A method for constructing a model of a semi-superconducting propulsion system

Abstract

This application provides a method for constructing a model of a semi-superconducting propulsion system, belonging to the technical field of aerospace electric propulsion. Specifically, it includes: establishing a dynamic mathematical sub-model of the semi-superconducting propulsion system containing shared rotor dynamic equations for the semi-superconducting motor and ducted fan; quantifying key coupling effect parameters in the shared rotor dynamic equations for the semi-superconducting motor and ducted fan and evaluating the impact of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system; constructing a cold source flow controller sub-model, which correlates the heat dissipation in the dynamic mathematical sub-model with the heat exchanger's heat exchange capacity in real time; dynamically predicting the cold source flow based on the current temperature field distribution and the real-time speed command signal of the semi-superconducting motor; and dynamically adjusting the cold source flow based on the predicted cold source flow. This application's processing method provides accurate modeling support for optimizing the temperature control strategy of actual semi-superconducting propulsion systems.

Description

Technical Field

[0001] This application relates to the field of aviation electric propulsion, and in particular to a method for constructing a model of a semi-superconducting propulsion system. Background Technology

[0002] Semi-superconducting propulsion systems combine conventional electromagnetic materials with superconducting materials, exhibiting advantages such as high power density and low loss. Their core lies in utilizing the zero-resistance characteristic of superconductors to achieve high current carrying capacity, thereby improving thrust output efficiency. This makes them particularly suitable for applications with stringent energy density requirements, such as electric aircraft and vertical takeoff and landing drones. However, existing design technologies still face the following key challenges:

[0003] Conventional models typically treat superconducting materials as idealized zero-resistance elements, neglecting their temperature dependence and dynamic response hysteresis. Under transient conditions, superconductors can experience localized overheating due to eddy current losses or delayed heat conduction, easily leading to performance degradation or even quench failure. Simultaneously, superconductors require maintenance at specific low temperatures, which conventional PID control or fixed-threshold temperature control strategies cannot adapt to rapidly changing thermal loads. During high-power output phases, the system may experience sudden temperature rises due to cooling response hysteresis, while excessive cooling at low loads wastes energy. Furthermore, there is a lack of ability to coordinate model accuracy and control real-time performance under dynamic conditions. PID stands for Proportional-Integral-Derivative, a widely used engineering control technique.

[0004] Traditional models typically treat propulsion system modeling and temperature control as independent components, failing to establish a closed-loop feedback mechanism. Therefore, a collaborative approach integrating dynamic modeling and intelligent temperature control is urgently needed to optimize system energy efficiency while ensuring the safe operation of the superconductor. Summary of the Invention

[0005] In view of this, this application provides a method for constructing a model of a semi-superconducting propulsion system, which solves the problems in the prior art, improves the realism of the simulation of the real semi-superconducting propulsion system by the model, and provides accurate modeling support for optimizing the temperature control strategy of the actual semi-superconducting propulsion system.

[0006] The method for constructing a model of a semi-superconducting propulsion system provided in this application adopts the following technical solution:

[0007] A method for constructing a model of a semi-superconducting propulsion system includes:

[0008] A dynamic mathematical sub-model of the semi-superconducting propulsion system is formed by establishing the stator voltage equation, electromagnetic torque equation, shared rotor dynamics equation of the semi-superconducting motor and ducted fan, and heat generation equation of the semi-superconducting motor. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan quantifies key coupling effect parameters. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan is used to evaluate the impact of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system. The key coupling effect parameters include the moment of inertia of the connecting shaft of the semi-superconducting motor and ducted fan, the rotational speed of the semi-superconducting motor, the electromagnetic torque of the semi-superconducting motor, the torque of the aerodynamic and mechanical load of the ducted fan, and the mechanical angular velocity of the semi-superconducting motor.

[0009] Based on the dynamic mathematical sub-model of the semi-superconducting propulsion system, a cold source flow control optimization strategy is constructed based on the coupling relationship between heat and cold source flow to form a cold source flow controller sub-model. The cold source flow controller sub-model correlates the heat dissipation in the dynamic mathematical sub-model with the heat exchanger's heat exchange capacity in real time. Based on the current temperature field distribution and the real-time speed command signal of the semi-superconducting motor, the cold source flow is dynamically predicted, and the cold source flow is dynamically adjusted according to the predicted cold source flow.

[0010] Optionally, the shared rotor dynamics equations for the semi-superconducting motor and the ducted fan are:

[0011] ;

[0012] in, , The moment of inertia of the shaft connecting the semi-superconducting motor and the ducted fan. Let be the moment of inertia of the ducted fan shaft. The moment of inertia of a semi-superconducting motor; The rotational speed of the semi-superconducting motor; For time; The transmission efficiency of the connecting shaft between the semi-superconducting motor and the ducted fan; The electromagnetic torque of a semi-superconducting motor; The torque of the ducted fan is the aerodynamic and mechanical load. The damping coefficient; This represents the mechanical angular velocity of the semi-superconducting motor.

[0013] Optionally, the steps for the cold source flow controller sub-model to predict the cold source flow include:

[0014] The heat of the semi-superconducting motor is segmented, and a mathematical expression for heat distribution is constructed.

[0015] The Newton-Raphson iteration method is used to set the ideal cold source outlet temperature and solve for the cold source flow rate by gradient.

[0016] The flow rate of the cold source is corrected by using the speed command of the semi-superconducting motor and environmental conditions.

[0017] Optionally, the mathematical expression for heat distribution is:

[0018] ;

[0019] ;

[0020] in, For the first Section of heat, The heat generated by the semi-superconducting motor For the first Section heat percentage coefficient; This represents the total number of segments; It is the heat decay factor.

[0021] Optionally, using the Newton-Raphson iteration method, the steps of setting the ideal cold source outlet temperature and calculating the cold source flow rate by gradient include:

[0022] Set up a cold source property parameter table to obtain property parameters in real time through interpolation of cold source temperature and pressure, i.e.:

[0023] ;

[0024] in, The specific heat capacity at constant pressure of the cold source; Represents a two-dimensional interpolation function; Given the current pressure of the cold source medium, These are the current temperatures of the cold source medium;

[0025] The outlet interface temperature is obtained by summing the segmented temperatures.

[0026] ;

[0027] in, For the first Section outlet cross-sectional temperature, For the first -1 section outlet section temperature, let , The inlet temperature of the cold source. For the first Next iteration mass flow rate guess. For the first Section of heat, This represents the total number of segments; For the first Specific heat capacity at constant pressure;

[0028] Calculate temperature error :

[0029] ;

[0030] in, For the ideal cold source outlet temperature, For the first Section outlet cross-sectional temperature;

[0031] judge Is it below the iteration threshold? If the flow rate is below the iteration threshold, the iteration converges, and the converged cold source flow rate is obtained. ,like If the value exceeds the iteration threshold, a gradient perturbation is applied to the mass flow rate guess, i.e.:

[0032] ;

[0033] ;

[0034] ;

[0035] in, For the first The mass flow gradient perturbation value in the next iteration. For the first Next iteration mass flow rate guess. No. Temperature disturbance value at the outlet section; These are the gradient coefficients; For the first Temperature disturbance value at the outlet section. No. The isobaric specific heat capacity perturbation value of the segment. For the first Section of heat.

[0036] Alternatively, a method for correcting the cold source flow rate using the speed command of the semi-superconducting motor and environmental conditions is as follows:

[0037] Calculate the prediction correction factor:

[0038] ;

[0039] Calculate the corrected cold source flow rate:

[0040] ;

[0041] in, To solve for the cold source flow rate using gradient gradation, To predict the correction factor; This represents the change in the rotational speed command of the semi-superconducting motor. This represents the actual change in rotational speed of the semi-superconducting motor. Due to environmental pressures, Ambient temperature; This represents the function for calculating the prediction correction factor. The form can be selected from empirical formulas or neural network data fitting. Minimum limit for cold source output; This is the corrected cold source flow rate.

[0042] Optionally, the modeling method for semi-superconducting propulsion systems also includes constructing an active fault response mechanism:

[0043] Data from virtual measurement points in the semi-superconducting motor and cooling circuit are collected to form a measurement data package. The measurement data packet This includes the temperature at each virtual measuring point, the actual rotational speed of the semi-superconducting motor, and the actual flow rate of the cold source;

[0044] For real-time acquired measurement data packets Feature extraction is performed to construct a temporal prediction sub-model based on a Long Short-Term Memory (LSTM) network. The temporal prediction sub-model is based on the previous time step. Measurement data packets Input: Current time; Output: Current time. Temperature of preset virtual measuring points Predicted values ​​and rate of change The predicted value;

[0045] Combined with the current moment Temperature of preset virtual measuring points Predicted values ​​and rate of change Based on the predicted values, a dynamic risk assessment function is established:

[0046] ;

[0047] in, for Real-time dynamic risk value; , These are the weighting coefficients; This is the upper limit of the safe temperature range; The maximum permissible rate of temperature rise;

[0048] Calculate the predicted risk value:

[0049] ;

[0050] in, To predict risk values, for Real-time dynamic risk value for Real-time dynamic risk value;

[0051] Based on the preset risk threshold range, the predicted risk values ​​are divided into normal level, warning level and emergency level from low to high.

[0052] If the fault is at the normal level, the proactive emergency response mechanism will maintain data monitoring and predictive diagnosis functions. If the fault is at the early warning level, a response strategy of adjusting the cold source flow and the speed and power of the semi-superconducting motor will be adopted. The cold source flow will be increased according to a preset ratio, and the maximum speed of the semi-superconducting motor will be reduced. If local phase overheating of the semi-superconducting motor still occurs after adjustment, the motor phase redundancy strategy will be adopted to stop power supply to the faulty phase. If the fault is at the emergency level, the cold source flow will be adjusted to the maximum, the maximum speed limit of the semi-superconducting motor will be set to the stop state, and power supply to the semi-superconducting motor will be stopped.

[0053] Optionally, the stator voltage equation for the semi-superconducting motor is:

[0054] ;

[0055] in, For semi-superconducting motors Shaft-stator voltage components, For semi-superconducting motors Shaft-stator voltage components; For the stator resistance of a semi-superconducting motor; Semi-superconducting motor Shaft stator current components, Semi-superconducting motor Shaft stator current components; Semi-superconducting motor Shaft stator inductance component, For semi-superconducting motors Shaft-stator inductance components; The angular velocity of a semi-superconducting motor; For permanent magnet flux linkages in semi-superconducting motors. For time;

[0056] The electromagnetic torque equation of a semi-superconducting motor is:

[0057] ;

[0058] in, The number of pole pairs of a semi-superconducting motor; It is the electromagnetic torque of a semi-superconducting motor.

[0059] In summary, this application includes the following beneficial technical effects:

[0060] The dynamic mathematical sub-model of the semi-superconducting propulsion system in this application includes shared rotor dynamic equations for the semi-superconducting motor and the ducted fan. It directly correlates parameters such as the mechanical angular velocity and electromagnetic torque of the semi-superconducting motor, and quantifies the dynamic coupling effects, including the moment of inertia of the connecting shaft between the semi-superconducting motor and the ducted fan, and the aerodynamic and mechanical loads of the ducted fan. This allows the dynamic mathematical sub-model to accurately predict transient interactions between multiple components, significantly improving the stability of the semi-superconducting propulsion system model under scenarios of sudden load changes or command step jumps, reducing the risk of response divergence, and providing a high-precision physical basis for temperature control optimization and fault protection.

[0061] The cold source flow controller sub-model in this application can dynamically correlate the temperature field distribution, the rotational speed command of the semi-superconducting motor, and environmental parameters to generate a predicted cold source flow. This strategy significantly improves the energy efficiency ratio and energy utilization rate while ensuring that the superconductor always operates within a safe temperature range.

[0062] By collecting multi-point data in real time through a distributed sensing network and combining it with a predictive diagnostic model, the risk of hysteresis can be identified in advance, and adaptive response strategies can be triggered in stages. From early warning-level preventive intervention to emergency-level controllable degradation procedures, a closed-loop system for rapid fault response is achieved. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating the method for constructing a model of a semi-superconducting propulsion system according to an embodiment of this application. Detailed Implementation

[0065] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0066] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0068] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0069] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0070] This application provides a method for constructing a model of a semi-superconducting propulsion system.

[0071] like Figure 1 As shown, a method for constructing a model of a semi-superconducting propulsion system includes:

[0072] A dynamic mathematical sub-model of the semi-superconducting propulsion system is constructed: the stator voltage equation, electromagnetic torque equation, shared rotor dynamics equation of the semi-superconducting motor and ducted fan, and heat generation equation of the semi-superconducting motor are established to form a dynamic mathematical sub-model of the semi-superconducting propulsion system. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan quantifies key coupling effect parameters. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan is used to evaluate the impact of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system. The key coupling effect parameters include the moment of inertia of the connecting shaft of the semi-superconducting motor and ducted fan, the rotational speed of the semi-superconducting motor, the electromagnetic torque of the semi-superconducting motor, the torque of the aerodynamic and mechanical load of the ducted fan, and the mechanical angular velocity of the semi-superconducting motor.

[0073] Constructing a cold source flow controller sub-model: Based on the dynamic mathematical sub-model of the semi-superconducting propulsion system, a cold source flow control optimization strategy is constructed based on the coupling relationship between heat and cold source flow to form a cold source flow controller sub-model. The cold source flow controller sub-model correlates the heat dissipation in the dynamic mathematical sub-model with the heat exchanger's heat exchange capacity in real time. Based on the current temperature field distribution and the real-time speed command signal of the semi-superconducting motor, the cold source flow is dynamically predicted, and the cold source flow is dynamically adjusted according to the predicted cold source flow.

[0074] This application improves modeling accuracy and addresses the shortcomings of multi-component separate models in predicting dynamic interaction relationships by establishing a dynamic mathematical sub-model that includes the shared rotor dynamics equations of the semi-superconducting motor and the ducted fan. Furthermore, the constructed cold source flow controller sub-model can dynamically predict the cold source flow based on the current temperature field distribution and the real-time speed command signal of the semi-superconducting motor, and dynamically adjust the cold source flow according to the predicted cold source flow. This ensures that the temperature of the semi-superconducting propulsion system model is below the safe temperature threshold, thereby optimizing energy consumption and improving the dynamic response quality of temperature control.

[0075] The stator voltage equation for a semi-superconducting motor is:

[0076] ;

[0077] in, For semi-superconducting motors Shaft-stator voltage components, For semi-superconducting motors Shaft-stator voltage components; The stator resistance of the semi-superconducting motor is given. The stator temperature of the semi-superconducting motor is less than or equal to the superconducting critical temperature. The maximum current that the semi-superconducting propulsion system model can carry is assumed to be lower than the superconducting critical current, i.e., under superconducting conditions. =0, otherwise it is a non-zero but very small variable; For semi-superconducting motors Shaft stator current components, For semi-superconducting motors Shaft stator current components; For semi-superconducting motors Shaft stator inductance component, For semi-superconducting motors Shaft-stator inductance components; The angular velocity of a semi-superconducting motor; For permanent magnet flux linkages in semi-superconducting motors. For time.

[0078] The electromagnetic torque equation of a semi-superconducting motor is:

[0079] ;

[0080] in, The number of pole pairs of a semi-superconducting motor; It is the electromagnetic torque of a semi-superconducting motor.

[0081] By combining the mechanical motion equations of the semi-superconducting motor and the rotor power balance equations of the ducted fan, we obtain the shared rotor dynamics equations for the semi-superconducting motor and the ducted fan. Specifically:

[0082] The mechanical equations of motion for a semi-superconducting motor are:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] in, The moment of inertia of a semi-superconducting motor. The mechanical angular velocity of the semi-superconducting motor; The torque of the ducted fan is the aerodynamic and mechanical load. The electromagnetic torque of a semi-superconducting motor; The damping coefficient; The rotational speed of the semi-superconducting motor; The rotor position angle of the semi-superconducting motor. For time, It is an extreme logarithm.

[0088] The ducted fan rotor power balance equation is:

[0089] ;

[0090] in, This refers to the ducted fan speed; The transmission efficiency of the connecting shaft between the semi-superconducting motor and the ducted fan; Input torque to the ducted fan; This refers to the aerodynamic load torque of the ducted fan. Let be the moment of inertia of the ducted fan shaft.

[0091] The shared rotor dynamics equations for the semi-superconducting motor and the ducted fan are:

[0092] ;

[0093] in, , The moment of inertia of the shaft connecting the semi-superconducting motor and the ducted fan. Let be the moment of inertia of the ducted fan shaft. The moment of inertia of a semi-superconducting motor; The rotational speed of the semi-superconducting motor; For time; The transmission efficiency of the connecting shaft between the semi-superconducting motor and the ducted fan; The electromagnetic torque of a semi-superconducting motor; The torque of the ducted fan is the aerodynamic and mechanical load. The damping coefficient; This represents the mechanical angular velocity of the semi-superconducting motor.

[0094] Regarding the construction of the heat generation equation for a semi-superconducting motor: the heat generation of a semi-superconducting motor mainly includes eddy current losses in the rotor permanent magnet, stator core losses, and rotor surface friction, among other factors. This application simplifies the heat generation equation for a semi-superconducting motor to:

[0095] ;

[0096] in, The heat generated by the semi-superconducting motor; This is an empirical interpolation formula related to the rotational speed of a semi-superconducting motor.

[0097] The steps for the cold source flow controller sub-model to predict the cold source flow include:

[0098] The heat of the semi-superconducting motor is segmented, and a mathematical expression for heat distribution is constructed.

[0099] The mathematical expression for heat distribution is:

[0100] ;

[0101] ;

[0102] in, For the first Section of heat, The heat generated by the semi-superconducting motor For the first Section heat percentage coefficient; This represents the total number of segments; It is the heat decay factor.

[0103] The Newton-Raphson iteration method is used, with an ideal cold source outlet temperature set, and the cold source flow rate is calculated using gradients; specifically, this includes:

[0104] Set up a table of liquid nitrogen physical property parameters for the cold source, and obtain the physical property parameters in real time by interpolating the temperature and pressure of the cold source.

[0105] ;

[0106] in, The specific heat capacity at constant pressure of the cold source; Represents a two-dimensional interpolation function; Given the current pressure of the cold source medium, These are the current temperatures of the cold source medium;

[0107] The outlet interface temperature is obtained by summing the segmented temperatures.

[0108] ;

[0109] in, For the first Section outlet cross-sectional temperature, For the first -1 section outlet section temperature, let , The inlet temperature of the cold source. For the first Next iteration mass flow rate guess. For the first Section of heat, This represents the total number of segments; For the first Specific heat capacity at constant pressure;

[0110] Calculate temperature error :

[0111] ;

[0112] in, For the ideal cold source outlet temperature, For the first Section outlet cross-sectional temperature;

[0113] judge Is it below the iteration threshold? If the flow rate is below the iteration threshold, the iteration converges, and the converged cold source flow rate is obtained. ,like If the value exceeds the iteration threshold, a gradient perturbation is applied to the mass flow rate guess, i.e.:

[0114] ;

[0115] ;

[0116] ;

[0117] in, For the first The mass flow gradient perturbation value in the next iteration. For the first Next iteration mass flow rate guess. No. Temperature disturbance value at the outlet section; These are the gradient coefficients; For the first Temperature disturbance value at the outlet section. No. The isobaric specific heat capacity perturbation value of the segment. For the first Section of heat.

[0118] The cold source flow rate is corrected using the speed command of the semi-superconducting motor and environmental conditions; specifically including:

[0119] Calculate the prediction correction factor:

[0120] ;

[0121] Calculate the corrected cold source flow rate:

[0122] ;

[0123] in, To solve for the cold source flow rate using gradient gradation, To predict the correction factor; This represents the change in the rotational speed command of the semi-superconducting motor. This represents the actual change in rotational speed of the semi-superconducting motor. Due to environmental pressures, Ambient temperature; This represents the function for calculating the prediction correction factor. The form can be selected from empirical formulas or neural network data fitting. Minimum limit for cold source output; This is the corrected cold source flow rate.

[0124] The modeling method for semi-superconducting propulsion systems also includes constructing an active fault response mechanism:

[0125] Data from virtual measurement points in the semi-superconducting motor and cooling circuit are collected to form a measurement data package. The measurement data packet This includes the temperature of each virtual measuring point, the actual speed of the semi-superconducting motor, and the actual flow rate of the cold source. The virtual measuring points can be distributed at each phase winding of the semi-superconducting motor, the inlet, middle section, and outlet of the cooling circuit, etc.

[0126] For real-time acquired measurement data packets Feature extraction is performed to construct a temporal prediction sub-model based on a Long Short-Term Memory (LSTM) network. The temporal prediction sub-model is based on the previous time step. Measurement data packets Input: Current time; Output: Current time. Temperature of preset virtual measuring points Predicted values ​​and rate of change The predicted value;

[0127] Combined with the current moment Temperature of preset virtual measuring points Predicted values ​​and rate of change Based on the predicted values, a dynamic risk assessment function is established:

[0128] ;

[0129] in, for Real-time dynamic risk value; , These are the weighting coefficients; This is the upper limit of the safe temperature range; The maximum permissible rate of temperature rise;

[0130] Calculate the predicted risk value:

[0131] ;

[0132] in, To predict risk values, for Real-time dynamic risk value for Real-time dynamic risk value;

[0133] Based on the preset risk threshold range, the predicted risk values ​​are divided into normal level, warning level and emergency level from low to high.

[0134] If the fault is at the normal level, the proactive emergency response mechanism will maintain data monitoring and predictive diagnosis functions. If the fault is at the early warning level, a response strategy of adjusting the cold source flow and the speed and power of the semi-superconducting motor will be adopted. The cold source flow will be increased according to a preset ratio, and the maximum speed of the semi-superconducting motor will be reduced. If local phase overheating of the semi-superconducting motor still occurs after adjustment, the motor phase redundancy strategy will be adopted to stop power supply to the faulty phase. If the fault is at the emergency level, the cold source flow will be adjusted to the maximum, the maximum speed limit of the semi-superconducting motor will be set to the stop state, and power supply to the semi-superconducting motor will be stopped.

[0135] This application establishes a proactive emergency response mechanism to overcome the limitations of traditional passive protection based on data collection and judgment. It uses real-time monitoring data from a distributed sensing network to predict overheating and overload trends online, enabling early assessment of safety conditions. Through a multi-level adaptive response strategy, it achieves a paradigm shift from passive alarm to proactive intervention.

[0136] The working principle of the semi-superconducting propulsion system model constructed in this application is as follows: the semi-superconducting motor efficiently converts the input electrical energy into mechanical torque to drive the ducted fan to rotate. Both share the same rotor system to ensure real-time synchronization of parameters such as angular velocity and torque. The ducted fan generates rotational motion, accelerating the inlet airflow and producing directional thrust. The semi-superconducting propulsion system model receives the speed command signal from the semi-superconducting motor and achieves speed closed-loop control through multi-stage PI control. The cold source flow controller sub-model senses the stator temperature field distribution of the semi-superconducting motor, cold source pipeline measurement point data, environmental parameters, and command signal change trends in real time, dynamically calculates prediction correction factors, and obtains real-time cold source flow. Furthermore, the semi-superconducting propulsion system model monitors and predicts alarm signals in real time, automatically triggering a graded response strategy under abnormal operating conditions to actively adjust system parameters, achieving coordinated optimization of system energy efficiency, dynamic response, and safety margin. PI stands for Proportional-Integral, and PI control is a linear control method based on proportional-integral regulation.

[0137] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of constructing a semi-superconducting electric propulsion system model, characterized by, include: A dynamic mathematical sub-model of the semi-superconducting propulsion system is formed by establishing the stator voltage equation, electromagnetic torque equation, shared rotor dynamics equation of the semi-superconducting motor and ducted fan, and heat generation equation of the semi-superconducting motor. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan quantifies key coupling effect parameters. The shared rotor dynamics equation of the semi-superconducting motor and ducted fan is used to evaluate the impact of each key coupling effect parameter on the overall performance of the semi-superconducting propulsion system. The key coupling effect parameters include the moment of inertia of the connecting shaft of the semi-superconducting motor and ducted fan, the rotational speed of the semi-superconducting motor, the electromagnetic torque of the semi-superconducting motor, the torque of the aerodynamic and mechanical load of the ducted fan, and the mechanical angular velocity of the semi-superconducting motor. Based on the dynamic mathematical sub-model of the semi-superconducting propulsion system, a cold source flow control optimization strategy is constructed based on the coupling relationship between heat and cold source flow to form a cold source flow controller sub-model. The cold source flow controller sub-model associates the heat dissipation in the dynamic mathematical sub-model with the heat exchanger's heat exchange capacity in real time. Based on the current temperature field distribution and the real-time speed command signal of the semi-superconducting motor, the cold source flow is dynamically predicted, and the cold source flow is dynamically adjusted according to the predicted cold source flow. The shared rotor dynamics equations for the semi-superconducting motor and the ducted fan are: ； in, , The moment of inertia of the shaft connecting the semi-superconducting motor and the ducted fan. Let be the moment of inertia of the ducted fan shaft. The moment of inertia of a semi-superconducting motor; The rotational speed of the semi-superconducting motor; For time; The transmission efficiency of the connecting shaft between the semi-superconducting motor and the ducted fan; The electromagnetic torque of a semi-superconducting motor; The torque of the ducted fan is the aerodynamic and mechanical load. The damping coefficient; The mechanical angular velocity of the semi-superconducting motor; The steps for the cold source flow controller sub-model to predict the cold source flow include: The heat of the semi-superconducting motor is segmented, and a mathematical expression for heat distribution is constructed. The Newton-Raphson iteration method is used to set the ideal cold source outlet temperature and solve for the cold source flow rate by gradient. The flow rate of the cold source is corrected by using the speed command of the semi-superconducting motor and environmental conditions; The mathematical expression for heat distribution is: ； ； in, For the first Section of heat, The heat generated by the semi-superconducting motor For the first Section heat percentage coefficient; This represents the total number of segments; It is the heat decay factor; Using Newton's iterative method, the steps for setting the ideal cold source outlet temperature and calculating the cold source flow rate by gradient include: Set up a cold source property parameter table to obtain property parameters in real time through interpolation of cold source temperature and pressure, i.e.: ； in, The specific heat capacity at constant pressure of the cold source; Represents a two-dimensional interpolation function; Given the current pressure of the cold source medium, These are the current temperatures of the cold source medium; The outlet interface temperature is obtained by summing the segmented temperatures. ； in, For the first Section outlet cross-sectional temperature, For the first -1 section outlet section temperature, let , The inlet temperature of the cold source. For the first Next iteration mass flow rate guess For the first Section of heat, This represents the total number of segments; For the first Specific heat capacity at constant pressure; Calculate temperature error : ； in, For the ideal cold source outlet temperature, For the first Section outlet cross-sectional temperature; judge Is it below the iteration threshold? If the flow rate is below the iteration threshold, the iteration converges, and the converged cold source flow rate is obtained. ,like If the value exceeds the iteration threshold, a gradient perturbation is applied to the mass flow rate guess, i.e.: ； ； ； in, For the first The mass flow gradient perturbation value in the next iteration. For the first Next iteration mass flow rate guess No. Temperature disturbance value at the outlet section; These are the gradient coefficients; For the first Temperature disturbance value at the outlet section. No. The isobaric specific heat capacity perturbation value of the segment. For the first Sectional heat; The method for correcting the cold source flow rate using the speed command of the semi-superconducting motor and environmental conditions is as follows: Calculate the prediction correction factor: ； Calculate the corrected cold source flow rate: ； in, To solve for the cold source flow rate using gradient gradation, To predict the correction factor; This represents the change in the rotational speed command of the semi-superconducting motor. This represents the actual change in rotational speed of the semi-superconducting motor. Due to environmental pressures, Ambient temperature; This represents the function for calculating the prediction correction factor. The form can be selected from empirical formulas or neural network data fitting. Minimum limit for cold source output; This is the corrected cold source flow rate.

2. The method for constructing a semi-superconducting propulsion system model according to claim 1, characterized in that, The modeling method for semi-superconducting propulsion systems also includes constructing an active fault response mechanism: Data from virtual measurement points in the semi-superconducting motor and cooling circuit are collected to form a measurement data package. The measurement data packet This includes the temperature at each virtual measuring point, the actual rotational speed of the semi-superconducting motor, and the actual flow rate of the cold source; For real-time acquired measurement data packets Feature extraction is performed to construct a temporal prediction sub-model based on a Long Short-Term Memory (LSTM) network. The temporal prediction sub-model is based on the previous time step. Measurement data packets Input: Current time; Output: Current time. Temperature of preset virtual measuring points Predicted values ​​and rate of change The predicted value; Combined with the current moment Temperature of preset virtual measuring points Predicted values ​​and rate of change Based on the predicted values, a dynamic risk assessment function is established: ； in, for Real-time dynamic risk value; , These are the weighting coefficients; This is the upper limit of the safe temperature range; The maximum permissible rate of temperature rise; Calculate the predicted risk value: ； in, To predict risk values, for Real-time dynamic risk value for Real-time dynamic risk value; Based on the preset risk threshold range, the predicted risk values ​​are divided into normal level, warning level and emergency level from low to high. If the fault is at the normal level, the proactive emergency response mechanism will maintain data monitoring and predictive diagnosis functions. If the fault is at the early warning level, a response strategy of adjusting the cold source flow and the speed and power of the semi-superconducting motor will be adopted. The cold source flow will be increased according to a preset ratio, and the maximum speed of the semi-superconducting motor will be reduced. If local phase overheating of the semi-superconducting motor still occurs after adjustment, the motor phase redundancy strategy will be adopted to stop power supply to the faulty phase. If the fault is at the emergency level, the cold source flow will be adjusted to the maximum, the maximum speed limit of the semi-superconducting motor will be set to the stop state, and power supply to the semi-superconducting motor will be stopped.

3. The method for constructing a semi-superconducting propulsion system model according to claim 1, characterized in that, The stator voltage equation of the semi-superconducting motor is: ； in, For semi-superconducting motors Shaft-stator voltage components, For semi-superconducting motors Shaft-stator voltage components; For the stator resistance of a semi-superconducting motor; For semi-superconducting motors Shaft stator current components, For semi-superconducting motors Shaft stator current components; For semi-superconducting motors Shaft stator inductance component, Semi-superconducting motor Shaft-stator inductance components; The angular velocity of a semi-superconducting motor; For permanent magnet flux linkages in semi-superconducting motors. For time; The electromagnetic torque equation of a semi-superconducting motor is: ； in, The number of pole pairs of a semi-superconducting motor; It is the electromagnetic torque of a semi-superconducting motor.

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