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Probabilistic multiple cracking model of elastic-brittle matrix composite reflecting randomness in matrix, reinforcement and bond
Published in Günther Meschke, Bernhard Pichler, Jan G. Rots, Computational Modelling of Concrete Structures, 2018
M. Vořechovský, R. Chudoba, Y. Li, R. Rypl
Carbon fibers have a random tensile 1) breaking strain ξ determined by the weakest flaw in the material structure. The 2) bond strength τ is random due to the irregular matrix penetration into the yarn structure and the 3) effective matrix tensile strength is random due to its heterogeneous material structure and (quasi) brittle failure mode. The remaining parameters are assumed to be deterministic: From the product specifications. The matrix modulus of elasticity, fiber radius, and fiber modulus of elasticity were set deterministic based on based on tests performed at RWTH Aachen, see the model parameters are summarized in Table 1.
Image-Based Photonic Techniques for Microfluidics
Published in Sushanta K. Mitra, Suman Chakraborty, Fabrication, Implementation, and Applications, 2016
David S. Nobes, Mona Abdolrazaghi, Sushanta K. Mitra
The algorithm then loops through and considers this approach for each particle found within the region of interest and results in an irregular matrix of velocity vectors. This particle-tracking approach can easily be extended to three dimensions. Other approaches have been developed to search for and track particles, which include the use of multiple frames to track particles through time (Malik et al., 1993), the use of neural networks to find particle trajectories (Labonté, 1999), and the use of a method based on velocity gradient tensor in the case of strong deformation in the fluid (Ishikawa et al., 2000).
Numerical simulation study of shale gas reservoir with stress-dependent fracture conductivity using multiscale discrete fracture network model
Published in Particulate Science and Technology, 2018
Mi Lidong, Jiang Hanqiao, Mou Shanbo, Li Junjian, Pei Yanli, Chuanbin Liu
Based on the equivalent volume assumption, irregular matrix blocks can be transformed to corresponding cuboid of the same volume. Therefore, we can define the pathway length of each rectangular block as the length of each fracture. The gas flow inside each block is regarded as one-dimensional flow and it moves out of a block from the fracture midpoint to that of another block. The discrete form of Equation (18) can be written as Equation (26) by discretizing the effective matrix volume into nb blocks centered at xi with length Δxi where i = 1,…, nb.
An intelligent ensemble classification method based on multi-layer perceptron neural network and evolutionary algorithms for breast cancer diagnosis
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Saeed Talatian Azad, Gholamreza Ahmadi, Amin Rezaeipanah
Finally, the best model produced by the IEC-MLP method based on the irregular matrix for all instances is shown in Table 10. The results show that only 7 out of 241 malignant instances were misidentified by the IEC-MLP as benign. Moreover, the IEC-MLP misidentified 6 of the 458 benign instances.
An adaptive approach for compression format based on bagging algorithm
Published in International Journal of Parallel, Emergent and Distributed Systems, 2023
Cui Huanyu, Han Qilong, Wang Nianbin, Wang Ye
In this section, we first provide the criteria for selecting the sparse matrix type, and make the data set be used separately on a single GPU, where the matrix is selected according to the following information: Select a matrix suitable for the global memory space used by a single GPU graphics card. Analysis in terms of data size, if a given sparse matrix's DIA compression format or ELL format size is suitable for GPU memory (8 * DIA data length/ELL data length for double precision calculations) less than 80% of the available GPU global memory.The values of non-zero elements in sparse matrix do not have complex values.The length and width of matrices are the same, that is to say, shape of the matrices are square. If the length and width of the matrix are not the same, when long rows * short columns, with the increase of the number of columns of different matrices, the performance of SPMV will gradually increase until stable. But increasing the number of columns in the matrix will precipitate a performance decline. When short rows * long columns, the performance of SPMV decreases faster as the number of matrix rows decreases. Therefore, in order to avoid other factors except the performance changes caused by matrix type, the sparse matrix shape with length and width of same matrix is selected.Matrix is asymmetric or symmetric. The matrix type is mainly for ELL or HYB sparsematrix compression format.The total number of rows in the matrix should be at least equal to the warp concurrency, which is the ratio of the total number of threads in all stream multiprocessor SM to the size of warp. Each SM can simultaneously process 2 thread blocks in parallel. Each thread block contains 1024 threads, so an SM can simultaneously process 2048 threads.Matrices contains a variety of matrix types, including diagonal matrix and irregular matrix. These two matrix types are mainly selected for DIA and CSR compression format to determine whether they can effectively handle the corresponding type of matrix, and to test the prediction accuracy of adaptive compression format.The matrix data is derived from the sparse matrix collection at the University of Florida at https://sparse.tamu.edu/. The file format of the data is mtx. Most mtx files contain sparse matrix rows, columns, and values of non-zero elements. Row and column values are integers, and non-zero elements values may be complex,integers, decimals, and zeros.