Reduction In Drag Coefficient Research Articles

Efficient aerodynamic shape optimization using transfer learning based multi-fidelity deep neural network

Computational efficiency and precision pose a classic contradiction in aerodynamic shape optimization. To address this challenge, this study introduces an effective optimization framework based on multi-fidelity fully connected neural network (MFFCN). The framework utilizes transfer learning (TL) to train a multi-fidelity surrogate model that establishes direct mappings between geometric configuration parameters and aerodynamic performance by adaptively capturing linear or nonlinear relationships concealed between high-fidelity (HF) and low-fidelity (LF) information. The HF and LF data are derived from fine and coarse grids, respectively, evaluated using the same computational fluid dynamics (CFD) model. The MFFCN-TL framework is applied to optimize the National Advisory Committee for Aeronautics 0012 (NACA0012) airfoil (12 design variables) and the Office National d'Études et de Recherches Aérospatiales M6 (ONERA M6) wing (50 design variables). Simulation results demonstrate that the NACA0012 airfoil achieves a 69.47% enhancement in lift–drag ratio in 1.069 s, compared to a 12.76% gain over 24.8 h in single-fidelity CFD-based optimization. The ONERA M6 wing achieves a 24.66% reduction in drag coefficient in 694 ms compared to 18.37% over 237.3 h in the CFD model. Statistical results show that the MFFCN-TL framework can reduce optimization cost by more than 90% compared to the single-fidelity CFD-based model. These findings suggest that the MFFCN-TL framework significantly enhances optimization efficiency and provides superior feasible solutions over single-fidelity methods.

Computational Analysis of Flow Around Two Wall-Mounted Trapezoidal Bluff Bodies Arranged in Tandem Position

Abstract Flow around two identical wall-mounted trapezoidal bluff bodies, arranged in tandem, is numerically investigated at a Reynolds number of 750,000. The investigation employs Reynolds-averaged Navier–Stokes (RANS) equations and the k–ω SST turbulence model. The effect due to the change of the angular orientation of the inclined faces (α) of this bluff body and the pitch distance (L/D) on hydrodynamic quantities and turbulence quantities is investigated. Furthermore, the drag coefficient and Strouhal number have also been evaluated to understand the vortex shedding and flow pattern-related phenomena. For d and intermediate types of bluff bodies, the streamwise mean velocity, cross-stream mean velocity, turbulence kinetic energy, and recirculation length decrease with the increase of α or L/D. Significant changes are also observed in the case of Strouhal number. Reduction in drag coefficient and recirculation length is observed with increased L/D at a constant α for d-type bluff bodies. The change of α and L/D also creates the formation of a periodic von Kármán vortex street at downstream of the second bluff body in the case of L/D = 7, making the flows more complex and unstable. The maximum size of the recirculation bubble occurs in the case of L/D = 10 at α = 30 deg. The investigation provides valuable insight into the complex dynamics of tandem configurations of wall-mounted bluff bodies.

Active control of transonic airfoil flutter using synthetic jets through deep reinforcement learning

This paper presents a novel framework for the active control of transonic airfoil flutter using synthetic jets through deep reinforcement learning (DRL). The research, conducted in a wide range of Mach numbers and flutter velocities, involves an elastically mounted airfoil with two degrees of freedom of pitching and plunging oscillations, subjected to transonic flow conditions at varying Mach numbers. Synthetic jets with zero-mass flux are strategically placed on the airfoil's upper and lower surfaces. This fluid–structure interaction (FSI) problem is treated as the learning environment and is addressed by using the arbitrary Lagrangian–Eulerian lattice Boltzmann flux solver (ALE-LBFS) coupled with a structural solver on dynamic meshes. DRL strategies with proximal policy optimization agents are introduced and trained, based on the velocities probed around the airfoil and the dynamic responses of the structure. The results demonstrate that the pitching and plunging motions of the airfoil in the limited cycle oscillation (LCO) can be effectively alleviated across an extended range of Mach numbers and critical flutter velocities beyond the initial training conditions for control onset. Furthermore, the aerodynamic performance of the airfoil is also enhanced, with an increase in lift coefficient and a reduction in drag coefficient. Even in previously unseen environments with higher flutter velocities, the present strategy is achievable satisfactory control results, including an extended flutter boundary and a reduction in the transonic dip phenomenon. This work underscores the potential of DRL in addressing complex flow control challenges and highlights its potential to expedite the application of DRL in transonic flutter control for aeronautical applications.

Effects of combined-type wind barriers on the aerodynamic characteristics of train–bridge system for a long-span suspension bridge

The development tendency of lightweight and higher-speed regarding trains goes against the safety and comfort requirement of high-speed trains, especially in the scenario of the train running on bridges within crosswind environments. Wind barriers have been proven to be an effective measure to guarantee the train's operation security. In this paper, a new type of wind barrier consisting of the fence-type wind barrier form and open hole-type wind barrier form has been proposed. Then, a series of wind tunnel tests about the large-scale section model of a large-span suspension bridge installed with different wind barriers have been conducted. The effects of three different forms of wind barrier including fence-type, open hole-type, and combined-type on the aerodynamic characteristics of the train-bridge system have been analyzed. The windproofing effectiveness and the protection mechanism of the combined-type wind barrier on the train-bridge system have been revealed through the measured aerodynamic characteristics. The results show that the installation of open hole-type, fence-type, and combined-type wind barriers at a height of 4 m and a ventilation rate of 19% yields a maximum reduction in the train drag coefficient of 67.70%, 72.92%, and 97.21%, respectively. Moreover, the combined-type wind barrier can improve the train-bridge system's aerodynamic performance and trains' running safety on bridges when comparing with the open hole-type and fence-type wind barriers. The proposed combined-type wind barrier can provide a more efficiency and practical way for the high-speed train's running safety on the bridge.

Deep reinforcement learning-based active flow control of an elliptical cylinder: Transitioning from an elliptical cylinder to a circular cylinder and a flat plate

We study the adaptability of deep reinforcement learning (DRL)-based active flow control (AFC) technology for bluff body flows with complex geometries. It is extended from a cylinder with an aspect ratio Ar = 1 to a flat elliptical cylinder with Ar = 2, slender elliptical cylinders with Ar less than 1, and a flat plate with Ar = 0. We utilize the Proximal Policy Optimization (PPO) algorithm to precisely control the mass flow rates of synthetic jets located on the upper and lower surfaces of a cylinder to achieve reduction in drag, minimization of lift, and suppression of vortex shedding. Our research findings indicate that, for elliptical cylinders with Ar between 1.75 and 0.75, the reduction in drag coefficient ranges from 0.9% to 15.7%, and the reduction in lift coefficient ranges from 95.2% to 99.7%. The DRL-based control strategy not only significantly reduces lift and drag, but also completely suppresses vortex shedding while using less than 1% of external excitation energy, demonstrating its efficiency and energy-saving capabilities. Additionally, for Ar from 0.5 to 0, the reduction in drag coefficient ranges from 26.9% to 43.6%, and the reduction in lift coefficient from 50.2% to 68.0%. This reflects the control strategy's significant reduction in both drag and lift coefficients, while also alleviating vortex shedding. The interaction and nonlinear development of vortices in the wake of elliptical cylinders lead to complex flow instability, and DRL-based AFC technology shows adaptability and potential in addressing flow control problems for this type of bluff body flow.

Optimal configuration of passive flow control devices to reduce the aerodynamics drag of a tractor-trailer model

Aerodynamic drag plays an important role in heavy vehicles, especially when traveling at high speeds. In this context, the application of passive flow control devices emerges as a potential approach to reduce the aerodynamic drag of vehicles, leading to improvements in fuel efficiency and environmental pollution. Consequently, this study focuses on using cab side extenders and rear flaps to reduce the aerodynamic drag of a 1:8 small-scale simplified tractor-trailer model. The main parameters of this device configuration, including the length LR and deflection angle θG of the cab side extenders, the length LR and deflection angle θR of the rear flaps, are optimized to minimize the model aerodynamic drag coefficient. Numerical studies are performed using the Computational Fluid Dynamics method, with the steady k-ω SST and the unsteady DES turbulence model. The optimal solution is implemented based on a 30-level Latin Hypercube design of the experiment and a full quadratic regression response surface method. The obtained results show that a maximum reduction in the aerodynamic drag coefficient of the simplified tractor-trailer model by 20.8 % is achieved with an optimized configuration of control devices. The mechanism of aerodynamic drag reduction is also evaluated based on the analysis of flow fields around the vehicle model.

Improving the energy efficiency and carbon dioxide reduction of a long-haul bus through aerodynamic design optimization

<p>Exhaustion of fossil fuel resources, inconsistent fuel costs and the difficulty of adopting electric vehicle technology in commercial vehicles support the idea that there is an opportunity for research in public transit regarding the correlation between energy efficiency and aerodynamic drag. The turbulent external airflow over a bus at high speeds impacts acceleration, speed, and fuel economy. The fundamental bus's design is intended to carry enough passengers for a reasonable run. Envisaging the factors influencing aerodynamic drag is defiant due to the convoluted relationship between the moving bus and the air. Consequently, a comprehensive numerical and experimental exploration is executed on the bodywork of a bus to improve its aerodynamic efficiency. The aerodynamic drag is directly proportional to the variations in the air density, frontal area, freestream velocity and the drag coefficient. Minimal design reforms are performed on a distinctive long-haul bus. The exertion aims to minimize the drag coefficient, thereby improving the flow characteristics of the bus's bodywork. Through the shape optimization of the bus's bodywork, the modified design has attained a forty-five percent reduction in the drag coefficient. This substantial reduction in drag coefficient directly impacts the reduction of drag force, energy efficiency improvement, and carbon emissions reduction.</p>

Use of machine learning to optimize actuator configuration on an airfoil

Machine learning was used to optimize the geometric arrangement of a pair of unsteady actuators on flow separation over an efficient low Reynolds number airfoil in post-tall conditions. Large eddy simulation was used to validate the results. Two actuators: one with blowing and the other with suction openings were installed on the top surface of an airfoil at low Reynolds number of 60,000. An SD7003 airfoil at a post stall angle of attack of 13° was utilized. The boundary layer flow of the top surface was manipulated by the actuators to control flow separation. The influence of several actuator parameters: frequency, energy input, opening area, location and orientation angle were considered in an optimization of the dual actuator configuration. A genetic algorithm-based optimization was implemented to find the most effective configuration of this coupling. Since the optimization process is time-consuming, machine learning was used to train artificial neural networks to be coupled with genetic algorithm to reduce the computational cost. The artificial neural networks and their training was constantly upgraded during the optimization cycle. Results for the optimal case indicated an increase in lift coefficient and the objective function in comparison to uncontrolled case by factors of 1.88 and 3.33 respectively. We also found a reduction in drag coefficient. It was also found that using a pair of actuators was more efficient than using a single actuator.

Passive flow-field control using dimples for improved aerodynamic flow over a wing

This study explores the efficacy of dimples in influencing the aerodynamic performance of a straight rectangular wing. Computational Fluid Dynamics based numerical simulations were performed to model turbulent flow and quantify the forces exerted on the wing. The k-ω Shear-Stress Transport turbulence model was chosen to solve the underlying equations. To ascertain reliability, the results of numerical simulations were compared with both experimental and simulation results of the previous studies. The impact of various dimple configurations, placed at 15%, 50% and 85% of the chord length, on the aerodynamic performance of the wing was investigated. The evaluation involved analyzing the drag coefficient (CD), lift coefficient (CL), lift-to-drag (L/D) ratio, streamlines and the flow field around wing in both chordwise and spanwise directions. The findings indicated that a wing with a dimpled surface could yield a reduced drag coefficient of up to 6.6% compared to the unmodified wing. This reduction is attributed to the dimples ability to sustain attached airflow and delay flow separation. The results demonstrated negligible deviation in the lift coefficient with the incorporation of dimples. The incorporation of dimples on the wing surface has been demonstrated to enhance the aerodynamic performance of lifting surfaces.

Effect of wing morphing on stability and energy harvesting in albatross dynamic soaring

Dynamic soaring, which harvests energy from the wind, can enhance Unmanned Aerial Vehicles’ (UAVs’) range and endurance. However, energy harvesting efficiency issues hinder UAV applications, which can be addressed by wing morphing. Therefore, this study investigates the influence of albatross wing morphing during dynamic soaring. By constructing a parametric model, the shape of the albatross wing can be modeled and achieve morphing based on joints. From the video data, this paper summarizes the typical wing morphing patterns of the albatross and notices that changes primarily occur during the leeward descent phase. This paper first analyzes the aerodynamic performance of different wing morphing patterns and finds that the drag coefficient can be reduced by 7.75% with a suitable morphing pattern. This paper also explores the drag coefficient reduction mechanism and finds that downwash airflow decreases by 30.32% after wingtip anhedral. Interestingly, the lift-to-drag ratio shows minimal variation under different morphing patterns, within 2%. From the stability perspective, this study finds that the neutral point position changes after morphing. The maximum longitudinal static margin change is 4.9%, enhancing longitudinal stability by increasing the restorative moment arm. The lateral neutral point is 4.87% closer to the center of gravity, decreasing the roll and yaw moments. It can be observed that wingtip anhedral significantly increases the stability of the albatross. Moreover, a flight simulation is carried out to study the morphing influence on trajectory and energy harvesting. The results show that maximum energy gained is improved by 47.99%, and endurance is increased by 13.05%. The results also indicate that the effects of wing morphing are global rather than limited to the phase of morphing occurrence. Finally, based on the results, this paper proposes wing morphing regularity about the wingtip for UAVs. Wingtip bends downward can significantly increase the UAVs’ stability and reduce drag, but the overall trajectory needs to be reconsidered after introducing wing morphing.

Research on Energy Loss of Optimization of Inducer–Impeller Axial Fit Dimensions Based on Wave-Piercing Theory

With the development of modern fluid machinery, the energy density of pumps is gradually being improved, and at the same time, higher demands are being placed on the cavitation performance, hence the introduction of the inducer and centrifugal impeller to form a dynamic–dynamic series structure. However, there are strict constraints on the axial size of pumps in fields such as firefighting and aerospace. The traditional empirical formula no longer satisfies the need to fit the axial dimensions between the induced wheel and the impeller at high velocities. Therefore, based on the wave-piercing theory, the drag reduction coefficient is introduced to explore the optimal axial fit size from the perspective of energy characteristics. This paper focuses on the influence of the inducer’s wake on the energy characteristics of downstream impellers, and conducts the following research: by adjusting the axial matching dimensions between the upstream inducer and the centrifugal impeller in the initial model, ten sets of axial distance models with matching dimensions of KD are designed, and the drag reduction coefficient is embedded to determine the optimal axial distance. The results show that the optimal axial distance is 0.2D, which is far lower than the axial distance value of 0.42D obtained from the traditional empirical formula for axial matching dimensions. Meanwhile, this paper uses tangential velocity, the inlet flow angle of the impeller, entropy production theory, and other indicators to analyze the internal energy loss of the high-speed vehicular fire pumps one by one. All of them confirm that the impeller in the high-speed vehicular fire pump has the lowest energy loss and optimal performance at an axial distance of 0.2D. Specifically, at this axial distance, the head can reach 259 m, and the hydraulic efficiency is as high as 83.62%. Thus, the feasibility of determining the axial placement of the impeller using the drag coefficient is validated. This research provides new insights into determining the axial coordination dimensions between the inducer and the impeller.

Multi-fidelity deep learning for aerodynamic shape optimization using convolutional neural network

Aerodynamic shape design is essential for improving aircraft performance and efficiency. First, this study introduces a data-driven optimization framework utilizing a multi-fidelity convolutional neural network (MFCNN) for aerodynamic shape optimization. To achieve better optimization results with reduced computational cost, the framework dynamically incorporates new data in each optimization cycle. Specifically, it constantly involves the optimal solution from previous cycle as a new high-fidelity sample and employs a low-fidelity infilling strategy that maximizes the minimum Euclidean distance for selecting new low-fidelity samples. Moreover, a standard synthetic benchmark is used to elaborate the procedure of optimization and show the capability and effectiveness of the framework. Finally, the framework is applied to two aerodynamic shape optimization problems: maximizing the lift-to-drag ratio for the Royal Aircraft Establishment 2822 (RAE2822) airfoils and minimizing the cruise drag coefficient for the three-dimensional (3D) drooped and scarfed non-axisymmetric nacelles. The framework increases the lift-to-drag ratio by 51.21% over the baseline and achieves an 18.79% reduction in the cruise drag coefficient for nacelle optimization, outperforming traditional multi-fidelity deep neural network optimization framework. Sufficiently utilizing the implicit relations between different fidelity levels of data through defined local perceptual fields and convolution, our MFCNN-based optimization framework signifies a step forward in the efficiency and accuracy of aerodynamic shape optimization.

Reduction of Drag Coefficient Due To Misaligned Wind‐Waves

AbstractRecent field observations suggest that the air‐sea momentum flux (or the drag coefficient) is significantly reduced when the dominant wind‐forced surface waves are misaligned from local wind. Such conditions may occur under rapidly changing strong winds (such as under tropical cyclones) or in coastal shallow waters where waves are refracted by bottom topography. A recent Large Eddy Simulation (LES) study also shows that the drag coefficient is reduced by a misaligned strongly forced wave train (with a small wave age of 1.37). In order to investigate more realistic field conditions, this study employs LES to examine the effect of a misaligned (up to 90°) surface wave train over a wide range of wave age up to 10.95. For all wave ages examined, the drag coefficient is reduced compared to the flat surface condition when the misalignment angle exceeds around 22.5°–45°. The drag reduction may occur even if the form drag of the wave train is positive.

A Mesh-Based Approach for Computational Fluid Dynamics-Free Aerodynamic Optimisation of Complex Geometries Using Area Ruling

In this paper, an optimisation procedure is introduced that uses a significantly cheaper, and CFD-free, objective function for aerodynamic optimisation than conventional CFD-driven approaches. Despite the reduced computational cost, we show that this approach can still drive the optimisation scheme towards a design with a similar reduction in drag coefficient for wave drag-dominated problems. The approach used is ‘CFD-free’, i.e., it does not require any computational aerodynamic analysis. It can be applied to geometries discretised using meshes more conventionally used for ‘standard’ CFD-based optimisation approaches. The approach outlined in this paper makes use of the transonic area rule and its supersonic extension, exploiting a mesh-based parameterisation and mesh morphing methodology. The paper addresses the following question: ‘To what extent can an optimiser perform (wave) drag minimisation if using ‘area ruling’ alone as the objective (fitness) function measurement?’. A summary of the wave drag approximation in transonic and supersonic regimes is outlined along with the methodology for exploiting this theory on a typical CFD surface mesh to construct an objective function evaluation for a given geometry. The implementation is presented including notes on the considerations required to ensure stability, and error minimisation, of the numerical scheme. The paper concludes with the results from a number of (simple and complex geometry) examples of a drag-minimisation optimisation study and the results are compared with an approach using full-fidelity CFD simulation. The overall conclusions from this study suggest that the approach presented is capable of driving a geometry towards a similar shape to when using full-fidelity CFD at a significantly lower computational cost. However, it cannot account for any constraints, driven by other aerodynamic factors, that might be present within the problem.

Thermal inspection of hybrid nanofluid flows over a stretched cylinder at an oblique stagnation point with variable characteristics

AbstractThe enhanced thermal characteristics of hybrid nanofluids make them more versatile compared to conventional fluids. These improved thermal properties render hybrid nanomaterials highly practical for a wide range of applications, including solar systems, energy production, and cooling processes. In line with this perspective, the current study concentrates on evaluating the thermal performance of two unique hybrid nanofluid flows that impinge obliquely on a stretched cylinder. Two base fluids, FC‐77 and a binary mixture of water and ethylene glycol (50:50)%, have been considered, with the addition of nanoparticles such as single‐walled carbon nanotubes (SWCNTs) and multiwalled carbon nanotubes (MWCNTs). The said model's novelty is enhanced by the temperature dependent viscosity and thermal conductivity. Appropriate transformations are applied to derive a system of ordinary differential equations (ODEs), which are then solved numerically using the bvp4c method. A thorough examination is conducted on the physical phenomenon of pertinent parameters, accompanied by graphical representations. The results revealed that, for the FC‐77 coolant‐based hybrid nanofluid, mounting the particle volume fraction leads to a significant reduction in temperature distribution. Additionally, it is perceived that the presence of a variable viscosity parameter causes a reduction in the surface drag coefficient as well as the axial and tangential velocities. The validity of the proposed flow model is demonstrated by comparing the results with those from an earlier study.

Aerodynamic shape optimization using design-variables-screening method

Aerodynamic shape optimization involving a complex geometric model or problem may have tens or hundreds of design variables, necessitating multiple accurate but time-consuming computational fluid dynamics simulations to produce optimal designs, which greatly affects the efficiency of optimization and. To address this challenge, this article proposes an efficient optimization method based on design-variables-screening. Within the framework of the method, a complicated input–output relationship is broken down into quantitative effects. The influence of design variables on the objective function is calculated by the Kriging regression model and functional analysis of variance. In the meantime, a screening strategy is proposed to facilitate the selection of design variables for optimization. The less important design variables in the problems of interest are fixed so that the dimensionality of the problems is reduced to save computational cost. Experimental results on the National Advisory Committee for Aeronautics airfoil (NACA0012) demonstrate that the simplified model with the screening strategy achieves nearly the same reduction in drag coefficient as the conventional method that optimizes all design variables. Moreover, it significantly enhances the efficiency of optimization and contributes to the enhancement of flow stability.

Aerodynamic drag improvements on a circular cylinder using passive Venturi actuators

Scale-adaptive simulations (SAS) of three-dimensional flow around a circular cylinder fitted with a passive version of a novel flow control method (passive Venturi actuator, PVA) are performed at a diameter-based Reynolds number of Re = 28 000. The PVA consists of one or more narrow slits located at the top and/or bottom sides of the cylinder that connected to the throat of the axial Venturi slit in this cylinder. The main purpose of the study is to investigate the influence of divergence angle and narrow slit location relative to the axial Venturi slit on the aerodynamic performance of the cylinder. To this end, four models were designed with various PVAs. Additional models, a plain cylinder (unmodified model) and a cylinder fitted with an axial Venturi slit (model without a narrow slit), were also used for quantitative comparison. SAS predicts that an additional 5% reduction in the time-averaged drag coefficient, ⟨CD⟩, was observed when two narrow slits located on the surface at an angle of ±80° from the front stagnation line were fitted to the cylinder with an axial Venturi slit. Reducing the divergence angle of the PVA leads to improvements in ⟨CD⟩ and root mean square of fluctuating force coefficients, CD−rms and CL−rms. It is found that a cylinder with a PVA that has two narrow slits and a divergence angle of 6° can produce a 28.6% reduction in ⟨CD⟩, a 58.5% reduction in CD−rms, and an 81.2% reduction in CL−rms, when compared to the plain cylinder.

DESIGN, EXPERIMENTAL STUDIES, AND CFD INVESTIGATIONS OF BIO-INSPIRED SLOTTED REAR DIFFUSER ATTACHMENT ON CAR MODEL FOR ENHANCING AERODYNAMIC PERFORMANCE

The performance enhancement of vehicle design is still a challenging phenomenon for technologists. A suitable pathway for performance enrichment is naturally available in living species. One adaptable design solution arrived through the penguin microbubble concept, which was used to reduce drag. The present work attempts to design rear-slotted diffuser attachments on car models to reduce drag force through the diversion of air. The drag reduction enhances the vehicle’s aerodynamic performance and reduces lift force to increase stability. Normally, high-speed vehicle performance and stability are determined by aerodynamic forces exerted on the body surface. The higher level of drag and lift forces leads to more fuel utilization and, in the case of electric vehicles, more power consumption. The drag force on the vehicle is mainly due to the formation of the wake region behind the body in the downstream region. The above forces are reduced using a rearward-slotted diffuser at the rear end underneath the vehicle. The aerodynamic performance of the vehicle models was estimated through wind tunnel experiments and numerical simulation for various wind speeds. The experimental work was conducted on selected car models with and without rearward slotted diffusers to compare the forces and performances of vehicle models. Interestingly, increased pressure was observed downstream of the vehicle while providing a slotted diffuser, consequently diminishing the wake area behind the vehicle. The drag and lift coefficient reduction were attained as 4.74% and 11.02% at 50 m/s (180 km/hr) on the chosen vehicle model. Also, a significant reduction in fuel consumption was calculated at higher speeds while attaching a slotted diffuser at the rear end of the car.

Optimizing flow control with deep reinforcement learning: Plasma actuator placement around a square cylinder

This study introduces a deep reinforcement learning-based flow control approach to enhance the efficiency of multiple plasma actuators on a square cylinder. The research seeks to adjust the control inputs of these actuators to diminish both drag and lift forces on the cylinder, ensuring flow stability in the process. The proposed model uses a two-dimensional direct numerical simulation of flow past a square cylinder to represent the environment. The control approach involves adjusting the AC voltage across three specific configurations of the plasma actuators. Initially tested at a Reynolds number (ReD) of 100, this strategy was later applied at ReD of 180. We observed a 97% reduction in the mean drag coefficient at ReD = 100 and a 99% reduction at ReD = 180. Furthermore, the findings suggest that increasing the Reynolds number makes it harder to mitigate vortex shedding using plasma actuators on just the cylinder's rear surface. However, an optimized configuration of these actuators can fully suppress vortex shedding under the proposed control scheme.

Spacing effects on blast loading characteristics of two tandem square columns under planar shock waves

To effectively evaluate the structural responses, it is crucial to possess relevant knowledge regarding the blast load on the structure. The loading characteristics of two columns differ from those of a single column due to complex flow-field interference. The spacing effect on blast loading characteristics of two tandem rigid square columns is examined numerically and experimentally for different center spacing between the columns (L = 1.2B–12B, where B represents the side length of a column). This includes studying drag characteristics, distribution patterns of reflection overpressure and impulse, and understanding the physical mechanism under blast load. The experimental tests use a shock tube device powered by high-pressure gas, while numerical simulations solve Euler equations using computational fluid dynamics techniques. Our findings reveal that spacing cases can be categorized into four situations: small and medium spacings (L/B = 1.2–2 and 2.5–4), where the upstream column exhibits a significant shielding effect on the downstream column, resulting in the reduced drag coefficient for the downstream column; large spacings (L/B = 5–7), where drag coefficient improves but remains lower than that of a single column; superlarge spacings with negligible shielding effect (L/B = 8–12), leading to similar blast loading for the downstream column compared to a single column.