CN107247833B - CAE mass data lightweight method under cloud computing - Google Patents
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Abstract
The invention belongs to the field of data processing, and discloses a CAE mass data lightweight method under cloud computing. The method comprises the following steps: (1) adopting an edge collapse coarsening algorithm to reduce the number of grids in the CAE initial geometric model to be processed, thereby obtaining a needed coarsening model; (2) and on the basis of the coarsening model, calculating a physical field to be mapped of the coarsening model according to a corresponding expression. According to the invention, a large amount of redundant information of CAE data during display can be reduced, and a large amount of original CAE information is transmitted to a user at a higher speed, so that the high efficiency of browser rendering is ensured, the speed and the performance are considered, and the analysis efficiency of the user is improved.
Description
Technical Field
The invention belongs to the field of data processing, and particularly relates to a CAE data lightweight method under cloud computing.
Background
The computer numerical simulation (often called CAE) involves a large amount of data, and particularly, the resulting data of CAE is often massive, the file volume is very large, and the physical field file of a single time step has a size of tens of MB, hundreds of MB, or even several GB.
When the traditional CAE software is used, in order to facilitate visual analysis of CAE results, researchers usually reduce the size of result files by taking measures of reducing output physical quantity, reducing output result precision, reducing resolution, reducing output time step, even reducing data dimensionality and the like, so that although visual display is facilitated, real three-dimensional result data cannot be directly obtained, and accuracy of the researchers using numerical simulation technology is affected; however, for the CAE based on cloud computing, in addition to the volume of the above-mentioned CAE result file, there is a more critical problem that the receiving of the CAE data is realized through the internet, and the data is generally displayed on a Web browser, which requires that the data transmission and display of the CAE must be simple and quick, and has the characteristic of light weight.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a CAE massive data lightweight method under cloud computing, and the number of grids in a CAE geometric model is reduced through an edge collapse coarsening algorithm, so that the technical problem of huge CAE data is solved.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for lightening CAE mass data under cloud computing, the method including:
(1) continuously coarsening the CAE initial geometric model to be processed by adopting an edge collapse coarsening algorithm for reducing the number of grids in the model until the total collapse cost generated by the coarsened model meets a preset requirement, wherein the model meeting the requirement is a required coarsening model, the total collapse cost F (e) is the change of the volume and the shape of the coarsened model and is calculated according to the following expression, F (V) is the volume cost of an edge, F (epsilon) is the shape cost of the edge, and k is the shape cost of the edgeVIs a volume cost weight, kεFor the local shape cost weight,
F(e)＝kVf(V)+kεf(ε)；
(2) the physical field T to be mapped of the needed coarsening model is calculated according to the following expression, wherein P is an object node to be solved, n is the number of nodes of the coarsening model in the sphere with the radius r, and liDistance of the ith node to point P, TiIs the physical field of the ith node, i is any positive integer between 1 and n, r is any given value,
further preferably, the initial geometric model of CAE to be processed is a triangular patch model.
Further preferably, in step (1), the edge collapse coarsening algorithm calculates the position of the collapse vertex using a quadratic measurement criterion.
Further preferably, the second measurement criterion is preferably implemented by calculating a minimum value of the energy functional.
Further preferably, in step (2), the physical field is a scalar field or a vector field, the scalar field including temperature or concentration, and the vector field including velocity, stress or strain.
Further preferably, before calculating the physical field T to be mapped, the following steps are required:
(I) converting the coordinate system of the coarsening model into a calculation coordinate system;
and (II) mapping the physical quantity in the grid to be calculated to the grid of the coarsening model by adopting an interpolation function.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
1. the CAE geometric model is coarsened to the grids with different grid orders, so that the grid number of the model is reduced, and meanwhile, a physical field is mapped to the coarsened model, so that a large amount of redundant information of CAE data during display is reduced;
2. the number of grids in the model is reduced by adopting an edge collapse coarsening algorithm, the algorithm is simple in calculation method, interference on the original model is small on the basis of not changing the shape of the original geometric model, and the generated total collapse cost is low;
3. the method calculates the optimal position of the collapse point by adopting a method of minimizing the energy functional based on the quadratic error measurement criterion, has simple calculation and high efficiency, and can quickly obtain the position of the high-quality collapse point without complex iteration;
4. according to the method, the volume and shape cost are used as the total collapse cost, the volume cost can reflect the geometric consistency of the collapsed grid and an original geometric model, the shape cost can reflect the detail change of a local area of the grid, and the loss of the local detail of the model is avoided;
5. the CAE geometric model is coarsened to lighten the data, and various physical fields are concerned to be displayed in practical application, so the method maps the required physical fields onto the coarsened model to realize the practical application of data lightening and has practical value.
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Fig. 1 is a flow chart of a CAE data weight reduction method under cloud computing constructed in accordance with a preferred embodiment of the present invention;
FIG. 2 is a flow chart of an edge collapse coarsening algorithm constructed in accordance with a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of a physical field map interpolation method constructed in accordance with a preferred embodiment of the present invention;
FIGS. 4(a) - (c) are grid scenarios of a coarsened model of an automotive engine block casting constructed in accordance with a preferred embodiment of the present invention;
FIGS. 5(a) - (c) are temperature field distribution clouds of a coarsening model of an automotive engine block casting constructed in accordance with a preferred embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The essence of the coarsening of the triangular meshes is to reduce the number of triangular faces and vertices thereof, while keeping the shape of the original model as much as possible. At present, a triangle mesh coarsening algorithm is gradually matured, and a plurality of mesh coarsening algorithms are available, such as: a coarsening algorithm for vertex deletion, a coarsening algorithm for grid repartitioning, a coarsening algorithm for region merging, a coarsening algorithm for edge collapse, and the like. Among them, edge collapse has become a very important class of coarsening algorithms, and most of the last two years of research on mesh coarsening algorithms is based on edge collapse. The system performs coarsening processing of multiple detail levels on the geometric model by using an edge collapse method.
The technical scheme provided by the invention is a CAE mass data lightweight method under cloud computing, fig. 1 is a flow chart of the CAE data lightweight method under cloud computing constructed according to the preferred embodiment of the invention, and as shown in fig. 1, the scheme is realized as follows:
(1) reducing the number of meshes of a model using an edge collapse coarsening algorithm for a CAE geometric model
Aiming at a CAE geometric model, particularly a model with a large number of triangular patches concentrated at a sharp corner or a region with large curvature, edge collapse and coarsening are carried out on the CAE geometric model to reduce the number of patches. Two problems to be solved when performing edge collapse are how to select a collapsed edge and how to determine the location of a new vertex on the collapsed edge. The method for solving the two problems is to select a certain cost function to express the cost generated by the original geometric model after the vertex substitution edge. Because the process of coarsening the geometric model does not change the shape of the geometric model, but reduces the interference to the original shape as much as possible by reducing the number of meshes, the lower the cost generated after the edge collapse, and the better the edge collapse scheme is.
Fig. 2 is a flowchart of an edge collapse coarsening algorithm constructed according to a preferred embodiment of the present invention, and as shown in fig. 2, the flowchart of the edge collapse coarsening algorithm is specifically as follows:
(a) before coarsening, obtaining the optimal position of the edge collapse vertex according to an energy functional minimum method based on a quadratic error measurement criterion, and calculating the optimal vertices of all edges;
(b) obtaining the optimal vertex after each edge collapses, then calculating the volume cost and the local shape cost, and obtaining the total coarsening cost;
(c) sequencing all edges according to the sequence of the collapse cost from small to large, establishing a priority queue of the edges, and performing edge collapse operation on the edge with the minimum cost in the priority queue;
(d) judging whether the coarsening result caused by the current collapse meets the preset coarsening requirement or not, if so, finishing coarsening treatment, and if not, entering the next step;
(e) and (3) searching which edges have influence on the collapse cost of the local edges when the edge collapses last time, calculating the collapse cost of the influenced edges, then updating the priority queue, returning to the step (c), and continuing to collapse the next edge, wherein the influenced edges refer to the edges connected with the optimal top point.
In order to reduce the interference on the original shape as much as possible, the edge collapse coarsening algorithm provided by the invention uses the volume cost and the local shape cost to comprehensively consider the error influence of the edge collapse on the model, and the scheme with the minimum cost function is the most coarsening scheme.
Based on the coarsening of the geometric model, the system can transmit the model to the browser through the network at a higher speed, and greatly reduces the rendering load of the browser. However, for casting simulation, the various physical fields in the calculation results are the analysis focus of researchers, and the results of the physical fields are obtained through the calculation grids, and the grids of the models with different levels of detail are inconsistent with the calculation grids, so that the physical fields of the grids of different models must be matched to make the models with different levels of detail displayed more accurately.
(2) Performing physical field mapping for the surface grids of different models after coarsening
Fig. 3 is a schematic diagram of a physical field mapping interpolation method constructed according to a preferred embodiment of the present invention, and as shown in fig. 3, for a case that meshes of a coarsened geometric model and a computational model are inconsistent, an interpolation function is used to implement matching of different types of meshes and equivalence of a physical field. In addition, in order to be able to see the condition of the inner part of the part in the process of cutting, the physical field data of the cutting plane is quickly constructed and displayed by using a visibility removing method.
Physical field mapping this step involves two key processes:
(I) transferring the relative coordinate system of the CAE calculation model to a world coordinate system of the coarsening model;
(II) using an interpolation function to map the results of the computational model onto the upscaled model.
The present invention will be further illustrated with reference to specific examples in view of the flow chart provided in fig. 1.
Taking the CAE temperature field analysis result of the engine cylinder block as an example, the cloud CAE data lightweight coarsening method provided in this embodiment is implemented as follows:
(1) for the CAE geometric model, the number of meshes of the model is reduced by using an edge collapse coarsening algorithm, as shown in fig. 1, specifically:
the optimal position of the edge collapse vertex is first obtained using an energy functional minimization method based on quadratic error measurement criteria, with the optimal vertex generally being near one plane, near the intersection of two planes, near the intersection of more than three planes.
And then calculating the volume change and the shape change caused by the integral model after each edge collapses to obtain a corresponding volume cost function and a corresponding shape cost function, and adding the volume cost function and the shape cost function to obtain the total cost of each edge, wherein the formula (1) is shown.
F(e)＝kVf(V)+kεf(ε) (1)
Where f (V) and f (ε) are the volume cost and shape cost, k, of the respective edgesVAnd kεRepresenting a volume cost weight and a local shape cost weight, respectively.
After the collapse cost of each edge is obtained, sequencing the edges from small to large, establishing a priority queue of edge collapse sequence, selecting the edge with the minimum collapse cost to perform edge collapse operation, and reducing one vertex, two triangular faces and three edges by one edge collapse operation;
and then judging whether the coarsening result caused by the current collapse meets the preset coarsening requirement, if so, quitting the coarsening treatment, and if not, searching for the influence on the collapse cost of the local edges when the edges collapse last time, updating the collapse cost of the influenced edges, updating the priority queue, and continuing to perform the collapse of the next edge.
FIGS. 4(a) - (c) are grid scenarios of a coarsened model of an automotive engine block casting constructed in accordance with a preferred embodiment of the present invention; as shown in fig. 4, fig. 4(a) is an original casting model, the number of grids in the graph is 910644, the size of the model file is 43.42MB, fig. 4(b) is a temperature field mapping cloud map after one round of collapse and coarsening treatment, the number of grids in the graph is 91200, the size of the model file is 4.34MB, which is 10% of the original model (the ratio is the preset coarsening requirement), fig. 4(c) is a temperature field mapping cloud map after two rounds of collapse and coarsening treatment, the number of grids in the graph is 9068, the size of the model file is 0.43MB, which is 1% of the original model (the ratio is the preset coarsening requirement); it can be found that after the edge is collapsed and coarsened, the shape of the casting is basically not changed, but the size of the file is reduced by 99 percent, and the size of the common complex casting can be basically reduced to be within 1MB after the coarsening treatment is used.
(2) Performing physical field mapping for the surface grids of different models after coarsening
After the geometric model is coarsened, mapping is needed to be performed on physical field data most concerned by a user, specifically, a relative coordinate system of the CAE calculation model is first transferred to a world coordinate system of the coarsened model, and assuming that a calculation model coordinate system is O '-X' Y 'Z' and a coarsened model coordinate system is O-XYZ, the conversion method is mainly divided into two steps:
(A) solving the minimum enveloping hexahedron of the two models, aligning the position directions of the two enveloping hexahedrons, and obtaining a rotation angle epsilon from a CAE calculation coordinate system to a coarsening model coordinate systemx、εy、εz。
(B) And aligning the centers of the two enveloping hexahedrons, translating the CAE calculation model coordinate system to a coarsening model coordinate system to coincide with the CAE calculation model coordinate system, and obtaining translation vectors (delta x, delta y and delta z).
After unifying the coordinate system, then mapping the physical field by using an interpolation function, as shown in fig. 3, an interpolation algorithm schematic diagram is shown, a dotted line in the diagram is taken as an original grid of the calculation model, a solid line is a grid of the coarsening model, assuming that an object node to be solved is P, a temperature field to be mapped is T, the number of nodes of the calculation model in a sphere with a radius of r is n, and the distance from the ith node to the P point is liThen can use
As the calculation weight of the node i to the object node P, the temperature field size of the P point mapped to the coarsening model by the calculation model can be obtained according to the following formula (2)
The physical quantity in the computational grid can be mapped to the grid of the coarsening model through the algorithm, so that a user can quickly and qualitatively know the overall distribution of the physical quantity.
FIGS. 5(a) - (c) are temperature field distribution cloud diagrams of an initial model part coarsening model of an automobile engine constructed according to a preferred embodiment of the invention, as shown in FIG. 5, wherein FIG. 5(a) is the temperature field distribution cloud diagram of an original casting, and the grid number is 910644; fig. 5(b) is a cloud image of the temperature field map after the one-round collapse and coarsening process, in which the number of meshes is 91200, which is 10% of the original model (the ratio is the predetermined coarsening requirement), fig. 5(c) is a cloud image of the temperature field map after the two-round collapse and coarsening process, in which the number of meshes is 9068, which is 1% of the original model (the ratio is the predetermined coarsening requirement). It can be seen that the temperature field distribution can be completely expressed through a physical field mapping algorithm, and the precision is very high. The data size of the temperature field after the first collapse coarsening is 10% of the original temperature field, but the data size of the temperature field after the second collapse coarsening is almost no difference compared with the original temperature field, and although the data size of the temperature field after the second collapse coarsening is only 1% of the data size of the original temperature field, the difference is very small and almost negligible compared with the original temperature field.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A CAE mass data lightweight method under cloud computing is characterized by comprising the following steps:
(1) continuously coarsening the CAE initial geometric model to be processed by adopting an edge collapse coarsening algorithm for reducing the number of grids in the model until the total collapse cost generated by the coarsened model meets a preset requirement, wherein the model meeting the requirement is a required coarsening model, the total collapse cost F (e) is the change of the volume and the shape of the coarsened model and is calculated according to the following expression, F (V) is the volume cost of an edge, F (epsilon) is the shape cost of the edge, and k is the shape cost of the edgeVIs a volume cost weight, kεFor the local shape cost weight,
F(e)＝kVf(V)+kεf(ε)；
(2) firstly, converting a coordinate system of the coarsening model into a calculation coordinate system, and then mapping physical quantities in a grid to be calculated to the grid of the coarsening model by adopting an interpolation function; the physical field T to be mapped of the coarsening model is calculated according to the following expression, wherein P is an object node to be solved, n is the number of nodes of the coarsening model in the sphere with the radius r, and liDistance of the ith node to point P, TiIs the physical field of the ith node, i is any positive integer between 1 and n, r is any given value,
2. the CAE mass data lightweight method under cloud computing according to claim 1, wherein the CAE initial geometric model to be processed is a triangular patch model.
3. The CAE mass data lightweight method under cloud computing as claimed in claim 1 or 2, wherein in step (1), the edge collapse coarsening algorithm uses a secondary measurement criterion to calculate the position of the collapse vertex.
4. The CAE mass data lightweight method under cloud computing as claimed in claim 3, wherein the secondary measurement criterion is implemented by calculating a minimum value of an energy functional.
5. The CAE mass data lightweight method under cloud computing according to claim 1, wherein in the step (2), the physical field is a scalar field or a vector field, the scalar field includes temperature or concentration, and the vector field includes velocity, stress or strain.
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| CN103914869A (en) * | 2014-02-26 | 2014-07-09 | 浙江工业大学 | Light-weight three-dimensional tree model building method supporting skeleton personalization edition |
| CN104077434A (en) * | 2014-06-09 | 2014-10-01 | 国家超级计算深圳中心(深圳云计算中心) | CAE (Computer Aided Engineering) analysis based product structure optimization method and system |
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| CN103914869A (en) * | 2014-02-26 | 2014-07-09 | 浙江工业大学 | Light-weight three-dimensional tree model building method supporting skeleton personalization edition |
| CN104077434A (en) * | 2014-06-09 | 2014-10-01 | 国家超级计算深圳中心(深圳云计算中心) | CAE (Computer Aided Engineering) analysis based product structure optimization method and system |
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