Local Flow Characteristics Research Articles

The Effect of wind barriers on the local wind field on a bridge deck

Abstract The adverse effect of crosswinds on road vehicles on a bridge deck or traffic lanes is of increasing concern. Local flow characteristics depend on the bridge's architecture and wind barriers are commonly used to alleviate wind-induced instability for vehicles. Terrain orography is another dominant factor. This study uses a terrain model with a coastal bridge (Chen-Kung Bridge, Penghu, Taiwan). The bridge is located between a reservoir and the Taiwan Strait. Numerous accidents have occurred due to strong monsoon winds in autumn and winter. In wind tunnel experiments, an atmosphere boundary layer is established and the test conditions (wind speed and wind incidence) are taken from local meteorological data. The effect of the height of solid wind barriers on local wind direction and speed is determined. The results can be used to remodel wind barriers to increase safety for road vehicles.

A compressible solver for two phase-flows with phase change for bubble cavitation

The present work describes the development of a novel fully compressible solver for two-phase flows with liquid-vapour phase change. Phase change phenomena in compressible two-phase flows are driving mechanisms for a multitude of phenomena including cavitation, droplet evaporation in acoustic fields, phase change in closed environments. Those are fundamental for a large number of technological applications including combustion chambers, propellant management in space and submarine engine design. The accurate simulation of phase change phenomena in compressible flows requires a direct numerical solver thermodynamically consistent and able to describe the smallest driving scales and to handle capillary effects. The present solver has these characteristics. It solves the full system of conservation equations for real fluid compressible flows of single species written in primitive variables using a semi-implicit pressure based solver. It leads to the energy equation being a Screened Poisson equation for the pressure that can be solved as a linear system. For the sake of thermodynamic consistency, several cubic equations of state are proposed to describe liquid and vapour phases as well as evolving saturation conditions at the interface. The proposed solver can be considered as an extension of classical incompressible solvers for nucleate boiling based on sharp interface approaches towards configurations with temporally and spatially evolving saturation conditions at the interface. It enables a complete coupling between local liquid-vapour phase change at the interface and pressure variations due to local flow features or time evolving thermodynamic conditions. Its interest is demonstrated with several benchmarks of increasing complexity with an ultimate configuration involving bubble cavitation due to the pressure drop in a convergent pipe.

Drifter Deployment Strategies to Determine Lagrangian Surface Convergence in Submesoscale Flows

Abstract Surface convergence in the ocean is associated with accumulation of buoyant pollutants as well as with vertical transport that is important to biological activity. Such surface convergence regions are marked by a high dilation rate, i.e., the finite time Lagrangian average divergence. Dilation-rate observations are most easily derived from the change of the area encompassed by a drifter swarm over time. The technological advances that have enabled the deployment of large numbers of drifters in a single experiment have raised new questions about optimal deployment strategies for extracting dilation-rate information with acceptable accuracy and as much spatial coverage as possible. Using a submesoscale-resolving operational model of the Mediterranean Sea, we analyze synthetic trajectories of drifter polygons to evaluate the impact of the number of drifters and their initial separation on the accuracy of the resulting dilation-rate estimates. The results confirm that estimates improve as the circumradius of the polygon decreases and as more drifters are added, but with only a marginal improvement for drifter polygons containing more than four drifters. Moreover, GPS positions obtained from drifters in the ocean are subject to uncertainty on the order of 2–50 m, and when this uncertainty is taken into account, an optimal circumradius can be identified that balances uncertainty from position measurements with that from the area approximations. Significance Statement Locating regions of convergence over a finite time interval on the ocean surface can help in pollution mitigation, locating biological hotspots, and even search-and-rescue operations. Finite time convergence can be quantified using the dilation rate, but it is hard to measure in the ocean. Hence, we present a method to estimate the dilation rate using trajectories of drifters, which are instruments widely used by oceanographers during field experiments to understand the local flow features. We show that even though the drifter-based dilation rates are prone to error as a result of a finite number of drifters and limited GPS accuracy, the estimates locate around 90% of the strongest convergent features in our model.

The role of bionic tubercle leading-edge in a centrifugal pump as turbines(PATs)

The structure of the blade's leading edge significantly affects the centrifugal pump as turbines (PATs) performance. This paper has attempted to investigate the hydraulic performance and pressure pulsations characteristics of PATs with bionic tubercle leading edge impellers by numerical calculation and experiment methods. The results showed that the bionic tubercles structure at the leading edge of the blade does not change the PAT's best efficiency point and helps improve the energy recovery efficiency under all operating conditions, especially with efficiency increased by 6.04 % under the over-load operation condition. Moreover, the bionic tubercles structure at the blade's leading edge can improves the impeller's local flow characteristics and effectively reduces the amplitude of dominant frequency in the main domains of PATs. The maximum reduction amplitude of pressure pulsations in the impeller with the bionic tubercle leading-edge under 0.7Qb, Qb, and 1.3Qb operating conditions is 29.8 %, 23.3 % and 61.1 %, respectively. This research provides a reference for the use of bionic blades to improve the energy recovery efficiency and operation stability of PATs.

Characteristics of local flow structure with different particle properties in a gas-solid diameter-varying fluidized bed

The effects of particle property on gas-solid flow in the diameter-varying riser are experimentally examined using the optical fiber probe in this research. Local flow features are obtained by signal analysis method under the operating conditions of 3–7 m/s and 8–63 kg/m2s. The results show that solid holdup distribution of glass beads is wider than that of catalyst particle particles in the wall region but is narrow in the center region. Solid holdup fluctuations tend to decrease with the increase of particle density under low-concentration conditions but increase at high-concentration conditions. The results from power spectrum density (PSD) show that the increase in particle density strengthens the periodic flow features in the enlarged section. The motion intensity decreases at the particle level but increases at the cluster level for high-density particles. Additionally, as particle density rises, the solid concentration discrepancies between the straight section and the enlarged section widen. In contrast to the upper straight section, the effects of particle density on gas-solid flow are minimal in the enlarged section.

Detailed numerical investigations of the in operando adjustable flow field in a diamond unit cell-based interpenetrating periodic open cellular structure (interPOCS)

In the field of heterogeneous catalysis, additively manufactured so-called Periodic Open Cellular Structures (POCS) applied as catalyst supports have demonstrated their advantages over conventional randomly packed bed reactors in terms of heat management and pressure drop (Busse et al., 2018; Inayat et al., 2016). Since the internal flow field and the pressure drop are mainly influenced by the geometrical properties of the unit cell (unit cell type, strut diameter and cell size), the resulting transport characteristics of the structure is already determined during the design phase of the unit cell (Horneber, 2015). By inserting one POCS based on a diamond unit cell (offset structure) into the cavities of a second diamond cell structure (fix structure), a completely new type of so-called interpenetrating Periodic Open Cellular Structures (interPOCS) is formed which allows for an in operando adjustment of the global and local morphological properties of the structure (Do et al., 2017; Do et al., 2020). With detailed, spatially resolved computational fluid dynamics (CFD) simulations using the open source toolbox OpenFOAM® (Weller et al., 1998) and an additional in-house random-walk particle tracking implementation called disTrackFoam (Trunk et al., 2021), we systematically investigated the local and global flow characteristics within the interPOCS in dependency of the relative positioning of the fixed and offset structure. The obtained results show that this highly adjustable structure allows for a broad variation of flow characteristics in a chemical reactor without changing the system’s periphery. This study demonstrates that interPOCS represent a completely new application of additively manufactured catalyst support structures enabling highly flexible in operando tuning of the flow field and mass transport in a heterogeneously catalyzed reactor system.

Experimental Investigation of Local Heat Transfer Coefficient in a Plate Heat Exchanger Using a Thin Heat Transfer Surface

Abstract This paper presents an experimental investigation of local heat transfer characteristics of single-phase flow in a plate heat exchanger (PHE). The local heat transfer coefficient is evaluated using a test section with PHE geometry for measuring wall temperature distribution. The test section of 1.5 mm thickness is employed to consider the heat conduction effect of the heat transfer plate. The results indicated that the local heat transfer coefficient is influenced by the development of the thermal boundary layer along the flow direction and the maldistribution of water flows along both the direction perpendicular to the flow and the stacking direction. The harmonic mean heat transfer coefficient calculated by the measured local heat transfer coefficient agrees with the average heat transfer coefficient evaluated by the modified Wilson plot method within ±25% and within ±16% for the hot side and the cold side, respectively.

Study of the Thermal-Hydraulic Characteristics of a Printed Circuit Heat Exchanger Using a Novel Iterative Inversion Method Combined with Genetic Algorithm

Printed circuit heat exchangers are getting more and more applications in various energy systems. Detailed data monitoring is essential to understand their operation situations in real time. Nevertheless, the commonly monitored parameters are inadequate to evaluate the local flow and heat transfer characteristics when the fluid undergoes an obvious property variation in the channel. In this work, a measurement system for printed circuit heat exchangers similar to those in industrial applications was set up to study the thermal-hydraulic characteristics of supercritical CO2 with non-linear physical properties. The experimental results show that the heat transfer coefficient can be increased by 82.9–109.6% when the mass flow rate increases from 0.15 to 0.42 kg/s. By comparison, the heat transfer coefficient can be increased by 70.7% when the fluid state approaches the critical point. A novel iterative inversion method was proposed to deduce the coefficients of the heat transfer and friction factor correlations based on the collected fundamental parameters. The deviations of the heat transfer rate and friction coefficient between the measured data and the predicted results by the derived correlations were both within 10%, indicating the feasibility and reliability of the inversion method.

Numerical Study on Local Flow and Heat Transfer Characteristics of Supercritical CO2 in PCHE with Sinusoidal Channels

Numerical Study on Local Flow and Heat Transfer Characteristics of Supercritical CO2 in PCHE with Sinusoidal Channels

Revealing the hot deformation behavior of AZ42 Mg alloy by using 3D hot processing map based on a novel NGO-ANN model

The hot deformation behavior of AZ42 alloy was observed using thermal compression tests at a temperature scope of 250–400 °C and strain rate scope of 0.001–1 s−1. True stress-strain curves exhibited a combination of work hardening and dynamic softening features. A Northern Goshawk algorithm (NGO)-optimized artificial neural network (ANN) model was proposed. The established NGO-ANN model demonstrated impressive prediction accuracy, achieving a high determination coefficient of 0.991, a mean absolute percentage error of 3.51 %, and a root mean square error of 2.73. Subsequently, three-dimensional (3D) hot processing map based on the dynamic material model (DMM) theory was created. There were three different regions within the processing maps: the flow instability region (region A: 250–260 °C, 0.02–1 s−1, and region B: 300–400 °C, 0.01–0.1 s−1), high-power dissipation coefficient region (region C: 350–400 °C, 0.001–0.02 s−1, and region D: 300–350 °C, 0.5–1 s−1), and low power dissipation efficiency safety region (region E: the rest ones). Microstructural analysis revealed significant local plastic flow features in the flow instability region and a combination of coarse initial deformation grains and fine dynamic recrystallization (DRX) grains in the low power dissipation efficiency safety region. Fine and uniform grains were observed in the high-power dissipation efficiency region with DRX degree VDRX as high as 85.6 %, resulting in the best mechanical properties. Based on the established 3D hot processing map, the optimal process domains were determined.

Local turbulence generation using conditional generative adversarial networks toward Reynolds-averaged Navier–Stokes modeling

Data-driven turbulence modeling has been extensively studied in recent years. To date, only high-fidelity data from the mean flow field have been used for Reynolds-averaged Navier–Stokes (RANS) modeling, while the instantaneous turbulence fields from direct numerical simulation and large eddy simulation simulations have not been utilized. In this paper, a new framework is proposed to augment machine learning RANS modeling with features extracted from instantaneous turbulence flow data. A conditional generative model is trained to model the probability distribution of the local instantaneous turbulence field given local mean flow features. Then, the generative model is transferred to machine learning RANS modeling. The present work is mainly focused on generating a local instantaneous turbulence field using conditional generative adversarial networks (CGANs). Several GANs are trained first on the turbulence data from channel flow and periodic hill flow to generate complete one-dimensional and two-dimensional turbulence fields. Then, a CGAN is trained on the periodic hill flow data to generate local turbulence fields. Statistical analysis is performed on the generated samples from the GAN models. The first and second moments, the two-point correlation, and the energy spectra conform well to those of real turbulence. Finally, the information learned by the CGAN is used for machine learning RANS modeling by multitask learning, and the feasibility of the framework proposed in this paper is initially verified.

Numerical analysis on flow distribution of nuclear reactor core inlet under ocean conditions

The high consumption of traditional energy is difficult to support the development of marine resources. Floating Nuclear Power Plant (FNPP) can be used for energy supply due to their advantages such as high power and low cost. Considering that ocean conditions can seriously cause flow fluctuations at the core inlet, this paper establishes a Reactor Pressure Vessel (RPV) model that includes a porous medium core model for numerical analysis. The momentum source is added to the control equation to achieve introduction of ocean conditions, which solves the problem of plane data errors in dynamic mesh method. Therefore, the flow distribution at the core inlet can be obtained under ocean conditions and the local flow characteristics in the lower chamber can be comprehensively observed. The results shown that nonlinear motion can promote more uniform flow distribution at the core inlet, enhancement of the additional force in motion can effectively suppress vortices. In the early stages of motion, rolling motion will cause inlet flow of most fuel assemblies to be too high or too low, it is necessary to adjust the reactor power output accordingly. In linear motion, surging motion does not improve the non-uniformity of flow distribution at the core inlet, but it can increase the stability of coolant flow in the lower chamber and reduces the number of vortices. Heaving motion has very little impact on flow distribution at the core inlet due to it is consistent with the mainstream direction. Coupled motion can enhance the uniformity of core inlet flow compared to single degree of freedom motion and more attention should be paid to adjusting initial reactor power output.

Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

Performance of printed circuit heat exchangers in SCO2 Brayton/Organic Rankine combined cycle: Assessment of simplified boiling temperature and heat flux type of boundary conditions

In this study, a simplified boiling temperature type of boundary condition (BTBC) is developed and used to study the performance of printed circuit heat exchanger (PCHE) with semicircular channels. CFD is used to simulate both fluid and heat flows in PCHE semicircular channels are used as a precooler in a combined supercritical CO2 Brayton/Organic Rankine cycle. Based on local heat transfer and flow characteristics of SCO2, the results of BTBC are compared to heat flux type of boundary condition (HFBC). The classical (Gnielinski) correlation and the modified heat transfer correlation (modified Jackson) which are frequently used to capture the heat transfer behavior of SCO2 are quantitatively evaluated for the two types of boundary conditions. The results show that the junction of different temperature sections of PCHE has the greatest influence on heat transfer coefficients, and the flow from the overheated section into the evaporation section accelerates the increase of heat transfer coefficients and enables them to reach their peak values faster. It is also found that the buoyance influence along the flow direction is significant and different for the two tested boundary conditions. In addition, results show that BTBC have bigger effects on the heat transfer coefficient at the different junctions of the precooler, compared to HFBC. Also, compared to the CFD results using BTBc, more than 90% of the data points predicted by the modified Jackson correlation are predicted within + 35% and −15% error margin, which is a better matching compared to the predictions of Gnielinski correlation. This type of boundary conditions can have a great influence on heat transfer of SCO2, and thus it is necessary to adopt a more accurate boiling model to capture well the coupled heat transfer between SCO2 and the organic working medium.

Inf uence of the variable geometry of the diverter nozzle on the metrological characteristics of a calibration unit with weighing devices

This article discusses the issues of improving the metrological characteristics, weight-dimensional and technical and economic indicators of calibration installations with weighing devices due to the introduction of a fl ow switch with a variable nozzle exit geometry into their design. It is assumed that the use of this design of the fl ow switch will solve the problem of fi lling the nozzle exit with water, reduce the dimensions of the hydraulic path, simplify the maintenance procedure, and reduce the cost of installations. A description of the design, principles of operation and a timing diagram of a fl ow switch with a variable nozzle exit geometry are presented. The local hydrodynamic characteristics of the water fl ow by a Pitot tube in the nozzle exit are studied with a change in its width in the mass fl ow rate range from 25 to 550 t/h. The optimal values of the nozzle cut width are experimentally determined depending on the value of the mass fl ow rate of the liquid, at which it is completely fi lled with liquid. A study of the metrological characteristics of a calibration installation with weighing devices, which includes a fl ow switch with a variable nozzle exit geometry, methods of indirect measurements and comparisons using a comparison standard in a wide range of liquid mass fl ow rates, has been carried out. The impact on the metrological characteristics of a calibration installation with weighing devices of the nozzle cut width in the investigated range of liquid fl ow is established. Graphical dependences of the expanded measurement uncertainty of the installation when reproducing the unit of liquid mass fl ow rate on the measurement time interval and on the mass fl ow rate of the liquid are presented. Based on the research results, the optimal values of the nozzle cut width and measurement time intervals were determined depending on the mass fl ow rates of the liquid when using a fl ow switch with a variable nozzle cut geometry. The expansion of the range of operation of weighing devices, which are part of the verifi cation installations with weighing devices, is justifi ed. According to the results of the research, the proposed design of the fl ow switch with a variable geometry of the nozzle exit is recommended for use in the design of new and modernization of installations for calibration with weight devices of fl ow switches.

Cost vs Accuracy: DNS of turbulent flow over a sphere using structured immersed-boundary, unstructured finite-volume, and spectral-element methods

We report a comparative study of three numerical solvers for the direct numerical simulation of the flow over a sphere at Re = 3700. A high-order spectral-element code (Nek5000), a general purpose, unstructured finite-volume solver (OpenFOAM) and an in-house Cartesian solver using the immersed-boundary method (IBM) are employed for the analysis; results are compared against previous numerical and experimental data. Numerical results show that Nek5000 and the IBM code operate within a similar computational performance range, in terms of cost-vs-accuracy analysis, for both global parameters as well as local flow features. On the other hand, OpenFOAM needed a significantly higher number of degrees of freedom (and, overall, a higher cost) to match some of the basic features of the flow, such as the length of the recirculation bubble forming downstream the sphere. For the finest grid resolutions, the three codes are in good agreement for most of the analyzed flow metrics. Overall, our results suggest that high-order methods and second-order, energy-conserving approaches based on the IBM may be both viable options for high-fidelity scale-resolving simulations of turbulent flows with separation.

Effect of flow structures on the transport of gametes in broadcast-spawning sea urchin

Broadcasted fertilization for the reproduction of invertebrates is accomplished through a complicated interaction between spawned gametes and the surrounding flows. Most gametes encounter each other in the vicinity of the sea urchin body where unique flow structures develop, and so analysis of local flow characteristics allows us to better understand the effect of flow on the fertilization process. This study applied a Lagrangian framework based on computational fluid dynamics to estimate the fertilization rate of eggs in a range of flow velocities (0.025–0.2 m/s) and the fertilization rate was the highest at U = 0.1 m/s, which is an intermediate flow speed. Among the four classified sub-zones, such as the aboral surface, wake, substrate, and water column, fertilization occurred most frequently in the wake and substrate regions. The relationship between fertilization rate and flow structures was investigated using three parameters: 1) standardized Morisita index to quantify the pattern of gamete distribution, 2) length of the recirculation zone to specify the areas where the eggs are most frequently fertilized, and 3) integral scale to estimate the dimension of vortex structures downstream. The results of this study show that the fertilization rate is higher inside the recirculation zone, especially when strong vortex structures are observed because the flow provides a favorable condition for the gametes to aggregate and collide with each other.

Combining Weather Station Data and Short-Term LiDAR Deployment to Estimate Wind Energy Potential with Machine Learning: A Case Study from the Swiss Alps

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energy. However, the country’s topography makes the assessment very challenging. We present two measurement campaigns at Lukmanier and Les Diablerets, as representative areas of the complex terrain of the Swiss Alps. A general understanding of local wind flow characteristics is achieved by comparing wind speed measurements from a near-surface ultra-sonic anemometer and from light detection and ranging (LiDAR) measurements further aloft. The measurements show how the terrain modifies synoptic wind for example through katabatic flows and effects of local topography. We use an artificial neural network (ANN) to combine the data from the measurement campaign with wind speed measured by weather stations in the surrounding area of the study sites. The ANN approach is validated against a set of LiDAR measurements which were not used for model calibration and also against wind speed measurements from a 25-meter mast, previously installed at Lukmanier. The statistics of the ANN output obtained from multi-year time series of nearby weather stations match accurately the ones of the mast data. However, for the rather short validation periods from the LiDAR, the ANN has difficulties in predicting lowest wind speeds at both sites, and highest wind speeds at Les Diablerets.

Attention-Based Multiple Graph Convolutional Recurrent Network for Traffic Forecasting

Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal correlations in traffic data. The proposed model comprises two spatial feature extraction modules. Firstly, a dot product attention mechanism is utilized to construct an adaptive graph to extract the similarity of road structure. Secondly, the graph attention mechanism is leveraged to enhance the extraction of local traffic flow features. The outputs of these two spatial feature extraction modules are integrated through a gating mechanism and fed into a Gated Recurrent Unit (GRU) to make spatiotemporal interaction predictions. Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest that the proposed model is effective in capturing complex spatiotemporal correlations and achieving about 1% improvements in traffic forecasting.

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. In a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.