CN103793559B - Numerical computations are combined parameter collaboration optimization design of electrical motor method with analytical analysis - Google Patents

Abstract

The invention belongs to the field of electrical technology, and specifically relates to a method for synergistically optimizing motor design by combining numerical calculation and analytical analysis. Numerical calculation is used to study the changes in physical parameters such as electromagnetic, temperature, fluid, thermal stress, vibration, and noise in the motor, and the structure is summarized. Analytical expression function clusters of electromagnetic performance with component size as a variable, analytical expression functions of maximum operating temperature and maximum temperature difference of different components, analytical expression function of maximum thermal stress, change function of electromagnetic noise of motor, analytical expression of maximum vibration mode value and natural frequency of components in different directions function, and then comprehensively consider the performance of all aspects of the motor to carry out the refined design of the size of the structural parts, which greatly improves the calculation accuracy of various performance indicators; the non-balanced relative two-way weighting method is used to transform the objective function, eliminating the differences in the values of different performance indicators. The impact of settlement results; Quantum computing is introduced into the intelligent optimization algorithm, so that the algorithm has better population diversity, global optimization ability and faster convergence speed.

Description

Parameter Collaborative Optimization Motor Design Method Combining Numerical Calculation and Analytical Analysis

technical field

The invention belongs to the field of electrical technology, and in particular relates to a method for designing a motor by combining numerical calculation and analytical analysis to optimize parameters collaboratively.

Background technique

The size of the air gap and the shape of the slots of the motor not only affect the magnetic circuit structure and output performance parameters of the motor, but also affect the air path and heat conduction path of the cooling air flowing through the motor, thereby affecting the temperature distribution; on the other hand, Changes in the structural dimensions of the motor cogging, iron core and magnetic poles will cause changes in the harmonic components of the air-gap magnetic field, thereby affecting the electromagnetic noise and vibration of the motor during operation. The structure of the motor is directly related to the size of the magnetic circuit, the heat conduction path and the fluid path of the cooling medium. In the basic design and optimization design of the motor, the influence of the structural size on the performance of electromagnetic, temperature, vibration and noise should be considered as a whole.

The existing motor optimization design methods seldom consider the above-mentioned problems, and the existing defects are as follows: 1) The existing motor optimization design is mostly carried out to improve the performance of a certain aspect such as electromagnetic, temperature, vibration and noise, and does not consider the impact of the optimization scheme on other aspects of the motor performance. 2) Existing motor optimization design methods are mostly carried out based on analytical calculation programs such as "magnetic circuit" and "thermal circuit", without considering specific conditions such as magnetic field, temperature field, vibration mode, etc. The subtle distribution of parameters changes; 3) For the motor optimization design with multi-objective, multi-variable and large data calculations, the existing optimization algorithms have shortcomings in terms of global convergence and iteration speed.

Contents of the invention

The technical problem to be solved by the invention is to provide a motor design method for synergistic optimization of parameters by combining numerical calculation and analytical analysis in view of the deficiencies of the prior art.

In order to achieve the purpose of the above invention, the present invention proposes a numerical calculation and analytical analysis combined parameter collaborative optimization motor design method, including the following steps:

Step 1) Calculation and analysis of nonlinear electromagnetic field: Through the numerical calculation of the nonlinear electromagnetic field in the motor, the distribution of the motor magnetic field and the electromagnetic performance parameters change with the size of the motor structural parts, and the electromagnetic performance analysis expression function cluster with the size of the structural parts as a variable is summarized;

Step 2) Electromagnetic, fluid, and temperature multiple convergent iterative physical field coupling analysis: Through multiple convergent iterative physical field coupling calculations of electromagnetic, fluid, and temperature in the motor, determine the transient temperature distribution law of the entire motor, and find out the maximum operating temperature and temperature of different components of the motor. The maximum temperature difference varies with the structure size, and the maximum working temperature change function and the maximum temperature difference change analysis function of different components with the structure size as a variable are summarized;

Step 3) Global transient thermal stress field analysis: Considering the thermal conductivity and expansion coefficient of different component materials, based on the global transient temperature field of the motor, the thermal stress distribution in the motor whose expansion or contraction is hindered during operation is obtained. Analytical expression function of maximum thermal stress of different components with variable size;

Step 4) Numerical calculation of multi-order vibration modes at working frequency: Numerical calculation of the variation law of the harmonic component of the air gap of the motor under different structural parts sizes, calculation and derivation of the motor electromagnetic noise variation function with the structural part size as a variable;

Step 5) Numerical calculation and analysis of the air-gap harmonic magnetic field and magnetic density wave components of the motor: taking into account the elastic modulus and Pogge ratio of different component materials, numerical calculation of multi-order vibration modes at the operating frequency of the motor to obtain the stator core, winding, and rotor Analytical expression functions of the maximum vibration mode value and natural frequency of the main components in different directions, as the size of the motor changes;

Step 6) Determine the basic constraints of the function and determine the variable range;

Step 7) weighting;

Step 8) Find the optimal solution through calculation;

Step 9) Improve the overall design scheme of the motor according to the size variables of the structural parts obtained from the optimal solution;

Step 10) Draw the processing drawings of each component of the motor, wire cutting mold, die, lamination, winding, embedding, dipping, assembly, test and measure the actual electromagnetic, temperature rise, vibration and noise of the motor after passing the test, the plan is finalized and finalized Mass production.

Structural part size variable: X=(x ₁ ,x ₂ ,x ₃ ,...,x _k ) ^T ;

Electromagnetic performance analytical expression function cluster: F _e =(f _e1 ,f _e2 ,f _e3 ,……,f _en );

Maximum working temperature change function: F _tmax =(f _tmax1 ,f _tmax2 ,f _tmax3 ,……,f _tmaxm );

Analytical function of maximum temperature difference change: F _tdet =(f _tdet1 ,f _tdet2 ,f _tdet3 ,……,f _tdetm );

Analytical expression function of maximum thermal stress: F _smax =(f _smax1 ,f _smax2 ,f _smax3 ,…,f _smaxo );

Motor electromagnetic noise variation function: F _en =(f _en );

Maximum vibration mode value: F _{mmax =(f mmax1 ,} f _mmax2 ,f _mmax3 ,……,f _mmaxp );

The analytical expression function of the natural frequency: F _if =(f _if1 ,f _if2 ,f _if3 ,...,f _ifp ).

The step 6) also includes: determining the basic constraints of the function as follows: the electromagnetic performance is higher than the original design F _eod <F _e , and the temperature, vibration and noise performance is lower than the design performance limit requirements F _tmax , F _tdet , F _smax , F _en , F _mmax < F _or .

The step 7) also includes: through the weighted set, the above-mentioned electromagnetic output performance parameter function, temperature distribution function, thermal stress function, electromagnetic noise function, and vibration natural frequency function are integrated into a single comprehensive optimization objective optimization function, and the weighting factor ω _i satisfies

The step 7) also includes: the sub-objective function weighting operation adopts an unbalanced relative two-way weighting method to transform the objective function, and assigns different weighting coefficients ω _i ≠ ω _c (0<i≤j, 0<c ≤j), highlight the key objects in the optimization target system; at the same time, take each value under the rated working condition as the benchmark correction weighting coefficient Eliminate the influence of the numerical value of various physical performance parameters on the optimization results; according to the requirements of performance indicators, positive weights and negative weights are used for the improvement and reduction goals, and the direction of the extreme value of the optimization function is unified, and normalized to the maximum value or extreme value. Small value search.

Use the optimization algorithm to find the global optimal solution of a single comprehensive optimization objective optimization function G, and optimize and design the component size with the best overall performance in all aspects of the motor

The step 8) also includes: using an improved intelligent optimization algorithm for global optimization; the improved intelligent optimization algorithm introduces quantum computing, uses a quantum revolving door to update qubits, and updates operator speed and position, The quantum NOT gate is used to realize the mutation of the qubit and increase the diversity of the operator population. At the same time, it adopts adaptive iterative algebra, scattered data crossover, Gaussian mutation strategy and forward migration method. Compared with the traditional intelligent optimization algorithm, it has better population diversity, global optimization ability and faster convergence speed; the intelligent optimization algorithm includes but not limited to genetic algorithm, ant colony algorithm, particle swarm algorithm, immune algorithm etc.

The size of the structural parts is summarized as a three-dimensional size, and the optimal design is carried out for the space structure of the motor structural components. The variable function of the three-dimensional size of the structural parts is X=(x ₁ ,x ₂ ,x ₃ ,...,x _k ;y ₁ ,y ₂ ,y ₃ ,...,y _k ; z ₁ ,z ₂ ,z ₃ ,...,z _k ) ^T .

The benefits of the present invention lie in that: comprehensive consideration of the performance of the electromagnetic output of the motor, working temperature rise, vibration and noise, significantly improving the scientific rationality of the target group for the size optimization design of the motor structural parts; based on the numerical calculation of the comprehensive physical field in the motor, Compared with the optimization of the existing analytical calculation program as the core, the calculation accuracy of various performance indicators is greatly improved, and the refined design of the size of structural parts can be carried out; the objective function is modified by using an unbalanced relative two-way weighting method, eliminating the need for different performance indicators. The influence of numerical value on the settlement result; Quantum computing is introduced into the intelligent optimization algorithm, so that the algorithm has better population diversity, global optimization ability and faster convergence speed.

Description of drawings

Fig. 1 is the step flow chart of design method of the present invention;

Figure 2 is a flow chart of electromagnetic, fluid and temperature multiple convergence iterative physical field coupling;

Fig. 3 is the design flowchart of embodiment 1 of the present invention;

Fig. 4 is a design flowchart of embodiment 2 of the present invention.

detailed description

A more complete and better understanding of the invention, and many of its attendant advantages, will readily be learned by reference to the following detailed description when considered in conjunction with the accompanying drawings, but the accompanying drawings illustrated herein are intended to provide a further understanding of the invention and constitute part of the invention.

In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

Embodiment 1: as shown in Figure 1 to Figure 3,

Step 1) Numerical calculation of the nonlinear electromagnetic field in the motor, the distribution of the magnetic field and the electromagnetic performance parameters of the motor change with the size of the motor structural parts, and it is concluded that the size of the structural parts X=(x ₁ , x ₂ , x ₃ ,..... .,x _k ) ^T is the variable electromagnetic performance analytical expression function cluster F _e =(f _e1 ,f _e2 ,f _e3 ,……,f _en );

The electromagnetic performance of the motor is different according to the design requirements, including but not limited to armature current I(X), efficiency eff(X), power factor pf(X), starting current Ist(X), starting torque Tst(X ).

Step 2) Multiple convergent iterative physical field coupling calculations of electromagnetic, fluid, and temperature in the motor, as shown in Figure 2, determine the transient temperature distribution law of the entire motor, find out the maximum operating temperature and maximum temperature difference of different components of the motor with the structural size change law, _{Summarize the maximum working temperature change function F tmax} ⁼ _{( f tmax1 , f tmax2 ,} f _tmax3 ,...,f _tmaxm ) and the maximum temperature difference change analytical function F _tdet =(f _tdet1 ,f _tdet2 ,f _tdet3 ,...,f _tdetm );

The temperature distribution of the motor can be optimized by selecting different components according to the structure and working state of the cooling system, including but not limited to the maximum temperature rise of the stator core TMsc(X), the maximum temperature rise of the stator winding TMsw(X), and the maximum temperature rise of the rotor core TMrc (X), the maximum temperature difference TDsc(X) of the stator core, the maximum temperature difference TDsw(X) of the stator winding, and the maximum temperature difference TDrc(X) of the rotor core.

Step 3) Considering the thermal conductivity and expansion coefficient of different component materials, based on the global transient temperature field of the motor, the thermal stress distribution in the motor that is hindered by expansion or contraction during operation is obtained, and the structural component size X=(x ₁ ,x ₂ ,x ₃ ,...,x _k ) ^T is the variable maximum thermal stress analytical expression function F _smax =(f _smax1 ,f _smax2 ,f _smax3 ,...,f _smaxo );

The investigation of the maximum thermal stress of each component of the motor includes, but is not limited to, the maximum thermal stress of the stator winding SMsw(X), the maximum thermal stress of the stator core SMsc(X), and the maximum thermal stress of the rotor core SMrc(X).

Step 4) Numerically calculate the change law of the harmonic component of the air gap of the motor under different structural parts sizes, and calculate and deduce that the structural part size X=(x ₁ ,x ₂ ,x ₃ ,...,x _k ) ^T The motor electromagnetic noise variation function F _en =(f _en ) is a variable;

Step 5) Taking into account the elastic modulus and Pogge ratio of different component materials, the numerical calculation of the multi-order vibration mode at the operating frequency of the motor is carried out, and the maximum vibration mode value F _mmax in different directions of the main components such as the stator core, winding and rotor is obtained =(f _mmax1 ,f _mmax2 ,f _mmax3 ,……,f _mmaxp ) and the analytical expression function F _if =(f _if1 ,f _if2 ,f _if3 ,……,f _ifp ) of the natural frequency, with the motor size X=(x ₁ , x ₂ ,x ₃ ,...,x _k ) change of ^T ;

In the optimal design of the motor, the vibration mode target objects include, but are not limited to, the maximum vibration mode MMsc(X) of the stator core, the maximum vibration mode MMsw(X) of the stator winding, the maximum vibration mode MMrc(X) of the rotor core, and the maximum vibration mode of the stator core MMrc(X). Natural frequency IFsc(X), stator winding natural frequency IFsw(X), rotor core natural frequency IFrc(X).

Step 6) Determine the basic constraints of the function as follows: the electromagnetic performance is higher than the original design F _eod <F _e , and the temperature, vibration and noise performance is lower than the design performance limit requirements F _tmax , F _tdet , F _smax , F _en , F _mmax <F _or ; Determine the variable range to meet the basic size relationship of motor design, 0<X<X _n ;, X∈R ⁿ

Step 7) After weighted aggregation, the above electromagnetic output performance parameter function, temperature distribution function, thermal stress function, electromagnetic noise function, and vibration natural frequency function are integrated into a single comprehensive optimization objective optimization function, and the weighting factor ω _i satisfies

Step 8) Use the optimization algorithm to find the global optimal solution of the single comprehensive optimization objective optimization function G, and optimize and design the component size with the best overall performance in all aspects of the motor.

Step 9) Improve the overall design of the motor according to the size variables of the structural parts obtained from the optimal solution, adjust the structural size design appropriately according to the processing technology, and compare the electromagnetic, temperature rise, vibration and noise indicators of the optimized and adjusted design motor with the original design Compare the program indicators, if the expected performance improvement effect is not achieved, adjust the weighting factor and re-optimize the design, and determine the design plan if the expected performance improvement effect is achieved;

Step 10) Draw the processing drawings of each component of the motor, wire cutting mold, die, lamination, winding, embedding, dipping, assembly, test and measure the actual electromagnetic, temperature rise, vibration and noise of the motor after passing the test, the plan is finalized and finalized Mass production.

Embodiment 2: As shown in Figure 1, Figure 2 and Figure 4, other steps are the same as in Embodiment 1, wherein step 7) sub-objective function weighting operation adopts an unbalanced relative two-way weighting method to transform the objective function, according to the priority of the optimization target Assign different weighting coefficients ω _i ≠ ω _c (0<i≤j, 0<c≤j), highlighting key objects in the optimization target system;

At the same time, each value under the rated working condition is taken as the benchmark correction weighting coefficient Eliminate the influence of the numerical value of various physical performance parameters on the optimization results;

According to the requirements of performance indicators, positive weights and negative weights are used respectively for the improvement and reduction goals, and the direction of the extreme value goal of the optimization function is unified, which is normalized as the search for the maximum value or the minimum value.

Among them, step 8) adopts an improved intelligent optimization algorithm for global optimization.

The improved intelligent optimization algorithm introduces quantum computing, uses quantum revolving gates to update qubits, updates operator speed and position, uses quantum NOT gates to realize qubit variation, and increases the diversity of operator populations. At the same time, it adopts adaptive iterative algebra, scattered data crossover, Gaussian mutation strategy and forward migration method. Compared with the traditional intelligent optimization algorithm, it has better population diversity, global optimization ability and faster convergence speed.

The intelligent optimization algorithm includes but not limited to genetic algorithm, ant colony algorithm, particle swarm algorithm, immune algorithm and so on.

The above is a detailed introduction to the motor design method of numerical calculation and analytical analysis combined with parameter collaborative optimization provided by the present invention, and the exemplary implementation of the present application is described above with reference to the accompanying drawings. It should be understood by those skilled in the art that the above-mentioned embodiments are only examples for the purpose of illustration, and are not used for limitation. Any modifications, equivalent replacements, etc. All should be included in the protection scope of this application.

Claims (6)

1. A numerical calculation and analytical analysis combined parameter collaborative optimization motor design method, characterized in that it comprises the following steps: Step 1) Calculation and analysis of nonlinear electromagnetic field: through the numerical calculation of the nonlinear electromagnetic field in the motor, the distribution of the magnetic field of the motor and the variation law of the electromagnetic performance parameters with the size of the structural parts of the motor are obtained, and the analytical expression function cluster of electromagnetic performance with the size of the structural parts as a variable is summarized; Step 2) Electromagnetic, fluid, and temperature multiple convergent iterative physical field coupling analysis: through the electromagnetic, fluid, and temperature multiple convergent iterative physical field coupling calculations in the motor, determine the transient temperature distribution law of the entire motor, and find out the maximum operating temperature and temperature of different components of the motor. The maximum temperature difference varies with the structure size, and the maximum working temperature change function and the maximum temperature difference change analysis function of different components with the structure size as a variable are summarized; Step 3) Global transient thermal stress field analysis: Considering the thermal conductivity and expansion coefficient of different component materials, based on the global transient temperature field of the motor, the thermal stress distribution in the motor whose expansion or contraction is hindered during operation is obtained. Analytical expression function of maximum thermal stress of different components with variable size; Step 4) Numerical calculation of multi-order vibration modes at operating frequencies: Numerical calculation of the variation law of the harmonic component of the air gap of the motor under different structural parts sizes, calculation and derivation of the motor electromagnetic noise variation function with the size of the structural parts as a variable; Step 5) Numerical calculation and analysis of the air-gap harmonic magnetic field and magnetic density wave components of the motor: taking into account the elastic modulus and Pogge ratio of different component materials, numerical calculation of multi-order vibration modes at the operating frequency of the motor to obtain the stator core, winding, and rotor The analytical expression function of the maximum vibration mode value and natural frequency in different directions, with the change of the motor size; Step 6) Determine the basic constraints of the function, and determine the variable range; Step 7) weighting, through the weighted set, the above-mentioned electromagnetic output performance parameter function, temperature distribution function, thermal stress function, electromagnetic noise function, and vibration natural frequency function are integrated into a single comprehensive optimization objective optimization function, and the weighting factor ω _i satisfies j=n+m+o+p, n is the number of independent variables of the electromagnetic performance analysis expression function cluster function, m is the number of independent variables of the temperature change function, o is the number of independent variables of the thermal stress analysis expression function, p is the number of independent variables of the analytical expression function of the natural frequency of vibration; Step 8) find the optimal solution by calculation; Step 9) improve the overall design of the motor according to the size variable of the structural part obtained from the optimal solution; Step 10) Draw the processing drawings of each component of the motor, wire cutting mold, punching die, lamination, winding, embedding, dipping, assembly, test and determine the actual electromagnetic, temperature rise, vibration and noise indicators of the motor after passing the test, the plan is finalized and batched Production. 2. numerical calculation and analytical analysis according to claim 1 combine parameter collaborative optimization motor design method, it is characterized in that: Structural part size variable: X＝(x ₁ ,x ₂ ,x ₃ ,...,x _k ) ^T ; Analytical expression function cluster of electromagnetic properties: F _e = (f _e1 , f _e2 , f _e3 ,...,f _en ); Maximum working temperature change function: F _tmax = (f _tmax1 , f _tmax2 , f _tmax3 ,..., f _tmaxm ); Analytical function of maximum temperature difference change: F _tdet = (f _tdet1 , f _tdet2 , f _tdet3 ,..., f _tdetm ); Analytical expression function of maximum thermal stress: F _smax ＝(f _smax1 ,f _smax2 ,f _smax3 ,…,f _smaxo ); Motor electromagnetic noise variation function: F _en ＝(f _enoise ); Maximum vibration mode value: F _{mmax ＝(f mmax1 ,} f _mmax2 ,f _mmax3 ,……,f _mmaxp ); The analytical expression function of the natural frequency: F _if =(f _if1 ,f _if2 ,f _if3 ,...,f _ifp ). 3. numerical calculation and analytical analysis according to claim 2 combine parameter collaborative optimization motor design method, it is characterized in that described step 6) also comprises: determine the basic constraint bar of function is: electromagnetic performance is higher than original design F _eod < F _e , temperature, vibration and noise performance are lower than the design performance limit requirements F _tmax , F _tdet , F _smax . 4. numerical calculation and analytical analysis according to claim 1 combine parameter cooperative optimization motor design method, it is characterized in that described step 7) also comprises: sub-objective function weighting operation adopts non-equilibrium relative two-way weighting method to transform objective function, Assign different weighting coefficients ω _i ≠ ω _c (0<i≤j, 0<c≤j) according to the priority and weight of the optimization target, highlighting the key objects in the optimization target system; at the same time, take each value under the rated working condition as the benchmark Modified weighting coefficient (0<i≤j), eliminating the influence of various physical performance parameters on the optimization results; according to the requirements of performance indicators, positive weights and negative weights are used for the improvement and reduction goals, and the direction of the extreme value of the optimization function is unified. Normalized for maximum or minimum search. 5. numerical calculation and analytical analysis according to claim 1 combine parameter cooperative optimization motor design method, it is characterized in that described step 8) also comprises: Adopting improved intelligent optimization algorithm to carry out global optimization; Described improved The intelligent optimization algorithm introduces quantum computing, uses quantum revolving gates to update qubits, updates operator speed and position, uses quantum NOT gates to realize qubit variation, and increases the diversity of operator populations; at the same time, it adopts adaptive iterative algebra , scattered data crossover, Gaussian mutation strategy and forward migration; compared with the traditional intelligent optimization algorithm, it has better population diversity, global optimization ability and faster convergence speed; the intelligent optimization algorithm includes genetic Algorithm, ant colony algorithm, particle swarm algorithm, immune algorithm. 6. According to claim 1 or 2, the numerical calculation and analytical analysis combined parameter collaborative optimization motor design method is characterized in that it also includes: the size of the induced structural parts is a three-dimensional size, and the optimal design is carried out for the spatial structure of the motor structural components , the three-dimensional size variable function of the structure is X=(x ₁ ,x ₂ ,x ₃ ,...,x _k ; y ₁ ,y ₂ ,y ₃ ,...,y _k ; z ₁ ,z ₂ ,z ₃ ,...,z _k ) ^T .