CN-122018348-A - Method for constructing semi-superconducting propulsion system model

CN122018348ACN 122018348 ACN122018348 ACN 122018348ACN-122018348-A

Abstract

The application provides a method for constructing a semi-superconducting propulsion system model, which belongs to the technical field of aviation electric propulsion, and specifically comprises the steps of establishing a dynamic mathematical submodel of the semi-superconducting propulsion system comprising a shared rotor dynamics equation of a semi-superconducting motor and a ducted fan, quantifying key coupling effect parameters by the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan, evaluating the influence of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system, constructing a cold source flow controller submodel, and dynamically predicting cold source flow based on the current temperature field distribution and a rotational speed command signal of the real-time semi-superconducting motor by the cold source flow controller submodel, and dynamically adjusting the cold source flow according to the predicted cold source flow. By the processing scheme, accurate modeling support is provided for optimizing the temperature control strategy of the actual semi-conductive propulsion system.

Inventors

XIAO BING
CHEN HAO
HU SIYUAN
WANG YE
JI CHUNSHENG

Assignees

太行国家实验室

Dates

Publication Date: 20260512
Application Date: 20260416

Claims (8)

1. A method of modeling a semi-superconducting propulsion system, comprising: Establishing a stator voltage equation of a semi-superconducting motor, an electromagnetic torque equation of the semi-superconducting motor, a shared rotor dynamics equation of the semi-superconducting motor and a ducted fan and a heat equation generated by the semi-superconducting motor to form a dynamic mathematical submodel of the semi-superconducting propulsion system, wherein the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan quantifies key coupling effect parameters, and the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan is used for evaluating the influence of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system, and the key coupling effect parameters comprise the rotational inertia of a connecting shaft of the semi-superconducting motor and the ducted fan, the rotational speed of the semi-superconducting motor, the electromagnetic torque of the semi-superconducting motor, the pneumatic and mechanical load torque of the ducted fan and the mechanical angular speed of the semi-superconducting motor; And constructing a cold source flow control optimization strategy based on a heat and cold source flow coupling relation on the basis of a power mathematical submodel of the semi-superconducting propulsion system to form a cold source flow controller submodel, wherein the cold source flow controller submodel is used for correlating heat dissipation in the power mathematical submodel with heat exchange capacity of a heat exchanger in real time, dynamically predicting the cold source flow based on current temperature field distribution and a rotating speed command signal of a real-time semi-superconducting motor, and dynamically regulating the cold source flow according to the predicted cold source flow.
2. The method of claim 1, wherein the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan is: ; Wherein, the , The rotational inertia of the connecting shaft of the semi-superconducting motor and the ducted fan, For the rotational inertia of the ducted fan shaft, The rotary inertia of the semi-superconducting motor is adopted; The rotation speed of the semi-superconducting motor; Time is; the transmission efficiency of the connecting shaft of the semi-superconducting motor and the ducted fan is improved; is the electromagnetic torque of the semi-superconducting motor; Torque for pneumatic and mechanical loads of the ducted fan; is a damping coefficient; is the mechanical angular velocity of the semi-superconducting motor.
3. The method of constructing a semi-superconducting propulsion system model according to claim 1, wherein the step of predicting the cold source flow by the cold source flow controller submodel includes: carrying out sectional treatment on the heat of the semi-superconducting motor to construct a heat distribution mathematical expression; Setting an ideal cold source outlet temperature by adopting a Newton iteration method, and solving the cold source flow in a gradient way; And correcting the cold source flow by utilizing the rotating speed instruction and the environmental condition of the semi-superconducting motor.
4. A method of modeling a semi-superconducting propulsion system according to claim 3, wherein the mathematical expression of the heat distribution is: ; ; Wherein, the Is the first The heat quantity of the section is equal to the heat quantity of the section, Is the heat generated by the semi-superconducting motor, Is the first The heat duty cycle of the segment; Is the total number of segments; Is a thermal decay factor.
5. The method for constructing a model of a semi-superconducting propulsion system according to claim 4, wherein the step of setting an ideal cold source outlet temperature and solving the cold source flow in a gradient manner by adopting a newton iteration method comprises the steps of: setting a cold source physical property parameter table, and obtaining physical property parameters in real time through cold source temperature and pressure interpolation, namely: ; Wherein, the The constant pressure specific heat capacity is used as a cold source; representing a two-dimensional interpolation function; As the pressure of the medium of the current cold source, Respectively the current cold source medium temperature; calculating the sectional temperature accumulation to obtain the outlet interface temperature: ; Wherein, the Is the first The temperature of the section outlet cross section, Is the first -1 Section outlet section temperature, let , Is the inlet temperature of the cold source, Is the first The mass flow guess value is iterated for a number of times, Is the first The heat quantity of the section is equal to the heat quantity of the section, Is the total number of segments; is the first Section constant pressure specific heat capacity; Calculating temperature error : ; Wherein, the For the ideal outlet temperature of the cold source, Is the first Section outlet cross-section temperature; Judging Whether or not it is below the iteration threshold, if Below the iteration threshold the iteration converges, obtaining the flow of the convergent cold source If (if) And (3) performing gradient disturbance on the guess value of the mass flow above the iteration threshold, namely: ; ; ; Wherein, the Is the first The mass flow gradient disturbance value is iterated for the next time, Is the first The mass flow guess value is iterated for a number of times, First, the A section outlet section temperature disturbance value; Is a gradient coefficient; is the first The temperature disturbance value of the section outlet section, First, the The constant pressure specific heat capacity disturbance value of the segment, Is the first Heat of the segment.
6. The method for constructing a semi-superconducting propulsion system model according to claim 3, wherein the method for correcting the flow of the cold source by using the rotating speed command and the environmental condition of the semi-superconducting motor comprises the following steps: calculating a prediction correction factor: ; calculating the corrected cold source flow: ; Wherein, the The flow of the cold source is solved for the gradient, Is a predictive correction factor; is the rotating speed command variable quantity of the semi-superconducting motor, Is the actual rotational speed variation of the semi-superconducting motor, In the event of an ambient pressure level, Is ambient temperature; Representing the predictive modifier calculation function, The form of (a) is selected from an empirical formula or neural network data fitting, The minimum limit is output for the cold source; And the corrected cold source flow is obtained.
7. The method for modeling a semi-superconducting propulsion system according to claim 1, wherein, the method for constructing the semi-superconducting propulsion system model further comprises the steps of constructing a fault active emergency mechanism: Collecting virtual measuring point data of semi-superconducting motor and cooling loop to form measuring data packet The measurement data packet The method comprises the steps of temperature of each virtual measuring point, actual rotating speed of the semi-superconducting motor and actual cold source flow; for real-time collected measurement data packet Performing feature extraction to construct a time sequence prediction sub-model based on a long-short-time memory network, wherein the time sequence prediction sub-model is at the previous moment Is a measurement data packet of (a) To input and output the current time Temperature of preset virtual measuring point Predicted value and rate of change of (2) Is a predicted value of (2); In combination with the current time Temperature of preset virtual measuring point Predicted value and rate of change of (2) Establishing a dynamic risk assessment function: ; Wherein, the Is that A moment dynamic risk value; 、 Is a weight coefficient; is a safe upper limit of temperature; Is the maximum allowable temperature rise rate; calculating a predicted risk value: ; Wherein, the In order to predict the risk value(s), Is that The time of day dynamic risk value is calculated, Is that A moment dynamic risk value; dividing the predicted risk value into a normal level, an early warning level and an emergency level in sequence from low to high according to a preset risk threshold range; If the fault active emergency mechanism is at a normal level, the data monitoring and predictive diagnosis functions are kept, if the fault active emergency mechanism is at an early warning level, the cold source flow rate adjustment and the semi-superconducting motor rotating speed and power adjustment response strategy are adopted, the cold source flow rate is increased according to a preset proportion, the maximum rotating speed of the semi-superconducting motor is reduced, if the local phase of the semi-superconducting motor is overheated after adjustment, the motor phase redundancy strategy is adopted, the power supply to the fault phase is stopped, if the fault active emergency mechanism is at an emergency level, the cold source flow rate is adjusted to the maximum, the maximum rotating speed limit of the semi-superconducting motor is set to be in a stop state, and the power supply to the semi-superconducting motor is stopped.
8. The method of modeling a semi-superconducting propulsion system according to claim 1, wherein the stator voltage equation of the semi-superconducting motor is: ; Wherein, the Is a semi-superconducting motor The voltage component of the shaft stator, Is a semi-superconducting motor A shaft stator voltage component; Is the stator resistance of the semi-superconducting motor; is a semi-superconducting motor The component of the shaft stator current, Is a semi-superconducting motor A shaft stator current component; is a semi-superconducting motor The inductance component of the shaft stator, Is a semi-superconducting motor An axial stator inductance component; is the angular velocity of the semi-superconducting motor; is the permanent magnet flux linkage of the semi-superconducting motor, Time is; The electromagnetic torque equation of the semi-superconducting motor is: ; Wherein, the Is the pole pair number of the semi-superconducting motor; Is the electromagnetic torque of the semi-superconducting motor.

Description

Method for constructing semi-superconducting propulsion system model Technical Field The application relates to the field of aviation electric propulsion, in particular to a method for constructing a semi-superconducting propulsion system model. Background The semi-superconducting propulsion system combines the conventional electromagnetic material and the superconducting material, and has the advantages of high power density, low loss and the like. The core is that the zero resistance characteristic of the superconductor is utilized to realize heavy current bearing, thereby improving the thrust output efficiency, and the method is particularly suitable for application scenes of electric airplanes, vertical take-off and landing unmanned aerial vehicles and the like with strict requirements on energy density. The prior art design still has the following key problems: Conventional models generally treat superconducting materials as idealized zero-resistance elements, without taking into account their temperature dependence and dynamic response hysteresis characteristics. Under transient working conditions, the superconductor is locally overheated due to eddy current loss or heat conduction delay, and performance degradation and even quench risks are easily caused. Meanwhile, superconductors need to be maintained in a specific low temperature environment, and conventional PID control or fixed threshold temperature control strategies cannot accommodate rapidly changing thermal loads. In the high power output stage, the system may cause local temperature sudden rise due to lag of cooling response, and at low load, excessive refrigeration wastes energy, and the collaborative optimization capability of model precision and control instantaneity under dynamic working conditions is lacking, wherein PID is fully called pro-Integral-Derivative, chinese is interpreted as Proportional-Integral-Derivative, and the PID is a widely applied engineering control technology. Conventional models typically treat propulsion system modeling and temperature control as independent links, which do not form a closed-loop feedback mechanism. Therefore, a synergistic method of integrating dynamic modeling and intelligent temperature control is needed to realize the optimization of the energy efficiency of the system on the premise of ensuring the safe operation of the superconductor. Disclosure of Invention In view of the above, the application provides a method for constructing a semi-superconducting propulsion system model, which solves the problems in the prior art, improves the authenticity of the semi-superconducting propulsion system model to the simulation of a real semi-superconducting propulsion system, and provides accurate modeling support for optimizing the temperature control strategy of the real semi-superconducting propulsion system. The application provides a method for constructing a semi-superconducting propulsion system model, which adopts the following technical scheme: A semi-superconducting propulsion system model construction method comprises the following steps: Establishing a stator voltage equation of a semi-superconducting motor, an electromagnetic torque equation of the semi-superconducting motor, a shared rotor dynamics equation of the semi-superconducting motor and a ducted fan and a heat equation generated by the semi-superconducting motor to form a dynamic mathematical submodel of the semi-superconducting propulsion system, wherein the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan quantifies key coupling effect parameters, and the shared rotor dynamics equation of the semi-superconducting motor and the ducted fan is used for evaluating the influence of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system, and the key coupling effect parameters comprise the rotational inertia of a connecting shaft of the semi-superconducting motor and the ducted fan, the rotational speed of the semi-superconducting motor, the electromagnetic torque of the semi-superconducting motor, the pneumatic and mechanical load torque of the ducted fan and the mechanical angular speed of the semi-superconducting motor; And constructing a cold source flow control optimization strategy based on a heat and cold source flow coupling relation on the basis of a power mathematical submodel of the semi-superconducting propulsion system to form a cold source flow controller submodel, wherein the cold source flow controller submodel is used for correlating heat dissipation in the power mathematical submodel with heat exchange capacity of a heat exchanger in real time, dynamically predicting the cold source flow based on current temperature field distribution and a rotating speed command signal of a real-time semi-superconducting motor, and dynamically regulating the cold source flow according to the predicted cold source flow. Optionally, the shared roto