Simulation method of intelligent flight control of sea-air multi-habitable vehicle based on Fluent

Abstract

The method for simulating the intelligent control of the airborne flight of a multi-purpose sea-air vehicle based on Fluent belongs to the field of flight control simulation technology. The present invention addresses the problem that the force model of the existing multi-purpose sea-air vehicle is complex when considering the influence of fixed wings, which makes the flight control simulation results unreliable. It includes establishing a geometric model of the vehicle; then establishing a geometric model of the flow field calculation domain, and then obtaining a mesh model of the flow field calculation domain, importing the initial settings of the rotor rotation domain into Fluent, and obtaining the initialized flow field information through steady-state calculation of the flow field; writing a first UDF program to realize the calculation of the vehicle motion parameters within any time step, and then realizing the calculation of the rotor speed control quantity; then writing a second UDF program to realize the setting of the motion conditions of the foreground area and the rotor rotation domain; loading the two UDF programs in Flunet, performing transient calculation of the flow field, and obtaining the rotor speed control quantity, vehicle motion parameters and external flow field calculation results for each time step. The present invention is used for the simulation of intelligent control of airborne flight.

Description

Technical Field

[0001] The present invention relates to a Fluent-based intelligent control simulation method for air-sea multi-habitat aircraft flight, belonging to the technical field of flight control simulation. Background Art

[0002] Aircraft can be divided into two main categories based on their structure and flight principles. The first category is fixed-wing aircraft, and the second category is rotary-wing aircraft.

[0003] Compared to fixed-wing aircraft, rotorcraft are more flexible, capable of vertical takeoff and landing, and hovering, and have a wider range of applications. A multi-purpose marine and airborne vehicle is a fixed-wing vehicle when navigating underwater and a rotorcraft when flying in the air. Research on rotorcraft in aerial flight has become increasingly mature in recent years. Theoretically, corresponding dynamic models have been established for aircraft with different numbers of rotors. These dynamic models describe the motion laws of a rotorcraft in its six degrees of freedom. Combined with the kinematic equations of motion, it is possible to establish a relationship between the rotor speed and the aircraft's flight position and attitude. Based on this relationship, a control system can be further constructed to control the aircraft's flight state by intelligently controlling the speed of each rotor.

[0004] Currently, simulation of rotorcraft motion and intelligent control primarily relies on Matlab / Simulink. The basic principle of this simulation is to construct mathematical model equations for the vehicle's dynamics, kinematics, and rotor aerodynamics using block diagrams, then integrate them to form an integrated system to simulate the vehicle's flight state. Therefore, a definitive mathematical model of the vehicle is essential for simulation using Simulink. However, due to the complexity of solving fluid equations and the influence of turbulence, the fluid loads generated by the high-speed rotation of the rotors during flight are difficult to describe using a definitive mathematical model. Furthermore, for multi-purpose marine and air vehicles with fixed wings, while they primarily rely on the aerodynamic effects of the rotors to maintain flight and can be considered rotorcraft, the influence of the fixed wings on the external flow field cannot be ignored. This further complicates the force model of the vehicle, making it difficult to establish an accurate simulation system in Simulink. Furthermore, when using Simulink for simulation, the results obtained are often merely abstract and ambiguous graphs describing the vehicle's motion, failing to visualize the model's true state as finite element simulations can. Summary of the Invention

[0005] Aiming at the problem that the force model of existing sea-air multi-purpose vehicles is complex when considering the influence of fixed wings, which makes the flight control simulation results unreliable, the present invention provides a Fluent-based sea-air multi-purpose vehicle air flight intelligent control simulation method.

[0006] The present invention provides a Fluent-based intelligent control simulation method for air-sea multi-habitat flight, comprising:

[0007] Step 1: Divide the aircraft into the rotor part, the frame part and the fixed-wing part to establish the aircraft geometric model;

[0008] Step 2: Based on the aircraft geometry model, a geometric model of the flow field calculation domain is established, including the rotor rotation domain, foreground area, and background area. The background area is divided into hexahedral meshes, and the rotor rotation domain and foreground area are divided into tetrahedral meshes and then converted into polyhedral meshes to obtain the flow field calculation domain mesh model.

[0009] Step 3: Import the flow field calculation domain grid model into the Fluent flow field simulation calculation module, then set the foreground area grid and the background area grid to overlapping grids, and set the grid boundary of the rotor rotation domain to the sliding grid calculation interface; then, in the initial settings of the rotor rotation domain, enter the initial rotor speed, rotation axis orientation, and rotation center coordinates of the aircraft in the coordinate system of the flow field calculation domain geometry model, perform a steady-state calculation of the aircraft's external flow field, and use the calculation results as the initial flow field information for the transient external flow field calculation;

[0010] Step 4: Write the first UDF program to calculate the vehicle motion parameters at any time step under transient external flow field simulation, and use the control algorithm to calculate the rotor speed control value based on the motion parameter calculation results;

[0011] Step 5: Write a second UDF program to set the speed and position of the foreground area relative to the background area, as well as the speed and position of the rotor rotation domain relative to the foreground area, based on the motion parameters obtained in step 4, to calculate the transient external flow field.

[0012] Step 6: Run the UDF program written in steps 4 and 5 in Flunet to perform iterative calculations to obtain the rotor speed control quantity, aircraft motion parameters and external flow field calculation results for each time step, thereby realizing intelligent control of the aircraft in the air.

[0013] According to the Fluent-based intelligent control simulation method for the air-sea multi-purpose vehicle flight of the present invention, the flow field cloud map of the vehicle geometric model is obtained according to the external flow field calculation results; the altitude curve and rotor speed curve of the vehicle geometric model are obtained according to the vehicle motion parameters.

[0014] According to the Fluent-based intelligent control simulation method for airborne multi-purpose aircraft flight of the present invention, in step 4, the calculation of the aircraft motion parameters is achieved by writing a first UDF program;

[0015] In the first UDF program, the inherent attribute variables and the motion parameters of the sea-air multi-purpose vehicle at the initial moment are used as global variables. Under the DEFINE_EXECUTE_AT_END macro, the Compute_Force_And_Moment statement is used to extract the surface flow field load of the vehicle at the end of the current time step, and the acceleration is calculated using the linearly simplified vehicle dynamics equation; then, the motion parameters of the vehicle in the next time step are obtained based on the linearly discretized kinematic equation.

[0016] According to the Fluent-based airborne multi-purpose vehicle flight intelligent control simulation method of the present invention, the surface flow field load includes force F and moment M;

[0017] The calculation methods of acceleration include:

[0018] F=ma,

[0019] M=Iβ,

[0020] Where m is mass, a is acceleration, I is moment of inertia, and β is angular acceleration;

[0021] The calculation method of the motion parameters of the spacecraft includes:

[0022] v t+Δt =v t +aΔt,

[0023] x t+Δt =x t +v t+Δt Δt,

[0024] Where v t+Δt is the velocity of the next time step, v t is the speed of the current time step, Δt is the interval between adjacent time steps, x t+Δt is the displacement of the next time step, x t is the displacement at the current time step.

[0025] According to the Fluent-based airborne multi-role vehicle flight intelligent control simulation method of the present invention, in step 4, the calculation of the rotor speed control amount is implemented by the first UDF program;

[0026] In the first UDF program, the expected position or attitude of the aircraft geometric model is used as a global variable. Under the DEFINE_EXECUTE_AT_END macro, the expected position or attitude is subtracted from the calculated aircraft position or attitude to obtain a position or attitude error. The position or attitude error is multiplied by a preset proportional coefficient to obtain an outer loop control variable of the aircraft geometric model. The outer loop control variable is then used as the expected speed and the speed is subtracted from the real-time speed of the aircraft to obtain a speed error. The speed error is processed using a discrete PID controller to obtain an inner loop control variable, and the rotor speed control variable of the aircraft geometric model is obtained from the inner loop control variable.

[0027] The outer loop is the spacecraft position and attitude control loop; the inner loop is the spacecraft speed and angular velocity control loop.

[0028] According to the Fluent-based airborne multi-purpose vehicle flight intelligent control simulation method of the present invention, in step five, the calculation of the transient external flow field is achieved by writing a second UDF program;

[0029] In the second UDF program, under the DEFINE_ZONE_MOTION macro, the velocity of the foreground area and the rotor rotation domain during translation and the center of rotation, axis orientation, and angular velocity during Euler rotation are specified in each time step;

[0030] The speed of the foreground area during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion parameters of the aircraft; the speed of the rotor rotation domain during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion conditions of the foreground area and the rotor speed control amount.

[0031] The beneficial effects of the present invention are as follows: the present invention is used for simulation of aircraft, and performs intelligently controlled flow field simulation on the position and attitude of a fixed-wing multi-purpose sea and air vehicle when flying in the air.

[0032] The present invention solves the external flow field of an aircraft during flight through Fluent, and utilizes the UDF program to combine the obtained flow field load with the mathematical model of the aircraft motion control. While taking into account the complexity and time-varying nature of the aircraft's external flow field, it avoids the tedious process of establishing a mathematical model for the flow field load force.

[0033] Based on the computational fluid dynamics software Fluent, the present invention solves the external flow field conditions of the aircraft during flight in real time, and extracts the flow field load in each time step of the aircraft through UDF. It then combines it with the dynamic and kinematic equations that describe its motion laws to solve the motion parameters of the aircraft. At the same time, a control algorithm is added to the UDF to obtain the expected rotor speed control value based on the motion parameters of the aircraft in each time step, ultimately realizing the simulation of the entire motion and intelligent control process of the aircraft, and ensuring the accuracy of the flight control simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the Fluent-based airborne multi-role vehicle flight intelligent control simulation method of the present invention;

[0035] Figure 2 is a schematic diagram of a geometric model of an aircraft in a specific embodiment;

[0036] Figure 3 2. It is a schematic diagram of a flow field calculation domain grid model in a foreground area of ​​a vehicle flow field in a specific embodiment;

[0037] Figure 4 1 is a schematic diagram of a grid model of a flow field calculation domain in a rotor rotation domain of an aircraft flow field in a specific embodiment;

[0038] Figure 5 Schematic diagram of the operation process of two UDF programs in the first specific embodiment;

[0039] Figure 6 is a surface pressure cloud map of the aircraft fuselage at the first selected moment in the first embodiment; in the figure, contour represents the cloud map, and Static Pressure represents the static pressure;

[0040] Figure 7 is a surface pressure cloud map of the aircraft fuselage at a second selected time in the first embodiment; the second selected time is a number of time steps away from the first selected time;

[0041] Figure 8 is a schematic diagram of the pressure of the Oyz plane of the vehicle's external flow field at the first selected moment in the first specific embodiment;

[0042] Figure 9 is a schematic diagram of the pressure of the Oyz plane of the outer flow field of the aircraft at the second selected moment in the first specific embodiment;

[0043] Figure 10 is a graph of aircraft altitude position in the first specific embodiment;

[0044] Figure 11 is a graph of the rotor speed of the aircraft in the first embodiment;

[0045] Figure 12 is a surface pressure cloud diagram of the aircraft fuselage at the third selected moment in the second specific embodiment;

[0046] Figure 13 is a surface pressure cloud map of the aircraft fuselage at the fourth selected time in the second specific embodiment; the fourth selected time is a number of time steps away from the third selected time;

[0047] Figure 14 is a schematic diagram of the pressure of the Oyz plane of the vehicle's external flow field at the third selected moment in the second specific embodiment;

[0048] Figure 15 is a schematic diagram of the pressure of the Oyz plane of the vehicle's external flow field at the fourth selected moment in the second specific embodiment;

[0049] Figure 16 is the angle attitude curve graph;

[0050] Figure 17 is a graph of the forward rotor speed of the aircraft along the Z axis in the second specific embodiment;

[0051] Figure 18 This is a curve diagram of the negative rotor speed of the aircraft Z axis in the second specific embodiment. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0054] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0055] Specific implementation method 1. Combination Figures 1 to 4 As shown, the present invention provides a Fluent-based intelligent control simulation method for air-sea multi-habitat aircraft flight, comprising:

[0056] Step 1: Divide the aircraft into the rotor, airframe, and fixed-wing sections to create a simplified aircraft geometry model. Simplification should fully consider the aerodynamic performance of the structure and adhere to the principle of not significantly affecting the aircraft flow field simulation results. The airframe, with its complex structure and minimal impact on the flow field, is the primary component to be simplified.

[0057] Step 2: Based on the aircraft geometry model, a geometric model of the flow field calculation domain is established, including the rotor rotation domain, foreground area, and background area. The background area is divided into hexahedral meshes using ICEM, and the rotor rotation domain and foreground area are divided into tetrahedral meshes using MESH. The meshes are then converted into polyhedral meshes in Fluent to obtain the flow field calculation domain mesh model.

[0058] The background region is typically constructed as a rectangular parallelepiped, representing the entire external flow field of the aircraft during flight. The rotor rotation domain is the small flow field region surrounding the aircraft's rotors and is typically constructed as a cylinder. The foreground region is created by creating a shell for the rotor rotation domain and the aircraft, and is the flow field region surrounding the rotation domain and the aircraft.

[0059] Considering the iteration rate of the flow field simulation and the difficulty of meshing, a hexahedral mesh was used for the background area. The rotor rotation domain and foreground area were first meshed with tetrahedrons, which were then converted to polyhedral meshes in Fluent.

[0060] Step 3: Import the flow field calculation domain grid model into the Fluent flow field simulation calculation module, then set the foreground area grid and the background area grid to overlapping grids (overset), and set the grid boundary of the rotor rotation domain to the sliding grid calculation interface (interface); then in the initial setting of the rotor rotation domain, input the initial rotor speed, rotation axis orientation and rotation center coordinates of the aircraft in the flow field calculation domain geometric model coordinate system, and then select the appropriate turbulence model and iterative settings to perform steady-state calculations on the aircraft's external flow field, and use the calculation results as the initialization flow field information for transient external flow field calculations, thereby replacing the initialization operation, which can effectively increase the calculation efficiency and accelerate the convergence speed; when the sea and air multi-purpose vehicle is flying in the air, there are both overall translation and rotation in the world coordinate system, and relative rotation of the rotor in the body coordinate system; when performing external flow field calculations, overlapping grids are used to simulate the overall motion of the aircraft, and sliding grids are used to simulate the rotation of the aircraft's rotors. The combination of the two realizes the simulation of the entire motion process.

[0061] Step 4: Write the first UDF program to calculate the vehicle's motion parameters at any time step under transient external flow field simulation, and use the control algorithm to calculate the rotor speed control variable based on the motion parameter calculation results; the motion parameters include force parameters, weight, acceleration, velocity, and flow field information;

[0062] Step 5: Write a second UDF program to set the speed and position of the foreground area relative to the background area, as well as the speed and position of the rotor rotation domain relative to the foreground area, based on the motion parameters obtained in step 4, to calculate the transient external flow field.

[0063] Step 6: Run the UDF program written in steps 4 and 5 in Flunet to perform iterative calculations to obtain the rotor speed control quantity, aircraft motion parameters and external flow field calculation results for each time step, thereby realizing intelligent control of the aircraft in the air.

[0064] Furthermore, the flow field cloud map of the aircraft geometric model is obtained according to the calculation results of the external flow field; and the height curve and rotor speed curve of the aircraft geometric model are obtained according to the aircraft motion parameters.

[0065] Furthermore, in step 4, a UDF program is written to calculate the vehicle motion parameters at any time step under the flow field transient simulation, and to substitute the calculated parameters into the control algorithm to obtain the rotor speed control variable. The calculation of the vehicle motion parameters is achieved by writing a first UDF program;

[0066] In the first UDF program, the inherent attribute variables such as the mass, moment of inertia, geometric center and the motion parameters at the initial moment of the sea-air multi-purpose vehicle are used as global variables. Under the DEFINE_EXECUTE_AT_END macro, the Compute_Force_And_Moment statement is used to extract the surface flow field load of the vehicle at the end of the current time step, and the acceleration is calculated using the linearly simplified vehicle dynamics equation; then, the motion parameters of the vehicle in the next time step are obtained based on the linearly discretized kinematic equation. The motion parameters include the speed, displacement and attitude of the vehicle. The first UDF program uses the principle of calculus to discretize continuous time into many equal time steps. In these time steps, the motion parameters of the vehicle are approximately regarded as constants and brought into the control equation for calculation, so that the accuracy of the solution depends on the length of the time step.

[0067] The surface flow field load includes force F and moment M;

[0068] The calculation methods of acceleration include:

[0069] F=ma,

[0070] M=Iβ,

[0071] Where m is mass, a is acceleration, I is moment of inertia, and β is angular acceleration;

[0072] The calculation method of the motion parameters of the spacecraft includes:

[0073] v t+Δt =v t +aΔt,

[0074] x t+Δt =x t +v t+ΔtΔt,

[0075] Where v t+Δt is the velocity of the next time step, v t is the speed of the current time step, Δt is the interval between adjacent time steps, x t+Δt is the displacement of the next time step, x t is the displacement at the current time step.

[0076] In step 4, the calculation of the rotor speed control variable is realized by the first UDF program;

[0077] In the first UDF program, the expected position or attitude of the aircraft geometric model is used as a global variable. Under the DEFINE_EXECUTE_AT_END macro, the expected position or attitude is subtracted from the calculated aircraft position or attitude to obtain a position or attitude error; the position or attitude error is multiplied by a preset proportional coefficient to obtain an outer loop control variable of the aircraft geometric model; the outer loop control variable is then used as the expected speed and the speed is subtracted from the real-time speed of the aircraft to obtain a speed error. The speed error is processed using a discrete PID controller to obtain an inner loop control variable, and the rotor speed control variable of the aircraft geometric model is obtained from the inner loop control variable; the speed includes linear velocity and angular velocity;

[0078] The outer loop is the spacecraft position and attitude control loop; the inner loop is the spacecraft speed and angular velocity control loop.

[0079] After obtaining the real-time motion parameters of the aircraft, the control algorithm (inner and outer loop control) is incorporated into the program to achieve intelligent control of the aircraft's flight position (or attitude). The inner loop control variable is converted into the aircraft's rotor speed control variable for each time step, thereby changing the flow field load force applied to the aircraft in real time, so that its flight position and attitude reach the desired values. In addition, a statement is written at the end of the program to export and save the aircraft's motion parameters at each time step. To avoid duplication of the exported data, this statement needs to be processed in parallel using a UDF.

[0080] In step 5, the calculation of the transient external flow field is realized by writing the second UDF program;

[0081] In the second UDF program, under the DEFINE_ZONE_MOTION macro, the velocity of the foreground area and the rotor rotation domain during translation and the center of rotation, axis orientation, and angular velocity during Euler rotation are specified in each time step;

[0082] The speed of the foreground area during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion parameters of the aircraft; the speed of the rotor rotation domain during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion conditions of the foreground area and the rotor speed control amount.

[0083] Because the foreground region and the rotor domain move differently, separate motion macros are required for parameter setting. The foreground region's translational velocity and Euler rotation parameters are communicated in real time with the vehicle's motion parameters via UDF global variables. The rotor domain's Euler rotation velocity relative to the foreground region is also communicated in real time with the rotor speed control variable calculated by the control algorithm using UDF global variables.

[0084] In this embodiment, a UDF program is written to set the motion law of the mesh in the corresponding flow field calculation domain of the aircraft based on the obtained aircraft motion parameters and rotor speed control variable. This part of the program uses the DEFINE_ZONE_MOTION macro, which can specify the velocity of the calculation domain during translation and the rotation center, axis orientation, and angular velocity during Euler rotation within each time step. The setting of the foreground area motion macro parameters is completed by setting global variables in the UDF, which are interconnected with the aircraft motion parameters calculated in step 4. As for the rotor rotation domain, its motion is more complex. It not only has translation and rotation following the foreground area, but also rotation around its own geometric center rotation axis. Its complex motion cannot be directly specified through the motion macro. By setting the rotor rotation domain to move relative to the foreground area in Fluent, the setting of the rotor rotation domain's complex motion can be converted into a simple rotation around the axis. The geometric center of the rotor's rotation domain and the orientation of the rotation axis are determined by the position and attitude of the aircraft. The rotation speed is also determined by the UDF global variable method and is communicated with the rotor speed control variable obtained by the control algorithm in step 4.

[0085] In step 6, the prepared UDF file is loaded into Fluent. The macros for calculating the vehicle's motion parameters and rotor control variables, as well as the macros for specifying the rotor's rotation domain and the motion state of the foreground region, are attached to the corresponding locations. Based on the initial flow field information obtained in step 3, an appropriate time step is selected for the transient flow field calculation. Finally, the obtained vehicle motion parameters and flow field results are post-processed. Specific embodiment one:

[0087] according to Figure 1 The process shown is to simulate the vertical ascent and altitude control process of the sea-air multi-purpose vehicle. The specific steps are as follows:

[0088] Step 1: Establish a simplified geometric model of the sea-air multi-purpose vehicle; Figure 2 As shown in the figure, the geometric model is mainly divided into three parts: rotor, fixed wing and frame, and the frame part is appropriately simplified.

[0089] Assume that the entire structure of the spacecraft is a rigid body with completely symmetrical parts. At the initial moment, the spacecraft's body coordinate system B(oxyz) coincides with the inertial coordinate system E(OXYZ); the spacecraft's center of mass is located at its geometric center and coincides with the origin of the body coordinate system.

[0090] Step 2: Based on the overlapping grid and sliding grid method, the corresponding aircraft flow field calculation domain is established - the background area, the foreground area and the four rotor rotation domains. The background area is divided into hexahedron grids, and the rotor rotation domain and foreground area are divided into tetrahedron grids first, and then the tetrahedron grids are converted into polyhedron grids in Fluent. The grid division of the foreground area and the rotor rotation domain is as follows Figure 3 and Figure 4 As shown, it can be seen that the grids in the rotor rotation domain and near the surface of the aircraft have been encrypted to a considerable extent.

[0091] Step 3: Import the divided computational domain mesh information into Fluent. Set the foreground and background meshes to overlap. Establish a sliding mesh computation interface between the rotor rotation domain mesh boundary and the foreground mesh boundary. Select a motion reference system for each of the four rotor rotation domains and set the rotational speed, rotation axis orientation, and rotation center coordinates. Select the SST k-ω turbulence model and the Coupled solver to perform steady-state calculations of the vehicle's external flow field. Use this flow field result as the initial flow field information for subsequent transient calculations.

[0092] Step 4: Assume that the velocity, displacement, and attitude angle of the aircraft in all directions are all zero at the initial moment. In the coordinate system set in step 1, the vertical ascent direction of the aircraft is the negative direction of the Y axis. The main forces acting on the aircraft are the lift generated by the rotation of the rotor along the Y axis and its own gravity. The forces and moments in other directions can be ignored. The dynamic equation of the aircraft in the Y axis direction is:

[0093] F Y =ma Y +mg

[0094] Where F Y is the force in the Y-axis direction, a Y is the acceleration in the Y-axis direction, g is the acceleration due to gravity;

[0095] The kinematic equation is:

[0096] and

[0097] v Y is the speed in the Y-axis direction, t is the time, x Y is the displacement in the Y-axis direction;

[0098] In the UDF, the inherent properties of the spacecraft, such as mass, moment of inertia, geometric center, and the motion parameters of the spacecraft at the initial moment, such as velocity, displacement, and attitude, are written as global variables. Then, under the DEFINE_EXECUTE_AT_END macro, the Compute_Force_And_Moment statement is used to extract the surface flow field load F of the spacecraft at the end of each time step. Y , and substitute it into the linearly simplified dynamic equation of the spacecraft: F Y =ma Y + mg. Get acceleration a Y Finally, we further combine the linear discretized kinematic equations: t+Δt =v t +aΔt and x t+Δt =x t +v t+Δt Δt, get the velocity v in the next time step Y and displacement x Y .

[0099] Next, the aircraft's altitude control algorithm is written. First, the aircraft's expected altitude is set as a global variable in the UDF. Within the DEFINE_EXECUTE_AT_END macro, the expected altitude is subtracted from the aircraft's actual altitude to obtain an error. The altitude error is then multiplied by a proportional coefficient of 5 to output the outer loop control variable, which is used as the error between the expected velocity and the aircraft's actual velocity. The velocity error is then fed into a discrete PID controller to output the inner loop control variable. The PID controller consists of a proportional, differential, and integral component, with corresponding coefficients set to 6, 1, and 0.05. The lift generated by the rotor is assumed to be proportional to the square of its rotational speed, and the proportional coefficient is derived from steady-state calculations. Based on the relationship between the lift forces of the aircraft's four rotors during vertical ascent, the inner loop control variable is treated as the desired total lift and converted into the rotor speed control variable for each time step. Finally, at the end of the program, statements are written to export and save the aircraft's motion parameters at each time step. To avoid duplication in the exported data, these statements are processed in parallel within the UDF.

[0100] Step 5: Then, in the UDF, write the DEFINE_ZONE_MOTION macros to specify the foreground area and the four rotor rotation domain mesh motions. The vehicle is performing vertical upward motion along the negative Y axis, so for the foreground area motion macro, simply call the velocity v calculated in step 4 through the UDF global variable. Y , to set the speed along the Y axis. For the four rotor rotation domains, their motion macros define their rotation relative to the foreground area around their own rotation center axis. The rotation center is the geometric center coordinate of each of the four rotor rotation domains; the rotation axis orientation is a unit vector parallel to the Y axis; and the rotation speed at each time step is determined by calling the rotor speed control value set in step 4 via a global variable.

[0101] Step 6: Load the prepared UDF file in Fluent, and respectively mount the macros for calculating the aircraft motion parameters and rotor control quantities, as well as the macros for specifying the rotor rotation domain and the motion state of the foreground area, to the corresponding positions. Then, based directly on the initial flow field information of the aircraft obtained in step 3, select a time step of 0.001s to perform the flow field transient calculation. After the calculation is completed, the flow field results can be processed to obtain the following Figures 6 to 9 The cloud diagram of the flow field of the aircraft at different times is shown in the figure. By processing the derived aircraft motion parameters, the height curve of the aircraft during the entire motion process can be obtained as shown in the figure. Figure 10 , rotor speed curve is as follows Figure 11 shown. Specific embodiment two:

[0103] according to Figure 1 The process shown is to simulate the vertical ascent and altitude control process of the sea-air multi-purpose vehicle. The specific steps are as follows:

[0104] Step 1: Establish the following for the sea-air multi-habitable vehicle: Figure 2 The geometric model shown.

[0105] Assume the entire structure of the spacecraft is a rigid body with completely symmetrical components. Initially, the spacecraft's body coordinate system B(oxyz) coincides with the inertial coordinate system E(OXYZ). The spacecraft's center of mass is located at its geometric center and coincides with the origin of the body coordinate system. The spacecraft's rotation about the x-axis of the body coordinate system is defined as roll motion, and the angle of rotation is defined as the roll angle.

[0106] Step 2: Based on the overlapping grid and sliding grid method, the corresponding aircraft flow field calculation domain is established - the background area, the foreground area and the four rotor rotation domains, and the grid division is performed. The grid division of the foreground area and the rotor rotation domain is as follows: Figure 3 and Figure 4 shown.

[0107] Step 3: Import the divided computational domain mesh information into Fluent, select the motion reference system under each of the four rotor rotation domains, and set their respective speeds, rotation axis orientations, and rotation center coordinates. Select the SST k-ω turbulence model and the Coupled solver to perform steady-state calculations on the vehicle's external flow field.

[0108] Step 4: Assume that the velocity, displacement and attitude angle of the aircraft in all directions are all 0 at the initial moment. In the body coordinate system, when the aircraft rolls around the X-axis, it is mainly affected by the lift generated by the rotor in the Y-axis direction, the X-axis torque generated by the lift difference between the two relative rotors in the Z-axis direction, and its own vertical downward gravity. The forces and torques in other directions can be ignored. Now convert the forces and torques it receives to the torque in the X-axis direction, the gravity in the Y-axis direction, and the components of the rotor lift in the Y-axis and Z-axis in the inertial coordinate system. The dynamic equation of the aircraft when rolling is:

[0109]

[0110] Where F Z is the force in the Z-axis direction, a Z is the acceleration in the Z-axis direction, M X is the torque in the X-axis direction, I X is the moment of inertia in the X-axis direction, β X is the angular acceleration in the X-axis direction;

[0111] The kinematic equation is:

[0112]

[0113] v Z is the speed in the Z-axis direction, Z is the displacement in the Z-axis direction; ω X is the angular velocity in the X-axis direction, X is the angular displacement in the X-axis direction.

[0114] In the UDF, the inherent properties of the spacecraft, such as mass, moment of inertia, geometric center, and the motion parameters of the spacecraft at the initial moment, such as velocity, displacement, and attitude, are written as global variables. Then, under the DEFINE_EXECUTE_AT_END macro, the Compute_Force_And_Moment statement is used to extract the surface flow field load F of the spacecraft at the end of each time step. Y 、F Z and M X , substitute it into the linear simplified dynamic equation of the spacecraft; get the acceleration a Y 、a Z and β X After that, we further combine the linear discretized kinematic equation to get the velocity v in the next time step.Y 、v Z 、ω X and displacement x Y 、x Z ,θ X .

[0115] Next, the angular displacement control algorithm for the vehicle is developed. First, the desired vehicle angle is written as a global variable in the UDF. Then, within the DEFINE_EXECUTE_AT_END macro, the desired angle is subtracted from the vehicle's real-time angular displacement to obtain the error. The angular error is then multiplied by a proportional coefficient of 3 to output the outer loop control variable, which is used as the desired angular velocity and then subtracted from the vehicle's real-time angular velocity to obtain the error. The angular velocity error is then fed into a discrete PID controller to output the inner loop control variable. The PID controller consists of a proportional, differential, and integral component, with corresponding coefficients set to 2, 1, and 0.05. The lift generated by the rotors is assumed to be proportional to the square of their rotational speed, and the proportional coefficient is derived from steady-state calculations. Before the vehicle begins to rotate, it remains in a hovering state, with all four rotors rotating at the same speed and the lift generated equal to the weight. By maintaining the speed of the two opposing rotors in the X-axis constant, the speed of the two opposing rotors in the Z-axis is adjusted, causing the vehicle to rotate due to a torque about the X-axis. According to this principle, the inner loop control quantity obtained in each time step in the angle control algorithm is regarded as the expected torque, and then converted into the control quantity of the two rotor speeds in the Z-axis direction.

[0116] Step 5: Continue in the UDF and write the DEFINE_ZONE_MOTION macros to specify the foreground area and the four rotor rotation domain mesh motions. The aircraft rotates around the X axis and also moves in the Z and Y axes. Therefore, for the foreground area motion macro, first call the velocity v obtained in the step through the UDF global variable. Y 、v Z Specify its translational velocity; then write the orientation of the vehicle's rotation axis - a vector parallel to the X axis, and calculate the displacement x obtained in step 4 based on the initial geometric center coordinates. Y 、x Z , write the center of rotation; finally, call the speed ω of step 4 through the global variable X Specify its rotation speed. For the four rotor rotation domains, their motion macros set their rotation relative to the foreground area around their own rotation center axis. The rotation center and rotation axis of each rotation domain are determined by the angular displacement θ of the aircraft. X Combined with the initial state, the rotation speeds of the two rotor rotation domains in the Z-axis direction are written by calling the rotor control value obtained in step 4 through the global variable method. The rotation speeds of the other two rotor rotation domains remain unchanged at the initial speed.

[0117] Step 6: Load the prepared UDF file in Fluent, and respectively mount the macros for calculating the aircraft motion parameters and rotor control quantities, as well as the macros for specifying the rotor rotation domain and the motion state of the foreground area, to the corresponding positions. Then, based directly on the initial flow field information of the aircraft obtained in step 3, select a time step of 0.001s to perform the flow field transient calculation. After the calculation is completed, the flow field results can be processed to obtain the following Figures 12 to 15 The cloud diagram of the flow field of the spacecraft at different times is shown in the figure. By processing the derived spacecraft motion parameters, the angle attitude curve of the spacecraft during the entire motion process can be obtained as shown in the figure. Figure 16 、Some rotor speed curves are as follows Figure 17 and Figure 18 shown.

[0118] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the invention. It should be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in conjunction with other described embodiments.

Claims

1. A Fluent-based intelligent flight control simulation method for multi-role sea and air vehicles, characterized by include, Step 1: Divide the aircraft into the rotor part, the frame part and the fixed-wing part to establish the aircraft geometric model; Step 2: Establish a flow field calculation domain geometric model including the rotor rotation domain, foreground area and background area based on the aircraft geometric model; The background area is divided into hexahedral meshes, and the rotor rotation domain and foreground area are divided into tetrahedral meshes and then converted into polyhedral meshes to obtain the flow field calculation domain mesh model; Step 3: Import the flow field calculation domain grid model into the Fluent flow field simulation calculation module, then set the foreground area grid and the background area grid to overlapping grids, and set the grid boundary of the rotor rotation domain to the sliding grid calculation interface; then, in the initial settings of the rotor rotation domain, enter the initial rotor speed, rotation axis orientation, and rotation center coordinates of the aircraft in the coordinate system of the flow field calculation domain geometry model, perform a steady-state calculation of the aircraft's external flow field, and use the calculation results as the initial flow field information for the transient external flow field calculation; Step 4: Write the first UDF program to calculate the vehicle motion parameters at any time step under transient external flow field simulation, and use the control algorithm to calculate the rotor speed control value based on the motion parameter calculation results; Step 5: Write a second UDF program to set the speed and position of the foreground area relative to the background area, as well as the speed and position of the rotor rotation domain relative to the foreground area, based on the motion parameters obtained in step 4, to calculate the transient external flow field. Step 6: Run the UDF program written in steps 4 and 5 in Flunet to perform iterative calculations to obtain the rotor speed control quantity, aircraft motion parameters and external flow field calculation results for each time step, thereby realizing intelligent control of the aircraft in the air.

2. The method for simulating the intelligent control of the airborne multi-habitable vehicle based on Fluent according to claim 1 is characterized in that: The flow field cloud map of the aircraft geometric model is obtained according to the calculation results of the external flow field; the height curve and rotor speed curve of the aircraft geometric model are obtained according to the aircraft motion parameters.

3. The method for simulating the intelligent control of the airborne multi-habitable vehicle based on Fluent according to claim 2, characterized in that: In step 4, the calculation of the aircraft motion parameters is achieved by writing a first UDF program; In the first UDF program, the inherent attribute variables and the motion parameters of the sea-air multi-purpose vehicle at the initial moment are used as global variables. Under the DEFINE_EXECUTE_AT_END macro, the Compute_Force_And_Moment statement is used to extract the surface flow field load of the vehicle at the end of the current time step, and the acceleration is calculated using the linearly simplified vehicle dynamics equation; then, the motion parameters of the vehicle in the next time step are obtained based on the linearly discretized kinematic equation.

4. The method for simulating the intelligent control of the airborne multi-habitable vehicle flight based on Fluent according to claim 3 is characterized in that: The surface flow field load includes force F and moment M; The calculation methods of acceleration include: F=ma, M=Iβ, Where m is mass, a is acceleration, I is moment of inertia, and β is angular acceleration; The calculation method of the motion parameters of the spacecraft includes: v t+Δt =v t +aΔt, x t+Δt =x t +v t+Δt Δt, Where v t+Δt is the velocity of the next time step, v t is the speed of the current time step, Δt is the interval between adjacent time steps, x t+Δt is the displacement of the next time step, x t is the displacement at the current time step.

5. The method for simulating the intelligent control of the airborne multi-habitable vehicle based on Fluent according to claim 4 is characterized in that: In step 4, the calculation of the rotor speed control variable is realized by the first UDF program; In the first UDF program, the expected position or attitude of the aircraft geometric model is used as a global variable. Under the DEFINE_EXECUTE_AT_END macro, the expected position or attitude is subtracted from the calculated aircraft position or attitude to obtain a position or attitude error. The position or attitude error is multiplied by a preset proportional coefficient to obtain an outer loop control variable of the aircraft geometric model. The outer loop control variable is then used as the expected speed and the speed is subtracted from the real-time speed of the aircraft to obtain a speed error. The speed error is processed using a discrete PID controller to obtain an inner loop control variable, and the rotor speed control variable of the aircraft geometric model is obtained from the inner loop control variable. The outer loop is the spacecraft position and attitude control loop; the inner loop is the spacecraft speed and angular velocity control loop.

6. The method for simulating the intelligent control of the airborne multi-habitable vehicle based on Fluent according to claim 5 is characterized in that: In step 5, the calculation of the transient external flow field is realized by writing the second UDF program; In the second UDF program, under the DEFINE_ZONE_MOTION macro, the velocity of the foreground area and the rotor rotation domain during translation and the center of rotation, axis orientation, and angular velocity during Euler rotation are specified in each time step; The speed of the foreground area during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion parameters of the aircraft; the speed of the rotor rotation domain during translation and the center of rotation, axis orientation and angular velocity during Euler rotation are determined according to the motion conditions of the foreground area and the rotor speed control amount.

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