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Most models employed for fluid forces in drill-string dynamics are of reduced order. The simplified nature of these models often fails to describe the complex behavior of the fluid force, in particular, when the drill-string movement is non-trivial or even when non-Newtonian behavior is predominant. In this work, the fluid forces are estimated by modeling the dynamic of the drilling fluid for both Newtonian and non-Newtonian fluids, and compared with reduced-order models. To achieve it, the lattice-Boltzmann method is implemented to solve the fluid model, while prescribed movements are set for the tool-joint. With the obtained fields, the forces are calculated and compared with recurrent models. Finally, it is observed that some models are capable of describing the interaction as long as the dynamic of the tool-joint is simple. In the presence of other trajectories--e.g., bouncing and chaotic--the models fail to describe the details of the dynamic. [ABSTRACT FROM AUTHOR]

In this research, a novel solar latent heat thermal energy storage (LHTES) system, including the cylindrical enclosures filled with a phase change material (PCM), is proposed, which can be installed on the building windows to alleviate the drawbacks of traditional PCM‐filled double‐glazed windows, such as daylight hindrance and leakage. The lattice Boltzmann method (LBM) is used to simulate the volumetric radiation‐conduction melting of the PCM within a single cylinder of the proposed LHTES system with considering more realistic conditions such as convective boundary condition, shadow effect, and variable solar radiation angle compared with the available works in the literature. As such, several boundary conditions are assessed, and parameters such as cylinder diameter, extinction coefficient, scattering albedo, solar angle, shadow effect, and natural convection heat transfer coefficient are studied on the time history of the melting fraction and charging time. The results revealed that considering the applied conditions, such as convection heat loss to the environment and shadow, significantly affects the charging time of the system. It is shown that the charging time for convective boundary condition with h=4$$ h=4 $$, 8$$ 8 $$, and 12Wm−2K−1$$ 12\ \mathrm{W}\ {\mathrm{m}}^{-2}\ {\mathrm{K}}^{-1} $$ increases, respectively, by 11%, 30%, and 50% relative to a case with the insulated boundary condition without the shadow effect and 38%, 91%, and 175% compared with the insulated case with a 90° shadow. [ABSTRACT FROM AUTHOR]

An improved pseudopotential lattice–Boltzmann model was proposed for simulating multiphase flow dynamics to describe fuel droplets, and its thermodynamic consistency was tested against the Peng–Robinson equation of state. The studied liquid fuels included paraffinic hydrocarbons with a different number of carbon atoms (C 1 –C 10), methanol (CH 3 OH), hydrogen (H 2), ammonia (NH 3), and water (H 2 O). To improve accuracy and reduce the magnitude of the spurious currents, the multi-relaxation times collision operator was implemented and the forcing term was computed using the hybrid pseudopotential interaction force with an eighth-order isotropic degree. The pseudopotential lattice–Boltzmann model accurately predicted the equilibrium densities and captured satisfactorily the thermodynamic vapor-liquid coexistence curve given by the analytical solution of the Peng–Robinson equation of state for acentric factors ranging from − 0.22 to 0.56, keeping the maximum average error for the liquid and vapor branches below 0.8% and 3.7%, respectively. Nevertheless, Peng–Robinson was found to be insufficiently accurate to replicate the actual thermodynamic state, especially for H 2 O and CH 3 OH, for which the results strongly deviated from the experimental vapor-liquid equilibrium densities and reached average errors for the vapor phase of nearly 28%. Furthermore, the surface tension (γ) was retrieved using the multiphase pseudopotential lattice–Boltzmann results and served to verify the thermodynamic consistency of the pseudopotential lattice–Boltzmann with respect to the parachor model. Lastly, the pseudopotential lattice–Boltzmann model was also shown to predict accurately the transient behavior of oscillating droplets. Overall, the enhanced model satisfactorily predicted the properties and behavior of the substances for a wide range of conditions. [ABSTRACT FROM AUTHOR]

Numerical solutions of the reaction–diffusion system of equations in three dimensions provide a systematic procedure to generate a variety of porous media structures of a chosen porosity. A way to characterize such geometries is by their Minkowski functional densities and their average pore diameter. We generated multiple porous geometries of the granular and foam-like types. Hydraulic flow through these series of geometries is simulated to study the dependence of the permeability k and the Forchheimer parameter β on the chosen geometrical identifiers. Both k and β exhibit two distinct behaviors aligned with the two distinct generated types of geometries when scaled with the Euler characteristic. A good correlation for dimensionless k as a function of the Minkowski functional densities and the average pore diameter was found for both types of geometries with an accuracy of 15% and 18%, respectively, while no good correlation was found for β , implying that more geometrical information is needed beyond the proposed one. From the correlations, it was found that, together with the porosity, the mean curvature is also a good parameter to characterize the permeability. Article Highlights: Porous media generation based on solutions to reaction-diffusion equations Model porous structures characterized through Minkowski functionals and average pore diameter Scaling based on the Euler characteristic reveals two distinct flow behaviors. [ABSTRACT FROM AUTHOR]

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance ( R ^2 = 0.9) when tested on extremely diverse real domains at multiple scales.

[Display omitted] • A microfluidic DSSC with a gapless configuration minimizes mass transport resistance. • Reducing series resistance in µDSSC increases efficiency near the theoretical limit. • μDSSC is less sensitive to light color temperature variations than Si cells. • The electrolyte flow does not decrease the efficiency of μDSSC. • The thermal efficiency of μDSSC is not limited by heat conduction within the cell. A dye-sensitized solar cell (DSSC) is a type of photoelectrochemical cell that converts sunlight directly into electricity using a photoanode with a dye-sensitized, nonporous semiconductor layer. This study investigates the impact of integrating microfluidic capabilities into DSSCs, resulting in a flow-cell configuration that introduces new electrical characteristics without compromising its performance. The primary goal of the work was to define and determine the values of heat, mass, and charge transport resistances, and to explore their relationships with the cell's current – voltage characteristics, temperature, light intensity, and spectra. We employed both experimental methods and computational fluid dynamics using the Lattice-Boltzmann solver to analyze the cell's dynamics. A novel multilayered microfluidic DSSC (µDSSC) was designed, featuring an innovative "gapless" configuration with a photoactive surface area exceeding 7 cm2, integrated directly with a microfluidic heat exchanger. This configuration allowed the µDSSC to achieve a power conversion efficiency (PCE) of at least 6 % under standard test conditions, with a fill factor reaching up to 68 %. By gradually covering the cell surface, the impact of series resistance (Rs) on PCE was minimized, enabling a maximum PCE of 19–22 %. A new integral definition of the fill factor (IFF) was proposed, which accurately describes the performance of cells constrained by ion mass transport resistance. For the gapless µDSSC, the IFF ranged from 58 % to 88 %. Additionally, we introduced a maximum cell efficiency (MCE) parameter to represent the cell's efficiency at the theoretical limit of Rs = 0, determined to be 20.7 % for the gapless µDSSC. This MCE value is specific to the cell and is independent of the illuminated surface area and incident light intensity. The thermal efficiency of µDSSC cells is generally limited by heat conduction through the multilayer structure, but at practical cell surface temperatures of 50–80 °C, this limitation is 3 to 6 times less significant than the limitation imposed by the maximum solar energy flux reaching the cell surface. The integration of microchannels into the cell led to a 58 % reduction in hydraulic resistance along the main flow direction. While electrolyte flow typically reduces efficiency in wide-gap DSSCs, the gapless configuration minimizes mass transport resistance, ensuring that fluid flow does not adversely affect the performance of gapless µDSSCs. [ABSTRACT FROM AUTHOR]

Multiphase flow properties of fractures are important in engineering applications such as hydraulic fracturing, evaluating the sealing capacity of caprocks, and the productivity of hydrocarbon-bearing tight rocks. Due to the computational requirements of high fidelity simulations, investigations of flow and transport through fractures typically rely on simplified assumptions applied to large fracture networks. These simplifications ignore the effect of pore-scale capillary phenomena and 3D realistic fracture morphology (for instance, tortuosity, contact points, and crevasses) that lead to macro-scale effective transport properties. The effect of these properties can be studied through lattice Boltzmann simulations, but they require high performance computing clusters and are generally limited in their domain size. In this work, we develop a technique to represent 3D fracture geometries and fluid distributions in 2D without losing any information. Using this innovative approach, we present a specialized machine learning model which only requires a few simulations for training but still accurately predicts fluid flow through 3D fractures. We demonstrate our technique using simulations of a water filled fracture being displaced by supercritical CO 2 . By generating highly efficient simulations of micro-scale multiphase flow in fractures, we hope to investigate a wide range of fracture types and generalize our method to be incorporated into larger discrete fracture network simulations. [ABSTRACT FROM AUTHOR]

• Identified the contact angle on ZrO 2 using Molecular Dynamics for both main crystallographic planes at Boiling Water Reactor in-situ operating conditions. • Connected the nanoscale Molecular Dynamics simulations to mesoscale Lattice Boltzmann simulations through the contact angle. • Modelled the nucleation of bubbles on a ZrO 2 surface at Boiling Water Reactor in-situ operating conditions taking into account the micro-roughness. • Obtained a correlation between the roughness and the shape of the surface and the nucleation site density. The mechanism of bubble nucleation in boiling water at solid surface remains poorly understood. In this study, the water boiling and bubble nucleation density under nuclear reactor operation conditions were investigated by multiphase Lattice Boltzmann and Molecular Dynamics simulations. The developed multiscale model takes into account the surface energy of ZrO 2 cladding in different crystallographic planes obtained by molecular dynamics simulations and uses it to inform the corresponding contact angle of two-phase fluid in the thermal multiphase Lattice Boltzmann model. The model describes the bubble formation on a rough surface due to boiling and predicts the number of active nucleation sites on surfaces of different roughness. Further, the effect of the different contact angles on bubble dynamics while in close contact with the surface is investigated. The obtained results are in good agreement with experimental observations and provide functional relationships between interface properties (roughness, surface energy) and the nucleation site density, necessary for macroscopic simulations of the boiling phenomena. [ABSTRACT FROM AUTHOR]

MPLBM-UT is a specialized lattice-Boltzmann library that makes running single- and two-phase flow simulations in porous media accessible to everyone. We provide a suite of tools to pre-process computational domains for simulation, to set up custom boundary conditions, to run simulations, to post-process simulation outputs, and to visualize simulation results and data. All of these tools are easily accessible to users through the mplbm_utils Python package included in and automatically installed with MPLBM-UT. The high-performance, highly parallel library Palabos is used as the solver backend. MPLBM-UT is easily deployed in a variety of systems, from laptops to supercomputer clusters. MPLBM-UT also features multiple examples and benchmark templates that allow for fast prototyping of different porous media problems. We also provide an interface for reading in different file types and downloading domains from the Digital Rocks Portal to perform simulations.

[Display omitted] • Packed-bed adsorbers with randomly packed spheres or cylinders at same porosity. • Characterization of how the particle shape determines the bed microstructure. • Numerical evaluations of permeability, dispersion, conversion and liquid transport. • Interstitial liquid affects bed performance by filling of smaller pores. • Intermittent interstitial liquid influences parameters in reduced-order models. Several industrial applications use packed-bed reactors for heterogeneous processes with intermittent presence of interstitial liquid. One such example is steam-regenerated adsorption systems. Here, we computationally generate two randomly packed beds of the same voidage – one with spheres and one with cylinders – to study the role of particle shape in such a process. We analyze the geometrical characteristics and determine the flow, transport and reaction properties at the same driving pressure difference. We also establish the effect of liquid on these characteristics. The bed of spheres exhibits 69% higher permeability due to differences in microstructure, and its shorter retention time and lower specific surface yields lower conversion in a first-order heterogeneous reaction. However, at the same flow rate, the spheres could be expected to outperform the cylinders. The bed of cylinders exhibits more pronounced local concentration variations due to a dominance of smaller pores, which are not as readily accessible to the flow. The presence of interstitial liquid reduces the permeability and significantly changes the streamwise velocity distributions inside both beds, effectively homogenizing the geometries by filling up the smaller pores. The implications of the present findings for reduced-order modelling of packed-bed adsorbers are discussed. [ABSTRACT FROM AUTHOR]

Direct numerical simulation and pore-network modeling are common approaches to study the physics of two-phase flow through natural rocks. For assessment of the long-term performance of geological sequestration of CO2, it is important to model the full drainage-imbibition cycle to provide an accurate estimate of the trapped CO2. While direct numerical simulation using pore geometry from micro-CT rock images accurately models two-phase flow physics, it is computationally prohibitive for large rock volumes. On the other hand, pore-network modeling on networks extracted from micro-CT rock images is computationally efficient but utilizes simplified physics in idealized geometric pore elements. This study uses the lattice-Boltzmann method for direct numerical simulation of CO2-brine flow in idealized pore elements to develop a new set of pore-level flow models for the pore-body filling and snap-off events in pore-network modeling of imbibition. Lattice-Boltzmann simulations are conducted on typical idealized pore-network configurations, and the interface evolution and local capillary pressure are evaluated to develop modified equations of local threshold capillary pressure of pore elements as a function of shape factor and other geometrical parameters. The modified equations are then incorporated into a quasi-static pore-network flow solver. The modified model is applied on extracted pore-network of sandstone samples, and saturation of residual trapped CO2 is computed for a drainage-imbibition cycle. The modified model yields different statistics of pore-level events compared with the original model; in particular, the occurrence of snap-off in pore-throats is reduced resulting in a more frontal displacement pattern along the main injection direction. Compared to the original model, the modified model is in closer agreement with the residual trapped CO2 obtained from core flow experiments and direct numerical simulation.

Featured Application: A microfluidic solar cell with energy storage capability–the solar redox flow batteries (SRFB). The dye-sensitized solar cells microfluidically integrated with a redox flow battery (µDSSC-RFB) belong to a new emerging class of green energy sources with an inherent opportunity for energy storage. The successful engineering of microfluidically linked systems is, however, a challenging subject, as the hydrodynamics of electrolyte flow influences the electron and species transport in the system in several ways. In the article, we have analyzed the microflows hydrodynamics by means of the lattice-Boltzmann method, using the algebraic solution of the Navier-Stokes equation for a duct flow and experimentally by the micro particle image velocimetry method. Several prototypes of µDSSC were prepared and tested under different flow conditions. The efficiency of serpentine µDSSC raised from 2.8% for stationary conditions to 3.1% for electrolyte flow above 20 mL/h, while the fill factor increased about 13% and open-circuit voltage from an initial 0.715 V to 0.745 V. Although the hexagonal or circular configurations are the straightforward extensions of standard photo chambers of solar cells, those configurations are hydrodynamically less predictable and unfavorable due to large velocity gradients. The serpentine channel configuration with silver fingers would allow for the scaling of the µDSSC-RFB systems to the industrial scale without loss of performance. Furthermore, the deterioration of cell performance over time can be inhibited by the periodic sensitizer regeneration, which is the inherent advantage of µDSSC. [ABSTRACT FROM AUTHOR]

The permeability of complex porous materials is of interest to many engineering disciplines. This quantity can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as the simulation domains become less porous or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e., porosity and tortuosity) have been proposed, but these features only partly summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains' solid boundaries. However, prior approaches building on the convolutional neural network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a representative elementary volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be modeled properly. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large ( > 512 3 ) images in approximately one second on a single graphics processing unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer. We validate our method with a laminar fluid flow case using vuggy samples and fractures. As a result of viewing the entire domain at once, our model is able to perform accurate prediction on domains exhibiting a large degree of heterogeneity. We expect the methodology to be applicable to many other transport problems where complex geometries play a central role. [ABSTRACT FROM AUTHOR]

Multiphase flow properties of fractures are important in engineering applications such as hydraulic fracturing, evaluating the sealing capacity of caprocks, and the productivity of hydrocarbon-bearing tight rocks. Due to the computational requirements of high fidelity simulations, investigations of flow and transport through fractures typically rely on simplified assumptions applied to large fracture networks. These simplifications ignore the effect of pore-scale capillary phenomena and 3D realistic fracture morphology (for instance, tortuosity, contact points, and crevasses) that lead to macro-scale effective transport properties. The effect of these properties can be studied through lattice Boltzmann simulations, but they require high performance computing clusters and are generally limited in their domain size. In this work, we develop a technique to represent 3D fracture geometries and fluid distributions in 2D without losing any information. Using this innovative approach, we present a specialized machine learning model which only requires a few simulations for training but still accurately predicts fluid flow through 3D fractures. We demonstrate our technique using simulations of a water filled fracture being displaced by supercritical CO2. By generating highly efficient simulations of micro-scale multiphase flow in fractures, we hope to investigate a wide range of fracture types and generalize our method to be incorporated into larger discrete fracture network simulations.

Lattice Boltzmann simulations have been carried out in order to study the flow of blood in normal and constricted blood channels using Newtonian and non-Newtonian rheological models. Instead of using parameters from previous works as is usually done, we propose a new optimization methodology that provides in a consistent manner the complete set of parameters for the studied models, namely Newtonian, Carreau-Yassuda and Kuang-Luo. The optimization was performed simultaneously using experimental data from several sources. Physical observables such as velocity profiles, shear rate profiles and pressure fields were evaluated. For the normal channel case, it was found that the Newtonian model predicts both the highest velocity and shear rates profiles followed by the Carreau-Yassuda and the Kuang-Luo models. For a constricted channel, important differences were found in the velocity profiles among the studied models. First, the Newtonian model was observed to predict the velocity profile maximum at different channel width positions compared to the non-Newtonian ones. Second, the obtained recirculation region was found to be longer for the Newtonian models. Finally, concerning the constriction shape, the global velocity was found to be lower for a rectangular geometry than for a semi-circular one.

In the emerging field of 3D bioprinting, cell damage due to large deformations is considered a main cause for cell death and loss of functionality inside the printed construct. Those deformations, in turn, strongly depend on the mechano-elastic response of the cell to the hydrodynamic stresses experienced during printing. In this work, we present a numerical model to simulate the deformation of biological cells in arbitrary three-dimensional flows. We consider cells as an elastic continuum according to the hyperelastic Mooney–Rivlin model. We then employ force calculations on a tetrahedralized volume mesh. To calibrate our model, we perform a series of FluidFM ® compression experiments with REF52 cells demonstrating that all three parameters of the Mooney–Rivlin model are required for a good description of the experimental data at very large deformations up to 80%. In addition, we validate the model by comparing to previous AFM experiments on bovine endothelial cells and artificial hydrogel particles. To investigate cell deformation in flow, we incorporate our model into Lattice Boltzmann simulations via an Immersed-Boundary algorithm. In linear shear flows, our model shows excellent agreement with analytical calculations and previous simulation data. [ABSTRACT FROM AUTHOR]

Purpose: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. Methods: We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. Results: The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. Conclusion: A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information. [ABSTRACT FROM AUTHOR]

We introduce a structured approach to the construction of linear BGK-type collision operators ensuring that the resulting Lattice-Boltzmann methods are stable with respect to a weighted L 2 -norm. The results hold for particular boundary conditions including periodic, bounce-back, and bounce-back with flipping of sign boundary conditions. This construction uses the equivalent moment-space definition of BGK-type collision operators and the notion of stability structures as guiding principle for the choice of the equilibrium moments for those moments influencing the error term only but not the order of consistency. The presented structured approach is then applied to the 3D isothermal linearized Euler equations with non-vanishing background velocity. Finally, convergence results in the strong discrete L ∞ -norm highlight the suitability of the structured approach introduced in this manuscript. [ABSTRACT FROM AUTHOR]

We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to a lattice Boltzmann application (LBMHD) that historically has made poor use of scalar microprocessors due to its complex data structures and memory access patterns. We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon E5345 (Clovertown), AMD Opteron 2214 (Santa Rosa), AMD Opteron 2356 (Barcelona), Sun T5140 T2+ (Victoria Falls), as well as a QS20 IBM Cell Blade. Rather than hand-tuning LBMHD for each system, we develop a code generator that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned LBMHD application achieves up to a 15x improvement compared with the original code at a given concurrency. Additionally, we present detailed analysis of each optimization, which reveal surprising hardware bottlenecks and software challenges for future multicore systems and applications.

Circulatory models can significantly help develop new ways to alleviate the burden of stroke on society. However, it is not always easy to know what hemodynamics conditions to impose on a numerical model or how to simulate porous media, which ineluctably need to be addressed in strokes. We propose a validated open-source, flexible, and publicly available lattice-Boltzmann numerical framework for such problems and present its features in this chapter. Among them, we propose an algorithm for imposing pressure boundary conditions. We show how to use the method developed by Walsh et al. (Comput Geosci 35(6):1186-1193, 2009) to simulate the permeability law of any porous medium. Finally, we illustrate the features of the framework through a thrombolysis model., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Lattice Boltzmann simulations have been carried out in order to study the flow of blood in normal and constricted blood channels using Newtonian and non-Newtonian rheological models. Instead of using parameters from previous works as is usually done, we propose a new optimization methodology that provides in a consistent manner the complete set of parameters for the studied models, namely Newtonian, Carreau- Yassuda and Kuang-Luo. The optimization was performed simultaneously using experimental data from several sources. Physical observables such as velocity profiles, shear rate profiles and pressure fields were evaluated. For the normal channel case, it was found that the Newtonian model predicts both the highest velocity and shear rates profiles followed by the Carreau-Yassuda and the Kuang-Luo models. For a constricted channel, important differences were found in the velocity profiles among the studied models. First, the Newtonian model was observed to predict the velocity profile maximum at different channel width positions compared to the non- Newtonian ones. Second, the obtained recirculation region was found to be longer for the Newtonian models. Finally, concerning the constriction shape, the global velocity was found to be lower for a rectangular geometry than for a semi-circular one. [ABSTRACT FROM AUTHOR]

Wave energy from the ocean is currently a very popular renewable energy, and its development has primarily focused on the shape of the wave energy converter (WEC) used to efficiently convert wave energy into electrical energy. However, the free surface ocean wave problem is very complex and the parameters affecting WEC behavior are difficult to understand. In this paper, based on the Lattice-Boltzmann method, we present particle-based CFD simulation results for the pivoted-type WEC that exhibits both vertical and horizontal motions. In this method, the computation domain need not be a mesh and complex geometry is not a limiting factor. Using a free-surface turbulence model, we simulated the fluid–structure interaction. We detail our simulation results, which show good agreement with those in the literature. [ABSTRACT FROM AUTHOR]

Highlights • LB-IB-DEM simulation of pebble bed recirculation in HTGR is conducted. • Velocity, FFT and correlation analyses are used to explore gas-pebble two phase flows in HTGR. • Slow gas-pebble flows in HTGR have intermittent and periodic mass flow pattern. • Gas-pebble interaction follows a simultaneous and linearly-dependent two-phase flow pattern. • Helium-pebble flow in real HTGR is predicted to be dominated by pebble recirculation. Abstract The pebble bed is one type of the core of the high temperature gas-cooled reactor (HTGR), which is regarded as the candidate of the generation IV advanced reactor. It is important to explore the gas-pebble flow characteristics and the pebble recirculation under the helium atmosphere. In this work, we presented a lattice Boltzmann (LB) method – immersed boundary (IB) method – discrete element method (DEM) coupled approach to simulate a test facility of pebble bed under the recirculation mode of operation. After model validation by an experiment of sphere sedimentation, the process of pebble recirculated at five constant rates are simulated. The correlations of gas motion and pebble motion in the upper and lower half beds are analyzed to uncover the inter-phase relationships for such intermittent pebble flows. Based on the systematic analyses of the two-phase flows, including the mean field and r.m.s field, the historical variation, inter-correlation, and the spectrum and phase space representations, we found sufficient evidences for the characteristics of intermittency, simultaneity, periodicity, and linear dependence for the inter-phase interaction of gas-pebble flows. [ABSTRACT FROM AUTHOR]

The goal of this paper is to investigate the effects of flow confinement on the noise generated by a small unmanned aerial system rotor measured and simulated in a partially closed test room. More specifically, the influence of the flow recirculation in the test room and the consequent blade turbulence interaction on both the tonal unsteady loading noise and the broadband laminar separation noise components are scrutinized. The study also aims at improving our prediction capabilities of drone rotor noise, and deepening our fundamental knowledge of rotor aeroacoustics in transitional boundary layer regimes. Numerical simulations are conducted by means of a scale-resolving wall-modelled lattice Boltzmann solver to calculate the unsteady flow and the noise generated by the same reference configuration used in our previous works, which consists in a rotor operated at a chord-based Reynolds number of about 70000 at 75% of the tip radius, both in hover and axial flow conditions. Results for a rotor in pristine flow conditions are compared to results obtained by simulating the rotor in a confined environment that reproduces the A-Tunnel test chamber of Delft University of Technology. On one hand, our study reveals that previously observed discrepancies between measured and predicted noise spectra in hover conditions are not due to the influence of the flow confinement on the laminar separation mechanisms, as hypothesized in our previous study, rather to a lack of numerical resolution. On the other hand, the new results reveal that, in hover conditions, the dominant effect of the flow recirculation is to increase the unsteady loading tonal noise components, whereas, in axial flow conditions, no significant effect due to the test environment is observed. Some of our conclusive observations are supported by results obtained using a source identification technique herein presented for the first time. [ABSTRACT FROM AUTHOR]

We present a validation study comparing results from a patient-specific lattice-Boltzmann simulation to transcranial Doppler (TCD) velocity measurements in four different planes of the middle cerebral artery (MCA). As part of the study, we compared simulations using a Newtonian and a Carreau-Yasuda rheology model. We also investigated the viability of using downscaled velocities to reduce the required resolution. Simulations with unscaled velocities predict the maximum flow velocity with an error of less than 9%, independent of the rheology model chosen. The accuracy of the simulation predictions worsens considerably when simulations are run at reduced velocity, as is for example the case when inflow velocities from healthy individuals are used on a vascular model of a stroke patient. Our results demonstrate the importance of using directly measured and patient-specific inflow velocities when simulating blood flow in MCAs. We conclude that localized TCD measurements together with predictive simulations can be used to obtain flow estimates with high fidelity over a larger region, and reduce the need for more invasive flow measurement procedures.

The problem of calcium leaching kinetics of ordinary Portland cement based mortars is revisited via a mesoscale approach. Based on state-of-the-art lattice-Boltzmann technique, a comprehensive suite of leaching analysis is undertaken to address a number of open questions such as (i) the competing effects of interface transition zone (ITZ) and aggregates, (ii) relative leaching rates of calcium between ITZ and mortar cement paste, and (iii) the influence of different water to cement ratios, volume fraction of aggregates and hence of ITZ on leaching kinetics. The mesoscale model is not only able to correctly reproduce experimentally observed trends but also confirm the commonly accepted hypothesis that ITZ and aggregates counter-balance their effect leading to similar portlandite leaching rates in cement paste and mortars. The model also confirms the applicability of simple scaling approach for upscaling leaching kinetics from pure cement paste to mortar scale under the condition that ITZ is fully percolated. [ABSTRACT FROM AUTHOR]

We present a validation study comparing results from a patient-specific lattice-Boltzmann simulation to transcranial Doppler (TCD) velocity measurements in four different planes of the middle cerebral artery (MCA). As part of the study, we compared simulations using a Newtonian and a Carreau-Yasuda rheology model. We also investigated the viability of using downscaled velocities to reduce the required resolution. Simulations with unscaled velocities predict the maximum flow velocity with an error of less than 9%, independent of the rheology model chosen. The accuracy of the simulation predictions worsens considerably when simulations are run at reduced velocity, as is for example the case when inflow velocities from healthy individuals are used on a vascular model of a stroke patient. Our results demonstrate the importance of using directly measured and patient-specific inflow velocities when simulating blood flow in MCAs. We conclude that localized TCD measurements together with predictive simulations can be used to obtain flow estimates with high fidelity over a larger region, and reduce the need for more invasive flow measurement procedures. [ABSTRACT FROM AUTHOR]

Urban traffic-related air pollution is a major source of environmental and health damage and is difficult to quantify due to its inherent physical complexity. We construct a CFD-based simulation framework coupling an efficient numerical method for turbulent fluid flows with a microscopic traffic model and an emissions model to simulate road transport pollutant dispersion at the urban microscale. We improve the open-source Lattice-Boltzmann based CFD software OpenLB to overcome its original stability deficiencies for high Reynolds number flows. A stable recursive regularization procedure with a double distribution function approach is proposed to solve an advection diffusion equation for passive scalar transport at high Reynolds number. The code is successfully validated on three reference cases of increasing complexity and the traffic model SUMO along with a physical engine emissions model are coupled with OpenLB to simulate traffic-induced pollution from a road network in a realistic complex geometry. Transient flow features are analysed and the time-averaged concentration levels in different neighbourhoods of the considered geometry are evaluated: high concentration levels are observed close to the streets but also inside specific building infrastructures due to complex wind dynamics. Analyses of altitudinal concentration variations show that flow recirculations located close to traffic lights can drive pollutant over the buildings and increase concentration levels inside inner courtyards. Time-averaged concentration maps are constructed using both spatially uniform and non-uniform line sources and it is shown that using uniform sources leads to up to 20% local concentration overestimations inside the urban canopy. • The code OpenLB is used to simulate high-Reynolds scalar dispersion with DDF LBM-LES • Traffic emissions are simulated with a traffic simulator and a physical engine model • Spatial uniformization of the emissions can lead to 20% concentration underestimation [ABSTRACT FROM AUTHOR]