and evolution of glaciers in this region.

Physical processes explaining the second force peak generated during a surge impact on a vertical wall

This paper presents an in-depth study of the impact of a surge on a vertical wall using incompressible and compressible RANS model simulations of a classical dam break experiment over a dry bed. The model allows access to the detailed flow structure and pressure field at any instant, which provides valuable complementary information to measurements. Our study focuses on the second force peak, which is often the largest one and for which the literature does not really provide a clear explanation. Before and after this peak, the pressure on the wall is governed by the flow kinematics in the area. Before the peak, an overpressure appears at the root of the reflected jet, corresponding to the violent interaction between the incoming surge and the run-down flow. At the peak instant, the situation suddenly changes, due to the collapse of the reflected jet onto the incoming flow, trapping an air cavity. As in the classical case of direct wave impact on a wall with a trapped air pocket, this process generates an additional strong uniform pressure field in the air cavity, which propagates to the water and nearby boundaries due to the water confinement effect. This compressible effect, which varies depending on the capacity of air to escape the cavity, explains the formation of the second force peak. Finally, the 3D incompressible model provides a much more reliable estimate of the second force peak than the 2D incompressible model. This is likely due to the air escape phenomenon, which occurs when the experimental initial conditions are not perfectly 2D. Although it is unlikely that the 3D simulation perfectly reproduces the experimental flow, nevertheless, with more or less comparable air escape, the computation results appear consistent.

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

The resolution of flow fields represents a significant factor influencing the accuracy of turbulent flow analysis. Nevertheless, the acquisition of high-resolution turbulence data remains a challenge due to the limitations imposed by computing resources. The interpolation method, while capable of achieving high-resolution turbulence at low cost, faces challenges in capturing details of turbulent flows. In this study, a local and global feature fusion network (LGFN) is designed for the reconstruction of high-resolution turbulent flows with high quality. First, dual parallel branches made of dense blocks are introduced into the LGFN to extract local features of turbulent flows. Moreover, the features after the first dense block of each branch are summarized into the self-attention block to obtain global features. The extracted local and global features are aggregated through learnable weight parameters to achieve feature fusion. Finally, the fused turbulence features are scaled to the same dimensional size as the high-resolution turbulence through the implementation of multilayer pixel shuffle layers and convolution layers. The effectiveness of the proposed network was evaluated using datasets of forced isotropic turbulence and channel turbulence. The results demonstrate that the reconstructed velocity fields of the LGFN exhibit the highest degree of similarity to the direct numerical simulation results, in comparison with bicubic interpolation, static convolutional neural network, and super-resolution dense connection network results. In addition, compared to alternative methods, the proposed network effectively captures the characteristics of isotropic or anisotropic turbulence even at larger scales.

High-resolution simulation of coastal flooding under extreme storm tides and sea level rise: A case study of Macau

In the face of climate change, coastal cities are challenged with growing risks from rising sea levels and intensified storm surges which could turn urban areas into temporary waterways. To address this, our study developed a high-resolution coastal urban flood model, OUC-CUFM (Ocean University of China – Coastal Urban Flood Model), designed to help municipal governments make precise coastal flood forecasts and reduce potential threats to people and property. This model is based on two-dimensional nonlinear Navier–Stokes shallow water equations, incorporating factors like local acceleration, convection, bottom friction, wind stress, and shallow water effects. Using the finite difference method with upwind spatial discretization and a leapfrog time scheme, it can simulate detailed street-level water flow interactions with buildings. In a case study on Macau, we used a 5-m resolution digital elevation model to simulate storm tide flooding from Typhoons Hato (2017) and Mangkhut (2018), with the former for model calibration and the later for validation. Comparisons between simulated and observed water levels showed that the model accurately captured storm water arrival, overtopping, and flood extent and depth. The model also projected flood changes under sea level rise (SLR) scenarios of 0.5 m (by 2070) and 1.0 m (by 2100) for Typhoon Hato, indicating that such SLRs would significantly increase storm-induced flood depth and extent. This model serves as a valuable tool for street-scale flood risk analysis, hazard mapping, and assessing the impact of coastal defenses, like dyke upgrades or new dyke construction, in densely built coastal cities.

NCRAD: Academic Biorepository to Support Standardization and Efficacy of Clinical Research Biospecimens for the Advancement of AD/ADRD Research

AbstractBackgroundNCRAD is a National Institute on Aging (NIA) cooperative grant, awarded to Indiana University since 1990, whose purpose is to serve as a biorepository for AD/ADRD researchers. With 74 participating across 150 unique institutions, NCRAD links specimens to clinical research data. NCRAD maintains over 2 million aliquots from more than 126,000 research participants spanning a wide range of AD/ADRD related phenotypes as well as healthy controls.MethodTo ensure standardization and uniformity, NCRAD develops a manual of procedures specific to each study protocol that includes detailed pictures, schematics, and flow charts for each sample type collected. NCRAD conducts web‐based training for site staff and provides supplemental training videos to ensureResearchers receiving samples from NCRAD agree to share the data generated from the samples. Since its inception, NCRAD has distributed over 410,000 samples to more than 200 researchers. NCRAD biospecimens and data have been reported in more than 850 publications.ResultNCRAD’s mission is to maintain uniformity in collection and processing of samples sites involved in each study. Centralized biobanking and broad sharing of biospecimens has accelerated research discovery.ConclusionUsing standardized biospecimen collection, processing, shipping, storage, and distribution procedures established at NCRAD, in conjunction with attention to detail, strict compliance with regulations, and oversight from the NIA, has resulted in a biorepository with highest quality samples available to researchers investigating the etiology, early detection and therapeutic development for AD/ADRD.

Hidden field discovery of turbulent flow over porous media using physics-informed neural networks

This study utilizes physics-informed neural networks (PINNs) to analyze turbulent flow passing over fluid-saturated porous media. The fluid dynamics in this configuration encompass complex features, including leakage, channeling, and pulsation at the pore-scale, which pose challenges for detailed flow characterization using conventional modeling and experimental approaches. Our PINN model integrates (i) implementation of domain decomposition in regions exhibiting abrupt flow changes, (ii) parameterization of the Reynolds number in the PINN model, and (iii) Reynolds Averaged Navier–Stokes (RANS) k−ε turbulence model within the PINN framework. The domain decomposition method, distinguishing between non-porous and porous regions, enables turbulent flow reconstruction with a reduced training dataset dependency. Furthermore, Reynolds number parameterization in the PINN model facilitates the inference of hidden first and second-order statistics flow fields. The developed PINN approach tackles both the reconstruction of turbulent flow fields (forward problem) and the prediction of hidden turbulent flow fields (inverse problem). For training the PINN algorithm, computational fluid dynamics (CFD) data based on the RANS approach are deployed. The findings indicate that the parameterized domain-decomposed PINN model can accurately predict flow fields while requiring fewer internal training datasets. For the forward problem, when compared to the CFD results, the relative L2 norm errors in PINN predictions for streamwise velocity and turbulent kinetic energy are 5.44% and 18.90%, respectively. For the inverse problem, the predicted velocity magnitudes at the hidden low and high Reynolds numbers in the shear layer region show absolute relative differences of 8.55% and 4.39% compared to the CFD results, respectively.

Influence of head surface conditions of projectiles on initial cavitation and supercavity

Abstract To investigate the inception of cavitation, the morphology of supercavity, and the detailed flow field influenced by the surface morphology of underwater high-speed projectiles, a high-precision large eddy simulation method was developed. The method accurately simulates cavitation inception and its fine structure under the influence of small-scale changes, enabling the high-precision numerical simulation of the projectile’s supercavitating flow field. The results indicate that the diameter of the cavitator and the height of the rough belt significantly affect the initial cavitation flow field of the projectile. As the cavitator’s diameter decreases, the initial cavitation number of the projectile notably decreases. Compared to conditions without a rough belt, increasing the rough belt alters the distribution of the primary cavity and the flow field structure near the wall. When the rough belt height is minimal, the formation and collapse of the primary cavity occur farther from the projectile’s head and the near-wall region. Introducing a rough zone of appropriate height can increase the pressure in the near-wall flow field and enhance vapor content, thereby promoting cavitation inception. A small rough zone height inhibits cavitation inception, while a large rough zone height promotes it.

Transformer based deep learning accelerated numerical simulation for incompressible flow

Numerical simulation of fluid plays an important role in the research of engineering, weather, and climate; the classical methods solve incompressible Navier–Stokes equations, providing the most detailed flow information in many cases. However, an increase in the number of grids causes the computing cost to increase significantly. In this paper, we propose a deep learning-based numerical solver for incompressible flow to improve the accuracy of numerical simulation on coarse-resolution grids. The solver uses the Swin Transformer—a hierarchical vision transformer using shifted windows—to build independent subnetworks and learn the interpolating coefficients for the variable values on the cell edge and, thus, to obtain fluxes in different directions. Numerical experiments show that our proposed solver can perform better than the traditional numerical scheme, predicting the solution well and maintaining long-term computational stability.

Detection of Two-Phase Slug Flow Film Thickness by Ultrasonic Reflection

Slug flow is a common phenomenon in closed pipes, occurring in two-phase flow where liquid and gas phases mix, impacting the custody transfer or the efficiency of chemical reactions. This study aims to detect and analyze slug flow film thickness in two-phase flow, providing detailed structural flow information. The ultrasonic or Doppler reflection method is employed to identify slug flow and detect detailed thickness. Additionally, electrical resistance tomography (ERT) is used to image and confirm the presence of slug flow. A high-speed camera records the slug flow's shape in real time, validating its existence. The ultrasonic reflection method offers high accuracy, with a measurement error rate of less than 1% based on experimental results. The study uses a homogeneous block calibration method to measure slug flow thickness. Graphical results reveal clear differences between the slug flow regime, inner pipe wall, and outer pipe wall, with the first echo of slug flow being easily observable. The accuracy of results is attributed to the combination of FPGA (Field-Programmable-Gate-Array) instruments and measurement methods, showcasing the study's novel approach. This research introduces a new perspective or novelty on slug flow in multiphase flow studies, highlighting an innovative method for detecting film thickness.

Characterization and visualization of gas–liquid two-phase flow based on wire-mesh sensor

Gas–liquid two-phase flow is prevalent in both industrial production and the natural environment, making the exploration of its flow behavior critically important. In this study, we design a wire-mesh sensor and measurement system to conduct dynamic experiments on vertical upward gas–liquid two-phase flow, obtaining multi-channel data under various conditions. We develop an integrated limited penetrable visibility graph network (ILPVGNet) to quantitatively assess the dynamic transition from slug flow to bubble flow using network metrics. The results demonstrate that this approach effectively integrates multi-channel measurements, revealing the evolution dynamics and internal coupling mechanisms of gas–liquid two-phase flow. To better observe fine flow structures and details, we perform visualization and super-resolution analysis on the multi-channel data, significantly enhancing image clarity and restoring detailed features, thereby improving our ability to intuitively analyze complex gas–liquid flow behaviors.

Multi-Scale Computational Fluid Dynamics Simulation of Alkaline Water Electrolyzers: A Numerical Investigation

The utilization of water electrolyzers for hydrogen production represents one of the promising technologies in the global transition away from fossil fuels towards sustainable energy sources. Among the various types of electrolyzers, alkaline electrolyzers in the zero-gap configuration with different separator types, like diaphragms and anion conducting membranes, have garnered significant attention, among others, due to their potential to utilize non-precious metal catalysts such as iron and nickel for the electrodes. This characteristic holds promise for reducing costs and increasing accessibility to hydrogen production technologies, including the establishment of largescale stationary systems and hydrogen production powered by renewable energy.In order to accelerate the development process and to understand the important mechanisms in more detail within these cell types, numerical Computational Fluid Dynamics (CFD) simulations are conducted and combined with experimental cell testing at the Juelich Research Center. The studies focus on investigating the influence of variations in the porous transport anode-electrode, particularly nickel fiber fleece and nickel foam, along with their characteristic properties on the performance of the anion exchange membrane water electrolysis cells. This includes investigating porosity gradients, optional additional catalyst layers and their loadings, and the effects of changing types and thicknesses of porous transport electrodes. These studies are conducted at varying electrolyte concentrations and operating conditions.Using the open-source platform OpenFOAM® and the developed libraries of OpenFuelCell21, CFD simulations are performed at both the micro and meso scales to examine the aforementioned questions (see Fig. 1).The model incorporates all key transport phenomena, such as two-phase fluid flow, described via an Eulerian-Eulerian approach, heat and mass transfer, electrochemical reactions, species transfer, and charge transfer across the various functional layers of the cell. It allows for the analysis of electrochemical reactions by employing the Butler-Volmer equation to describe electrokinetics across various types of electrochemically active porous electrodes.For the multi-physical simulation of the detailed flow within the porous electrodes at micro scale, microtomography images are used as geometrical input. In addition, the microscopic data are subsequently utilized for characterizing the porous electrodes in the macro-homogeneous simulations performed at the meso-scale.Literature: Zhang, S. Hess, H. Marschall, U. Reimer, S. Beale, W. Lehnert, openFuelCell2: A new computational tool for fuel cells electrolyzers, and other electrochemical devices and processes, Comput. Phys. Commun. 298 (2024) 109092. Figures:Figure 1: Numerical simulation of the flow through different micro-porous electrodes (left) which results are used for the macro-homogeneous simulations of alkaline electrolysis cell in 2-D and 3-D (right). Figure 1

Mesh adaptation and dynamic positioning for the efficient simulation of lifting hydrofoil flows

Accurate simulations of the flow around lifting hydrofoils are challenging, since they need to capture the flow near the foil surface precisely, represent the free surface, and take into account body motion and deformation. Therefore, these simulations are often computationally expensive.This paper studies numerical methods to limit the costs of hydrofoil simulation. Mesh adaptation is used to efficiently capture the free-surface flow, to resolve flow details around the foil surface, and to ensure the accuracy of mesh motion techniques, like overset meshing. For maximum precision of the boundary-layer flow, adaptation is started from dedicated body-aligned meshes.Hydrofoil flexibility is taken into account through a linear eigenmode-based reduced-order model of the structural response. This approach removes the need to couple directly the fluid and structure solvers and reduces the computational overhead for fluid–structure simulation. Equilibrium positions for flexible and rigid motion are determined with a fixed-point iteration based on quasi-Newton and approximate models for the forces respectively, which eliminate the need for costly time-accurate simulation.Test cases demonstrate that these methods work together, providing accurate simulations of realistic hydrofoils with reasonable computational costs. Mesh adaptation allows to target a specific numerical uncertainty through the refinement threshold parameter. Together with the body-aligned ‘sock’ meshes, adaptive refinement produces the same accuracy as non-adapted meshes for up to 10 times less CPU hours. Both fluid–structure interaction methods lead to computations which have the same convergence speeds as for non-moving bodies. Thus, foil flexibility can be simulated for a computational overhead below 40% and it leads to significantly better agreement with experiments.

Effect of foulant on temperature and steam quality profiles in once-through steam generator tubes

Once-through steam generators (OTSGs) are commonly used in the oil sands industry for steam generation forin-situthermal recovery of extra heavy oil. These are expensive operations with challenges associated with the deposition of foulants, arising from solids and hydrocarbons in the boiler feed water, on the inner tube wall that leads to a decreasedheat transfer rate. The foulant deposition also causes elevated tube-wall temperatures, which may lead to tube failure. This study examines the impact of foulant on the tube-wall temperature profile and vapor-to-liquid ratio in an OTSG. A three-dimensional finite volume model of multiphase flow and heat transfer in a single pass of a field-scale OTSG unit is described. Detailed flow behavior and heat transfer characteristics of water and steam from the CFD model were compared to field data. Thereafter, fouling was added to the model to explore overheating and erosion zones in the OTSG tubes. Velocity, temperature distribution, and steam generation in the OTSG tubes in the convection section and radiant section were investigated for areas prone overheating. It was found that the CFD model simulation results closely matched the bulk fluid temperature of the field unit. In the presence of fouling, the outer skin temperature from the model was well within the range of the thermocouple data from the field unit, enabling the application of the model to identify the foulant thickness based on the outer skin temperature. Lastly, the model demonstrated that the bends are the most likely sites for erosion within the pass.