Submersible motor temperature field joint calculation method based on finite volume method and analytical method

Abstract

The invention discloses a submersible motor temperature field joint calculation method based on a finite volume method and an analytical method, and the method builds an efficient coupling temperature field calculation method through building a temperature dependence model of key parameters and integrating the temperature dependence model in fluid-solid coupling calculation. According to the method, through parameterization updating instead of complex multi-physical field direct coupling, the huge calculation burden of a traditional two-way coupling model is avoided while the temperature-loss interaction is accurately reflected, and the balance between the calculation precision and the calculation efficiency is achieved. According to the method, the calculation dimension can be reduced to be extremely low based on the temperature field reduced-order model constructed by the intrinsic orthogonal decomposition. Therefore, when temperature prediction is carried out aiming at different working conditions, a time-consuming full-order model does not need to be repeatedly operated, and only quick calculation needs to be carried out in the low-dimensional subspace, so that quick response to variable working conditions is realized, and the design and analysis efficiency is greatly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of performance research of three-phase permanent magnet synchronous motor, and relates to a motor temperature field calculation method, in particular to an oil-submerged motor temperature field calculation method based on a reduced-order model. BACKGROUND

[0002] In current oil-submerged permanent magnet motor temperature calculation, mainstream methods include thermal circuit method, finite element method and finite volume method. The thermal circuit method is efficient but has limited accuracy and relies on experimental correction. The finite element method has moderate accuracy and time consumption, but it is difficult to accurately simulate fluid heat dissipation. The finite volume method has the highest accuracy, but the calculation cost is too large.

[0003] In terms of calculation strategy, it mainly includes one-way calculation and electromagnetic-thermal coupled calculation. The former assumes fixed temperature rise to calculate loss, and the process is simple but the accuracy is limited. The latter updates the material electromagnetic parameters (such as winding resistance and permanent magnet performance) through iteration to reflect the temperature-loss coupling effect, and has higher accuracy but longer time consumption.

[0004] Existing coupling methods mostly use software joint simulation, which is complex in modeling, large in resource consumption, and needs to be calculated repeatedly when calculating different working conditions, which is time-consuming and cannot realize rapid iteration of motor design scheme. Therefore, it is urgent to develop a calculation method that can greatly shorten the time while ensuring the calculation accuracy, so as to realize the rapid iteration of the design scheme. SUMMARY

[0005] The application provides an oil-submerged motor temperature field joint calculation method based on finite volume method and analytical method, which realizes efficient and accurate calculation of motor temperature field under different working conditions by constructing a reusable parameterized solution system, and effectively improves the motor development progress.

[0006] The purpose of the application is realized by the following technical solutions:

[0007] An oil-submerged motor temperature field joint calculation method based on finite volume method and analytical method, comprising the following steps:

[0008] Step 1: fitting the relationship between temperature-sensitive parameters and temperature by cubic spline interpolation;

[0009] Step 2: establishing a functional relationship between current and permanent magnet temperature based on the constraint of constant output torque;

[0010] Step 3: integrating the parameterized model and performing temperature rise iteration calculation;

[0011] Step 4: constructing a temperature field reduced-order model to realize fast calculation of temperature field.

[0012] Compared with the prior art, the application has the following advantages:

[0013] 1. Achieves high-precision coupled calculations, significantly improving efficiency: This invention establishes a temperature-dependent model for key parameters (such as the magnetic properties of permanent magnets and the viscosity of lubricating oil) and integrates it into fluid-structure interaction calculations, constructing a highly efficient coupled temperature field calculation method. This method, through parameterized updates rather than complex multi-physics direct coupling, accurately reflects temperature-loss interactions (such as current changes caused by a decrease in magnetic properties) while avoiding the enormous computational burden of traditional two-way coupled models, achieving a balance between computational accuracy and efficiency.

[0014] 2. A general temperature field reduction model is constructed, giving the method rapid response capability: Compared with any traditional numerical method that requires "calculation from scratch", the temperature field reduction model constructed based on intrinsic orthogonal decomposition in this invention can reduce the computational dimensionality to an extremely low level. This allows for rapid calculation in a low-dimensional subspace, eliminating the need to repeatedly run the time-consuming full-order model when predicting temperatures for different operating conditions. This enables a rapid response to varying operating conditions and greatly improves design and analysis efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention;

[0016] Figure 2 This is a finite element model diagram of the three-phase permanent magnet synchronous motor in the embodiment;

[0017] Figure 3 This is a graph showing the relationship between current and permanent magnet temperature under rated operating conditions in the embodiment.

[0018] Figure 4 The fluid-structure interaction temperature rise calculation model in the embodiment is as follows: 1-external fluid domain of motor, 2-motor housing, 3-stator core, 4-equivalent winding area, 5-equivalent insulation layer, 6-magnetic groove, 7-rotor core, 8-shaft, 9-lubricating oil in the hollow flow channel of the shaft, 10-permanent magnet, 11-lubricating oil area at the air gap;

[0019] Figure 5 These are sampling distribution points obtained based on the operating condition range;

[0020] Figure 6 This is a comparison chart of temperature calculation results without considering compensation current in the embodiments;

[0021] Figure 7 The output torque of the two-way coupling model that considers the temperature change of the permanent magnet is compared with that of the model described in this invention (including current compensation).

[0022] Figure 8 This is a comparison chart of the results of the reduced-order model and the bidirectional coupling calculation model considering current compensation;

[0023] Figure 9 This is a comparison chart of the computation time of the two models. Detailed Implementation

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0025] This invention provides a joint calculation method for the temperature field of a submersible motor based on the finite volume method and analytical methods. The method first parameterizes parameters strongly related to losses and temperature, such as the magnetic properties of the permanent magnet and the viscosity of the lubricating oil, and couples these parameters into a fluid-structure interaction model to obtain a baseline temperature field through iterative calculation. Subsequently, representative sample points are selected in a variable operating condition space using Latin hypercube sampling, and the temperature field "snapshot" set obtained from the sample calculations is reduced in order based on intrinsic orthogonal decomposition to extract dominant characteristic modes and construct a low-dimensional subspace model. Finally, this reduced-order model enables rapid and accurate prediction of the motor temperature field under different operating conditions. Figure 1 As shown, the specific steps are as follows:

[0026] Step 1: Fit the relationship between the temperature-sensitive parameter and temperature using cubic spline interpolation. The steps are as follows:

[0027] For key parameters related to system losses and sensitive to temperature, namely the permanent magnet BH curve and the internal lubricating oil dynamic viscosity, a continuous relationship model of their variation with temperature is established using cubic spline interpolation. , In the formula This refers to the magnetic flux passing through a unit cross-sectional area of ​​the permanent magnet in the motor. The external magnetizing force applied to the permanent magnet, The temperature of the permanent magnet. This refers to the dynamic viscosity of the lubricating oil. This refers to the temperature of the lubricating oil.

[0028] Step 2: Based on the constraint of constant output torque, establish the functional relationship between current and permanent magnet temperature. The specific steps are as follows:

[0029] Step 2: Establish the finite element electromagnetic model of the motor.

[0030] Step 22: Under rated operating conditions and with the motor output torque kept constant, calculate the input current values ​​corresponding to a series of permanent magnet temperature points using the finite element electromagnetic model of the motor established in Step 21.

[0031] Step Two and Three: Based on the discrete (temperature, current) data points calculated in Step Two and Two, cubic spline interpolation is used to perform curve fitting, thereby obtaining a curve that can accurately describe the input current. With the temperature of the permanent magnet Changing continuous function model .

[0032] Step 3: Integrate the parameterized model and perform iterative temperature rise calculations. The specific steps are as follows:

[0033] Step 3: 1. Establish a fluid-structure interaction temperature rise calculation model for the motor, and use the parameterized relationships obtained in Step 1 and Step 2 as inputs for coupling variables.

[0034] Step 3.2 During the solution process, the motor fluid-structure interaction temperature rise calculation model dynamically updates the input current, lubricating oil viscosity and winding resistance according to the real-time temperature field, thereby achieving synchronous correction of oil wear loss and copper loss.

[0035] Step 33: Iterative calculations are performed using the temperature-loss feedback mechanism from Step 32 until the system reaches thermal equilibrium (i.e., the temperature rise distribution satisfies the convergence criterion), ultimately obtaining the accurate steady-state temperature rise distribution of the motor under rated operating conditions.

[0036] Step 4: Construct a temperature field reduced-order model. The specific steps are as follows:

[0037] Step 41: Using the Latin hypercube method, the operating environment temperature of the motor and the external well fluid velocity are sampled together, and temperature field snapshots under each sampling condition are calculated based on the full-order fluid-structure interaction model.

[0038] Step 4.2: By performing intrinsic orthogonal decomposition and singular value decomposition on the snapshot set, the main characteristic modes of the temperature field are extracted, and a low-dimensional subspace that can characterize the core characteristics of the submersible motor temperature field is constructed. This results in a temperature field reduced-order model with a significantly reduced computational scale, enabling rapid calculation of the temperature field.

[0039] Example:

[0040] This embodiment uses a 4-pole, 24-slot, single-layer winding three-phase permanent magnet synchronous motor as the implementation object, and its finite element model is as follows: Figure 2 As shown in the figure. The motor has a rated speed of 4000 r / min and a rated frequency of 133 Hz, and adopts a vector control strategy with Id=0. While maintaining a constant output torque, the stator current values ​​corresponding to different temperatures are obtained by changing the permanent magnet temperature. The relationship between the current and the permanent magnet temperature is shown in the figure. Figure 3 As shown in the figure. Furthermore, function fitting was performed on the key temperature-sensitive parameters affecting motor losses, and the fitting results are summarized in Table 1.

[0041] Table 1 Fitting table of temperature-sensitive parameters

[0042]

[0043] The above fitting relationship is integrated into the motor fluid-structure interaction calculation model. Figure 4 In each iteration, the coupled model dynamically corrects loss parameters such as copper loss and oil friction loss that change with temperature based on the real-time updated winding temperature, lubricating oil temperature, and permanent magnet temperature, until the relative error of the temperature field results calculated in two adjacent iterations is less than 5%, at which point it is considered converged.

[0044] Based on the established coupled model, bottom hole ambient temperature and well fluid velocity were selected as input variables, and their value ranges are shown in Table 2. The Latin hypercube method was used to sample within the above variable space, and the sampling point distribution is shown below. Figure 5 As shown. For each sampling condition, temperature rise calculation is performed and a snapshot of the temperature field is saved. The data matrix formed by all snapshots is subjected to intrinsic orthogonal decomposition and singular value decomposition to extract the dominant characteristic modes and their corresponding coefficients, thereby constructing a reduced-order model of the motor temperature field.

[0045] Table 2 Motor operating parameters and their value ranges

[0046]

[0047] To verify the effectiveness and efficiency of the method proposed in this invention, this embodiment conducted a multi-dimensional comparative analysis. Figure 6 The calculation results of the traditional magnetothermal coupling model that does not consider the change in current caused by the temperature rise of the permanent magnet and the reduced-order model of the present invention are shown. The maximum error between the two is only 2.58℃, which shows that the method of the present invention has high accuracy in temperature field calculation.

[0048] Figure 7 Further comparison of the output torque of the reduced-order model and the strongly coupled model reveals that the traditional bidirectional coupling calculation suffers from torque output deviation because it does not fully consider the current boost effect caused by the increase in permanent magnet temperature. In contrast, Figure 8 The invention demonstrates the calculation results after considering the compensation current while maintaining constant torque output, and its temperature field distribution is more consistent with the actual operating conditions of the motor's rated torque output.

[0049] Figure 9 The listed computation time shows that the reduced-order model constructed in this embodiment significantly improves computational efficiency while ensuring the accuracy of output torque and temperature field calculation.

Claims

1. A method for jointly calculating the temperature field of a submersible generator based on the finite volume method and analytical methods, characterized in that... The method includes the following steps: Step 1: Fit the relationship between the temperature-sensitive parameter and temperature using cubic spline interpolation; Step 2: Based on the constraint of constant output torque, establish the functional relationship between current and permanent magnet temperature; Step 3: Integrate the parameterized model and perform iterative temperature rise calculations; Step 4: Construct a temperature field reduction model to achieve rapid calculation of the temperature field.

2. The method for joint calculation of the temperature field of a submersible motor based on the finite volume method and analytical method according to claim 1, characterized in that... Step one has the following steps: For the BH curve of the permanent magnet and the dynamic viscosity of the internal lubricating oil, a continuous relationship model of their variation with temperature is established using cubic spline interpolation. , In the formula This refers to the magnetic flux passing through a unit cross-sectional area of ​​the permanent magnet in the motor. The external magnetizing force applied to the permanent magnet, The temperature of the permanent magnet. This refers to the dynamic viscosity of the lubricating oil. This refers to the temperature of the lubricating oil.

3. The method for joint calculation of the temperature field of a submersible generator based on the finite volume method and analytical method according to claim 1, characterized in that... The specific steps of step two are as follows: Step 2:

1. Establish the finite element electromagnetic model of the motor; Step 22: Under rated operating conditions and with the motor output torque kept constant, calculate the input current values ​​corresponding to a series of permanent magnet temperature points using the finite element electromagnetic model of the motor established in Step 21. Step Two and Three: Based on the discrete (temperature, current) data points calculated in Step Two and Two, cubic spline interpolation is used to perform curve fitting, thereby obtaining a curve that can accurately describe the input current. With the temperature of the permanent magnet Changing continuous function model .

4. The method for jointly calculating the temperature field of a submersible generator based on the finite volume method and analytical method according to claim 1, characterized in that... The specific steps of step three are as follows: Step 3:

1. Establish a fluid-structure interaction temperature rise calculation model for the motor, and use the parameterized relationships obtained in Step 1 and Step 2 as inputs for coupling variables; Step 3.2 During the solution process, the motor fluid-structure interaction temperature rise calculation model dynamically updates the input current, lubricating oil viscosity and winding resistance according to the real-time temperature field, thereby realizing the synchronous correction of oil wear loss and copper loss. Step 33: Iterative calculations are performed using the temperature-loss feedback mechanism from Step 32 until the system reaches thermal equilibrium, i.e., the temperature rise distribution satisfies the convergence criterion, and finally the accurate steady-state temperature rise distribution of the motor under rated operating conditions is obtained.

5. The method for jointly calculating the temperature field of a submersible generator based on the finite volume method and analytical method according to claim 1, characterized in that... The specific steps of step four are as follows: Step 41: Using the Latin hypercube method, the operating environment temperature of the motor and the external well fluid velocity are sampled together, and temperature field snapshots under each sampling condition are calculated based on the full-order fluid-structure interaction model. Step 4.2: By performing intrinsic orthogonal decomposition and singular value decomposition on the snapshot set, the main characteristic modes of the temperature field are extracted, and a low-dimensional subspace that can characterize the core characteristics of the submersible motor temperature field is constructed, thereby obtaining a reduced-order model of the temperature field and realizing rapid calculation of the temperature field.

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