Systems and methods for designing and optimizing tools are provided. A computational fluid dynamics (CFD) simulation model implemented by a processor is provided to simulate a fluid flow inside the tool to generate a particle density distribution of fluids inside the tool. The particle density distribution is converted to a spatial distribution of fluid concentration of the mixture which allows for high resolution visualization of fluid flow in the tool.

Systems and methods for designing and optimizing tools are provided. A computational fluid dynamics (CFD) simulation model implemented by a processor is provided to simulate a fluid flow inside the tool to generate a particle density distribution of fluids inside the tool. The particle density distribution is converted to a spatial distribution of fluid concentration of the mixture which allows for high resolution visualization of fluid flow in the tool.

A calculation method for real-time physical engine enhancement based on a neural network includes: dynamically constructing a multi-layer and multi-surface pre-collision shell according to key concave and convex vertices of an object to be subjected to collision detection; obtaining an initial collision detection correspondence matrix according to the multi-layer and multi-surface pre-collision shell; and setting a collision detection condition, inputting a relevant parameter of the collision detection condition into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after the parameter screening. When the collision condition satisfies the safety condition, a collision detection correspondence matrix is not updated. When the collision condition does not satisfy the safety condition, the matrix is updated, and the multi-layer and multi-surface pre-collision shell is reconstructed according to the updated matrix. A calculation system for the real-time physical engine enhancement based on a neural network is further provided.

The present invention discloses an optimization method for a screen surface dynamic load of a vibrating screen. The method includes the following steps: step 1. selecting design variables, and establishing an experimental matrix; step 2. performing a response curved surface experiment; step 3. establishing two double-objective optimization models and solving the same to obtain two groups of Pareto solution sets, wherein the solution sets respectively represent screening efficiency optimization paths of the vibrating screen under the conditions of a high screen surface dynamic load and a low screen surface dynamic load; and step 4. calculating an optimization space for a screen surface dynamic load under a high screening efficiency. According to the method of the present invention, the screen surface dynamic load can be directly reduced, and the service life of the screen surface and the whole vibrating screen is prolonged.

The present invention discloses an optimization method for a screen surface dynamic load of a vibrating screen. The method includes the following steps: step 1. selecting design variables, and establishing an experimental matrix; step 2. performing a response curved surface experiment; step 3. establishing two double-objective optimization models and solving the same to obtain two groups of Pareto solution sets, wherein the solution sets respectively represent screening efficiency optimization paths of the vibrating screen under the conditions of a high screen surface dynamic load and a low screen surface dynamic load; and step 4. calculating an optimization space for a screen surface dynamic load under a high screening efficiency. According to the method of the present invention, the screen surface dynamic load can be directly reduced, and the service life of the screen surface and the whole vibrating screen is prolonged.

Optimizing a processes for the manufacture of amorphous silicon by generating molecular dynamics simulations of amorphous silicon across a range of manufacturing conditions, applying a local order metric algorithm to each of the simulations to detect formation of crystalline structures within each of the simulations based on a local order metric determined by the algorithm, determining material quality for each simulation as a function of the local order metric and the detected crystalline structures, modifying manufacturing conditions according to a desired material quality and applying the modified manufacturing conditions to the manufacturing process of amorphous silicon. A molecular dynamics simulation of amorphous silicon may be generated using modified manufacturing conditions, applying the local order metric to the modified simulation to detect whether the crystalline structures are formed, if the crystalline structures are formed, repeating the modifying and dynamics simulation steps until no crystalline structures are detected.

Optimizing a processes for the manufacture of amorphous silicon by generating molecular dynamics simulations of amorphous silicon across a range of manufacturing conditions, applying a local order metric algorithm to each of the simulations to detect formation of crystalline structures within each of the simulations based on a local order metric determined by the algorithm, determining material quality for each simulation as a function of the local order metric and the detected crystalline structures, modifying manufacturing conditions according to a desired material quality and applying the modified manufacturing conditions to the manufacturing process of amorphous silicon. A molecular dynamics simulation of amorphous silicon may be generated using modified manufacturing conditions, applying the local order metric to the modified simulation to detect whether the crystalline structures are formed, if the crystalline structures are formed, repeating the modifying and dynamics simulation steps until no crystalline structures are detected.

Provided is a method for analyzing an effect of a hygroscopic seeding material sprayed through an airborne cloud seeding experiment on ground aerosol concentrations, including the steps of: inputting information of meteorological fields and seeding spraying of the airborne cloud seeding experiment to a numerical cloud seeding model to execute a numerical simulation; calculating a mass concentration of the hygroscopic seeding material on ground, based on results of the numerical simulation; and calculating a contribution degree of the hygroscopic seeding material to mass concentrations of aerosols, based on comparison between the calculated mass concentration of the hygroscopic seeding material and the mass concentrations of the aerosols observed on an execution date of the airborne cloud seeding experiment.

Provided is a method for analyzing an effect of a hygroscopic seeding material sprayed through an airborne cloud seeding experiment on ground aerosol concentrations, including the steps of: inputting information of meteorological fields and seeding spraying of the airborne cloud seeding experiment to a numerical cloud seeding model to execute a numerical simulation; calculating a mass concentration of the hygroscopic seeding material on ground, based on results of the numerical simulation; and calculating a contribution degree of the hygroscopic seeding material to mass concentrations of aerosols, based on comparison between the calculated mass concentration of the hygroscopic seeding material and the mass concentrations of the aerosols observed on an execution date of the airborne cloud seeding experiment.

An elastic body is modelled as a plurality of models of particles and models of geometric elements. Each particle model has a position attribute. Each geometric element has a boundary defined by two or more of the particles as nodes of the geometric element, nodes being points shared with neighbouring geometric elements. The interior area or volume of the geometric elements is modelled via non-linear interpolation functions that use the positions of the particles of the respective geometric elements as inputs to compute position and/or strain of interior points of the geometric elements. Energy is summed over the interior region of the element, the energy computation based on (a) position and/or stress of the geometric element computed by the non-linear interpolation functions and/or (b) non-linear material parameters that depend on position and/or strain at the interior points of the geometric elements. New positions of the particles are computed based minimizing total energy of the element.