A dynamic vibration control method

Abstract

This invention discloses a dynamic vibration reduction control method. The method involves acquiring parameter combination data of the vibration reduction equipment and inputting it into computational fluid dynamics software for calculation, outputting combined data of the results. Using the combined data of the results as independent variables and the expected loss as the dependent variable, a deep neural network is used for training to obtain a vibration loss function. An optimization algorithm is used to obtain parameter combination data with the minimum expected loss, and then an optimal vibration reduction control strategy function is constructed. The difference between the expected loss and the minimum expected loss is calculated to obtain the expected benefit. The work required to overcome external forces when the appendage switches from normal operating conditions to optimal control conditions is obtained to obtain the control cost function. An exhaustive search or swarm intelligence method is used to obtain the combination that maximizes the comprehensive evaluation function. The optimal comprehensive control strategy function under different flow velocities is obtained. The optimal comprehensive control strategy function is deployed to the controller of the vibration reduction equipment, and the relative position of the appendage is dynamically adjusted according to the measured different inflow velocities.

Description

Technical Field

[0001] This invention relates to the field of dynamic vibration reduction control, and more specifically to a dynamic vibration reduction control method. Background Technology

[0002] Slender structures such as cables and pipelines on offshore platforms and cables of large bridges are prone to fatigue failure due to vortex-induced vibration, resulting in huge maintenance costs every year. Currently, the methods used to suppress vortex-induced vibration to protect related equipment are often only suitable for a few limited working conditions and are difficult to adapt to the ever-changing actual working environment. Therefore, the protective effect on related equipment is limited. Thus, there is a need for an intelligent dynamic vibration reduction control method that can adapt to a variety of working conditions, effectively suppress vortex-induced vibration of slender structures in complex and variable environments, effectively protect related equipment, and greatly reduce equipment maintenance costs. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that the current methods for suppressing vortex-induced vibration to protect related equipment are often only suitable for a few limited working conditions and are difficult to adapt to the changing actual working environment. The present invention also provides a dynamic vibration reduction control method that is adaptable to various working conditions, can effectively suppress vortex-induced vibration of slender structures, and greatly reduce the maintenance cost of equipment, so as to solve the defects caused by the existing technology.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solutions:

[0005] A dynamic vibration reduction control method, comprising the following steps:

[0006] Step 1: Obtain the parameter combination data of the vibration reduction equipment and input it into the Computational Fluid Dynamics (CFD) software for calculation and output of the result combination data. The correspondence between the parameter combination data and the result combination data is the eddy-induced vibration relationship spectrum. The parameter combination data includes the flow velocity of the vibration reduction equipment, denoted as U, the distance between the structure and the attachment, denoted as r, and the angle between the structure and the attachment, denoted as θ. The result combination data includes the vibration amplitude, denoted as A, and the vibration frequency, denoted as f.

[0007] Step 2: Using the combined data as the independent variable and the expected loss as the dependent variable, train a deep neural network to obtain the vibration loss function: Or E = h(r,θ), where E is the expected loss. h is a function;

[0008] Step 3: Under the same flow rate, use an optimization algorithm to obtain a parameter combination data that minimizes the expected loss, i.e., E. min =h(r * ,θ* Then, construct the optimal control strategy function for vibration reduction: (r * θ * ) = p(U), where E min To minimize the expected loss, r * The distance between the minimum structure and the appendage, θ * The angle between the smallest structure and the attachment, p is a function;

[0009] Step 4: Under the same flow rate, calculate the expected loss E and the minimum expected loss E. min The difference between the expected return and the expected return is denoted as EA, where EA = EE. min =h(r,θ)-h(r) * ,θ * ) = g(r,θ), where g is a function;

[0010] Step 5: Under the same flow rate, the work required by the control system of the vibration damping device to overcome external forces when switching the appendage from normal operating condition to optimal control condition is recorded as W. Based on this work, the control cost function is obtained: PR = KW(r,θ), where P is a function, PR is the control cost, K is the proportional coefficient (the value range needs to be determined according to the actual application scenario), and W(r,θ) = 0.5*(FF). * )*(rr * )+0.5*(MM * )*(θ-θ * ), where F is the force at the center of the main body of the vibration damping device, F * M is the minimum force at the center of the vibration damping device, and M is the torque around the center of the vibration damping device. * The minimum torque at the center of the vibration damping equipment body;

[0011] Step 6: Under the same flow rate, use exhaustive search or swarm intelligence methods to obtain the combination r that maximizes the comprehensive evaluation function, denoted as S. best θ best S max =g(r bset ,θ besr )-KW(r bset ,θ besr ), the combination (r best θ best ) represents a certain flow velocity U * The corresponding comprehensive optimal control strategy, the comprehensive evaluation function is: S=EA-PR=g(r,θ)-KW(r,θ);

[0012] Step 7: Repeat steps 3-6 to obtain the optimal integrated control strategy function for different flow velocities:

[0013] (r best θ best ) = q(U), where q is a function;

[0014] Step 8: Deploy the optimal integrated control strategy function to the controller of the vibration reduction equipment, and dynamically adjust the relative position of the attachment according to the measured different inflow velocities.

[0015] In the aforementioned dynamic vibration reduction control method, the input in step 1 is denoted as in, in = [(U,r,θ,…)1, (U,r,θ,…)2,…], and the output is denoted as out, out = [(A,f,…)1, (A,f,…)2,…].

[0016] The aforementioned dynamic vibration reduction control method includes an optimization algorithm in step 3, which includes an enumeration method and a swarm intelligence method.

[0017] The technical solution provided by the dynamic vibration reduction control method of the present invention, as described above, has the following technical effects:

[0018] It can adapt to various working conditions, effectively suppress vortex-induced vibration of slender structures, and greatly reduce equipment maintenance costs. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an attached vibration damping device. Detailed Implementation

[0020] In order to make the technical means, inventive features, objectives and effects of the invention easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to specific illustrations. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0021] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings of this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0023] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0024] A preferred embodiment of the present invention provides a dynamic vibration reduction control method, which aims to adapt to various working conditions, effectively suppress vortex-induced vibration of slender structures, and greatly reduce equipment maintenance costs.

[0025] A dynamic vibration reduction control method, comprising the following steps:

[0026] Step 1: Obtain the parameter combination data of the vibration reduction equipment and input it into the Computational Fluid Dynamics (CFD) software for calculation and output of the result combination data. The correspondence between the parameter combination data and the result combination data is the eddy-induced vibration relationship spectrum. The parameter combination data includes the flow velocity of the vibration reduction equipment, denoted as U, the distance between the structure and the attachment, denoted as r, and the angle between the structure and the attachment, denoted as θ. The result combination data includes the vibration amplitude, denoted as A, and the vibration frequency, denoted as f.

[0027] Since there are many methods to suppress vortex-induced vibration, this article takes a common dual-attachment vibration damping device as an example, such as... Figure 1 As shown, the large circle represents the slender structure to be protected (the main body), such as a cable or duct; the small circles represent appendages used to suppress vortex-induced vibration, which are generally arranged symmetrically. The position between the appendages and the main body can be represented by the distance r and the included angle θ. Under the same inflow, different relative positions of the appendages and the main body correspond to different vibration frequencies and amplitudes of the main body. Therefore, it is necessary to use CFD simulation software to calculate the amplitude A and frequency f of the overall structure composed of the main body and appendages under different combinations of flow velocity U, distance r, and included angle θ, as well as the load L on the appendage structure. The load L includes the force F pointing towards the center of the main body and the moment M around the center of the main body. Based on the magnitude of the calculated force, the step sizes for the variation of r and θ are set to Δr and Δθ, respectively.

[0028] Step 2: Using the combined result data as the independent variable and the expected loss as the dependent variable, train a deep neural network to obtain the vibration loss function: Or E = h(r,θ), where E is the expected loss. h is a function;

[0029] Step 3: Under the same flow rate, use an optimization algorithm to obtain a parameter combination data that minimizes the expected loss, i.e., E. min =h(r * ,θ *Then, construct the optimal control strategy function for vibration reduction: (r * θ * ) = p(U), where E min To minimize the expected loss, r * The distance between the minimum structure and the appendage, θ * The angle between the smallest structure and the attachment, p is a function;

[0030] Step 4: Under the same flow rate, calculate the expected loss E and the minimum expected loss E. min The difference between the expected return and the expected return is denoted as EA, where EA = EE. min =h(r,θ)-h(r) * ,θ * ) = g(r,θ), where g is a function;

[0031] Step 5: Under the same flow rate, the work done by the control system of the vibration damping equipment to overcome the external force under normal operating conditions is recorded as W. Based on the work done, the control cost function is obtained: PR = KW(r,θ), where P is a function, PR is the control cost, K is the proportional coefficient (the value range needs to be determined according to the actual application scenario), and W(r,θ) = 0.5*(FF). * )*(rr * )+0.5*(MM * )*(θ-θ * ), where F is the force at the center of the main body of the vibration damping device, F * M is the minimum force at the center of the vibration damping device, and M is the torque around the center of the vibration damping device. * The minimum torque at the center of the vibration damping equipment body;

[0032] Step 6: Under the same flow rate, use exhaustive search or swarm intelligence methods to obtain the combination r that maximizes the comprehensive evaluation function, denoted as S. best θ best S max =g(r bset ,θ besr )-KW(r bset ,θ besr ), the combination (r best θ best ) represents a certain flow velocity U * The corresponding comprehensive optimal control strategy, the comprehensive evaluation function is: S=EA-PR=g(r,θ)-KW(r,θ);

[0033] Step 7: Repeat steps 3-6 to obtain the optimal integrated control strategy function for different flow velocities:

[0034] (rbest θ best ) = q(U), where q is a function;

[0035] Step 8: Deploy the optimal integrated control strategy function to the controller of the vibration damping equipment, and dynamically adjust the relative position of the appendages according to the measured different inflow velocities.

[0036] In the aforementioned dynamic vibration reduction control method, the input in step 1 is denoted as in, in = [(U,r,θ,…)1, (U,r,θ,…)2,…], and the output is denoted as out, out = [(A,f,…)1, (A,f,…)2,…].

[0037] In the aforementioned dynamic vibration reduction control method, the optimization algorithm in step 3 includes enumeration and swarm intelligence methods.

[0038] In summary, the dynamic vibration reduction control method of the present invention can adapt to various working conditions, effectively suppress vortex-induced vibration of slender structures, and greatly reduce equipment maintenance costs.

[0039] The specific embodiments of the invention have been described above. It should be understood that the invention is not limited to the specific embodiments described above, and the devices and structures not described in detail should be understood to be implemented in a manner common to the art; those skilled in the art can make various modifications or alterations within the scope of the claims, and make several simple deductions, variations or substitutions, which do not affect the substantive content of the invention.

Claims

1. A dynamic vibration reduction control method, characterized in that, Includes the following steps: Step 1: Obtain the parameter combination data of the vibration reduction equipment and input it into the computational fluid dynamics software for calculation and output of the result combination data. The correspondence between the parameter combination data and the result combination data is a vortex-induced vibration relationship spectrum. The parameter combination data includes the flow velocity of the vibration reduction equipment, denoted as U, the distance between the structure and the attachment, denoted as r, and the angle between the structure and the attachment, denoted as θ. The combined data of the results includes the vibration amplitude denoted as A and the vibration frequency denoted as f; Step 2: Using the combined data as the independent variable and the expected loss as the dependent variable, train a deep neural network to obtain the vibration loss function: or Where E is the expected loss. h is a function; Step 3: Under the same flow rate, use an optimization algorithm to obtain a parameter combination that minimizes the expected loss. Then, the optimal control strategy function for vibration reduction is constructed: , of which E min To minimize expected loss, The distance between the minimum structure and the appendage, The angle between the smallest structure and the attachment, p is a function; Step 4: Under the same flow rate, calculate the expected loss E and the minimum expected loss E. min The difference between the expected return and the expected return is denoted as EA. where g is a function; Step 5: Under the same flow rate, obtain the work done by the control system of the vibration damping device to overcome external forces when switching the appendage from normal operating condition to optimal control condition, denoted as W. Based on this work done, obtain the control cost function: Where P is a function, PR is the control cost, and K is a scaling factor, the range of which needs to be determined based on the actual application scenario. Where F is the force at the center of the main body of the vibration damping device, F * M is the minimum force at the center of the vibration damping device, and M is the torque around the center of the vibration damping device. * The minimum torque at the center of the vibration damping equipment body; Step 6: Under the same flow rate, use exhaustive search or swarm intelligence methods to obtain the combination that maximizes the comprehensive evaluation function, denoted as S. , ,Right now The combination For a certain flow rate The corresponding comprehensive optimal control strategy, the comprehensive evaluation function is: ; Step 7: Repeat steps 3-6 to obtain the optimal integrated control strategy function for different flow velocities: , where q is a function; Step 8: Deploy the optimal integrated control strategy function to the controller of the vibration reduction equipment, and dynamically adjust the relative position of the appendages according to the measured different inflow velocities.

2. The dynamic vibration reduction control method as described in claim 1, characterized in that, The input in step 1 is denoted as in. The output is denoted as out. .

3. The dynamic vibration reduction control method as described in claim 2, characterized in that, The optimization algorithm described in step 3 includes enumeration and swarm intelligence methods.

Images