CN111460743B - Data-driven bionic propulsion water power performance parameter acquisition method - Google Patents
Data-driven bionic propulsion water power performance parameter acquisition method Download PDFInfo
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Abstract
The invention discloses a data-driven acquisition method of hydrodynamic performance parameters of bionic propulsion, which comprises the steps of acquiring a moving image of a research object, analyzing the acquired moving image by a template matching method, establishing a motion mode database, establishing a geometric model of the research object according to first frame of moving image data, carrying out grid division on the established geometric model, updating and iterating the geometric model and the grid division according to the data of the motion mode database, solving hydrodynamic performance parameters of the iterated geometric model based on a fluid dynamics method to obtain hydrodynamic performance parameter values of the research object, accurately controlling the motion form of the research object in a simulation process according to an experimental result, and overcoming the problems of inaccurate simulation calculation and narrow application range caused by the traditional simplified motion model; the hydrodynamic performance analysis of bionic propulsion is directly driven by the database, so that the dilemma that theoretical analysis is inconsistent with experiments due to the fact that a motion model is simplified is avoided.
Description
Technical Field
The invention belongs to the technical field of underwater bionic robots, and particularly relates to a data-driven bionic propulsion water power performance parameter acquisition method.
Background
The aquatic organisms in the nature have remarkable underwater movement capability after the evolution of hundreds of millions of years, and the swimming performance of the aquatic organisms far exceeds that of an artificial underwater vehicle. The bionic propulsion is widely concerned by the inspiration of aquatic organisms in the nature, and in the past two decades, numerous researchers open the research of a new generation of underwater propellers based on the principle of the bionics so as to break through the technical bottlenecks of low efficiency, high noise, poor concealment and the like caused by the propulsion of the traditional propellers.
In order to explore the generation mechanism of the high-efficiency movement of aquatic organisms, researchers develop various researches such as theoretical analysis, simulation, experimental verification and the like based on the kinematics principle of the aquatic organisms. The biokinematics lays a foundation for dynamics research, hydrodynamic research and experimental research, and establishing a mathematical model according to the biokinematics is the first step of developing the research. However, the biological motion in the nature is complex, most motion modes cannot establish an accurate mathematical model, and in the traditional fish propulsion research field, a simplified model is established by fitting a fish body swing curve through a complex function of a polynomial and a sine function. However, a simplified model can not be provided for each biological motion, for example, the gait of the kalman motion of the fish, even the result of theoretical analysis and simulation is inconsistent with the experiment due to the simplified motion model, so that the research lacks a general application range, effective research data of the hydrodynamic performance of the bionic propulsion water cannot be effectively provided, and the real motion of the aquatic organisms cannot be restored to the maximum extent.
Disclosure of Invention
The invention aims to provide a data-driven bionic propulsion water power performance parameter acquisition method to overcome the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data-driven bionic propulsion water power performance parameter acquisition method comprises the following steps:
step 1), acquiring a moving image of a research object in one motion cycle;
step 2), carrying out image analysis on the acquired motion image, positioning the position information of the research object in each frame of image of the acquired motion image by adopting a template matching method, and establishing a motion mode database of the research object in one motion period;
and 3) establishing a geometric model of the research object by adopting a dynamic grid method according to the first frame of motion image data in one period in the motion mode database obtained in the step 2), carrying out grid division on the established geometric model, updating and iterating the geometric model and the grid division according to the data of the motion mode database, and carrying out hydrodynamic performance parameter solution on the iterated geometric model based on a fluid dynamics method so as to obtain a hydrodynamic performance parameter value of the research object.
Furthermore, based on a hydrodynamic experiment platform, a digital particle image velocimeter is adopted to collect motion images of aquatic organism research objects in multiple periods, and a complete motion period corresponding to the research organisms is reserved according to the collected motion images.
Further, the step 2) specifically comprises the following steps:
firstly, aiming at one frame of image in an acquired moving image, positioning the head of a research object by using a template matching method, marking the front end point of the head and the body contour, and establishing a coordinate system;
secondly, removing the ambient factors of the research object by binarization;
then, marking the central line of the research object through the body contour and the front end point, and fitting by adopting a spline curve;
then, uniformly cutting small sections on a central line, wherein a cutting line is vertical to the central line, and recording the coordinates of the intersection point position of the cutting line and the body contour line to obtain the motion mode data of one frame of image;
and finally, repeating the steps for each frame of image in one period, and constructing a motion mode database of the research object in one motion period.
Furthermore, in the motion mode data of one frame of image, the head end point of the study object is taken as the origin of coordinates, and the horizontal and vertical directions are taken as coordinate axes.
Further, in step 3), a flow field state model of the research object is established, and hydrodynamic performance parameters including boundary conditions, a speed and pressure decoupling algorithm, a space and time discrete format and a time step are set.
Further, updating and iterating the grid partitioning comprises the steps of: and establishing a grid updating model containing calling file data and data interpolation, and compiling and linking the grid updating model into a solver, so that the solver calls a corresponding program when the grid is updated every time, the spatial position of the boundary grid at the next time point is determined, and the grid division updating is realized.
Further, by setting the time step and the total duration, the meshing update is iterated according to the set time step and the total duration.
Furthermore, an interpolation method is adopted to explicitly perform meshing updating by using the result obtained by the motion mode database when each computational iteration step meshing updating is performed.
Furthermore, a third-order Catmull-Rom interpolation method is specifically adopted.
Furthermore, each frame of image data constructs a data file, and each data file contains the position information of all discrete points of one frame of image.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention relates to a data-driven acquisition method of hydrodynamic performance parameters of bionic propulsion, which comprises the steps of firstly acquiring a moving image of a research object, analyzing the acquired moving image by a template matching method, establishing a moving mode database of the research object in a moving period, establishing a geometric model of the research object according to the first frame of moving image data in the obtained moving mode database, carrying out grid division on the established geometric model, updating and iterating the geometric model and the grid division according to the data of the moving mode database, carrying out hydrodynamic performance parameter solution on the iterated geometric model based on a fluid dynamics method so as to obtain hydrodynamic performance parameter values of the research object, closely combining the actual movement and the simulation calculation of the research object, accurately controlling the movement form of the research object in the simulation process according to an experimental result, the problems of inaccurate simulation calculation and narrow application range caused by the traditional simplified motion model are solved; the hydrodynamic performance analysis of the bionic propulsion is directly driven by the database, the dilemma that theoretical analysis is inconsistent with experiments due to the fact that a motion model is simplified is avoided, the real motion of aquatic organisms is reduced to the maximum extent, and a novel research idea is provided for researching the hydrodynamic performance of the bionic propulsion.
Furthermore, the grid updating is performed explicitly by using the result obtained by the motion mode database when each calculation iteration grid is updated, so that the motion of a research object is consistent with the motion of image acquisition during simulation calculation, and the calculation precision is improved.
Furthermore, interpolation operation is adopted to explicitly perform meshing updating by using the result obtained by the motion mode database when each computational iteration step meshing updating, so that the stability and the precision of numerical computation are ensured.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a DPIV acquisition motion image of the present invention.
Fig. 3 is a flowchart of image processing according to the present invention.
FIG. 4 is a schematic diagram of entity dispersion in the image processing process according to the present invention.
Fig. 5 is a flow chart of a numerical calculation process according to the present invention.
Fig. 6 is an explanatory diagram of the temporal-spatial mismatch problem according to the present invention.
Detailed Description
As shown in fig. 1, the method for acquiring the power performance parameters of the data-driven bionic propulsion water according to the present invention directly drives the grid updating process in the simulation calculation process through the motion mode database, thereby avoiding simplifying the motion model, and the following describes the method in detail with reference to the accompanying drawings and specific embodiments:
step 1), acquiring a moving image of a research object in a motion cycle:
based on a hydrodynamic experiment platform, a Digital Particle Image Velocimeter (DPIV) is adopted to directly acquire moving images of aquatic organism research objects in multiple periods, and a complete movement period corresponding to the research organisms is reserved according to the acquired moving images. The experimental research object is usually fish, and the experimental environment is mainly the basin, consequently, the high frequency image of gathering is mostly black and white picture, is favorable to the later stage to carry out image analysis. The images acquired by DPIV are shown in fig. 2.
Step 2), carrying out image analysis on the acquired motion image, positioning the position information of the research object in each frame of image of the acquired motion image by adopting a template matching method, and establishing a motion mode database of the research object in one motion period;
the image analysis process is illustrated by taking fish as an example, and as shown in fig. 3, the image analysis process comprises the following steps: firstly, aiming at a first frame image of a motion cycle, positioning the head of a fish by using a template matching method, marking the front end point of the head and the body contour, and establishing a coordinate system; secondly, the influence of environmental factors around the research object is removed by binarization, and under a high-frequency black-and-white image, the colors of an experimental environment (water and a water tank) and aquatic organisms are greatly different, so that the accurate body contour of the research object is easily obtained by binarization; then, marking the central line (spinal line) of the fish body through the body contour and the front end endpoint, and fitting by adopting a spline curve, as shown in fig. 4; then, uniformly cutting small sections on a central line, wherein a cutting line is perpendicular to the central line and intersects with a body contour line at a left point and a right point, a head end point is taken as a coordinate origin, the horizontal direction and the vertical direction are taken as coordinate axes, the position coordinates of the intersection point of the cutting line and the body contour line are recorded, and finally, the operation is repeatedly carried out for each frame of image in one period, and the position coordinates of each discrete point in each frame of image are determined; each frame of image comprises a plurality of discrete points, each frame of image data constructs a data file, each data file comprises position information of all the discrete points of one frame of image, all image processing is completed, and a motion mode database of the research object in one motion cycle is constructed.
The template matching method is to compare and identify an acquired moving image with a template, which is an image of a body part to be identified of a study object prepared in advance, and is easy to implement because the acquired moving image is usually a black-and-white image under high-frequency acquisition of the DPIV.
And 3) establishing a geometric model of the research object by adopting a dynamic grid method according to the first frame of motion image data in one period in the motion mode database obtained in the step 2), carrying out grid division on the established geometric model, updating and iterating the geometric model and the grid division according to the data of the motion mode database, and carrying out hydrodynamic performance parameter solution on the geometric model after the grid division based on a fluid dynamics method so as to obtain a hydrodynamic performance parameter value of the research object.
As shown in fig. 5, the hydrodynamic performance parameters are solved, and the turbulence model, the boundary conditions, the velocity-pressure decoupling algorithm, the spatial and temporal discrete format, and the time step are solved. According to the motion data of the first frame image in the single period of the research object in the motion mode database, a SolidWorks three-dimensional modeling software is adopted to establish a geometric model of the research object, then the geometric model of the research object is firstly converted into an intermediate file format (igs format), and is led into a mesh division software ICEM for mesh division.
Establishing a flow field state model of a research object: in the research process, a flow field governing equation adopts a Navier-Stokes equation, the flow field solution is to solve the partial differential equation set, and the Reynolds average Navier-Stokes RANS equation of the flow field is as follows:
wherein the content of the first and second substances,
is the Reynolds stress, δ ij Is the kronecker function, p is the pressure, μ is the kinetic viscosity coefficient, ρ is the fluid density;
in the research process, high Reynolds number is mainly involved, so the flow field state is turbulent flow, the turbulent flow model to be solved is a readable k-epsilon model, and the equation of the model is as follows:
wherein G is k And G b Turbulent kinetic energy generation term, Y, due to flow field mean velocity and buoyancy, respectively M Representing the contribution of the pulsating expansion in the compressible turbulence to the total dissipation factor, σ k And σ ε Is the turbulent Plantt number, C 2 ,C 1ε And C 3ε Is a constant number, S k And S ε Is a source item defined by a user;
setting boundary conditions including the inlet and outlet conditions and the wall surface conditions of the flow field, wherein the wall surface function of the wall surface conditions is processed by adopting an enhanced wall surface function; the velocity pressure decoupling algorithm adopts a PISO algorithm, the time term adopts a first-order implicit Euler format, the pressure gradient interpolation adopts a second-order format, and the convection term adopts a second-order windward format; the relaxation factor of the pressure term is reduced to 0.15 to improve convergence of numerical calculations.
Performing secondary development on the existing flow field solver by using commercial flow field solver software ANSYFLUENT, wherein the secondary development is to write a corresponding algorithm program by using C language, and comprises calling document data and interpolating data; writing a corresponding user self-defining program (UDF) by using C language, wherein a DEFINE _ GRID _ MOTION macro function is mainly used in the writing process, a MOTION mode database file is called in the function, corresponding file data is read and interpolation processing is carried out, the position of a boundary point is updated according to an interpolation result, the user-defined program written by a user is compiled and then is linked into a solver, so that the solver calls the corresponding program when the GRID is updated every time to determine the space position of the boundary GRID at the next time point.
And setting a dynamic grid division updating method, adopting a spring fairing method and a reconstruction method, updating the updating frequency once for each time step, setting the time step and the total duration parameter, and starting iterative solution.
In the process, an interpolation method is adopted to explicitly update grids by using the result obtained by the motion mode database when each calculation iteration step grid division is updated, so that the motion of a research object is consistent with the motion of image acquisition during simulation calculation. Specifically, as shown in fig. 6, in order to solve the two mismatch problems, the present application performs two times of interpolation operations, and in order to ensure the stability and accuracy of numerical value calculation, an interpolation formula of at least three orders is adopted, and the present application adopts a three-order Catmull-Rom interpolation method; the interpolation function can be expressed as a convolution of a discrete function and an interpolation kernel function:
in a third order Catmull-Rom interpolation method
Wherein a is 0.5.
The x and y direction displacement variables for the interpolated points can be written as:
the next time step position coordinate of the grid division point in the flow field that needs to be updated is written as:
the underwater thrust is an effect force and cannot be directly solved, and the thrust is generally composed of a pressure part and a friction part; the pressure magnitude depends on the shape, while the friction is influenced by the viscosity of the fluid. The pressure and friction produce a propulsive or retarding action on the organism, the total thrust value consisting essentially of a propulsive action and a retarding action, i.e. a thrust value
F(t)＝F T (t)-D(t)
Wherein F T The (t) and d (t) profiles represent the propulsive and retarding effects of pressure and friction.
F T (t) and D (t) are specifically defined as:
where p is the unit pressure,. tau ix Is the viscous stress tensor component of the x-direction, n j Is the direction vector, Σ is the total area.
The total power can be decomposed into useful power and lateral power, the lateral power being
The swimming efficiency is
In the flow field numerical solving process, the physical quantity required by the calculation is saved, and the main hydrodynamic parameters of thrust, resistance and efficiency are calculated, so that the bionic propulsion hydrodynamic analysis can be completed.
In summary, the invention provides a data-driven acquisition method for bionic propulsion hydrodynamic performance parameters, aiming at the problem of motion model simplification existing in the traditional hydrodynamic performance research, compared with the traditional research method, the research method provided by the invention tightly combines the actual motion of a research object with simulation calculation, accurately controls the motion form of the research object in the simulation process according to the experimental result, overcomes the main problems of inaccurate simulation calculation, narrow application range and the like brought by the traditional simplified motion model, and provides a practical and reliable research approach for developing aquatic organism complex motion research.
The above-mentioned contents are only for explaining the technical idea of the invention of the present application, and can not be used as the basis for limiting the protection scope of the invention, and any modifications and substitutions made on the technical solution according to the design concept and technical features proposed by the present invention are within the protection scope of the claims of the present invention.
Claims (7)
1. A data-driven bionic propulsion water power performance parameter acquisition method is characterized by comprising the following steps:
step 1), acquiring a moving image of a research object in a motion cycle; based on a hydrodynamic experiment platform, a digital particle image velocimeter is adopted to collect motion images of aquatic organism research objects in a plurality of periods, and a complete motion period corresponding to the research organisms is reserved according to the collected motion images;
step 2), carrying out image analysis on the acquired motion image, positioning the position information of the research object in each frame of image of the acquired motion image by adopting a template matching method, and establishing a motion mode database of the research object in one motion period; firstly, aiming at one frame of image in an acquired moving image, positioning the head of a research object by using a template matching method, marking the front end point of the head and the body contour, and establishing a coordinate system;
secondly, removing the ambient factors of the research object by binarization;
then, marking the central line of the research object through the body contour and the front end point, and fitting by adopting a spline curve;
then, uniformly cutting small sections on a central line, wherein a cutting line is vertical to the central line, and recording the coordinates of the intersection point position of the cutting line and the body contour line to obtain the motion mode data of one frame of image;
finally, the steps are repeated for each frame of image in a period, and a motion mode database of the research object in a motion period is constructed;
step 3), establishing a geometric model of the research object by adopting a dynamic grid method according to the first frame of motion image data in one period in the motion mode database obtained in the step 2), carrying out grid division on the established geometric model, updating and iterating the geometric model and the grid division according to the data of the motion mode database, and carrying out hydrodynamic performance parameter solution on the iterated geometric model based on a fluid dynamics method so as to obtain a hydrodynamic performance parameter value of the research object;
in the motion mode data of one frame of image, the head end point of the research object is taken as a coordinate origin, and the horizontal direction and the vertical direction are taken as coordinate axes.
2. The method for acquiring the performance parameters of the bionic propulsion water driven by the data according to claim 1, wherein in the step 3), a flow field state model of a research object is established, and the set performance parameters of the water power comprise boundary conditions, a speed and pressure decoupling algorithm, a space and time discrete format and a time step length.
3. The method for acquiring the dynamic performance parameters of the bionic propulsion water driven by the data as claimed in claim 2, wherein the updating and the iteration of the meshing comprise the following steps: and establishing a grid updating model containing calling file data and data interpolation, and compiling and linking the grid updating model into a solver, so that the solver calls a corresponding program when the grid is updated every time, the spatial position of the boundary grid at the next time point is determined, and the grid division updating is realized.
4. The method for acquiring the kinetic performance parameters of the bionic propulsion water driven by the data as claimed in claim 3, wherein the gridding updating is iterated according to the set time step and the total duration by setting the time step and the total duration.
5. The method of claim 3, wherein the gridding update is performed explicitly by using the result from the motion mode database at each computational iteration step gridding update by using an interpolation method.
6. The data-driven bionic propulsion water power performance parameter acquisition method as claimed in claim 5, wherein a third-order Catmull-Rom interpolation method is adopted.
7. The method for acquiring the kinetic performance parameters of the bionic propulsion water according to claim 1, wherein each frame of image data is constructed into a data file, and each data file contains the position information of all discrete points of one frame of image.
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