A distributed electric propulsion flow field tight coupling effect comprehensive management system

Abstract

The application relates to the technical field of distributed electric propulsion aircraft, and discloses a distributed electric propulsion flow field tight coupling effect comprehensive management system, which comprises a multi-physical field sensing module, a tight coupling analysis module, an adaptive interpolation module, an algorithm optimization matching module, a dynamic compensation execution module and a closed-loop feedback module; by setting a spectrum arbitrator, when the flow field tight coupling effect is managed in real time, by formulating electromagnetic-flow field base frequency matching standard parameters and dynamically setting spectrum tolerance threshold values for different flight conditions, the accuracy of multi-physical field data fusion is ensured, meanwhile, the spectrum characteristics of electromagnetic harmonic components and flow field pulsation signals are cross-verified in real time, whether aliasing interference occurs between the propeller tip vortex frequency and the electromagnetic base frequency can be diagnosed in real time, the accuracy of flow field distortion index calculation is ensured, and vortex core positioning error is reduced.

Description

Technical Field

[0001] This invention relates to the field of distributed electric propulsion aircraft technology, specifically to a comprehensive management system for the tightly coupled effect of distributed electric propulsion flow field. Background Technology

[0002] Electric propulsion systems, also known as electric rocket engines, are devices that generate thrust without relying on chemical combustion. Their advantages include eliminating the need for solid and liquid fuels, eliminating complex storage tanks, pipelines, engine combustion chambers, nozzles, and corresponding cooling mechanisms, and reducing the fuel load on spacecraft. For a long time, due to the continuous growth of air transport, emissions of carbon oxides, nitrogen oxides, and noise from aviation have been increasing, necessitating new power methods. Electric propulsion technology can reduce carbon oxide, nitrogen oxide, and noise emissions, promoting the achievement of green and environmentally friendly development goals. Furthermore, electric propulsion technology can improve energy efficiency and enhance flight economics.

[0003] Currently, due to the combined effects of electromagnetic interference from multiple thrusters, high-speed airflow pulsation, and structural elastic deformation in distributed electric propulsion systems, the equipped spectrum analysis module cannot detect spectral aliasing between electromagnetic harmonic frequencies and flow field vortex frequencies during real-time integrated management of the tightly coupled flow field effect. When spectral aliasing is not identified, the system misinterprets electromagnetic noise as real vortex signals, resulting in a flow field distortion index calculation error exceeding 18.7%, which cannot guarantee the accuracy of aerodynamic efficiency assessment. Furthermore, during flow field-structure coupling state monitoring, it is impossible to detect the mechanical... Abnormal deviations in the wing-thruster attitude coupling relationship can cause velocity field reconstruction errors exceeding 12 m / s, and when the thruster installation angle deviation exceeds 1.2°, real-time correction commands cannot be generated. In electromagnetic-flow-structure multiphysics coupling analysis, due to the nonlinear characteristics of cross-domain interactions, it is impossible to achieve hierarchical decoupling and tracing of vortex intensity, electromagnetic stress, and strain energy density when performing quantitative evaluation of tight coupling effects. This may lead to disordered weight allocation in the tight coupling control strategy, affecting the safety margin and control effect of the distributed electric propulsion system's integrated management.

[0004] Therefore, a comprehensive management system for the tightly coupled effect of distributed electric propulsion flow field is proposed to solve the above problems. Summary of the Invention

[0005] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a comprehensive management system for the tightly coupled effect of distributed electric propulsion flow fields, which solves the problems mentioned in the background section.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a comprehensive management system for the tightly coupled effect of a distributed electric propulsion flow field, the system comprising: The multi-physics sensing module includes an airborne high-frequency pressure sensor array, an electromagnetic spectrum analyzer, and a distributed fiber Bragg grating sensor. The tightly coupled analysis module is configured to perform flow field distortion index calculation and spectrum consistency judgment. The adaptive interpolation module uses a nonlinear resampling algorithm to achieve spatiotemporal alignment of multi-source data; The algorithm optimization matching module includes a pre-stored optimization algorithm library and a real-time multi-objective particle swarm optimization engine; The dynamic compensation execution module drives the thruster vector nozzle and electromagnetic suppression coil according to the optimal control strategy. The closed-loop feedback module achieves system self-optimization through the decay rate evaluation unit and iterative trigger.

[0007] Preferably, the system includes the following steps: S1. Collect dynamic parameters of the flow field, electromagnetic interference parameters, and structural deformation parameters of the distributed electric propulsion system in flight state through a multi-source sensor array to generate a multi-physics field coupled dataset; S2. Based on the multi-physics coupling dataset, perform quantization of the flow field tight coupling effect intensity to construct the flow field distortion index matrix and electromagnetic-structure coupling feature vector; S3. Perform spectral consistency analysis on the flow field distortion index matrix and generate spectral matching judgment results: if the fundamental frequency of the flow field dynamic parameters is consistent with that of the electromagnetic interference parameters, then execute S5; if they are inconsistent, then execute S4. S4. The sampling frequency of the dynamic parameters of the flow field is adaptively adjusted using a nonlinear interpolation algorithm to align it with the fundamental frequency of the electromagnetic interference parameters, thereby generating a calibration flow field dataset. S5. Align the calibration flow field dataset, the original flow field dataset, and the electromagnetic-structure coupling feature vector according to the spatiotemporal coordinates to construct a tight coupling effect synergistic analysis matrix; S6. Based on the tight coupling effect collaborative analysis matrix, and combined with the pre-stored optimization algorithm library, perform multi-objective optimization algorithm matching to generate the optimal control strategy identifier; wherein the algorithm matching process adopts an improved multi-objective particle swarm optimization algorithm, with flow field uniformity, electromagnetic compatibility and structural stability as fitness functions; S7. Based on the optimal control strategy identifier, call the corresponding flow field compensation model to dynamically correct the thruster output parameters in real time and generate an anti-interference propulsion command set. S8. Execute the anti-interference propulsion instruction set through the distributed actuator network, output the flow field tight coupling effect compensation result, and update the multiphysics coupling dataset in a closed loop.

[0008] Preferably, step S1 specifically includes: S11. Deploy high-frequency pressure sensor arrays at the leading edge of the aircraft wing, the outlet of the propeller duct, and the vortex region at the wingtip to collect flow field pressure pulsation data in real time. S12. Install a three-axis fluxgate sensor around the electromagnetic coil of the thruster to synchronously capture the time-varying electromagnetic field intensity and spectrum characteristics. S13. Monitor the micro-strain distribution of the wing-propeller coupled structure through a fiber optic grating deformation sensor network and generate a structural deformation gradient map. S14. The pressure pulsation data, electromagnetic field strength and spectral characteristics, and structural deformation gradient map are fused into a spatiotemporally aligned multiphysics coupling dataset.

[0009] Preferably, the operation of quantifying the intensity of the tight coupling effect in the flow field in step S2 includes: S21. Based on the computational fluid dynamics vortex identification model, extract the vortex core position and intensity from the dynamic parameters of the flow field to generate a vortex interference topology map. S22. Construct the Maxwell stress tensor based on electromagnetic interference parameters to quantify the distortion effect of electromagnetic force on the flow field. S23. Integrate the aforementioned vortex interference topology diagram and electromagnetic force distortion effect, and calculate the flow field distortion index matrix using a weighting function: in The flow field distortion index. Here, represents the aerodynamic coupling weighting coefficient, and represents the time-varying rate of the velocity field. For the velocity field, For electromagnetic coupling weighting coefficients, For electromagnetic stress tensor, Let t be the pressure field gradient, and t be the time variable. This is the symbol for partial differentials.

[0010] Preferably, the nonlinear interpolation algorithm in step S4 is as follows: S41. Construct the time-frequency analysis matrix of the dynamic parameters of the flow field: in This is the time-frequency analysis matrix. Let s be the time coordinate. This is the original flow field pressure signal. For adaptive window functions, For window function time shift operators, It is a complex exponential kernel function. It is an integral infinitesimal element; S42. Reconstruct the interpolation nodes based on the fundamental frequency of the electromagnetic interference parameter, and remap the non-uniform sampling points using a cubic spline function: in This is the resampled flow field signal. These are the cubic spline interpolation coefficients. Let be the spline basis function, and t be the time variable. is the fundamental frequency of electromagnetic interference, and k is the index of the basis function.

[0011] Preferably, the process of constructing the tight coupling effect synergistic analysis matrix in step S5 includes: S51. Map the dynamic parameters of the flow field into a two-dimensional velocity contour matrix. ; S52. Convert electromagnetic interference parameters into electromagnetic field strength contour maps. ; S53. Project the structural deformation gradient diagram onto the flow field plane through coordinate transformation to generate the structure-flow field coupling matrix. ; S54, according to time and space reference , , The superimposed matrix forms a three-dimensional tightly coupled effect synergistic analysis matrix: in This is the synergistic analysis matrix for tight coupling effects.

[0012] Preferably, the improved multi-objective particle swarm optimization algorithm in step S6 includes: S61. Establish an algorithm library ,in Corresponding to the The feature matrix of the optimization algorithms includes eddy current control algorithm, electromagnetic shielding optimization algorithm, and structural vibration suppression algorithm, among which... To optimize the collection of algorithm libraries; S62. Initialize particle swarm positions: in Let be the initial position vector of the particle. As the lower bound of the dimension of the algorithm library, The upper bound of the algorithm library dimension. A random number within a range; S63. During the exploration phase, particle velocity is updated based on fitness values: in The particle velocity vector This is the particle's current position. For particle swarm inertia weights, , For individual and social learning factors, This is the optimal position in the particle's history. This is the best historical position for the group. , A random number within a range; The fitness function is: in The overall fitness value, For the curl of the velocity field, The peak intensity of electromagnetic interference. For the structural strain energy density, As the weight for flow field uniformity, For electromagnetic compatibility weights, For structural stability weights; S64. During the development phase, Pareto dominance relations are introduced to filter the non-dominated solution set and output the optimal control strategy identifier.

[0013] Preferably, the dynamic adjustment rule for the fitness function weight coefficients in step S63 is as follows: S71, When the flow field distortion index At that time, increase aerodynamic weight ,in This is the safe threshold for distortion intensity; S72. When the electromagnetic interference intensity exceeds the threshold At that time, improve Up to 1.5 times the original value; S73. When the structural resonant frequency is detected, Set to adaptive gain mode: in For structural stability weighting coefficients, Basic gain coefficient, It is a natural constant. As a frequency-sensitive adjustment factor, To measure the structural vibration frequency, This is the structure's natural frequency.

[0014] Preferably, the flow field compensation model execution process in step S7 includes: S81. Select one of the following compensation mechanisms based on the optimal control strategy identifier: Eddy current control algorithm: Changing the phase angle of the thruster blade tip vortex through phase modulation: in The adjusted vortex phase angle, The original phase angle of the thruster, This is the phase angle correction amount. The vortex angular velocity vector. It is a position vector; Electromagnetic shielding algorithm: Injecting anti-phase electromagnetic pulses between the thrusters: in To compensate for the magnetic field strength, The differential gain coefficient, Let be the rate of change of electric field. The vector of the interfering electric field; S82. Verify the feasibility of compensation commands based on Lyapunov stability theory and generate an anti-interference propulsion command set.

[0015] Preferably, the closed-loop update mechanism in step S8 is as follows: S91. Implement an anti-interference propulsion instruction set through a distributed actuator network; S92. Real-time acquisition of compensated flow field parameters and calculation of actual coupling attenuation rate: in The attenuation rate of the tight coupling effect. To compensate for the norm of the front flow field distortion index, To compensate for the post-flow field distortion index norm; S93, when This triggers iterative optimization from S2 to S7; when The new dataset will then be injected into the historical database update algorithm library, where... This is the minimum threshold for the decay rate.

[0016] (III) Beneficial Effects Compared with the prior art, the present invention provides a comprehensive management system for the tightly coupled effect of distributed electric propulsion flow field, which has the following beneficial effects: 1. In this invention, by setting up a spectrum arbitrator, when performing real-time management of the tight coupling effect of the flow field, the accuracy of multi-physics data fusion is ensured by formulating electromagnetic-flow field fundamental frequency matching standard parameters and dynamically setting spectrum tolerance thresholds for different flight conditions. At the same time, the spectrum characteristics of electromagnetic harmonic components and flow field pulsation signals are cross-verified in real time, which can diagnose in real time whether there is aliasing interference between the thruster tip vortex frequency and the electromagnetic fundamental frequency, ensuring the accuracy of the flow field distortion index calculation and reducing vortex core positioning error.

[0017] 2. In this invention, by setting up a Lie group mapper, when performing flow field-structure coupling compensation, the spatial Lie algebra matrix of wing deflection and thruster deflection angle is solved to determine in real time whether the wing-thruster spatial attitude has been coupled off, so that the system can reduce velocity field reconstruction error; and when an attitude coupling anomaly is detected, a spatial transformation matrix can be generated in real time through the exponential mapping from Lie group to Lie algebra, so that the thrust air vector error is corrected and the control lift efficiency is guaranteed.

[0018] 3. In this invention, by setting up a cross-domain tracer, when performing comprehensive management of multi-physics coupling effects, a tensor decomposition model of electromagnetic stress, aerodynamic pressure gradient and structural strain energy is constructed to separate the coupling contribution of vortex intensity, electromagnetic stress and strain energy density in real time; control weights are automatically allocated according to the hierarchical decoupling results, so that the system can achieve electromagnetic-aerodynamic-structural third-order coupling traceability for magnetohydrodynamic vortices, reduce the failure rate of control strategies under emergency conditions, and ensure the safety margin of the comprehensive management system under complex conditions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall system architecture of the present invention.

[0020] Figure 2 This is a flowchart of the overall system steps of the present invention.

[0021] Figure 3 This is a flowchart of the S1 auxiliary step of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1-3 This is a comprehensive management system for the tightly coupled effect of a distributed electric propulsion flow field. The system includes: The multi-physics sensing module includes an airborne high-frequency pressure sensor array, an electromagnetic spectrum analyzer, and a distributed fiber Bragg grating sensor. The tightly coupled analysis module is configured to perform flow field distortion index calculation and spectrum consistency judgment. The adaptive interpolation module uses a nonlinear resampling algorithm to achieve spatiotemporal alignment of multi-source data; The algorithm optimization matching module includes a pre-stored optimization algorithm library and a real-time multi-objective particle swarm optimization engine; The dynamic compensation execution module drives the thruster vector nozzle and electromagnetic suppression coil according to the optimal control strategy. The closed-loop feedback module achieves system self-optimization through the decay rate evaluation unit and iterative trigger.

[0024] The system includes the following steps: S1. Collect dynamic parameters of the flow field, electromagnetic interference parameters, and structural deformation parameters of the distributed electric propulsion system in flight state through a multi-source sensor array to generate a multi-physics field coupled dataset; S2. Based on the multi-physics coupling dataset, the intensity of the tight coupling effect of the flow field is quantified, and the flow field distortion index matrix and electromagnetic-structure coupling feature vector are constructed. S3. Perform spectral consistency analysis on the flow field distortion index matrix and generate spectral matching judgment results: if the fundamental frequency of the flow field dynamic parameters is consistent with that of the electromagnetic interference parameters, then execute S5; if they are inconsistent, then execute S4. S4. Adaptively adjust the sampling frequency of the dynamic parameters of the flow field using a nonlinear interpolation algorithm to align it with the fundamental frequency of the electromagnetic interference parameters, thereby generating a calibration flow field dataset. S5. Align the calibration flow field dataset, the original flow field dataset, and the electromagnetic-structure coupling feature vector according to the spatiotemporal coordinates to construct a tight coupling effect synergistic analysis matrix. S6. Based on the tight coupling effect collaborative analysis matrix, and combined with the pre-stored optimization algorithm library, multi-objective optimization algorithm matching is performed to generate the optimal control strategy identifier; the algorithm matching process adopts an improved multi-objective particle swarm optimization algorithm, with flow field uniformity, electromagnetic compatibility and structural stability as fitness functions; S7. Based on the optimal control strategy identifier, call the corresponding flow field compensation model to dynamically correct the thruster output parameters in real time and generate an anti-interference propulsion command set. S8. Execute the anti-interference propulsion instruction set through the distributed actuator network, output the compensation results of the tight coupling effect of the flow field, and update the multi-physics coupling dataset in a closed loop.

[0025] Step S1 specifically includes: S11. Deploy high-frequency pressure sensor arrays at the leading edge of the aircraft wing, the outlet of the propeller duct, and the vortex region at the wingtip to collect flow field pressure pulsation data in real time. S12. Install a three-axis fluxgate sensor around the electromagnetic coil of the thruster to synchronously capture the time-varying electromagnetic field intensity and spectrum characteristics. S13. Monitor the micro-strain distribution of the wing-propeller coupled structure through a fiber optic grating deformation sensor network and generate a structural deformation gradient map. S14. Integrate pressure pulsation data, electromagnetic field strength and spectral characteristics, and structural deformation gradient maps into a spatiotemporally aligned multiphysics coupled dataset.

[0026] The operations in step S2 for quantifying the intensity of the tight coupling effect in the flow field include: S21. Based on the computational fluid dynamics vortex identification model, extract the vortex core position and intensity from the dynamic parameters of the flow field to generate a vortex interference topology map. S22. Construct the Maxwell stress tensor based on electromagnetic interference parameters to quantify the distortion effect of electromagnetic force on the flow field. S23. By integrating the vortex interference topology and electromagnetic force distortion effects, the flow field distortion index matrix is ​​calculated using a weighted function: in The flow field distortion index. Here, represents the aerodynamic coupling weighting coefficient, and represents the time-varying rate of the velocity field. For the velocity field, For electromagnetic coupling weighting coefficients, For electromagnetic stress tensor, Let t be the pressure field gradient, and t be the time variable. This is the symbol for partial differentials.

[0027] The nonlinear interpolation algorithm in step S4 is as follows: S41. Construct the time-frequency analysis matrix of the dynamic parameters of the flow field: in This is the time-frequency analysis matrix. Let s be the time coordinate. This is the original flow field pressure signal. For adaptive window functions, For window function time shift operators, It is a complex exponential kernel function. It is an integral infinitesimal element; S42. Reconstruct the interpolation nodes based on the fundamental frequency of the electromagnetic interference parameter, and remap the non-uniform sampling points using a cubic spline function: in This is the resampled flow field signal. These are the cubic spline interpolation coefficients. Let be the spline basis function, and t be the time variable. is the fundamental frequency of electromagnetic interference, and k is the index of the basis function.

[0028] The process of constructing the tight coupling effect synergistic analysis matrix in step S5 includes: S51. Map the dynamic parameters of the flow field into a two-dimensional velocity contour matrix. ; S52. Convert electromagnetic interference parameters into electromagnetic field strength contour maps. ; S53. Project the structural deformation gradient diagram onto the flow field plane through coordinate transformation to generate the structure-flow field coupling matrix. ; S54, according to spatiotemporal reference , , The superimposed matrix forms a three-dimensional tightly coupled effect synergistic analysis matrix: in This is the synergistic analysis matrix for tight coupling effects.

[0029] The improved multi-objective particle swarm optimization algorithm in step S6 includes: S61. Establish an algorithm library ,in Corresponding to the The feature matrix of the optimization algorithms includes eddy current control algorithm, electromagnetic shielding optimization algorithm, and structural vibration suppression algorithm, among which... To optimize the collection of algorithm libraries; S62. Initialize particle swarm positions: in Let be the initial position vector of the particle. As the lower bound of the dimension of the algorithm library, The upper bound of the algorithm library dimension. A random number within a range; S63. During the exploration phase, particle velocity is updated based on fitness values: in The particle velocity vector This is the particle's current position. For particle swarm inertia weights, , For individual and social learning factors, This is the optimal position in the particle's history. This is the best historical position for the group. , A random number within a range; The fitness function is: in The overall fitness value, For the curl of the velocity field, The peak intensity of electromagnetic interference. For the structural strain energy density, As the weight for flow field uniformity, For electromagnetic compatibility weights, For structural stability weights; S64. During the development phase, Pareto dominance relations are introduced to filter the non-dominated solution set and output the optimal control strategy identifier.

[0030] The dynamic adjustment rule for the fitness function weight coefficients in step S63 is as follows: S71, When the flow field distortion index At that time, increase aerodynamic weight ,in This is the safe threshold for distortion intensity; S72. When the electromagnetic interference intensity exceeds the threshold At that time, improve Up to 1.5 times the original value; S73. When the structural resonant frequency is detected, Set to adaptive gain mode: in For structural stability weighting coefficients, Basic gain coefficient, It is a natural constant. As a frequency-sensitive adjustment factor, To measure the structural vibration frequency, This is the structure's natural frequency.

[0031] The flow field compensation model execution process in step S7 includes: S81. Select one of the following compensation mechanisms based on the optimal control strategy identifier: Eddy current control algorithm: Changing the phase angle of the thruster blade tip vortex through phase modulation: in The adjusted vortex phase angle, The original phase angle of the thruster, This is the phase angle correction amount. The vortex angular velocity vector. It is a position vector; Electromagnetic shielding algorithm: Injecting anti-phase electromagnetic pulses between the thrusters: in To compensate for the magnetic field strength, The differential gain coefficient, Let be the rate of change of electric field. The vector of the interfering electric field; S82. Verify the feasibility of compensation commands based on Lyapunov stability theory and generate an anti-interference propulsion command set.

[0032] The closed-loop update mechanism in step S8 is as follows: S91. Implement an anti-interference propulsion instruction set through a distributed actuator network; S92. Real-time acquisition of compensated flow field parameters and calculation of actual coupling attenuation rate: in The attenuation rate of the tight coupling effect. To compensate for the norm of the front flow field distortion index, To compensate for the post-flow field distortion index norm; S93, when This triggers iterative optimization from S2 to S7; when The new dataset will then be injected into the historical database update algorithm library, where... This is the minimum threshold for the decay rate.

[0033] Example 1: Cancellation of Spectrum Aliasing Interference: When the aircraft performs transonic cruise missions, the spectrum arbitration module activates preset electromagnetic-flow field matching parameters: the fundamental frequency difference threshold is set to 200 Hz, the tip vortex reference frequency is 1600 Hz, and the electromagnetic interference main frequency is 4500 Hz. The data acquisition unit acquires electromagnetic field data from the triaxial fluxgate sensor at a sampling rate of 256 kHz through two independent 16-bit analog-to-digital conversion channels, while simultaneously acquiring flow field pulsation signals from the leading-edge pressure sensor array at a sampling rate of 48 kHz. When real-time spectrum analysis detects that the overlapping region in the 1400-1800 Hz frequency band exceeds 45% of the total bandwidth, the system automatically activates the adaptive resampling protocol. This protocol uses a cubic spline interpolation algorithm to calibrate the pressure sensor data to ensure strict synchronization with the electromagnetic fundamental frequency. The core calculation function calls the signal_resample() function from the patent library. After 15 seconds of continuous closed-loop testing, the distortion index calculation error decreased from 18.7% to 0.6%. Verification using laser particle image velocimetry technology confirmed that the vortex core center positioning accuracy remained stable within ±0.03 meters, a 30-fold improvement compared to traditional methods. The entire processing time was less than 5 milliseconds, meeting the real-time requirements of the flight control system. The system also simultaneously recorded optimal threshold parameters at different altitudes, forming a dynamic database for future mission optimization.

[0034] Example 2: Real-time pose coupling correction: During the aircraft's sharp turn maneuver, a distributed fiber optic grating sensor network monitors structural deformation at a refresh rate of 500 Hz. When the system detects a 0.38-degree torsional deformation in the left wingtip region and a 1.15-degree deviation in the No. 2 thruster's mounting angle, the attitude coupling compensation module is immediately activated. This module first constructs a spatial attitude transformation matrix for the wing-thruster system, containing a 3×3 rotation matrix and a 3×1 translation vector, fully describing the attitude relationships in a three-dimensional special Euclidean group space. Based on this matrix, the system uses the logarithmic mapping principle from Lie groups to Lie algebras to calculate the required correction for the flow field velocity vector. Upon receiving the correction command, the actuator drives the vector nozzle to make precise adjustments at an angular velocity of 120 degrees per second. Test data shows that attitude calibration is completed in just 3 milliseconds, reducing the mounting angle deviation to 0.08 degrees and decreasing the flow field reconstruction velocity error from the initial 12 m / s to 0.7 m / s. In 20 subsequent repeated maneuver tests, the system successfully kept lift loss below 0.5%, an improvement of 18.8 times compared to the 9.4% loss without the correction system. Redundant control strategies were employed during the correction process to ensure safety in the event of a single sensor failure, and all pose correction data was recorded via an onboard black box.

[0035] Example 3: Multi-field Coupled Hierarchical Source Tracing Control When the aircraft encounters complex extreme conditions, the cross-domain source analysis module initiates a multi-physics coupling and decoupling procedure. The system first performs tensor decomposition on the real-time acquired distortion index, quantifying the contribution of each physical component: electromagnetic stress component 0.092, aerodynamic curl component 0.063, and structural strain component 0.021. Based on this quantification, the system generates three levels of optimized control commands: first, activate electromagnetic shielding; second, adjust eddy current control; and finally, tune structural damping. During command execution, the No. 2 thruster injects a counter-phase compensation magnetic field, with the field strength calculated based on the electromagnetic field change rate and a differential gain coefficient of 0.8. Simultaneously, the vector nozzle adjusts the tip phase angle based on the vortex rotation direction detection, with a maximum adjustment range of 15 degrees. The wing's active damping system is tuned to a 120 Hz counter-phase resonance frequency for vibration suppression. After a 150-millisecond control cycle, comprehensive evaluation shows that the tight coupling effect attenuation rate jumped from the initial 41% to 89%, and the control strategy failure rate decreased from 34.1% to 2.8%. The additional power consumed by the system during the entire process accounts for only 0.8% of the total power of the propulsion system, which is lower than the energy consumption level of 3.5% for traditional solutions. The system automatically records the optimal parameter combination under combined operating conditions to enhance the intelligence of the control algorithm library.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive management system for the tightly coupled effect of a distributed electric propulsion flow field, characterized in that: The system includes: The multi-physics sensing module includes an airborne high-frequency pressure sensor array, an electromagnetic spectrum analyzer, and a distributed fiber Bragg grating sensor. The tightly coupled analysis module is configured to perform flow field distortion index calculation and spectrum consistency judgment. The adaptive interpolation module uses a nonlinear resampling algorithm to achieve spatiotemporal alignment of multi-source data; The algorithm optimization matching module includes a pre-stored optimization algorithm library and a real-time multi-objective particle swarm optimization engine; The dynamic compensation execution module drives the thruster vector nozzle and electromagnetic suppression coil according to the optimal control strategy. The closed-loop feedback module achieves system self-optimization through a decay rate evaluation unit and an iterative trigger. The system includes the following steps: S1. Collect dynamic parameters of the flow field, electromagnetic interference parameters, and structural deformation parameters of the distributed electric propulsion system in flight state through a multi-source sensor array to generate a multi-physics field coupled dataset; S2. Based on the multi-physics coupling dataset, perform quantization of the flow field tight coupling effect intensity to construct the flow field distortion index matrix and electromagnetic-structure coupling feature vector; S3. Perform spectral consistency analysis on the flow field distortion index matrix and generate spectral matching judgment results: if the fundamental frequency of the flow field dynamic parameters is consistent with that of the electromagnetic interference parameters, then execute S5; if they are inconsistent, then execute S4. S4. Adaptively adjust the sampling frequency of the dynamic parameters of the flow field using a nonlinear interpolation algorithm to align it with the fundamental frequency of the electromagnetic interference parameters, thereby generating a calibration flow field dataset. S5. Align the calibration flow field dataset, the original flow field dataset, and the electromagnetic-structure coupling feature vector according to the spatiotemporal coordinates to construct a tight coupling effect synergistic analysis matrix; S6. Based on the tight coupling effect collaborative analysis matrix, and combined with the pre-stored optimization algorithm library, perform multi-objective optimization algorithm matching to generate the optimal control strategy identifier; wherein the algorithm matching process adopts an improved multi-objective particle swarm optimization algorithm, with flow field uniformity, electromagnetic compatibility and structural stability as fitness functions; S7. Based on the optimal control strategy identifier, call the corresponding flow field compensation model to dynamically correct the thruster output parameters in real time and generate an anti-interference propulsion command set. S8. Execute the anti-interference propulsion instruction set through the distributed actuator network, output the flow field tight coupling effect compensation result, and update the multiphysics coupling dataset in a closed loop. The operations in step S2 for quantifying the intensity of the tight coupling effect in the flow field include: S21. Based on the computational fluid dynamics vortex identification model, extract the vortex core position and intensity from the dynamic parameters of the flow field to generate a vortex interference topology map. S22. Construct the Maxwell stress tensor based on electromagnetic interference parameters to quantify the distortion effect of electromagnetic force on the flow field. S23. Integrate the aforementioned vortex interference topology diagram and electromagnetic force distortion effect, and calculate the flow field distortion index matrix using a weighting function: in The flow field distortion index. Here, represents the aerodynamic coupling weighting coefficient, and represents the time-varying rate of the velocity field. For the velocity field, For electromagnetic coupling weighting coefficients, For electromagnetic stress tensor, Let t be the pressure field gradient, and t be the time variable. The symbol is for partial differentials; The nonlinear interpolation algorithm in step S4 is as follows: S41. Construct the time-frequency analysis matrix of the dynamic parameters of the flow field: in This is the time-frequency analysis matrix. Let s be the time variable. This is the original flow field pressure signal. For adaptive window functions, For window function time shift operators, It is a complex exponential kernel function. It is an integral infinitesimal element; S42. Reconstruct the interpolation nodes based on the fundamental frequency of the electromagnetic interference parameter, and remap the non-uniform sampling points using a cubic spline function: in This is the resampled flow field signal. These are the cubic spline interpolation coefficients. Let be the spline basis function, and t be the time variable. is the fundamental frequency of electromagnetic interference, and k is the index of the basis function.

2. The integrated management system for tightly coupled flow field effects of distributed electric propulsion according to claim 1, characterized in that: Step S1 specifically includes: S11. Deploy high-frequency pressure sensor arrays at the leading edge of the aircraft wing, the outlet of the propeller duct, and the vortex region at the wingtip to collect flow field pressure pulsation data in real time. S12. Install a three-axis fluxgate sensor around the electromagnetic coil of the thruster to synchronously capture the time-varying electromagnetic field intensity and spectrum characteristics. S13. Monitor the micro-strain distribution of the wing-propeller coupled structure through a fiber optic grating deformation sensor network and generate a structural deformation gradient map. S14. The pressure pulsation data, electromagnetic field strength and spectral characteristics, and structural deformation gradient map are fused into a spatiotemporally aligned multiphysics coupling dataset.

3. The integrated management system for tightly coupled flow field effects of distributed electric propulsion according to claim 1, characterized in that: The process of constructing the tight coupling effect synergistic analysis matrix in step S5 includes: S51. Map the dynamic parameters of the flow field into a two-dimensional velocity contour matrix. ; S52. Convert electromagnetic interference parameters into electromagnetic field strength contour maps. ; S53. Project the structural deformation gradient diagram onto the flow field plane through coordinate transformation to generate the structure-flow field coupling matrix. ; S54, according to time and space reference , , The superimposed matrix forms a three-dimensional tightly coupled effect synergistic analysis matrix: in This is the synergistic analysis matrix for tight coupling effects.

4. The integrated management system for tightly coupled flow field effects of distributed electric propulsion according to claim 1, characterized in that: The improved multi-objective particle swarm optimization algorithm in step S6 includes: S61. Establish an algorithm library ,in Corresponding to the The feature matrix of the optimization algorithms includes eddy current control algorithm, electromagnetic shielding optimization algorithm, and structural vibration suppression algorithm, among which... To optimize the collection of algorithm libraries; S62. Initialize particle swarm positions: in Let be the initial position vector of the particle. As the lower bound of the dimension of the algorithm library, The upper bound of the algorithm library dimension. A random number within a range; S63. During the exploration phase, particle velocity is updated based on fitness values: in The particle velocity vector This is the particle's current position. For particle swarm inertia weights, , For individual and social learning factors, This is the optimal position in the particle's history. This is the best historical position for the group. , A random number within a range; The fitness function is: in The overall fitness value, For the curl of the velocity field, The peak intensity of electromagnetic interference. For the structural strain energy density, As the weight for flow field uniformity, For electromagnetic compatibility weights, For structural stability weights; S64. During the development phase, Pareto dominance relations are introduced to filter the non-dominated solution set and output the optimal control strategy identifier.

5. A distributed electric propulsion flow field tight coupling effect integrated management system according to claim 4, characterized in that: The dynamic adjustment rule for the fitness function weight coefficients in step S63 is as follows: S71, When the flow field distortion index At that time, increase aerodynamic weight ,in This is the safe threshold for distortion intensity; S72. When the electromagnetic interference intensity exceeds the threshold At that time, improve Up to 1.5 times the original value; S73. When the structural resonant frequency is detected, Set to adaptive gain mode: in For structural stability weighting coefficients, Basic gain coefficient, It is a natural constant. As a frequency-sensitive adjustment factor, To measure the structural vibration frequency, This is the structure's natural frequency.

6. The integrated management system for tightly coupled flow field effects of distributed electric propulsion according to claim 1, characterized in that: The flow field compensation model execution process in step S7 includes: S81. Select one of the following compensation mechanisms based on the optimal control strategy identifier: Eddy current control algorithm: Changing the phase angle of the thruster blade tip vortex through phase modulation: in The adjusted vortex phase angle, The original phase angle of the thruster, This is the phase angle correction amount. The vortex angular velocity vector. It is a position vector; Electromagnetic shielding algorithm: Injecting anti-phase electromagnetic pulses between the thrusters: in To compensate for the magnetic field strength, The differential gain coefficient, Let be the rate of change of electric field. The vector of the interfering electric field; S82. Verify the feasibility of compensation commands based on Lyapunov stability theory and generate an anti-interference propulsion command set.

7. A distributed electric propulsion flow field tight coupling effect integrated management system according to claim 1, characterized in that, The closed-loop update mechanism in step S8 is as follows: S91. Implement an anti-interference propulsion instruction set through a distributed actuator network; S92. Real-time acquisition of compensated flow field parameters and calculation of actual coupling attenuation rate: in The attenuation rate of tight coupling effect. To compensate for the norm of the front flow field distortion index. To compensate for the post-flow field distortion index norm; S93, when This triggers iterative optimization from S2 to S7; when The new dataset will then be injected into the historical database update algorithm library, where... This is the minimum threshold for the decay rate.

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