Heat exchange enhancement control method and device

Abstract

An embodiment of the present invention discloses a heat exchange enhancement control method and device, the method comprising: obtaining pressure data measured by multiple pressure measurement units in the first k time steps, and forming a first matrix and a second matrix based on the pressure data; calculating a predictive control matrix based on the first matrix, the second matrix and the control amount of the k-th time step; controlling the vibration frequency of a vibration component based on the predictive control matrix to maximize boundary layer separation, wherein the vibration component is located in the flow field and is installed on the inner wall of the flow field in the upstream area of ​​the heating device. The above implementation scheme uses system identification technology to measure the natural vibration frequency of the flow field in real time, and inputs the calculated frequency as an input signal to the vibration component through a piezoelectric drive vibration plate to form a resonance effect in the flow field, thereby maximizing the heat exchange effect of the flow field.

Description

Technical Field

[0001] The present invention relates to intelligent control technology, and more particularly to a heat exchange enhancement control method and device. Background Art

[0002] As the heat generation power of electronic devices continues to increase and the size of electronic products continues to shrink, the demand for efficient heat dissipation in limited space is becoming increasingly stronger and more urgent.

[0003] Currently, improving the heat transfer capacity of a flow field primarily involves modifying the geometry of the heat transfer channel or applying disturbances through mechanical structures. Both approaches essentially aim to enhance heat transfer by disrupting the boundary layer within the flow field. However, current heat transfer enhancement solutions are still less than ideal and cannot achieve optimal flow field heat transfer performance. Summary of the Invention

[0004] In view of this, the present invention provides a heat exchange enhancement control method and device to overcome the problem of poor flow field heat exchange effect in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A heat exchange enhancement control method, comprising:

[0007] Obtaining pressure data measured by a plurality of pressure measurement units in the first k time steps, and constructing a first matrix and a second matrix based on the pressure data, where k is a positive integer, the plurality of pressure measurement units are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix is ​​used to store the pressure data of the first k-1 time steps measured, and the second matrix is ​​used to store the pressure data of the second to kth time steps measured;

[0008] Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix; the predictive control matrix is ​​the control matrix at the k+1th time step;

[0009] The vibration frequency of a vibration component is controlled based on the predictive control matrix to maximize boundary layer separation. The vibration component is located in the flow field and mounted on an inner wall of the flow field in an upstream region of the heat generating device.

[0010] Optionally, based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix, including:

[0011] Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step is calculated [A k , B k ];

[0012] Based on the state matrix of the kth time step [A k , B k ] Using the first preset control algorithm, the control matrix K under the kth time step is calculated k and the X matrix x at the kth time step k+1 ;

[0013] Based on the X matrix x at the kth time step k+1 Using the second preset control algorithm, the state matrix of the k+1th time step is calculated;

[0014] The control matrix at the k+1th time step is calculated based on the state matrix at the k+1th time step using the first preset control algorithm.

[0015] Optionally, the elements in the first matrix and the second matrix are the absolute values ​​of the reciprocals of the pressure data; and the first preset control algorithm is a linear quadratic regulator LQR.

[0016] Optionally, the calculating of the state matrix of the k+1th time step by using a second preset control algorithm based on the X matrix at the kth time step includes:

[0017] Based on the X matrix at the kth time step, the formula is used Calculate the first parameter γ;

[0018] Based on the state matrix of the kth time step, the X matrix and the first parameter γ, the formula is used Calculate the state matrix G of the k+1th time step k+1 .

[0019] Optionally, the calculating of the state matrix of the k+1th time step by using a second preset control algorithm based on the X matrix at the kth time step includes:

[0020] Determine the first matrix X at the k+1th time step based on the X matrix at the kth time step k+1 and the control matrix U k+1 ;

[0021] Based on the first matrix Xk+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1 .

[0022] A heat exchange enhancement control device, comprising:

[0023] a data acquisition module, configured to acquire pressure data measured by a plurality of pressure measurement units in the first k time steps, and to construct a first matrix and a second matrix based on the pressure data, where k is a positive integer, and the plurality of pressure measurement units are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix being used to store the pressure data of the first k-1 time steps measured, and the second matrix being used to store the pressure data of the second to kth time steps measured;

[0024] A matrix processing module is used to process the matrix X based on the first matrix X. k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix; the predictive control matrix is ​​the control matrix at the k+1th time step;

[0025] A vibration control module is used to control the vibration frequency of a vibration component based on the predictive control matrix to maximize boundary layer separation, wherein the vibration component is located in the flow field and is installed on the inner wall of the flow field in the upstream area of ​​the heating device.

[0026] Optionally, the matrix processing module includes:

[0027] A first processing module is used to process the first matrix X k The second matrix Y k and the control matrix U at the kth time step k Using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step [A k , B k ];

[0028] The second processing module is used to calculate the state matrix [A k , B k ] Using the first preset control algorithm, the control matrix K under the kth time step is calculated k and the X matrix x at the kth time step k+1 ;

[0029] The third processing module is used to calculate the X matrix x based on the k-th time step. k+1Using the second preset control algorithm, the state matrix of the k+1th time step is calculated;

[0030] The fourth processing module is used to calculate the control matrix at the k+1th time step based on the state matrix of the k+1th time step using the first preset control algorithm.

[0031] Optionally, the elements in the first matrix and the second matrix are the absolute values ​​of the reciprocals of the pressure data; and the first preset control algorithm is a linear quadratic regulator LQR.

[0032] Optionally, the third processing module includes:

[0033] The parameter determination module is used to determine the X matrix based on the k-th time step, using the formula Calculate the first parameter γ;

[0034] A state matrix determination module is configured to determine the state matrix of the kth time step, the X matrix and the first parameter γ using the formula Calculate the state matrix G of the k+1th time step k+1 .

[0035] Optionally, the third processing module includes:

[0036] LQR update module, used to determine the first matrix X at the k+1th time step based on the X matrix at the kth time step k+1 and the control matrix U k+1 ;

[0037] A state matrix updating module is used to update the state matrix based on the first matrix X k+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1 .

[0038] It can be seen from the above technical solution that, compared with the prior art, the embodiment of the present invention discloses a heat exchange enhancement control method and device, the method comprising: obtaining pressure data measured by multiple pressure measurement units in the first k time steps, and forming a first matrix and a second matrix based on the pressure data; calculating a predictive control matrix based on the first matrix, the second matrix and the control amount of the kth time step; controlling the vibration frequency of the vibration component based on the predictive control matrix to maximize boundary layer separation, the vibration component is located in the flow field and is installed on the inner wall of the flow field in the upstream area of ​​the heating device. The above implementation scheme uses system identification technology to measure the natural vibration frequency of the flow field in real time, and inputs the calculated frequency as an input signal to the vibration component through a piezoelectric drive vibration plate to form a resonance effect in the flow field, thereby maximizing the heat exchange effect of the flow field. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 A schematic diagram of the flow field structure arrangement disclosed in an embodiment of the present invention;

[0041] Figure 2 This is a flow chart of a heat exchange enhancement control method disclosed in an embodiment of the present invention;

[0042] Figure 3 A flowchart of determining a predictive control matrix disclosed in an embodiment of the present invention;

[0043] Figure 4 A flowchart of obtaining the next time step state matrix disclosed in an embodiment of the present invention;

[0044] Figure 5 Another flow chart for obtaining the next time step state matrix disclosed in an embodiment of the present invention;

[0045] Figure 6 This is a structural schematic diagram of a heat exchange enhancement control device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] For the purpose of reference and clarity, the following technical terms, abbreviations or abbreviations are summarized as follows:

[0047] LQR (linear quadratic regulator) is a type of linear quadratic regulator (LQR). It can generate optimal control laws for linear state feedback, making it easy to implement closed-loop optimal control. LQR optimal control can achieve good performance in the original system at low cost (in fact, it can also be used to tune unstable systems), and the method is simple and easy to implement.

[0048] oDMDC (online Dynamic Mode Decomposition Control) is an online dynamic mode decomposition control algorithm. Dynamic mode decomposition (DMD) is a dimensionality reduction algorithm. Given a set of spatiotemporal coupled data, DMD can easily extract the temporal variations (patterns) corresponding to the spatial components. These patterns are associated with fixed oscillation frequencies and decay / growth rates. DMD is widely used in nonlinear mechanics research, primarily by fitting linear equations to nonlinear dynamic problems and conducting research.

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] In order to better understand the heat exchange enhancement control method disclosed in the embodiment of the present invention, the application scenarios of the method and the existing technical solutions for increasing the heat dissipation effect of the flow field are first introduced as follows: Figure 1 This is a schematic diagram of the flow field structure arrangement disclosed in the embodiment of the present invention, see Figure 1 As shown, in a flow channel (referred to as flow channel) for a fluid (such as liquid, gas, etc.), the fluid flows from left to right, and is close to the heating device below the outer wall of the flow channel. The fluid flows through the flow channel and takes away the heat generated by the heating device to achieve heat dissipation. In a conventional flow channel, the fluid boundary layer will continue to thicken as the distance from the inlet increases, and the thickening of the boundary layer will lead to a deterioration in the heat dissipation effect. The heat dissipation optimization method of the embodiment of the present invention revolves around the technology of boundary layer separation and destruction, and the boundary layer separation is reflected in the measurement as a reverse pressure gradient. In the implementation scenario of the present invention, a feedback control loop can be achieved by measuring the local pressure distribution and feeding it back to the vibration component, such as Figure 1 As shown, a vibrating component is arranged on the inner wall of the flow channel upstream of the heating device. Figure 1In the embodiment, a piezoelectric driven vibration plate is used as an example, and a series of pressure measuring units are arranged at the inner wall of the flow field near the heating device downstream of the piezoelectric driven vibration plate to measure the local pressure distribution, such as pressure sensors. These pressure measuring units can be arranged equidistantly or non-equidistantly, and are used to measure the pressure value near the wall inside the flow field; multiple pressure measuring units are arranged in sequence along the flow direction of the fluid. Among them, the local pressure distribution measured by the pressure measuring unit is the pressure distribution corresponding to the local area where the heating device is located in the flow. It can be understood that in actual applications, the vibration component can also be other vibration devices capable of controlling the vibration frequency in addition to the piezoelectric driven vibration plate, and the embodiments of the present invention do not limit this.

[0051] Among them, boundary layer separation is a phenomenon, but if we want to use this phenomenon, we need a quantitative method. When the boundary layer separates, negative pressure will appear near the wall, that is, reverse pressure gradient; the greater the negative pressure, the greater the degree of boundary layer separation, and the better the local heat transfer effect. One way to separate the boundary layer is to apply disturbance, that is, Figure 1 The role of the medium voltage electric drive vibration plate.

[0052] In the embodiment of the present invention, it can be set:

[0053] u k =-K k x k (1)

[0054]

[0055] Among them, u k is the input of the piezoelectric drive vibration piece, and x k It is a vector composed of the absolute value of the reciprocal of the pressure data measured by the pressure sensor, and is also the state quantity of the system, K k is the control matrix. Since the above formula corresponds to a time-varying system, the three quantities in formula (1) are all quantities that change with time. The k in formula (1) and (2) represents the quantity of the kth time step. p is the pressure data (i.e., pressure value) measured by the pressure sensor. Figure 1 For example, if there are 16 pressure sensors, the number of measured pressure data is 16. There are 16 elements in this vector, and each element is the absolute value of the reciprocal of the pressure data measured by a pressure sensor.

[0056] It should be noted that, based on the aforementioned description of the reverse pressure gradient, since negative pressure appears near the wall during boundary layer separation, the maximum negative pressure occurs when the absolute value of the negative pressure is the largest. Therefore, to maximize boundary layer separation, it is necessary to maximize the absolute value of the local pressure. At the same time, due to the inherent characteristics of the control algorithm, the problem of finding the maximum value needs to be converted into a problem of finding the minimum value. Therefore, the elements in formula (2) are the absolute value plus the reciprocal of the measured pressure data.

[0057] In order to achieve the change from state quantity to input quantity, it is necessary to select a suitable control algorithm to determine the control matrix K. In the embodiment of the present invention, the control algorithm can use a linear quadratic regulator, that is, LQR. LQR can achieve precise control when the system model information is available. Its specific form is:

[0058] K=(R+B T PB) -1 B T PA (3)

[0059] P=A T PA-A T PB(B T PB+R) -1 (A T PB) T +Q (4)

[0060] x k+1 =Ax k +Bu k =[AB][x k u k ] T (5)

[0061] X k ≡[x1, x2, ..., x k-1 ] (6)

[0062] U k ≡[u1, u2, ..., u k-1 ] (7)

[0063] Formula (4) is used to solve P, which needs to be solved iteratively. There are mature solvers with low computational cost, which can achieve real-time solution. Among them, Q and R are design values ​​(Q and R are values ​​obtained by adjusting parameters in the design stage. By changing Q and R, the performance of the controller can be changed), which can be obtained through experimental parameter adjustment. Formula (5) is a model for estimating the boundary layer separation state. In the embodiment of the present invention, it is a state space model; the state quantity x is the pressure distribution downstream of the piezoelectric drive vibration plate (downstream of the flow channel), and A and B are matrices of the state space model, which are obtained through the system identification algorithm. In the above formula, x is the state quantity of the system (pressure), u is the input quantity of the system (vibration frequency), and Q and R need to be tried and errored in the design process to obtain appropriate values. In addition, the subscripts 1, 2, ... k-1, k, k+1 in the formulas of this article represent the corresponding time steps, such as x k+1 Represents the state quantity of the kth time step. The superscript "T" in the formula means to transpose the matrix.

[0064] In the state space model, that is, in formula (5), x k+1 =Ax k +Bu k is the standard state space model representation, [AB][x k u k ] T It is an equivalent deformation of the state space model. The reason why the state space model needs to be deformed is that it needs to be better applied to the heat transfer enhancement control scheme.

[0065] Combined with the above formula (5), as the degree of boundary layer separation increases, x k+1 The smaller it is, the more x k+1 , and inform the controller of this quantity, so that the controller can play a role. Formula (5) is to find x k+1 , calculate x k+1 The purpose is to allow the controller to function.

[0066] In the above formulas, formula (5) is a model for estimating the boundary layer separation state, and formulas (3) and (4) are control algorithms. In practical applications, the functions are realized by deploying formulas (3)-(7) into the controller.

[0067] Combine Figure 1 As mentioned above, since the purpose of control is to make the pressure distribution unstable (the increase in the absolute value of pressure indicates that the boundary layer is destroyed and the heat transfer is enhanced), so that resonance occurs at the positions of the 16 pressure sensors, maximizing the absolute value of pressure is the control target. The LQR algorithm calculates the control matrix by minimizing the objective function, so the inverse of the absolute value of pressure is used in formula (2).

[0068] A and B are obtained by solving the pressure data collected by the pressure sensor and the system is identified using the online modal dynamic decomposition control algorithm (oDMDc). After solving (4) to obtain P, it can be inserted into (3) to obtain the control matrix K. The modal dynamic decomposition control algorithm can predict the signals in the next few time steps based on the measured signals in the previous few time steps. For nonlinear systems without obvious regularities, the time window in which the modal dynamic decomposition control algorithm can effectively predict is relatively short, which means that the model generated by the modal dynamic decomposition control algorithm has a time limit. However, since the modal dynamic decomposition control algorithm is a lightweight algorithm, the oDMDc algorithm, which was later improved, can continuously update the model according to the evolution of the measured values, so that it can always effectively predict the signals in the next few time steps. At the same time, the LQR controller can also update its own control matrix based on the model updated by oDMDc, thereby achieving effective control.

[0069] Based on the above, the present invention proposes a heat exchange enhancement control method to achieve quantitative processing of boundary layer separation.

[0070] Figure 2 This is a flow chart of a heat exchange enhancement control method disclosed in an embodiment of the present invention, see Figure 2 As shown, the heat exchange enhancement control method may include:

[0071] Step 201: Obtain pressure data measured by multiple pressure measurement units in the first k time steps, and construct a first matrix and a second matrix based on the pressure data.

[0072] Wherein, k is a positive integer, the multiple pressure measurement units (such as pressure sensors) are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix is ​​used to store the pressure data of the first k-1 time steps of the measurement, and the second matrix is ​​used to store the pressure data of the 2nd to kth time steps of the measurement.

[0073] Specifically, the elements in the first matrix and the second matrix can be the absolute values ​​of the reciprocals of the pressure data. The data contained in the first matrix and the second matrix are not completely identical, and their data are misaligned. For example, the first matrix includes [A, B, C, D, E, F], and the second matrix includes [B, C, D, E, F, G]. Because the system is time-varying, new measurement data is continuously generated. Therefore, the two matrices have a misalignment difference, and only by combining other algorithms can the prediction of subsequent related data be achieved.

[0074] Step 202: Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k, calculate the predictive control matrix; the predictive control matrix is ​​the control matrix under the k+1th time step.

[0075] Based on the relationship between the above-mentioned different formulas, the first matrix and the second matrix are substituted into the formula containing the first matrix and the second matrix to obtain the current control matrix. Specifically, a linear quadratic regulator can be used to obtain the current control matrix to achieve the transformation from state quantity to input quantity. After obtaining the current control matrix, the data already obtained can be further used to calculate the predictive control matrix for the next time step. Specifically, how to implement the control matrix based on the first matrix X k , the second matrix Y k and the predicted control matrix U at the kth time step k , calculate the control matrix, which will be introduced in detail in the following content and will not be explained in detail here.

[0076] Step 203: Controlling the vibration frequency of a vibration component based on the predictive control matrix to maximize boundary layer separation, wherein the vibration component is located in the flow field and mounted on an inner wall of the flow field in an upstream region of the heating device.

[0077] The control principle of the heat exchange enhancement control method disclosed in the embodiment of the present invention is to control the vibrating component to vibrate at the natural vibration frequency of the flow field in real time to form a resonance effect, maximize boundary layer separation, and thus achieve the best heat exchange optimization effect. Therefore, the predictive control matrix is ​​related to the natural frequency of the flow field at the next time step. Specifically, the present invention uses model-based control based on sensor feedback to provide the optimal vibration frequency in real time according to the characteristics of the flow field, thereby maximizing the effectiveness of vibration-enhanced heat exchange.

[0078] It should be noted that the purpose of the present invention is to generate resonance at the pressure sensor to destroy the separation layer. A macroscopic idea is: the purpose is to enhance local heat exchange - enhancing local heat exchange requires the destruction of the local boundary layer - to destroy the local boundary layer, the local pressure distribution needs to become unstable (it is the pressure distribution, so a row of pressure sensors needs to be arranged) - the local pressure distribution becomes unstable and needs to be expressed in mathematical language so that the controller can understand it - using mathematical formulas to express the local pressure distribution becomes unstable is Reached minimum value.

[0079] The heat exchange enhancement control method described in this embodiment uses system identification technology to measure the natural vibration frequency of the flow field in real time, and inputs the calculated frequency as an input signal to the vibration component through the piezoelectric drive vibration plate to form a resonance effect in the flow field, thereby maximizing the heat exchange effect of the flow field.

[0080] Figure 3 This is a flowchart of determining the predictive control matrix disclosed in an embodiment of the present invention, see Figure 3 As shown, the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix, which can include:

[0081] Step 301: Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step is calculated [A k , B k ].

[0082] Step 302: Based on the state matrix [A k , B k ] Using the first preset control algorithm, the control matrix K under the kth time step is calculated k and the X matrix x at the kth time step k+1 .

[0083] In an example, the control matrix K at the kth time step is calculated k Implementations may include:

[0084] Collect the pressure data of the first k steps (column vector) x1, x2, .., x k , and form the first matrix X k and the second matrix Y k , where Y k is defined as:

[0085] Y k ≡[x2, x3, ..., x k ] (8)

[0086] Define Z k =[X k U k ] T , and obtain

[0087]

[0088] Definition G k =[A k B k ], and find G k :

[0089]

[0090] Formula (8), formula (9) and formula (10) constitute the online modal dynamic decomposition control algorithm. Formula (8), formula (9) and formula (10) are used to obtain the state matrix [A] of the kth time step. k , B k ], calculate the control matrix K at the kth time step according to formulas (3) to (7): k .

[0091] And for the X matrix x at the kth time step k+1 , when the state matrix of the kth time step is known [A k , B k ], the state matrix [A k , B k ]Substitute into formula (5) to obtain the X matrix x at the kth time step k+1 .

[0092] Step 303: Based on the X matrix x at the kth time step k+1 The state matrix of the k+1th time step is calculated using the second preset control algorithm.

[0093] Step 304: Based on the state matrix of the k+1th time step, a first preset control algorithm is used to calculate a control matrix at the k+1th time step, that is, a predictive control matrix.

[0094] In the above description, the elements in the first matrix and the second matrix are the absolute values ​​of the inverses of the pressure data. The first preset control algorithm is a linear quadratic regulator (LQR). Of course, the first preset control algorithm can also be implemented differently, for example, it can also be a neural network.

[0095] Figure 4 This is a flowchart of obtaining the next time step state matrix disclosed in an embodiment of the present invention, see Figure 4 As shown, in one implementation, the calculation of the state matrix of the k+1th time step using the second preset control algorithm based on the X matrix at the kth time step may include:

[0096] Step 401: Determine the first matrix X at the k+1th time step based on the X matrix at the kth time step. k+1 and the control matrix U k+1 .

[0097] Step 402: Based on the first matrix X k+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1.

[0098] In this implementation, the latest X matrix is ​​used to iteratively calculate the state matrix for the next time step. The linear quadratic regulator LQR is repeatedly applied to obtain the control matrix for the next time step, which is also known as the predictive control matrix. Specifically, after obtaining the current control matrix, it is used to iteratively update the model matrix. Based on formulas (3)-(7), the predictive control matrix representing the next time step is calculated. The predictive control matrix is ​​related to the natural frequency of the flow field at the next time step.

[0099] In practical applications, since there are no values ​​at the beginning, the above algorithm needs to iterate for several time steps to take effect, and the initial value of the input matrix can be randomly selected, usually using the identity matrix.

[0100] Figure 5 Another flow chart for obtaining the next time step state matrix disclosed in an embodiment of the present invention is shown in FIG. Figure 5 As shown, in another implementation, the state matrix of the k+1th time step is calculated based on the X matrix at the kth time step using the second preset control algorithm, which may include:

[0101] Step 501: Based on the X matrix at the kth time step, the first parameter γ is calculated using formula (11).

[0102]

[0103] Step 502: Based on the state matrix of the kth time step, the X matrix and the first parameter γ, the state matrix G of the k+1th time step is calculated using formula (12): k+1 .

[0104]

[0105] Relative to Figure 4 Corresponding implementation, this application calculates the state matrix G of the k+1th time step k+1 The realization of does not require repeated iterative calculations, but the state matrix G of the k+1th time step can be calculated by formula (11) and formula (12) k+1 , the process greatly reduces the amount of calculation and is easier to implement.

[0106] Formulas (8) through (10) are implemented using the online modal dynamic decomposition control algorithm, oDMDc. While oDMDc is a relatively recent development, LQR has a long history. LQR has not been widely used over the years because it requires model information as input to achieve good control results. While oDMDc can generate high-quality model information from measurements, the advent of oDMDc makes it possible to use LQR for precise control. There are many control algorithms similar to LQR, but the key is that oDMDc provides valuable model information for precise control. Therefore, oDMDc can also be combined with other control algorithms (such as neural networks) to achieve control.

[0107] The control algorithm executed according to the above steps can evaluate the boundary layer separation by measuring the pressure distribution on the contact surface between the fluid and the flow channel wall, and maximize the local boundary layer separation by using the mechanism of the LQR algorithm to achieve the method of enhancing heat transfer.

[0108] For simplicity of description, the aforementioned method embodiments are described as a series of actions. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, as certain steps can be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also be aware that the embodiments described in this specification are preferred embodiments, and the actions and modules involved are not necessarily required for the present invention.

[0109] The above embodiments disclosed in the present invention describe the method in detail. The method of the present invention can be implemented using various devices. Therefore, the present invention also discloses a device, which will be described in detail in the following specific embodiments.

[0110] Figure 6 This is a schematic diagram of the structure of a heat exchange enhancement control device disclosed in an embodiment of the present invention, see Figure 6 As shown, the heat exchange enhancement control device 60 may include:

[0111] The data acquisition module 601 is used to acquire pressure data measured by multiple pressure measurement units in the previous k time steps, and to construct a first matrix and a second matrix based on the pressure data.

[0112] Wherein, k is a positive integer, the multiple pressure measurement units are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix is ​​used to store the pressure data of the first k-1 time steps of the measurement, and the second matrix is ​​used to store the pressure data of the 2nd to kth time steps of the measurement.

[0113] The matrix processing module 602 is configured to process the matrix X based on the first matrix X. kThe second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix; the predictive control matrix is ​​the control matrix under the k+1th time step.

[0114] The vibration control module 603 is used to control the vibration frequency of the vibration component based on the predictive control matrix to maximize boundary layer separation. The vibration component is located in the flow field and installed on the inner wall of the flow field in the upstream area of ​​the heating device.

[0115] The heat exchange enhancement control device described in this embodiment uses system identification technology to measure the natural vibration frequency of the flow field in real time, and inputs the calculated frequency as an input signal to the vibration component through the piezoelectric drive vibration plate to form a resonance effect in the flow field, thereby maximizing the heat exchange effect of the flow field.

[0116] In one implementation, the matrix processing module includes:

[0117] A first processing module is used to process the first matrix X k The second matrix Y k and the control matrix U at the kth time step k Using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step [A k , B k ];

[0118] The second processing module is used to calculate the state matrix [A k , B k ] Using the first preset control algorithm, the control matrix K under the kth time step is calculated k and the X matrix x at the kth time step k+1 ;

[0119] The third processing module is used to calculate the X matrix x based on the k-th time step. k+1 Using the second preset control algorithm, the state matrix of the k+1th time step is calculated;

[0120] The fourth processing module is used to calculate the control matrix at the k+1th time step based on the state matrix of the k+1th time step using the first preset control algorithm.

[0121] In one implementation, the elements in the first matrix and the second matrix are absolute values ​​of the reciprocals of the pressure data; and the first preset control algorithm is a linear quadratic regulator LQR.

[0122] In one implementation, the third processing module includes:

[0123] The parameter determination module is used to determine the X matrix based on the k-th time step, using the formula Calculate the first parameter γ;

[0124] A state matrix determination module is configured to determine the state matrix of the kth time step, the X matrix and the first parameter γ using the formula Calculate the state matrix G of the k+1th time step k+1 .

[0125] In one implementation, the third processing module includes:

[0126] LQR update module, used to determine the first matrix X at the k+1th time step based on the X matrix at the kth time step k+1 and the control matrix U k+1 ;

[0127] A state matrix updating module is used to update the state matrix based on the first matrix X k+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1 .

[0128] Any one of the thermal strengthening control devices described in the above embodiments includes a processor and a memory. The data acquisition module, matrix processing module, vibration control module, first processing module, second processing module, third processing module, fourth processing module, parameter determination module, state matrix determination module, LQR update module, state matrix update module, etc. in the above embodiments are all stored in the memory as program modules, and the processor executes the above program modules stored in the memory to realize corresponding functions.

[0129] The processor contains a kernel, which retrieves the corresponding program module from the memory. There can be one or more kernels, and the kernel parameters can be adjusted to process the returned data.

[0130] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0131] An embodiment of the present invention provides a storage medium having a program stored thereon, which, when executed by a processor, implements the heat exchange enhancement control method described in the above embodiment.

[0132] An embodiment of the present invention provides a processor, which is used to run a program, wherein the heat exchange enhancement control method described in the above embodiment is executed when the program is run.

[0133] Furthermore, this embodiment provides an electronic device, comprising a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute the heat exchange enhancement control method described in the above embodiment by executing the executable instructions.

[0134] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0135] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0136] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0137] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A heat exchange enhancement control method, characterized in that: include: Obtaining pressure data measured by a plurality of pressure measurement units in the first k time steps, and constructing a first matrix and a second matrix based on the pressure data, where k is a positive integer, the plurality of pressure measurement units are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix is ​​used to store the pressure data of the first k-1 time steps measured, and the second matrix is ​​used to store the pressure data of the second to kth time steps measured; Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix; the predictive control matrix is ​​the control matrix under the k+1th time step; U k is a matrix composed of the vibration frequencies of the control vibration component input at the previous k-1 moments; based on the predictive control matrix, the vibration frequency of the vibration component is controlled using the formula u=-Kx to maximize boundary layer separation, the vibration component is located in the flow field and installed on the inner wall of the flow field in the upstream area of ​​the heating device, u is the vibration frequency of the vibration component, K is the predictive control matrix, and x is the pressure data; Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix, including: Based on the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , and the state space model x k =Ax k-1 +Bu k-1 =[AB][x k-1 u k-1 ] T , using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step is calculated [A k , B k ]; A, B are matrices of the state space model; Based on the state matrix of the kth time step [A k , B k ], and the equation P = A T PA-A T PB(B T PB+R) -1 (A T PB) T +Q and the formula K=(R+B T PB) -1 B T PA, using the first preset control algorithm, calculates the control matrix K at the kth time step k and the X matrix x at the kth time step k+1 ; The first preset control algorithm is a linear quadratic regulator LQR, Q, R are pre-set matrices of the linear quadratic regulator LQR, and P is a symmetric positive definite matrix derived based on the obtained; Based on the X matrix x at the kth time step k+1 Using the second preset control algorithm, the state matrix of the k+1th time step is calculated; The control matrix at the k+1th time step is calculated based on the state matrix at the k+1th time step using the first preset control algorithm.

2. The heat exchange enhancement control method according to claim 1, characterized in that: The elements in the first matrix and the second matrix are absolute values ​​of the reciprocals of the pressure data.

3. The heat exchange enhancement control method according to claim 2, characterized in that: The method of calculating a state matrix of the k+1th time step based on the X matrix at the kth time step using a second preset control algorithm includes: Based on the X matrix at the kth time step, the formula is used Calculate the first parameter γ; Based on the state matrix of the kth time step, the X matrix and the first parameter γ, the formula is used Calculate the state matrix G of the k+1th time step k+1 , G k =[A k B k ].

4. The heat exchange enhancement control method according to claim 1, characterized in that: The method of calculating a state matrix of the k+1th time step based on the X matrix at the kth time step using a second preset control algorithm includes: Determine the first matrix X at the k+1th time step based on the X matrix at the kth time step k+1 and the control matrix U k+1 ; Based on the first matrix X k+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1 , G k+1 =[A k+1 B k+1 ].

5. A heat exchange enhancement control device, characterized in that: include: a data acquisition module, configured to acquire pressure data measured by a plurality of pressure measurement units in the first k time steps, and to construct a first matrix and a second matrix based on the pressure data, where k is a positive integer, and the plurality of pressure measurement units are located in the flow field and arranged on the inner wall of the flow field close to the heating device, the first matrix being used to store the pressure data of the first k-1 time steps measured, and the second matrix being used to store the pressure data of the second to kth time steps measured; A matrix processing module is used to process the matrix X based on the first matrix X. k The second matrix Y k and the control matrix U at the kth time step k , calculate the predictive control matrix; the predictive control matrix is ​​the control matrix under the k+1th time step; U k A matrix composed of the vibration frequencies of the control vibration components input at the previous k-1 moments; a vibration control module, configured to control the vibration frequency of a vibration component based on the predictive control matrix using a formula u=-Kx to maximize boundary layer separation, wherein the vibration component is located in the flow field and mounted on an inner wall of the flow field in an upstream region of the heating element, where u is the vibration frequency of the vibration component, K is the predictive control matrix, and x is pressure data; The matrix processing module includes: A first processing module is used to process the first matrix X k The second matrix Y k and the control matrix U at the kth time step k , and the state space model x k =Ax k-1 +Bu k-1 =[AB][x k-1 u k-1 ] T , using the online modal dynamic decomposition control algorithm, the state matrix of the kth time step is calculated [A k , B k ]; A, B are matrices of the state space model; The second processing module is used to calculate the state matrix [A k ,B k ], and the equation P = A T PA-A T PB(B T PB+R) -1 (A T PB) T +Q and the formula K=(R+B T PB) -1 B T PA, using the first preset control algorithm, calculates the control matrix K at the kth time step k and the X matrix x at the kth time step k+1 The first preset control algorithm is a linear quadratic regulator LQR, Q and R are pre-set matrices of the linear quadratic regulator LQR, and P is a symmetric positive definite matrix derived based on the linear quadratic regulator; The third processing module is used to calculate the X matrix x based on the k-th time step. k+1 Using the second preset control algorithm, the state matrix of the k+1th time step is calculated; The fourth processing module is used to calculate the control matrix at the k+1th time step based on the state matrix of the k+1th time step using the first preset control algorithm.

6. The heat exchange enhancement control device according to claim 5, characterized in that: The elements in the first matrix and the second matrix are absolute values ​​of the reciprocals of the pressure data.

7. The heat exchange enhancement control device according to claim 6, characterized in that: The third processing module includes: The parameter determination module is used to determine the X matrix based on the k-th time step, using the formula Calculate the first parameter γ; A state matrix determination module is configured to determine the state matrix of the kth time step, the X matrix and the first parameter γ using the formula Calculate the state matrix G of the k+1th time step k+1 , G k =[A k B k ].

8. The heat exchange enhancement control device according to claim 5, characterized in that: The third processing module includes: LQR update module, used to determine the first matrix X at the k+1th time step based on the X matrix at the kth time step k+1 and the control matrix U k+1 ; A state matrix updating module is used to update the state matrix based on the first matrix X k+1 , the control matrix U k+1 and the second matrix Y at the k+1th time step k+1 , calculate the state matrix G of the k+1th time step k+1 , G k+1 =[A k+1 B k+1 ].

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