Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational s...Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational speed in numerical simulation of casting thermal stress based on FDM, one for handling of nonconstant material properties and the other for dealing with the various coefficients in discretization equations. The use of the two techniques has been discussed and an application in wave-guide casting is given. The results show that the computational speed is almost tripled and the computer storage needed is reduced nearly half compared with those of the original method without the new technologies. The stress results for the casting domain obtained by both methods that set the temperature steps to 0.1 ℃ and 10 ℃, respectively are nearly the same and in good agreement with actual casting situation. It can be concluded that both handling the material properties as an assumption of stepwise profile and eliminating the repeated calculation are reliable and effective to improve computational speed, and applicable in heat transfer and fluid flow simulation.展开更多
We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new conden...We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos. 50827102 and 50931004)National Basic Research Program of China (Grant No. 2010CB631202 and No. 2006CB605202)High Technology Research and Development Program of China (Grant No. 2007AA03Z552)
文摘Finite difference method (FDM) was applied to simulate thermal stress recently, which normally needs a long computational time and big computer storage. This study presents two techniques for improving computational speed in numerical simulation of casting thermal stress based on FDM, one for handling of nonconstant material properties and the other for dealing with the various coefficients in discretization equations. The use of the two techniques has been discussed and an application in wave-guide casting is given. The results show that the computational speed is almost tripled and the computer storage needed is reduced nearly half compared with those of the original method without the new technologies. The stress results for the casting domain obtained by both methods that set the temperature steps to 0.1 ℃ and 10 ℃, respectively are nearly the same and in good agreement with actual casting situation. It can be concluded that both handling the material properties as an assumption of stepwise profile and eliminating the repeated calculation are reliable and effective to improve computational speed, and applicable in heat transfer and fluid flow simulation.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0403301, 2017YFB0503301, and2018YFB0504302)the National Natural Science Foundation of China (Grant Nos. 11991073, 61975229, and Y8JC011L51)+2 种基金the Key Program of CAS (Grant No. XDB17030500)the Civil Space Project (Grant No. D040301)the Science Challenge Project (Grant No. TZ2018005)。
文摘We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.
