Estimation Of Drag Coefficient Research Articles

Experimental and numerical estimation of wall effect and drag coefficient on hollow frustum settling in annular and non-annular channels

The wall effect and drag coefficient of hollow frustum particles (Tapered hollow cylinders) settling in cylindrical non-annular and annular channels were experimentally investigated. The terminal velocity, V, of the hollow frustum particles is measured by considering flow channels of different diameters filled with various working fluids. The present work deals with the study of drag coefficient for a range of 0.12 ≤ deq/D ≤ 0.44, 0.22 ≤ deq/L ≤ 0.43, and 0.22 ≤ di/do ≤ 0.77. The hollow frustum particle's terminal velocity, V, decreases with increased blockage ratios and di/do ratios. The experiment revealed that the frustum attains a steady-state orientation. The hollow frustum particle is affected by the wall's retardation, which is described by the wall factor. The hollow frustum particle's wall factor (f) depends on the Reynolds number (Re) and blockage ratio. The wall factor increases with increasing Reynolds number up to a certain point called critical Reynolds number, Rec; after that, the wall factor remains constant with increasing Reynolds number. The hollow frustum particles settling in the cylindrical non-annular channel were more subjected to wall effects than those in the annular channel. Simple correlations between the wall factor and the blockage ratio are developed. The current research also focuses on the relationship between Reynolds number (Re) and drag coefficient (CD). The wall effect is considered while estimating the hollow frustum particle's drag coefficient (CD). The numerical estimate of the experimental drag coefficient is made using Ansys- fluent 18.1, and the experimental and numerical results are consistent with each other.

A new model for quantifying wave damping by vegetation in combined wave–current flow

The propagation of waves in nearshore areas is often accompanied by the presence of tidal currents, which have an impact on the dissipation of wave energy in coastal vegetation regions. However, the existing models generally do not consider the in-canopy velocity, resulting incomplete energy dissipation terms and thus inaccurate estimation of drag coefficients. In this study, we developed a new analytical model to incorporate vertical velocity distribution and full energy dissipation terms in energy flux balance equation. This model is applied to interpret the data from a new flume experiment of vegetation in combined wave-current flows. New CD-Re relationships were derived by applying the new model. Additionally, we extracted the wave energy passage time and decay rate from both the experimental data and the model results, which shows that the wave energy passage time of opposing currents is up to 75% longer than that of following currents. This disparity in passage time is the primary factor contributing to the greater wave dissipation observed in the cases of opposing currents. This study offers novel insights into the process of wave dissipation, and can potentially improve our capability in predicting wave dissipation.

New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data

Abstract. The effect that sea ice topography has on the momentum transfer between ice and atmosphere is not fully quantified due to the vast extent of the Arctic and limitations of current measurement techniques. Here we present a method to estimate pan-Arctic momentum transfer via a parameterization that links sea ice–atmosphere form drag coefficients with surface feature height and spacing. We measure these sea ice surface feature parameters using the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Though ICESat-2 is unable to resolve as well as airborne surveys, it has a higher along-track spatial resolution than other contemporary altimeter satellites. As some narrow obstacles are effectively smoothed out by the ICESat-2 ATL07 spatial resolution, we use near-coincident high-resolution Airborne Topographic Mapper (ATM) elevation data from NASA's Operation IceBridge (OIB) mission to scale up the regional ICESat-2 drag estimates. By also incorporating drag due to open water, floe edges and sea ice skin drag, we produced a time series of average total pan-Arctic neutral atmospheric drag coefficient estimates from November 2018 to May 2022. Here we have observed its temporal evolution to be unique and not directly tied to sea ice extent. By also mapping 3-month aggregates for the years 2019, 2020 and 2021 for better regional analysis, we found the thick multiyear ice area directly north of the Canadian Archipelago and Greenland to be consistently above 2.0×10-3, while most of the multiyear ice portion of the Arctic is typically around ∼1.5×10-3.

Estimation of Drag Coefficient From Radar Tracked Trajectory Data of an Artillery Shell

An attempt is made to estimate aerodynamic parameters using radar tracked coordinates of a free flight artillery shell. The present paper proposes schemes using maximum likelihood method to estimate average values of drag coefficient and its functional dependence on Mach number by processing trajectory data of an artillery shell. The proposed schemes were validated by applying these on simulated trajectory data of a routinely used artillery shell. Based on the results obtained, it can be concluded that both schemes could advantageously be used to estimate drag coefficient using radar tracked trajectory data of an artillery shell.

Numerical Modeling of the Flow around a Cylinder using FEATool Multiphysics

The current study examines the numerical analysis of the laminar flow around a cylinder at various Reynolds numbers (0.1, 1.1, 20, 26, 50, 100, and 195). The research found that a steady state can exist for Reynolds number values of 0.1, 1.1, 20, and 26. However, the flow pattern becomes unstable at Reynolds numbers 50, 100, and 195, leading to the development of the Kármán vortex street. The FEATool Multiphysics software in MATLAB (R2019b) was utilized to numerically solve the steady 2D Navier-Stokes equation. The study compared the estimated drag coefficient to previous experimental and analytical studies in Abaqus/CFD. The lift and pressure coefficients were also calculated, and their results were found to be in strong agreement with earlier investigations in terms of predicting pressure and velocity distribution. The analysis provided insight into how the flow field changes with increasing Reynolds numbers.

Nondestructive Estimation of Hazelnut (Corylus avellana L.) Terminal Velocity and Drag Coefficient Based on Some Fruit Physical Properties Using Machine Learning Algorithms.

Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential that an understanding of hazelnuts' aerodynamic properties, i.e., terminal velocity and drag coefficient, is acquired. In this study, the moisture, mass, density, projected area, surface area, and geometric diameter were used as independent variables in the data set, and the dependent variables terminal velocity and drag coefficient estimation were determined. In this study, logistic regression (LR), support vector regression (SVR), and artificial neural networks (ANNs) were used based on machine learning methods. When the results were evaluated according to R2 (determination coefficient), MSE (mean squared error), and MAE (mean absolute error) metrics, it was seen that the most successful models were the ANN, SVR, and LR, respectively. According to the R2 metric, the ANN method achieved 91.5% for the terminal velocity of hazelnuts and 85.9% for the drag coefficient of hazelnuts. Using the independent variables in the study, it was seen that the terminal velocity and drag coefficient value of hazelnuts could be successfully estimated.

Numerical investigations into the influence of geometric configurations on hydrodynamic characteristics of torpedo anchors

Torpedo anchors are an innovative and cost-effective technology in marine foundation engineering; however, there is a lack of systematic and comprehensive studies on the influence of torpedo anchor geometry on its hydrodynamic characteristics, especially the effect of anchor fin configuration on the hydrodynamic characteristics is rarely reported in the existing literature. Therefore, this study investigates the influence of geometric characteristics of both finless and finned torpedo anchors on their terminal velocity, drag coefficient and installation directional stability in water through CFD numerical analysis in a systematical manner. The considered geometric characteristics include the center of gravity position, shape and angle of anchor tip, shaft and fin aspect ratio, fin number, fin thickness, fin shape, fin position and fin area. Based on the obtained numerical results, some practical design recommendations and impact weighting charts of different anchor geometric factors are provided, which enables a quick qualitative and quantitative assessment of torpedo anchors. In addition, a simple weight-based approach for estimation of terminal velocity and drag coefficient of torpedo anchors considering multiple geometric configuration factors is proposed, which may provide some reference and scientific guidance for experimental and engineering design of torpedo anchors.

Development of analytical method of estimation of drag coefficient for flexible structures

Flexible structures used in devices, such as micro/nano resonators, sensors, etc. are subjected to fluid forces. Estimation of damping coefficient due to the fluid forces in flexible structures is of utmost importance for the designs of these structures. In existing literatures, empirical correlation for the estimation of damping coefficient are available for the non-flexible structures only. Therefore, development of, preferably simple, analytical method of estimation of damping coefficient in flexible structures subjected to various mode shapes. In this work we attempt to develop such a model from the first principle. The results of the model are validated with the data available for non-flexible structure. Analysis of three mode shapes is performed using the analytical model and the results are compared with the numerical data. Computations are conducted using the finite difference method-based IB2d fluid–structure interaction solver, which solves Navier-Stokes equation for fluid-region using the Eulerian and an equation of motion for solid-region using the Lagrangian approach. A connectivity between fluid and structure is made through a forcing function which is a source term in the momentum equation. From the numerical simulations the average damping coefficients respectively, for first, second and third mode shape are found to be 0.79×10-3, 1.49×10-3, and 2.01×10-3. It is concluded that, with the given flow velocity (low Re), reasonable predictions of damping coefficients can be made using the proposed analytical model for the flexible structures.

Constrained control for a class of vessel operations: Positioning under environmental and shielding effects

This paper investigates Dynamic Positioning (DP) of an vessel next to a larger vessel subject to unknown wind and wave shielding effects in time-varying ocean environments. For safe operations, the vessels need to maintain a distance from each other which can be formulated as constrained control. Under certain conditions, the larger vessel shields the smaller vessel, and when the conditions change or due to the change in heading, the shielding reduces. For the DP system, in order to detect the shielding effect, and monitor the variation of environmental forcing on the vessel, an environment condition detector is designed based on low-frequency force learning and adaptive neural network. In the detector, estimates of wind drag coefficients are used to determine whether or not the shielding effect is present when upper thresholds are crossed. In this case, the neural network is activated in the detector and control when the small vessel moves out from the shadow of the large vessel. Prescribed performance control is proposed to meet the side-by-side safe constraint leveraging the H∞ approach to guarantee the vessel can keep its position against the exogenous disturbance. Numerical simulations demonstrate the effectiveness of the proposed design.

Drag coefficient estimation in FSI for PWR fuel assembly bowing

Assembly bowing in a PWR core can be a serious problem for managing the power during normal operation or for periodical maintenance. One of the causes of this phenomenon is the transverse flow in the core, generated by the non-uniformity of the axial flow. Modelling the mechanical behaviour of a PWR core is made difficult by the complex geometry and the numerous friction points that lead to non-linearities. A simple but efficient way to deal with these issues is the porous medium model proposed by Ricciardi et al. (2009). In this model, the equations used at the fluid–structure interface require empirical parameters such as the added mass, or axial and normal drag coefficients. Using a new experimental setup at CEA, Eudore, which hosts 3 half-scale fuel assemblies in a line, the forces acting on the assembly by a non-uniform flow profile were measured. An analytical model to retrieve the normal drag coefficient was then proposed. This coefficient is then used in FSCORE, a numerical software based on the porous medium approach. The experimental and numerical results are presented and show good agreement.

Estimation of a battery electric vehicle output power and remaining driving range under subfreezing conditions

This manuscript proposes a hybrid machine learning approach to estimate the battery output power and remaining driving range in a battery electric vehicle (BEV). These two metrics are among the most significant factors in the market penetration of BEVs, specifically in countries with harsh winters. The proposed hybrid method comprises a recurrent dynamic network, nonlinear autoregressive with exogenous inputs (NARX), and a recursive algorithm, Kalman filter (KF). The NARX neural network utilizes the information from the weather, the vehicle's speed, and road conditions to predict the battery output power, and the remaining driving range. KF, unlike the other similar studies, is employed to account for the dynamic changes in different road conditions by providing the vehicle rolling resistance and aerodynamic drag coefficient estimates to the network. To validate the performance of the proposed method, several driving tests under different ambient conditions are conducted using a real BEV (Kia Soul 2017), and the performance of the suggested approach is compared with a conventional model-based method. The comparison between the measured battery output power and the estimated one shows that the proposed hybrid method provides more accurate estimates than a model-based approach with over 50 % of percentage change in terms of root mean squared error. Furthermore, through a systemic network optimization, it is shown that the data from two trips (out of 14 trips) are sufficient to successfully predict the real-time battery output power, energy consumption, and the remaining driving range in different weather conditions, including winter. Therefore, the proposed NARX network is particularly suitable to circumvent the range anxiety issues and hence predict the remaining range better than the traditional vehicle dynamics-based approach.

Thermal analysis of flowing stream in partially heated double forward-facing step by using artificial neural network

The regulators for thermal energy transfer, performances of heat exchangers, turbine blades subject to cooling structure, and energy storage procedures claim the use of a heated fluid with partially heated circular obstructions rooted in confined domains. Owing to such importance we consider a partially heated double forward-facing step (DFFS). To be more specific, from the inlet of DFFS, the viscous stream flows in parabolic form and the Neumann condition is implemented at the outlet. At each wall, no slip is incorporated. The mathematical formulation is constructed to narrate the flow field. The translation of the centers of mounted heated obstructions is considered in three separate situations. For every event, the strength of the Nusselt number is debated numerically. For all cases, the drag coefficient for partially heated obstruction is found a decreasing function of the Reynolds number. Besides this, for better estimation of Drag Coefficient (DC) and Lift Coefficient (LC), an artificial neural network (ANN) model with multilayer perceptron (MLP) is developed. MoD values shows that the error rates of the ANN model are very low. The findings show that the constructed ANN model can accurately predict DC and LC values with very low error rates.

Drag force-internal volume relationship for underwater gliders and drag coefficient estimation using machine learning

The relationship between drag force and internal volume of torpedo shaped underwater glider hulls has been investigated using an ensemble analysis method. Basis dimensions of 1.50 m for length and 0.20 m for diameter are selected by considering dimensions of commercial and academic gliders. 9 different forms are used for analyses in two groups (constant length and constant diameter). Nose and back sections are kept constant for all forms. The forms are analyzed in 4 different velocities and 6 different angles of attacks using the open source CFD (Computational Fluid Dynamics) software OpenFOAM. The ensemble resulted in 216 simulations. Simulation results are then used to estimate volumetric drag coefficient values for streamlined body of revolution hull forms under angle of attack. Multivariate linear regression machine learning method was used for the estimations and generation of empirical relation. The results show that volume increase effects gliders much more when angle of attack and glider velocity increases. However, for glider with velocities ranging from 0.3 to 0.6 m/s the increase in the volume does not contribute to drag force as much. Resulting empirical formula gives accurate estimation of drag coefficients for bodies of revolution experiencing 0–10 degrees of attack angle.

A Lagrangian roughness model integrated with the vortex method for drag coefficient estimation and flow control investigations around circular cylinder for a wide range of Reynolds numbers

A Lagrangian roughness model integrated with the vortex method for drag coefficient estimation and flow control investigations around circular cylinder for a wide range of Reynolds numbers

Drag Coefficient and Its Sea State Dependence under Tropical Cyclones

Abstract The drag coefficient under tropical cyclones and its dependence on sea states are investigated by combining upper-ocean current observations [using electromagnetic autonomous profiling explorer (EM-APEX) floats deployed under five tropical cyclones] and a coupled ocean–wave (Modular Ocean Model 6–WAVEWATCH III) model. The estimated drag coefficient averaged over all storms is around 2–3 × 10−3 for wind speeds of 25–55 m s−1. While the drag coefficient weakly depends on wind speed in this wind speed range, it shows stronger dependence on sea states. In particular, it is significantly reduced when the misalignment angle between the dominant wave direction and the wind direction exceeds about 45°, a feature that is underestimated by current models of sea state–dependent drag coefficient. Since the misaligned swell is more common in the far front and in the left-front quadrant of the storm (in the Northern Hemisphere), the drag coefficient also tends to be lower in these areas and shows a distinct spatial distribution. Our results therefore support ongoing efforts to develop and implement sea state–dependent parameterizations of the drag coefficient in tropical cyclone conditions.

Study of the bubble motion in a centrifugal rotor based on visualization in a rotating frame of reference

Single bubbles flowing in a centrifugal rotor were investigated through numerical and experimental methods, where the rotating speed, flow coefficient, and bubble diameter were varied to analyze their influence on the bubbles trajectories and drag coefficient. Results show that bubble trajectory is influenced by the flow rate and rotating speed, while drag coefficient curves are influenced by the bubble diameter and rotating speed. Finally, a comparison of the estimated drag coefficient against several drag coefficient correlations is shown. It was noted a good agreement with a model based on the Reynolds, Weber, and Eötvos numbers. Additionally, it was found a dependence between the drag coefficient and a dimensionless number, which considers the rotational acceleration of the rotor.

<i>Corrigendum to</i>: 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.

Wake stabilization behind a cylinder by secondary flow over the leeward surface

Porous coating and blowing jets are both effective flow control methods for a bluff body. In the present study, we conducted wind tunnel experiments to investigate the combined control effects on a circular cylinder. The flow control was achieved by active steady blowing flows through the structured porous surface on the leeward side of the cylinder. The Reynolds number Re in the experiments, based on the cylinder outer diameter, was 1.0×104. The control effects were evaluated by a non-dimensional blowing momentum coefficient Cμ, which was determined by various blowing mass flow rates, incoming wind speed, and the geometry of the porous surface. Reduced-order models, including proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), were employed to analyze the wake stabilization effects of the secondary jet flows. We found that, under the control of secondary flows ejected from the porous region of the cylinder, POD modal characteristics in the global flow wake were changed; temporal and spatial properties of DMD transformed; frequency and mode of the vortex shedding process shifted; statistical turbulent flow characteristics ameliorated; and the estimated drag coefficients restrained. Experimental results in the present study demonstrated that the secondary flow ejected from the structured porous surface and the resultant small-scale vortices could stabilize the cylinder wake with proper Cμ values.

Estimation of drag coefficient of emergent and submerged vegetation patches with various densities and arrangements in open channel flow

ABSTRACT A large proportion of flow resistance can be due to vegetation in open-channel flows. Classic methods are unable to estimate flow resistance in the presence of vegetation. In this study, drag coefficient was estimated as a parameter to represent the flow resistance in emergent and submerged vegetation patches with different densities. Emergent vegetation was simulated by wooden cylinders 8 mm in diameter and 0.35 m high. Four emergent patches with different densities, arrangements, tandem, random, and circular, were examined. Also, submerged vegetation was simulated by nails 4 mm in diameter and 4.5 cm in height. The phenomenon of dip occurred throughout the dense patches because of secondary flow and the drag force generated by vegetation. Four methods were selected to estimate the drag coefficient and the balance approach-blockage factor method was used as the main method to estimate the patch drag coefficient. Results showed that velocity profiles were S-shaped and the drag coefficient of random vegetated patches was greater than the coefficient of tandem patches. The drag coefficient revealed a direct relation with vegetation density. In all patches, the initial rows had the highest contribution to flow resistance, so the drag coefficient decreased through vegetation patches.

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%.