Method for calculating oil friction loss of high-speed oil-filled motor rotor

Abstract

This invention relates to a method for calculating oil friction loss in the rotor of a high-speed oil-filled motor, belonging to the field of motors. The method involves: establishing a finite element simulation model coupling the electromagnetic and temperature fields of the motor; using this model to obtain a multivariate functional relationship with the motor's optimizeable parameter [E] as the independent variable and the motor's operating temperature T as the dependent variable; establishing a composite functional relationship between fluid viscosity and the motor's optimizeable parameter [E]; based on the analysis mechanism of fluid-rotor friction loss, obtaining the functional relationship between Reynolds number Re and fluid viscosity, and the functional relationship between Reynolds number Re and the motor's optimizeable parameter [E]; combining the analytical expression of Reynolds number Re to obtain an analytical calculation model for fluid friction loss; setting an axial flow velocity in the simulation model, comparing it with the analytical calculation results, and introducing a correction coefficient to obtain an analytical calculation model for oil friction loss considering axial flow; and using the multivariate function extremum theory to obtain the relationship between oil friction loss and the motor's optimizeable parameter [E]. This invention is used for calculating oil friction loss in the rotor of a high-speed oil-filled motor.

Description

Technical Field

[0001] This invention belongs to the field of electric motors, specifically relating to a method for calculating rotor oil friction loss in a high-speed oil-filled motor. Background Technology

[0002] Spacecraft equipment wastes a significant amount of energy during exhaust emissions. To utilize this energy and reduce the load on the spacecraft's power modules, a turbine-powered system capable of reusing exhaust gases is incorporated into the spacecraft's power supply system. However, due to the unpredictable attitude of spacecraft, the cooling circuit of the generator in the power generation system cannot be designed normally; therefore, oil-filled cooling is employed. In this cooling method, the air gap is filled with cooling oil, resulting in substantial rotor oil friction losses during high-speed operation, severely reducing the generator's power generation efficiency. There are generally two methods for calculating rotor oil friction losses in motors: analytical methods and finite element simulation. Analytical methods are fast, but because they do not consider actual operating conditions such as axial flow, the calculated results have a large error compared to actual conditions. While finite element simulation can simulate actual operating conditions to a greater extent and calculate more accurate results, the fluid simulation is time-consuming, which limits the progress of motor optimization for oil friction losses. Currently, there is no analytical algorithm that approximates the simulation results, which is one of the main technical limitations for improving the calculation speed of high-speed motor rotor oil friction losses. Summary of the Invention

[0003] The purpose of this invention is to solve the problem that motor optimization for oil-filled motors cannot simultaneously achieve fast calculation speed and accurate results for oil friction loss, and proposes a method for calculating rotor oil friction loss of high-speed oil-filled motors.

[0004] The method of this invention is a derivation and modification of the analytical calculation method, which can make the results closer to the finite element simulation calculation results (actual results). Based on fluid mechanics theory, this invention considers high-speed fluid turbulence and the axial flow of cooling oil in the motor air gap, and introduces a correction coefficient into the analytical formula for oil friction loss of the motor rotor. The correction coefficient can be fitted by a finite number of finite element simulation results, and finally obtains a more accurate oil friction loss calculation formula (i.e., multivariate function expression).

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for calculating rotor oil friction loss of a high-speed oil-filled motor, the method comprising the following steps:

[0007] A. Establish a finite element simulation model of the electromagnetic and temperature fields of the motor using finite element analysis software, and obtain a multivariate function relationship with the motor's optimizable parameter [E] as the independent variable and the oil-filled motor's operating temperature T as the dependent variable using the finite element simulation model;

[0008] B. Based on the relationship between fluid viscosity and temperature, establish a composite function relationship between fluid viscosity and the motor optimizable parameter [E] in step A above;

[0009] C. Based on the analysis mechanism of the air gap fluid and rotor friction loss of the motor rotor, the dimensionless Coulter Reynolds number Re and the fluid viscosity in step B above are obtained under the working condition of tangential flow in the air gap of the motor rotor. The functional relationship between the Reynolds number Re and the motor optimizable parameter [E] is further obtained, and the flow state of the fluid is judged at the same time.

[0010] D. Since the formulas for fluid friction differ between laminar and turbulent states, the laminar or turbulent calculation formula should be determined based on the fluid flow state in step C. Combined with the analytical expression of the Reynolds number Re in step C, an analytical calculation model for fluid friction loss can be obtained.

[0011] E. Perform finite element simulation with the same parameters as the analytical calculation model above, and set the axial flow velocity that was not considered in the analytical calculation in the simulation model. Compare it with the analytical calculation results in step D, and introduce a correction coefficient K on the basis of the analytical calculation model in step D to obtain an analytical calculation model of oil friction loss considering axial flow. Use the multivariate function extremum theory to obtain the relationship between oil friction loss and the change of the motor optimizable parameter [E].

[0012] Furthermore, step A specifically includes:

[0013] The fluid viscosity decreases as the operating temperature T of the oil-filled motor increases. Therefore, it is necessary to first consider the influence of the motor's optimizable parameters on the temperature field. By calculating the temperature field through finite element simulation, the motor's optimizable parameters are changed to obtain the relationship between the operating temperature of the oil-filled motor and the motor's optimizable parameters, as shown in equation (3).

[0014] T = Fun([E]) (3)

[0015] Where Fun() is a multivariate function relationship, T is the operating temperature of the oil-filled motor, and [E] is an optimizable parameter of the motor that affects its electromagnetic performance.

[0016] Furthermore, in step B, equation (4) is used to establish a composite function relationship between the fluid viscosity and the motor optimizable parameters in step A above;

[0017] μ=F(T) (4)

[0018] Where μ is the fluid viscosity and F(T) is a composite function of the motor's optimizable parameters.

[0019] Furthermore, step C specifically involves:

[0020] The fluid flow properties are determined by the ratio between inertial force and viscous force, and are described by the dimensionless Coulter Reynolds number Re. Under the working conditions of tangential flow in the air gap of the motor rotor, the functional relationship between the Reynolds number Re and the fluid viscosity is expressed by equation (5). Combining the influence of the working temperature T of the oil-filled motor mentioned above, the relationship between the Reynolds number Re and the motor's optimizable parameter function (multivariate function) is further obtained by equation (6).

[0021]

[0022] Re=Fun(ρ,ω,r,δ,μ(T([E]))) (6)

[0023] Where ρ is the fluid density, ω is the angular velocity, r is the rotor radius, δ is the air gap length, and μ is the fluid viscosity;

[0024] If Re is below 2000, the fluid flows in the same direction and is in a laminar state; if the flow velocity increases further, velocity fluctuations will occur in the flow; if Re is above 2300, it becomes a turbulent state.

[0025] Furthermore, in step D, the friction coefficient C of the fluid in the turbulent state... f Calculated using equation (7);

[0026]

[0027] Where σ is the tangential stress of the fluid and v is the fluid flow velocity;

[0028] Since the tangential stress σ of the fluid represents the force at a point, the force F on the entire cylindrical surface of the air gap is obtained by integrating over the surface that generates the force, as shown in equation (8); the frictional torque T is obtained by combining equations (7) and (8). m The expression for the fluid friction loss P is given in equation (9); and the fluid friction loss P is obtained. m The analytical calculation model is shown in equation (10);

[0029] F=2σπrl (8)

[0030] T m =C f ρπω 2 r 4 l (9)

[0031] P m =C f ρπω 3 r 4 l (10)

[0032] Where l is the axial length.

[0033] Furthermore, in step E, the above analytical calculation parameters are input into the finite element simulation model as initial conditions, and the axial flow velocity of the fluid is added. The simulation results are compared with the above analytical calculation results. A correction coefficient K is introduced into the analytical calculation model (10) to obtain the analytical calculation model (11) considering the axial flow velocity.

[0034] P m =KC f ρπω 3 r 4 l (11)

[0035] Regarding the correction coefficient K and friction coefficient C in equation (11) f Determine equations (12) and (13); the correction coefficient K is changed by altering the axial flow velocity V in the finite element simulation. l Other variables [M] are used to obtain the fitted function curve, where [M] includes motor structural parameters related to fluid mechanics;

[0036] C f =Fun(T,Re,r,δ) (12)

[0037] K = Fun(V) l ,[M]) (13)

[0038] By combining equations (6), (11), (12), and (13), the relationship between oil friction loss and the change of motor optimizable parameters is obtained through equation (14).

[0039] P m =Fun(T([E]),[E],V l ,[M]) (14)

[0040] Equation (14) combines with finite element simulation to determine the axial flow velocity V. l The influence of the operating temperature T of the oil-filled motor is taken into account in the calculation formula of oil friction loss.

[0041] Furthermore, the analytical calculation parameters include fluid density ρ, rotor radius r, angular velocity ω, motor optimizable parameters [E], air gap length δ, and axial length l.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. The influence of temperature on fluid viscosity is considered. The Reynolds number and friction coefficient are determined by the fluid viscosity. The final friction loss calculation method (i.e., Equation (11)) also takes into account the dynamic influence of temperature.

[0044] 2. The coolant in the air gap of the motor needs to flow to dissipate heat. This method, by combining it with finite element simulation, takes into account the influence of the axial flow velocity of the fluid on the friction coefficient and introduces a correction coefficient to further correct the formula for oil friction loss, so that the analytical results are closer to the actual working conditions. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method for calculating the oil friction loss of the rotor of a high-speed oil-filled motor according to the present invention. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0047] Specific Implementation Method 1: This implementation method discloses a method for calculating the rotor oil friction loss of a high-speed oil-filled motor, the method including the following steps:

[0048] A. Establish a finite element simulation model of the electromagnetic and temperature fields of the motor using finite element analysis software, and obtain a multivariate function relationship with the motor's optimizable parameter [E] as the independent variable and the oil-filled motor's operating temperature T as the dependent variable using the finite element simulation model;

[0049] B. Based on the relationship between fluid viscosity and temperature, establish a composite function relationship between fluid viscosity and the motor optimizable parameter [E] in step A above;

[0050] C. Based on the analysis mechanism of the air gap fluid and rotor friction loss of the motor rotor, the dimensionless Coulter Reynolds number Re and the fluid viscosity in step B above are obtained under the working condition of tangential flow in the air gap of the motor rotor. The functional relationship between the Reynolds number Re and the motor optimizable parameter [E] is further obtained, and the flow state of the fluid is judged at the same time.

[0051] D. Since the formulas for fluid friction differ between laminar and turbulent states, it is necessary to determine whether to use a laminar or turbulent calculation formula based on the fluid flow state in step C (laminar flow calculation is already very mature, and this invention modifies the formula for turbulent flow). Combined with the analytical expression of the Reynolds number Re in step C, an analytical calculation model for fluid friction loss is obtained.

[0052] E. Perform finite element simulation with the same parameters as the analytical calculation model above, and set the axial flow velocity that was not considered in the analytical calculation in the simulation model. Compare it with the analytical calculation results in step D, and introduce a correction coefficient K on the basis of the analytical calculation model in step D to obtain an analytical calculation model of oil friction loss considering axial flow. Using the multivariate function extremum theory, obtain the relationship between oil friction loss and the motor optimizable parameter [E] (find the dimension that has a greater impact on the optimizable parameters, which greatly improves the speed of motor optimization for oil friction loss).

[0053] Furthermore, step A specifically includes:

[0054] The fluid viscosity decreases as the operating temperature T of the oil-filled motor increases (the higher the operating temperature T of the oil-filled motor, the lower the viscosity of the fluid. Since the operating temperature of the oil-filled motor is relatively high, the viscosity of the fluid is greatly affected by the temperature). Therefore, it is necessary to first consider the influence of the motor's optimizable parameters on the temperature field. By calculating the temperature field through finite element simulation, the motor's optimizable parameters are changed to obtain the relationship between the operating temperature of the oil-filled motor and the motor's optimizable parameters, as shown in equation (3).

[0055] T = Fun([E]) (3)

[0056] Where Fun() is a multivariate function relationship, T is the operating temperature of the oil-filled motor, and [E] is the motor's optimizable parameters that affect the motor's electromagnetic performance (the motor's optimizable parameters [E] include, but are not limited to, air gap length, motor stator tooth yoke size, winding parameters, rotor parameters, etc. (depending on which parameters need to be optimized in the actual project)).

[0057] Furthermore, in step B, equation (4) is used to establish a composite function relationship between the fluid viscosity and the motor optimizable parameters in step A above;

[0058] μ=F(T) (4)

[0059] Where μ is the fluid viscosity and F(T) is a composite function of the motor's optimizable parameters;

[0060] Fluid viscosity is one of the physical properties of fluids, which varies with the type and state of matter. It only becomes apparent when fluid is flowing. In engineering, the effect of pressure on viscosity is generally ignored.

[0061] Furthermore, step C specifically involves:

[0062] The fluid flow properties are determined by the ratio between inertial force and viscous force, and are described by the dimensionless Coulter Reynolds number Re. Under the working conditions of tangential flow in the air gap of the motor rotor, the functional relationship between the Reynolds number Re and the fluid viscosity is expressed by equation (5). Combining the influence of the working temperature T of the oil-filled motor mentioned above, the relationship between the Reynolds number Re and the motor's optimizable parameter function (multivariate function) is further obtained by equation (6).

[0063]

[0064] Re=Fun(ρ,ω,r,δ,μ(T([E]))) (6)

[0065] Where ρ is the fluid density, ω is the angular velocity, r is the rotor radius, δ is the air gap length, and μ is the fluid viscosity;

[0066] If Re is below 2000, the fluid flows in the same direction and is in a laminar state; if the flow velocity increases further, velocity fluctuations will occur in the flow; if Re is above 2300, it becomes a turbulent state (high-speed motors have small air gaps and high speeds, so their flow state is usually turbulent).

[0067] Furthermore, in step D, the friction coefficient C of the fluid in the turbulent state... f Calculated using equation (7);

[0068]

[0069] Where σ is the tangential stress of the fluid and v is the fluid flow velocity;

[0070] Since the tangential stress σ of the fluid represents the force at a point, the force F on the entire cylindrical surface of the air gap is obtained by integrating over the surface that generates the force, as shown in equation (8); the frictional torque T is obtained by combining equations (7) and (8). m The expression for the fluid friction loss P is given by equation (9), and the fluid friction loss P is obtained. m The analytical calculation model is shown in equation (10);

[0071] F=2σπrl (8)

[0072] T m =C f ρπω 2 r 4 l (9)

[0073] P m =C f ρπω 3 r 4 l (10)

[0074] Where l is the axial length.

[0075] Furthermore, in step E, the above analytical calculation parameters are input into the finite element simulation model as initial conditions, and the axial flow velocity of the fluid is added. The simulation results are compared with the above analytical calculation results (i.e., the results obtained using equation (10)). A correction coefficient K is introduced into the analytical calculation model equation (10) to obtain the analytical calculation model equation (11) considering the axial flow velocity.

[0076] P m =KC f ρπω 3 r 4 l (11)

[0077] Regarding the correction coefficient K and friction coefficient C in equation (11) f Determining equations (12) and (13) (combining equations (1) and (2), we know that the coefficient of friction C) f It is related to the operating temperature T and Reynolds number R of the oil-filled motor. e The coefficients related to the rotor radius r and the air gap length δ are expressed by equation (12); the correction coefficient K is related to the axial flow velocity V. l Other variables [M] are the variables in the motor's optimizable parameters that affect fluid dynamics. The relevant coefficients need to be determined in conjunction with actual engineering, and are expressed by equation (13). The correction coefficient K is changed by altering the axial flow velocity V in the finite element simulation. l Other variables [M] are used to obtain the fitted function curve, where [M] includes motor structural parameters related to fluid mechanics;

[0078] C f =Fun(T,Re,r,δ) (12)

[0079] K = Fun(V) l ,[M]) (13)

[0080] Combining equations (6), (11), (12), and (13), equation (14) yields the relationship between oil friction loss and the optimizable parameters of the motor (i.e., a multivariate function expression). This function simultaneously considers the operating temperature T of the oil-filled motor, the variable motor parameters (i.e., other variables [E]), and the axial flow velocity V. l (impact);

[0081] P m =Fun(T([E]),[E],V l ,[M]) (14)

[0082] Equation (14) combines with finite element simulation to determine the axial flow velocity V. l The influence of the operating temperature T of the oil-filled motor is taken into account in the calculation formula of oil friction loss, which effectively improves the accuracy of analytical calculation and the speed of multi-objective optimization of motor electromagnetic performance and oil friction loss.

[0083] Furthermore, the analytical calculation parameters include fluid density ρ, rotor radius r, angular velocity ω, motor optimizable parameters [E], air gap length δ, and axial length l.

[0084] The difference between conventional methods and the method of this invention:

[0085] Conventional methods employ different fixed surface friction coefficients C for flow modes with different Reynolds numbers. f The calculation formula (using cooling oil as the medium) is shown in equation (1); where δ is the air gap length and R is the air gap length. e K is the Reynolds number. c This is a calculation coefficient related to fluid materials, taken as 0.0325 in the oil-cooled environment of this paper. However, it is not applicable to scenarios where the Reynolds number changes significantly due to temperature variations, and it does not consider axial fluid flow. Axial fluid flow in the air gap will add vortex flow to the already turbulent tangential fluid flow, greatly affecting the surface friction coefficient C. f Furthermore, this effect also varies with the axial flow velocity.

[0086]

[0087] The method of this invention combines the temperature field with fluid finite element simulation and incorporates the surface friction coefficient C. f In the calculation, the following parameters were obtained: operating temperature T of the oil-filled motor, rotor radius r, Reynolds number Re, air gap length δ, and axial flow velocity V. l Formula (2) for calculating the friction coefficient of other variables [M]. The oil friction loss calculated by this friction coefficient also takes into account the changes in temperature and axial flow velocity.

[0088] KC f =Fun(T,R) e ,r,δ,V l ,[M]) (2)

[0089] like Figure 1 As shown, 1. First, the parameters affecting motor performance and temperature are used as inputs to establish a thermomagnetic coupling field, and the final system temperature and motor size parameters are obtained.

[0090] 2. By combining system temperature with fluid material properties, a thermal-fluid coupling field is established; by combining motor size with fluid material properties, the fluid flow state is obtained.

[0091] 3. Based on the thermal-fluid coupled field, a finite element simulation of fluid friction loss is performed, and the result of the finite element simulation is obtained as a; after obtaining the fluid flow state, an analytical calculation model of fluid friction loss is established, and the analytical calculation result is obtained as b.

[0092] 4. By comparing the simulation results with the analytical calculation results, a correction coefficient K is introduced into the analytical results to make the analytical results closer to the simulation results.

[0093] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0094] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for calculating rotor oil friction loss of a high-speed oil-filled motor, characterized in that: The method includes the following steps: A. Establish a finite element simulation model of the electromagnetic and temperature field coupling of the motor using finite element analysis software, and obtain the optimizable parameters of the motor using the finite element simulation model. The independent variable is the operating temperature of the oil-filled motor. The relationship is a multivariate function of the dependent variable; B. Based on the relationship between fluid viscosity and temperature, establish the relationship between fluid viscosity and the optimizable parameters of the motor in step A above. The composite function relationship; C. Based on the analytical mechanism of rotor air gap fluid and rotor friction loss, the dimensionless Couet Reynolds number is obtained under the working condition of tangential flow in the rotor air gap. The Reynolds number is further obtained by relating the fluid viscosity to the fluid viscosity obtained in step B above. With motor optimizable parameters The functional relationship is determined, and the flow state of the fluid is judged simultaneously. D. Since the formulas for fluid friction differ between laminar and turbulent flow, the appropriate formula (laminar or turbulent) must be determined based on the fluid flow state described in step C, combined with the Reynolds number from step C. The analytical expression yields the analytical calculation model for fluid friction loss; E. Perform a finite element simulation with the same parameters as the analytical calculation model described above, and set the axial flow velocity in the simulation model that was not considered in the analytical calculation. Compare the simulation model with the analytical calculation results in step D, and introduce a correction coefficient based on the analytical calculation model in step D. An analytical calculation model for oil friction loss considering axial flow was obtained; using the multivariate function extremum theory, the relationship between oil friction loss and the motor's optimizable parameters was derived. The changing relationship.

2. The method for calculating rotor oil friction loss of a high-speed oil-filled motor according to claim 1, characterized in that: Step A is as follows: Fluid viscosity varies with the operating temperature of the oil-filled motor The temperature field decreases as the temperature increases, so the influence of the motor's optimizable parameters on the temperature field needs to be considered first. By calculating the temperature field through finite element simulation, the motor's optimizable parameters are changed, and the relationship between the working temperature of the oil-filled motor and the motor's optimizable parameters is obtained, as shown in equation (3). （3） in, It is a multivariate functional relationship. This refers to the operating temperature of the oil-filled motor. These are the parameters that can be optimized for motors that affect their electromagnetic performance.

3. The method for calculating rotor oil friction loss of a high-speed oil-filled motor according to claim 1, characterized in that: In step B, the composite function relationship between fluid viscosity and the optimizable parameters of the motor in step A is established using equation (4); （4） in, For fluid viscosity, For the composite function of the motor's optimizable parameters, This refers to the operating temperature of the oil-filled motor.

4. The method for calculating rotor oil friction loss of a high-speed oil-filled motor according to claim 1, characterized in that: Step C specifically involves: The flow properties of a fluid are determined by the ratio between inertial forces and viscous forces, expressed by the dimensionless Couitton number. To describe, under the operating conditions of tangential flow in the air gap of the motor rotor, the Reynolds number... The functional relationship with fluid viscosity is expressed by equation (5); combined with the influence of the operating temperature T of the oil-filled motor mentioned above, the Reynolds number is further obtained by equation (6). The relationship between the motor and its optimized parameter functions; （5） （6） in, For fluid density, Angular velocity, The rotor radius is... The length of the air gap. For fluid viscosity, It is a multivariate functional relationship. The parameters for the motor can be optimized. This refers to the operating temperature of the oil-filled motor. like Below 2000, the fluid flows in the same direction, which is a laminar flow state; as the flow velocity increases further, velocity fluctuations will appear in the flow; if Above 2300, it becomes a turbulent state.

5. The method for calculating rotor oil friction loss of a high-speed oil-filled motor according to claim 4, characterized in that: In step D, the coefficient of friction of the fluid in turbulent state Calculated using equation (7); （7） in, For fluid tangential stress, For fluid flow velocity, For fluid density; Due to fluid tangential stress The force at the representative point is integrated over the air gap cylindrical surface that generates the force to obtain the force on the entire cylindrical surface. See equation (8); combining equations (7) and (8) yields the friction torque. The expression for the fluid friction loss is shown in equation (9); and the fluid friction loss is obtained. The analytical calculation model is shown in equation (10). (8) (9) (10) in, axial length The rotor radius is... ω is the angular velocity.

6. The method for calculating rotor oil friction loss of a high-speed oil-filled motor according to claim 5, characterized in that: In step E, the analytical calculation parameters are input into the finite element simulation model as initial conditions, and the axial flow velocity of the fluid is added. The simulation results are compared with the analytical calculation results, and a correction coefficient is introduced into the analytical calculation model (10). The analytical calculation model (11) considering axial flow velocity for oil friction loss is obtained; the analytical calculation parameters include fluid density. Rotor radius angular velocity Air gap length axial length and the motor's optimizable parameters ; (11) in, For fluid friction loss, The coefficient of friction of the fluid in turbulent conditions; Regarding the correction coefficient in equation (11) and the coefficient of friction Determine equations (12) and (13); correction coefficients By changing the axial flow velocity in the finite element simulation Other variables The fitted function curve is obtained, where It includes motor structural parameters related to fluid mechanics; （12） （13） in, It is a multivariate functional relationship. This refers to the operating temperature of the oil-filled motor. It is the Reynolds number; By combining equations (6), (11), (12), and (13), the relationship between oil friction loss and the change of motor optimizable parameters is obtained through equation (14). (14) Equation (14) combines with finite element simulation to determine the axial flow velocity. Operating temperature of oil-filled motor The impact is taken into account in the calculation formula of oil friction loss.

Images