Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational B 'ezier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.
There are also studies on the reverse design problem where functional curves like airfoils are synthesized from functional parameters ( , the pressure distribution and the lift drag coefficient) @cite_19 @cite_16 . These methods use neural networks to learn the complicated relationship between the curve geometry and its corresponding functional parameters. While the goal and method in our work are different, we share the idea of using neural networks to learn parametric curves.
@cite_19 @cite_16
2088405726 1971254313
Abstract Determining the airfoil geometry from a given C p -distribution is an inverse problem of paramount importance specially in the context of variable geometry aerodynamic platforms. This work describes the implementation of artificial neural nets for the airfoil geometry determination. Instead of using full coordinates of the airfoil, Bezier–PARSEC 3434 parameters have been used to describe an airfoil. Some of these parameters have been determined using a Genetic Algorithm. In the second stage C p -distribution in terms of c l , c d and c m for 10 angles of attack has been input into three different neural nets for learning and then estimating the corresponding BP3434 parameters. Feed-forward backpropagation, Generalized regression and Radial basis neural nets have been trained and then compared in terms of performance and regression statistics. The work establishes the superiority of feed-forward backpropagation neural nets. The result is partly due to good function approximation properties of the neural architecture and partly due to the use of Bezier–PARSEC 3434 parameterization scheme. A novel self-adaptive geometric primitive for functional geometric shape synthesis is presented. This novel geometric primitive, for CAD use, is specifically designed to reproduce geometric shapes with functional requirements, such as the aerodynamic and hydrodynamic ones, once the functional parameters are furnished. It produces a typical CAD representation of a functional profile: a set of Bezier curves. The proposed primitive follows a generate-and-test approach and takes advantage of the use of a properly designed artificial neural network (BNN). It combines the properties of a geometric primitive and the capability to manage the engineering knowledge in a specific field of application. The proposed evolutionary primitive is applied to a real engineering application: the automatic synthesis of airfoils. Some examples are simulated in order to test the effectiveness of the proposed method. The results obtained by an original prototypal software are presented and critically discussed.