Showing papers on "Airfoil published in 2022"

Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations

Abstract: Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations. We employ PINNs for solving the Reynolds-averaged Navier–Stokes equations for incompressible turbulent flows without any specific model or assumption for turbulence and by taking only the data on the domain boundaries. We first show the applicability of PINNs for solving the Navier–Stokes equations for laminar flows by solving the Falkner–Skan boundary layer. We then apply PINNs for the simulation of four turbulent-flow cases, i.e., zero-pressure-gradient boundary layer, adverse-pressure-gradient boundary layer, and turbulent flows over a NACA4412 airfoil and the periodic hill. Our results show the excellent applicability of PINNs for laminar flows with strong pressure gradients, where predictions with less than 1% error can be obtained. For turbulent flows, we also obtain very good accuracy on simulation results even for the Reynolds-stress components.

Airfoil design and surrogate modeling for performance prediction based on deep learning method

Abstract: Aiming at the problems of a long design period and imperfect surrogate modeling in the field of airfoil design optimization, a convolutional neural network framework for airfoil design and performance prediction (DPCNN) is established based on the deep learning method. The airfoil profile parameterization, physical field prediction, and performance prediction are achieved. The results show that the DPCNN framework can generate substantial perfect airfoil profiles with only three geometric parameters. It has significant advantages such as good robustness, great convergence, fast computation speed, and high prediction accuracy compared with the conventional machine learning method. When the train size is 0.1, the predicted results can be obtained within 5 ms. The prediction absolute errors of physical field of most sample points are lower than 0.002, and the relative errors of aerodynamic performance parameters are lower than 2.5%. Finally, the optimization attempt of operating parameters is completed by gradient descent method, which shows good stability and convergence. Overall, the DPCNN framework in this paper has outstanding advantages in time cost and prediction accuracy.

The third golden age of aeroacoustics

Abstract: The present review covers the latest evolution of computational aeroacoustics, the field that deals with the noise generated by fluid flows and its propagation in the medium. It highlights the latest findings in both free flows (jet noise) and wall-bounded flows (airfoil, airframe, and turbomachinery noise) in more and more complex environments. Among the computational aero-acoustics methods, high-order schemes of the Navier–Stokes equations on unstructured grids and the lattice Boltzmann method on Cartesian grids have emerged as excellent candidates to tackle noise problems in realistic complex geometries. The latter is also shown to be particularly efficient for both noise generation and propagation, allowing to directly estimate the noise in the far field. Two examples of application of such methods to complex jet noise and to installed airfoil noise are first presented. The first one involves compressible subsonic and supersonic flows in dual-stream nozzles and the second one subsonic flow around an airfoil embedded in the potential core of the open-jet anechoic wind tunnel as in the actual trailing-edge noise experiment. For airframe noise, large eddy simulations of scaled nose landing gear noise and three-element high-lift devices can be tackled to decipher noise sources. For turbomachinery noise, simulations of installed low-speed fans have already unveiled a wealth of details on their noise sources, whereas high-speed turbofans remain a challenge giving the high Reynolds numbers and small tip gaps involved.

Deep reinforcement learning based synthetic jet control on disturbed flow over airfoil

Abstract: This paper applies deep reinforcement learning (DRL) on the synthetic jet control of flows over an NACA (National Advisory Committee for Aeronautics) 0012 airfoil under weak turbulent condition. Based on the proximal policy optimization method, the appropriate strategy for controlling the mass rate of a synthetic jet is successfully obtained at [Formula: see text]. The effectiveness of the DRL based active flow control (AFC) method is first demonstrated by studying the problem with constant inlet velocity, where a remarkable drag reduction of 27.0% and lift enhancement of 27.7% are achieved, accompanied by an elimination of vortex shedding. Then, the complexity of the problem is increased by changing the inlet velocity condition and reward function of the DRL algorithm. In particular, the inlet velocity conditions pulsating at two different frequencies and their combination are further applied, where the airfoil wake becomes more difficult to suppress dynamically and precisely; and the reward function additionally contains the goal of saving the energy consumed by the synergetic jets. After training, the DRL agent still has the ability to find a proper control strategy, where significant drag reduction and lift stabilization are achieved, and the agent with considerable energy saving is able to save the energy consumption of the synergetic jets for 83%. The performance of the DRL based AFC proves the strong ability of DRL to deal with fluid dynamics problems usually showing high nonlinearity and also serves to encourage further investigations on DRL based AFC.

Summary from the 1st AIAA Ice Prediction Workshop

Abstract: The AIAA 1st ice prediction workshop focused on establishing current 2D and 3D simulation capabilities towards ice accretion on aircraft by bringing together scientists from diverse backgrounds such as code developers, experimentalists and users. To that end, experimentally and publicly available rime and glaze ice accretion data were provided for a wide range of applications. The geometries include single and multi-element airfoils, wings, engine inlet and horizontal tail configurations in flow with different droplet size distributions including Supercooled Large Droplets. In addition to ice accretion, data such as pressure distribution, ice mass and collection efficiency were provided, when available. Results from various Computational Fluid Dynamics workflows were provided by academia, research/federal centers and industries. The submitted data is analyzed via code-to-code and code-to-experiment comparison plots. While the many comparisons are now publicly available, the paper presents the detailed analysis on some selected baseline and optional cases. A methodology and technology gap assessment concludes on the current state-of-the-art and presents possible future workshops directions to improve our understanding and modeling of the various phenomena at play.

Three-Dimensional Analysis of Biomimetic Aerofoil in Transonic Flow

Abstract: Since the invention of the aircraft, there has been a need for better surface design to enhance performance. This thirst has driven many aerodynamicists to develop various types of aerofoils. Most researchers have strongly assumed that smooth surfaces would be more suitable for air transport vehicles. This ideology was shattered into pieces when biomimetics was introduced. Biomimetics emphasized the roughness of a surface instead of smoothness in a fluid flow regime. In this research, the most popular 0012 aerofoils of the National Advisory Committee for Aeronautics (NACA) are considered to improve them, with the help of a surface pattern derived from the biological environment. Original and biomimetic aerofoils were designed in three dimensions with the help of Solidworks software and analyzed in the computational flow domain using the commercial code ANSYS Fluent. The implemented biomimetic rough surface pattern upgraded the NACA 0012 aerofoil design in the transonic flow regime. Lift and viscous forces of the aerofoil improved up to 5.41% and 9.98%, respectively. This research has proved that a surface with a little roughness is better than a smooth surface.

Recent progress on flutter‐based wind energy harvesting

Abstract: Wind energy harvesting technology can convert wind energy into electric energy to supply power for microelectronic devices. It has great potential in many specific applications and environments, such as remote areas, sea surfaces, mountains, and so on. Over the past few years, flutter‐based wind energy harvesting, which generates electric energy based on the limit cycle oscillation created by structural aeroelastic instability, has received increasing attention, and as a consequence, different energy harvesting structures, theories, and methods have been proposed. In this paper, three types of flutter‐based energy harvesters (FEHs) including airfoil‐based, flat plate‐based, and flexible body‐based FEHs are reviewed, and related concepts and theoretical models are introduced. The recent progress in FEH performance enhancement methods is classified into structural improvement and optimization, the introduction of nonlinearity, and hybrid structures and mechanisms. Finally, the main FEH challenges are summarized, and future research directions are discussed.

Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview.

Abstract: During the past few decades, several significant progresses have been made in exploring complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil models. Additionally, some new challenges have arisen. To the best of the author's knowledge, most studies are concerned with the deterministic case; however, the effects of stochasticity encountered in practical flight environments on the nonlinear dynamical behaviors of the airfoil systems are neglected. Crucially, coupling interaction of the structure nonlinearities and uncertainty fluctuations can lead to some difficulties on the airfoil models, including accurate modeling, response solving, and vibration suppression. At the same time, most of the existing studies depend mainly on a mathematical model established by physical mechanisms. Unfortunately, it is challenging and even impossible to obtain an accurate physical model of the complex wing structure in engineering practice. The emergence of data science and machine learning provides new opportunities for understanding the aeroelastic airfoil systems from the data-driven point of view, such as data-driven modeling, prediction, and control from the recorded data. Nevertheless, relevant data-driven problems of the aeroelastic airfoil systems are not addressed well up to now. This survey contributes to conducting a comprehensive overview of recent developments toward understanding complex dynamical behaviors and vibration suppression, especially for stochastic dynamics, early warning, and data-driven problems, of the conceptual two-dimensional airfoil models with different structural nonlinearities. The results on the airfoil models are summarized and discussed. Besides, several potential development directions that are worth further exploration are also highlighted.

High-Lift Common Research Model: RANS, HRLES, and WMLES perspectives for CLmax prediction using LAVA

Abstract: A unified assessment of three turbulence treatments: Reynolds Averaged Navier-Stokes (RANS), Hybrid RANS/LES (HRLES) and Equilibrium Wall-Modelled Large Eddy Simulation (WMLES) is presented for the High-Lift Common Research Model (CRM-HL). For the free-air configuration, steady-state RANS simulations show very accurate drag polar predictions in the low-AoA linear regime. However, strong grid sensitivity is reported near the maximum lift-state (CLmax ), with finer-grids showing larger errors and predicting erroneous flow topologies on the wing. Our RANS simulations show that several corrections for the Spalart-Allmaras (SA) turbulence model widely used in the community lead to more erroneous results compared to the baseline closure, without exception. Both scale-resolving methods (HRLES and WMLES) address these drawbacks and predict an outboard separation pattern on the main element that is in good agreement with the oil flow photographs taken from the QinetiQ wind tunnel experiments, when LES-appropriate grids and numerical discretizations are used. While RANS simulations with the baseline SA closure do not show any wing-root separation post CLmax , both HRLES and WMLES show onset of corner flow separation with varying degrees of progression, along with a weak pitch break in the wing-contribution of the overall pitching moment. This post-CLmax pitch break seen in the free-air simulations is weaker than the break observed in experiments, with a weaker break reported in WMLES for each iteration of grid-refinement. In-tunnel simulations using both SA-baseline RANS and WMLES show a much stronger post-CLmax break with the WMLES predictions showing excellent agreement with the experiment in terms of both the flow-topology observed and the pressure-coefficients at various spanwise stations. Sensitivity to the tunnel wall boundary layer is characterized via comparisons between viscous and inviscid treatments for the tunnel walls. WMLES predictions show moderate sensitivity at the predicted inboard flow-state at CLmax along with the progression towards a post-CLmax stall; however, this stalled state at AoA ≈ 20◦ (inside the tunnel) obtained with both tunnel wall treatments appears to be largely identical.

Transition, intermittency and phase interference effects in airfoil secondary tones and acoustic feedback loop

Abstract: A large eddy simulation is performed to study secondary tones generated by a NACA0012 airfoil at angle of attack of $\alpha = 3^{\circ}$ with freestream Mach number of $M_{\infty} = 0.3$ and Reynolds number of $Re = 5 \times 10^4$. Laminar separation bubbles are observed over the suction side and near the trailing edge, on the pressure side. Flow visualization and spectral analysis are employed to investigate vortex shedding aft of the suction side separation bubble. Vortex interaction results in merging or bursting such that coherent structures or turbulent packets are advected towards the trailing edge leading to different levels of noise emission. Despite the intermittent occurrence of laminar-turbulent transition, the noise spectrum depicts a main tone with multiple equidistant secondary tones. To understand the role of flow instabilities on the tones, the linearized Navier-Stokes equations are examined in its operator form through bi-global stability and resolvent analyses, and by time evolution of disturbances using a matrix-free method. These linear global analyses reveal amplification of disturbances over the suction side separation bubble. Non-normality of the linear operator leads to further transient amplification due to modal interaction among eigenvectors. Two-point, one time autocovariance calculations of pressure along the spanwise direction elucidate aspects of the acoustic feedback loop mechanism in the non-linear solutions. This feedback process is self-sustained by acoustic waves radiated from the trailing edge, which reach the most sensitive flow location between the leading edge and the separation bubble, as identified by the resolvent analysis. Leading edge disturbances arising from secondary diffraction and phase interference among the most unstable frequencies computed in the eigenspectrum are also shown to have an important role in the feedback loop.

Multi-Objective Optimization and Optimal Airfoil Blade Selection for a Small Horizontal-Axis Wind Turbine (HAWT) for Application in Regions with Various Wind Potential

Abstract: The type of airfoil with small wind turbine blades should be selected based on the wind potential of the area in which the turbine is used. In this study, 10 low Reynolds number airfoils, namely, BW-3, E387, FX 63-137, S822, S834, SD7062, SG6040, SG6043, SG6051, and USNPS4, were selected and their performance was evaluated in a 1 kW wind turbine in terms of the power coefficient and also the startup time, by performing a multi-objective optimization study. The blade element momentum technique was utilized to perform the calculations of the power coefficient and startup time and the differential evolution algorithm was employed to carry out the optimization. The results reveal that the type of airfoil used in the turbine blade, aside from the aerodynamic performance, completely affects the turbine startup performance. The SG6043 airfoil has the highest power coefficient and the BW-3 airfoil presents the shortest startup time. The high lift-to-drag ratio of the SG6043 airfoil and the low inertia of the turbine blades fitted with the BW-3 airfoil make them suitable for operation in windy regions and areas with low wind speeds, respectively.

Coupled particle and mesh method in an Euler frame for unsteady flows around the pitching airfoil

Abstract: Finite difference method (FDM) is a grid-based method which is convenient for implementing multiple resolutions and has high computational efficiency. Smoothed particle hydrodynamics (SPH) is a meshless and particle method which has been widely applied into the fluid flows with free surfaces. In this study, a coupled SPH-FDM approach in an Euler frame is developed to simulate the unsteady flow around a pitching airfoil. In addition, an improved iterative shifting particle technology (IPST) is proposed to satisfy the movement of pitching airfoils. Steady flows around the static airfoils are simulated by SPH-FDM, and the results are compared with results of finite volume mothed (FVM) and literatures. The flows around the pitching airfoil with different conditions are simulated, and the results by coupled SPH-FDM are compared with the results by FVM which uses mesh deformation technology to control the movement of mesh. The numerical results by SPH-FDM in an Euler frame are agreement with the results of FVM or literatures, which verifies the effectiveness of the present method. The results of pithing airfoils show the direction of lift hysteresis loop changes from clockwise to counterclockwise as the pitching axis moves from the leading edge to the trailing edge, which can be well predicted by present SPH-FDM in the Euler frame. In addition, some parameters which may affect the lift hysteresis loop are discussed in this study. The results indicate that the acceleration of the airfoil surface has little effect on the lift hysteresis loop; the shape of airfoil has a weak effect on the lift hysteresis loop; the velocity of the airfoil surface, the position of the pitching axis and the free-stream velocity have predominant effect on the lift hysteresis loop.