Parameter configuration method and system for computational fluid dynamics simulation
A technology of computational fluid dynamics and parameter configuration methods, applied in the field of computational fluid dynamics simulation calculations, to achieve the effects of getting rid of uncertainty, strong versatility, and improving ease of use
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Embodiment 1
[0053] A parameter configuration method for computational fluid dynamics simulation, such as figure 1 shown, including:
[0054] Step 1: setting the experimental values of the parameters of the computational fluid dynamics simulation, obtaining the experimental result data set of the computational fluid dynamics simulation by means of a simulation experiment, and performing error evaluation based on the experimental result data set to obtain error data;
[0055] Step 2: determining an error model and a performance model based on the experimental values of the parameters, the error data and the simulation experiment result data set;
[0056] Step 3: Using the error model and the performance model as constraints respectively, perform calculations to obtain the optimal value of parameter configuration for computational fluid dynamics simulation;
[0057] The parameters include grid density and discretization order.
[0058] Step 1: setting the experimental parameters of com...
Embodiment 2
[0074] Based on the same inventive concept, the present invention also provides a parameter configuration system for computational fluid dynamics simulation, including:
[0075] A parameter configuration system for computational fluid dynamics simulation, such as figure 2 As shown, including error module, model module and optimal solution module;
[0076] Error module: used to set the experimental values of the parameters of the computational fluid dynamics simulation, obtain the experimental result data set of the computational fluid dynamics simulation by the simulation experiment method, and perform error evaluation based on the experimental result data set to obtain error data;
[0077] Model module: used to determine an error model and a performance model based on the experimental value of the parameter, the error data and the simulation experiment result data set;
[0078] Optimal solution module: used to use the error model and the performance model as constraints, ...
Embodiment 3
[0095] In numerical simulation, error is generally used to measure the accuracy of simulation. In actual engineering applications or scientific research, a threshold is usually specified for the error. When the actual simulation error is less than or equal to this threshold, the simulation accuracy is considered to meet the requirements. This is also the mainstream grid-independent verification standard. For example, it is generally believed that the error is less than 10 -2 When meeting engineering precision, and for scientific computing programs, the minimum error requirement is 10 -6 . When the error meets a certain accuracy requirement, further reducing the grid spacing can further reduce the error, but on the one hand, the degree of error reduction is very low; on the other hand, the resulting overhead will increase sharply. Similarly, when the discretization order can also be changed, there should exist a set of grid order configurations that allow the required accurac...
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