The adaptive FEM analysis of the temperature field of the piston in one diesel engine is given by using the ANSYS software. By making full use of the post results provided by the software, the posteriori error estimat...The adaptive FEM analysis of the temperature field of the piston in one diesel engine is given by using the ANSYS software. By making full use of the post results provided by the software, the posteriori error estimation and adaptive accuracy meshing algorithm is developed. So the blindness of the mesh design through experiences can be avoided, and the accuracy requirement is adapted to the relative temperature gradient distribution across the entire domain. Therefore the meshes and solutions can be obtained at the same time. Based on the temperature field analysis, the thermal stress and deformation fields are calculated as well. The results show that the stress concentrates on the edge of the piston pin boss and the inside surface of the first ring groove, and the deformation of the head of the piston is greatest. But the difference between the long and short axes of the bottom cross section is greatest.展开更多
Simultaneous-source acquisition has been recog- nized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of...Simultaneous-source acquisition has been recog- nized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method （RLSRTM） using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a fiat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneoussource data, incomplete data and noisy data.展开更多
文摘The adaptive FEM analysis of the temperature field of the piston in one diesel engine is given by using the ANSYS software. By making full use of the post results provided by the software, the posteriori error estimation and adaptive accuracy meshing algorithm is developed. So the blindness of the mesh design through experiences can be avoided, and the accuracy requirement is adapted to the relative temperature gradient distribution across the entire domain. Therefore the meshes and solutions can be obtained at the same time. Based on the temperature field analysis, the thermal stress and deformation fields are calculated as well. The results show that the stress concentrates on the edge of the piston pin boss and the inside surface of the first ring groove, and the deformation of the head of the piston is greatest. But the difference between the long and short axes of the bottom cross section is greatest.
基金financial support from the National Natural Science Foundation of China (Grant Nos. 41104069, 41274124)National Key Basic Research Program of China (973 Program) (Grant No. 2014CB239006)+2 种基金National Science and Technology Major Project (Grant No. 2011ZX05014-001-008)the Open Foundation of SINOPEC Key Laboratory of Geophysics (Grant No. 33550006-15-FW2099-0033)the Fundamental Research Funds for the Central Universities (Grant No. 16CX06046A)
文摘Simultaneous-source acquisition has been recog- nized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method （RLSRTM） using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a fiat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneoussource data, incomplete data and noisy data.
