Local Flow Variations Research Articles

Real-time data acquisition inside high-pressure PEM water electrolyzer

This study used the micro-electro-mechanical systems (MEMS) technology to develop a high-pressure resistant flexible 7-in-1 microsensor (voltage, current, humidity, temperature, pressure, flow, and hydrogen). The microsensor is integrated with a flexible printed circuit (FPC) for real-time data acquisition of local voltage, current, humidity, temperature, pressure, flow, and hydrogen variations in different positions inside a high-pressure proton exchange membrane water electrolyzer (PEMWE). The goal of developing such microsensor is to observe the influence of temperature and pressure on the PEMWE. The operational safety of hydrogen is ensured by using a micro hydrogen sensor.

A cluster analysis-based shock wave pattern recognition method for two-dimensional inviscid compressible flows

Compressible flows typically exhibit multiple shock waves which interact with each other, making the detection of these shock waves crucial for various aspects of flow studies including construction of high-order numerical schemes (e.g., shock-fitting), adaptive grid refinement, and flow visualization. This study aims to effectively identify and localize multiple shock waves and their interaction points in two-dimensional inviscid steady and unsteady flows. A novel shock wave pattern recognition method based on cluster analysis is proposed, including three processes. First, a series of grid-cells located at the transition zones of captured shock waves are extracted using a shock wave detection approach based on local flow variation. Subsequently, these grid-cells are grouped into numerous clusters using the classical K-means clustering algorithm, with categorization based on nearest neighbor features. Finally, a strategy is introduced to merge relevant adjacent clusters and further localize the points where shock waves interact. The Bézier curve fitting technique is then employed to obtain the high-quality shock-lines. Several numerical cases demonstrate that this method achieves high localization accuracy for shock-lines while being minimally affected by grid type and scale variations. Moreover, it enables clear and effective identification of the shock interaction patterns in both steady and unsteady flows, providing an effective visualization means for analyzing the motion and evolution of shock wave configurations.

Improvement of turbulence model for predicting shock-wave–boundary-layer interaction flows by reconstructing Reynolds stress discrepancies based on field inversion and machine learning

Reynolds-averaged Navier–Stokes (RANS) models have been the mainstay of engineering applications in recent years, and this trend will likely persist in the coming decades. However, the ability of RANS methods to predict complex flows, such as shock-wave/boundary-layer interaction (SWBLI) flows, is inadequate. In the absence of a breakthrough in traditional turbulence modeling, data-driven modeling has emerged as a new paradigm in turbulence research. In this study, a field-inversion and machine-learning framework based on the regularized ensemble Kalman filter (REnKF) was implemented to enhance the predictive ability of the Menter shear-stress transport (SST) model for SWBLI flows. This approach directly modifies the norm and eigenvalues of the Reynolds stress tensor obtained by RANS methods to overcome the limitations of the Boussinesq hypothesis. The spatial distributions of Reynolds stress discrepancies are initially obtained from experimental data using the REnKF method and a parameterization method based on geometric transformation for improved prediction of the training case. Then, a mapping function from local flow variables to discrepancy fields is constructed by selecting appropriate input features and combining them with a random forest algorithm. Our results verify the effectiveness of the framework for SWBLI flows of varying strengths and types. The results also demonstrate that the SST model's simulation ability for SWBLI flows has been significantly improved, providing more accurate predictions of separation and reattachment, considerably lowering the prediction errors for relevant flow variables, and verifying the generalization ability of the proposed framework.

An improved lift model for Euler-Lagrange simulations of bubble columns

The objective of this work is to establish a lift model which can account for the variation in the bubble spreading mechanism with different column aspect ratios. To this end, a strategic approach has been developed and applied to explore the dependency of the lift variation with local flow variables. Numerical simulations are performed by using Computational Fluid Dynamics - Discrete Particle Method (CFD-DPM) coupled with Volume of Fluid (VOF) method in the open-source framework OpenFOAM. Both homogeneous and heterogeneous bubbly flows are considered with the results from reported experiments in literature. It has been found that the local gas fraction and the vorticity are strongly correlated with the lift coefficient. However, using one of these two variables alone cannot describe the lift variation within the bubble columns. Finally, a unified lift model is established that depends on both local gas fraction and vorticity. Euler–Lagrange simulations using this lift model are demonstrated to provide excellent agreement on the bubbly dynamics with experiments of homogeneous bubbly flows and bubble swarms.

Elucidating the atmospheric boundary layer turbulence by combining UHF radar wind profiler and radiosonde measurements over urban area of Beijing

The knowledge of atmospheric turbulence over urban environment remains limited albeit its great importance for air pollution and convection initiation. This study investigates turbulence characteristics in boundary layer based on concurrent radar wind profiler (RWP) and radiosonde observations for the year 2019 at an urban site in Beijing, China. The eddy dissipation rate (ϵ) is retrieved based on Doppler spectral width measurements from RWP. The radiosonde measurements are utilized for estimating the Brunt–Väisälä frequency which is further collocated with RWP based ϵ retrievals for the estimation of vertical eddy diffusivity, outer scale length and inner scale length profiles. Contrasting seasonality is revealed in diurnal cycle of height-resolved turbulence parameters over Beijing. Within the lowermost 1 km altitude, ϵ is estimated to be more than 10−2 m2s−3 during all seasons (except for winter), which possibly represents the highly turbulent state of the atmosphere near ground surface due to the local mountain-plain flow variation. However, above 1 km altitude, the ϵ magnitude varies between 10−4 and 10−2 m2s−3. The vertical eddy diffusivity is found to be higher in the spring and summer seasons while being approximately an order of magnitude lower in the autumn and winter seasons, which agrees with few existing studies.

Influence of compression garments on fatigue behaviour during running based on nonlinear dynamical analysis

ABSTRACT The purpose of this study was to assess quantitatively the effects of compression garments (CGs) on fatigue behaviour during sport activities such as running, which are the subject of a series of qualitative and physiological studies. A quantitative biomechanical analysis of the effects of CGs could assist coaches and athletes to adopt these types of performance enhancement garments. In this research, kinematic changes are measured using 2D phase portraits to study the influence of CGs on fatigue behaviour. Fifteen healthy male intermediate athletes participated in this study and the kinematic data of hip repetitive movements with and without CG were measured during running tasks. These data are used to reconstruct the state space and the local flow variation method is adopted to quantify the trajectory drifts caused by fatigue in the state space. The effects of CGs on the complexity of kinematic changes are also evaluated using permutation entropy. The results indicate that fluctuations in the kinematics are reduced when compression garments are used. It is also shown that adoption of CGs results in a reduction of the rate of fatigue development and decreased complexities in the movement kinematics.

Computational fluid dynamics modelling of hydrodynamics, mixing and oxygen transfer in industrial bioreactors with Newtonian broths

Industrial aerobic fermentation processes are performed in large-scale bioreactors (> 20 m3). Understanding the local values of the velocity field, the eddy dissipation rate and the gas volume fraction is of interest, as these parameters affect mixing and mass transfer and hence fermentation process performance and profitability. Despite the industrial and academic importance of these flow variables in large-scale bioreactors, there is scarce literature addressing it. This article provides a numerical comparison using Computational Fluid Dynamics (CFD) of different industrially relevant reactor types (bubble columns and stirred tanks with different impeller configurations) operated within a realistic range of industrial conditions (40 – 90 m3, 0.3 – 6 kW m-3, 0.5 – 1 vvm). Local flow variables and mixing times are evaluated for all cases studied. The collection of these data allows the prediction of the typical values of mixing time (10 – 206 s) and oxygen transfer rate (1 – 8 kg m-3 h-1) in industrial bioreactors, and serves as basis for the comparison between different reactor types.

Altered Media Flow and Tablet Position as Factors of How Air Bubbles Affect Dissolution of Disintegrating and Non-disintegrating Tablets Using a USP 4 Flow-Through Cell Apparatus

This study investigated how air bubbles in media affect tablet dissolution in a flow-through cell system (USP 4) using disintegrating (USP prednisone) and non-disintegrating (USP salicylic acid) tablets. Cell hydrodynamics were studied using particle image velocimetry (PIV) and computational fluid dynamics (CFD). The PIV analysis showed periodic changes in the local flow corresponding to the discharge and suction of the pump cycles. The absence of prior deaeration induced small air bubbles in the media and lower maximum flow during the cycle, explaining the slower dissolution of the USP salicylic acid tablets. Bubbles, occurring during the USP prednisone tablets study, induced the transition of floating disintegrated particles towards the cell outlet, whereas the particles precipitated to form a white layer on the glass beads used in the study with prior deaeration. CFD analysis showed local flow variation in multiple positions of small (ID 12 mm) and large (ID 22.6 mm) cells, explaining the different rates of dissolution of prednisone tablet particles depending on their distribution. These results emphasize the importance of prior deaeration in dissolution studies using a flow-through system. Bubbles in the flow-through cell system affected tablet dissolution by reducing the area in contact with the media (wettability), lowering the maximum instantaneous flow (pressure buffering), and altering the position of disintegrated particles in the cell.

Machine learning for adjoint vector in aerodynamic shape optimization

Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.Graphical abstract

Scale law analysis of the curved boundary layer evolving around a horizontal cylinder at Pr > 1

The convective boundary layer flow on the external surface of an isothermally heated horizontal cylinder is investigated in this study. Numerical simulations are first carried out for a wide range of flow parameters, i.e., Rayleigh and Prandtl numbers, and scale relations quantifying the boundary layer flow are then determined from the simulation data. The numerical results suggest that the curved boundary layer experiences an initial growth state, a transitional state, and a developed state, which are essentially identical to the extensively studied flat boundary layers. Scale relations quantifying the local flow variables are obtained, and the proposed scale laws indicate that during the initial growth, the present curved boundary layer flow follows a two-dimensional growth rather than the well-known one-dimensional startup of flat boundary layers. It is further demonstrated that the characteristic velocity of the boundary layer flow maximizes at π/2, but its thickness is circumferential location independent. In the steady state, however, the maximum streamwise velocity of the boundary layer shifts to approximately 7π/9 and the thickness consistently increases with the circumferential location. It is also shown that the thickness of the inner viscous boundary layer could be obtained by properly considering the Prandtl number effect, i.e., by the term (1 + Pr−1/2)−1. The proposed scale relations could reasonably describe the curved boundary layer flow, and all regression constants are above 0.99.

Merging computational fluid dynamics and machine learning to reveal animal migration strategies

Abstract Understanding how migratory animals interact with dynamic physical environments remains a major challenge in migration biology. Interactions between migrants and wind and water currents are often poorly resolved in migration models due to both the lack of high‐resolution environmental data, and a lack of understanding of how migrants respond to fine‐scale structure in the physical environment. Here we develop a generalizable, data‐driven methodology to study the migration of animals through complex physical environments. Our approach combines validated computational fluid dynamic (CFD) modelling with animal tracking data to decompose migratory movements into two components, namely movement caused by physical forcing and movement due to active locomotion. We then use a flexible recurrent neural network model to relate local environmental conditions to locomotion behaviour of the migrating animal, allowing us to predict a migrant's force production, velocity and trajectory over time. We apply this framework to a large dataset containing measured trajectories of migrating Chinook salmon through a section of river in California's Sacramento‐San Joaquin Delta. We show that the model is capable of describing fish migratory movements as a function of local flow variables, and that it is possible to accurately forecast migratory movements on which the model was not trained. After validating our model, we show how our framework can be used to understand how migrants respond to local‐flow conditions, how migratory behaviour changes as overall conditions in the system change and how the energetic cost of migratory movements depends on environmental conditions in space and time. Our framework is flexible and can readily be applied to other species and systems.

Bubble cloud configuration effect on the added mass

Understanding the mechanisms that control the dynamics of bubble clouds is essential to many industrial processes in the energy and chemical realms. Due to the complexity of the two-phase flow configurations, modeling the physics of the phenomena driving the mixing of bubbles within a liquid matrix is still a major challenge. One of the weaknesses of most existing two-phase flow models is due to the incomplete handling of bubble dispersion. This difficulty comes from the fact that dispersion can be driven by numerous complex phenomena, such as turbulence, local pressure distribution, bubble to bubble interaction, etc. In this study, we introduce the effect of added mass fluctuations on the dispersion of small bubbles. Existing models based on the Euler–Euler approach do not take into account local flow variations due to bubble distributions. Therefore, these models do not correctly describe fine dispersion features. Solving the potential flow around N bubbles allows to take into account the effect of the added mass on bubble cloud distributions. To this aim, a complete added mass model, which includes local bubble configurations via the void fraction gradient, is developed. The void fraction gradient allows us to account for the asymmetry of the bubble cloud around a single central bubble. Consequently, the proposed model can only represent regular and irregular bubble cloud distributions. This methodology results in a more consistent consideration of the added mass effects as well as Meshchersky's force, which should be included in hydrodynamic two-phase flow models. The proposed approach can be implemented in Euler–Euler models intended to consider the dispersion of bubbles caused by the effect of added mass.

Influence of local heat flow variations on geometrical deflections, microstructure, and tensile properties of Ti-6Al-4 V products in powder bed fusion systems

Abstract This research investigated the geometry, microstructure and tensile properties of Ti-6Al-4 V samples produced by Selective Laser Melting (SLM) and Electron Beam Melting (EBM), which vary due to different heat transfer mechanisms occurring at different locations at the same time. The geometric variations were evaluated by comparing the cranial implants manufactured in this way with their Computer-Aided Designed files using a 3D scanner. The SLM samples were strongly deformed, while the EBM process affected the size but not the shape of the samples. The cooling rate varied from the core to the surface zones, so that the microstructural morphology varied between them. The size and orientation of the precipitated α phase varied significantly from the core to the surface zones in the EBM. Two new α colonies were identified; one was nucleated and grew from the prior β-grain boundary and the other formed parallelograms of equal size consisting of α lamellae. The tensile samples that were built up in the Z-direction in the EBM had the lowest yield strength and the highest elongation, due to the effect of the slowest cooling rate, which promoted continuous dynamic recrystallization, and α colonies precipitated from the α grain boundary. This can provide useful guidance to engineers in developing the functionally graded structure.

Disturbed Flow Promotes Atherosclerotic Plaque Development in Human 3D Tissue-Engineered Vessels On-a-Chip

Atherosclerosis is an arterial disease characterized by intravascular plaques. Disease hallmarks are vessel stenosis and hyperplasia, eventually escalating into plaque rupture and acute clinical presentations. Innate immune cells and local flow variations are core players in the pathology, but their combined effects have never been investigated before in human vessel replicas due to the lack of modeling systems with adequate degree of complexity. Here, we combined computational fluid dynamics and tissue-engineering to achieve full human atherosclerotic plaque development on-a-chip. Our model incorporates induced pluripotent stem cell-derived populations into small-caliber arteries that are cultured in atheroprone conditions. Using machine-learning-aided immunophenotyping, as well as molecular and nanoprobe-based tensile analyses, we found that immune cells and extracellular matrix were comparable between in vitro and ex vivo human plaques. Our results provide further insights into the relation between disturbed flow dynamics and vascular inflammation, introducing a versatile, scalable modeling tool to study atherosclerosis onset and progression.

Quantification of the effect of compression garments on fatigue behavior in cycling

Effectiveness of compression garments to enhance athletic performance is the subject of numerous qualitative studies. This study aims at quantification of the effect of compression garments using nonlinear dynamics approach. Kinematic data of fifteen healthy male athletes was obtained and the state space was reconstructed. The trajectory drifts caused by fatigue in the state space were quantified using local flow variation technique. The study illustrates that compression garments (CGs) decrease rate of fatigue development and the body exhibits a more restricted complexity (more predictable and smaller fluctuations) when CGs are worn.

Full characterization of the peak-locking error by means of orthogonal functions and application to the flow behind a helicopter fuselage model

This work offers a 1D fully analytic complete modeling of the peak-locking error. Besides error assessment, this model allows for measurement correction. The paradigm of peak locking is revisited. The expressions of the functional dependence of the error are obtained for the two most relevant components of the measurement statistics: (1) the local velocity average, and (2) the local velocity rms. The prediction of the peak-locking error is related to complex interactions between the particle images size and shape and the measurement algorithms (peak fitting, image interpolation, etc.). The proposed model incorporates the different interactions by using a generalized approach that only requires the calibration of a few coefficients. This calibration is done by means of an inexpensive multiple Δt strategy, consisting in measuring the same flow field with different Δt subsets. Differently to previous empirical models from the authors, a robust theoretical foundation has been established. Besides providing rationale to the procedure, the results are significantly more accurate. A calibration using only three coefficients may correct up to 90% of the peak-locking error, while the previous empirical models would correct just in the order of 50%. This leaves a 10% of residual peak-locking error instead of 50%, reducing the final incidence by a factor of 5. The methodology is tested, in the context of turbulent flows, on real images corresponding to the measurement of the flow around the fuselage of a helicopter model. The test campaign was performed in the Italian Aerospace Research Centre (CIRA) CT-1 low speed wind tunnel. The proposed method, combined with the multiple Δt strategy, results in an error prediction accuracy that is good enough for correcting the measurements. Corrections to the average flow are in the order of 0.1 pixel (~ 3% of the convective velocity but in the order of 100% of the local flow variations). Corrections to the rms are in the same order; being the turbulence small, they cancel very large relative rms errors (~ 100%). The coherence between predicted error and actual error in real cases is further supported by the consistent matching that can be observed on their spatial patterns, associated with flow field statistics. An orthogonal expansion allows for full assessment of PIV peak-locking bias errors. The turbulent flow behind a helicopter fuselage model is used as test case. The expansion is calibrated and validated using a multiple Δt strategy. The resulting assessment is good enough to allow for correcting errors in the order of 0.1 pixel in the measured average as well as in the rms.

Linear amplification factor transport equation for stationary crossflow instabilities in supersonic boundary layers

Based on the database from linear stability theory (LST) analysis, a local amplification factor transport equation for stationary crossflow (CF) waves in low-speed boundary layers was developed in 2019. In this paper, the authors try to extend this transport equation to compressible boundary layers based on local flow variables. The similarity equations for compressible boundary layers are introduced to build the function relations between non-local variables and local flow parameters. Then, compressibility corrections are taken into account to modify the source term of the transport equation. Through verifications of different sweep angles, Reynolds numbers, angles of attack, Mach numbers, and different cross-section geometric shapes, the rationality and correctness of the new transport equation established in this paper are illustrated.

Assisting and opposing mixed convection with conjugate heat transfer in a differentially heated cavity filled with coarse-grained porous media

We report numerical simulations of assisting and opposing mixed convection in a side-heated, side-cooled cavity packed with relatively large solid spheres. The mixed convection is generated by imposing a movement on the isothermal vertical walls, either in or opposite to the direction of natural convection flow. For a fluid Prandtl number of 5.4 and fluid Rayleigh numbers of 106 and 107, we varied the modified Richardson number from 0.025 to 500. As in fluids-only mixed convection, we find that the mutual interaction between forced and natural convection, leading to a relative heat transfer enhancement in assisting - and a relative heat transfer suppression in opposing - mixed convection, is most prominent at a Richardson number of approximately one, when the Richardson number is modified with the Darcy number Da and the Forchheimer coefficient Cf = 0.1 as Rim = Ri × Da0.5/Cf. We focus on local flow and heat transfer variations in order to explain differences in local and average heat transfer between a coarse grained and fine grained (Darcy-type) porous medium, at equal porosity and permeability. We found that the ratio between the thermal boundary layer thickness at the isothermal walls and the average pore size plays an important role in the effect that the grain and pore size have on the heat transfer. When this ratio is relatively large, the thermal boundary layer is locally disturbed by the solid objects and these objects cause local velocities and flow recirculation perpendicular to the walls, resulting in significant differences in the wall-averaged heat transfer. The local nature of the interactions between flow and solid objects cannot be captured by a volume averaged approach, such as a Darcy model.

Multiscale Parallelized Computational Fluid Dynamics Modeling Toward Resolving Manufacturable Roughness

Abstract Typical turbomachinery aerothermal problems of practical interest are characterized by flow structures of wide-ranging scales, which interact with each other. Such multiscale interactions can be observed between the flow structures produced by surface roughness and by the bulk flow patterns. Moreover, additive manufacturing (AM) may sooner or later open a new chapter in the way components are designed by granting designers the ability to control the shape and patterns of surface roughness. As a result, surface finish, which so far has been treated largely as a stochastic trait, can be shifted to a set of design parameters that consist of repetitive, discrete micro-elements on a wall surface (“manufacturable roughness”). Considering this prospective capability, the question would arise regarding how surface microstructures can be incorporated in computational analyses during designing in the future. Semi-empirical methods for predicting aerothermal characteristics and the impact of manufacturable roughness could be used to minimize computational cost. However, the lack of element-to-element resolution may lead to erroneous predictions, as the interactions among the roughness micro-elements have been shown to be significant for adequate performance predictions (Kapsis and He, 2018, “Analysis of Aerothermal Characteristics of Surface Micro-Structures,” ASME J. Fluids Eng., 140(5), p. 051104). In this paper, a new multiscale approach based on the novel block spectral method (BSM) is adopted. This method aims to provide efficient resolution of the detailed local flow variation in space and time of the large-scale microstructures. This resolution is provided without resorting to modeling every single ones in detail, as a conventional large-scale computational fluid dynamics (CFD) simulation would demand, but still demonstrating similar time-accurate and time-averaged flow properties. The main emphasis of this work is to develop a parallelized solver of the method to enable tackling large problems. The work also includes a first of the kind verification and demonstration of the method for wall surfaces with a large number of microstructured elements.

Curie point depth, thermal gradient, and heat flow in the Colombian Caribbean (northwestern South America)

Geothermal gradients were estimated at the points of a grid with cell size of 50 km by 50 km in northwestern Colombia. Depths to the bottom of magnetic sources were assumed to represent Curie depths, and were estimated by means of statistical-spectral analysis of aeromagnetic data contained in square windows of 100 km by 100 km, 200 km by 200 km, and 300 km by 300 km. The centroid method and a variation of it that considers the fractal distribution of magnetization were applied. The modified centroid method provided better estimations of Curie depths, which range between 13 km and 47 km. Obtained Curie depths are comparable to those reported in other studies of regional character. The corresponding estimated geothermal gradients correlate quite well with estimations based on the bottom simulating reflector and bottom hole temperatures. Observed differences are small and can be accounted for by local heat flow variations due to shallow ground water flow, or recent sedimentation. Elaborated geothermal gradient and heat flow maps for northwestern Colombia are accurate and consistent with estimates of the thickness of the oceanic crust and continental cortical thicknesses, reported by previous authors. The maps presented in this study represent a contribution to the heat flow studies in northwestern South America.