2 research outputs found
Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN
The heat transfer performance of Plate Fin Heat Sink (PFHS) has been
investigated experimentally and extensively. Commonly, the objective function
of the PFHS design is based on the responses of simulations. Compared with
existing studies, the purpose of this study is to transfer from analysis-based
model to image-based one for heat sink designs. Compared with the popular
objective function based on maximum, mean, variance values etc., more
information should be involved in image-based and thus a more objective model
should be constructed. It means that the sequential optimization should be
based on images instead of responses and more reasonable solutions should be
obtained. Therefore, an image-based reconstruction model of a heat transfer
process for a 3D-PFHS is established. Unlike image recognition, such procedure
cannot be implemented by existing recognition algorithms (e.g. Convolutional
Neural Network) directly. Therefore, a Reconstructive Neural Network (ReConNN),
integrated supervised learning and unsupervised learning techniques, is
suggested and improved to achieve higher accuracy. According to the
experimental results, the heat transfer process can be observed more detailed
and clearly, and the reconstructed results are meaningful for the further
optimizations
Variational Auto-Encoder Based Approximate Bayesian Computation Uncertian Inverse Method for Sheet Metal Forming Problem
In this study, an image-assisted Approximate Bayesian Computation (ABC)
parameter inverse method is proposed to identify the design parameters. In the
proposed method, the images are mapped to a low-dimensional latent space by
Variational Auto-Encoder (VAE), and the information loss is minimized by
network training. Therefore, an effective trade-off between information loss
and computational cost can be achieved by using the latent variables of VAE as
summary statistics of ABC, which overcomes the difficulty of selecting summary
statistics in the ABC. Besides, for some practical engineering problems,
processing the images as objective function can effective show the response
result. Meanwhile, the relationship between design parameters and the latent
variables is constructed by Least Squares Support Vector Regression (LSSVR)
surrogate model. With the well-constructed LSSVR model, the simulation
coefficient vectors under given parameters will be determined effectively.
Then, the parameters to be identified are determined by comparing the simulated
and observed coefficient vectors in ABC. Finally, a sheet forming problem is
investgated by the suggested method. The material parameters of the blank and
the process parameters of the forming process are identified. Results show that
the method is feasibility and effective for the identification of sheet forming
parameters