Airfoil Optimization Research Articles

Aerodynamic optimization of airfoil based on deep reinforcement learning

The traditional optimization of airfoils relies on, and is limited by, the knowledge and experience of the designer. As a method of intelligent decision-making, reinforcement learning can be used for such optimization through self-directed learning. In this paper, we use the lift–drag ratio as the objective of optimization to propose a method for the aerodynamic optimization of airfoils based on a combination of deep learning and reinforcement learning. A deep neural network (DNN) is first constructed as a surrogate model to quickly predict the lift–drag ratio of the airfoil, and a double deep Q-network (double DQN) algorithm is then designed based on deep reinforcement learning to train the optimization policy. During the training phase, the agent uses geometric parameters of the airfoil to represent its state, adopts a stochastic policy to generate optimization experience, and uses a deterministic policy to modify the geometry of the airfoil. The DNN calculates changes in the lift–drag ratio of the airfoil as a reward, and the environment constantly feeds the states, actions, and rewards back to the agent, which dynamically updates the policy to retain positive optimization experience. The results of simulations show that the double DQN can learn the general policy for optimizing the airfoil to improve its lift–drag ratio to 71.46%. The optimization policy can be generalized to a variety of computational conditions. Therefore, the proposed method can rapidly predict the aerodynamic parameters of the airfoil and autonomously learn the optimization policy to render the entire process intelligent.

An efficient global optimization algorithm combining revised expectation improvement criteria and Kriging

The efficient global optimization (EGO) algorithm is a kind of Bayesian optimization algorithm that uses the Kriging interpolation model and expectation improvement (EI) criteria as surrogate model and acquisition function, respectively. However, the greediness of EI criteria can lead the EGO algorithm to fall into local optima. Owing to this, revised expectation improvement (REI) criteria are proposed by introducing a balance factor to adjust the exploitation and exploration of EI criteria, and the corresponding algorithm is called the revised efficient global optimization (REGO) algorithm. In order to motivate exploration, and ensure that the computational cost is acceptable, a Latin hypercube based indicator is proposed to denote a balance factor from the viewpoint of sample distribution. Several test functions and an airfoil optimization problem are applied to verify the performance of the REGO algorithm. The results show that the REGO algorithm has acceptable computational cost, a strong ability to find global optima, and good robustness.

Shape Optimization of an Asymmetric Airfoil for Low Wind Speed Region having Adjoint-Based Optimization Technique

The land needed to install wind turbines is shrinking as power generation from renewable energy sources increases significantly. A large number of studies are being conducted to maximize the power extraction from wind turbines in areas with low wind speeds. Wind turbine blades play a significant role in utilizing the maximum amount of energy from the wind. The aerodynamic performance of a wind turbine blade depends on the airfoil shape. The shape optimization of an asymmetric S2027 airfoil for a low wind speed region was investigated using the adjoint-based optimization technique. The primary objectives of this study were to maximize the lift coefficient, minimize the drag coefficient, and maximize the lift-to-drag ratio. The optimization is based on the adjoint method for Reynolds number variation in the range of 2 × 105 to 5 × 105 and an angle of attack variation from 0° to 12°. A two-dimensional Reynolds–Averaged Navier–Strokes Computational Fluid Dynamics model was created with all the operating parameters and used for optimization. The aerodynamic performance was validated experimentally. For each optimization function, approximately 16 shapes were obtained. The aerodynamic performance for each optimized shape was determined under different operating conditions. Different airfoil shapes with a specific chord, leading and trailing edges, and span arrangement was obtained. The drag coefficient was reduced by 2%–30%; the lift coefficient was improved by 2%–35%, and the lift-to-drag ratio was improved up to 40%.

Study on design characteristics of airfoil pressure distribution for low Mach number general aircraft

This paper studies several problems that may exist in the aerodynamic design of low Mach number general aircraft, and discusses the design characteristics of the pressure distribution of some related airfoils while part of the characteristics is verified by a multi-point laminar airfoil design case. Firstly, several issues which exist in the aerodynamic design of low Mach number general aircraft, such as relatively large zero-lift drag coefficient, high stall performance requirements, and large variation range of Reynolds number, are discussed and analyzed. Then, to address these issues, the paper extracts some useful design features that are beneficial to the airfoil aerodynamic performance from the study on the design characteristics of the pressure distribution of several turbulent flow airfoils and laminar flow airfoils that are designed for low Mach number general aircraft. Finally, by considering the typical design conditions of a light multi-purpose single-turboprop-engine general aircraft, including the cruise, climb and stall conditions, a multi-point laminar airfoil optimization is offered by using the GAW-1 airfoil as the baseline. The optimized foil has a 9.6 improvement in the lift-to-drag ratio of the cruise condition, a 16.3 improvement in the lift-to-drag ratio of the climb condition but a 0.115 decrease in the maximum lift coefficient of the stall condition. The results show that the requirements from the cruise, climb and stall conditions are contradictory to some degree in the considering multi-point optimization case, and the designers should deal with this trade-off carefully based on the features of the low Mach number general aircraft.

Multi-Objective Airfoil Optimization Under Unsteady-Freestream Dynamic Stall Conditions

Aimed at alleviating dynamic stall, an optimization method for rotor airfoils is proposed considering the unsteady-freestream circumstance and pitching motion. The method is based on the unsteady Reynolds-averaged Navier-Stokes equations, kriging model, and non-dominated sorting genetic algorithm II (NSGA-II). Three objective functions are built for minimizing the area of lift hysteresis loop, value of peak drag, and peak pitching moment. An optimized airfoil with larger maximum thickness, maximum camber, and upper leading-edge radius is obtained based on the baseline SC1095 airfoil. Besides, the optimized airfoil shows smaller local thickness and camber near the trailing edge. The results show that the flow separation and dynamic stall are suppressed or alleviated under both design and off-design conditions, attributed to a significant reduction of negative pressure peak and adverse pressure gradient. The dynamic stall is also alleviated for the rotor configured with the optimized airfoil in forward flight. This indicates that the optimized airfoil performs well in a rotor environment.

Deep-learning-based aerodynamic shape optimization of rotor airfoils to suppress dynamic stall

The use of computational fluid dynamics (CFD) to optimize the aerodynamic shape of rotor airfoils with the aim of suppressing dynamic stall is computationally expensive and inefficient. To address this issue, a surrogate model based on deep learning (DL) is employed to replace a CFD module of optimization process in this study. The optimization framework is demonstrated by optimizing a SC1095 rotor airfoil in subsonic flow. The airfoil shapes are parameterized using the class function/shape function transformation method, and an airfoil dataset for a deep neural network (DNN) is generated by Latin hypercube sampling. The surrogate model for predicting the aerodynamic coefficients of the airfoils is established by training the DNN, which can get the predicted results within a second. This surrogate model is then combined with the multi-island genetic algorithm for rotor airfoil optimization. Finally, the aerodynamic performance of a rotor with the optimized airfoil is investigated to verify the dynamic stall suppression effect. The results demonstrate that the peak drag and moment coefficients of the optimized airfoil can be reduced by 82.5% and 88.6%, respectively, compared with the baseline airfoil, while the lift coefficients increase during almost all of the pitching period. This means that the optimized airfoil has significantly improved dynamic stall characteristics. Moreover, by suppressing the dynamic stall, the rotor with the optimized airfoil achieves better aerodynamic performance than the baseline rotor. Statistical data show that our use of a DL-based surrogate model instead of the CFD module will reduce the optimization time by at least one order of magnitude.

Optimizing airfoil shape for small, low speed, unmanned gliders: A homemade investigation

Motivated by an interest to increase the efficiency of static airfoils, the objective of this study was to optimize airfoil lift generation based solely on airfoil shape. Enhancing the lift generated by airfoils without flaps can lead to longer flight times for unmanned gliders and possibly reduce fuel requirements for ground and air vehicles (1,2,3). We employed a Bernoullian model of airfoil lift generation to predict an optimized wing shape which was then constructed and compared with five others in a wind tunnel. We assembled airfoils from Styrofoam and conducted experiments in a small wind tunnel, measuring air speeds with a handheld anemometer. Our Bernoullian model related lift generation to the ratio of surface areas above and below an airfoil’s chord, and because this ratio was maximized in our optimal airfoil, we predicted that this wing would generate more lift force than the other tested wings with lower ratios. However, we identified a variety of confounding variables including imperfections in wing constructions, turbulent airflow and air resistance within the wind tunnel, and low resolution of the anemometer, all of which contributed to unpredicted and unreliable results. Moreover, we concluded that our mathematical model was not rigorous enough to be generalized to a wider set of experimental conditions. Despite these shortcomings, our results did point to a correlation between airfoil shape and lift generation, and will allow for more informed, less error prone future studies.

Simulation research on hydrodynamic performance of wave glider airfoil

To improve the wave energy utilization rate of the wave glider as the research goal, research on the improvement of the hydrodynamic performance of the airfoil of the wave glider was carried out. First, we extract the bionic airfoil A and the standard airfoil C and draw the design airfoil B and the regular airfoil D. Secondly, using ANSYS Fluent, the hydrodynamic performance of the four airfoils under different inflow velocities and angles of attack is studied. Finally, data analysis shows that under the same attack angle, the lift coefficient, drag coefficient, and lift-drag ratio of the airfoil are not affected by the incoming flow velocity. Under the same incoming flow velocity, when the attack angle is 5°-13°, the bionic wing Type A is slightly better than the design airfoil B, obviously better than the standard airfoil C and regular airfoil D. Considering the hydrodynamic performance and material cost, it can be seen that the B airfoil is the optimal airfoil in this study. The comparative analysis of four airfoils provides a research basis for improving the wave energy utilization rate of the wave glider.

Unsteady RANS-based DMD analysis of airfoil NACA0015 with Gurney flap

The present investigation summarizes the numerical prediction and DMD analysis of the flow in the vicinity of a Gurney flap set on an airfoil NACA0015 at Re = 10,300 tested in a water tunnel with time-resolved Particle Image Velocimetry. A series of two-dimensional U-RANS simulations has been accomplished using an incompressible, time-accurate solver of OpenFOAM. A purely data-based DMD analysis has followed to extract the spatio-temporal coherent structures from the time-evolving flowfields, to mimic the experiments. It is stressed that this work focuses on the analysis of this kind of flap by applying DMD to datasets generated with numerical simulations, which has not been previously investigated, but using experimental datasets. Under this approach, the comparison of the numerics with the experimental counterpart shows a remarkable agreement for both the structures and major frequencies, which stresses the suitability of the incompresible U-RANS approach in this class of studies. Special attention has been focused on the modelling of the experimental facility, and a quantification of the prediction accuracy is provided by assessing the deviation of the spectral content. The simulations clearly show the key role of having a good characterization of the water tunnel to be successful at capturing the spectrum and flow unsteadiness. Furthermore, they reveal quite promising for those industrial oriented simulations, which deal with lift-enhancement devices and lift-to-drag ratio optimization of airfoils since they permit an accurate prediction.

Investigation of a Newly Developed Slotted Bladed Darrieus Vertical Axis Wind Turbine: A Numerical and Response Surface Methodology Analysis

Abstract In the past few years, wind energy became the most reliable and clean energy source throughout the world. This research broadly has focused on the 2D design of the conventional (without slot) wind turbine blades as well as slotted airfoil blades for places having a low power density of wind. For vertical axis wind turbines, optimum airfoil design plays a vital role in the aerodynamic efficiency of the wind turbine. To get better aerodynamic efficiency, a feasible airfoil criterion of selection, played an important role in the chosen blade design. In this paper, the conventional NACA0018 profile without slots and slotted airfoil profile is selected for measuring the turbine blade performance. The geometry of the computational domain has been created using the solid works software and the computational investigation has been performed using the computational fluid dynamics (CFD) solver ansys fluent 2020 R2 with the help of the shear stress transport (SST k–ω) turbulence model. The simulations are conducted initially with base airfoil and then varying the different structures of slots. After introducing slots in the base airfoil, efficiency was increased in terms of lift coefficient (Cl) and power coefficient (Cp) by 2.32% and 17.94%, respectively at the angle of attack of 15 deg. The results indicate that slotted airfoils have a better lift coefficient and power coefficient compared to an airfoil without a slot. The best turbine operating parameters were found to be 14.82 deg of angle of attack, 1.73 coefficient of lift, and 2.99 tip speed ratio (TSR) by using the response surface methodology (RSM). At these optimal settings, the best Cp response was 0.406. A field experiment was carried out to verify the modeling-optimization outcomes, and the results were within 7% of the model projected results. Thus, this type of slotted airfoil designed for a vertical axis wind turbine (VAWT) can be used to harness wind energy potential more efficiently.

Airfoil optimization methodology and CFD validation for Mars atmospheric conditions

Airfoil optimization methodology and CFD validation for Mars atmospheric conditions

Aerodynamic shape optimization of co-flow jet airfoil using a multi-island genetic algorithm

The co-flow jet is a zero-net-mass-flux active flow control strategy and presents great potential to improve the aerodynamic efficiency of future fuel-efficient aircrafts. The present work is to integrate the co-flow jet technology into aerodynamic shape optimization to further realize the potential of co-flow-jet technology and improve co-flow jet airfoil performance. The optimization results show that the maximum energy efficiency ratio of lift augmentation and drag reduction increased by 203.53% (α = 0°) and 10.25% (α = 10°) at the Power-1 condition (power coefficient of 0.3), respectively. A larger curvature is observed near the leading edge of the optimized aerodynamic shape, which leads to the early onset of flow separation and improves energy transfer efficiency from the jet to the free stream. In addition, the higher mid-chord of the optimized airfoil is characterized by accelerating the flow in the middle of the airfoil, increasing the strength of the negative pressure zone, thus improving the stall margin and enhancing the co-flow jet circulation.

Aerodynamic Shape Optimization of a Symmetric Airfoil from Subsonic to Hypersonic Flight Regimes

Hypersonic flight has been the subject of numerous research studies during the last eight decades. This work aims to optimize the aerodynamic performance of a two-dimensional baseline airfoil (NACA0012) at distinct flight regimes from subsonic to hypersonic speeds. A mission profile has been defined, where four points representing the subsonic, transonic, supersonic, and hypersonic flow conditions have been selected. A framework has been implemented based on high-fidelity RANS computational fluid dynamics simulations. Gradient-based optimizations have been conducted with the objective of minimizing the drag. The optimization results show an overall improvement in aerodynamic performance, including a decrease in the drag coefficient of up to 79.2% when compared to the baseline airfoil. In the end, a morphing strategy has been laid out based on the optimal shapes produced by the optimization.

Aerodynamic optimization of a propeller airfoil at low Reynolds numbers

The aerodynamic optimization method was presented to design a propeller airfoil for a stratosphere airship at low Reynolds numbers. The delta class and shape transformation were adopted to describe the airfoil geometry as the design variables. The flow field was calculated by an open-source code XFOIL to obtain the aerodynamic characteristics as the design targets. The separated particle swarm optimization was employed as the optimization algorithm. The optimization case started with a typical low Reynolds number airfoil E387 and obtained a thicker and more cambered airfoil using the proposed method. The flow fields of the optimized and initial airfoils were analysed by CFD. Results suggested that the lift-to-drag ratio of the optimized airfoil changed from 35.7 to 45.9 with an increasing rate by 28.5% at design cruise condition. The overall aerodynamic performance of the optimized airfoil has been significantly improved at both design and off-design conditions within a wide range of attack angle. The optimization platform presented here is an efficient and effective method for the low-Reynolds-number airfoil designs.

Design, optimization and application of two-element airfoils for tactical UAV

Owning to the requirements for longer endurance, larger payloads as well as simpler mechanical structure of tactical UAV, the two-element airfoil technology is introduced. Firstly, the aerodynamic characteristics of five common low-speed airfoils are calculated and compared. The airfoil with good aerodynamic performance after segmentation is selected as initial airfoil. Then, the influence of different segmentation types on the aerodynamic characteristics of airfoils is analyzed, and the segmentation type which brings the best aerodynamic benefits is selected. The slot parameters are optimized by NSGA-II optimization algorithm under cruising, take-off, and a large angle of attack states to obtain a high-performance self-adaption tactical UAV airfoil. Finally, the new airfoil is applied to Sparrow-hawk tactical UAV, the CL1.5 /CD of acceleration state increases 15.5%, the CL of large angle of attack increases 20.4% and the CL1.5 /CD of cruising state after enlarge the aspect ratio increase12.3%.

Optimal airfoil design through particle swarm optimization fed by CFD and XFOIL

Optimal airfoil design through particle swarm optimization fed by CFD and XFOIL

Airfoil optimization based on multi-objective bayesian

Airfoil optimization based on multi-objective bayesian

Design optimization of a blunt trailing-edge airfoil for wind turbines under rime ice conditions

A new optimization method is developed for the design of a blunt trailing-edge airfoil for wind turbines under rime ice conditions. The parametric representation of the airfoil is given using the profile integration theory and B-spline function. The rime ice shape from wind tunnel tests is fitted using a linear interpolation algorithm with equidistant and equiangular steps to preserve the same number of ice shape feature points. The blunt trailing-edge thickness and distribution ratio on the upper side of the middle arc are included in the design variables. The optimizer, based on the Bare-Bones Multi-Objective Particle Swarm Optimization (BB-MOPSO) algorithm integrated with ICEM-CFD and FLUENT software, seeks the solutions maximizing the lift coefficient and lift-drag ratio. A new airfoil NACA0012BT (with BT denoting the blunt trailing-edge) with the trailing-edge thickness of 2.2004 %c (with c denoting the chord length) and distribution ratio of 1:55.8482 is obtained, and the lift and drag coefficients, lift-drag ratios, pressure distributions, and flow characteristics are investigated using the Computational Fluid Dynamics (CFD) method. Significant improvements of the NACA0012 airfoil design are achieved in this process, confirming that the developed method constitutes a valuable tool for designing and optimizing the wind turbine airfoil operating in icing conditions.

Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network.

As compared with the computational fluid dynamics(CFD), the airfoil optimization based on deep learning significantly reduces the computational cost. In the airfoil optimization based on deep learning, due to the uncertainty in the neural network, the optimization results deviate from the true value. In this work, a multi-network collaborative lift-to-drag ratio prediction model is constructed based on ResNet and penalty functions. Latin supersampling is used to select four angles of attack in the range of 2°–10° with significant uncertainty to limit the prediction error. Moreover, the random drift particle swarm optimization (RDPSO) algorithm is used to control the prediction error. The experimental results show that multi-network collaboration significantly reduces the error in the optimization results. As compared with the optimization based on a single network, the maximum error of multi-network coordination in single angle of attack optimization reduces by 16.0%. Consequently, this improves the reliability of airfoil optimization based on deep learning.

Robust Optimization of Natural Laminar Flow Airfoil Based on Random Surface Contamination

Natural laminar-flow (NLF) airfoils are one of the most promising technologies for extending the range and endurance of aircrafts. However, there is a lack of methods for the optimization of airfoils based on the surface contamination that destroys the laminar flow. In order to solve this problem, a robust optimization process is proposed using the Non-dominated Sorting genetic algorithm- II (NSGA-II) evolutionary algorithm, and Monte Carlo simulation combined with an aerodynamic calculation software Xfoil. Firstly, the airfoil is optimized normally and the aerodynamic performance of optimized airfoil under surface contamination is analyzed. Then, the original airfoil is robustly optimized under random surface contamination based on the assumption that its locations follow triangular and uniform probability distributions. Finally, all the optimized results and original airfoil are compared. It is found that robust optimization reduces the sensitivity of the airfoil to random surface contamination, hence, improving the robustness of the airfoil. The proposed methods make it possible to improve the aerodynamic performance of NLF airfoils considering surface contamination.