Intelligent control device and method for eliminating wake of bluff body

Abstract

The invention discloses an intelligent control device and method for eliminating a bluff body wake. The device comprises a bluff body model, wherein holes are formed in the lower part and the rear part of the bluff body model; the flow suction pipe is arranged at the opening below the bluff body model and is used for sucking the fluid in the flow field; the fluid conveying pipe is connected with the fluid suction pipe and used for conveying sucked fluid. The flow controller is connected with the flow conveying pipe and used for controlling and adjusting excitation parameters; the servo motor is connected with the flow controller, the direction and the strength of the exciter are adjusted through parameters output by the flow controller, the exciter is arranged at a rear opening of the bluff body model, and a rear flow field of the bluff body is interfered through the exciter, so that wake flow is controlled and eliminated; the data processing training system is connected with the flow controller and the CCD high-speed camera through wireless signals, and the optical axis of the CCD high-speed camera is perpendicular to the light plane of the laser sheet emitted by the high-frequency laser. According to the invention, the wake formed behind the bluff body can be dynamically and intelligently controlled, adjusted and eliminated in real time.

Description

Technical Field

[0001] The invention belongs to the technical field of flow field intelligent control, and in particular relates to an intelligent control device and method for eliminating blunt body wakes. Background Art

[0002] Blunt bodies, as a classic structural form, have received extensive attention in civil and marine engineering fields such as high-rise buildings, long-span bridges, offshore platforms, and underwater vehicles. When a fluid flows through a bluff body, flow separation will occur, forming a significant wake behind the bluff body. At the same time, its resistance will cause asymmetric periodic vortex shedding. These alternating vortex shedding not only reduces the stability of the near wake, but also generates unsteady forces on the bluff body. For structures such as high-rise buildings and long-span bridges, these wakes will cause the structures to produce vibrations and wind-induced responses of varying amplitudes, seriously threatening the safety and stability of the structures. For marine structures such as underwater vehicles, the wakes they produce will greatly affect the stealth performance and cause a lot of flow noise. Therefore, the control and elimination of bluff body wakes are crucial to the safety, performance, and stability of the structure.

[0003] The control method of fluid is often divided into active flow control and passive flow control according to whether there is external energy input. Among them, passive control does not require additional energy input and is achieved by modifying the shape of the structure, such as optimizing the geometric structure, arranging small structures on the surface, etc. This method is simple to control, but generally can only produce useful control effects on specific flows or motion states. This flow control method is determined in advance, but when the flow field environment or the motion state of the object changes, it is impossible to achieve a good flow control effect. Active flow control requires the introduction of external auxiliary energy and the addition of appropriate interference to the original flow field to achieve the purpose of controlling the flow. For the flow control of objects, some scholars have proposed some means and methods, such as patent number 201410621524.2, patent name A method for reducing vortex-induced vibration of parallel double cylinders, which discloses controlling the wake by actively jetting into the flow field, but they only blindly control the flow field and cannot control the optimal jet volume and direction, etc., and cannot actively adjust the optimal flow control according to the flow environment or the motion state of the object in real time. Its scope of application and control effect are very limited. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent control device and method for eliminating the wake of a blunt body, which can intelligently control, adjust and eliminate the wake behind the blunt body in real time by using an artificial intelligence algorithm.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] An intelligent control device for eliminating the wake of a blunt body comprises: a blunt body model, wherein holes are opened below and behind the blunt body model; a suction pipe is arranged at the opening below the blunt body model and is used to absorb fluid in a flow field; a flow delivery pipe is connected to the suction pipe and is used to transport the absorbed fluid; a flow controller is connected to the flow delivery pipe and is used to control and adjust excitation parameters; a servo motor is connected to the flow controller and the direction and intensity of an exciter are adjusted by the parameters output by the flow controller, the exciter is arranged at the rear opening of the blunt body model and the flow field behind the blunt body is disturbed by the exciter, thereby controlling and eliminating the wake; a data processing training system is connected to the flow controller and a CCD high-speed camera through wireless signals, and the optical axis of the CCD high-speed camera is perpendicular to the light plane of the laser sheet emitted by the high-frequency laser.

[0007] Furthermore, the CCD high-speed camera is used to capture images of tracer particles in the flow field, and the captured particle images are transmitted to the data processing training system via Ethernet, and the output control parameters are input into the flow controller.

[0008] Furthermore, the bluff body model is made of acrylic material.

[0009] Furthermore, the high-frequency laser emits a light source with a wavelength of 523 nm to illuminate particles in the flow field.

[0010] Furthermore, the data processing training system includes a PIV flow field data set, a training linear genetic algorithm (LGP) network, and control parameter evaluation and output.

[0011] Furthermore, the generation of the PIV flow field data set in the wake first obtains the original velocity field distribution of the flow field area to be measured through particle image velocimetry technology, then adds random illumination and noise to the particle image, and changes the particle grayscale value according to the illumination intensity to compensate for the random error of the measured velocity field caused by laser intensity interference, and finally obtains the velocity difference J of the entire flow field.

[0012] Furthermore, in the initial stage of training the linear genetic algorithm LGP network, a feedback signal s is added as an input of the system, and the control parameters are calculated by the formula b=J(s,h), where: J is the speed difference, s is the feedback signal, h is the multi-frequency open-loop control, and the vector b=[b1, b2...] contains all control instructions, and the control instructions include excitation intensity and excitation direction. Through genetic operations, the speed difference in the wake is minimized under a specific b control.

[0013] Furthermore, in the control parameter evaluation and output, the effect and efficiency of the estimated control rate are first estimated through the objective function K. Based on the value of the objective function, LGP decides whether to retain, optimize or abandon the test of the current control parameters. This learning process is repeated until the optimal control law is selected, and the optimal control parameters are input into the controller to perform real-time control adjustments on the actuator.

[0014] The present invention may also include: a control method using the above-mentioned intelligent control device for eliminating the wake of a bluff body, when conducting the experiment, choose to be in a dark place to avoid the influence of ambient light, and at the same time, the surface of the bluff body model is painted with black matte paint;

[0015] First, fix the suction hole and the actuator inside the blunt body, aim the high-frequency laser at the wake area to be measured, arrange the CCD high-speed camera in the vertical direction of the laser plane in the area to be measured, turn on the high-frequency laser and the CCD high-speed camera, obtain the particle image in the flow field and input it into the data processing training system for processing to obtain the velocity information of the flow field, input the flow field information into the self-developed genetic programming algorithm for training and learning, so as to obtain the current optimal control parameters, and pass them to the flow controller to control the actuator and servo motor, so as to change the wake, and then use PIV to obtain the flow field information again, and repeat the cycle until the wake is stable.

[0016] The beneficial effects of the present invention are:

[0017] The present invention can dynamically and real-time intelligently adjust, control and eliminate the wake generated behind the object, and has the characteristics of wide application range, easy adjustment and simple structure. On the one hand, the wake can be dynamically controlled by intelligently and autonomously adjusting the excitation intensity and direction according to the change of the flow field environment or the motion state of the object. On the other hand, through real-time measurement of the flow field information, the flow field information can be extracted and learned by using the self-developed genetic programming algorithm network to obtain the current optimal excitation control parameters and feed them back to the controller in real time to adjust the exciter. The exciter changes the wake field by adjusting the excitation intensity and direction, and the PIV inputs the changed flow field information into the genetic algorithm in real time, thereby forming a closed-loop control. Through repeated cycles, the optimal control parameters can be finally obtained to weaken the unsteady wake disturbance produced after the fluid flows through the blunt body.

[0018] The present invention obtains particle images in the flow field through PIV and inputs them into a data processing training system for processing to obtain velocity information of the flow field, inputs the flow field information into a self-developed network based on a genetic programming algorithm, extracts and learns the flow field information, thereby obtaining the current optimal excitation control parameters and feeding them back to the controller in real time to adjust the exciter, and the exciter changes the wake field by adjusting the excitation intensity and direction, etc., and PIV inputs the changed flow field information into the genetic algorithm in real time, thereby forming a closed-loop control, and ultimately the optimal control parameters can be obtained through repeated cycles, the unsteady wake disturbance produced after the fluid flows through the bluff body is weakened and reaches a stable state, and the wake formed behind the bluff body can be dynamically and intelligently controlled, adjusted and eliminated in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Attached Figure 1 It is a structural schematic diagram of the present invention.

[0020] Attached Figure 2 It is an intelligent control flow chart of the present invention based on the linear genetic programming algorithm LGP network.

[0021] Attached Figure 3 It is a linear genetic programming algorithm (LGP) diagram of the present invention.

[0022] In the attached figure: 1: blunt body model, 2: suction hole, 3: flow tube, 4: flow controller, 5: servo motor, 6: actuator, 7: data processing training system, 8: CCD high-speed camera, 9: high-frequency laser. DETAILED DESCRIPTION

[0023] The present invention is further described below in conjunction with the accompanying drawings.

[0024] This embodiment discloses an intelligent control device for eliminating the wake of a blunt body, as shown in the attached Figure 1 As shown, it includes: a bluff model 1, a suction hole 2, a flow pipe 3, a flow controller 4, a servo motor 5, an exciter 6, a data processing training system 7, a CCD high-speed camera 8, and a high-frequency laser 9. The bluff model 1 is made of acrylic material, and holes are opened at the bottom and rear of the bluff model 1. The suction pipe 2 is arranged at the lower opening to absorb the fluid in the flow field. The flow pipe 3 is connected to the suction pipe 2 to transport the absorbed fluid. The flow controller 4 is connected to the flow pipe 3 to control and adjust the excitation parameters; the servo motor 5 and the flow controller 4 adjust the direction and strength of the exciter 6 through the parameters output by the controller. The exciter 6 is arranged at the rear opening of the bluff model 1. The exciter 6 can interfere with the rear flow field of the bluff body, thereby controlling and eliminating the wake.

[0025] The high-frequency laser 9 emits a light source with a wavelength of 523nm, which is used to illuminate the particles in the flow field. The optical axis of the CCD high-speed camera 8 is perpendicular to the light plane of the laser sheet emitted by the high-frequency laser 9, which is used to capture images of tracer particles in the flow field; the particle images captured by the CCD high-speed camera 8 are transmitted to the data processing training system 7 via Ethernet, and the output control parameters are input into the flow controller 4.

[0026] The data processing training system 7 described in this embodiment is as shown in the attached Figure 3 As shown, it includes: PIV flow field data set, training linear genetic algorithm LGP network, control parameter evaluation and output.

[0027] The generation of the PIV flow field data set in the wake first obtains the original velocity field distribution of the flow field area to be measured by particle image velocimetry technology, and then adds random illumination and noise to the particle image, and changes the particle grayscale value according to the illumination intensity. This is to simulate the intensity of laser scattering and the change of spatial position over time under experimental conditions, so as to compensate for the random error caused by laser intensity interference to the measured velocity field, and finally obtain the velocity difference J of the entire flow field. Then, a control network based on a linear genetic algorithm is constructed. The genetic algorithm is constructed as a tree structure, which can be easily evaluated recursively and described by Lisp expressions. These trees are called individuals, and the population of individuals is a generation. LGP is an iterative process in which the candidate solution population is repeatedly genetically modified according to its fitness and evolves towards a better solution.

[0028] To train the linear genetic algorithm LGP network, the first generation of individuals is randomly generated, all individuals are evaluated, and the fitness value is determined according to the degree to which they minimize the objective function. Then the second generation of individuals is generated using genetic operations, mainly genetic operations: elite, replication, mutation and crossover. The elite operation is set to N = 1, that is, the best individuals in one generation are copied to the next generation, and the probabilities of replication, crossover and mutation operations are set to 15%, 70% and 15% respectively. In the initial stage of the learning process, we add a feedback signal s as an input to the system, and calculate the control parameters by the formula b = J (s, h), where: J is the speed difference, h is the multi-frequency open-loop control, the vector b = [b1, b2...] contains all control instructions, such as excitation intensity, excitation direction, etc., J is the speed difference, and the speed difference in the wake is minimized under the specific b control through genetic operations.

[0029] In the control parameter evaluation and output, the objective function K is first used to estimate the effect and efficiency of the estimated control rate. Based on the value of the objective function, LGP decides whether to keep, optimize or abandon the test of the current control parameters. This learning process is repeated until the optimal control law is selected. The optimal control parameters are input into the controller to make real-time control adjustments to the actuator 6.

[0030] The control method of the intelligent control device for eliminating the wake of a blunt body in this embodiment is as follows: Figure 2 As shown, when conducting the experiment, it is best to do it in a dark place to avoid the influence of ambient light, and at the same time, the surface of the bluff model 1 is painted with black matte paint. First, fix the suction hole 2 and the actuator 6 inside the bluff, aim the high-frequency laser 9 at the wake area to be measured, arrange the CCD high-speed camera 8 in the vertical direction of the laser plane of the area to be measured, turn on the high-frequency laser 9 and the CCD high-speed camera 8, obtain the particle image in the flow field and input it into the data processing training system 7 for processing to obtain the velocity information of the flow field, input the flow field information into the self-developed genetic programming algorithm for training and learning, so as to obtain the current optimal control parameters, and pass it to the flow controller 4 to control the actuator 6 and the motor 5, so as to change the wake, and then use PIV to obtain the flow field information again, and repeat the cycle until the wake reaches stability.

[0031] The present invention uses PIV technology to measure the wake velocity field formed after the fluid flows through the bluff body, and inputs the measured velocity parameters into the independently developed linear genetic programming algorithm (LGP) to analyze and train the data, thereby adjusting the excitation parameters of the exciter, and changing the exciter through the control system to intelligently control the flow field, greatly reducing the disturbance of the wake, and then repeating the cycle until the wake behind the bluff body reaches stability. Through the above method, the wake formed behind the bluff body can be controlled, adjusted and eliminated in real time.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent control device for eliminating the wake of a blunt body, characterized in that: include: A bluff body model (1), wherein holes are opened at the bottom and rear of the bluff body model (1); A suction pipe (2) is arranged at an opening below the bluff body model (1) and is used to suck fluid in the flow field; The fluid delivery tube (3) is connected to the fluid suction tube (2) and is used to transport the sucked fluid; A flow controller (4) is connected to the flow pipe (3) and is used to control and adjust the excitation parameters; a servo motor (5) is connected to the flow controller (4) and adjusts the direction and intensity of the exciter (6) through the parameters output by the flow controller (4); the exciter (6) is arranged at the rear opening of the bluff body model (1) and interferes with the rear flow field of the bluff body through the exciter (6), thereby controlling and eliminating the wake; a data processing training system (7) is connected to the flow controller (4) and a CCD high-speed camera (8) through wireless signals, and the optical axis of the CCD high-speed camera (8) is perpendicular to the light plane of the laser sheet emitted by the high-frequency laser (9).

2. The intelligent control device for eliminating the wake of a blunt body according to claim 1, characterized in that: The CCD high-speed camera (8) is used to capture images of tracer particles in the flow field. The captured particle images are transmitted to the data processing training system (7) via Ethernet, and the output control parameters are input into the flow controller (6).

3. The intelligent control device for eliminating the wake of a blunt body according to claim 1, characterized in that: The bluff body model (1) is made of acrylic material.

4. The intelligent control device for eliminating the wake of a blunt body according to claim 1, characterized in that: The high-frequency laser (9) emits a light source with a wavelength of 523 nm, which is used to illuminate particles in the flow field.

5. The intelligent control device for eliminating the wake of a blunt body according to claim 2, characterized in that: The data processing training system (7) includes a PIV flow field data set, a training linear genetic algorithm LGP network, and control parameter evaluation and output.

6. The intelligent control device and method for eliminating the wake of a blunt body according to claim 5, characterized in that: The generation of the PIV flow field data set in the wake flow first obtains the original velocity field distribution of the flow field area to be measured through the particle image velocimetry technology, then adds random illumination and noise to the particle image, and changes the particle grayscale value according to the illumination intensity to compensate for the random error caused by the laser intensity interference on the measured velocity field, and finally obtains the velocity difference J of the entire flow field.

7. The intelligent control device for eliminating the wake of a blunt body according to claim 5, characterized in that: In the initial stage of training the linear genetic algorithm LGP network, a feedback signal s is added as an input of the system, and the control parameters are calculated by the formula b=J(s,h), where: J is the speed difference, s is the feedback signal, h is the multi-frequency open-loop control, and the vector b=[b1, b2...] contains all control instructions, and the control instructions include excitation intensity and excitation direction. Through genetic operations, the speed difference in the wake is minimized under a specific b control.

8. The intelligent control device for eliminating the wake of a blunt body according to claim 5, characterized in that: In the control parameter evaluation and output, the objective function K is first used to estimate the effect and efficiency of the estimated control rate. Based on the value of the objective function, LGP decides whether to retain, optimize or abandon the test of the current control parameters. This learning process is repeated until the optimal control law is selected, and the optimal control parameters are input into the controller to perform real-time control adjustments on the actuator.

9. A control method using the intelligent control device for eliminating the wake of a blunt body according to any one of claims 1 to 8, characterized in that: When conducting the experiment, a dark place was chosen to avoid the influence of ambient light, and the surface of the blunt body model (1) was painted with black matte paint; First, the suction hole (2) and the actuator (6) are fixed inside the bluff body, the high-frequency laser (9) is aimed at the wake area to be measured, the CCD high-speed camera (8) is arranged in the vertical direction of the laser plane of the area to be measured, the high-frequency laser (9) and the CCD high-speed camera (8) are turned on, the particle image in the flow field is obtained and input into the data processing training system (7) for processing to obtain the velocity information of the flow field, the flow field information is input into the self-developed genetic programming algorithm for training and learning, so as to obtain the current optimal control parameters, and the parameters are transmitted to the flow controller (4) to control the actuator (6) and the servo motor (5), so as to change the wake, and then the flow field information is obtained by using PIV, and the reciprocating cycle is repeated until the wake reaches stability.

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