9 results about "Particle method" patented technology

Aspects herein describe new methods of determining optimal actions to achieve high-level objectives based on an optimized chosen statistic. At least one high-level objective, along with various observational data about the world, is identified by a computational unit. The computational unit determines, through a particle method, an optimal course of action. The particle method is doubly-exponentially accelerated based on one or more acceleration methods. The doubly-exponentially accelerated particle method comprises alternating backward and forward sweeps of a coupled induction loop to optimize a selection policy and test for convergence to determine said optimal course of action. The doubly-exponentially accelerated particle method may be applied in multiple different time scales.

A data processing method suitable for multi-resolution particle methods includes the following steps: reading initial data and establishing an original model array to start calculation; detecting and judging all particles separately to determine whether particles split or merge, with the judgment condition being whether particles of different sizes flow across precisions; marking and recording the corresponding number of particles that split or merge; constructing two intermediate arrays to store particle information after splitting and merging; updating particle information in conjunction with time nodes, and updating the final particle data and corresponding particle information to the original model array for subsequent calculations, thereby improving overall computational efficiency.

This invention relates to the field of geological disaster prevention and control, specifically to a method for improving the accuracy of landslide disaster chain simulation using the smooth particle method. The method comprises the following steps: Step 1: Collecting survey data on the target landslide and its impact range to be simulated, and based on this data, clarifying the geological conditions, topography, landslide body physical and mechanical parameters, and water body physical and mechanical parameters of the target landslide and its impact range; Step 2: Constructing a numerical analysis model for landslide disaster chain numerical simulation based on the smooth particle method; Step 3: Setting initial calculation parameters based on the numerical analysis model constructed in Step 2; Step 4: Starting the simulation calculation, updating the effective radius of any calculation time step according to a preset rule, and continuing the calculation for the next time step until all calculations are completed, obtaining the numerical simulation result of the landslide disaster chain. This invention can significantly improve the accuracy of landslide disaster chain simulation using the smooth particle method.

A method for evaluating melt fragmentation in a sodium-cooled fast reactor (SFR), in which a high-order accurate particle method is adopted to discretely solve a corresponding model and calculate the melt fragmentation process in the SFR. A least-squares moving particle semi-implicit method with high-order accuracy for gradient and Laplace models is used, and a particle-shifting technique is used to maintain a more uniform particle distribution. A geometry-based free surface identification technique is adopted. An equation-of-state model is introduced to enable acquisition of more realistic physical parameters. The solid-liquid coupling model and the phase transition model are combined to carry out the high-order accurate discretization. An evaluation system is also provided. The melt fragmentation data can be updated to the system in real time, and a real-time dynamic graph of the melt fragmentation process is automatically generated, and real-time dynamic cloud graphs of different influencing factors are automatically generated.

The invention discloses a multiphase composite structure dynamic fracture prediction method based on deep learning. The method comprises the following steps: generating fracture evolution data of a multiphase composite structure under dynamic loading as sample data by adopting a GPU accelerated finite mass point method and combining with a rate-related cohesive zone model; the method comprises the following steps of: constructing a multi-dimensional feature system comprising static space features and dynamic time features by taking a bonding unit of a potential cracking region of a multi-phase composite structure as a description object, and carrying out standardization processing on different types of features; constructing a space-time cooperative encoder-decoder deep learning framework; the structure of the framework is as follows: starting from an input layer, a graph attention network space encoder, a feature fusion layer, a gating cycle unit time decoder and a double-branch output layer are connected in sequence; training and verifying the space-time collaborative encoder-decoder deep learning framework for training to obtain a final deep learning framework; and inputting the characteristic data of the test set or the to-be-predicted multi-phase composite structure into the final deep learning framework to complete the rapid prediction of the dynamic fracture evolution of the multi-phase composite structure. According to the method, efficient prediction of dynamic fracture evolution of the multiphase composite structure can be realized, calculation is simple, convenient and efficient, and the defects of the content in current research are overcome.

This invention discloses a blowout prevention system and method for auger excavators based on SPH-DEM coupling, belonging to the field of tunnel engineering construction technology. The system integrates an SPH fluid simulation module, a DEM particle simulation module, a bidirectional coupling interface, a parametric model of the auger excavator, and a blowout early warning module. It uses the Lagrange particle method to discretize the fluid, Hertz-Mindlin theory to solve for particle contact forces, and GPU parallel computing to achieve bidirectional exchange of forces between the fluid and particles. It also integrates multiple key physical quantities to construct a blowout risk criterion. The method includes multi-scale model construction, coupling calculation parameter setting, bidirectional coupling calculation, blowout feature extraction, risk early warning, and parameter optimization steps. This invention simulates the particle migration and blowout mechanism caused by water pressure disturbance in water-rich strata, improving the accuracy of blowout risk identification and early warning capabilities, and providing a scientific basis for the structural design and construction parameter control of auger excavators.

The invention provides an energy-controllable turbulence simulation method and device based on continuous convolution, and relates to the technical field of computer graphics fluid simulation. The method comprises the following steps: generating particle method physical turbulence data through various physical solvers, sampling and calculating particle speed to obtain various model data sets; training the continuous convolution network model to obtain a plurality of continuous convolution approximation model units, and performing particle displacement prediction according to the model units; and inputting the particle displacement prediction result into an energy interpolation network to obtain an interpolation proportionality coefficient, and carrying out hybrid interpolation on the particle displacement prediction results of the plurality of continuous convolution approximation model units according to the interpolation proportionality coefficient to obtain a final simulation result. The turbulence energy change level generated by the method keeps positive correlation with the control parameters, the effectiveness of the energy intensity coefficient on turbulence energy control is proved, and the obvious trend of transition from laminar flow to turbulence is presented in the visual effect.

Aspects herein describe new methods of determining optimal actions to achieve high-level objectives based on an optimized chosen statistic. At least one high-level objective, along with various observational data about the world, is identified by a computational unit. The computational unit determines, through a particle method, an optimal course of action. The particle method is doubly-exponentially accelerated based on one or more acceleration methods. The doubly-exponentially accelerated particle method comprises alternating backward and forward sweeps of a coupled induction loop to optimize a selection policy and test for convergence to determine said optimal course of action. The doubly-exponentially accelerated particle method may be applied to a meta-control problem to determine an optimal course of action for achieving the goals of a plurality of different control systems having respective computational units.

The application discloses a reactor core meltdown collector design optimization method based on a least square particle method, which is based on a least square moving particle method in a Lagrange method, has higher simulation accuracy and no space consistency limitation, adds a fluid-solid coupling model (PMS model) for calculating the interaction between a coolant and solid melt, and finally calculates the force between solid particles of the melt in the flow field and between the solid particles of the melt and a solid wall through a solid collision model (DEM method). In addition, an optimized particle slip model is used to relieve the particle particle aggregation effect, the calculation density and dynamic viscosity are processed to improve the calculation stability, and the accuracy of particle type determination is improved through a double detection method of algebraic calculation and geometric calculation. The method is a method with the ability of simulating the fluid-solid coupling problem and the solid collision problem in the reactor core meltdown collector and is used for the design optimization of the reactor core meltdown collector.