CN108133504B - Three-dimensional flow field multivariable data visualization method based on polyhedral pipeline - Google Patents
Three-dimensional flow field multivariable data visualization method based on polyhedral pipeline Download PDFInfo
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Abstract
The invention relates to a flow field multivariable data visualization method based on a pipeline, and belongs to the field of flow field data visualization in scientific calculation visualization. The method of the invention visualizes the three-dimensional flow field multivariable data by constructing a colorful polyhedral pipeline. The method comprises the following basic steps: calculating a flow line, constructing a polyhedral pipeline along the flow line, visualizing the flow field vector direction through the shape of the pipeline, and then visualizing a plurality of scalar attribute data on the side surface of the pipeline through color mapping, thereby realizing the combined visualization of the scalar attribute data and the flow field vector data. The flexibility of multivariate correlation analysis on the three-dimensional flow field is increased, and the efficiency of visual analysis on multivariate data is improved.
Description
Technical Field
The invention relates to a three-dimensional flow field multivariable data visualization method, in particular to a flow field multivariable data visualization method based on a pipeline, and belongs to the field of flow field data visualization in scientific calculation visualization.
Background
The three-dimensional flow field is a data field widely existing in the fields of computational fluid mechanics, meteorological numerical simulation and the like. In order to analyze the three-dimensional flow field data, the three-dimensional flow field needs to be visualized, and the phenomenon inside the flow field is known through visualized graphs or images.
Streamlines (streamlines) and streamtubes (streamtube) are common three-dimensional flow field visualization methods. The flowing direction of the flow field can be effectively known by virtue of the streamline or the flow pipe, and the mode structure of the three-dimensional flow field can be explored. However, due to its geometry, both the streamlines and the flowtube have limited visualization capabilities. The geometry of the flow lines is only suitable for representing the flow direction of the flow field. In addition to representing the flow field direction, the conventional flow tube can represent an additional physical quantity (such as the magnitude of the flow velocity) through the thickness of the pipeline. If color attributes are added, both the stream line and the stream tube can visualize another physical quantity (e.g., temperature) by color mapping. In addition, if there are more physical quantities to visualize, both the flow lines and the flow tubes are difficult to carry. In practical applications, the three-dimensional flow field is generally a multivariate data field, and usually has a plurality of physical quantities such as temperature, pressure, density, and the like in addition to vector data describing flow velocity. These physical quantities are typically scalar data describing some physical property. In order to analyze the interrelationship or the internal connection between the physical attribute data, it is generally desirable to be able to jointly visualize the physical attribute data.
In view of the above requirements, the present invention provides a visualization method suitable for three-dimensional flow field multivariable data. The method constructs a polyhedral pipeline according to vector data of a three-dimensional flow field, and then encodes different scalar attribute data on each side surface of the pipeline through color mapping, thereby realizing the joint visualization of a plurality of scalar attribute data and flow field vector data.
Disclosure of Invention
The invention aims to provide a multivariate data visualization method suitable for a three-dimensional flow field, which realizes the joint visualization of a plurality of scalar attribute data and flow field vector data so as to improve the visualization analysis efficiency of the multivariate data of the three-dimensional flow field.
The three-dimensional flow field multivariate data as referred to herein comprises: vector data describing flow rates of the three-dimensional flow field, and a plurality of scalar attribute data describing physical quantities of the flow field; if the attribute data is not scalar data, it can be split into multiple scalar data.
The purpose of the invention is realized by the following technical scheme.
A three-dimensional flow field multivariable data visualization method based on a polyhedral pipeline comprises the following steps:
Step 2, selecting a plurality of physical quantities needing visualization from the attribute data, and respectively marking the physical quantities as a1、a2、…、aMWhere M is the number of selected physical quantities and M is greater than or equal to 3.
And 3, selecting one or more streamline seed points in the three-dimensional flow field, and calculating a streamline according to the streamline seed points and the flow field vector data, wherein the method for calculating the streamline comprises but is not limited to Euler algorithm and Runge-Kutta algorithm.
And 4, selecting part or all of the streamlines obtained in the step 3, and executing a step 5 aiming at each streamline L, wherein the method for selecting the streamlines comprises but is not limited to random selection or sequential equal-interval selection according to streamline numbers.
Step 5, starting from the first sample point on the streamline L, sequentially taking two adjacent sample points PiAnd Pi+1Step 6 to step 8 are performed.
Step 6, obtaining a sample point P from the input dataiAnd Pi+1Vector data T ofiAnd Ti+1Respectively at PiAnd Pi+1Establishing a local orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1The method for establishing the local orthogonal coordinate system comprises the following steps: the sample points on the streamline are connected with the viewpoint to establish a unitized sight line vector V, and then, according to the vector T of the sample points, B ═ T × V, N ═ B × T is obtained, and three mutually perpendicular vectors N, B, T together constitute a local orthogonal coordinate system NBT of the sample points.
Step 7, setting a pipeline radius parameter lambda in a local coordinate system NiBiTiN of (A)iBiEstablishing regular M polygon G with external circle radius lambda by taking origin as center in planeiLet G beiOne vertex of (2) is at NiOn the shaft; in the same way in the local coordinate system Ni+1Bi+1Ti+1N of (A)i+1Bi+1Establishing regular M polygon G in planei+1(ii) a A polygon GiAnd Gi+1The corresponding vertexes are respectively connected to obtain a small segment of polyhedral pipeline Fi(ii) a In NiBiIn-plane from NiThe shaft starts to move F in a counterclockwise orderiAre marked with f1、f2、…、fM。
Step 8, obtaining a sample point P from the input dataiPhysical quantity of a1、a2、…、aMEach of which isCorresponding value m1、m2、…、mMAccording to m1、m2、…、mMDetermining M color values C1、C2、…、CM(ii) a Using colour values C1、C2、…、CMTo FiSide f of1、f2、…、fMAnd (4) coloring in sequence. Wherein color value C1、C2、…、CMIt can be determined according to a certain consistent color mapping rule, or by looking up a preset color table.
Advantageous effects
The three-dimensional flow field multivariable data visualization method based on the polyhedral pipeline actually visualizes the flow field vector direction through the shape of the polyhedral pipeline, and then visualizes a plurality of scalar attribute data on the side surface of the pipeline through color mapping. Compared with the traditional streamline or flow pipe visualization method, the method has the advantages that: the joint visualization of a plurality of scalar attribute data and flow field vector data is realized, and the upper limit of the number of scalar attributes is not limited in theory. The flexibility of multivariate correlation analysis on the three-dimensional flow field is increased, and the efficiency of visual analysis on multivariate data is improved.
Drawings
Example streamlines for the embodiment of FIG. 1;
FIG. 2 is a schematic view of a constructed polyhedral pipe;
FIG. 3 is a front view of a small segment of a colored polyhedral duct;
FIG. 4 is an oblique side view of a small segment of the colored polyhedral pipe;
FIG. 5 illustrates a front view of a color polyhedral pipe of a flow line;
FIG. 6 illustrates a back view of a color polyhedral pipe of a streamline.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
A three-dimensional flow field multivariable data visualization method based on a polyhedral pipeline comprises the following steps:
The present invention uses a disclosed hurricane simulation dataset as an example. The data set is three-dimensional multivariable flow field data generated by the national weather research center for the Isabel hurricane simulation of 2003. The data field size is 500 × 500 × 100. In addition to the vector data describing the flow rate, the data set also includes attribute data of 10 physical quantities. The invention selects the data at the moment T-30 in the data set as the input data of the embodiment, and performs normalization processing on each attribute data.
Step 2, selecting a plurality of physical quantities needing visualization from the attribute data, and respectively marking the physical quantities as a1、a2、…、aMWhere M is the number of selected physical quantities and M is greater than or equal to 3.
In this embodiment, let M be 6, 6 physical quantities needing visualization are selected from hurricane attribute data: p (pressure), TC (temperature), QVAPOR (Water vapor), QGRAUP (Graupel), QRAIN (rain), QSNOW (snow), wherein these physical quantities are respectively marked as a1、a2、…、a6。
And 3, selecting one or more streamline seed points in the three-dimensional flow field, and calculating a streamline according to the streamline seed points and the flow field vector data, wherein the method for calculating the streamline comprises but is not limited to Euler algorithm and Runge-Kutta algorithm.
Without loss of generality, the embodiment selects a streamline seed point inside the hurricane field, and calculates to obtain a streamline by adopting a fourth-order Runge-Kutta algorithm, wherein the streamline is shown in FIG. 1.
And 4, selecting part or all of the streamlines obtained in the step 3, and executing a step 5 aiming at each streamline L, wherein the method for selecting the streamlines comprises but is not limited to random selection or sequential equal-interval selection according to streamline numbers.
In this embodiment, streamlines are sequentially selected according to the streamline numbers, and the streamline number 1 is selected as an example streamline, which is designated as the streamline L. Since only one streamline is obtained in step 3 of the present embodiment, all the streamlines of step 3 are selected here.
Step 5, starting from the first sample point on the streamline L, sequentially taking two adjacent sample points PiAnd Pi+1Step 6 to step 8 are performed.
Step 6, obtaining a sample point P from the input dataiAnd Pi+1Vector data T ofiAnd Ti+1Respectively at PiAnd Pi+1Establishing a local orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1The method for establishing the local orthogonal coordinate system comprises the following steps: the sample points on the streamline are connected with the viewpoint to establish a unitized sight line vector V, and then, according to the vector T of the sample points, B ═ T × V, N ═ B × T is obtained, and three mutually perpendicular vectors N, B, T together constitute a local orthogonal coordinate system NBT of the sample points.
Let two adjacent sample points P without loss of generalityiAnd Pi+1As shown in FIG. 2, assuming the viewpoint position is E, the viewpoint E is connected to the sample point PiObtaining a unitized eye vector
In this case, let again
Bi＝Ti×Vi
Ni＝Bi×Ti
Three mutually perpendicular vectors N are thus obtainedi、Bi、TiWhich together constitute a sample point PiLocal orthogonal coordinate system NiBiTi. Sample point Pi+1Local coordinate system Ni+1Bi+1Ti+1The construction process of (2) is similar to that of (2), and the result is shown in FIG. 2.
Step 7, setting a pipeline radius parameter lambda in a local coordinate system NiBiTiN of (A)iBiEstablishing regular M polygon G with external circle radius lambda by taking origin as center in planeiLet G beiOne vertex of (2) is at NiOn the shaft; in the same way in the local coordinate system Ni+1Bi+1Ti+1N of (A)i+1Bi+1Establishing regular M polygon G in planei+1(ii) a A polygon GiAnd Gi+1The corresponding vertexes are respectively connected to obtain a small segment of polyhedral pipeline Fi(ii) a In NiBiIn-plane from NiThe shaft starts to move F in a counterclockwise orderiAre marked with f1、f2、…、fM。
Without loss of generality, in this embodiment, the radius parameter λ of the pipeline is set to 1, and the local coordinate system N is setiBiTiN of (A)iBiThe positive 6-sided polygon G with the radius of the circumscribed circle of 1 is established by taking the origin as the center in the planeiLet G beiOne vertex of (2) is at NiOn the shaft; in the same way in the local coordinate system Ni+1Bi+1Ti+1N of (A)i+1Bi+1In-plane establishment of regular 6-sided polygon Gi+1(ii) a A polygon GiAnd Gi+1The corresponding vertexes are respectively connected to obtain a small segment of the polyhedral pipeline F shown in figure 2i(ii) a In NiBiIn-plane from NiThe shaft starts to move F in a counterclockwise orderiAre marked with f1、f2、…、f6The results are shown in FIG. 2.
Step 8, obtaining a sample point P from the input dataiPhysical quantity of a1、a2、…、aMEach corresponding value m1、m2、…、mMAccording to m1、m2、…、mMDetermining M color values C1、C2、…、CM(ii) a Using colour values C1、C2、…、CMTo FiSide f of1、f2、…、fMAnd (4) coloring in sequence. Wherein color value C1、C2、…、CMIt can be determined according to a certain consistent color mapping rule, or by looking up a preset color table.
The color determination method of the embodiment is as follows: according to the HSV color model, namely a 'hue-saturation-brightness' color model, all colors are set to be completely saturated, the saturation value is fixed to 1.0, and then the saturation value is a physical quantity a1、a2、…、aMRespectively specifying maximum equally spaced tone values h1、h2、…、hMAnd taking the value m of each physical quantity1、m2、…、mMAs the brightness values of the colors, M colors C are obtained1、C2、…、CM。
In the present embodiment, 6 physical quantities are selected: p, TC, QVAPOR, QGRAUP, QRAIN, QSNOW (a)1、a2、…、a6) According to the above color determination method, the maximum equally spaced hues specified for these physical quantities are each red (h)10), yellow (h)260), green (h)3120), cyan (h)4180), blue (h)5240) and purple (h)6300), assume a sample point PiPhysical quantity of a1、a2、…、aMEach value m1、m2、…、m6Are all 1.0, 6 defined colors C are thereby obtained1、C2、…、C6. By C1、C2、…、C6To FiSide f of1、f2、…、f6The results of the coloration are shown in FIGS. 3 and 4, where FIG. 3 is FiA front view after coloring, and FIG. 4 is FiOblique side view after coloring.
For the exemplary flow line numbered 1 in this embodiment, after step 5 (which includes steps 6 to 8) is performed, the colored polyhedral tube shown in fig. 5 and 6 is obtained, wherein fig. 5 is a front view and fig. 6 is a back view after rotation. Comparing fig. 1 and 5, it can be seen that: the direction of the polyhedral pipeline is consistent with the flow line, and the flowing direction of a flow field is reflected; the side surface of each pipeline shows the value conditions of the corresponding physical quantities at different positions through the light and shade change of the color. For example, the yellow surface at the tail of the pipeline (top right corner of fig. 5) is brighter, which indicates that the value of TC (temperature) corresponding to yellow is higher in the area; thus, when looking along the pipe to the spiral part, the TC (temperature) value gradually decreases from the change of color brightness, and then rises to the upper part of the spiral area. In addition, there is a clear bright and dark stripe on the green surface at the upper part of the spiral region shown in fig. 5, which indicates that the qvapre value corresponding to green is unstable in this region.
Due to spatial occlusion, all the pipe faces cannot be viewed from one viewing angle. For this reason, the established tube is subjected to a rotational translation operation or the like, resulting in a back view as shown in fig. 6, from which the tube faces, violet, cyan, blue, etc., which are not visible in fig. 5, can be seen. However, the blue color plane of fig. 6 is mostly black, which indicates that the QRAIN value corresponding to blue is very low (equal to or close to 0.0) in these regions. Similar features are also found in QGRAUP corresponding to the cyan surface of FIG. 6. In connection with fig. 5 and 6, some correlations between different physical properties can also be seen, for example: the areas where QSNOW values are high (purple area in fig. 6), and QRAIN values are low (corresponding to blue being darkened to black).
The visual visualization results reveal the change mode and the incidence relation among the multivariable data of the three-dimensional flow field, are favorable for efficiently exploring the phenomenon or rule in the three-dimensional flow field, and are also the embodiment of the beneficial effect of the invention.
The steps illustrate the whole process of the three-dimensional flow field multivariable data visualization method based on the polyhedral pipeline.
It should be understood that the present embodiments are only specific examples for implementing the invention, and should not be used for limiting the protection scope of the invention. It is intended that all equivalent modifications and variations of the above-described aspects be included within the scope of the present invention as claimed, without departing from the spirit and scope of the invention.
Claims (5)
1. A three-dimensional flow field multivariable data visualization method based on a polyhedral pipeline is characterized by comprising the following steps:
inputting three-dimensional flow field multivariable data, wherein the three-dimensional flow field multivariable data comprises vector data for describing flow velocity of a three-dimensional flow field and scalar attribute data for describing physical quantity of the flow field, and normalizing each attribute data;
step two, selecting a plurality of physical quantities needing visualization from the attribute data, and respectively marking the physical quantities as a1、a2、…、aMWherein M is the number of selected physical quantities, M is greater than or equal to 3;
selecting one or more streamline seed points in the three-dimensional flow field, and calculating a streamline according to the streamline seed points and flow field vector data;
step four, selecting part or all of the streamlines obtained in the step three, and executing the step five aiming at each streamline L;
and step five, sequentially taking two adjacent sample points P from the first sample point on the streamline LiAnd Pi+1Executing the step six to the step eight;
step six, obtaining a sample point P from input dataiAnd Pi+1Vector data T ofiAnd Ti+1Respectively at PiAnd Pi+1Establishing a local orthogonal coordinate system NiBiTiAnd Ni+1Bi+1Ti+1The method for establishing the local orthogonal coordinate system comprises the following steps: connecting the sample points on the streamline with the viewpoint to establish a unitized sight line vector V, then obtaining a B (T multiplied by V, N) and a B multiplied by T according to the vector T of the sample points, and forming a local orthogonal coordinate system NBT of the sample points by three mutually perpendicular vectors N, B, T;
step seven, setting a pipeline radius parameter lambda in a local coordinate system NiBiTiN of (A)iBiEstablishing regular polygon G with external circle radius lambda by taking origin as center in planeiLet G beiOne vertex of (2) is at NiOn the shaft; in the same way in the local coordinate system Ni+1Bi+ 1Ti+1N of (A)i+1Bi+1Building regular polygon G in planei+1(ii) a A polygon GiAnd Gi+1The corresponding vertexes are respectively connected to obtain a small segment of polyhedral pipeline Fi(ii) a In NiBiIn-plane from NiThe shaft starts to move F in a counterclockwise orderiAre marked with f1、f2、…、fM；
Step eight, obtaining a sample point P from the input dataiPhysical quantity of a1、a2、…、aMEach corresponding value m1、m2、…、mMAccording to m1、m2、…、mMDetermining M color values C1、C2、…、CM(ii) a Using colour values C1、C2、…、CMTo FiSide f of1、f2、…、fMAnd (4) coloring in sequence.
2. The three-dimensional flow field multivariable data visualization method based on the polyhedral pipeline as recited in claim 1, wherein: the method for calculating the streamline in the third step includes but is not limited to Euler algorithm or Runge-Kutta algorithm.
3. The three-dimensional flow field multivariable data visualization method based on the polyhedral pipeline as recited in claim 1, wherein: the method for selecting the streamline in the fourth step includes, but is not limited to, random selection or sequential equal interval selection according to the streamline number.
4. The three-dimensional flow field multivariable data visualization method based on the polyhedral pipeline as recited in claim 1, wherein: color value C in the step eight1、C2、…、CMIt can be determined according to a certain consistent color mapping rule, or by looking up a preset color table.
5. A device according to claim 1 or 4, based onThe three-dimensional flow field multivariable data visualization method of the polyhedral pipeline is characterized in that the color determination scheme in the step eight comprises the following steps: according to the HSV color model, namely a 'hue-saturation-brightness' color model, all colors are set to be completely saturated, the saturation value is fixed to 1.0, and then the saturation value is a physical quantity a1、a2、…、aMRespectively specifying maximum equally spaced tone values h1、h2、…、hMAnd taking the value m of each physical quantity1、m2、…、mMAs the brightness values of the colors, M colors C are obtained1、C2、…、CM。
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