CN117077322A - Design method of motor cooling flow channel - Google Patents
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Abstract
The invention discloses a design method of a motor cooling flow passage, which comprises the following steps: determining a variable to be optimized of a cooling flow channel of the motor, and acquiring a plurality of first sample data based on the variable to be optimized, wherein the plurality of first sample data form an initial sample point set; based on a CFD model of a motor cooling flow channel, simulating an initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein a plurality of first static pressure differences form a first static pressure difference set; fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between the design variable and the static pressure difference; and checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet a first preset condition. According to the technical scheme, the calculation is accurate and simple, and the optimal design parameters can be accurately obtained so as to effectively reduce the flow resistance of the cooling flow channel of the motor.
Description
Technical Field
The invention relates to the technical field of motor cooling flow channels, in particular to a design method of a motor cooling flow channel.
Background
The driving motor is a core component of the new energy vehicle and provides power for the whole vehicle to run. The heat generated during the operation of the driving motor is discharged through a cooling device, and liquid cooling is commonly used as a cooling means at present, and a flow channel for cooling liquid to flow is arranged in the driving motor shell.
The flow resistance is an important performance index of a cooling flow channel of the driving motor and is used for measuring the resistance of the cooling liquid flowing in the flow channel. The reduction of the flow resistance of the flow channel has important significance for improving the operation safety of the motor and reducing the model selection cost of the water supply pump of the whole vehicle factory. At present, regarding the optimal design of a cooling flow channel of a driving motor, a quantitative design method for flow resistance of the flow channel is lacking.
Disclosure of Invention
The invention provides a design method of a motor cooling flow channel, which can optimally design one or more design variables, has high degree of freedom, avoids the limitation of judging the optimal design parameter combination only by a limited sample of a CFD calculation result, is accurate and simple in calculation, and can accurately acquire the optimal design parameters to effectively reduce the flow resistance of the motor cooling flow channel.
The embodiment of the invention provides a design method of a motor cooling flow passage, which comprises the following steps:
determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set;
based on a CFD model of the motor cooling flow channel, simulating the initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein a plurality of first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel;
fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between a design variable and the static pressure difference;
and checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet a first preset condition.
Optionally, verifying the fitting accuracy of the first functional relation includes:
acquiring a plurality of second sample data based on the design variable to be optimized, wherein the second sample data form a test sample point set;
simulating the test sample point set based on a CFD model of the motor cooling flow channel to obtain second static pressure differences corresponding to each second sample data, wherein a plurality of second static pressure differences form a second static pressure difference set;
inputting the test sample point set into the first functional relation, solving to obtain a third static pressure difference corresponding to each second sample data, wherein a plurality of third static pressure differences form a third static pressure difference set;
and calculating to obtain an evaluation parameter for evaluating the fitting precision of the first functional relation according to the second static pressure difference set and the third static pressure difference set.
Optionally, according to the second static pressure difference set and the third static pressure difference set, an evaluation parameter for evaluating the fitting accuracy of the first functional relation is obtained by calculation, including:
according to the formulaCalculating to obtain a determination coefficient R for evaluating the overall fitting precision of the first function relation 2 Wherein, k is more than or equal to 1 and less than or equal to nt, nt is the number of second sample data, p tk To testSecond sample data X in sample point set tk Corresponding second static pressure difference, p tfk For testing the second sample data X in the sample point set tk Corresponding third static pressure difference, ++>Is the average of the second set of static pressure differences;
according to the formulaCalculating to obtain a relative maximum absolute error RMAE for evaluating the local fitting accuracy of the first functional relation, wherein +.>Is the standard deviation of the second set of static pressure differences.
Optionally, the first preset condition includes: 1-R 2 ＜ε 1 And RMAE < ε 2 Wherein ε is 1 And epsilon 2 Are all arbitrary positive numbers less than 0.01.
Optionally, the design method of the cooling flow channel of the motor further includes: and updating the initial sample point set when the fitting precision of the first functional relation is judged to not meet a first preset condition.
Optionally, updating the initial sample point set includes:
judging R 2 Whether or not it is less than epsilon 3 And in determining R 2 Less than epsilon 3 At that time, determining the corresponding second sample data at which RMAE is maximum, wherein ε 3 An arbitrary positive number less than 0.1;
acquiring a plurality of third sample data in a first preset range to which second sample data corresponding to the maximum RMAE belongs, wherein the plurality of third sample data form a correction sample point set;
combining the test sample point set and the corrected sample point set to the initial sample point set.
Optionally, the design method of the cooling flow channel of the motor further includes: when determining R 2 Greater than or equal toε 3 At that time, the test sample point set is merged to the initial sample point set.
Optionally, fitting the initial sample point set and the first static pressure difference set to obtain a first functional relation between a design variable and a static pressure difference, including:
constructing a Kriging agent model, and calculating and fitting to obtain an initial functional relation between the initial sample point set and the first static pressure difference set;
and determining a first functional relation according to the initial functional relation.
Optionally, before determining the design variable to be optimized of the motor cooling flow passage, the method includes:
determining design variables of a cooling flow channel of the motor, wherein the design variables comprise the design variables to be optimized;
based on the design variables, a StarCCM+ simulation software platform is adopted to construct a CFD model of the motor cooling flow channel.
Optionally, before determining the design variable to be optimized of the motor cooling flow channel, after constructing the CFD model of the motor cooling flow channel, the method further comprises:
and acquiring a plurality of measured test data, and correcting the CFD model of the motor cooling flow channel according to the measured test data.
According to the scheme provided by the invention, the design variable to be optimized of the motor cooling flow channel is determined, a plurality of first sample data are obtained based on the design variable to be optimized to form an initial sample point set, then the initial sample point set is simulated based on the CFD model of the motor cooling flow channel to obtain a corresponding first static pressure difference set, then a first functional relation between the design variable and the static pressure difference is obtained according to fitting of the initial sample point set and the first static pressure difference set, fitting accuracy of the first functional relation is checked, when the fitting accuracy of the first functional relation is judged to meet a first preset condition, the parameter value of the design variable corresponding to the minimum static pressure difference is obtained based on solving of the first functional relation, so that limitation of judging the optimal design parameter combination only through a limited sample of a CFD calculation result is avoided, the first functional relation has good prediction capability, the parameters of each design variable can be adjusted at will, optimal design can be carried out aiming at one or more design variables, meanwhile, the corresponding static pressure difference can be obtained rapidly after the parameters of the design variable are input, and the efficiency is far higher than CFD calculation efficiency.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, a brief description will be given below of the drawings required for the embodiments or the description of the prior art, and it is obvious that although the drawings in the following description are specific embodiments of the present invention, it is obvious to those skilled in the art that the basic concepts of the device structure, the driving method and the manufacturing method, which are disclosed and suggested according to the various embodiments of the present invention, are extended and extended to other structures and drawings, and it is needless to say that these should be within the scope of the claims of the present invention.
FIG. 1 is a flow chart of a design method of a cooling flow channel of a motor according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a cooling flow channel of a motor according to an embodiment of the present invention;
FIG. 3a is a front view of FIG. 2;
FIG. 3b is a partial side view of FIG. 2;
FIG. 3c is a schematic view of a portion of the structure of FIG. 2;
FIG. 3d is a schematic view of another partial structure of FIG. 2;
FIG. 4 is a flow chart of another design method for a cooling flow channel of a motor according to an embodiment of the present invention;
FIG. 5 is a flow chart of a design method of a cooling flow passage of a motor according to an embodiment of the present invention;
fig. 6 is a flowchart of a design method of a cooling flow channel of a motor according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described by means of implementation examples with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the basic concepts disclosed and suggested by the embodiments of the present invention are within the scope of the present invention.
Fig. 1 is a flowchart of a design method of a cooling flow channel of a motor according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
s101, determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set.
It will be appreciated that, according to the specific structure of the motor cooling flow channel, the number of design variables of the motor cooling flow channel and the specific representation meaning of each design variable will also be different, which is not limited in the embodiment of the present invention, where the design variable to be optimized of the motor cooling flow channel may refer to all the design variables involved in the whole structure of the motor cooling flow channel, or may be a part or one of the design variables, which is not limited in the embodiment, and may be set according to actual requirements. It should be noted that, the number of design variables to be optimized for different motor cooling channels, and the value of each design variable to be optimized are different, and the influence of the values on the flow resistance of the whole motor cooling channel is also different. The flow resistance of the motor cooling flow channel means that when the cooling liquid flows in the cooling flow channel, energy loss can be generated due to factors such as friction of the inner wall of the flow channel, change of the cross section shape of a pipeline and the like, the apparent static pressure of the fluid is reduced, namely, due to the existence of the flow resistance of the motor cooling flow channel, the static pressure of the fluid at the water outlet is smaller than the static pressure of the fluid at the water inlet, and the specific magnitude of the static pressure reduction is positively correlated with the flow resistance of the motor cooling flow channel.
Exemplary, fig. 2 is a schematic structural view of a motor cooling flow channel according to an embodiment of the present invention, fig. 3a is a front view of fig. 2, fig. 3b is a partial side view of fig. 2, fig. 3c is a partial schematic structural view of fig. 2, fig. 3d is another partial schematic structural view of fig. 2, and referring to fig. 2, fig. 3a to 3d, the motor cooling flow channel includes a motor cooling flow channel body 10, a water inlet 20 and a water outlet 30 respectively communicating with the motor cooling flow channel body 10, a water inlet 40 communicating with the water inlet 20, and a water outlet 50 communicating with the water outlet 30; the motor cooling flow path body 10 includes a plurality of first body portions 11 and a plurality of second body portions 12, the water inlet 20 and the water outlet 30 are respectively located on two adjacent first body portions 11, the second body portions 12 connect the two adjacent first body portions 11, and no second body portion 12 is disposed between the two adjacent first body portions 11. For the convenience of structure understanding and description, the global coordinate system of the motor cooling flow channel is defined as a space rectangular coordinate system XYZ, the X axis, the Y axis and the Z axis are mutually perpendicular, the origin of coordinates is marked as O, and the Y axis is parallel to the axial direction of the motor cooling flow channel. As such, design variables of the motor cooling flow passage may include: inner arc radius R of motor cooling runner in Outer arc radius R of motor cooling flow channel out Corresponding central angle Deg of the first main body part axle Corresponding central angle Deg of the second main body part cir Intersection point P of water inlet axis and outer arc surface of motor cooling flow channel in Intersection point P of coordinates of water outlet axis and outer arc surface of motor cooling flow passage out Is Deg of the circumferential inclination of the water inlet in_cir The circumferential inclination angle Deg of the water outlet out_cir Straight line P in Angle Deg between projection of O in XZ plane and Z axis in_proj Straight line P out Angle Deg between projection of O in XZ plane and Z axis out_proj Number N of first body portions axle Number N of second body portions cir Axial length L of first body portion axle Axial length L of second body portion cir 、P in Y-direction coordinate Y of (2) in 、P out Y-direction coordinate Y of (2) out Water inlet and outletRadius R of nozzle io Radius R of water inlet nozzle and water outlet nozzle tube Bending length L of water inlet nozzle and water outlet nozzle tube Chamfer radius R at the junction of the first body portion and the second body portion fill 、P in Distance from water inlet to water inlet nozzle interface and P out Distance H from water outlet to water outlet nozzle interface io Height H of water inlet nozzle and water outlet nozzle ver Height H of bending water tap hor Axial inclination Deg of water inlet in_axle Axial inclination Deg of water outlet out_axle . Thus, the design variable to be optimized for the motor cooling flow passage may be one or more of the design variables for the motor cooling flow passage described above.
Specifically, after determining the design variable to be optimized of the cooling flow channel of the motor, determining an upper limit value and a lower limit value of a value of each design variable to be optimized, so that different multiple parameter combinations can be set according to different value parameters of each design variable to be optimized, each parameter combination is one sample data, multiple first sample data can be obtained according to actual conditions based on the determined design variable to be optimized, and multiple first sample data form an initial sample point set for facilitating subsequent data processing.
S102, based on a CFD model of the motor cooling flow channel, simulating an initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein the first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel.
The CFD model is a simulation model for performing computational fluid dynamics, and is used for, for example, researching the flow condition and heat transfer of a fluid. The CFD model of the motor cooling flow channel may be a simulation model built by an existing professional software platform, such as, but not limited to, starccm+.
Specifically, parameters of design variables to be optimized corresponding to each first sample data in the initial sample point set are input into a CFD model of the motor cooling flow channel, and the difference value (namely, first static pressure difference) between the static pressure of fluid at the water inlet of the motor cooling flow channel and the static pressure of fluid at the water outlet of the motor cooling flow channel is obtained through simulation calculation under each first sample data of the motor cooling flow channel.
S103, fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between the design variable and the static pressure difference.
Specifically, since each first sample data corresponds to one first static pressure difference, a first functional relation between a design variable and a static pressure difference can be obtained by fitting through an existing data analysis processing method according to an initial sample point set formed by a plurality of first sample data and a first static pressure difference set formed by a plurality of first static pressure differences.
S104, checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet the first preset condition.
Specifically, after the first functional relation between the design variable and the static pressure difference is obtained through fitting the initial sample point set and the first static pressure difference set, the fitting precision of the first functional relation needs to be checked, and after the fitting precision of the first functional relation is judged to meet a first preset condition, the calculation precision of the first functional relation can be considered to be the same as the simulation result of the CFD model of the motor cooling flow channel, and the first functional relation has higher accuracy. Therefore, when the parameter value of any design variable is changed later, simulation processing is not needed through the CFD model of the motor cooling flow channel, and the calculation can be performed through the first functional relation, so that the calculation efficiency is improved. Furthermore, the parameter value of the design variable corresponding to the minimum static pressure difference can be obtained by solving based on the first functional relation with higher fitting precision, so that the motor cooling flow passage is designed according to the design variable corresponding to the minimum static pressure difference, and the motor cooling flow passage is ensured to have smaller flow resistance.
Illustratively, m design variables to be optimized [ x ] are selected 1 ,x 2 ,…,x i ,…,x m ]Forming a first sample data in which each of the samples is to be optimizedDesign variable x i The value range of (2) is x imin ≤x i ≤x imax I is more than or equal to 1 and less than or equal to m, and m is more than or equal to 1. Based on mathematical sampling method, the design variable is sampled at a specified sampling resolution (the sampling resolution represents the number of samples in the sampling process, the higher the resolution, the more samples are sampled), or the number of samples, to generate an initial sample point set X containing n pieces of first sample data 0 ＝[X 1 ,X 2 ,…,X j ,…,X n ]Wherein j is more than or equal to 1 and less than or equal to n, n is more than 1, X j Is the design variable to be optimized [ x ] 1 ,x 2 ,…,x i ,…,x m ]A set of combinations of values representing a design of the cooling flow path. Then based on CFD model of motor cooling flow channel, for initial sample point set X 0 Simulation is carried out to obtain an initial sample point set X 0 Corresponding first set of static pressure differences P loss0 ＝[p 1 ,p 2 ,…,p j ,…,p n ]Wherein the first static pressure difference p j Is the initial sample X j And (5) a corresponding CFD solving result. And then based on the initial sample point set X 0 And a first set of static pressure differences P loss0 Fitting to obtain a first functional relation P of the design variable and the static pressure difference loss ＝f Krig (X), wherein X is any one set of design variables [ X ] 1 ,x 2 ,…,x i ,…,x m ]Is a combination of values of X including X j ，P loss And fitting the first static pressure difference of the cooling flow channel corresponding to the value combination X. Finally, by checking the fitting precision of the first functional relation, when the fitting precision of the first functional relation meets the first preset condition, the parameter value of the design variable corresponding to the minimum static pressure difference is obtained based on the solving of the first functional relation, so that the motor cooling flow passage can be designed according to the design variable corresponding to the minimum static pressure difference, the motor cooling flow passage is ensured to have smaller flow resistance, the limitation of judging the optimal design parameter combination only through a limited sample of the CFD calculation result is avoided, the first functional relation has good prediction capability, the parameters of each design variable can be randomly adjusted, and one or more of concerned design variables can be aimed atThe design variables are optimally designed, the degree of freedom is high, and meanwhile, after a group of parameters of the design variables are input, the corresponding static pressure difference can be obtained quickly, and the efficiency is far higher than the CFD calculation efficiency.
In the embodiment, the to-be-optimized design variable of the motor cooling flow channel is determined, a plurality of first sample data are obtained based on the to-be-optimized design variable to form an initial sample point set, then the initial sample point set is simulated based on a CFD model of the motor cooling flow channel to obtain a corresponding first static pressure difference set, then a first functional relation between the design variable and the static pressure difference is obtained according to fitting of the initial sample point set and the first static pressure difference set, fitting accuracy of the first functional relation is checked, when the fitting accuracy of the first functional relation is judged to meet a first preset condition, the parameter value of the design variable corresponding to the minimum static pressure difference is obtained based on solving of the first functional relation, so that limitation of judging the optimal design parameter combination only through a limited sample of a CFD calculation result is avoided, the first functional relation has good prediction capability, the parameters of each design variable can be adjusted at will, optimal design can be conducted on one or more design variables concerned, meanwhile, the corresponding static pressure difference can be obtained rapidly after the parameters of the design variable are input, and the efficiency is far higher than the CFD calculation efficiency.
Optionally, in step S103 of fig. 1, a first functional relation between the design variable and the static pressure difference is obtained by fitting according to the initial sample point set and the first static pressure difference set, including: constructing a Kriging agent model, and calculating and fitting to obtain an initial functional relation between an initial sample point set and a first static pressure difference set; a first functional relation is determined from the initial functional relation.
The Kriging agent model is a fitting method, can fit and express complex change relation of independent variables along with dependent variables into an explicit functional relation, and has high fitting precision and good prediction effect. After the Kriging agent model is established, an initial functional relation between the initial sample point set and the first static pressure difference set can be obtained through calculation, and then the first functional relation can be determined according to the initial functional relation, so that the finally obtained first functional relation can effectively reduce the calculated amount of engineering problems, and the calculation efficiency is improved.
Exemplary, the initial sample point set is X 0 ＝[X 1 ,X 2 ,…,X j ,…,X n ]X is defined as the initial sample point set 0 The first static pressure difference set obtained by simulation is P loss0 ＝[p 1 ,p 2 ,…,p j ,…,p n ]Constructing a Kriging proxy model, and calculating and fitting to obtain an initial sample point set X 0 And a first set of static pressure differences P loss0 The initial functional relation between can be expressed as P loss0 ＝f Krig (X 0 ) Further, the first functional relation is determined to be P according to the initial functional relation loss ＝f Krig (X), wherein X is any one set of design variables [ X ] 1 ,x 2 ,…,x i ,…,x m ]Is a combination of values of X including X j ，P loss And fitting the first static pressure difference of the cooling flow channel corresponding to the value combination X.
Optionally, fig. 4 is a flowchart of another design method of a cooling flow channel of a motor according to an embodiment of the present invention, as shown in fig. 4, on the basis of fig. 1, the fitting accuracy of the first functional relation is checked, including: acquiring a plurality of second sample data based on the design variable to be optimized, wherein the plurality of second sample data form a test sample point set; simulating the test sample point set based on a CFD model of the motor cooling flow channel to obtain second static pressure differences corresponding to each second sample data, wherein the second static pressure differences form a second static pressure difference set; inputting the test sample point set into a first functional relation, solving to obtain a third static pressure difference corresponding to each second sample data, wherein a plurality of third static pressure differences form a third static pressure difference set; and calculating according to the second static pressure difference set and the third static pressure difference set to obtain an evaluation parameter for evaluating the fitting precision of the first functional relation.
Therefore, the design method specifically comprises the following steps:
s201, determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set.
S202, based on a CFD model of the motor cooling flow channel, simulating an initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein the first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel.
S203, fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between the design variable and the static pressure difference.
S204, acquiring a plurality of second sample data based on the design variable to be optimized, wherein the plurality of second sample data form a test sample point set.
The parameter value of the second sample data can be different from that of the first sample data, so that the first functional relation can be checked by adopting parameter combinations except the first sample data, and the reliability of the checking result is ensured.
S205, based on a CFD model of the motor cooling flow channel, simulating the test sample point set to obtain second static pressure differences corresponding to each second sample data, wherein the second static pressure differences form a second static pressure difference set.
S206, inputting the test sample point set into the first functional relation, solving to obtain a third static pressure difference corresponding to each second sample data, and forming a third static pressure difference set by the plurality of third static pressure differences.
S207, calculating to obtain an evaluation parameter for evaluating the fitting precision of the first functional relation according to the second static pressure difference set and the third static pressure difference set.
And S208, when the fitting precision of the first functional relation is judged to meet the first preset condition, solving based on the first functional relation to obtain the parameter value of the design variable corresponding to the minimum static pressure difference.
For example, when a plurality of second sample data are obtained based on the design variable to be optimized, [ x ] may be obtained by using the Lat Ding Chao cube method 1 ,x 2 ,…,x i ,…,x m ]Global sampling is carried out to obtain a test sample point set X containing nt pieces of second sample data t ＝[X t1 ,X t2 ,…,X tk ,…,X tnt ]Wherein k is more than or equal to 1 and less than or equal to nt, nt is more than 1, X tk For the design variable to be optimized [ x ] 1 ,x 2 ,…,x i ,…,x m ]Is a combination of values. It can be understood that the latin hypercube method is a random sampling method, which can generate a sampling sample point set with relatively uniform distribution, ensure the randomness of sampling, obtain good sampling effect by using fewer samples, reduce the calculation cost, and enable the value of nt to be smaller than that of n. Then based on CFD model of motor cooling flow channel, test sample point set X t Simulation is carried out to obtain and test a sample point set X t Corresponding second set of static pressure differences P losst ＝[p t1 ,p t2 ,…,p tk ,…,p tnt ]Wherein the second static pressure difference p tk For testing sample point set X t Second sample data X of (1) tk And (5) a corresponding CFD solving result. At the same time, the test sample point set X t Input to a first functional relation to obtain a point set X of the test sample t Corresponding third set of static pressure differences P losstf ＝[p tf1 ,p tf2 ,…,p tfk ,…,p tfnt ]Wherein the third static pressure p tfk For testing sample point set X t Second sample data X of (1) tk Is a result of the calculation of the first functional relation of (a). And then can be based on the second set of static pressure differences P losst And a third set of static pressure differences P losstf And calculating to obtain an evaluation parameter for evaluating the fitting precision of the first functional relation, so that whether the fitting precision of the first functional relation meets a first preset condition can be judged according to the result of the evaluation parameter, and when the fitting precision of the first functional relation meets the first preset condition, the parameter value of the design variable corresponding to the minimum static pressure difference is obtained based on the first functional relation.
Optionally, calculating to obtain the fitting accuracy of the first functional relation according to the second static pressure difference set and the third static pressure difference setEvaluation parameters, including: according to the formulaCalculating to obtain a determination coefficient R for evaluating the overall fitting precision of the first function relation 2 Wherein, k is more than or equal to 1 and less than or equal to nt, nt is the number of second sample data, p tk For testing the second sample data X in the sample point set tk Corresponding second static pressure difference, p tfk For testing the second sample data X in the sample point set tk Corresponding third static pressure difference, ++>Is the average of the second set of static pressure differences; according to the formula->Calculating to obtain a relative maximum absolute error RMAE for evaluating the local fitting accuracy of the first functional relation, wherein +.>Is the standard deviation of the second set of static pressure differences.
Specifically, R is 2 Refers to a determination coefficient for evaluating the integral fitting precision of a function, R 2 The closer to 1 means the higher fitting accuracy of the first functional relation. RMAE refers to the relative maximum absolute error, which indicates how good the local fitting accuracy of the function is, and the closer the RMAE is to 0, the higher the fitting accuracy of the first functional relation is. Wherein the standard deviation of the second static pressure difference setThe calculation formula of (2) is +.>
Optionally, the first preset condition includes: 1-R 2 ＜ε 1 And RMAE < ε 2 Wherein ε is 1 And epsilon 2 Are all arbitrary positive numbers less than 0.01.
Wherein ε 1 And epsilon 2 The specific value of epsilon can be set according to the actual requirement 1 And epsilon 2 May be the same or different, and is not particularly limited herein 1 And epsilon 2 The closer the value of (c) is to 0, the higher the evaluation requirement on the first functional relation is, and the higher the fitting accuracy of the first functional relation satisfying the first preset condition is.
Specifically, when 1-R 2 ＜ε 1 ，ε 1 May be a value close to 0, R is stated 2 Is close to 1, while RMAE < ε 2 ，ε 2 When the value is close to 0, the value of RMAE is close to 0, so that the calculated evaluation parameter R for evaluating the fitting accuracy of the first functional relation 2 When the RMAE meets the first preset condition, the fitting precision of the first functional relation is high, so that the first functional relation can accurately calculate the static pressure difference of the corresponding motor cooling flow channel according to the parameters of any design variable, the high reliability of the design result is ensured, and the method can be directly used for guiding engineering design.
With continued reference to fig. 2, the method of designing a motor cooling flow passage may further include the steps of:
and S209, updating the initial sample point set when the fitting precision of the first functional relation is judged to not meet the first preset condition.
Specifically, when it is determined that the fitting accuracy of the first functional relation does not satisfy the first preset condition, it may be considered that the fitting accuracy of the first functional relation is low, possibly because the first sample data sampled in the initial sample point set is insufficient, and cannot be used as a representative of the whole sample. In this way, the initial sample point set needs to be updated, a new first static pressure difference set is obtained by re-simulating the updated initial sample point set, then a first functional relation between the design variable and the static pressure difference is obtained by re-fitting the updated initial sample point set and the new first static pressure difference set, the fitting accuracy of the first functional relation is further verified, and the cyclic treatment is carried out until the fitting accuracy of the obtained first functional relation meets a first preset condition, and then the updating of the initial sample point set is stopped.
Optionally, updating the initial sample point set includes: judging R 2 Whether or not it is less than epsilon 3 And in determining R 2 Less than epsilon 3 At that time, determining the corresponding second sample data at which RMAE is maximum, wherein ε 3 An arbitrary positive number less than 0.1; acquiring a plurality of third sample data in a first preset range to which the corresponding second sample data belong when the RMAE is maximum, wherein the plurality of third sample data form a correction sample point set; the test sample point set and the corrected sample point set are merged into the initial sample point set.
Wherein ε 3 The specific values of (2) may be set according to actual requirements.
Specifically, when it is determined that the fitting accuracy of the first functional relation does not satisfy the first preset condition, that is, according to the above formulaThe calculated decision coefficient R for evaluating the overall fitting accuracy of the first functional relation 2 Not satisfy 1-R 2 ＜ε 1 And/or, according to the above formula +.>The calculated relative maximum absolute error RMAE of the accuracy of the partial fitting of the evaluation first function relation does not satisfy RMAE < epsilon 2 . Thus, when the initial sample set is updated, R can be judged first 2 Whether or not it is less than epsilon 3 If in the judgment of R 2 Less than epsilon 3 When epsilon 3 At a value close to 0, R 2 ＜ε 3 Description R 2 Approaching 0, it can be determined that the fitting accuracy of the first functional relation is low. At this time, the second sample data corresponding to the maximum RMAE can be determined according to the above formula, and the method continues to obtain a plurality of third sample data within the first preset range to which the second sample data corresponding to the maximum RMAE belongs based on the design variable to be optimized, that is, a plurality of third sample data are obtained around the second sample data corresponding to the maximum RMAE, the plurality of third sample data form a corrected sample point set,the specific size of the first preset range may be set according to actual requirements, which is not limited herein. And finally, combining the test sample point set and the correction sample point set into an initial sample point set to update the initial sample point set.
Further, when R is determined 2 Greater than or equal to epsilon 3 In this case, the test sample point set may be combined with the initial sample point set to update the initial sample point set.
Exemplary, the test sample point set contains nt second sample numbers, i.e., the test sample point set is X t ＝[X t1 ,X t2 ,…,X tk ,…,X tnt ]Wherein k is more than or equal to 1 and less than or equal to nt, nt is more than 1, X tk For the design variable to be optimized [ x ] 1 ,x 2 ,…,x i ,…,x m ]Is a combination of values. When determining R 2 Greater than or equal to epsilon 3 At the time, test sample point set X t Merging to initial sample Point set X 0 The resulting updated initial set of sample points will include (n+nt) sample data. When determining R 2 Less than epsilon 3 When the RMAE is maximum, corresponding second sample data can be determined according to the formula, nc pieces of third sample data are acquired around the second sample data, and the plurality of third sample data form a corrected sample point set X c ＝[X c1 ,X c2 ,…,X cl ,…,X cnc ]Wherein, 1.ltoreq.l.ltoreq.nc, nc > 1, X cl For the design variable to be optimized [ x ] 1 ,x 2 ,…,x i ,…,x m ]Is a combination of values. Test sample Point set X t And a correction sample point set X c Merging to initial sample Point set X 0 The resulting updated initial set of sample points will include (n+nt+nc) sample data.
In summary, the initial sample point set is updated, the new first static pressure difference set is obtained by re-simulating the initial sample point set after updating, then the first functional relation between the design variable and the static pressure difference is obtained by re-fitting the initial sample point set after updating and the new first static pressure difference set, so that the fitting precision of the first functional relation can be further improved, the static pressure difference of the corresponding motor cooling flow channel can be accurately calculated by the first functional relation according to the parameters of any design variable, the high reliability of the design result is ensured, and the method can be directly used for guiding engineering design.
Optionally, fig. 5 is a flowchart of a design method of a cooling flow channel of a motor according to an embodiment of the present invention, as shown in fig. 5, before determining a design variable to be optimized of the cooling flow channel of the motor, including: determining design variables of a cooling flow channel of the motor, wherein the design variables comprise design variables to be optimized; based on design variables, a StarCCM+ simulation software platform is adopted to construct a CFD model of the motor cooling flow channel. Therefore, the design method specifically comprises the following steps:
s301, determining design variables of a motor cooling flow channel, wherein the design variables comprise design variables to be optimized.
The design variables may be any variables related to the motor cooling flow channel, and the number and specific physical meaning of the corresponding design variables may be different according to different structures of the motor cooling flow channel, which are not specifically limited herein, and may be set according to actual requirements. The design variables to be optimized may be one or more of the design variables, even all of the design variables, and are not particularly limited herein. It can be understood that the more the number of design variables to be optimized, the longer the simulation time of the CFD model of the motor cooling flow channel is in simulation operation, and the more complicated the internal data processing process.
S302, constructing a CFD model of the motor cooling flow channel by adopting a StarCCM+ simulation software platform based on design variables.
The StarCCM+ is a common computational fluid dynamics simulation tool, and the StarCCM+ software platform can draw a parameterized CFD model of the motor cooling flow channel according to the initial value of the design variable of the motor cooling flow channel, namely the CFD model of the motor cooling flow channel. It should be noted that, the simulation software platform for constructing the CFD model of the motor cooling flow channel includes, but is not limited to, starCCM+, and other simulation software platforms, such as Fluent, CFX, numeca, COMSOL or OpenFOAM software, may also be used.
S303, determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set.
S304, based on a CFD model of the motor cooling flow channel, simulating the initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein the plurality of first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel.
S305, fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between the design variable and the static pressure difference.
S306, checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet the first preset condition.
Optionally, fig. 6 is a flowchart of another design method of a cooling flow channel of a motor, where, as shown in fig. 6, after constructing a CFD model of the cooling flow channel of the motor before determining a variable to be optimized of the cooling flow channel of the motor, the method further includes: and acquiring a plurality of actual measurement test data, and correcting the CFD model of the motor cooling flow passage according to the actual measurement test data. Therefore, the design method specifically comprises the following steps:
s401, determining design variables of a motor cooling flow channel, wherein the design variables comprise design variables to be optimized.
S402, constructing a CFD model of the motor cooling flow channel by adopting a StarCCM+ simulation software platform based on design variables.
S403, acquiring a plurality of actual measurement test data, and correcting the CFD model of the motor cooling flow passage according to the actual measurement test data.
The actual test data, namely data obtained by actual motor cooling flow passage operation, comprises static pressure difference values corresponding to different motor cooling flow passage design parameters.
S404, determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set.
S405, based on a CFD model of the motor cooling flow channel, simulating the initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein the first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel.
S406, fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between the design variable and the static pressure difference.
S407, checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet the first preset condition.
Specifically, design parameters (i.e., parameters of each design variable) of the motor cooling flow channel in the actually measured test data are input into a CFD model of the motor cooling flow channel, a corresponding static pressure difference value is obtained through simulation, the static pressure difference value is compared with a static pressure difference value corresponding to the same design parameters in the actually measured test data, if the static pressure difference value and the static pressure difference value are the same, the CFD model of the motor cooling flow channel can be considered to be in accordance with reality, the model precision and accuracy are very high, and if the static pressure difference value and the model precision and the model accuracy are different, a set of parameter combinations which enable deviation between a simulation value and a test value to be minimum can be found through adjusting setting parameters which possibly affect simulation results in the CFD model and a simulation solving algorithm, such as a wall function, a turbulence model, a boundary layer grid and the like, so as to correct the CFD model. Therefore, the simulation accuracy of the CFD model after correction is improved, and the accuracy of the first functional relation established by the follow-up simulation depending on the CFD model is also improved.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements, combinations, and substitutions can be made by those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method of designing a cooling flow path for an electric machine, comprising:
determining a design variable to be optimized of a motor cooling flow channel, and acquiring a plurality of first sample data based on the design variable to be optimized, wherein the plurality of first sample data form an initial sample point set;
based on a CFD model of the motor cooling flow channel, simulating the initial sample point set to obtain first static pressure differences corresponding to each first sample data, wherein a plurality of first static pressure differences form a first static pressure difference set, and the static pressure differences refer to differences between static pressure of fluid at a water inlet of the motor cooling flow channel and static pressure of fluid at a water outlet of the motor cooling flow channel;
fitting according to the initial sample point set and the first static pressure difference set to obtain a first functional relation between a design variable and the static pressure difference;
and checking the fitting precision of the first functional relation, and solving the parameter value of the design variable corresponding to the minimum static pressure difference based on the first functional relation when the fitting precision of the first functional relation is judged to meet a first preset condition.
2. The method of claim 1, wherein verifying the fitting accuracy of the first functional relationship comprises:
acquiring a plurality of second sample data based on the design variable to be optimized, wherein the second sample data form a test sample point set;
simulating the test sample point set based on a CFD model of the motor cooling flow channel to obtain second static pressure differences corresponding to each second sample data, wherein a plurality of second static pressure differences form a second static pressure difference set;
inputting the test sample point set into the first functional relation, solving to obtain a third static pressure difference corresponding to each second sample data, wherein a plurality of third static pressure differences form a third static pressure difference set;
and calculating to obtain an evaluation parameter for evaluating the fitting precision of the first functional relation according to the second static pressure difference set and the third static pressure difference set.
3. The method of designing a cooling flow path for an electric machine according to claim 2, wherein calculating an evaluation parameter for evaluating fitting accuracy of the first functional relation from the second set of static pressure differences and the third set of static pressure differences includes:
according to the formulaCalculating to obtain a determination coefficient R for evaluating the overall fitting precision of the first function relation 2 Wherein, k is more than or equal to 1 and less than or equal to nt, nt is the number of second sample data, p tk For testing the second sample data X in the sample point set tk Corresponding second static pressure difference, p tfk For testing the second sample data X in the sample point set tk Corresponding third static pressure difference, ++>Is the average of the second set of static pressure differences;
according to the formulaCalculating to obtain a relative maximum absolute error RMAE for evaluating the local fitting accuracy of the first functional relation, wherein +.>Is the standard deviation of the second set of static pressure differences.
4. The method of claim 3, wherein the first preset condition includes: 1-R 2 ＜ε 1 And RMAE < ε 2 Wherein ε is 1 And epsilon 2 Are all arbitrary positive numbers less than 0.01.
5. The method of designing a motor cooling flow passage according to claim 3, further comprising: and updating the initial sample point set when the fitting precision of the first functional relation is judged to not meet a first preset condition.
6. The method of claim 5, wherein updating the initial set of sample points comprises:
judging R 2 Whether or not it is less than epsilon 3 And in determining R 2 Less than epsilon 3 At that time, determining the corresponding second sample data at which RMAE is maximum, wherein ε 3 An arbitrary positive number less than 0.1;
acquiring a plurality of third sample data in a first preset range to which second sample data corresponding to the maximum RMAE belongs, wherein the plurality of third sample data form a correction sample point set;
combining the test sample point set and the corrected sample point set to the initial sample point set.
7. The method of designing a motor cooling flow passage according to claim 6, further comprising: when determining R 2 Greater than or equal to epsilon 3 At that time, the test sample point set is merged to the initial sample point set.
8. The method of claim 1, wherein fitting the initial set of sample points to the first set of static pressure differences to obtain a first functional relationship between the design variable and the static pressure differences comprises:
constructing a Kriging agent model, and calculating and fitting to obtain an initial functional relation between the initial sample point set and the first static pressure difference set;
and determining a first functional relation according to the initial functional relation.
9. The method of designing a motor cooling flow passage according to claim 1, comprising, before determining a design variable to be optimized of the motor cooling flow passage:
determining design variables of a cooling flow channel of the motor, wherein the design variables comprise the design variables to be optimized;
based on the design variables, a StarCCM+ simulation software platform is adopted to construct a CFD model of the motor cooling flow channel.
10. The method of designing a motor cooling flow passage according to claim 9, further comprising, after constructing a CFD model of the motor cooling flow passage before determining the design variables to be optimized of the motor cooling flow passage:
and acquiring a plurality of measured test data, and correcting the CFD model of the motor cooling flow channel according to the measured test data.
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