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An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.

Aerodynamic reduced-order model (ROM) is a useful tool to predict nonlinear unsteady aerodynamics with reasonable accuracy and very low computational cost. The efficacy of this method has been validated by many recent studies. However, the generalization capability of aerodynamic ROMs with respect to different flow conditions and different aeroelastic parameters should be further improved. In order to enhance the predicting capability of ROM for varying operating conditions, this paper presents an unsteady aerodynamic model based on long short-term memory (LSTM) network from deep learning theory for large training dataset and sampling space. This type of network has attractive potential in modeling temporal sequence data, which is well suited for capturing the time-delayed effects of unsteady aerodynamics. Different from traditional reduced-order models, the current model based on LSTM network does not require the selection of delay orders. The performance of the proposed model is evaluated by a NACA 64A010 airfoil pitching and plunging in the transonic flow across multiple Mach numbers. It is demonstrated that the model can accurately capture the dynamic characteristics of aerodynamic and aeroelastic systems for varying flow and structural parameters.

The stall aerodynamic model based on Kirchhoff’s theory of flow separation is widely used in the identification and modeling of stall aerodynamic parameters. However, it has two defects. First, its model structure is significantly different from the pre-stall model used for a small attack angle, meaning the identification results cannot be combined with the pre-stall model to form the full flight envelope model. Second, the pitching moment model, which is used in conjunction with the Kirchhoff lift model, cannot accurately describe the aircraft stall pitching moment characteristics. To ensure the compatibility of the two models, this paper proposes a method to determine some unknown parameters in the stall model. The mechanism for the pitching moment generation of the aircraft stall is analyzed, and two high-order correction terms are added to the pitching moment model to better describe the longitudinal stall aerodynamic characteristics. Based on the identification of aerodynamic parameters, a longitudinal stall aerodynamic modeling method used for the aircraft stall process is developed. The identification and simulation validation results based on the quasi-steady stall flight data of a civil aircraft show that the improved stall model can accurately describe the quasi-steady stall pitching moment. The established stall aerodynamic model can accurately characterize the longitudinal quasi-steady stall aerodynamic characteristics of aircraft under different stall degrees.

The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \\times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

The aircraft conceptual design step requires a substantial number of aerodynamic configuration evaluations. Since the wing is the main aircraft lifting element, the focus is on solving direct and reverse design problems. The former could be solved using a low-cost computational model, but the latter is unlikely, even for these models. Surrogate modeling is a technique for simplifying complex models that reduces computational time. In this work, a surrogate aerodynamic model, based on the implementation of a multilayer perceptron (MLP), is presented. The input data consist of geometrical characteristics of the wing and airfoil and flight conditions. Some of the MLP hyperparameters are defined using evolutionary algorithms, learning curves, and cross-validation methods. The MLP predicts the aerodynamic coefficients (drag, lift, and pitching moment) with high agreement with the substituted aerodynamic model. The MLP can predict the aerodynamic characteristics of compressible flow up to 0.6 M. The developed MLP has achieved up to almost 800 times faster in computing time than the model on which it was trained. The application of the developed MLP will enable the rapid study of the effects of changes in various parameters and flight conditions on flight performance, related to the design and modernization of new vehicles.

Aerodynamic roughness length (z0${z}_{0}$ ) fundamentally affects land surface momentum loss and wind resource simulation, but ground truth data of z0${z}_{0}$are sparse in space, causing z0${z}_{0}$datasets used in atmospheric models are empirically estimated from land cover types through a look‐up table. In this study, we derived z0${z}_{0}$values from 101 anemometer towers in China. Taking them as ground truth, we show that existing gridded z0${z}_{0}$datasets determined from either a look‐up table or a machine‐learning method contain considerable uncertainty and fail to capture the variability of z0${z}_{0}$within each land cover type, although the latter performs better. Even for the widely used ERA5, its z0${z}_{0}$is overestimated in wind‐rich regions of China, causing an underestimation of near‐surface wind speed. This highlights the necessity to improve z0${z}_{0}$data in atmospheric models. Current rapidly expanding anemometer towers may substantially enrich z0${z}_{0}$truth data and thus provide potential to improve wind resource modeling. Plain Language Summary Aerodynamic roughness length (z0${z}_{0}$ ) is a key parameter for atmospheric modeling and wind energy assessment. However, z0${z}_{0}$is a virtual height that cannot be measured directly, and its truth values are sparse in space. Gridded z0${z}_{0}$datasets are generally estimated from land cover types. Their accuracy and applicability remain uncertain. The increasing number of anemometer towers built for wind resource assessment presents an opportunity to enrich z0${z}_{0}$truth values. In this study, we have derived z0${z}_{0}$values at 101 selected anemometer towers in China. Taking them as ground truth, we evaluated gridded z0${z}_{0}$datasets that were specified by a look‐up table with land cover types or derived by a machine‐learning method. The latter has better accuracy than the former, but they all contain considerable uncertainty and fail to capture the evident variability of z0${z}_{0}$within each land cover type. Such uncertainty of z0${z}_{0}$can cause systematic biases in the simulated near‐surface wind speed. Taking the widely used ERA5 as an example, we show that its z0${z}_{0}$has been overestimated in wind‐rich regions of China, resulting in an underestimation of near‐surface wind speed. This study highlights the potential of the emerging tens of thousands of anemometer towers to improve wind resource modeling. Key Points Ground truth data of aerodynamic roughness length can be substantially enriched with the data of rapidly expanding anemometer towers Current datasets of aerodynamic roughness length determined by land cover types or machine‐learning methods have large uncertainties Biases in the aerodynamic roughness length of ERA5 have led to its underestimation of wind power resources in China

To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier–Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.

This work proposes a multifidelity modeling approach to mitigate adverse characteristics of airfoil dynamic stall through aerodynamic shape optimization (ASO). Cokriging regression (CKR) is used to efficiently determine an optimum airfoil shape by combining data from high-fidelity (HF) and low-fidelity (LF) computational fluid dynamics simulations. The HF dynamic stall response is modeled using the unsteady Reynolds-averaged Navier–Stokes equations and Menter’s SST turbulence model, whereas the LF model is developed by simplifying the HF model with a coarser discretization and relaxed convergence criteria. The CKR model, constructed using various infill criteria to model the objective and constraint functions with six PARSEC parameters, is utilized to find the optimal design. The results show that the optimal shape from CKR delays the dynamic stall angle over 3° while reducing the peak values of the aerodynamic coefficients compared to the baseline airfoil (NACA 0012). Comparing the optimized shapes from the CKR and a HF Kriging regression (HF-KR) shows a similar delay in dynamic stall angle; however, the CKR optimum provides a better design for the current problem formulation while requiring 39% less computational time than the HF-KR approach. This work presents a new multifidelity modeling approach to saving the computational burden of dynamic stall mitigation through ASO. The approach used in this work is general and can be applied for other unsteady aerodynamic applications and optimization.

The airborne wind turbine (AWT) employs a flying energy conversion to harvest the stronger winds blowing at higher altitudes. This study presents an aeroelastic evaluation of the AWT, which carries a flying rotor installed inside a buoyant shell. A considerable aerodynamic impact on the structural integrity of the full-scale system is modeled using a fluid–structure interaction (FSI) approach. Both the fluid and structure models are formulated separately and validated using a series of benchmark numerical data. To analyze the structural aeroelasticity, the aerodynamic loads from the fully resolved computational model are coupled using a one-way FSI on the structural model of the blade and shell to perform the non-linear static analysis. For a detailed investigation, various wind loads from the bare and shell rotor configurations are imposed on the flexible structure. The generated torque, aerodynamic loads, tip deflection, stress estimation and operational stability of the proposed energy system are computed. The tip deflection is 18% more in the shell rotor compared to the bare rotor at rated conditions, while an average increase of 54% more tip deflection was observed for every 4 m/s increase in wind speed. The non-linear aeroelastic characteristics in each case are found to be within the chosen design criteria, according to material, operational speed and structural limits. Most importantly, the significant power gain justifies the structural response of the blade to withstand the shell-induced loads at rated conditions in the shell configuration.

Aeronautical engineers are frequently faced with subsonic flutter phenomena. Thus, in the quest for safety requirements and to avoid catastrophes, it is important to evaluate efficient and low-cost aeroelastic control strategies for flutter suppression. Within this aim, constrained viscoelastic layers appear as an interesting alternative to be used in such situations. However, the modeling of aeroelastic systems with viscoelastic materials is still a challenge due to their inherent frequency- and temperature-dependent behavior and the need to thoroughly understand their mechanical interactions with the aeroelastic system under subsonic conditions. In most of the cases, it is due to their frequency- and temperature-dependent behavior. In this study, a modeling method of aeroviscoelastic systems under subsonic airflows to alleviate the flutter effects is proposed. The finite element model of a three-layer sandwich curved panel coupled with the unsteady aerodynamic loadings generated by the nonplanar doublet-lattice method is used, using an in-house code AEROSOLVER. To solve the complex eigenvalue problem, a modified version of the p-k method has been used to predict the subsonic flutter boundary and to verify the possibility of increasing the critical speeds of the aeroelastic system using viscoelastic materials. Additionally, the influence of the thickness of the layers and the operational temperature of the system on its stability were investigated here.