2 results about "Cellular automaton" patented technology

The application discloses a kind of 3D printing microstructure and performance accurate regulation and control method by thermal field behavior monitoring feedback, belong to the technical field of additive manufacturing, including: step one, obtain the process parameters in the process of additive manufacturing and real-time monitoring thermal field behavior;Step two, optimize 3D thermal field model and generate a group of selected process parameters under temperature field;Step three, utilize cellular automaton coupling through the temperature field under selected process parameters generated by 3D thermal field model optimized in step two and its corresponding multi-physical field, to simulate the microstructure topography under corresponding process parameters;Step four, train microstructure accurate regulation and control model;Step five, target part printing and its microstructure distribution real-time accurate regulation and control.It can be seen from this that the application can accurately regulate the generation of microstructure, improve the overall mechanical properties of target printed parts.

The application discloses a lightning-caused fire spreading simulation method and system, and the method comprises the steps: acquiring multi-source spatio-temporal characteristic data corresponding to lightning-caused fire; performing feature extraction and fusion on the corresponding multi-source spatio-temporal characteristic data through a pre-constructed spatio-temporal feature fusion network, and outputting prediction parameters for representing fire state conversion; constructing a multi-state cellular automaton including a smoldering state and an open fire state, locating a lightning ignition point, initializing the state of the cell corresponding to the lightning ignition point to the smoldering state, and giving the cell corresponding to the lightning ignition point its initial thermal energy; performing phase change discrimination on the cell in the smoldering state based on the prediction parameters and dynamic time sequence characteristics; performing a neighborhood propagation rule based on vector correction based on the phase change discrimination result to simulate lightning-caused fire spreading, and outputting a simulation result. Through deep coupling of deep learning feature extraction and physical mechanism modeling, the accuracy and scientificity of lightning-caused fire simulation are significantly improved by the scheme.