Experimental optimization of plasma actuation for cylinder drag reduction using genetic algorithms

A closed-loop parameter optimization system around a cylinder is built by integrating the plasma actuation and genetic algorithms in this research, employing numerical simulations and experimental methods. The study aims to minimize the total drag on the cylinder by optimizing the reduced frequency. A pair of surface dielectric barrier discharge plasma actuators, powered by alternating-current high-voltage sources, is symmetrically positioned at ±90° azimuth angles on the two sides of a circular cylinder, and the Reynolds (Re) number is 1.5×104 based on the cylinder diameter. Numerical simulations were first used to determine the optimization space for the reduced frequency, followed by wind tunnel experiments to further search for the optimal research within this space. Particle image velocimetry and hot-wire anemometry were used to investigate the flow field's instantaneous and time-averaged characteristics. Ultimately, the optimal reduced frequency was identified based on duty-cycle frequency, free-stream velocity, and cylinder diameter. The results show that the optimal duty-cycle frequency obtained through genetic algorithm optimization in numerical simulations and wind tunnel experiments is the same, at 140 Hz, corresponding to a reduced frequency of approximately 1.372. The drag reduction rates are also similar, at 73.9% and 73.6%, respectively. During plasma flow control with the optimal reduced frequency, the dominant frequency of the overall motion of the separated vortex field is no longer the natural shedding frequency of the baseline flow. Still, it is instead controlled by the plasma duty-cycle frequency. Compared to the baseline flow, the plasma flow control at the optimal reduced frequency transforms the large-scale alternating vortices into small-scale shedding vortices, resulting in a time-averaged narrow and stable velocity deficit region, leading to reduced energy loss and significantly lower time-averaged drag coefficient. Meanwhile, the interaction between plasma-induced vortices and the Kármán vortex street in the cylinder wake enhances mixing, significantly suppressing turbulence intensity. The results demonstrate the effectiveness of genetic algorithms in identifying the global optimal reduced frequency of plasma actuation, achieving maximum drag reduction.

Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms

In this study, nano-silica oxide's effect as a Drag Reducing Agent (DRA) of water flow in a 12.7 and 25.4 mm galvanized pipe was investigated. The studied parameters include Nano silica oxide concentration, Flow rate, temperature, and tube pipe diameter. To develop the conditions in preparing the Nano-particle on Drag Reduction (DR), nano-particles were provided in the top water-based fluid. To have a comprehensive analysis of process folding conditions, the experiments were carried out with three different drag-reducing concentration agents with three various temperatures and three different flow rates. Moreover, as a new method in this study, the experimental (Drag reduction percent) outputs were evaluated and analyzed using the Artificial neural network which is optimized by a genetic algorithm. In the consequence of algorithm genetic, the highest rate of drag reduction occurred at a horizontal pipeline 12.7 mm, temperature 41.07 °C, and a concentration of 0.628 with a 1441.84 flow rate was 25.84%.

Regarding the drag reduction of ridge-like surface as optimization goal, this paper has optimized V-like and U-like surface using genetic algorithm in the context of given sizes and obtained the ridge-like structure size with the best drag reduction effect. Comparative analysis of the surface resistance and turbulence characteristics and the velocity distribution near the wall show that the optimized ridge-like surface has good drag reduction effect and the drag reduction of V-like surface is better. The drag reduction of optimized ridge-like surface has increased significantly and this shows that the method optimizing ridge-like structure using genetic algorithm is feasible. The optimization method can be used for navigation devices and other fluid mechanical shape optimization design.

In this study, the drag minimization problem around a circular cylinder was considered. Two plasma actuators (PAs) were installed on the upper and the lower side of the cylinder. Thus, the design problem is the multi-variate problem. A Kriging based genetic algorithm (GA) was employed to optimize the parameters of the operating conditions of PAs and the design knowledge discovery is also carried out for the multi-variate problem. The aerodynamic performance was evaluated in wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. This optimization methodology explores the optimum waveform of parameters for AC voltage by changing the waveform automatically. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment. An analysis of variance (ANOVA) and a scatter plot matrix (SPM) were introduced for design knowledge discovery. According to the discovered design knowledge, it was found that the modulation frequency for two PAs is an important parameter to reduce drag. In addition, the duty ratio for a PA on the model has two optimum points. Flow visualization is also carried out by particle image velocimetry (PIV). According to this result, the flow separation around the model is reduced by optimum design compared with the flow of the model without PAs.

Since 1947, when Schubauer and Skramstad (J Res: 69–78, 1947 [1]) established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326, [2]). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flow analysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.

Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speeds, Part II - Experimental validation using Infra-Red transition measurement from Wind Tunnel tests

A Clustering Genetic Algorithm for Cylinder Drag Optimization

The control of bluff-body wakes for reduced drag and enhanced stability has traditionally relied on the so-called direct-wake control approach. By the use of actuators or passive devices, one can manipulate the aerodynamic loads that act on the rear of the model. An alternative approach for the manipulation of the flow is to move the position of the actuator upstream, hence interacting with an easier-to-manipulate boundary layer. The present paper comprises a bluff-body flow study via large-eddy simulations to investigate the effectiveness of an upstream actuator (positioned at the leading edge) with regard to the manipulation of the wake dynamics and its aerodynamic loads. A rectangular cylinder with rounded leading edges, equipped with actuators positioned at the front curvatures, is simulated at $Re=40\,000$ . A genetic algorithm (GA) optimization is performed to find an effective actuation that minimizes drag. It is shown that the GA selects superharmonic frequencies of the natural vortex shedding. Hence, the induced disturbances, penetrating downstream in the wake, significantly reduce drag and lateral instability. A comparison with a side-recirculation-suppression approach is also presented, the latter case being worse in terms of reduced drag (only 8 % drag reduction achieved), despite the total suppression of the side recirculation bubble. In contrast, the GA optimized case contributes to a 20 % drag reduction with respect to the unactuated case. In addition, the large drag reduction is associated with a reduced shedding motion and an improved lateral stability.

The practical interest of flow control approaches is no more debated as flow control provides an effective mean for considerably increasing the performances of ground or air transport systems, among many others applications. Here a fundamental configuration is investigated by using non-thermal surface plasma discharge. A dielectric barrier discharge is installed at the step corner of a backward-facing step (Reh=30000, Re?=1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step. The primary objective of this paper is the coupling of a numerical optimizer with an experiment. More specifically, optimization by genetic algorithm is implemented experimentally in order to minimize the reattachment point downstream of the step model. Validation through inverse problem is firstly demonstrated. When coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm finds the optimum forcing conditions with a good convergence rate, the best control design variables being in agreement with the literature that uses other types of control devices than plasma. Indeed, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing phenomena. At the best forcing conditions, the mean flow reattachment is reduced by 20%. This article, with its experiment-based approach, demonstrates the robustness of a single-objective multi-design optimization method, and its feasibility for wind tunnel experiments.

The potential benefits of active flow control are no more debated. Among many others applications, flow control provides an effective mean for manipulating turbulent separated flows. Here, a nonthermal surface plasma discharge (dielectric barrier discharge) is installed at the step corner of a backward-facing step (U 0 = 15 m/s, Re h = 30,000, Re θ = 1650). Wall pressure sensors are used to estimate the reattaching location downstream of the step (objective function #1) and also to measure the wall pressure fluctuation coefficients (objective function #2). An autonomous multi-variable optimization by genetic algorithm is implemented in an experiment for optimizing simultaneously the voltage amplitude, the burst frequency and the duty cycle of the high-voltage signal producing the surface plasma discharge. The single-objective optimization problems concern alternatively the minimization of the objective function #1 and the maximization of the objective function #2. The present paper demonstrates that when coupled with the plasma actuator and the wall pressure sensors, the genetic algorithm can find the optimum forcing conditions in only a few generations. At the end of the iterative search process, the minimum reattaching position is achieved by forcing the flow at the shear layer mode where a large spreading rate is obtained by increasing the periodicity of the vortex street and by enhancing the vortex pairing process. The objective function #2 is maximized for an actuation at half the shear layer mode. In this specific forcing mode, time-resolved PIV shows that the vortex pairing is reduced and that the strong fluctuations of the wall pressure coefficients result from the periodic passages of flow structures whose size corresponds to the height of the step model.

Micro aerial vehicles flying at low speeds are becoming increasingly popular in military and daily life. Nevertheless, the short cruise time related to the poor aerodynamic efficiency of the wing at low Reynolds numbers is still a key issue. To deal with this, a spanwise plasma actuator array is used to reduce the zero-lift drag of a low-Reynolds-number airfoil, and experimental optimization of the electrical parameters is performed with intelligent algorithms. Results show that for efficient drag reduction, an unsteady unidirectional jet working mode should be preferred by the plasma actuator. In this mode, the drag reduction maps are mostly flat, and the drag reduction magnitude is insensitive to the variation of input voltage amplitude. There exists a threshold particle-observed Strouhal number (0.2) below which the drag reduction effectiveness drops sharply. As a comparison, the map of the power saving ratio shows a steep gradient, and its maximum always resides on the lower bound of duty cycle. With increasing freestream velocity, the mean drag reduction decreases monotonically. A genetic algorithm shows superior performance over surrogate-based optimization by reaching a maximum drag reduction of 40% and a peak power saving ratio of 0.7. Particle image velocimetry results reveal that there exists a laminar separation bubble on the airfoil. With plasma actuation, the transition location is shifted upstream, and the separation region is eliminated significantly. At low speeds, this pressure drag reduction exceeds the friction drag increase, resulting in a net drag decrease. However, transition-induced drag variation can only explain part of the total drag reduction, and the rest is inferred to be turbulent friction drag reduction.

Proc. 3rd Int. Symp. on Turbulence and Shear Flow Phenomena Sendai, Japan, June 25-27, 2003 We propose a new algebraic control scheme for drag reduction in wall-turbulence, which requires the streamwise wall-shear signal only. By assuming continuously distributed sensors and actuators, the controller is designed to reduce the near-wall Reynolds shear stress that is directly responsible for the turbulent skin friction drag. Intuitive and suboptimal control schemes are considered. The derived control laws are assessed by means of direct numerical simulation of turbulent pipe flow at Reτ 180. A clear drag reduction symptom associated with a negative near-wall Reynolds stress is observed when the control scheme derived by the suboptimal control theory is applied. INTRODUCTION For successful development of an active feedback control system for drag reduction in wall-bounded turbulent flow, the effectiveness of the control algorithm used as well as the performance of the hardware components such as sensors and actuators is of great importance. Control schemes may be classified into two types, i.e., explicit and implicit schemes. The explicit scheme is one in which the control input of the actuator i, φi , is given explicitly, e.g., φi( x, t) = F[s j( x, t′ | t′ ≤ t)], where s j is the sensor information and F is a mapping function. On the other hand, the implicit scheme, such as the optimal control (e.g., Bewley et al., 2001) only describes a relation to be satisfied (i.e. the control input minimizing the cost functional) and requires iterative procedures to determine the control input. While such implicit schemes are useful to explore the possibility of drag reduction control, the explicit schemes are easier to be implemented in the real applications. In the last decade, various explicit control algorithms were developed and assessed by using direct numerical simulation (DNS) of controlled turbulent flow. Choi et al. (1994) proposed so-called the opposition control, in which blowing/suction velocity is given at the wall so as to oppose the velocity components at a virtual detection plane located abovethe wall. They attained about 25 % drag reduction in their DNS of turbulent channelflow at low Reynoldsnumbers. Subsequently, several attempts were made to develop control algorithms using the information measurable at the wall. Lee et al. (1997) used a neural network and obtained an algorithm in which the control input is given as a weighted sum of the spanwise wall-shear stresses, ∂w/∂y|w , measured around the actuator. Lee et al. (1998) derived series of analytical solutions of the control input to minimize the cost function in the framework of the suboptimal control. Their DNS of channel flow at Reτ 110 showed 16-22% drag reduction when ∂w/∂y|w (in this case, the control law is quite similar to that obtained by using the neural network mentioned above) or the wall pressure, pw, was used as the sensor signal. From a practical point of view, it is desirable to use the streamwise wall-shear stress, τw = ∂u/∂y|w, or pw (or both) as a sensor signal because a streamwise wall-shear stress sensor (Yoshino et al., 2003) and a wall pressure sensor (Lofdahl et al., 1996) of sufficiently small size and high frequency response are becoming available. For the use of pw, in addition to the work by Lee et al. (1998), Koumoutsakos(1999) presented an algorithm to suppress the vorticity flux, and succeeded to reduce the friction drag in his DNS. For the use of τw, however, development of effective algorithm seems more difficult. Lee et al. (1998) also presented a suboptimal solution aiming at reduction of τw. This algorithm uses τw as the sensor signal only, but the friction drag (i.e., τw) was not reduced by that algorithm. Very recently, Lee et al. (2001) applied a two-dimensional linear-quadratic-Gaussian (LQG) controller to a linearized NavierStokes equation. About 10 % drag reduction was attained in their DNS of a channel flow at Reτ 100. They also attained 17 % drag reduction by making an ad hoc extension. Morimoto et al. (2002) employed a weighted sum of τw as the control input and optimized the weights by using the genetic algorithm (GA). The excellent gene (i.e., the pattern of weights) led to 12 % drag reduction in a channel flow at Reτ 100. The previous suboptimal control using the streamwise wall-shear signal only targeted at direct suppression of the streamwise wallshear. Namely, the cost functional may be expressed as

A Kriging based genetic algorithm (GA) was employed to optimize the parameters of the operating conditions of plasma actuators (PAs). In this study, the lift maximization problem around a circular cylinder was considered. Two PAs were installed on the upper and the lower side of the cylinder. This problem was similar to the airfoil design, because the circular has potential to work as airfoil due to the control of flow circulation by the PAs with four design parameters. The aerodynamic performance was assessed by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The developed optimization system explores the optimum waveform of parameters for AC voltage by changing the waveform automatically. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment. An analysis of variance and a parallel coordinate plot were introduced for design knowledge discovery. According to the discovered design knowledge, it was found that duty ratios for two PAs are an important parameter to create lift.KeywordsPlasma actuatorGenetic algorithmEfficient global optimizationExperimental evaluation

Kriging-based genetic algorithm (GA) was employed to optimise the parameters of the operating conditions of plasma actuators (PAs). In this study, the lift maximisation problem around a circular cylinder was considered. Two PAs were installed on the upper and the lower side of the cylinder. This problem was similar to the airfoil design, because the circular has potential to work as airfoil due to the control of flow circulation by the PAs with four design parameters. The aerodynamic performance was assessed by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The developed optimisation system explores the optimum waveform of parameters for AC voltage by changing the waveform automatically. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment. An analysis of variance and a scatter plot matrix were introduced for design knowledge discovery. According to the discovered design knowledge, it was found that duty ratios for two PAs are an important parameter to create lift.