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The shape of a bluff body section is of high importance to its aerostatic performance. Obtaining the aerostatic performance of a specific shape based on wind tunnel tests and CFD simulations takes a lot of time, which affects evaluation efficiency. This paper proposes a novel fully convolutional neural network model that enables rapid prediction fr...
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The planform-customized waverider developed from the osculating-cone method enables the design of a double swept waverider to remedy some deficiencies for the waverider. To analyze the aerodynamic performances, two models of the double swept waveriders, with cusp head and bend head shape, respectively, were fabricated for the wind-tunnel test, and...
Citations
... Artificial neural network (ANN) is a computational method inspired by biological neural networks found in brains and is frequently used for regression, prediction and approximation applications [7]. ANN finds vast applications in various fields of science and engineering [8,9]. ...
... A streamlined body, such as an airfoil, is characterized by a thin boundary layer attached to the entire surface, with a narrow and generally steady wake. Conversely, bluff bodies such as buildings and sign panels are characterized by substantial boundary layer separation, with larger and unsteady wakes [18,19]. The outer region of the separated flow features a thin layer of high shear and vorticity. ...
Road signs are prone to extreme winds that cause significant damage. Overhead sign structures can disrupt traffic and cause harm to the traveling public if a failure occurs under extreme wind conditions. In this paper, we employ Computational Fluid Dynamics (CFD) in a comparative study to understand the aerodynamics of standard, porous, and curved signs. The study shows the viability of porous and curved overhead boards for lessening aerodynamic loads, which can mini-mize damage and enhance safety on roadways. Porous overhead signs can decrease the drag forces; however, the size of the openings is a vital parameter in reducing wind loads. Small and uniform perforations lead to higher drag forces, compared to larger ones, under the same porosity ratio. Introducing porosity to a solid panel moves the vorticity region further downstream, reducing the magnitude of pressures on the leeward side and decreasing the drag force. However, curved panels further enhanced the force reduction.
... SDF and binary features were developed for CFD input learning of CNN and U-Net models in [37]. Li et al. [38] proposed a wall distance field and space coordinate field for the U-Net model's input features. In this study, we used a different approach to generate novel input features for our proposed model. ...
For industrial design and the improvement of fluid flow simulations, computational fluid dynamics (CFD) solvers offer practical functions and conveniences. However, because iterative simulations demand lengthy computation times and a considerable amount of memory for sophisticated calculations, CFD solvers are not economically viable. Such limitations are overcome by CFD data-driven learning models based on neural networks, which lower the trade-off between accurate simulation performance and model complexity. Deep neural networks (DNNs) or convolutional neural networks (CNNs) are good illustrations of deep learning-based CFD models for fluid flow modeling. However, improving the accuracy of fluid flow reconstruction or estimation in these earlier methods is crucial. Based on interpolated feature data generation and a deep U-Net learning model, this work suggests a rapid laminar flow prediction model for inference of Naiver–Stokes solutions. The simulated dataset consists of 2D obstacles in various positions and orientations, including cylinders, triangles, rectangles, and pentagons. The accuracy of estimating velocities and pressure fields with minimal relative errors can be improved using this cutting-edge technique in training and testing procedures. Tasks involving CFD design and optimization should benefit from the experimental findings.
... CFD methodology has been pursued by some researchers to study the integrated device in recent years [15]. With the huge improvement of computer hardware and the development of numerical algorithms, the use of CFD numerical simulation to analyze the complex flow conditions of airflow and particulate materials in an integrated device has become an indispensable tool for studying multiphase reactions [16][17][18]. This technique is particularly useful in the simulation of gas-solid two-phase flows in process engineering. ...
The briquetting technology of rice straw could increase the bulk density of the straw, reduce transportation and storage costs, and improve resource utilization. This paper analyzed the working principle of the air-conveying integrated device in briquetting machines. High-speed photography technology was used to track and record the movement process of crushed straw material in the air-conveying cylinder area. It was compared with the simulation results of the average velocity of crushed straw material to verify the reliability of the simulation. The results showed that the flow of straw scraps in the straw-shredding and air-conveying integrated device was relatively stable when the impeller speed was 630 r/min, the number of blades was three, the blades were tilted back 15°, and the radius of curvature of the air-conveying tube elbow was 700 mm. At the same time, the speed distribution was uniform, and the highest throwing speed reached 4.5 m/s to 4.8 m/s. After optimization, the average increase rate of briquette density was 2.61% and the average increase rate of briquette productivity was 2.52%. The fluid movement law of the straw-shredding and air-conveying integrated device studied in this paper could be used to optimize the air-conveying device, improve the efficiency of straw briquetting and the utilization rate of straw resources.
... To meet the objectives of progress and innovation in FSI in various scenarios of engineering applications and control schemes, this book includes 15 research studies and collects the most recent and cutting-edge developments on these relevant issues. The topics cover different areas associated with FSI, including wind loads [1][2][3], flow control [4][5][6][7][8][9], energy harvesting [10], buffeting and flutter [11,12], complex flow characteristics [13], train-bridge interactions [14] and the application of neural networks in related fields [15]. In summary, these complementary contributions in this publication provide a volume of recent knowledge in the growing field of FSI. ...
Fluid–structure interactions (FSI) play a crucial role in the design, construction, service and maintenance of many engineering applications, e [...]
