Estimation Of Drag Coefficient Research Articles

Structured porous surface for drag reduction and wake attenuation of cylinder flow

In the current study, a new passive flow control strategy characterized by 3D printing structured porous surface was introduced and experimentally investigated to manipulate the dynamic wake evolution and flow characteristics around a circular cylinder at a Reynolds number of 2.02×104. With the 3D printing structured porous surface, parts of the incoming flow could go through the cylinder and communicate with the wake flow spontaneously. As a result, the vortex shedding was suppressed and the drag was reduced. From POD analysis results, we observed shifted modes and decreased vorticity distribution, indicating that the periodic vortex shedding process was weakened. This was also verified by the analysis of dynamic wake evolution and by the power spectrum analysis. In addition, experimental results showed that the time-averaged turbulent kinetic energy (TKE) and the Reynolds shear stress (RSS) distributions were restrained in the wake of the porous cylinder. Moreover, the mean estimated drag coefficients witnessed a significant decrease up to 49.47%.

Bubble's rise characteristics in shear-thinning xanthan gum solution: a numerical analysis

The rise of gaseous bubbles in shear-thinning liquid is a fundamental issue in fluid physics, particularly the bubbles rise dynamics in water-soluble xanthan gum concentration have a strong link for enhancing the stability of foams that have been encountered extensively in oil recovery, methane hydrate formation processing, froth flotation, and food and beverage industries. Here, air bubble rise behavior in xanthan gum solution (XGs) was investigated by using a coupled volume of fluid with the level set approach in CFD (computational fluid dynamics) modelling. The Carreau-Yasuda rheological model was adopted to define the viscous properties of the XGs and the continuum surface force model was used to track the interface between bubble and XGs. Additionally, ANN (artificial neural network) modeling was demonstrated for estimating the outputs of CFD. Our estimated CFD results for different bubble terminal velocities in water and experimental data obtained from the literature showed that there was a maximum relative error of 4.51%. Then, the CFD setup was utilized to investigate the effect of different concentrations of XGs and liquid flow index (N) on bubble rise dynamics within the bubble Reynolds number (Reb) range up to 10.05 and Eotvos number (Eo) range up to 3.47. For a fixed bubble size, the dimensionless bubble terminal velocity decreased in increased flow index and concentration of XGs, which led to decrease Reb and increase Eo. For a given XGs, the dimensionless bubble terminal velocity significantly depends on Reb and Eo. It was found that XGs and flow index significantly affect the distribution of dimensionless liquid viscosity than that of the dimensionless liquid velocity close to the bubble's underneath region. In a comparison with the rheological power-law model, the Carreau-Yasuda model showed to predict more accurate results. The estimated drag coefficient showed a deviation from the empirical equation reported in the literature, in contrast, a more accurate estimation in drag coefficient was obtained based on modified Reynolds number and Archimedes number. ANN modelling outputs agreed with CFD results and indicated that the training and testing of ANN have great efficiency to predict unknown values.

Detailed study of single bubble behavior and drag correlations in Newtonian and non-Newtonian liquids for the design of bubble columns

A study of the effects of fluid type (shear-thinning, Newtonian, and shear-thickening) and periodic shape fluctuations of bubbles on the drag coefficient is presented for three bubble sizes (2 mm, 4 mm and 6 mm), three flow consistency indexes (μwater, 10μwater, 100μwater) and three flow behavior indexes (0.8, 1, 1.2). Computational Fluid Dynamics (CFD) simulations were performed in addition to previous measurements to obtain local data of the flow hydrodynamics. The results were used to evaluate 12 different drag coefficient estimation models, which are essential for the design of bubble columns. The Dijkhuizen et al. and Rodrigue correlations are suitable for the prediction of terminal velocity in both Newtonian and non-Newtonian liquids with high or intermediate viscosity. Finally, a modification of the correlations enables the prediction of small bubble terminal velocity also in low-viscosity liquids.

Physics-based estimates of drag coefficients for the impact pressure calculation of dense snow avalanches

In avalanche engineering and hazard mapping, computing impact pressures exerted by avalanches on rigid structures has long been a difficult task that requires combining empirical equations, rules of thumb, engineering judgment and experience. Until the 1990s, well-documented avalanches were seldom, and the main source of information included back-analysis of damage to structures and scarce field measurements. By the 1990s, several field sites were equipped across Europe, and since then they have provided new insights into the physics of impact. The main problem has been the difficulty in interpreting and generalizing the results to propose sound methods for estimating impact pressure. Testing a wide range of flow conditions has also been difficult in the field. To go a step forward in the elaboration of new guidelines for computing avalanche forces, we developed a numerical code based on the Discrete Element Method (DEM), which made it possible to simulate how an avalanche interacts with a rigid obstacle and to study how impact pressure depends on obstacle shape and size, as well as the avalanche flow regime. We extracted pressure and velocity data from the Vallée de la Sionne database to validate the DEM code, calibrate the model parameters, and elaborate avalanche scenarios. We studied four avalanches scenarios related to distinct flow regimes of the avalanche’s dense core. In these scenarios, snow cohesion and velocity were imposed at the upstream boundary of the computational domain. Building on earlier work, we generalized an empirical equation for computing impact pressure as a function of snow cohesion, velocity, flow regime, and structure shape and size. Various coefficients were defined and calibrated from our DEM data. Within the range of tested values, we found good agreement between estimated pressure and field data.

Identification of particular hydrodynamic parameters for a modular type 4 DOF underwater vehicle by means of CFD method

PurposeThe development of robust control algorithms for the position, velocity and trajectory control of unmanned underwater vehicles (UUVs) depends on the accuracy of their mathematical models. Accuracy of the model is determined by precise estimation of the UUV hydrodynamic parameters. The purpose of this study is to determine the hydrodynamic forces and moments acting on an underwater vehicle with complex body geometry and moving at low speeds and to achieve the accurate coefficients associated with them.Design/methodology/approachA three-dimensional (3D) computer-aided design (CAD) model of UUV is designed with one-to-one dimensions. 3D fluid flow simulations are conducted using computational fluid dynamics (CFD) software programme in the solution of Navier Stokes equations for laminar and turbulent flow analysis. The coefficients depending on the hydrodynamic forces and moments are determined by the external flow analysis using the CFD programme. The Flow Simulation k-ε turbulence model is used for the transition from laminar flow to turbulent flow. Hydrodynamic properties such as lift and drag coefficients and roll and yaw moment coefficients are calculated. The parameters are compared with the coefficient values found by experimental methods.FindingsAlthough the modular type UUV has a complex body geometry, the comparative results of the experiments and simulations confirm that the defined model parameters are accurate and close to the actual experimental values. In the proposed k-ε method, the percentage error in the estimation of drag and lifting coefficients is decreased to 4.2% and 8.39%, respectively.Practical implicationsThe model coefficients determined in this study can be used in high-level control simulations which leads to the development of robust real-time controllers for complex-shaped modular UUVs.Originality/valueThe Lucky Fin UUV with 4 degrees of freedom is a specific design and its CAD model is first extracted. Verification of simulation results by experiments is generally less referenced in studies. However, it provides more precise parameter identification of the model. Proposed study offers a simple and low-cost experimental measurement method for verification of the hydrodynamic parameters. The extracted model and coefficients are worthwhile references for the analysis of modular type UUVs.

Drag Coefficient Estimation in Fsi for Pwr Fuel Assembly Bowing

Drag Coefficient Estimation in Fsi for Pwr Fuel Assembly Bowing

Firebrand transport from a novel firebrand generator: numerical simulation of laboratory experiments

Firebrands (often called embers) increase the propagation rate of wildfires and often cause the ignition and destruction of houses. Predicting the motion of firebrands and the ignition of new fires is therefore of significant interest to fire authorities. Numerical models have the potential to accurately predict firebrand transport. The present study focuses on conducting a set of benchmark experiments using a novel firebrand generator, a device that produces controlled and repeatable sets of firebrands, and validating a numerical model for firebrand transport against this set of experiments. The validation is conducted for the transport of non-burning and burning cubiform firebrand particles at two flow speeds. Four generic drag sub-models used to estimate drag coefficients that are suited for a wide variety of firebrand shapes are verified for their applicability to firebrand transport modelling. The four sub-models are found to be good in various degrees at predicting the transport of firebrand particles.

In-Flight Estimation of Drag Parameters and Air Data for Unmanned Aircraft

Identifying the drag parameters of unmanned aerial vehicles (UAVs) is crucial for guaranteeing their aerodynamic efficiency. However, in contrast to commercial aircraft, obtaining accurate UAV drag parameters is challenging because the existing approaches rely on analytical models or require accurate modeling of the engine thrust, which highly depends on time-varying wind conditions. To address this challenge, this paper first proposes a novel in-flight estimation algorithm for the air data (airspeed, angle of attack, and sideslip angle) and drag parameters of UAVs. With this approach, there is no need to compute all of the contributing components for drag, to model the thrust of the UAVs, or to perform complicated wind tunnel testing/computational fluid dynamics analysis to obtain the drag parameter. Instead, the proposed algorithm requires only standard sensors such as inertial measurement units and air data systems during gliding flight. Then, an efficient glide phase detection algorithm for initiating the filter is proposed. Simulation and experimental flight testing of a UAV demonstrate that the proposed algorithm yields accurate zero lift drag coefficient, attitude, and air data estimation results according to thorough validation with newly derived metrics for performance evaluation.

A Scheme for Estimating Time-Varying Wind Stress Drag Coefficient in the Ekman Model with Adjoint Assimilation

In this study, the time-varying wind stress drag coefficient in the Ekman model was inverted by the cubic spline interpolation scheme based on the adjoint method. Twin experiments were carried out to investigate the influences of several factors on inversion results, and the conclusions were (1) the inverted distributions with the cubic spline interpolation scheme were in good agreement with the prescribed distributions of the wind stress drag coefficients, and the cubic spline interpolation scheme was superior to direct inversion by the model scheme and Cressman interpolation scheme; (2) the cubic spline interpolation scheme was more advantageous than the Cressman interpolation scheme even if there is moderate noise in the observations. The cubic spline interpolation scheme was further validated in practical experiments where Ekman currents and wind speed derived from mooring data of ocean station Papa were assimilated. The results demonstrated that the variation of the time-varying wind stress drag coefficient with time was similar to that of wind speed with time, and a more accurate inversion result could be obtained by the cubic spline interpolation scheme employing appropriate independent points. Overall, this study provides a potential way for efficient estimation of time-varying wind stress drag coefficient.

Generalization techniques of neural networks for fluid flow estimation

We demonstrate several techniques to encourage practical uses of neural networks for fluid flow estimation. In the present paper, three perspectives which are remaining challenges for applications of machine learning to fluid dynamics are considered: 1. interpretability of machine-learned results, 2. bulking out of training data, and 3. generalizability of neural networks. For the interpretability, we first demonstrate two methods to observe the internal procedure of neural networks, i.e., visualization of hidden layers and application of gradient-weighted class activation mapping (Grad-CAM), applied to canonical fluid flow estimation problems -- $(1)$ drag coefficient estimation of a cylinder wake and $(2)$ velocity estimation from particle images. It is exemplified that both approaches can successfully tell us evidences of the great capability of machine learning-based estimations. We then utilize some techniques to bulk out training data for super-resolution analysis and temporal prediction for cylinder wake and NOAA sea surface temperature data to demonstrate that sufficient training of neural networks with limited amount of training data can be achieved for fluid flow problems. The generalizability of machine learning model is also discussed by accounting for the perspectives of inter/extrapolation of training data, considering super-resolution of wakes behind two parallel cylinders. We find that various flow patterns generated by complex interaction between two cylinders can be reconstructed well, even for the test configurations regarding the distance factor. The present paper can be a significant step toward practical uses of neural networks for both laminar and turbulent flow problems.

Drag coefficient modeling of heterogeneous connected platooning vehicles via BP neural network and PSO algorithm

The operation of connected platooning vehicles (CPVs) with V2V and V2I techniques has the potential to decrease the overall aerodynamic drag and reduce fuel consumption. One of the key technologies for CPVs is the spacing control which is heavily dependent on the drag force. However, the existing studies on drag coefficient of CPVs seldom pay attention to heterogeneous platoon, and most analysis only consider the effect of vehicle spacing and ignore key information such as cross-sectional area of adjacent vehicles. In addition, most research only focuses on qualitative research, without concluding a quick formula for calculating vehicle drag coefficient in a platoon. In this work, we investigate the modeling and estimation of drag coefficient in terms of the inter-vehicle distances, the platoon configurations, and the cross-sectional area of the adjacent vehicles. We calculate the drag coefficients for ten categories of vehicles with different inter-vehicle distances via numerical analysis in the software FLUENT. According to hundreds of the analysis results, we propose a model to estimate the drag correction factor online. A hybrid algorithm combining the BP neural network (BPNN) and particle swarm optimization (PSO) is employed to optimize the parameters in the model. The model is validated via three kinds of regression evaluation indexes and additional simulation tests with different platooning configurations and different types of vehicles. In terms of the comparison results, the developed model is effective to estimate the drag coefficients of CPVs.

Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization

We focus on a convolutional neural network (CNN), which has recently been utilized for fluid flow analyses, from the perspective on the influence of various operations inside it by considering some canonical regression problems with fluid flow data. We consider two types of CNN-based fluid flow analyses: (1) CNN metamodeling and (2) CNN autoencoder. For the first type of CNN with additional scalar inputs, which is one of the common forms of CNN for fluid flow analysis, we investigate the influence of input placements in the CNN training pipeline. As an example, estimation of drag and lift coefficients of an inclined flat plate and two side-by-side cylinders in laminar flows is considered. For the example of flat plate wake, we use the chord Reynolds number \(\hbox {Re}_\mathrm{c}\) and the angle of attack \(\alpha \) as the additional scalar inputs to provide the information on the complexity of wake. For the wake interaction problem comprising flows over two side-by-side cylinders, the gap ratio and the diameter ratio are utilized as the additional inputs. We find that care should be taken for the placement of additional scalar inputs depending on the problem setting and the complexity of flows that users handle. We then discuss the influence of various parameters and operations on the CNN performance, with the utilization of autoencoder (AE). A two-dimensional decaying homogeneous isotropic turbulence is considered for the demonstration of AE. The results obtained through the AE highly rely on the decaying nature. Investigation on the influence of padding operation at a convolutional layer is also performed. The zero padding shows reasonable ability compared to other methods which account for the boundary conditions assumed in the numerical data. Moreover, the effect of the dimensional reduction/extension methods inside CNN is also examined. The CNN model is robust against the difference in dimension reduction operations, while it is sensitive to the dimensional extension methods. The findings of this paper will help us better design a CNN architecture for practical fluid flow analysis.

Hydrodynamic Modeling and Design of Robotic Fish using Slender Body Theory

Design of bio-inspired robotic fish has been an important area of research for applications such as water quality monitoring, aquatic animal behavioral study and underwater exploration. The modeling of robotic fish requires high dimensional analysis, which increases the design complexity. To address this complexity, approximate models are preferred and frequently used in the design of robotic fishes. The accuracy of these models mainly relies on various hydrodynamic parameter identification of which drag coefficient estimation is highly challenging. This paper utilizes a simple deceleration motion model to estimate the drag coefficient effectively so that the linearization error is reduced. The non-linearity in the deceleration model is simplified and mapped as a linear model to predict the drag coefficient. The estimated drag coefficient is used for dynamic modeling of robotic fish using slender body theory. The dynamic model is validated through experimental setup using accelerometer and visual sensor data. The maximum swimming speed of 0.32 ms-1 is achieved at 1.2 Hz caudal fin oscillations.

Understanding Physical Processes Represented by the Monin\u2013Obukhov Bulk Formula for Momentum Transfer

Physical processes represented by the Monin–Obukhov bulk formula for momentum are investigated with field observations. We discuss important differences between turbulent mixing by the most energetic non-local, large, coherent turbulence eddies and local turbulent mixing as traditionally represented by K-theory (analog to molecular diffusion), especially in consideration of developing surface-layer stratification. The study indicates that the neutral state in a horizontally homogeneous surface layer described in the Monin–Obukhov bulk formula represents a special neutrality regardless of wind speed, for example, the surface layer with no surface heating/cooling. Under this situation, the Monin–Obukhov bulk formula agrees well with observations for heights to at least 30 m. As the surface layer is stratified, stably or unstably, the neutral state is achieved by mechanically generated turbulent mixing through the most energetic non-local coherent eddies. The observed neutral relationship between u_* (the square root of the momentum flux magnitude) and wind speed V at any height is different from that described by the Monin–Obukhov formula except within several metres of the surface. The deviation of the Monin–Obukhov neutral u_*-V linear relation from the observed one increases with height and contributes to the deteriorating performance of the bulk formula with increasing height, which cannot be compensated by stability functions. Based on these analyses, estimation of drag coefficients is discussed as well.

Assessment of Wind and Vegetation Interactions in Constructed Wetlands

Meteorological data from vegetated and un-vegetated wetlands during wet and dry seasons, were collected and analyzed to evaluate the role of wind and vegetation on wetlands’ hydrology. Wind speed diminished by as much as 40%, accompanied by a measurable change in wind directions in the vegetated compared to the open water site. Wind speed and direction means were significantly different (p < 0.001 and <0.01), for vegetated and non-vegetated wetland, respectively. Cattails (Typha sp.) and open water estimates of wind drag coefficients using the log wind profile, were 0.016 and 0.009 for dry season, and 0.012 and 0.005 for wet season, respectively. Wind set up near the wetland outlet was more pronounced at shallow water depth (<20 cm). Measured velocity profile during inflow discharge event with a wind speed of 0.53 ms−1, showed two-layer flows; wind-generated surface water flow opposite to a sub-surface inflow. This opposing surface flow increases hydraulic residence time and improve nutrient uptake. Conversely, wind-generated flows aligned with inflow discharges, accelerates water flow towards the outlet, reduce the duration of water-biotic interactions, and decrease nutrient uptake.

Experimental investigation of aerodynamic characteristics of bat carcasses after collision with a wind turbine

Abstract. A large number of bat fatalities have been reported in wind energy facilities in different regions globally. Wind farm operators are required to monitor bat fatalities by conducting carcass surveys at wind farms. A previous study implemented the ballistics model to characterize the carcass fall zone distributions after a bat is struck by turbine blades. The ballistics model considers the aerodynamic drag force term, which is dependent upon the carcass drag coefficient. The bat carcass drag coefficient is highly uncertain; no measurement of it is available. This paper introduces a methodology for bat carcass drag coefficient estimation. Field investigation at Macksburg wind farm resulted in the discovery of three bat species: the hoary bat (Lasiurus cinereus), eastern red bat (Lasiurus borealis), and evening bat (Nycticeius humeralis). Carcass drop experiments were performed from a dropping platform at finite height, and carcass position time series data were recorded using a high-speed camera. Falling carcasses were subjected to aerodynamic drag and gravitational forces. Carcasses were observed to undergo rotation, often rotating around multiple axes simultaneously, as well as lateral translation. The complex fall dynamics, along with drop from a limited height, prohibit the carcasses from attaining terminal velocity. Under this limitation, the drag coefficient is estimated by fitting a ballistics model to the measured velocity. Multivariable optimization was performed to fit the ballistics model to the measured velocity resulting, in an optimized estimate of the drag coefficient. A sensitivity analysis demonstrated significant variation in the drag coefficient with a small change in initial position, highlighting the chaotic nature of carcass fall dynamics. Based on the limited sample, the bat carcass drag coefficient and terminal velocity were found to be between 0.70–1.23 and 6.63–17.57 m s−1, respectively. The maximum distance carcasses are predicted to fall after impact with a typical utility-scale onshore wind turbine was computed using a 2-D ballistics model. Based on the range of drag coefficients found in this study, hoary and evening bats are estimated to fall within the rotor plane up to a maximum distance of 92 and 62 m, respectively, from the wind turbine tower. The ballistics model of carcasses after being struck by wind turbine blades can be used to obtain fall distributions for bats, guide carcass survey efforts, and correct survey data for limited or unsearched areas.

From numerical prototypes to real models: a progressive study of aerodynamic parameters of nonconventional concrete structures with Computational Fluid Dynamics

Abstract The practical evaluation of aerodynamic coefficients in unconventional concrete structures requires specific studies, which are small-scale models evaluated in wind tunnels. Sophisticated facilities and special sensors are needed, and the tendency is for modern and slender constructions to arise with specific demands on their interaction with the wind. On the other hand, the advances obtained in modern multi-core processors emerge as an alternative for the construction of sophisticated computational models, where the Navier-Stokes differential equations are solved for fluid flow using numerical methods. Computations of this kind require specialized theoretical knowledge, efficient computer programs, and high-performance computers for large-scale calculations. This paper presents recent results involving two real-world applications in concrete structures, where the aerodynamic parameters were estimated with the aid of computational fluid dynamics. Conventional quad-core computers were applied in simulations with the Finite Volume Method and a progressive methodology is presented, highlighting the main aspects of the simulation and allowing its generalization to other types of problems. The results confirm that the proposed methodology is promising in terms of computational cost, drag coefficient estimation and versatility of simulation parameters. These results also indicate that mid-performance computers can be applied for preliminary studies of aerodynamic parameters in design offices.

The dynamics of a subglacial salt wedge

Marine-terminating glaciers, such as those along the coastline of Greenland, often release meltwater into the ocean in the form of subglacial discharge plumes. Though these plumes can dramatically alter the mass loss along the front of a glacier, the conditions surrounding their genesis remain poorly constrained. In particular, little is known about the geometry of subglacial outlets and the extent to which seawater may intrude into them. Here, the latter is addressed by exploring the dynamics of an arrested salt wedge – a steady-state, two-layer flow system where salty water partially intrudes a channel carrying fresh water. Building on existing theory, we formulate a model that predicts the length of a non-entraining salt wedge as a function of the Froude number, the slope of the channel and coefficients for interfacial and wall drag. In conjunction, a series of laboratory experiments were conducted to observe a salt wedge within a rectangular channel. For experiments conducted with laminar flow (Reynolds number Re < 800), good agreement with theoretical predictions are obtained when the drag coefficients are modelled as being inversely proportional to Re. However, for fully turbulent flows on geophysical scales, these drag coefficients are expected to asymptote toward finite values. Adopting reasonable drag coefficient estimates for this flow regime, our theoretical model suggests that typical subglacial channels may permit seawater intrusions of the order of several kilometres. While crude, these results indicate that the ocean has a strong tendency to penetrate subglacial channels and potentially undercut the face of marine-terminating glaciers.

Kinematic and Dynamic Scaling of Copepod Swimming

Calanoid copepods have two swimming gaits, namely cruise swimming that is propelled by the beating of the cephalic feeding appendages and short-lasting jumps that are propelled by the power strokes of the four or five pairs of thoracal swimming legs. The latter may be 100 times faster than the former, and the required forces and power production are consequently much larger. Here, we estimated the magnitude and size scaling of swimming speed, leg beat frequency, forces, power requirements, and energetics of these two propulsion modes. We used data from the literature together with new data to estimate forces by two different approaches in 37 species of calanoid copepods: the direct measurement of forces produced by copepods attached to a tensiometer and the indirect estimation of forces from swimming speed or acceleration in combination with experimentally estimated drag coefficients. Depending on the approach, we found that the propulsive forces, both for cruise swimming and escape jumps, scaled with prosome length (L) to a power between 2 and 3. We further found that power requirements scales for both type of swimming as L3. Finally, we found that the cost of transportation (i.e., calories per unit body mass and distance transported) was higher for swimming-by-jumping than for cruise swimming by a factor of 7 for large copepods but only a factor of 3 for small ones. This may explain why only small cyclopoid copepods can afford this hydrodynamically stealthy transportation mode as their routine, while large copepods are cruise swimmers.

Numerical Investigation of Fluid Forces Acting on a Confined Cylinder with Obstacle Subjected to Axial Flow

This paper focuses on fluid forces acting on a confined cylinder subjected to axial flow in application to fuel assembly dynamic behavior. From the literature, it is difficult to estimate the damping induced by the flow. Therefore, it is proposed to study numerically the damping fluid forces on a cylinder for various parameters. It has been observed that it increases with the smaller confinement and with the presence of an obstacle and decreases when the Reynolds number increases. Larger values correspond to a greater contribution of pressure forces. Dynamic simulations are compared to the steady ones and give different values, but the order of magnitude and general trend remain the same. Therefore, steady simulations are suitable to have a rough estimation of drag coefficients in dynamics.