Subjects: Computer Science >> Other Disciplines of Computer Science Subjects: Mechanics >> Other Disciplines of Mechanics submitted time 2023-06-15
Abstract： Topology optimization is widely used in the engineering design phase to maximize product performance by mathematically modeling and optimizing the distribution of materials in the design space. However, deep learning to solve the topology optimization problem suffers from insufficient data and weak adaptability of the training model boundary conditions. Therefore, a Topy library-based data sample generation method is used to generate 400,000 2D samples of four types of boundary conditions for random structures, cantilever beams, continuous beams and simply supported beams, each containing two types of resolution data, and to expose this dataset. An improved DoubleU-Net network is proposed for topology optimization with high accuracy prediction in real time. In the generated dataset, the average IoU accuracies of the models for four structures, namely, random beam, cantilever beam, continuous beam and simply supported beam, are 93.26%, 96.71%, 96.35% and 97.38, respectively, and the experimental results show that DoubleU-Net can better adapt to different resolution data. The model trained with the random structure dataset has strong generalization ability and has great potential for real-time structural optimization in large-scale projects.
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science Subjects: Mechanics >> Oscillation and Wave submitted time 2023-06-15
Abstract： This paper proposes a modal analysis strategy based on dilated residual convolutional broad network. In modal analysis, vibration analysis of large-scale structures or complex systems usually requires processing large amounts of data and complex calculations.The dilated residual convolution width architecture can reduce the number of parameters and computational complexity of the network, reduce the computational burden, and improve the efficiency of analysis.The dilated residual convolutional broad network applied to modal analysis tasks can improve the extraction ability of vibration features, improve the accuracy of modal identification, and enhance the sensitivity of structural damage detection, and has high computational efficiency and parameter efficiency. The experimental results show that our model achieves excellent performance in the regression task of modal analysis prediction.
Peer Review Status:Awaiting Review
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