Detailed Computational Fluid Dynamics Simulations Research Articles

On Using the Distributor as a Multi Degree- of-Freedom System to Mitigate the Pressure Pulsation in an Axial Turbine at Speed-No-Load

Abstract Hydraulic axial turbines are more frequently utilized for grid regulation purposes. Sometimes, they must be operated at Speed-No-Load (SNL) conditions, which for some machines is characterized by a varying number of large vortical flow structures extending from the vaneless space to the draft tube. In the current study, the configuration varies between two and three vortices, introducing detrimental pressure pulsations throughout the turbine. A recent study shows that the vortices can be mitigated by individually controlling the guide vanes. Since optimization of the distributor layout is linked with a large degree of freedom, machine learning is deployed to find the optimal setup cost-effectively. A reduced numerical Computational-Fluid-Dynamics (CFD) model is built and used to generate input for Gaussian process regression surrogate models by performing 2000 steady-state simulations with varying distributor layouts. The surrogate models predict that the optimal layout is to open seven out of 20 guide vanes in succession while keeping the remaining ones closed. However, detailed time-dependent CFD simulations show that placing opened guide vanes on opposite sides of the runner axis is better; it reduces the pressure amplitude peaks corresponding to a two- and three-vortex configuration, and the maximal pressure pulsations by as much as 88% in the vaneless space compared to regular SNL operation. Meanwhile, the radial force on the runner and pressure pulsations on the runner blades are reduced by more than 83% and 55%, respectively, compared to deploying the surrogate models' optimal layout prediction.

Deep learning-aided active subspace exploration of free-stream effects for fan-shaped film cooling

Film cooling plays a critical role in protecting engine components from high temperatures that can influence safety and performance of gas turbines. However, the process is fraught with uncertainties due to complex inflow conditions and geometrical configurations. These uncertainties significantly impact cooling effectiveness, underscoring the importance of identifying the dominant factors in a quantified manner. Traditional methods, such as the Monte Carlo approach, encounter the “curse of dimensionality,” making them computationally intensive as the number of variable increases. This study tackles these challenges by employing a deep learning strategy with a convolutional neural network (CNN) model to predict film cooling effectiveness efficiently, reducing computational loads compared to traditional computational fluid dynamics (CFD) simulations. It enables the CNN model to act as a surrogate for the CFD simulation and provides gradient information for subsequent analyses. Additionally, this study employs active subspace (AS) analysis for dimensionality reduction, identifying dominant parameters based on the trained CNN model. This approach not only enhances the speed of simulations but also provides an effective way to analyze dominant parameters and carry out optimizations. Results demonstrate a strong correlation between the CNN predictions and detailed CFD simulations, with all the mean absolute errors below 0.08, validating the model's efficacy in capturing complex cooling dynamics. The dominant analysis based on the AS method gives the blowing ratio as the most important factors, and the subsequent parameter dimension reduction and suggested optimization region are also explored by applying the low-dimensional active variable. The tested optimal factors yield higher spatially averaged cooling effectiveness above 0.35 and show reasonable pattern changes compared to the maximum and minimum results in the sample.

Proton exchange membrane-like alkaline water electrolysis using flow-engineered three-dimensional electrodes

For high rate water electrolysers, minimising Ohmic losses through efficient gas bubble evacuation away from the active electrode is as important as minimising activation losses by improving the electrode’s electrocatalytic properties. In this work, by a combined experimental and computational fluid dynamics (CFD) approach, we identify the topological parameters of flow-engineered 3-D electrodes that direct their performance towards enhanced bubble evacuation. In particular, we show that integrating Ni-based foam electrodes into a laterally-graded bi-layer zero-gap cell configuration allows for alkaline water electrolysis to become Proton Exchange Membrane (PEM)-like, even when keeping a state-of-the-art Zirfon diaphragm. Detailed CFD simulations, explicitly taking into account the entire 3-D electrode and cell topology, show that under a forced uniform upstream electrolyte flow, such a graded structure induces a high lateral velocity component in the direction normal to and away from the diaphragm. This work is therefore an invitation to start considering PEM-like cell designs for alkaline water electrolysis as well, in particular the use of square or rectangular electrodes in flow-through type electrochemical cells.

Recent advances and effectiveness of machine learning models for fluid dynamics in the built environment

ABSTRACT Indoor environmental quality is crucial for human health and comfort, necessitating precise and efficient computational methods to optimise indoor climate parameters. Recent advancements in machine learning (ML) and computational fluid dynamics (CFD) are promising. However, applying ML to complex building airflow presents challenges. This research aims to investigate the integration of ML with CFD in the context of built environment applications using a systematic review approach. It highlights a critical knowledge gap: the need to synthesise innovative approaches that address the limitations of indoor modelling using data-driven ML methods. The review examines contemporary literature, identifying current developments and suggesting potential future directions. It delves into the innovations in combining ML with CFD to predict thermal comfort and indoor air quality, uncovering key limitations such as the lack of high-quality experimental data for training and validation, the computational complexity of detailed CFD simulations, and the interpretability issues of ‘black-box’ ML models. The emergence of data-driven techniques in fluid mechanics offers promising prospects for modelling in the built environment. Future research should focus on incorporating physics-based rules in ML models, adapting turbulence closure models for indoor flows, and enhancing model validation using real-world datasets. The research emphasises the synergistic relationship between ML and CFD; it proposes pathways to overcome current limitations, aiming to enhance the precision and efficiency of indoor environment modelling through their integration.

Hydrodynamic simulation-informed compartment modelling of an annular centrifugal contactor

Abstract The geometrical and hydraulic parameters have a great impact on the mass transfer characteristics of annular centrifugal contactors. The objective of this study is to evaluate the mass transfer performance of a single annular centrifugal contactor by applying the computational fluid dynamics informed compartment modelling approach. In the study, a steady state compartment model of an annular centrifugal contactor is developed in gProms general purpose process modeller by using the hydrodynamic parameters obtained from computational fluid dynamics simulations performed in OpenFOAM with the GEneralised Multifluid Modelling Approach (GEMMA). The mass transfer rate predicted by the developed compartment model is compared with data obtained from uranium extraction with Tributyl Phosphate experiments performed with a laboratory-scale annular centrifugal contactor. Uranium concentrations in the organic and aqueous outlets and the mass transfer rate evaluated by the developed compartmented contactor model are in good agreement with the experimental data. The results reveal that the use of a hydrodynamic-informed compartment modelling approach raises the possibility of designing full-scale annular centrifugal contactors without the need for detailed computational fluid dynamics simulations and the prediction of mass transfer performance of the whole system from laboratory scale experiments.

Multi‐Scale Simulation of a Novel Integrated Reactor for Hydrogen Production by Ammonia Decomposition

AbstractA novel reactor concept for ammonia decomposition utilizing tail gas from a purification unit as heat supply is presented. The designed micro‐structured reactor integrates both endothermic ammonia decomposition and exothermic tail gas combustion. The reactor and corresponding process are simulated using a mathematical multi‐scale model, which combines the results of multiple detailed computational fluid dynamics simulations into a fast surrogate model. The latter is coupled with a process simulation software via a so‐called container to simulate the entire process. The efficiency of the presented reactor concept is determined and benefits over alternative approaches are highlighted.

Computational fluid dynamics modeling of a discrete feed atomic layer deposition reactor: Application to reactor design and operation

Novel transistor fabrication methods such as area-selective atomic layer deposition (AS-ALD) are crucial to improving nanopatterning, which is essential for facilitating transistor stacking in semiconducting wafers. However, transistor surfaces are subjected to nonuniformities during the initial AS-ALD adsorption reactions that are attributed to steric hindrance effects. To minimize the role of steric hindrance generated by an oversaturation of reagent on the substrate surface, a discrete feed method is proposed for an ALD reactor configuration where reagent is introduced in short pulses through a perpendicular delivery system. An optimal reactor configuration is developed by modifying the inlet geometries of the reactor to ensure ideal fluid dynamics conditions (e.g., minimal vortices, radial flow distribution) are achieved. Detailed computational fluid dynamics simulations demonstrate the performance of the new reactor configuration and operational strategy.

Multiscale Thermal-Hydraulic Analysis of the ATHENA Core Simulator

In the framework of the ALFRED research and development program, the ATHENA facility will be constructed for thermal-hydraulic analysis of full-scale ALFRED components and systems. The source system of the facility is the core simulator, which aims to be representative of an ALFRED average fuel assembly. Computational fluid dynamics (CFD) codes are gaining attention for the analysis of complex systems in pool-type reactors since they are able to reproduce three-dimensional phenomena. In this paper, a multiscale approach based on porous media is proposed to reduce the computational cost of the core simulator CFD model. The multiscale approach starts with the detailed simulation of the infinite lattice domain of the fuel assembly to characterize the porous media hydraulic behavior. Then the porous media are applied in the system model. Three different approaches are investigated: (1) adopting a single porous media for the entire fuel assembly, (2) representing the bundle with two porous domains, and (3) adopting the so-called hybrid medium. The results have been compared with the reference detailed CFD simulation for performance evaluation. The first step of the analysis is the application of the multiscale approach on the CIRCE fuel pin simulator to carry out a turbulence model validation against experimental data and a comparison of the three approaches with a proven CFD model. Then the approach is applied on the ATHENA core simulator exploiting the CIRCE results. The results obtained with the porous media models are compared with a detailed CFD simulation of the core simulator to evaluate the performance of the three approaches. Eventually, the best solution is applied on a model of the entire ATHENA core simulator integrated with the feeding region. The model is tested also in transient conditions. The numerical experiment demonstrates the effectiveness of the multiscale approach in reducing the computational cost while maintaining high accuracy in representing the quantities of interest.

Computational Modeling of Magnesium Hydroxide Precipitation and Kinetics Parameters Identification.

Magnesium is a critical raw material and its recovery as Mg(OH)2 from saltwork brines can be realized via precipitation. The effective design, optimization, and scale-up of such a process require the development of a computational model accounting for the effect of fluid dynamics, homogeneous and heterogeneous nucleation, molecular growth, and aggregation. The unknown kinetics parameters are inferred and validated in this work by using experimental data produced with a T2mm-mixer and a T3mm-mixer, guaranteeing fast and efficient mixing. The flow field in the T-mixers is fully characterized by using the k-ε turbulence model implemented in the computational fluid dynamics (CFD) code OpenFOAM. The model is based on a simplified plug flow reactor model, instructed by detailed CFD simulations. It incorporates Bromley's activity coefficient correction and a micro-mixing model for the calculation of the supersaturation ratio. The population balance equation is solved by exploiting the quadrature method of moments, and mass balances are used for updating the reactive ions concentrations, accounting for the precipitated solid. To avoid unphysical results, global constrained optimization is used for kinetics parameters identification, exploiting experimentally measured particle size distribution (PSD). The inferred kinetics set is validated by comparing PSDs at different operative conditions both in the T2mm-mixer and the T3mm-mixer. The developed computational model, including the kinetics parameters estimated for the first time in this work, will be used for the design of a prototype for the industrial precipitation of Mg(OH)2 from saltwork brines in an industrial environment.

Abstracts from The Aerosol Society Drug Delivery to the Lungs 33.

Abstracts from The Aerosol Society Drug Delivery to the Lungs 33.

Thermal hydraulic analysis of the AFIP-7 irradiation test in the Advanced test Reactor – Model correlation and performance evaluation

As a system level code, RELAP5 is widely used in the nuclear field for the reactor hydraulic analysis. In the component/experimental level, it is frequently employed as well. This paper demonstrates that the RELAP5 flow simulation of the AFIP-7 irradiation experiment which has a complicated 3D geometry/flow path deviates by ∼28% from the flow evaluation through a computational fluid dynamics (CFD) simulation, which has been validated against a flow test performed at Oregon State University. Thermal safety compliance analysis of an experiment planned to be irradiated in the Advanced Test Reactor is usually performed using the finite element analysis code, Abaqus. This research reveals the disadvantages of the Abaqus simulation in the flow instability and departure from nucleate boiling evaluations via a conjugate heat transfer analysis of the AFIP-7 irradiation experiment. As a result, a detailed CFD simulation is suggested for irradiation experiments with complicated flow paths, rather than a simplified RELAP5 simulation or hand calculations. The validated simulation approach will be integrated with the Boehmite correlations to investigate the oxide growth prediction on the fuel cladding in Part II of this research.

Wind Tunnel Validation of a Particle Tracking Model to Evaluate the Wind‐Induced Bias of Precipitation Measurements

AbstractA physical full‐scale experimental set‐up was designed and implemented in the wind tunnel to reproduce and capture the trajectories of falling water drops when approaching the collector of catching type precipitation gauges, reproducing rainfall measurements in windy conditions. The experiment allowed to collect, for the first time, a large data set of high‐resolution footages of the deviation of such trajectories, as induced by the bluff‐body aerodynamics of the outer gauge shape. By processing the collected images, a consistent quantitative interpretation of each drop pattern was possible, based on a detailed Computational Fluid Dynamics simulation of the airflow updraft and acceleration features above the collector of the gauge. Numerical airflow simulations were extensively validated in the wind tunnel, using local flow measurements and Particle Image Velocimetry. Capturing the deviation of the drop trajectories in the wind tunnel allowed a clear visualization of the physical reason for the wind‐induced undercatch of precipitation gauges, since drops were individually observed to fall outside instead of inside of the collector, contrary to what would be expected by extrapolating their undisturbed trajectory. The adopted Lagrangian Particle Tracking model and the formulation used for the drag coefficient were suitable to closely reproduce the observed drop trajectories when affected by the airflow deformation due to the bluff‐body aerodynamics of two investigated gauge geometries. The wind tunnel experiment provided the basis for the validation of the particle tracking model in terms of the difference between simulated and observed trajectories, after initial conditions were suitably set to represent the experimental setup.

Torch Simulator: An analytical model for rapid prediction of inductively coupled plasma parameters

In this study, a novel analytical model has been developed to predict the parameters of the radio-frequency (RF) inductively coupled plasma (ICP), including axial temperature, electron number density, ion number density, ionization percentage, neutral atom number density, and robustness. The developed model has been implemented in a MATLAB app. The core element of Torch Simulator is a mathematical model which is highly accurate in predicting the temperature of plasma for the Fassel torch. In this simulator, the results of a computational fluid dynamics (CFD) model have been used to train the model with respect to the functions that describe the induction zone's size and position. The Torch Simulator can predict the ICPs parameters within a few seconds allowing the ICPs to be easily simulated, analyzed, and optimized. The results of the simulator are also compared with the results of a detailed CFD simulation as well as experimental measurements. It is shown that there is good agreement between the predictions of the Torch Simulator and the available data.

Application and Benchmarking of a Novel Coupled Methodology for Simulating the Thermomechanical Evolution of Sodium-cooled Fast Reactors Fuel Subassemblies

We employ a novel methodology to simulate the irradiation of Sodium cooled Fast Reactors (SFR) subassemblies. The proposed approach allows considering the coupling between key thermal–hydraulic and thermomechanical phenomena, which is usually neglected in traditional simulation methods. Comparisons between results obtained from coupled and uncoupled methodologies show that the latter can significantly over predict the stresses and the strains of highly irradiated fuel bundles. Additionally, we explore different options for the pin mechanical modeling. In particular, we show that a relatively simple beam-based finite element model for the fuel pins can be used to efficiently compute swelling and irradiation creep strains averaged over the cross section of the fuel claddings. However, if a prediction of the cladding swelling gradients is needed, the beam model must be replaced by a more computationally expensive 3D fuel pin model. An efficient method to estimate the fuel cladding temperature evolution based on a limited number of Computational Fluid Dynamics (CFD) simulations is also presented in this work, and its performance is evaluated. Finally, a numerical benchmark against the state-of-the-art coupled code system for the simulation of SFR subassemblies is presented. This benchmark shows that the proposed coupled methodology allows obtaining results that are in good agreement with the reference methodology. However, two clear advantages of the proposed methodology were identified. Firstly, the use of detailed CFD simulations in our methodology, compared to the lower resolution thermal-hydraulic model employed in the state-of-the-art approach. Secondly, considering the reduction of the coolant mass flow rate caused by the fuel bundle deformation, neglected in the pre-existing approach, which is shown in this work to have a potentially large impact on the coolant temperature distribution.

Characterization of a modular Temkin reactor with experiments and computational fluid dynamics simulations

The determination of reaction kinetics without the influence of transport limitations is challenging, especially under industrially relevant conditions, particle shapes, and sizes. The Temkin reactor concept, developed in the 1960s, allows to measure reaction kinetics with a small number of unmodified catalyst particles operating under assumed isothermal and plug-flow conditions. In this study, a recently developed modular Temkin reactor design is studied experimentally and with detailed CFD simulations in order to shed light on the residence time distribution without reaction. Cold fluid volumetric flow rates are varied in the range of 50–200 mlNmin-1 and two different particle shapes (spheres and rings) are tested. Pulse experiments and passive scalar computational fluid dynamics (CFD) simulations verify that this reactor design behaves like a cascade of stirred tank reactors with Bodenstein numbers of each chamber smaller than three. Mean age of air CFD simulations highlight potentials to re-design the entrance of the chamber in order to minimize stagnation zones.

Size-Dependent Distribution of Patient-Specific Hemodynamic Factors in Unruptured Cerebral Aneurysms Using Computational Fluid Dynamics.

Purpose: To analyze size-dependent hemodynamic factors [velocity, shear rate, blood viscosity, wall shear stress (WSS)] in unruptured cerebral aneurysms using computational fluid dynamics (CFD) based on the measured non-Newtonian model of viscosity. Methods: Twenty-one patients with unruptured aneurysms formed the study cohort. Patient-specific geometric models were reconstructed for CFD analyses. Aneurysms were divided into small and large groups based on a cutoff size of 5 mm. For comparison between small and large aneurysms, 5 morphologic variables were measured. Patient-specific non-Newtonian blood viscosity was applied for more detailed CFD simulation. Quantitative and qualitative analyses of velocity, shear rate, blood viscosity, and WSS were conducted to compare small and large aneurysms. Results: Complex flow patterns were found in large aneurysms. Large aneurysms had a significantly lower shear rate (235 ± 341 s−1) than small aneurysms (915 ± 432 s−1) at peak-systole. Two times higher blood viscosity was observed in large aneurysms compared with small aneurysms. Lower WSS was found in large aneurysms (1.38 ± 1.36 Pa) than in small aneurysms (3.53 ± 1.22 Pa). All the differences in hemodynamic factors between small and large aneurysms were statistically significant. Conclusions: Large aneurysms tended to have complex flow patterns, low shear rate, high blood viscosity, and low WSS. The hemodynamic factors that we analyzed might be useful for decision making before surgical treatment of aneurysms.

Spray drying experiments and CFD simulation of guava juice formulation

This paper presents a computational fluid dynamics (CFD) approach to model the spray-drying of a formulation of guava juice. The computational model incorporates the characteristic drying rate curve (CDC) of the formulation. The CDC is derived from the drying history of single droplets suspended on a thin glass filament. The computational domain is based on the geometry of a small production spray-dryer with a capacity of 10 kg/h. A two-phase Eulerian-Lagrangian model implemented in a commercial CFD package (ANSYS-FLUENT) is used to simulate the gas-particle interaction in the dryer. Hot-wire anemometry (HWA) measurements of the air flow in the drying chamber are used to calibrate the CFD model. The URANS k-ω SST and the Scale-Adapted Simulation turbulence models are employed to compute the unsteady three-dimensional flow field. Model calibration suggests that the swirl number of the air flow entering the dryer is about 0.61. Simulations predict high axial velocities along the center-line and a narrow jet core in the upper part of the dryer. Lagrangian particle tracking is used to calculate the particle trajectories starting from the perimeter of the rotary disk atomizer and ending at the dryer walls and bottom outlet. Simulation results of particle size distribution, time-average temperature and humidity profiles are presented in order to analyze the overall drying performance. The procedure shown in this work emphasizes the need of implementing drying kinetics with experimental support, and suitable inlet flow conditions to perform detailed CFD simulations of the spray-drying of fruit juices.

Computational study of full-scale VHTR lower plenum for turbulent mixing assessment

Next-generation nuclear reactors are poised to efficiently provide reliable power at enhanced safety levels for many years to come. Among the fundamental designs for these reactors is the very-high-temperature reactor (VHTR), which employs helium as the primary coolant and has the potential of reaching elevated temperatures such that chemical processing (e.g., electrolysis for hydrogen production) is viable alongside traditional electricity generation. This and other next-generation reactors also represent a strategic bridge for ultimately transitioning from fossil fuel-dominated energy portfolios to one where renewable options abound. For the VHTR, the ultimate goal of constructing a new plant will be preceded by additional fundamental research aimed at generating trusted models for behavior prediction under normal and accident scenarios. This paper presents a detailed computational fluid dynamics simulation of the lower plenum, where hot coolant from the core mixes together in a turbulent fashion before traveling to power conversion equipment. Because the flow is expected to enter the lower plenum across a wide range of temperatures and velocities, concerns exist when the mixing is incomplete. The potential hot spots on cylindrical support pedestals and the uniformity of the main outlet are the primary metrics of interest in this study, along with flow velocities and temperatures at different locations within the lower plenum. Three locations are probed in detail and suggest a large range exists for approach velocities and temperatures. A large degree of stratification is also seen on the surfaces of the support pedestals, suggesting a facility should accommodate testing for such behavior. The characterizations presented provide the valuable data needed in order to design appropriate experimental testbeds, where scaled modeling can be carried out in a manner meaningful in predicting the full-scale behavior.

Sliding behavior of droplet on a hydrophobic surface with hydrophilic cavities: A simulation study

This paper presents a detailed computational fluid dynamics simulation study on water droplet mobility and cavity fillings resulting from a sliding droplet on a titled surface or substrate with round shaped cavities in a serial arrangement. The effects of different hydrophobicity (or contact angle) values for cavity and non-cavity surfaces, cavity depths, cavity spacings, plate tilting angles, and initial droplet volumes on its mobility were investigated. The findings are reported in terms of the total number of filled cavities, its duration, and the average volume of droplet per cavity. This study can be useful for the design and development of the passive microfluidics related devices and applications.

An engine size–scaling method for kinetically controlled combustion strategies

A substantial amount of research has recently focused on kinetically controlled combustion strategies such as reactivity-controlled compression ignition combustion. These strategies are promising methods to achieve high efficiency with near-zero NOx and soot emissions; however, despite promising results, very few attempts have been made to develop size-scaling relationships that would allow these results to be generalized to any engine design. Engine design is a long and arduous process that requires a substantial amount of experimental work. Consequently, it is of interest to develop scaling laws that allow results from one engine to be extrapolated to new designs. Several scaling laws have been proposed for diffusion combustion (i.e. mixing limited) that scale parameters such as liquid length and lift-off length. Such parameters have been deemed unimportant for highly premixed low-temperature combustion strategies; thus, a new methodology is needed. The present effort uses a combination of detailed computational fluid dynamics simulations and engine experiments in two engines with different bore sizes to develop a new engine size–scaling methodology for low-temperature kinetically controlled combustion strategies. The effects of pressure, temperature, and turbulence timescales are explored in order to replicate the large-bore engine performance in a small-bore engine. A size-scaling relationship based on the ignition timescale is proposed and used to generalize the results to an arbitrary bore size and fuel combination.