A bionic measurement and online estimation method for the flow field around the rudder blade of a submersible

Abstract

The present invention discloses a bionic measurement and online estimation method of a flow field around a rudder blade of a submersible, comprising the following steps: obtaining the pressure distribution on the rudder blade surface based on a computational fluid dynamics method, arranging a pressure sensor, and obtaining the actual pressure measurement value of the rudder blade flow field; establishing an online simulation mechanical model of the rudder blade flow field, and obtaining the virtual pressure prediction value of the rudder blade; establishing a physical association between the state value of the rudder blade flow field and the actual pressure measurement value; fusing the actual pressure measurement value and the virtual pressure prediction value, and estimating the state value of the rudder blade flow field. The present invention can accurately and real-timely estimate parameters such as the relative flow velocity and effective angle of attack of the rudder blade flow field, improve the output accuracy of the effective angle of attack of the submersible rudder blade, and improve the maneuverability of the submersible.

Description

Technical Field

[0001] The invention belongs to the technical field of ocean flow field perception, and in particular relates to a bionics measurement and online estimation method of a flow field around a rudder blade of a submersible. Background Art

[0002] Submersibles, such as underwater robots and submarines, play an indispensable role in the military field. Improving the maneuvering accuracy of submersibles is of great significance to improving the operating ability of submersibles in complex marine environments. In the manipulation and control of submersibles, the rudder blade plays a key role. The rudder blade mainly relies on the relative angle of attack with the external flow to generate lift for submersible manipulation. The relative velocity and angle of attack between the rudder blade and the ocean current determine the magnitude of the lift. Affected by random currents, eddies and other factors in the ocean, the ground speed (i.e., the speed relative to the geodetic coordinate system) and the turning angle of the rudder blade are not equal to the relative velocity and effective angle of attack, which in turn affects the actual lift of the rudder blade and reduces the maneuvering accuracy of the submersible. Therefore, estimating the relative velocity and effective angle of attack between the rudder blade and the ocean current is of great significance to improving the maneuvering accuracy of the submersible. Summary of the invention

[0003] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and to propose a bionic measurement and online estimation method for the flow field around the rudder blade of a submersible, so as to realize the online estimation of the relative flow velocity and the effective angle of attack parameters of the flow field around the rudder blade of the submersible, provide real-time feedback of key flow field parameters for the rudder blade control of the submersible, improve the accuracy of the effective rudder angle and lift output of the rudder blade, and thus improve the control accuracy of the submersible.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A bionics measurement and online estimation method for a flow field around a submersible rudder blade comprises the following steps:

[0006] S1. Obtain the pressure distribution on the rudder blade surface based on the computational fluid dynamics method, arrange the pressure sensor, and obtain the actual pressure measurement value of the flow field around the rudder blade;

[0007] S2, establishing an online simulation mechanical model of the flow field around the rudder blade to obtain a predicted value of the virtual pressure of the rudder blade;

[0008] S3, establishing a physical association between the flow field state value around the rudder blade and the actual pressure measurement value;

[0009] S4. The actual pressure measurement value and the virtual pressure prediction value are integrated to estimate the state value of the flow field around the rudder blade.

[0010] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0011] 1. The present invention is based on the bionic principle that the lateral line of fish senses the dynamic pressure of the flow field, and designs a method for measuring the pressure of the flow field around the rudder blade of a submersible. This method can enable the rudder blade of a submersible to sense the changes in the external flow field in real time, thus solving the problem that the rudder blade of a traditional submersible is difficult to sense the external time-varying flow field environment.

[0012] 2. The present invention establishes an online virtual simulation mechanics method for the flow field of a submersible rudder blade based on the theory of fluid mechanics, realizes real-time calculation of pressures at different positions of the rudder blade, and provides an online mechanical simulation tool for parameter estimation of the flow field of a submersible rudder blade, thus solving the problem that the existing submersible rudder blade cannot simulate the flow field in real time and it is difficult to establish a physical connection between the observed value of the flow field and the state value.

[0013] 3. In the method of the present invention, the actual pressure measurement information of the flow field around the rudder blade and the virtual pressure calculation information of the rudder blade mechanical model are effectively integrated, so as to obtain the approximate optimal estimation of key parameters such as the effective angle of attack and relative flow velocity of the rudder blade of the submersible relative to the external flow field, thereby improving the accuracy of the effective angle of attack and lift of the rudder blade and realizing precise control of the submersible. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the method of the present invention;

[0015] Figure 2 It is a schematic diagram of the arrangement of pressure sensing points on the rudder blade surface;

[0016] Figure 3 It is a schematic diagram of the three-dimensional design of rudder blade pressure sensing;

[0017] Figure 4 It is a schematic diagram of the vortex layer arrangement on the rudder blade surface and the trailing edge vortex;

[0018] Figure 5 is an example diagram of the estimation of the relative speed between the rudder blade and the external flow field according to the present invention;

[0019] Figure 6 is an example diagram of the estimation of the effective angle of attack of the rudder blade and the external flow field according to the present invention;

[0020] Description of the accompanying figures: 1-rudder blade body; 2-pressure conduction hole; 3-pressure sensor; 4-rudder blade shaft. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0022] Example

[0023] like Figure 1 As shown, the present invention is a bionic measurement and online estimation method of the flow field around the rudder blade of a submersible, comprising the following steps:

[0024] S1. Obtaining the pressure distribution on the rudder blade surface based on the computational fluid dynamics method, arranging the pressure sensor, and obtaining the actual pressure measurement value of the flow field around the rudder blade; in this embodiment, specifically including:

[0025] Taking the rudder blade of NACA airfoil section as an example, the three-dimensional geometric model of the rudder blade of the submersible is established by geometric modeling software and imported into computational fluid dynamics software for numerical simulation. By setting different flow field attack angles θ and flow velocities U, the pressure distribution on the rudder blade surface is obtained.

[0026] Calculate the derivative of the pressure cp at each position of the rudder blade with respect to the angle of attack θ and the flow velocity U of the surrounding flow field and

[0027] According to the theory of empirical observability of system state, and The larger rudder position is more sensitive to the changes in the system state angle of attack θ and flow velocity U. Therefore, the observability of the system state angle of attack θ and flow velocity U in this area is stronger. The pressure measurement points cp are arranged in the rudder area from front to back. 0 、cp 1 ,...,cp M , that is, setting a pressure sensor to obtain the actual pressure measurement value of the flow field around the rudder blade.

[0028] In actual implementation, the pressure difference between sensors can also be used as the measurement output, for example, the pressure difference between two adjacent sensors forms a vector: cp 0 -cp 1 ,cp 1 -cp 2 ,...,cp M -1-cp M .

[0029] like Figure 2 and Figure 3 As shown, a design example of rudder blade surface imitating fish lateral line pressure perception is shown. The rudder blade rotates around the rudder blade shaft 4 in the flow field. The pressure conduction hole 2 is arranged on the surface of the rudder blade body 1 to conduct the pressure of the external flow field to the pressure sensor 3. The pressure sensor 3 collects pressure data in real time to realize real-time measurement of the flow field around the rudder blade.

[0030] S2. Establishing an online simulation mechanical model of the flow field around the rudder blade to obtain the predicted value of the virtual pressure of the rudder blade; including:

[0031] S21. A rudder blade fluid dynamics model is established based on potential flow theory, and the arrangement method of the rudder blade attachment vortex, the rudder blade trailing edge Kutta condition and the free vortex motion model are set; specifically:

[0032] The potential flow model in fluid mechanics theory has the advantages of obtaining analytical expressions and fast calculation speed, and is suitable for real-time simulation calculation. Therefore, the potential flow theory is used to establish the rudder blade fluid mechanics model, set the arrangement method of the rudder blade attachment vortex, the rudder blade trailing edge Kutta condition and the free vortex motion model;

[0033] The linear vortex element is used to approximate the vortex intensity distribution of the rudder blade. Starting from the trailing edge of the rudder blade, the linear vortex elements are arranged in sequence in the counterclockwise direction along the surface of the rudder blade until it returns to the trailing edge of the rudder blade. There are a total of N+1 nodes and N linear vortex elements. The vortex intensity of each node is expressed by γ 0 , γ 1 , ..., γ N The linear vortex element between two adjacent nodes is represented by γ j.j+1 (j=0,1,...,N-1); a free vortex is generated at the tail of the rudder blade at each time step, and the vortex strength of the free vortex is expressed by Γ 0 , Γ 1 , ..., Γ S Represents, where S represents the Sth time step; Figure 4 The figure shows the arrangement of vortex layers on the rudder blade surface and the schematic diagram of the trailing edge vortex.

[0034] The Kutta condition at the trailing edge of the rudder blade is set as the unit pressure p on the trailing edge. 0,1 and the lower unit pressure p N-1,N equal; the rudder blade surface meets the impenetrable condition;

[0035] The motion of the free vortex is updated using the local velocity of the flow field;

[0036] The hydrodynamic model of the rudder blade is expressed as:

[0037]

[0038] Where U is the external flow velocity, n i is the normal vector of the rudder blade unit, A i,j With C i,j are the influence coefficients of linear vortex element and free vortex, respectively.

[0039] S22, solving the rudder blade fluid mechanics model by an iterative solution method, obtaining the vortex intensity distribution on the rudder blade surface, calculating the local speed of the free vortex after obtaining the rudder blade vortex intensity, and updating the position of the free vortex;

[0040] S23, using the Bernoulli principle to establish a virtual pressure measurement equation on the rudder blade surface, and obtain a predicted value of the virtual pressure on the rudder blade surface; wherein the virtual pressure measurement equation on the rudder blade surface established using the Bernoulli principle is specifically:

[0041]

[0042] Among them, Φ(X) represents the velocity potential at different positions of the rudder blade, ρ is the density of water, and the velocity potential Φ(X) is obtained by superimposing the velocity potential of each unit.

[0043] S3, establishing a physical association between the state value of the flow field around the rudder blade and the actual pressure measurement value; including:

[0044] S31, based on the unscented Kalman filter algorithm, a physical correlation model between the rudder blade flow field state value and the pressure measurement value is established, specifically:

[0045] The unscented Kalman filter algorithm is used to establish the physical correlation model between the state value of the flow field around the rudder blade and the pressure measurement value. The physical connection includes two parts: the system model f(·) and the measurement model h(·):

[0046]

[0047] Wherein, the subscript k represents the kth time step, x = [U, θ] is the state value of the flow field around the rudder blade, and z = [cp 0 ,cp 1 ,...,cp M ] is the observed pressure on the rudder blade surface, the system model f(·) is set as the rudder blade fluid mechanics model, the measurement model h(·) is set as the virtual pressure measurement equation on the rudder blade surface, v and w represent the system model white noise and the measurement model white noise respectively, and satisfy v~N(0,Q) and v~N(0,R), Q and R are the system noise matrix and the measurement noise matrix respectively.

[0048] S32, setting the initial values ​​of the system model and the measurement model, the system noise matrix and the measurement noise matrix, specifically:

[0049] Set the state variable x = [U, θ] of the system model, that is, the velocity and angle of attack of the rudder blade and the surrounding flow field;

[0050] Set the system observation z = [cp 0 ,cp 1 ,...,cp M ], i.e., the observed value of the rudder blade surface pressure;

[0051] The a posteriori estimate of the relative velocity U and the effective angle of attack θ of the flow field around the rudder is expressed as

[0052] S4. The actual pressure measurement value and the virtual pressure prediction value are integrated to estimate the state value of the flow field around the rudder blade.

[0053] Since the flow field around the rudder blade is time-varying, the state value of the flow field needs to be estimated and updated at each time step; the estimated update at each time step includes two parts: a priori estimation and a posteriori estimation.

[0054] The a priori estimate is:

[0055] The system model of step S31 is used to obtain the mean value x of the prior estimation of the flow field state around the rudder blade in the k+1 time step. k+1|k and its covariance matrix P k+1|k ;

[0056] The posterior estimate is:

[0057] The virtual pressure prediction value z of the rudder blade surface is obtained through the measurement model in step S31 k+1|k and its covariance matrix P z ;

[0058] Estimate x based on the state prior k+1|k and the virtual pressure prediction value z k+1|k Get the cross-covariance matrix P xz According to P xz With P z Get the Kalman gain K k :

[0059]

[0060] Calculate the a posteriori estimate of the flow field state based on the Kalman filter formula and its covariance matrix P k+1|k+1 :

[0061]

[0062] After obtaining the a priori estimate of the current time step, return to the a priori estimate step to estimate the state of the flow field around the rudder surface in the next time step; through online a priori and a posteriori iterative estimation, the actual and virtual pressure prediction values ​​of each time step are integrated to realize the dynamic estimation of the relative flow velocity U and the effective angle of attack θ of the flow field around the rudder surface.

[0063] like Figure 5 and Figure 6 As shown in the figure, the dotted line is the actual value, and the solid line is the estimated value according to the method of this embodiment. It can be seen that the present invention can not only estimate the relative flow velocity between the rudder blade and the external flow field online, but also dynamically estimate the changing effective angle of attack online. Through online flow field parameter estimation, state feedback can be provided for the angle adjustment of the rudder blade of the submersible, and the accuracy of the effective rudder angle and lift output of the rudder blade can be improved, thereby improving the control accuracy of the submersible.

[0064] The method of the present invention collects actual pressure measurement information of the flow field around the rudder blade by imitating the fish lateral line pressure sensing method, and at the same time establishes a virtual simulation mechanical model of the rudder blade of the submersible based on the potential flow theory to obtain the virtual pressure prediction value of the rudder blade surface, and uses the Kalman filter to fuse the actual pressure measurement information with the virtual simulation pressure information of the mechanical model, so as to realize the online estimation of the relative flow velocity and the effective angle of attack parameters of the flow field around the rudder blade of the submersible, and provide real-time feedback of key flow field parameters for the rudder blade control of the submersible.

[0065] It should also be noted that in this specification, terms such as "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A bionic measurement and online estimation method for the flow field around a submersible rudder blade, characterized in that: The following steps are involved: S1. Obtain the pressure distribution on the rudder blade surface based on the computational fluid dynamics method, arrange the pressure sensor, and obtain the actual pressure measurement value of the flow field around the rudder blade; S2, establishing an online simulation mechanical model of the flow field around the rudder blade to obtain a predicted value of the virtual pressure of the rudder blade; S3. Establishing a physical association between the state value of the flow field around the rudder blade and the actual pressure measurement value, including: S31, based on the unscented Kalman filter algorithm, a physical correlation model between the rudder blade flow field state value and the pressure measurement value is established, specifically: The unscented Kalman filter algorithm is used to establish a physical correlation model between the flow field state value of the rudder blade and the pressure measurement value. The physical correlation model consists of two parts: the system model f(·) and the measurement model h(·): Wherein, the subscript k represents the kth time step, x = [U, θ] is the state value of the flow field around the rudder blade, and z = [cp0, cp1, ..., cp M ] is the observed pressure on the rudder blade surface, the system model f(·) is set as the rudder blade fluid mechanics model, the measurement model h(·) is set as the virtual pressure measurement equation on the rudder blade surface, v and w represent the system model white noise and the measurement model white noise respectively, and they satisfy v~N(0,Q) and v~N(0,R), Q and R are the system noise matrix and the measurement noise matrix respectively; S32, setting the initial values ​​of the system model and the measurement model, the system noise matrix and the measurement noise matrix, specifically: Set the state variable x = [U, θ] of the system model, that is, the relative velocity and angle of attack between the rudder blade and the surrounding flow field; Set the system observation z = [cp0, cp1, ..., cp M ], i.e., the observed value of the rudder blade surface pressure; The a posteriori estimate of the relative velocity U and the effective angle of attack θ of the flow field around the rudder is expressed as S4, fusing the actual pressure measurement value and the virtual pressure prediction value to estimate the state value of the flow field around the rudder blade; Since the flow field around the rudder blade is time-varying, the state value of the flow field needs to be estimated and updated at each time step; the estimation update at each time step includes two parts: a priori estimation and a posteriori estimation; The a priori estimate is: The system model of step S31 is used to obtain the mean value x of the prior estimation of the flow field state around the rudder blade in the k+1 time step. k+1|k and its covariance matrix P k+1|k ; The posterior estimate is: The virtual pressure prediction value z of the rudder blade surface is obtained through the measurement model in step S31 k+1|k and its covariance matrix P z ; Estimate x based on the state prior k+1|k and the virtual pressure prediction value z k+1|k Get the cross-covariance matrix P xz According to P xz With P z Get the Kalman gain K k : Calculate the a posteriori estimate of the flow field state based on the Kalman filter formula and its covariance matrix P k+1|k+1 : After obtaining the a priori estimate of the current time step, return to the a priori estimate step to estimate the state of the flow field around the rudder surface in the next time step; through online a priori and a posteriori iterative estimation, the actual and virtual pressure prediction values ​​of each time step are integrated to realize the dynamic estimation of the relative flow velocity U and the effective angle of attack θ of the flow field around the rudder surface.

2. The bionic measurement and online estimation method of the flow field around the rudder blade of a submersible according to claim 1 is characterized in that: Step S1 specifically includes: The three-dimensional geometric model of the rudder blade of the submersible is established by geometric modeling software and imported into computational fluid dynamics software for numerical simulation. Different flow field attack angles θ and flow velocities U are set to obtain the pressure distribution on the rudder blade surface. Calculate the derivative of the pressure cp at each position of the rudder blade with respect to the angle of attack θ and the flow velocity U of the surrounding flow field and According to the theory of empirical observability of system state, and The larger rudder position is more sensitive to the changes in the system state angle of attack θ and flow velocity U. Therefore, the observability of the system state angle of attack θ and flow velocity U in this area is stronger. The pressure measurement points cp0, cp1, ..., cp are arranged in the rudder area from front to back. M , that is, setting a pressure sensor to obtain the actual pressure measurement value of the flow field around the rudder blade.

3. The bionic measurement and online estimation method of the flow field around the rudder blade of a submersible according to claim 1 is characterized in that: Step S2 includes: S21. Establish a rudder blade fluid dynamics model based on potential flow theory, set the arrangement method of the rudder blade attachment vortex, the rudder blade trailing edge Kutta condition and the free vortex motion model; S22, solving the rudder blade fluid mechanics model by an iterative solution method, obtaining the vortex intensity distribution on the rudder blade surface, calculating the local speed of the free vortex after obtaining the rudder blade vortex intensity, and updating the position of the free vortex; S23. Using Bernoulli's principle, a virtual pressure measurement equation for the rudder blade surface is established to obtain a predicted value of the virtual pressure on the rudder blade surface.

4. The bionic measurement and online estimation method of the flow field around the rudder blade of a submersible according to claim 3 is characterized in that: Step S21 is specifically as follows: The potential flow theory is used to establish the fluid dynamics model of the rudder blade, and the arrangement method of the rudder blade attachment vortex, the rudder blade trailing edge Kutta condition and the free vortex motion model are set; The linear vortex element is used to approximate the vortex intensity distribution of the rudder blade. Starting from the trailing edge of the rudder blade, the linear vortex elements are arranged in sequence in the counterclockwise direction along the surface of the rudder blade until it returns to the trailing edge of the rudder blade. There are a total of N+1 nodes and N linear vortex elements. The vortex intensity of each node is represented by γ0, γ1, ..., γ N The linear vortex element between two adjacent nodes is represented by γ j.j+1 , j = 0, 1, ..., N-1; the tail of the rudder blade escapes a free vortex at each time step, and the vortex strength of the free vortex is represented by Γ0, Γ1, ..., Γ S Represents, where S represents the Sth time step; The Kutta condition at the trailing edge of the rudder blade is set as the unit pressure p on the trailing edge. 0,1 and the lower unit pressure p N-1,N equal; the rudder blade surface meets the impenetrable condition; The motion of the free vortex is updated using the local velocity of the flow field; The hydrodynamic model of the rudder blade is expressed as: Where U is the external flow velocity, n i is the normal vector of the rudder blade unit, A i,j With C i,j are the influence coefficients of linear vortex element and free vortex, respectively.

5. The bionic measurement and online estimation method of the flow field around the rudder blade of a submersible according to claim 4 is characterized in that: Step S23, using Bernoulli's principle to establish the virtual pressure measurement equation on the rudder blade surface is specifically: Among them, φ(X) represents the velocity potential at different positions of the rudder blade, ρ is the density of water, and the velocity potential φ(X) is obtained by superimposing the velocity potential of each unit.

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