Method for calculating motor friction torque based on fluid temperature variation characteristics and gap flow state

Abstract

The application provides a motor friction torque calculation method based on fluid temperature change characteristics and gap flow state, tests the filled liquid by a physical property parameter measuring device to obtain liquid density and viscosity data, constructs a mathematical model based on the temperature change characteristics of the physical property parameters and determines undetermined coefficients by experimental data fitting, constructs an oil friction torque analytical method based on four typical flow states of Taylor-Couette flow system according to the gap flow characteristics of the motor stator and rotor, models the gap flow field of the motor stator and rotor by using the CFD method, and adds the above oil temperature change characteristic model into the numerical calculation. Based on the numerical calculation results, the undetermined coefficients in the oil friction torque analytical formula are determined, and a complete and efficient oil friction torque calculation method is formed. The application can effectively improve the calculation accuracy of the oil friction torque of the wet-type motor.

Description

Technical Field

[0001] This invention relates to the field of wet motor technology, and in particular to a method for calculating motor friction torque based on fluid temperature variation characteristics and gap flow state. Background Technology

[0002] Wet-type motors possess advantages such as lubrication compensation, thermal conductivity, and pressure balance. Their applications continue to expand in fields like aerospace and deep-sea development. However, because wet-type motors are filled with an oil-based medium, the viscosity of which is higher than that of a gaseous medium. This results in significant oil friction torque due to shear stress on the rotor walls within the gaps, leading to a substantial increase in the proportion of oil friction torque in mechanical losses under high-speed operating conditions. Therefore, predicting and mitigating this friction torque based on the motor's structure and operating parameters has become a critical challenge in the field of motor design and operation.

[0003] Currently, there are many reference models for calculating the friction torque or loss of wet-type motors, mainly focusing on motor structural parameters and drive parameters. However, current research on Taylor-Couet flow patterns in the fluid mechanics field and on object morphology in the materials science field will introduce errors into the calculation models.

[0004] On the one hand, existing computational models generally divide flow states into laminar and turbulent flow. However, according to cutting-edge research in the field of fluid dynamics, the actual evolution of Taylor-Couet flows has rich flow structures, and therefore the evolution of flow structures will cause errors.

[0005] On the other hand, while existing computational models take into account physical properties, they do not consider the changes in density and viscosity with temperature. According to cutting-edge research in the chemical industry, there is a linear mathematical relationship between the density and temperature of oil, while a non-linear exponential relationship exists between the viscosity and temperature.

[0006] Therefore, considering the flow regime evolution and the temperature-dependent properties of physical parameters is crucial in calculating oil friction torque. Furthermore, CFD numerical calculation methods for the stator-rotor clearance of motors cannot provide highly accurate results. Therefore, defining the temperature-dependent properties of materials and rotating the turbulence model are essential. Summary of the Invention

[0007] The purpose of this invention is to provide a method for calculating motor friction torque based on fluid temperature change characteristics and gap flow state, which effectively improves the calculation accuracy of oil friction torque in wet motors.

[0008] To achieve the above-mentioned invention, the present invention adopts the following technical solution:

[0009] The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow includes the following steps:

[0010] S1: Establish a modeling method for oil physical property parameters that takes into account temperature changes;

[0011] S2: Establish a CFD-based method for calculating the flow domain of motor gaps;

[0012] S3: Establish an analytical modeling method for oil friction torque based on flow regime and oil temperature change characteristics;

[0013] Step S1 includes the following steps:

[0014] S10: The density and viscosity of the oil at different temperatures are tested using physical property parameter testing equipment;

[0015] S11: Construct linear and nonlinear parametric models applicable to the temperature-dependent properties of density and viscosity, respectively;

[0016] S12: Determine the undetermined coefficients of the model to obtain a complete model of the temperature-dependent properties of oil physical parameters;

[0017] Step S2 includes the following steps:

[0018] S20: Based on the motor structure and operating parameters, determine the structural characteristics and parameters of the CFD numerical calculation model for the constant motor gap flow domain;

[0019] S21: Based on the temperature change characteristic model of oil physical property parameters determined in S12, the influence of temperature change characteristics on the physical property parameters of watershed materials is added using a user-defined function;

[0020] S22: Design a mesh node partitioning scheme and verify mesh independence;

[0021] S23: By comparing the results of different turbulence models with experimental and direct numerical simulations, a high-precision and low-cost turbulence model suitable for motor gap flow domain calculation is determined.

[0022] Step S3 includes the following steps:

[0023] S30: Establishment of a fundamental analytical model for oil friction torque based on four flow regimes;

[0024] S31: The temperature change characteristics of fluid physical properties are introduced to correct the original parameters;

[0025] S32: Modify the basic analytical model based on the correction parameters;

[0026] S33: Using the CFD numerical calculation model of S2, the undetermined coefficients in the basic model are fitted to obtain a complete analytical model of oil friction torque.

[0027] Furthermore, step S10 specifically includes:

[0028] The hydraulic oil filled inside the motor was measured using physical property testing equipment to obtain the results at different temperatures. T Density at K r Tf Dynamic viscosity m Tf and kinematic viscosity n Tf The test results;

[0029] Step S11 specifically includes:

[0030] Introducing the volume-temperature diffusivity α p Construct a linear parametric model of the relationship between density and temperature:

[0031]

[0032] in, r Tf At temperature T The numerical calculation results are as follows; r 0 and T 0 The density and temperature were obtained from experimental tests at a reference point.

[0033] Nonlinear parametric models relating dynamic viscosity and kinematic viscosity to temperature were constructed based on the Vogel and Walther models.

[0034]

[0035] in, m Tf and n Tf At temperature T The following numerical calculations are performed on the dynamic viscosity and kinematic viscosity; m 0 It is the experimentally measured value of the dynamic viscosity at the reference point; C , D , E , F , G These are the constants that the model needs to fit based on the experimental test results.

[0036] Furthermore, step S12 specifically includes:

[0037] An optimization algorithm is used to iteratively fit the parameterized function model proposed in S11 based on the physical property parameters obtained in S10, to determine the specific characterization forms of the density, dynamic viscosity and kinematic viscosity models, and to obtain a high-precision function model suitable for the temperature change characteristics of the internal oil physical property parameters of wet motors.

[0038] Furthermore, step S20 specifically includes:

[0039] Based on the integrity of the motor structural parameters, periodic characteristics, and Taylor-Couette flow characteristics, the structural features and parameters of the CFD numerical calculation model are determined; a sliding coordinate system and its rotating and stationary walls are set up.

[0040] Furthermore, step S22 specifically includes:

[0041] Considering the influence of the temperature variation characteristics of oil physical properties on the calculation of the first layer mesh thickness of the boundary layer, in the axial direction N z Zhou Xiang N θ and radial N r In terms of the number of nodes, multiple grid-independent schemes are designed using an incremental sequence. Dimensionless local transport parameters or global response parameters are used as evaluation parameters. When the rate of change between two adjacent evaluation parameters is less than 0.5%, the former is adopted as the optimal grid partitioning scheme for subsequent calculations.

[0042] Furthermore, step S23 specifically includes:

[0043] Based on existing literature regarding experimental or direct numerical simulation results of Taylor-Couet flow systems with similar characteristics, the computational accuracy and economy of turbulence models were compared and screened to select the turbulence model most suitable for numerical calculation of the flow between the stator and rotor of the motor.

[0044] Furthermore, step 30 specifically includes:

[0045] Based on the evolution of the Taylor-Couet flow pattern, a method for calculating the oil friction torque in the stator-rotor gap of an electric motor is proposed. T AC Basic analytical model:

[0046]

[0047] Among them, the stator inner diameter R s Rotor outer diameter R r Gap width d g = R s - R r Circumferential length L a radius ratio or = R r / Rs , length-to-diameter ratio Г= L a / d g Dynamic viscosity μ oh r ω is the angular velocity of the rotor. f , g , h For the undetermined coefficients that need to be solved using a CFD numerical calculation model, The c1 , The c2 and The c3 , respectively, are the critical Taylor numbers for flow regime transition.

[0048] Furthermore, step 31 specifically includes:

[0049] Based on the temperature-dependent characteristics of fluid properties, a functional model is adopted using density, dynamic viscosity, and kinematic viscosity models, with temperature as the primary factor. T Density at K r Tf Dynamic viscosity m Tf and kinematic viscosity n Tf Replace density in the system parameter definition formula r Dynamic viscosity m and kinematic viscosity n And corrects the Reynolds number, Taylor number, and Nusselt number:

[0050] Re T = u r d g / n T = R r oh r d g / n T ;

[0051] The T =(1+ or ) 4 (64 or 2 ) -1 d g ( R s + Rr ) 2 oh r 2 n T -2 ;

[0052] No ω =( J ω ) T / ( J ω lam ) T。

[0053] Furthermore, step 32 specifically includes:

[0054] The revised r Tf , m Tf , n Tf , Re T , The T and No ω Bring into T AC In, for different temperatures T The analytical model of the oil friction torque under the given conditions is modified.

[0055] Furthermore, step 33 specifically includes:

[0056] Based on the CFD numerical calculation model in S2, the local transport parameters and global response parameters of the Taylor-Couet flow system at different rotational speeds and temperatures are calculated, and these parameters are then substituted into the oil friction torque. T AC In the analytical model, fitting is performed to determine the undetermined coefficients, thus obtaining the complete analytical model of oil friction torque.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This invention establishes a parametric mathematical model of the changes in oil physical properties with temperature. The physical property parameters of the oil at different temperatures are obtained using physical property parameter testing equipment. The mathematical model containing undetermined parameters is fitted by experimental data, and finally, an accurate calculation model for the physical property parameters of wet motor oil is determined.

[0059] This invention establishes a CFD numerical solution domain based on motor structural parameters, determines the boundary layer meshing scheme based on driving strength parameters, explores a computational model with high efficiency and economy, and incorporates oil physical property parameters into the calculation using user-defined functions to construct a flow domain solution model suitable for the stator and rotor clearance of the motor.

[0060] Based on the development law of Taylor-Couette flow, this invention divides the motor operating range into four classical flow regimes and derives a semi-analytical model of oil friction torque according to these flow regimes. Based on CFD numerical calculation results, the undetermined coefficients in the analytical model are determined, thereby obtaining a precise calculation method for oil friction torque applicable to different motor temperature and speed ranges under various operating conditions. Attached Figure Description

[0061] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram comparing the test and fitting results of the physical property parameters in an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the CFD numerical calculation model in an embodiment of the present invention;

[0064] Figure 4 This is a schematic diagram comparing the accuracy and economy of the turbulence model in this embodiment of the invention.

[0065] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate this embodiment, some components in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings. Detailed Implementation

[0066] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0067] This embodiment provides a method for calculating motor friction torque based on fluid temperature variation characteristics and gap flow regime. It considers four typical flow regimes of Taylor-Cooette flow in the stator-rotor gap region of the motor and incorporates a functional model of the temperature variation characteristics of oil physical properties. The establishment of the analytical model includes 3 main steps and 12 sub-steps, such as... Figure 1 As shown. The three main steps include: S1, a modeling method for oil physical property parameters considering temperature change characteristics; S2, a CFD-based method for calculating the motor gap flow domain; and S3, an analytical modeling method for oil friction torque based on flow regime and oil temperature change characteristics.

[0068] The oil physical property parameter modeling method S1 considering temperature changes includes three sub-steps:

[0069] Step S10:

[0070] The density of the oil filling the inside of the wet motor was determined using a surface tension meter and a rheometer. r Tt and dynamic viscosity m Tt ,according to n Tt = m Tt / r Tt The relationship will be sought n Tt The experimental values.

[0071] Step S11:

[0072] By introducing the volume-temperature diffusivity α p Based on the Knežević model, a mathematical model can be constructed regarding the relationship between density and temperature:

[0073]

[0074] in, r Tf At temperature T The numerical calculation results are as follows. r 0 and T 0 The density and temperature are obtained from experimental tests at a reference point (here taken as...). T 0 =293.15K and r 0 =844.99kg / m 3 ).

[0075] The dependence of dynamic viscosity and kinematic viscosity on temperature is modeled based on the Vogel and Walther models:

[0076]

[0077] in, m Tf and n Tf At temperature T The following numerical calculations are performed on the dynamic viscosity and kinematic viscosity. m 0 =1.76×10 - 2 Pa·s is T =The experimentally measured value of dynamic viscosity at 293.15K. C ,D , E , F , G These are the constants that the model needs to fit based on the experimental test results.

[0078] Step S12:

[0079] Based on the experimental results and the Levenberg-Marquardt optimization algorithm, iterative fitting was performed to calculate... α p =3.52×10 -5 (fit coefficient) R 2 =0.949). The calculation error for the defined density. D ρ : D ρ =|( r Tt - r Tf ) / r Tt |×100%. Unlike the linear trend of density, the viscosity of hydraulic oil decreases exponentially with increasing temperature, and the decreasing trend gradually weakens. The Levenberg-Marquardt optimization algorithm was also used for iterative fitting, and the fitting result for the Vogel model is (fitting coefficients). R 2 =0.999): C =1.58×10 -3 , D =2.21×10 3 , E =49.19; The fitting result for the Walther model is (fit coefficient) R 2 =0.998): F =11.24, G =4.11. The calculation errors for dynamic viscosity and kinematic viscosity are defined as follows: D μ =|( m Tt - m Tf ) / m Tt |×100% and D ν =|( n Tt - n Tf ) / n Tt|×100%. The above experimental test results, analytical calculation models, and error statistics regarding density, dynamic viscosity, and kinematic viscosity within the temperature range of 273.15K-393.15K are presented in [the table / data]. Figure 2 .

[0080] The maximum error between the density-temperature function model based on the volume-temperature diffusivity coefficient and the experimental results is 0.06%, indicating that the proposed model can accurately reflect the trend of hydraulic oil density change with temperature. The maximum error between the dynamic viscosity-temperature function model based on the Vogel model and the experimental results is 3.01%, indicating that the proposed model can accurately reflect the trend of hydraulic oil dynamic viscosity change with temperature. Since the experimental results of dynamic viscosity are calculated based on the experimental results of density and dynamic viscosity, the maximum error between the kinematic viscosity-temperature function model based on the Walther model and the experimental results is 7.34%, indicating that the proposed model can relatively accurately reflect the trend of hydraulic oil kinematic viscosity change with temperature.

[0081] The CFD-based method S2 for calculating the motor gap flow domain includes four sub-steps:

[0082] Step S20:

[0083] Based on the actual stator inner diameter of the motor R s Rotor outer diameter R r Gap width d g = R s - R r Circumferential length L a radius ratio or = R r / R s , length-to-diameter ratio Г= L a / d g A CFD numerical model of the flow domain between the stator and rotor of the motor was established. Based on the characteristics of the complete Taylor vortex, a translational boundary condition was set in the axial direction (the number of periods was consistent with the existing DNS value of Г=3.1), and a rotational boundary condition was set in the circumferential direction (the number of periods was 20). Figure 3 As shown. The boundary conditions are set as follows: rotor rotating wall 100, stator stationary wall 101, rotational period boundary 102, and translational period boundary 103.

[0084] Step S21:

[0085] By using a user-defined function, the hydraulic oil temperature variation characteristic function model proposed in this invention is set into the properties of the fluidized bed material, introducing temperature. T For the variable values ​​of fluid domain physical properties.

[0086] Step S22:

[0087] During radial mesh generation, in order to ensure that the first mesh layer is located in the viscous sublayer of the boundary layer 104, the height of the first mesh layer of the boundary layer 104 between the rotor rotating wall 100 and the stator stationary wall 101 must satisfy 1 ≤ y + ≤5. In calculation y + It is necessary to take into account the physical properties of the oil involved.

[0088] Design 8 sets of mesh node division schemes, in the axial direction ( N z ), Zhou Xiang ( N θ ) and radial ( N r An increasing sequence is used, and dimensionless torque is selected. G = J ω / n 2 and Nusselt No ω = G / G lam The criteria for evaluating grid independence are shown in Table 1. J ω = T r / (2π L a r ) represents the angular velocity flux. n The dynamic viscosity of the oil. r Given the density of the oil, the dynamic viscosity μ can be derived from this. n×r , T r To maintain the speed of the drive rotor n The torque required at that time G lam = J ω lam / n 2 The dimensionless torque under laminar flow conditions. J ω lam =2 nR s 2R r 2 oh r / ( R s 2 - R r 2 ) represents the angular velocity flux under laminar flow conditions. oh r This represents the angular velocity of the rotor. Starting with scheme 7, G and No ω The volatility decreased to 0.47%, indicating that the scheme achieved a balance between computational efficiency and result accuracy. Therefore, scheme 7 was adopted for the mesh generation of subsequent numerical calculations.

[0089] Table 1 Mesh Independence Verification Scheme

[0090]

[0091] Step S23:

[0092] To determine the applicability of the turbulence model while also considering the economics of numerical computation, Realizable... k-e Model, SST k-oh Models and Stress -oh The RSM model was compared with Vent's experiments (reference: F. Vent, "Turbulence between two rotating coaxial cylinders," Archives of Applied Mechanics, Vol. 4, No. 6, pp. 577–595, December 1933, Digital Object Identifier: 10.1007 / BF02084936) and Scheupp's direct numerical simulation results (reference: A. Scheupp, E. Clement, D. Legendre, and C. Gabier, "Numerical simulation of bubble dispersion in turbulent Taylor-Couette flow," Physical Fluids, Vol. 26, No. 4, p. 043304, April 2014, Digital Object Identifier: 10.1063 / 1.4871728). The supercomputing node used for the calculations was configured as an AMD EPYC 7452@2.35GHz 64core 256G. Realizable k-e The model requires a minimum average kernel time of 1.44 × 10⁻⁶. 4 However, its calculations G The average error between the test and DNS reached a maximum of 11.56%. SST k-oh Models and Stress -oh The RSM model yielded smaller average errors of 5.75% and 3.49%, respectively, but the RSM model had the highest average kernel time of 1.82 × 10⁻⁶.4 In summary, SST k-oh The model has good accuracy and low computational cost, so it is the best choice for calculation of the object under study.

[0093] The analytical modeling method S3 for oil friction torque based on the temperature-dependent characteristics of flow regime and physical property parameters includes four sub-steps:

[0094] Step S30:

[0095] For a TC system with a smooth surface and constant temperature, the driving intensity of the system gradually increases with the continuous increase of the inner rotor speed. During this process, the flow state within the TC system changes from Couet flow to a turbulent state containing Taylor-Couet flows of varying development stages. Based on the flow characteristics, the flow states during this development process can be divided into four categories, and the rotor friction torque derived from this also has four forms:

[0096] ① Kuetz flow

[0097] when The When the velocity is sufficiently small, the flow is in a purely azimuthal laminar state. At this point, due to the conservation of radial angular momentum, the tangential velocity... u φ ( r Radial position only r The function, independent of f and z And the other two velocity components u r ( r )and u z ( r All are 0. Substituting the above conditions into the three-dimensional incompressible Navier-Stokes equations yields a second-order linear differential equation, the solution of which is:

[0098]

[0099] Simultaneously, by incorporating the boundary conditions of the Taylor-Couet system and applying Newton's law of internal friction, the frictional torque on the infinitesimal element of the rotor surface is obtained. t r Integrating this value over the rotor surface, the total frictional torque on the rotor surface under laminar flow conditions is obtained. T CFR :

[0100]

[0101] ②Laminar flow

[0102] When the driving force of the system is very small, energy transfer is limited by the laminar boundary layer. At this point, the boundary layer remains laminar, but the mainstream region becomes turbulent and exhibits spatiotemporally stable Taylor vortices. The and No ω The relationship between them satisfies the scaling power law ( α (Scaling factor for different TC systems under low-drive flow conditions) No ω -1~ The 1 / 3+α ,Right now No ω -1= aTa 1 / 3+α ( f (This refers to the offset coefficient of the function). Combining this with the parameters defined above, the oil friction torque under laminar flow conditions can be derived. T LR for:

[0103]

[0104] ③Classical Transient Flow

[0105] As the driving intensity is further increased, the flow regime within the boundary layer gradually changes from laminar to turbulent. At this point, the spatiotemporally stable Taylor vortices are disrupted, and the Taylor vortex flow state transitions to a modulated Taylor vortex state, resulting in its time dependence. In this transitional state, hairpin vortices (considered plumes in Rayleigh-Bénard flows) are ejected from the inner and outer cylinders, and these vortices contribute to the formation of large-scale mainstream structures. These structures, in turn, generate axial pressure gradients and couple back into the boundary layer, causing plumes to be ejected back to nearby locations. The and No ω The relationship between them satisfies the scaling power law ( β (Scaling factor for different Taylor-Couette flow systems under transitional flow conditions) No ω ~ The 1 / 3+β ,Right now No ω = bTa 1 / 3+β ( g (This refers to the offset coefficient of the function). Combining this with the parameters defined above, the oil friction torque under low-drive flow conditions can be derived. T CTR for:

[0106]

[0107] ④Limited turbulent flow state

[0108] When the driving force of the system is sufficiently large, the boundary layer becomes fully turbulent, and the system enters the so-called limiting flow state. The Taylor-Couet flow system with stator-rotor clearance in a motor exhibits... or →1 indicates a small gap characteristic, resulting in relatively less disruption to flow continuity. Therefore, the scaling exponent of the limiting flow state or the transitional flow state close to the limiting flow state has a significant impact on the flow's continuity. or The dependence on [the value of] is relatively weak, meaning that it exhibits a similar scaling power law under different stator and rotor size parameters. At this point... The and No ω The relationship between them satisfies the scaling power law ( c (Scaling factor for different Taylor-Couette flow systems under transitional flow conditions) No ω ~ The 0.39+γ ,Right now No ω = cTa 0.39+γ ( h (This refers to the offset coefficient of the function). Combining this with the parameters defined above, the oil friction torque under low-drive flow conditions can be derived. T UTR for:

[0109]

[0110] Because the critical Taylor number for flow regime transition varies among Taylor-Couette flow systems with different characteristics, studies have found that increasing the radius ratio accelerates the flow regime transition. Therefore, the critical Taylor number for flow regime transition needs to be determined based on the system under study; here, we assume that they are respectively... The c1 , The c2 and The c3 .

[0111] Step S31:

[0112] Based on the temperature-dependent characteristics of the oil's physical properties obtained in step 12, the existing parameters are corrected: temperature is used. T Density at K r Tf Dynamic viscosity m Tf and kinematic viscosity n Tf Replace density in the system parameter definition formula r Dynamic viscosity m and kinematic viscosity n And corrects the Reynolds number, Taylor number, and Nusselt number:

[0113] (a) ReT = u r d g / n T =R r oh r d g / n T ;

[0114] (b) The T =(1+ or ) 4 (64 or 2 ) -1 d g ( R s + R r ) 2 oh r 2 n T -2 ;

[0115] (c) No ω =( J ω ) T / ( J ω lam ) T。

[0116] Step 32:

[0117] The oil friction torque was derived based on the corrected parameters. T AC :

[0118]

[0119] Step S33:

[0120] For the latter three types of Taylor-Couette turbulent flows within the stator-rotor gap of the motor, the scaling factor cannot be obtained analytically. Using the CFD numerical calculation model proposed in this invention, the aforementioned power-law relationship is fitted, as shown in Table 2. Based on the data in this table, the oil friction torque of the motor can be accurately calculated.

[0121] Table 2 Power-law fitting coefficients

[0122]

[0123] The above embodiments are only used to illustrate and not limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention without departing from the spirit and scope of the present invention. Any modifications or partial substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow, characterized in that... Includes the following steps: S1: Establish a modeling method for oil physical property parameters that takes into account temperature changes; S2: Establish a CFD-based method for calculating the flow domain of motor gaps; S3: Establish an analytical modeling method for oil friction torque based on flow regime and oil temperature change characteristics; Step S1 includes the following steps: S10: The density and viscosity of the oil at different temperatures are tested using physical property parameter testing equipment; S11: Construct linear and nonlinear parametric models applicable to the temperature-dependent properties of density and viscosity, respectively; S12: Determine the undetermined coefficients of the model to obtain a complete model of the temperature-dependent properties of oil physical parameters; Step S2 includes the following steps: S20: Based on the motor structure and operating parameters, determine the structural characteristics and parameters of the CFD numerical calculation model for the constant motor gap flow domain; S21: Based on the temperature change characteristic model of oil physical property parameters determined in S12, the influence of temperature change characteristics on the physical property parameters of watershed materials is added using a user-defined function; S22: Design a mesh node partitioning scheme and verify mesh independence; S23: By comparing the results of different turbulence models with experimental and direct numerical simulations, a high-precision and low-cost turbulence model suitable for motor gap flow domain calculation is determined. Step S3 includes the following steps: S30: Establishment of a fundamental analytical model for oil friction torque based on four flow regimes; S31: The temperature change characteristics of fluid physical properties are introduced to correct the original parameters; S32: Modify the basic analytical model based on the correction parameters; S33: Using the CFD numerical calculation model of S2, the undetermined coefficients in the basic model are fitted to obtain a complete analytical model of oil friction torque. Step S10 specifically includes: The hydraulic oil filled inside the motor was measured using physical property testing equipment to obtain the results at different temperatures. T Density at K ρ Tf Dynamic viscosity μ Tf and kinematic viscosity ν Tf The test results; Step S11 specifically includes: Introducing the volume-temperature diffusivity α p Construct a linear parametric model of density and temperature; A nonlinear parametric model of the relationship between dynamic viscosity and kinematic viscosity and temperature was constructed based on the Vogel model and the Walther model. Step S12 specifically includes: An optimization algorithm is used to iteratively fit the parameterized function model proposed in S11 based on the physical property parameters obtained in S10, to determine the specific characterization forms of the density, dynamic viscosity and kinematic viscosity models, and to obtain a high-precision function model suitable for the temperature change characteristics of the physical property parameters of the oil inside the wet motor. Step 30 specifically includes: Based on the evolution of the Taylor-Couet flow pattern, a method for calculating the oil friction torque in the stator-rotor gap of an electric motor is proposed. T AC Basic analytical model: ； Among them, the stator inner diameter R s Rotor outer diameter R r Gap width δ g = R s - R r Circumferential length L a radius ratio η = R r / R s , length-to-diameter ratio Г= L a / δ g Dynamic viscosity μ ω r ω is the angular velocity of the rotor. f , g , h For the undetermined coefficients that need to be solved using a CFD numerical calculation model, Ta c1 , Ta c2 and Ta c3 These are the critical Taylor numbers for the flow regime transition; Step 31 specifically includes: Based on the temperature-dependent characteristics of fluid properties, a functional model is adopted using density, dynamic viscosity, and kinematic viscosity models, with temperature as the primary factor. T Density at K ρ Tf Dynamic viscosity μ Tf and kinematic viscosity ν Tf Replace density in the system parameter definition formula ρ Dynamic viscosity μ and kinematic viscosity ν And corrects the Reynolds number, Taylor number, and Nusselt number: Re T = u r δ g / ν T = R r ω r δ g / ν T ; Ta T =(1+ η ) 4 (64 η 2 ) -1 δ g ( R s + R r ) 2 ω r 2 ν T -2 ; Nu ω =( J ω ) T / ( J ω lam ) T。 2. The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow state according to claim 1, characterized in that, Step S20 specifically includes: Based on the integrity of the motor's structural parameters, periodic characteristics, and Taylor-Couette flow characteristics, the structural features and parameters of the CFD numerical calculation model are determined; a sliding coordinate system and its rotating and stationary walls are set up.

3. The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow state according to claim 2, characterized in that, Step S22 specifically includes: Considering the influence of the temperature variation characteristics of oil physical properties on the calculation of the first layer mesh thickness of the boundary layer, in the axial direction N z Zhou Xiang N θ and radial N r In terms of the number of nodes, multiple grid-independent schemes are designed using an incremental sequence. Dimensionless local transport parameters or global response parameters are used as evaluation parameters. When the rate of change between two adjacent evaluation parameters is less than 0.5%, the former is adopted as the optimal grid partitioning scheme for subsequent calculations.

4. The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow state according to claim 3, characterized in that, Step S23 specifically includes: Based on existing literature regarding experimental or direct numerical simulation results of Taylor-Couet flow systems with similar characteristics, the computational accuracy and economy of turbulence models were compared and screened to select the turbulence model most suitable for numerical calculation of the flow between the stator and rotor of the motor.

5. The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow state according to claim 1, characterized in that, Step 32 specifically includes: The revised ρ Tf , μ Tf , ν Tf , Re T , Ta T and Nu ω Bring into T AC In, for different temperatures T The analytical model of the oil friction torque under the given conditions is modified.

6. The method for calculating motor friction torque based on fluid temperature variation characteristics and interstitial flow state according to claim 5, characterized in that, Step 33 specifically includes: Based on the CFD numerical calculation model in S2, the local transport parameters and global response parameters of the Taylor-Couet flow system at different rotational speeds and temperatures are calculated, and these parameters are then substituted into the oil friction torque. T AC In the analytical model, fitting is performed to determine the undetermined coefficients and obtain a complete analytical model of oil friction torque.

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