CN113094867B - Train compartment noise environment modeling method based on digital twins - Google Patents
Train compartment noise environment modeling method based on digital twins Download PDFInfo
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- CN113094867B CN113094867B CN202110224897.6A CN202110224897A CN113094867B CN 113094867 B CN113094867 B CN 113094867B CN 202110224897 A CN202110224897 A CN 202110224897A CN 113094867 B CN113094867 B CN 113094867B
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- G06F30/20—Design optimisation, verification or simulation
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Abstract
The invention relates to the technical field of digital twins, Internet of things and computer modeling, in particular to a train compartment noise environment modeling method based on the digital twins. The invention combines the digital twin technology with the noise environment monitoring method, and carries out real-time and continuous monitoring on the noise in the physical space inside the carriage through the noise detection sensor; the adopted computer simulation visualization method intuitively displays the noise distribution of the physical space in the train carriage as a visual three-dimensional graph, supports the dynamic description of continuous space change and time change, and is beneficial to the intuitive understanding and feeling of engineering developers; the physical space information is completely mapped into the Sayboat space information through the digital twin calculation model, and a prediction model based on the digital twin is established in the computer system, so that the real situation in the physical space is effectively predicted, and the engineering realization and noise simulation experiment of the train carriage can be effectively supported.
Description
Technical Field
The invention relates to the technical field of digital twins, Internet of things and computer modeling, in particular to a train compartment noise environment modeling method based on the digital twins.
Background
The noise is serious environmental pollution along the railway, and the daily life of people along the railway is influenced by various noises caused by high-speed running of the train. Serious noise pollution not only seriously affects riding comfort, but also can injure optic nerves, generate a series of adverse physiological symptoms such as insomnia, neurasthenia, unstable blood pressure and the like, and possibly generate psychological stress, easy emotional dysphoria, slow response and the like. In order to reduce the physiological and psychological influence of noise on passengers, the noise reduction can be carried out in a noise reduction mode, and the noise reduction can be divided into two modes of passive noise reduction and active noise reduction, wherein the passive noise reduction mainly adopts more and better sound insulation materials or sound absorption materials to improve the internal space of a carriage, and the active noise reduction can be carried out in a noise interference mode.
At present, the noise in the physical space inside the train compartment is mainly collected and analyzed by adopting a sampling experiment method, continuous noise monitoring cannot be realized, and the noise data obtained by sampling data modeling is difficult to accurately predict the noise environment. The traditional method is difficult to establish a visual computer simulation model, and an abstract formal expression mode causes that an engineer is difficult to visually observe and understand the noise distribution and the change of the internal space of the carriage. After the engineer adjusts the design, process and materials of the car, the noise distribution in the interior space needs to be re-sampled to obtain new data. Not only is the cost high, but also rapid development is difficult to achieve.
The digital twinning technology is a key technology of a computer real-time system. The digital twin is a virtual model constructed in the Saybook space of a computer system aiming at the real object attributes in the physical entity space, such as geometric attributes, physical attributes, behavior rules and the like, and establishes a one-to-one virtual-real mapping relationship between the physical space and the system in the Saybook space.
Disclosure of Invention
The invention provides a train compartment noise environment modeling method based on digital twins, aiming at overcoming at least one defect in the prior art.
In order to solve the technical problems, the invention adopts the technical scheme that: a train compartment noise environment modeling method based on digital twins comprises the following steps:
s1, mapping a physical model in a train carriage space into an information model of a Sayboat space, discretizing a continuous physical space into points and three-dimensional grids of the Sayboat space to form a space division model of the physical space mapping, and simultaneously sensing noise analysis data in the carriage by adopting a sensor and mapping the noise analysis data into the Sayboat space model;
s2, acquiring noise distribution of the environment of the train compartment by using a noise sensor in the train compartment;
s3, after a three-dimensional description model of the internal space of the train compartment is established, simulating and visually describing a sound field simulation visual system;
s4, setting a noise propagation boundary condition of the physical space model, respectively establishing a propagation model and an interference model of noise in the carriage according to the three-dimensional grid division result in the step S1 and the boundary condition set according to the sound propagation physical characteristics in the physical space, obtaining a noise prediction distribution map and a noise interference prediction distribution map according to the model calculation result, and displaying the prediction result by using a visualization technology.
Further, the step S1 specifically includes:
s11, carrying out geometric three-dimensional modeling on the train carriage, converting the train carriage in a physical space into three-dimensional graphic information in a computer system by adopting three-dimensional CAD software, and dividing the train carriage space into three-dimensional grid models by adopting a Delaunay subdivision method;
s12, serializing three-dimensional geometric grid data: assuming that one mesh is composed of N triangles, the final mesh is represented as an N × 12 matrix; for each mesh, its 12-dimensional features are respectively 9-dimensional coordinates of three vertices of a triangle, and the normal vector of the face formed by each triangle is expressed in the following format:
wherein, { x1，y1,z1|x2,y2,z2|x3,y3,z3Is the three-dimensional coordinates of three vertices, { n }1,n2,n3Is its normal vector.
Furthermore, the noise sensors in the carriage are distributed discretely, and the spatial noise intensity between the sensors is estimated by adopting an interpolation or fitting method, so that the complete noise distribution condition in the whole carriage is obtained.
Further, the step S3 specifically includes:
s31, mapping the noise distribution of the physical space into a Saybook space, and corresponding the space positions one by one, so as to obtain a noise distribution model in a three-dimensional space; the noise data form time sequence data according to the time sequence T, namely noise change monitoring data in continuous time periods are formed;
s32, carrying out normalization processing on the noise value acquired in the physical space to enable the vibration amplitude of the sound source after normalization to be between [ -1,1], so that subsequent visualization processing is facilitated;
and S33, establishing a three-dimensional space noise distribution and propagation visualization model by adopting a computer visualization method.
Furthermore, noise among time sampling points is obtained by adopting an interpolation or fitting method; or, calculating the noise variation between time sampling points by adopting a sound propagation equation.
Further, in step S33, a tree-like hierarchical structure is used to describe the relationship of the physical space objects, including the spatial relationship and the combination relationship.
Further, in step S33, a visual spatial noise distribution is obtained by using a color mapping visualization method.
Further, the step S4 specifically includes:
s41, assuming that the noise transmission is spherical wave, setting a noise transmission boundary condition of a physical space model according to a three-dimensional grid division result in the step S1 and a sound transmission physical characteristic in a physical space according to a wave equation and the geometric characteristics of a carriage;
s42, establishing a transmission model and a superposition model of noise in the carriage:
assuming that the noise propagation is a spherical wave, the sound equation of the point sound source based on the simple harmonic wave is as follows:
wherein, P represents sound pressure, S and R represent the coordinates of the sound source point and the receiving point respectively, and are inversely proportional to the distance l; where ω is 2 pi f, and f denotes a sound wave vibration frequency;
when two or more noises collide with spherical waves in propagation, sound energy is superposed, and an energy superposition equation is adopted:
in the formula, p1And p2Is the power of the noise, t is the time;
finally, calculating the energy superposition value after the spherical wave collision;
s43, obtaining a noise prediction distribution map and a noise interference prediction distribution condition according to the noise propagation model and the energy superposition model, and establishing a real-time calculation model for predicting the change of the compartment noise environment;
and S44, obtaining the visual expression and interaction of the noise prediction distribution diagram and the noise interference prediction distribution condition result by utilizing the visualization technology in the step S3.
The invention also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
Compared with the prior art, the beneficial effects are:
1. the invention combines the digital twin technology with the noise environment monitoring method, and carries out real-time and continuous monitoring on the noise in the physical space inside the carriage through the noise detection sensor; meanwhile, the noise data and other data of the physical space, such as information of train speed per hour, wind speed, gradient, curvature, temperature and the like can be fused to establish a fused noise model;
2. the invention visually displays the noise distribution of the physical space in the train carriage as a visual computer three-dimensional graph, supports the dynamic description of continuous space change and time change, can quickly reflect time and space change, is favorable for the visual understanding and feeling of engineering developers, provides powerful support for engineering design and comfort analysis in the carriage, and can expand the technology into the noise description in other closed space environments;
3. according to the method, the physical space information is completely mapped into the Sayboat space information through the digital twin calculation model, and the prediction model based on the digital twin noise propagation and noise superposition is established in the computer system, so that the real situation in the physical space is effectively predicted, and the engineering realization and noise simulation experiment of the train compartment can be effectively supported.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The drawings are for illustration purposes only and are not to be construed as limiting the invention; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
As shown in fig. 1, a method for modeling a train car noise environment based on a digital twin includes the following steps:
step 1, mapping a physical model in a train carriage space into an information model of a Saybook space, discretizing a continuous physical space into points and three-dimensional grids of the Saybook space to form a space division model of the physical space mapping, and simultaneously sensing noise analysis data in the carriage by adopting a sensor and mapping the noise analysis data into the Saybook space model.
S11, carrying out geometric three-dimensional modeling on the train carriage, converting the train carriage in a physical space into three-dimensional graphic information in a computer system by adopting three-dimensional CAD software, and dividing the train carriage space into three-dimensional grid models by adopting a Delaunay subdivision method;
s12, serializing three-dimensional geometric grid data: assuming that one mesh is composed of N triangles, the final mesh is represented as an N × 12 matrix; for each mesh, its 12-dimensional features are respectively 9-dimensional coordinates of three vertices of a triangle, and the normal vector of the face formed by each triangle is expressed in the following format:
wherein, { x1,y1,z1|x2,y2,z2|x3,y3,z3Is the three-dimensional coordinates of three vertices, { n }1,n2,n3Is its normal vector.
And 2, acquiring the noise distribution of the environment of the train compartment by using a noise sensor in the train compartment. The distribution mode of the noise sensors in the carriage is discrete distribution, and the spatial noise intensity among the sensors is estimated by adopting an interpolation or fitting method, so that the complete noise distribution condition in the whole carriage is obtained.
And 3, after the three-dimensional description model of the internal space of the train compartment is established, simulating and visually describing the sound field simulation visual system.
S31, mapping the noise distribution of the physical space into a Saybook space, and corresponding the space positions one by one, so as to obtain a noise distribution model in a three-dimensional space; the noise data form time sequence data according to the time sequence T, namely noise change monitoring data in continuous time periods are formed; noise among the time sampling points is obtained by adopting an interpolation or fitting method; or, calculating the noise change condition between time sampling points by adopting a sound propagation equation;
s32, carrying out normalization processing on the noise value acquired in the physical space to enable the vibration amplitude of the sound source after normalization to be between [ -1,1], so that subsequent visualization processing is facilitated;
and S33, establishing a three-dimensional space noise distribution and transmission visualization model by adopting a computer visualization method. Describing the relationship of physical space objects by using a tree hierarchy, such as a spatial relationship, a combination relationship and the like; and obtaining visual spatial noise distribution by adopting a color mapping visualization method.
And 4, setting a noise propagation boundary condition of the physical space model, respectively establishing a propagation model and an interference model of the noise in the carriage according to the three-dimensional grid division result in the step S1 and the boundary condition set according to the sound propagation physical characteristics in the physical space, obtaining a noise prediction distribution map and a noise interference prediction distribution map according to the model calculation result, and displaying the prediction result by using a visualization technology.
S41, assuming that the noise transmission is spherical wave, setting a noise transmission boundary condition of a physical space model according to a three-dimensional grid division result in the step S1 and a sound transmission physical characteristic in a physical space according to a wave equation and the geometric characteristics of a carriage;
s42, establishing a propagation model and a superposition model of noise in the carriage:
assuming that the noise propagation is a spherical wave, the sound equation of the point sound source based on the simple harmonic wave is as follows:
wherein, P represents sound pressure, S and R represent the coordinates of the sound source point and the receiving point respectively, and are inversely proportional to the distance l; where ω is 2 pi f, and f denotes a sound wave vibration frequency;
when two or more noises collide with spherical waves in propagation, sound energy is superposed, and an energy superposition equation is adopted:
in the formula, p1And p2Is the power of the noise, t is the time;
finally, calculating the energy superposition value after the spherical wave collision;
s43, obtaining a noise prediction distribution map and a noise interference prediction distribution condition according to the noise propagation model and the energy superposition model, and establishing a real-time calculation model for predicting the change of the compartment noise environment;
and S44, obtaining the visual expression and interaction of the noise prediction distribution diagram and the noise interference prediction distribution condition result by utilizing the visualization technology in the step S3.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A train compartment noise environment modeling method based on digital twins is characterized by comprising the following steps:
s1, mapping a physical model in a train carriage space into an information model of a Sayboat space, discretizing a continuous physical space into points and three-dimensional grids of the Sayboat space to form a space division model of the physical space mapping, and simultaneously sensing noise analysis data in the carriage by adopting a sensor and mapping the noise analysis data into the Sayboat space model; step S1 specifically includes:
s11, carrying out geometric three-dimensional modeling on the train carriage, converting the train carriage in a physical space into three-dimensional graphic information in a computer system by adopting three-dimensional CAD software, and dividing the train carriage space into three-dimensional grid models by adopting a Delaunay subdivision method;
s12, serializing three-dimensional geometric grid data: assuming that one mesh is composed of N triangles, the final mesh is represented as an N × 12 matrix; for each mesh, its 12-dimensional features are respectively 9-dimensional coordinates of three vertices of a triangle, and the normal vector of the face formed by each triangle is expressed in the following format:
wherein, { x1,y1,z1|x2,y2,z2|x3，y3，z3Is the three-dimensional coordinates of three vertices, { n }1,n2,n3Is its normal vector;
s2, acquiring noise distribution of the environment of the train compartment by using a noise sensor in the train compartment;
s3, after a three-dimensional description model of the internal space of the train compartment is established, simulating and visually describing a sound field simulation visual system;
s4, setting a noise propagation boundary condition of the physical space model, respectively establishing a propagation model and an interference model of noise in the carriage according to the three-dimensional grid division result in the step S1 and the boundary condition set according to the sound propagation physical characteristics in the physical space, obtaining a noise prediction distribution map and a noise interference prediction distribution map according to the model calculation result, and displaying the prediction result by using a visualization technology.
2. The modeling method for the noise environment of the train carriage based on the digital twin as claimed in claim 1, wherein the distribution mode of the noise sensors inside the carriage is discrete distribution, and the spatial noise intensity among the sensors is estimated by adopting an interpolation or fitting method, so that the complete noise distribution condition inside the whole carriage is obtained.
3. The modeling method for the noise environment of the train carriages based on the digital twin as claimed in claim 2, wherein the step S3 specifically comprises:
s31, mapping the noise distribution of the physical space into a Saybook space, and corresponding the space positions one by one, so as to obtain a noise distribution model in a three-dimensional space; the noise data form time sequence data according to the time sequence T, namely noise change monitoring data in continuous time periods are formed;
s32, carrying out normalization processing on the noise value acquired in the physical space to enable the vibration amplitude of the sound source after normalization to be between [ -1,1], so that subsequent visualization processing is facilitated;
and S33, establishing a three-dimensional space noise distribution and propagation visualization model by adopting a computer visualization method.
4. The digital twin-based modeling method for noise environment of train carriages as claimed in claim 3, wherein the noise between time sampling points is obtained by interpolation or fitting; or, calculating the noise variation between time sampling points by adopting a sound propagation equation.
5. The modeling method for train compartment noise environment based on digital twin as claimed in claim 4, wherein in step S33, the relationship of physical space object is described by tree hierarchy structure, including spatial relationship and combination relationship.
6. The method for modeling a train car noise environment based on digital twin as claimed in claim 3, wherein in said step S33, a visual spatial noise distribution is obtained by using a color mapping visualization method.
7. The modeling method for a train car noise environment based on digital twins as claimed in claim 3, wherein said step S4 specifically includes:
s41, assuming that the noise transmission is spherical wave, setting a noise transmission boundary condition of a physical space model according to a three-dimensional grid division result in the step S1 and a sound transmission physical characteristic in a physical space according to a wave equation and the geometric characteristics of a carriage;
s42, establishing a propagation model and a superposition model of noise in the carriage:
assuming that the noise propagation is a spherical wave, the sound equation of the point sound source based on the simple harmonic wave is as follows:
wherein, P represents sound pressure, S and R represent the coordinates of the sound source point and the receiving point respectively, and are inversely proportional to the distance l; where ω is 2 pi f, and f denotes a sound wave vibration frequency;
when two or more noises collide with spherical waves in propagation, sound energy is superposed, and an energy superposition equation is adopted:
in the formula, p1And p2Is the power of the noise, t is the time;
finally, calculating the energy superposition value after the spherical wave collision;
s43, obtaining a noise prediction distribution map and a noise interference prediction distribution condition according to the noise propagation model and the energy superposition model, and establishing a real-time calculation model for predicting the change of the compartment noise environment;
and S44, obtaining the visual expression and interaction of the noise prediction distribution diagram and the noise interference prediction distribution condition result by utilizing the visualization technology in the step S3.
8. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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