CN112364450B - Multi-pipeline layout optimization method and system for aero-engine - Google Patents
Multi-pipeline layout optimization method and system for aero-engine Download PDFInfo
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Abstract
The invention relates to a multi-pipeline layout optimization method and system for an aeroengine, wherein the method comprises the following steps: s1, acquiring an endpoint coordinate of a pipeline and a bounding box of an engine surface accessory, and projecting the endpoint coordinate and a bottom surface coordinate of the bounding box to a plane to acquire a two-dimensional simplified diagram of a laying space; s2, aiming at the two-dimensional simplified diagram of the laying space, acquiring an initial path of a pipeline in the two-dimensional simplified diagram of the laying space; s3, grouping the pipelines in the two-dimensional simplified diagram of the laying space according to the initial paths of the pipelines in the two-dimensional simplified diagram of the laying space to obtain a plurality of pipeline groups; s4, sequencing the plurality of pipeline groups to obtain a first set; s5, aiming at any pipeline group in the first set, performing pipeline path planning to obtain an optimal pipeline path combination; s6, carrying out parallel processing on each pipeline according to the pipeline optimal path combination to obtain a pipeline final optimal path; and S7, acquiring a three-dimensional model corresponding to the final optimal path of the pipeline according to the final optimal path of the pipeline.
Description
Technical Field
The invention relates to the technical field of aeroengine engineering, in particular to a multi-pipeline layout optimization method and system for an aeroengine.
Background
As a typical complex electromechanical product, the aero-engine has a narrow casing surface space, complex constraint and a large number of pipelines are required to be arranged on the surface, and in the actual design process, the trend of pipeline paths is usually determined according to experience of a designer, which tends to cause low design efficiency, and the design result depends on the experience of the designer to a great extent. Especially, when the multi-pipeline system is laid out, a designer needs to consider not only various engineering constraints, but also the rationality of the arrangement sequence of pipelines, so that the design result is difficult to be optimized by manually finishing the pipeline system design with huge orders of magnitude, and the production cycle of the whole engine is seriously affected by reworking caused by the existence of certain design errors.
In the pipeline design process, as a plurality of pipelines are arranged, the pipelines which are laid firstly can influence the pipeline paths which are laid subsequently, even the situation that the pipelines cannot be laid sometimes occurs, so that the sequence of pipeline laying plays a very important role in the optimality of the whole pipeline system. On the other hand, the aero-engine has small available laying space, and excessive use of single-clamp-mount fixed pipelines reduces the space utilization, so that parallel layout among pipelines is required as much as possible.
At present, although many students study the problem of automatic pipeline layout and achieve a certain result, the study is generally directed at single pipeline or branch pipelines, but the study is less for the problem of multi-pipeline layout in the field of aeroengines, the realizing thought is mostly ordered according to expert experience, and then the problem is converted into single pipeline layout to be laid in sequence, and the interaction between pipelines, such as the problem of multi-pipeline parallel layout, is not considered. And the time used for the conventional layout method is also long. In summary, it can be seen that a complete set of theory and method is not formed for the problem of the multi-pipeline layout of the aero-engine at present.
Disclosure of Invention
First, the technical problem to be solved
In view of the above-mentioned drawbacks and shortcomings of the prior art, the present invention provides a method and a system for optimizing multi-pipeline layout of an aero-engine, which solve the technical problem of multi-pipeline layout of an aero-engine.
(II) technical scheme
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides a method for optimizing a multi-pipeline layout of an aeroengine, including:
s1, acquiring an endpoint coordinate of a pipeline in an aeroengine model and a bounding box of an engine surface accessory, and projecting the endpoint coordinate and a bottom surface coordinate of the bounding box to a plane to acquire a two-dimensional simplified diagram of a laying space;
the pipeline consists of a plurality of pipe sections;
s2, aiming at the two-dimensional simplified diagram of the laying space, acquiring an initial path of a pipeline in the two-dimensional simplified diagram of the laying space;
s3, grouping the pipelines in the two-dimensional simplified drawing of the laying space according to the initial paths of the pipelines in the two-dimensional simplified drawing of the laying space to obtain a plurality of pipeline groups;
s4, sequencing the plurality of pipeline groups to obtain a first set;
the first set comprises a plurality of pipeline groups in an arrangement order;
s5, aiming at any pipeline group in the first set, performing pipeline path planning to obtain an optimal pipeline path combination;
s6, carrying out parallel processing on each pipeline according to the pipeline optimal path combination to obtain a final optimal path of the pipeline;
and S7, acquiring a three-dimensional model corresponding to the final optimal path of the pipeline according to the final optimal path of the pipeline.
Preferably, the step S1 specifically includes:
according to a projection mode corresponding to the formula (1), the endpoint coordinates of the pipeline and the coordinates of the bottom surface of the bounding box are projected to a plane;
wherein ρ is a radial distance score of a cylindrical coordinate corresponding to a starting point of the pipeline or an end point of the bottom surface of the bounding box;
θ is an angle value of a cylindrical coordinate corresponding to a starting point of the pipeline or an end point of the bottom surface of the bounding box;
z is the height value of the cylindrical coordinate corresponding to the starting point of the pipeline or the end point of the bottom surface of the bounding box;
and x and y correspond to plane coordinates of each point after projection.
Preferably, the step S2 specifically includes:
for the two-dimensional simplified diagram of the laying space, determining an initial path of each pipeline based on a two-dimensional plane by adopting a preset differential evolution algorithm, wherein an evaluation function in the preset differential evolution algorithm is as follows:
wherein L is i Is the length of the i-th section of the pipeline;
n is the number of pipe sections in the pipeline;
α i an included angle between the ith pipe section and the (i+1) th pipe section in the pipeline;
ω 1 a first preset weight;
ω 2 a second preset weight;
p is a punishment term of interference between a preset pipeline and a plane obstacle.
Preferably, the step S3 specifically includes:
acquiring a difference value between initial paths of all pipelines by adopting a formula (3), and clustering and grouping all the pipelines based on the difference value to acquire a plurality of pipeline groups;
wherein each pipeline group comprises a plurality of pipelines;
equation (3):
wherein delta is the difference value between the initial paths of any two pipelines;
L coin1 the total weight length of each section of the initial path of any one of the two pipelines after projection of each section of the initial path of the other pipeline of the two pipelines;
L coin2 the total weight length of each section of the initial path of the other pipeline in the arbitrary two pipelines after projection of each section of the initial path of the other pipeline in the arbitrary two pipelines;
L path1 the total length of the initial path for any one of the two lines;
L path2 the total length of the initial path for any one of the two lines;
ED i is the distance between the end point of one pipe section and the projection point of the other pipe section;
m is the number of effective projection points; the effective projection point is a projection point of which the projection point is positioned on the projected pipe section;
k is a preset constant.
Preferably, the step S4 specifically includes:
s41, acquiring the total length of the pipeline in any pipeline group of the pipeline groups aiming at the pipeline groups;
the total length of the pipelines of the pipeline group is as follows: the total length of the initial paths of all the pipelines in the pipeline group;
s42, acquiring an interference degree value of any pipeline group by adopting a formula (4);
ζ＝m·t (4)；
wherein m is expressed as the number of pipelines in the pipeline group;
t represents the number of the pipelines interfering with the pipeline in any pipeline group in all the other pipeline groups except any pipeline group in the plurality of pipeline groups;
ζ is the interference value of the pipeline group;
s43, sorting the plurality of pipeline groups based on the total pipeline length of the pipeline groups and the interference degree value of any pipeline group to obtain a first set; the method specifically comprises the following steps:
sequencing the plurality of pipeline groups according to a first condition and a second condition to obtain a first set;
wherein the first condition is: sequencing the interference values of the pipeline groups from small to large;
wherein the second condition is: if the interference values of the pipeline groups are the same, arranging the pipeline group with the large pipeline total length in front of the pipeline group with the small pipeline total length.
Preferably, the step S5 specifically includes:
s51, aiming at any pipeline group in the first set, encoding pipelines in the pipeline group according to a preset mode, and obtaining an encoding group corresponding to the pipelines in the pipeline group;
wherein the first and last bits of the coding group are the extension distances of the pipeline starting section and the pipeline ending end respectively;
the first position and the last position of the coding group sequentially correspond to the column coordinates of the nodes in the pipeline;
the nodes in the pipeline are break points in the pipeline.
S52, completing planning of multiple pipeline paths by adopting a preset collaborative differential evolution algorithm based on the codes of the nodes in the pipeline, and obtaining an optimal path combination;
wherein the second evaluation function in the co-differential evolution algorithm is:
wherein the method comprises the steps of
In which L k Is the length of the kth section in a certain pipeline;
ρ i 、θ i 、z i the column coordinates of the ith node;
r is the bending radius of the pipeline;
α i an included angle between the ith pipe section and the (i+1) th pipe section in one pipeline;
F i an evaluation function for a single pipeline;
θ k is the included angle between two adjacent pipe sections;
is a constraint item of the distance between the pipeline and the surface of the casing;
l is the length of the target pipeline;
c is the interval distance when the pipeline is scattered;
d i the projection distance of the current discrete point on the axis of the case is set;
fc () is the case busbar equation;
F A the integral evaluation function of the same pipeline group after the clustering is completed;
F i a single pipeline evaluation function for an ith pipeline in the pipe group;
ω 3 third preset weight;
e is the energy value of the path.
Preferably, in step S52, based on the encoding of the nodes in the pipeline, a preset collaborative differential evolution algorithm is adopted to complete planning of multiple pipeline paths, and an optimal path combination is obtained, which specifically includes:
s521, regarding all pipelines in any pipe group, taking any pipeline in the pipe group as a population in a collaborative differential evolution algorithm;
s522, acquiring a first individual in any population corresponding to the tube group;
the first body includes a code for a node in a pipeline;
s523, performing intersection and mutation treatment on a first individual in the population to obtain a second individual in the population;
s524, combining the second individuals of the population with the first individuals of all the populations except the population in the tube group to obtain a first path combination;
s525, evaluating the first path combination by adopting a second evaluation function to obtain an evaluation value of the first path combination;
s526, determining whether to replace a first individual of the population with the second individual based on the evaluation value of the first path combination;
s527, repeating the steps S522 to S526 until all individuals in the population have corresponding evaluation values;
s528, obtaining individuals with highest evaluation values in each group in the tube group, and calculating an evaluation function value;
s529, repeating the steps S522 to S528 for a preset number of times to obtain an optimal path combination;
the optimal path combination is as follows: the individual combination corresponding to the highest evaluation function value.
Preferably, the step S6 specifically includes:
s61, aiming at the pipeline optimal path combination, randomly generating initial codes 0 or 1 of M straight line segments of the pipeline optimal path combination, wherein the initial codes 0 and 1 of the M straight line segments of the initial and the final extension segments are removed;
wherein, the initial code 1 represents parallelization processing of the pipe section, and the initial code 0 represents no processing;
s62, adjusting a path according to the initial coding;
s63, obtaining a final optimal path by adopting a simulated annealing algorithm.
Preferably, the step S62 includes:
s621, inquiring initial codes corresponding to each pipe section;
s622, judging whether the initial code corresponding to the pipe section is 1;
s623, if yes, calculating a parallelism value delta' between the pipe section and other pipe sections except the pipeline where the pipe section is located;
the parallelism value delta' is:
wherein r is 1 、r 2 Respectively the radius of the pipeline to which the two pipe sections of which the parallelism value is to be calculated belong;
δ max 、δ min respectively the maximum and minimum spacing between parallel pipe sections;
ω 4 a fourth preset weight;
ω 5 a fifth preset weight;
ω 6 a sixth preset weight;
θ is the angle between the two line direction vectors;
s624, performing parallel processing on the pipe section with the highest parallelism value;
s625, repeating the steps S621-S624 until all pipe sections are queried.
In a second aspect, an embodiment of the present invention further provides an aeroengine multi-pipeline layout optimization system, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform any of the aircraft engine multi-pipeline layout optimization methods described above.
(III) beneficial effects
The beneficial effects of the invention are as follows: according to the multi-pipeline layout optimization method of the aeroengine, information of a laying environment in an aeroengine model is extracted, accessories and port points in a three-dimensional environment are mapped to a two-dimensional plane, an initial path relation among the ports is determined by adopting a differential evolution algorithm, a clustering algorithm is adopted on the basis to divide a pipeline into a plurality of pipeline groups, then a pipeline group layout sequence is determined according to interference among the pipeline groups, and a collaborative differential evolution algorithm is adopted for each pipeline group in turn according to the sequence to complete the layout optimization process of the same group of pipelines. Because the parallel pipelines obtained at the moment are not exactly parallel, the parallel pipelines can be exactly parallelized by adopting a simulated annealing algorithm. The method is essentially different from the traditional multi-pipeline layout method, the method is more in line with the actual requirements of engineering, the parallel laying of multiple pipelines is realized, and the influence of the laying sequence on the optimality of pipeline design results is greatly reduced by adopting a method of co-evolution after grouping.
Drawings
FIG. 1 is a flow chart of a method for optimizing the layout of multiple pipelines of an aeroengine;
FIG. 2 is a flow chart of an aircraft engine multi-pipeline layout optimization method in an embodiment of the invention;
FIG. 3 is a flow chart of a pipeline grouping method based on hierarchical clustering algorithm in the invention;
FIG. 4 is a schematic diagram of the distribution of pipeline energy values in the present invention;
FIG. 5 is a pipeline individual encoding mode of the multi-pipeline layout algorithm in the present invention;
FIG. 6 is a schematic diagram of a co-differential evolutionary algorithm for a multi-pipeline layout in accordance with the present invention;
FIG. 7 is a flow chart of a co-differential evolutionary algorithm in accordance with the present invention;
FIG. 8 is a pipeline parallel processing encoding mode based on a simulated annealing algorithm in the invention;
FIG. 9 is a general flow of a pipeline parallelization process based on a simulated annealing algorithm in the present invention;
FIG. 10 is a layout result of the present invention for a multiple pipeline layout performed on a simplified aircraft engine model.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
In order that the above-described aspects may be better understood, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example 1
Referring to fig. 1, in this embodiment, a method for optimizing a multi-pipeline layout of an aeroengine is provided, including:
s1, acquiring end point coordinates of a pipeline in an aeroengine model and a bounding box of an engine surface accessory, and projecting the end point coordinates and bottom surface coordinates of the bounding box to a plane to acquire a two-dimensional simplified diagram of a laying space.
The pipeline is composed of a plurality of pipe sections.
S2, aiming at the two-dimensional simplified diagram of the laying space, acquiring an initial path of a pipeline in the two-dimensional simplified diagram of the laying space.
S3, grouping the pipelines in the two-dimensional simplified drawing of the laying space according to the initial paths of the pipelines in the two-dimensional simplified drawing of the laying space to obtain a plurality of pipeline groups.
S4, sequencing the plurality of pipeline groups to obtain a first set.
The first set comprises a plurality of pipeline groups in an arrangement order;
s5, aiming at any pipeline group in the first set, performing pipeline path planning to obtain an optimal pipeline path combination.
S6, carrying out parallel processing on each pipeline according to the pipeline optimal path combination, and obtaining a final optimal path of the pipeline.
And S7, acquiring a three-dimensional model corresponding to the final optimal path of the pipeline according to the final optimal path of the pipeline.
In this embodiment, preferably, the step S1 specifically includes:
according to a projection mode corresponding to the formula (1), the endpoint coordinates of the pipeline and the coordinates of the bottom surface of the bounding box are projected to a plane;
wherein ρ is the radial distance score of the cylindrical coordinates corresponding to the starting point of the pipeline or the end point of the bottom surface of the bounding box.
And θ is an angle value of a cylindrical coordinate corresponding to a starting point of the pipeline or an end point of the bottom surface of the bounding box.
Z is the height value of the cylindrical coordinate corresponding to the starting point of the pipeline or the end point of the bottom surface of the bounding box.
And x and y correspond to plane coordinates of each point after projection.
In the embodiment, step S1 is to read the coordinates of the end points of the pipeline through UGNX secondary development technology, establish bounding boxes of the surface accessory barriers of the aeroengine, and project the plane coordinates of the bottom of each bounding box and the center coordinates of the port to a plane.
In this embodiment, preferably, the step S2 specifically includes:
for the two-dimensional simplified diagram of the laying space, determining an initial path of each pipeline based on a two-dimensional plane by adopting a preset differential evolution algorithm, wherein an evaluation function in the preset differential evolution algorithm is as follows:
wherein L is i Is the length of the i-th section of pipe in the pipeline.
N is the number of pipe sections in the pipeline.
α i Is the included angle between the ith pipe section and the (i+1) th pipe section in the pipeline.
ω 1 A first predetermined weight.
ω 2 And a second preset weight.
P is a punishment term of interference between a preset pipeline and a plane obstacle.
In this embodiment, preferably, the step S3 specifically includes:
and (3) acquiring a difference value between initial paths of all the pipelines by adopting a formula, and clustering and grouping all the pipelines based on the difference value to acquire a plurality of pipeline groups.
Wherein each pipeline group comprises a plurality of pipelines.
Equation (3):
where δ is the difference value between the initial paths of any two lines.
L coin1 And the total combined length of each section of the initial path of any one of the two pipelines after projection of each section of the initial path of the other pipeline of the two pipelines.
L coin2 And the total combined length of each initial path section of the other pipeline in the two pipelines after projection of each initial path section of the other pipeline in the two pipelines.
L path1 For the total length of the initial path of either of the two lines.
L path2 For the total length of the initial path of either of the two lines.
ED i Is the distance between the end point of one pipe section and the projection point of the other pipe section.
M is the number of effective projection points; the effective projection point is a projection point of which the projection point is positioned on the projected pipe section.
K is a preset constant.
In the difference value in this embodiment, when the number of effective projection points of any pair of pipelines is 0, it is indicated that there is no parallel relationship between the two pipelines, and at this time, the difference value is made to be a larger constant, and if the number of effective projection points is not 0, the difference value is calculated according to the formula below the formula (3). The pipeline grouping method based on hierarchical clustering algorithm is shown in fig. 3.
In this embodiment, preferably, the step S4 specifically includes:
s41, acquiring the total length of the pipeline in any pipeline group of the pipeline groups aiming at the pipeline groups.
The total length of the pipelines of the pipeline group is as follows: the total length of the initial path of all the tubes in the tube set.
S42, acquiring the interference degree value of any pipeline group by adopting the formula (4).
ζ＝m·t (4)；
Where m is the number of lines in the line set.
t represents the number of the pipes interfering with the pipe in any one pipe group among all the other pipe groups except the any one pipe group among the plurality of pipe groups.
ζ is the interference value of the pipeline group.
S43, sorting the plurality of pipeline groups based on the total pipeline length of the pipeline groups and the interference degree value of any pipeline group to obtain a first set; the method specifically comprises the following steps:
and sequencing the plurality of pipeline groups according to the first condition and the second condition to obtain a first set.
Wherein the first condition is: the interference values of the pipeline groups are ordered from small to large.
Wherein the second condition is: if the interference values of the pipeline groups are the same, arranging the pipeline group with the large pipeline total length in front of the pipeline group with the small pipeline total length.
For example, in this embodiment, the tube group having a large interference ζ in the sorting is to be laid down in the rear of the tube group having a small ζ, and for the tube group having the same ζ, the total length of the piping is compared, and the tube group having a larger total length is laid down before the tube group having a small length.
In this embodiment, preferably, the step S5 specifically includes:
s51, aiming at any pipeline group in the first set, encoding pipelines in the pipeline group according to a preset mode, and obtaining an encoding group corresponding to the pipelines in the pipeline group.
Wherein the first and last bits of the coding group are the extension distances of the pipeline starting section and the ending end respectively.
The first position and the last position of the coding group sequentially correspond to the column coordinates of the nodes in the pipeline; referring to FIG. 5, where the first and last bits of the code are the extension distances of the beginning and ending ends of the pipeline, respectively, the extension distances are present to ensure that the resulting pipeline of the algorithm can be installed correctly; coding intermediate ρ i 、θ i 、z i The column coordinate of the ith node of the pipeline.
The nodes in the pipeline are break points in the pipeline.
And S52, completing planning of multiple pipeline paths by adopting a preset collaborative differential evolution algorithm based on the codes of the nodes in the pipeline, and obtaining an optimal path combination.
Wherein the second evaluation function in the co-differential evolution algorithm is:
wherein the method comprises the steps of
In which L k Is the length of the kth segment in a certain pipeline.
ρ i 、θ i 、z i Is the column coordinate of the ith node.
R is the bending radius of the pipeline.
α i Is the included angle between the ith pipe section and the (i+1) th pipe section in one pipeline.
F i Is an evaluation function of a single pipeline.
θ k Is the included angle between two adjacent pipe sections.
Is a constraint item of the distance between the pipeline and the surface of the casing.
L is the length of the target pipeline.
C is the spacing distance when the pipeline is discretized.
d i The projection distance of the current discrete point on the axis of the case is obtained.
fc () is the case busbar equation.
F A And (5) the whole evaluation function of the same pipeline group after the clustering is completed.
F i A single-tube evaluation function for the ith tube in the tube set.
ω 3 And a third preset weight.
E is the energy value of the path, as shown in FIG. 4, for a pipeline path, the energy value is expanded at equal intervals around the pipeline axis as the center, different energy values are provided in different distance ranges, a higher energy value is provided at a position closer to the pipeline axis, and the energy value of a region far from the axis exceeds the energy value region, and the energy value is 0.
The energy value E of the path in this embodiment is preset.
Referring to fig. 6 and 7, in this embodiment, preferably, in step S52, based on the codes of the nodes in the pipeline, a preset co-differential evolutionary algorithm is adopted to complete planning of multiple pipeline paths, so as to obtain an optimal path combination, which specifically includes:
s521, regarding all the pipelines in any pipe group, taking any pipeline in the pipe group as a population in the collaborative differential evolution algorithm.
S522, acquiring a first individual in any population corresponding to the tube group.
The first body includes an encoding of a node in a pipeline.
S523, performing crossing and mutation treatment on a first individual in the population to obtain a second individual in the population.
S524, combining the second individuals of the population with the first individuals of all the populations except the population in the tube group to obtain a first path combination.
And S525, evaluating the first path combination by adopting a second evaluation function, and obtaining an evaluation value of the first path combination.
S526, based on the evaluation value of the first path combination, determining whether to replace the first individual of the population with the second individual.
S527, repeating the steps S522 to S526 until all individuals in the population have corresponding evaluation values.
S528, obtaining the individuals with the highest evaluation values in each group in the tube group, and calculating the evaluation function value.
And S529, repeating the steps S522 to S528 for a preset number of times, and obtaining the optimal path combination.
The optimal path combination is as follows: the individual combination corresponding to the highest evaluation function value.
Referring to fig. 7, in this embodiment, in order to eliminate the influence of the pipeline sequence in the same group on the optimality of the overall layout result, a co-evolution strategy is introduced into the original differential evolution algorithm, so as to realize that multiple pipelines can jointly complete the evolution process on the premise of neglecting the layout sequence. The co-evolution strategy breaks down a large number of gauge module questions into a number of small questions, each representing a population. Various populations exist in an ecological system (large environment), information communication among the populations is achieved by transmitting and acquiring evolution information into the ecological system (large environment), and finally, a plurality of populations are continuously evolved to solve the whole problem.
Each pipeline independently completes an iteration process, in each iteration process, an algorithm needs to select a path scheme representing a population from respective population combinations for forming the iteration, the current scheme is evaluated through an evaluation function of a plurality of pipelines, a scheme with better quality is selected as a parent population in the next iteration process, and finally the algorithm is converged to an optimal path scheme through repeated iteration.
In the preset collaborative differential evolution algorithm in the embodiment, each pipeline in the pipeline group is regarded as a population, the individuals in each population are subjected to crossover and mutation operations respectively, then the population and the representative individuals of other populations are combined into a test scheme, the scheme is evaluated and selected by adopting a multi-pipeline layout evaluation function, and after all the individuals of the population complete the evolution operations, one individual is preferably selected as the representative and added into the representative set. After all the populations complete the iterative process, selecting the representative individuals of each population to form a layout scheme of the iterative process, calculating the evaluation value of the scheme, and repeatedly iterating the above processes until the iterative requirement is met, so as to complete the multi-pipeline co-evolution process.
In this embodiment, preferably, the step S6 specifically includes:
referring to fig. 8, S61, for the pipeline optimal path combination, initial codes 0 or 1 of M straight line segments of the pipeline optimal path combination, from which the extension segments of the start and end are removed, are randomly generated.
Wherein initial code 1 represents parallelization processing of the pipe section, and initial code 0 represents no processing.
In this embodiment, each line in the code of fig. 8 corresponds to one pipeline, and assuming that N pipelines to be processed exist in the same group, the extension section of each pipeline from which the start and stop are removed is composed of M straight line sections, and the start pipe section and the end pipe section are not modifiable because the directions of the start pipe section and the end pipe section are consistent with the normal direction of the port, so that the part is not considered in the coding process. The first M codes in FIG. 8 are coded values corresponding to each straight pipe section in the first pipeline, wherein 1 represents parallelization processing of the pipe section, and 0 represents no processing; the codes with the positions between M+1 and 2M-2 represent the adjustment modes of each pipe section during parallel processing, and 1 represents the rotation of the pipe section by taking the starting end point of the pipe section as the axis so as to realize the parallel operation with the pipe section with the highest parallelism; if the code is 0, the pipe section end point is used as the axis and is treated in the same way, the diagram X is a coding schematic diagram, and the red dotted line in the diagram represents the corresponding relation between the adjustment direction control code and each pipe section. The other pipeline coding modes to be processed in the same group are processed in the mode, and finally, all pipeline codes are combined, and the total length of the combined codes is 2N (M-1).
S62, adjusting a path according to the initial coding;
s63, referring to FIG. 9, a simulated annealing algorithm is adopted to obtain a final optimal path.
In this embodiment, the step S62 includes:
s621, inquiring the initial code corresponding to each pipe segment.
S622, judging whether the initial code corresponding to the pipe section is 1.
And S623, if so, calculating the parallelism delta' value between the pipe section and other pipe sections except the pipeline where the pipe section is located.
The parallelism value delta' is:
wherein r is 1 、r 2 The radii of the pipelines to which the two pipe sections to be calculated have parallelism values respectively.
δ max 、δ min The maximum and minimum spacing between parallel pipe segments, respectively.
ω 4 And a fourth preset weight.
ω 5 A fifth preset weight.
ω 6 A sixth predetermined weight.
θ is the angle between the two line direction vectors.
S624, performing parallel processing on the pipe segment with the highest parallelism value.
S625, repeating the steps S621-S624 until all pipe sections are queried.
Referring to fig. 10, in this embodiment, a visual studio-UG secondary development platform is jointly built, a corresponding dynamic link library is developed, and the multi-pipeline automatic layout method is integrated into software.
According to the multi-pipeline layout optimization method for the aeroengine, information of a laying environment in an aeroengine model is extracted, accessories and port points in a three-dimensional environment are mapped to a two-dimensional plane, an initial path relation among the ports is determined by adopting a differential evolution algorithm, a clustering algorithm is adopted to divide a pipeline into a plurality of pipeline groups on the basis, a pipeline group layout sequence is determined according to interference among the pipeline groups, and a collaborative differential evolution algorithm is adopted to each pipeline group in sequence according to the sequence to complete a layout optimization process of the same pipeline group. Because the parallel pipelines obtained at the moment are not exactly parallel, the parallel pipelines can be exactly parallelized by adopting a simulated annealing algorithm. The method is essentially different from the traditional multi-pipeline layout method, the method is more in line with the actual requirements of engineering, the parallel laying of multiple pipelines is realized, and the influence of the laying sequence on the optimality of pipeline design results is greatly reduced by adopting a method of co-evolution after grouping.
Example two
Taking an aero-engine model with an average diameter of 1170mm and an overall length of 2645mm as an example, the layout of a plurality of pipelines is completed according to the multi-pipeline layout optimization method of the aero-engine in the embodiment, wherein parameters in the multi-pipeline layout optimization method of the aero-engine in the embodiment are set as follows:
in this embodiment, the formula (2) specifically includes:
parameter setting in equation (3) in the present embodiment: omega 1 ＝5、ω 2 ＝1.2。
In this embodiment, the formula (5) specifically includes:
parameter setting in equation (6) in the present embodiment: omega 4 ＝1、ω 5 ＝3.5；ω 6 ＝0.02，δ min ＝20、δ max ＝45。
The multi-pipeline layout result obtained by performing layout according to the multi-pipeline layout optimization method of the aero-engine in the embodiment is shown in fig. 10, and the whole process is operated for 6.1min, wherein the time for constructing the two-dimensional connection relationship is 1.72s, the time for searching the path of the pipeline is 338.4s, and the time for parallel processing of the pipeline is 25.9s. In summary, the method of the present invention is shorter than the conventional prior art layout method, and achieves a high efficiency layout of a plurality of pipes on the outer surface of the aeroengine, while demonstrating the feasibility of the method.
According to the multi-pipeline layout optimization method for the aeroengine, information of a laying environment in an aeroengine model is extracted, accessories and port points in a three-dimensional environment are mapped to a two-dimensional plane, an initial path relation among the ports is determined by adopting a differential evolution algorithm, a clustering algorithm is adopted to divide a pipeline into a plurality of pipeline groups on the basis, a pipeline group layout sequence is determined according to interference among the pipeline groups, and a collaborative differential evolution algorithm is adopted to each pipeline group in sequence according to the sequence to complete a layout optimization process of the same pipeline group. Because the parallel pipelines are not exactly parallel, the parallel pipelines can be exactly parallelized by adopting a simulated annealing algorithm, and finally, the UGNX secondary development technology based on Visual C# is used for completing the generation of the entity pipelines. The method in the embodiment has essential difference from the traditional multi-pipeline layout method, the method in the embodiment is more in line with the actual requirements of engineering, the parallel laying of multiple pipelines is realized, the influence of the laying sequence on the optimality of pipeline design results is greatly reduced by adopting a method of collaborative evolution after grouping, and on the other hand, the method has important reference significance for the pipeline layout of other complex electromechanical products.
On the other hand, in this embodiment, there is also provided an aeroengine multi-pipeline layout optimization system, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein the memory stores program instructions executable by the processor, the processor invoking the program instructions to enable execution of an aero-engine multi-pipeline layout optimization method as described in any of the above.
Since the system described in the foregoing embodiments of the present invention is a system for implementing the method of the foregoing embodiments of the present invention, those skilled in the art will be able to understand the specific structure and modification of the system based on the method of the foregoing embodiments of the present invention, and thus will not be described in detail herein. All systems used in the methods of the above embodiments of the present invention are within the scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, systems according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third, etc. are for convenience of description only and do not denote any order. These terms may be understood as part of the component name.
Furthermore, it should be noted that in the description of the present specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with the embodiment or example being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art upon learning the basic inventive concepts. Therefore, the appended claims should be construed to include preferred embodiments and all such variations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, the present invention should also include such modifications and variations provided that they come within the scope of the following claims and their equivalents.
Claims (6)
1. An aircraft engine multi-pipeline layout optimization method is characterized by comprising the following steps:
s1, acquiring an endpoint coordinate of a pipeline in an aeroengine model and a bounding box of an engine surface accessory, and projecting the endpoint coordinate and a bottom surface coordinate of the bounding box to a plane to acquire a two-dimensional simplified diagram of a laying space;
the pipeline consists of a plurality of pipe sections;
s2, aiming at the two-dimensional simplified diagram of the laying space, acquiring an initial path of a pipeline in the two-dimensional simplified diagram of the laying space;
s3, grouping the pipelines in the two-dimensional simplified drawing of the laying space according to the initial paths of the pipelines in the two-dimensional simplified drawing of the laying space to obtain a plurality of pipeline groups;
s4, sequencing the plurality of pipeline groups to obtain a first set;
the first set comprises a plurality of pipeline groups in an arrangement order;
s5, aiming at any pipeline group in the first set, performing pipeline path planning to obtain an optimal pipeline path combination;
s6, carrying out parallel processing on each pipeline according to the pipeline optimal path combination to obtain a final optimal path of the pipeline;
s7, acquiring a three-dimensional model corresponding to the final optimal path of the pipeline according to the final optimal path of the pipeline;
the step S1 specifically includes:
according to a projection mode corresponding to the formula (1), the endpoint coordinates of the pipeline and the coordinates of the bottom surface of the bounding box are projected to a plane;
wherein ρ is a radial distance score of a cylindrical coordinate corresponding to a starting point of the pipeline or an end point of the bottom surface of the bounding box;
θ is an angle value of a cylindrical coordinate corresponding to a starting point of the pipeline or an end point of the bottom surface of the bounding box;
z is the height value of the cylindrical coordinate corresponding to the starting point of the pipeline or the end point of the bottom surface of the bounding box;
the x and y correspond to plane coordinates of each point after projection;
the step S2 specifically includes:
for the two-dimensional simplified diagram of the laying space, determining an initial path of each pipeline based on a two-dimensional plane by adopting a preset differential evolution algorithm, wherein an evaluation function in the preset differential evolution algorithm is as follows:
wherein L is i Is the length of the i-th section of the pipeline;
n is the number of pipe sections in the pipeline;
α i an included angle between the ith pipe section and the (i+1) th pipe section in the pipeline;
ω 1 a first preset weight;
ω 2 a second preset weight;
p is a punishment term of interference between a preset pipeline and a plane obstacle;
the step S3 specifically includes:
acquiring a difference value between initial paths of all pipelines by adopting a formula (3), and clustering and grouping all the pipelines based on the difference value to acquire a plurality of pipeline groups;
wherein each pipeline group comprises a plurality of pipelines;
equation (3):
wherein delta is the difference value between the initial paths of any two pipelines;
L coin1 the total weight length of each section of the initial path of any one of the two pipelines after projection of each section of the initial path of the other pipeline of the two pipelines;
L coin2 the total weight length of each section of the initial path of the other pipeline in the arbitrary two pipelines after projection of each section of the initial path of the other pipeline in the arbitrary two pipelines;
L path1 the total length of the initial path for any one of the two lines;
L path2 the total length of the initial path for any one of the two lines;
ED i is the distance between the end point of one pipe section and the projection point of the other pipe section;
m is the number of effective projection points; the effective projection point is a projection point of which the projection point is positioned on the projected pipe section;
k is a preset constant;
the step S4 specifically includes:
s41, acquiring the total length of the pipeline in any pipeline group of the pipeline groups aiming at the pipeline groups;
the total length of the pipelines of the pipeline group is as follows: the total length of the initial paths of all the pipelines in the pipeline group;
s42, acquiring an interference degree value of any pipeline group by adopting a formula (4);
ζ＝m·t (4)；
wherein m is expressed as the number of pipelines in the pipeline group;
t represents the number of the pipelines interfering with the pipeline in any pipeline group in all the other pipeline groups except any pipeline group in the plurality of pipeline groups;
ζ is the interference value of the pipeline group;
s43, sorting the plurality of pipeline groups based on the total pipeline length of the pipeline groups and the interference degree value of any pipeline group to obtain a first set; the method specifically comprises the following steps:
sequencing the plurality of pipeline groups according to a first condition and a second condition to obtain a first set;
wherein the first condition is: sequencing the interference values of the pipeline groups from small to large;
wherein the second condition is: if the interference values of the pipeline groups are the same, arranging the pipeline group with the large pipeline total length in front of the pipeline group with the small pipeline total length.
2. The method according to claim 1, wherein the step S5 specifically includes:
s51, aiming at any pipeline group in the first set, encoding pipelines in the pipeline group according to a preset mode, and obtaining an encoding group corresponding to the pipelines in the pipeline group;
wherein the first and last bits of the coding group are the extension distances of the pipeline starting section and the pipeline ending end respectively;
the first position and the last position of the coding group sequentially correspond to the column coordinates of the nodes in the pipeline;
the nodes in the pipeline are break points in the pipeline;
s52, completing planning of multiple pipeline paths by adopting a preset collaborative differential evolution algorithm based on the codes of the nodes in the pipeline, and obtaining an optimal path combination;
wherein the second evaluation function in the co-differential evolution algorithm is:
wherein the method comprises the steps of
In which L k Is the length of the kth section in a certain pipeline;
ρ i 、θ i 、z i the column coordinates of the ith node;
r is the bending radius of the pipeline;
α i an included angle between the ith pipe section and the (i+1) th pipe section in one pipeline;
F i an evaluation function for a single pipeline;
θ k is the included angle between two adjacent pipe sections;
is a constraint item of the distance between the pipeline and the surface of the casing;
l is the length of the target pipeline;
c is the interval distance when the pipeline is scattered;
d i the projection distance of the current discrete point on the axis of the case is set;
fc () is the case busbar equation;
F A the integral evaluation function of the same pipeline group after the clustering is completed;
F i a single pipeline evaluation function for an ith pipeline in the pipe group;
ω 3 third preset weight;
e is the energy value of the path.
3. The method according to claim 2, wherein the step S52 is based on the encoding of the nodes in the pipeline, and the planning of the multiple pipeline paths is completed by adopting a preset co-differential evolutionary algorithm, so as to obtain an optimal path combination, and the method specifically includes:
s521, regarding all pipelines in any pipe group, taking any pipeline in the pipe group as a population in a collaborative differential evolution algorithm;
s522, acquiring a first individual in any population corresponding to the tube group;
the first body includes a code for a node in a pipeline;
s523, performing intersection and mutation treatment on a first individual in the population to obtain a second individual in the population;
s524, combining the second individuals of the population with the first individuals of all the populations except the population in the tube group to obtain a first path combination;
s525, evaluating the first path combination by adopting a second evaluation function to obtain an evaluation value of the first path combination;
s526, determining whether to replace a first individual of the population with the second individual based on the evaluation value of the first path combination;
s527, repeating the steps S522 to S526 until all individuals in the population have corresponding evaluation values;
s528, obtaining individuals with highest evaluation values in each group in the tube group, and calculating an evaluation function value;
s529, repeating the steps S522 to S528 for a preset number of times to obtain an optimal path combination;
the optimal path combination is as follows: the individual combination corresponding to the highest evaluation function value.
4. A method according to claim 3, wherein said step S6 specifically comprises:
s61, aiming at the pipeline optimal path combination, randomly generating initial codes 0 or 1 of M straight line segments of the pipeline optimal path combination, wherein the initial codes 0 and 1 of the M straight line segments of the initial and the final extension segments are removed;
wherein, the initial code 1 represents parallelization processing of the pipe section, and the initial code 0 represents no processing;
s62, adjusting a path according to the initial coding;
s63, obtaining a final optimal path by adopting a simulated annealing algorithm.
5. The method according to claim 4, wherein the step S62 includes:
s621, inquiring initial codes corresponding to each pipe section;
s622, judging whether the initial code corresponding to the pipe section is 1;
s623, if yes, calculating a parallelism value delta' between the pipe section and other pipe sections except the pipeline where the pipe section is located;
the parallelism value delta' is:
wherein r is 1 、r 2 Respectively the radius of the pipeline to which the two pipe sections of which the parallelism value is to be calculated belong;
δ max 、δ min respectively the maximum and minimum spacing between parallel pipe sections;
ω 4 a fourth preset weight;
ω 5 a fifth preset weight;
ω 6 a sixth preset weight;
θ is the angle between the two line direction vectors;
s624, performing parallel processing on the pipe section with the highest parallelism value;
s625, repeating the steps S621-S624 until all pipe sections are queried.
6. An aircraft engine multi-pipeline layout optimization system, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the aeroengine multi-pipeline layout optimization method of any of claims 1-5.
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