Search

A deep learning framework is proposed for real-time transonic flow prediction. To capture the complex shock discontinuity of transonic flow, we introduce the residual network ResNet and deconvolutional neural networks to learn the nonlinear discontinuity phenomenon in transonic flow, which is affected by the Mach number, angle of attack, Reynolds number, and aerodynamic shape. In our framework, flow field variables on actual grid points are utilized in the neural network training to avoid the interpolation operation and the input of spatial position with a point cloud that is required with traditional convolutional neural networks. To investigate and validate the proposed framework, transonic flows around two-dimensional airfoils and three-dimensional wings are utilized to verify its effectiveness and prediction accuracy. The results prove that the model is able to efficiently learn the transonic flow field under the influence of the Mach number, angle of attack, Reynolds number, and aerodynamic shape. Significantly, some essential physical features, such as shock strength and location, flow separation, and the boundary layer, are accurately captured by this model. Furthermore, it is shown that our framework is able to make accurate predictions of the pressure distribution and aerodynamic coefficients. Thus, the present work provides an efficient and robust surrogate model for computational fluid dynamics simulation that enhances the efficiency of complex aerodynamic shape design optimization tasks and represents a step toward the realization of the digital twin concept.

Deep reinforcement learning (RL) is capable of identifying and modifying strategies for active flow control. However, the classic active formulation of deep RL requires lengthy active exploration. This paper describes the introduction of expert demonstration into a classic off-policy RL algorithm, the soft actor-critic algorithm, for application to vortex-induced vibration problems. This combined online-learning framework is applied to an oscillator wake environment and a Navier–Stokes environment with expert demonstration obtained from the pole-placement method and surrogate model optimization. The results show that the soft actor-critic framework combined with expert demonstration enables rapid learning of active flow control strategies through a combination of prior demonstration data and online experience. This study develops a new data-efficient RL approach for discovering active flow control strategies for vortex-induced vibration, providing a more practical methodology for industrial applications.

In the present work, we propose an optimization framework based on the active learning method, which aims to quickly determine the conditions of tandem flapping wings for optimal performance in terms of thrust or efficiency. Especially, multi-fidelity Gaussian process regression is used to establish the surrogate model correlating the kinematic parameters of tandem flapping wings and their aerodynamic performances. Moreover, the Bayesian optimization algorithm is employed to select new candidate points and update the surrogate model. With this framework, the parameter space can be explored and exploited adaptively. Two optimization tasks of tandem wings are carried out using this surrogate-based framework by optimizing thrust and propulsion efficiency. The response surfaces predicted from the updated surrogate model present the influence of the flapping frequency, phase, and separation distance on thrust and efficiency. It is found that the time-average thrust of the hind flapping wing increases with the frequency. However, the increase in frequency may lead to a decrease in propulsive efficiency in some circumstances.

Nanotip arrays with high aspect ratio, which have attracted much attention due to their potential applications, have been fabricated by many methods. Dry etching combined with self-assembly masks is widely used because of the convenience of dry etching and high throughput of self-assembly. In this paper, we report a method combining Cesium Chloride (CsCl) self-assembly with inductively coupled plasma (ICP) dry etching to fabricate silicon nanotip arrays with high aspect ratio and silicon nanotip arrays with aspect ratio 15 have been achieved after optimization of all parameters.