Air conditioner pipeline design method, storage medium, computing equipment and program product

Abstract

The invention provides an air conditioner pipeline design method, a storage medium, computing equipment and a program product. The method comprises the steps that the compressor load of an air conditioner is recognized; constraint conditions of pipeline parameters needing to be optimized and an objective function of pipeline optimization are obtained, and the constraint conditions comprise a first constraint condition determined according to a preset pipeline design specification; according to the recognized compressor load and the obtained constraint condition and target function, finite element analysis is carried out in combination with a preset multi-target optimization algorithm, and a pipeline parameter value combination solution set is obtained; and according to the obtained pipeline parameter value combination solution set, the pipeline design scheme of the air conditioner is determined. According to the scheme provided by the invention, trial and error times can be reduced, and simulation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of control, and in particular to an air-conditioning pipeline design method, storage medium, computing device and program product. Background Art

[0002] A core aspect of air conditioner structural design is the design of the piping. Beyond meeting the system's cooling requirements and the product's spatial structure, piping design also focuses on the vibration and noise generated by the piping during operation, a key concern and pain point for engineers. The compressor is the primary source of vibration in the air conditioner piping, and the piping system connecting the compressor is the primary channel for transmitting compressor vibration and noise. During operation, the compressor and piping can easily resonate, leading to serious consequences such as vibration, noise, stress, and fatigue in the piping system. In addition to performance, the effectiveness of piping design also plays a crucial role in controlling air conditioner manufacturing costs. Piping design in related technologies relies heavily on the experience of piping engineers. Engineers first develop a piping plan, connect a target prototype to the system for testing, and collect data on air conditioner noise and piping vibration. If the test data meets the specifications, the design is complete. If not, the process is repeated until the requirements are met. While this method is direct and accurate, it requires repeated prototype production and testing, making it less cost-effective. With the improvement of computer performance, the research on various numerical simulation methods has also made great progress. More and more simulation technologies are being applied to engineering practice, and it has become a trend to conduct simulation before the plan enters the prototype test stage.

[0003] In related technologies, pipeline optimization can be summarized as a cyclical process: pipeline simulation → target prototype testing → pipeline simulation → target prototype testing. This process of finding the optimal pipeline design through trial and error is time-consuming, labor-intensive, and inefficient. Summary of the Invention

[0004] The main purpose of the present invention is to overcome the defects of the above-mentioned related technologies and provide an air-conditioning pipeline design method, storage medium, computing device and program product to solve the problem in the related technologies of finding a better pipeline design solution through continuous trial and error, which is time-consuming, labor-intensive and inefficient.

[0005] On the one hand, the present invention provides an air conditioning pipeline design method, comprising: identifying the compressor load of the air conditioner; obtaining the constraints of the pipeline parameters that need to be optimized and the objective function of the pipeline optimization, the constraints including: a first constraint determined according to a preset pipeline design specification; performing finite element analysis based on the identified compressor load and the obtained constraints and objective function in combination with a preset multi-objective optimization algorithm to obtain a pipeline parameter value combination solution set; and determining the pipeline design scheme of the air conditioner based on the obtained pipeline parameter value combination solution set.

[0006] Optionally, identifying the compressor load of the air conditioner includes: obtaining vibration acceleration data of the compressor surface obtained through a modal experiment; and calculating load data at the center of mass of the compressor based on the obtained vibration acceleration data.

[0007] Optionally, calculating the load data at the center of mass of the compressor according to the acquired vibration acceleration data includes: calculating the load data at the center of mass of the compressor according to the acquired vibration acceleration data using the following load identification formula:

[0008]

[0009] Where M is the equivalent mass matrix of the compressor center of mass, K is the equivalent stiffness matrix of the compressor, and T 0p is the acceleration transfer matrix between the compressor surface and the compressor mass center, is the vibration acceleration data of the compressor surface.

[0010] Optionally, the constraint condition further includes: a second constraint condition; the second constraint condition includes: at least one of: structural strength constraint, vibration control constraint, and fluid performance constraint.

[0011] Optionally, the objective function of pipeline optimization includes at least one of: a pipeline natural frequency optimization objective function, a pipeline vibration amplitude optimization objective function, a pipeline stress value optimization objective function, a pipeline weight optimization objective function and a noise optimization objective function.

[0012] Optionally, performing finite element analysis based on the identified compressor load and the obtained constraints and objective function in combination with a preset multi-objective optimization algorithm to obtain a pipeline parameter solution set includes: establishing a finite element analysis model of the air conditioner; applying the identified compressor load to the finite element analysis model, and integrating the constraints and objective function;

[0013] According to the constraints and the objective function, a finite element analysis is run, and an iterative calculation is performed using the preset multi-objective optimization algorithm to obtain a combined solution set of pipeline parameter values.

[0014] Optionally, the pipeline design scheme of the air conditioner is determined based on the obtained pipeline parameter value combination solution set, including: determining the optimal pipeline parameter value combination that meets preset requirements from the obtained pipeline parameter value combination solution set as the pipeline design scheme of the air conditioner.

[0015] Another aspect of the present invention provides a storage medium having a computer program stored thereon, wherein the program implements the steps of any of the aforementioned methods when executed by a processor.

[0016] Another aspect of the present invention provides a computing device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the program.

[0017] In another aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any of the aforementioned methods are implemented.

[0018] According to the technical solution of the present invention, the parameter range under the air-conditioning pipeline design specification is added as a constraint condition to the pipeline optimization, so that the final pipeline indicators are more reasonable and meet the enterprise and industry standards.

[0019] The technical solution of this invention considers objective functions such as pipeline natural frequency, pipeline vibration amplitude, pipeline weight, and pipeline stress, taking into account both technical margins and economic efficiency. Furthermore, considering the impact of vibration on noise, acoustic objective functions in different environments are also considered.

[0020] According to the technical solution of the present invention, finite element analysis is performed in combination with a multi-objective optimization algorithm, which can reduce the number of trial and error times, improve simulation efficiency, provide a Pareto optimal solution in the multi-objective optimization method, and simultaneously explain the relative emphasis of the solution under multiple indicators, thereby providing designers with a design solution.

[0021] According to the technical solution of the present invention, compressor load identification is performed based on the vibration acceleration data and acceleration transfer matrix of the compressor surface, which serves as the input source for pipeline optimization design. This is more convenient and quick, and can improve the accuracy of pipeline optimization simulation.

[0022] According to the technical solution of the present invention, multi-objective optimization programming software, three-dimensional structure design software, and finite element simulation software are combined to realize the automation of parameter optimization to structural design optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0024] Figure 1 1 is a schematic diagram of an embodiment of the air conditioning pipeline design method provided by the present invention;

[0025] Figure 2 A flow chart showing a specific embodiment of the step of identifying the compressor load of the air conditioner according to the present invention;

[0026] Figure 3 A schematic diagram showing pipeline parameters of an exhaust pipe according to an embodiment of the present invention is shown;

[0027] Figure 4 This is a schematic diagram of a specific implementation of the steps of performing finite element analysis based on the identified compressor load and the obtained constraint conditions and objective function in combination with a preset multi-objective optimization algorithm to obtain a parameter value combination solution set of pipeline parameters;

[0028] Figure 5 An example of visualizing a solution set of pipeline parameter value combinations is shown. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all 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.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] In the related art, simulation methods for pipeline optimization design generally rely on trial and error to find the optimal pipeline design solution, which is time-consuming, labor-intensive, and inefficient. Furthermore, in the related art, simulation work for pipeline optimization design generally uses a unit load as the input load of the pipeline system, and then conducts horizontal comparisons of multiple pipeline design solutions. However, this unit load is difficult to represent the actual operating state of the pipeline during the operation of the air conditioning product, and the simulation accuracy is low. Naturally, the reliability of the horizontal comparison is also worth exploring.

[0032] The present invention provides an air conditioning pipeline design method, which is particularly suitable for variable frequency air conditioning pipeline design.

[0033] Figure 1It is a method schematic diagram of an embodiment of the air conditioning pipeline design method provided by the present invention.

[0034] like Figure 1 As shown, according to one embodiment of the present invention, the air conditioning pipeline design method includes at least step S110, step S120, step S130 and step S140.

[0035] Step S110 : identifying the compressor load of the air conditioner as a simulation input source.

[0036] In air conditioning pipeline simulation, the compressor acts as the vibration source of the pipeline, and the pipeline is subjected to the load applied by the compressor. Accurate load data is the key to calculating the structural response of the pipeline (such as stress, deformation, and displacement). Lack of load information will lead to inaccurate calculation of the structural response and affect the reliability of the simulation results.

[0037] Specifically, the compressor load is identified using a rigid body dynamics method. In one embodiment, the vibration response of key points inside the compressor (such as the center of mass) is obtained based on the vibration response collected at the surface measurement points of the compressor, combined with the transfer characteristics of the compressor body, and then the excitation load of the compressor is inversely determined.

[0038] Figure 2 A flow chart showing a specific embodiment of the steps of identifying the compressor load of the air conditioner according to the present invention is shown. Figure 2 As shown, step S110 may specifically include step S111 and step S112.

[0039] Step S111 : acquiring vibration acceleration data of the compressor surface obtained through a modal experiment.

[0040] Step S112: Calculate the load data at the center of mass of the compressor based on the acquired vibration acceleration data.

[0041] In the three-dimensional coordinate system, the compressor is regarded as a rigid body, and the acceleration of any point p on the rigid body is (Linear acceleration) can be measured by the acceleration of any other point c on the rigid body To express:

[0042]

[0043] in, is the angular acceleration of the rigid body, r cp is the vector distance from point p on the rigid body to point c on the rigid body.

[0044] The acceleration of any point on the rigid body can be decomposed along the x, y, and z axes. If the coordinates of the origin of the reference coordinate system (X r ,Y r ,Zr ) are all 0, then according to the above formula, the acceleration is decomposed along the axis:

[0045]

[0046] in, It is the acceleration collected along the x-axis, y-axis, and z-axis of the compressor rigid surface measurement point, which can be collected by installing a three-axis acceleration sensor on the compressor; is the acceleration of any point in the reference coordinate system, where are the accelerations decomposed along the x-axis, y-axis, and z-axis, respectively. are the angular accelerations decomposed along the x-axis, y-axis, and z-axis, respectively, which are unknown data to be solved; (X0, Y0, Z0) are the coordinate values ​​of the measuring point on the surface of the compressor rigid body along the three axes.

[0047] The above equation can be expressed by matrix as:

[0048]

[0049] A0=T0A r

[0050]

[0051] Among them, A0 is the acceleration vector collected from the compressor surface, which is a known vector; A r is an unknown vector, is the acceleration vector obtained in the reference coordinate system, T0 is the transfer matrix between the measuring point on the compressor surface and the reference coordinate system, and the transfer matrix can be obtained through the coordinates of the accelerometer installed on the rigid surface of the compressor. Therefore, the accelerometer measuring point position can be designed on the actual compressor or on the three-dimensional structural model (such as in three-dimensional modeling software such as Croe) to obtain the acceleration measuring point coordinate information. Therefore, the transfer matrix is ​​a known vector.

[0052] When the acceleration A0 of the compressor surface is collected, the acceleration information A of the key points inside the compressor can be obtained through the transfer matrix T0 between the compressor surface point and the key points inside the compressor (such as the center of mass). r .

[0053] Therefore, the coordinates and accelerations of the compressor surface measurement points and the transfer matrix T0 between the compressor surface points and the reference coordinate system are obtained. According to the obtained coordinates and accelerations of the compressor surface measurement points and the transfer matrix T0, the acceleration A of the center of mass of the compressor can be determined. r .

[0054] The plane motion of a rigid body can be decomposed into two parts: the translation of the center of mass and the rotation around the center of mass. The translation of the center of mass satisfies the momentum theorem, that is, the net external force is equal to the rate of change of the center of mass momentum. The rotation around the center of mass satisfies the moment of momentum theorem, that is, the net external torque is equal to the rate of change of the moment of momentum. Based on the formulas for the center of mass motion during translation and the moment of momentum during rotation, the dynamic equations for the plane motion of a rigid body can be obtained:

[0055]

[0056] Where m is the mass of the rigid body, and are the accelerations of the center of mass along the x-axis and y-axis respectively. ix is the net external force along the x-axis, ∑F iy is the net external force along the y-axis, J Cz is the moment of inertia about the center of mass, is the angular acceleration, ∑M Cz (F i ) is the net external moment about the center of mass.

[0057] Since the excitation force, torque and acceleration are all vectors, the three-dimensional space vector balance formula can be obtained:

[0058]

[0059] Among them, [F qx ,F qy ,F qz ] is the excitation force decomposed along the three axes when the excitation acts on the excitation point, [F cx ,F cy ,F cz ] is the centroid force decomposed along the three axes on the centroid after the excitation force is converted to the centroid. [M cx ,M cy ,M cz ] is the centroid moment decomposed along the three axes acting on the centroid after the excitation force is converted to the centroid. [x c ,y c ,z c ] is the three-axis coordinate of the center of mass, [x q ,y q ,z q ] are the three-axis coordinates of the excitation point.

[0060] In a specific embodiment, the excitation is obtained by striking the compressor surface with a hammer method. The hammer method requires a hammer and an accelerometer. The hammer strikes a pre-set excitation point (i.e., the hammering point), and the force accelerometer on the hammer collects the excitation force [F qx ,F qy ,F qz], and the accelerometer set at the measuring point on the compressor surface obtains acceleration data. Since the excitation point and the measuring point are both pre-set, [x c ,y c ,z c ],[x q ,y q ,z q ] are all known data.

[0061] Rewrite the above formula into transfer matrix form:

[0062]

[0063] F c =X cq F q

[0064]

[0065] Among them, F q is the force vector measured at the excitation point, F c is the force vector at the center of mass, X cq Transfers the matrix for the position of the force vector.

[0066] If the system composed of the compressor and the rubber foot pad is regarded as a six-degree-of-freedom system, a six-degree-of-freedom motion equation of the compressor can be listed as follows:

[0067]

[0068] Where F(t) is the equivalent force vector at the center of mass of the compressor, is the equivalent acceleration vector at the center of mass of the compressor, X c (t) is the equivalent displacement vector at the center of mass of the compressor. The above data are all time domain data transformed with time t, M is the equivalent mass matrix of the center of mass of the compressor, and K is the equivalent stiffness matrix of the compressor.

[0069] Perform Fourier transform on the above formula to convert the time domain data into frequency domain data. At this time, the data is converted from time t to frequency f. The Fourier transform formula is:

[0070]

[0071] Where f(t) is the time domain data and F(f) is the frequency domain data, then:

[0072]

[0073] get:

[0074]

[0075] For a closed compressor, the acceleration data at the center of mass cannot be directly measured. By measuring the p point on the compressor surface j Acceleration data The acceleration data at the center of mass is obtained through the acceleration transfer matrix T0, that is:

[0076]

[0077] It should be noted that since the center of mass acceleration considers six degrees of freedom, namely three translations and three rotations, and measuring the acceleration data of a point on the compressor surface usually only detects data in three directions, it is necessary to collect acceleration data from at least two points on the compressor, that is, the vibration acceleration data includes the acceleration data of two points on the compressor surface. After collecting the acceleration data of the compressor surface, it is substituted into the above formula:

[0078]

[0079] in Is a 3N×1 vector, corresponding to the acceleration transfer matrix T 0p is a 3N×6 vector.

[0080] In the above formula, the acceleration data of the two measuring points collected by the accelerometer is The coordinate data of the two measuring points (X1, Y1, Z1), (X2, Y2, Z2) and the centroid coordinates (X c ,Y c ,Z c ) is substituted into the transfer matrix T 0p Then the center of mass acceleration in the frequency domain is obtained by the least squares method:

[0081]

[0082] Substituting this into the Fourier transformed equation of motion, we obtain the load identification formula F(f):

[0083]

[0084] Where M is the equivalent mass matrix of the compressor center of mass, K is the equivalent stiffness matrix of the compressor, and T 0p is the acceleration transfer matrix between the compressor surface and the compressor mass center; is the vibration acceleration data of the compressor surface.

[0085] The equivalent mass matrix M is composed of the compressor mass and inertia parameters. The compressor mass can be obtained by weighing, and the inertia parameters can be obtained through direct testing methods such as inertia table measurement or indirect testing methods such as inertia parameter identification using mass lines. The equivalent stiffness matrix K can be obtained from the stiffness coefficient of the foot pad, which can be obtained by consulting the foot pad manufacturer's manual.

[0086] Given known compressor structural parameters, the equivalent load at the compressor's center of mass can be calculated using the load identification formula based on the compressor surface vibration acceleration data. Compressor surface vibration acceleration data can be obtained through modal testing (e.g., the hammer method), so compressor load data can be obtained through modal testing of the compressor.

[0087] In the related art, in the simulation work of pipeline optimization design, a unit load is generally used as the input load of the pipeline system, and then a horizontal comparison of multiple pipeline design schemes is performed. However, this unit load is difficult to represent the actual operation status of the pipeline during air conditioning operation, and the simulation accuracy is low. Naturally, the reliability of its horizontal comparison is also worth exploring. The present invention obtains the vibration acceleration data of the compressor surface obtained through modal experiments, and according to the obtained vibration acceleration data, the load data at the center of mass of the compressor can be calculated using the above-mentioned load identification formula. The method is convenient, fast, easy to implement, and can improve the accuracy of pipeline optimization simulation.

[0088] Step S120: Obtain the constraints of the pipeline parameters that need to be optimized and the objective function of the pipeline optimization.

[0089] The pipeline parameters include, for example, at least one of the straight pipe length, bend pipe length, and bend angle of the exhaust pipe. Figure 3 FIG2 shows a schematic diagram of pipeline parameters of an exhaust pipe according to an embodiment of the present invention. Figure 3 As shown, the pipeline parameters include straight pipe length PH1, bend pipe length PL1, bend angle PA1, bend pipe length PL2, bend angle PA2, bend pipe length PL3, bend angle PA3, straight pipe length PH2, and bend angle PA4.

[0090] In one specific embodiment, the constraint condition may include a first constraint condition determined according to a preset piping design specification. The first constraint condition may include a value range for a piping parameter, i.e., a constraint on the piping spatial layout, within which the piping parameters obtained by the optimized design fall. Incorporating the parameter ranges from the air conditioning piping design specification as constraints into the piping optimization process ensures that the resulting piping specifications are more reasonable and meet enterprise and industry standards.

[0091] Specifically, after determining the pipeline parameters that need to be optimized, such as pipe length and angle, these parameters are considered from the perspectives of assembly, fluid performance, structural stability, and safety margin. These parameters are typically based on corresponding design specifications, corporate standards, and industry standards. Therefore, these relevant design specifications, corporate standards, and industry standards can be used as constraints for multi-objective optimization.

[0092] by Figure 3 Taking the basic structure of the central exhaust pipe as an example, the following Table 1 is obtained according to relevant specifications and standards. The value range of each pipeline parameter in the table is used as one of the constraints for the pipeline optimization design.

[0093] Table 1

[0094]

[0095] Table 1 shows the pipeline parameters and value ranges of the exhaust pipe model according to a specific embodiment of the present invention. Figure 3 Only some commonly used parameters of the pipeline are used as optimization parameters. It should be understood that other parameters (for example, the limited distance between the pipeline and the air-conditioning component, and the bending processing specifications) can be considered as optimization parameters.

[0096] The constraint condition may further include a second constraint condition, and the second preset condition may specifically include at least one of a structural strength constraint, a vibration control constraint, and a fluid performance constraint.

[0097] (1) Structural strength constraints:

[0098] The structural strength constraint may specifically include a stress response constraint of the pipeline. In a specific embodiment, the stress response constraint of the pipeline may be calculated using a finite element analysis method.

[0099] First, in the finite element software, the pipeline structure is converted into a 3D geometric model and divided into elements, such as tetrahedrons or hexahedrons. Each element has several nodes, and the connections between the nodes define the shape and properties of the element. For example, for a four-node quadrilateral element, the element displacement is:

[0100] U(x,y)=a1+a2x+a3y+a4xy

[0101] Where U is the displacement vector, and a1, a2, a3, and a4 are interpolation coefficients. The interpolation coefficients are determined by combining shape functions and nodal displacements, and their specific form depends on the type and geometry of the element.

[0102] Then, define the material properties, define the physical properties of the material for each element, such as the elastic modulus E and Poisson's ratio, apply the external load F, run the solver, and solve the finite element equations:

[0103] KU=F

[0104] Where K is the stiffness matrix, U is the displacement vector, and F is the load vector.

[0105] In finite element analysis, strain is calculated from the displacement gradient:

[0106]

[0107] Where ∈ is the strain and △U is the displacement gradient tensor.

[0108] Finally, based on the displacement results, the strain of each element can be calculated, and then the stress can be obtained by Hooke's law. Hooke's law is the basic law of linear elastic materials, which states that stress and strain are proportional:

[0109] σ stress =E∈≤[S]

[0110] Among them, σ stress It represents the stress response of the pipeline, E represents the elastic modulus, and [S] represents the safety threshold of the stress response.

[0111] (2) Vibration control constraints:

[0112] The vibration control constraints may specifically include pipeline amplitude constraints. In one embodiment, the pipeline amplitude is calculated using finite element analysis. In finite element analysis, pipeline vibration can be represented using modal superposition, where the total amplitude can be expressed as the sum of the amplitudes of each modal order. Modes describe the inherent characteristics of a system during free vibration. Specifically, a mode is the spatial distribution of the vibration amplitude and phase at each point in the system when it vibrates at a specific frequency.

[0113] Each mode corresponds to a specific natural frequency and a unique vibration shape.

[0114]

[0115] A≤[δ]

[0116] Among them, A represents the amplitude of the pipeline, A i is the amplitude of the ith mode, n is the number of modes considered, F is the excitation force vector, is the vibration mode vector of the i-th mode, m is the pipeline mass, ω is the excitation frequency, ω0 is the natural frequency of the i-th mode, ζ is the damping ratio, and [δ] represents the safety threshold of the amplitude displacement.

[0117] In addition, vibration control also needs to consider boundary constraints. Parameters such as the natural frequency of the piping system will be different in the free state and under constrained conditions. The pipeline is installed during actual operation, so the boundary constraints of the pipeline are fixed at both ends, and the vibration frequency of the compressor is used as the excitation input.

[0118] (3) Fluid performance constraints:

[0119] Specifically, different medium flow rates in the pipeline will cause different degrees of impact force on the pipeline. In order to reduce the vibration effect of the impact force on the pipeline, the flow rate should meet the following conditions:

[0120]

[0121] Where u represents the flow rate, d represents the pipe diameter, and Q min With Q max These are the minimum and maximum flow rates in the air conditioning pipeline.

[0122] Since different pipelines are loaded with different media, the medium flow velocity u of the pipeline in the working state should meet the following requirements:

[0123] u min ≤u≤u max

[0124] Among them, u min with u max It is the minimum and maximum flow speed of the medium in the air conditioning pipeline when it is in working condition.

[0125] The objective function of pipeline optimization may specifically include: at least one of a pipeline natural frequency optimization objective function, a pipeline vibration amplitude optimization objective function, a pipeline stress value optimization objective function, a pipeline weight optimization objective function, and a noise optimization objective function;

[0126] (1) Pipeline natural frequency optimization objective function (used to adjust the pipeline natural frequency):

[0127] To prevent the pipeline from resonating with external excitation during operation, the natural frequency of the pipeline system is required to be outside the resonance range. This means that the difference between the natural frequency of the pipeline system and the rotational frequency of the compressor is maximized. The mathematical optimization model is as follows:

[0128] max|Δw|=|w i -w compressor |

[0129] Where Δw is the difference between the natural frequency of the pipeline system and the engine rotation frequency, w i is a certain order natural frequency of the piping system, w compressor is the operating frequency of the compressor.

[0130] (2) Pipeline vibration amplitude optimization objective function (used to adjust the pipeline vibration amplitude):

[0131] Excessive vibration amplitude can cause structural fatigue and wear of the pipeline, as well as loosening or breaking of pipeline connections, posing potential safety hazards. Furthermore, the amplitude of pipeline vibration directly affects the noise level during system operation. During pipeline optimization design, parameters are adjusted to minimize the vibration amplitude of the pipeline under external excitation (such as engine rotation frequency). The optimization model is as follows:

[0132] min A

[0133] Among them, min is the minimization operator. Under the premise that the damping matrix can be decoupled, the amplitude value can be obtained by performing the modal superposition method on the motion differential equation of the multi-degree-of-freedom system under external excitation in structural dynamics.

[0134] (3) Pipeline stress value optimization objective function (used to adjust the pipeline stress value):

[0135] When a pipe is bent, especially when it is bent at a small radius or large angle, it will not only retain a large amount of internal stress, but also easily form stress concentration, which can lead to stress fatigue fracture in the pipe. Compared with bends, straight pipes are less likely to experience stress fracture. Therefore, when optimizing the pipe design, it is required to minimize the stress at the bend as much as possible. The mathematical optimization model is:

[0136]

[0137] Among them, min is the minimization operator, F is the resultant force on the wall of the bend, d is the diameter of the pipe, and p is the m represents the average effective pressure, Δp represents the maximum amplitude of the pulsating pressure, and β represents the elbow angle.

[0138] (4) Pipeline weight optimization objective function (used to adjust pipeline weight):

[0139] From the perspective of cost saving, since the weight of the pipeline is proportional to the volume, for the convenience of calculation, when calculating the objective function of the pipeline weight, the total volume of the pipeline is used as the total objective function, that is, the minimum pipeline volume is taken as the optimization goal. The mathematical optimization model is:

[0140]

[0141] Where V is the total volume of the pipeline, V e is the volume of each unit under finite element division, A e is the cross-sectional area of ​​each unit under finite element division, L e is the length of each unit in the finite element division. This means that in finite element analysis, the total volume of the pipeline is the sum of the volumes of all discretized units.

[0142] The variables that affect the pipe volume are the pipe length, angle, and pipe diameter mentioned above.

[0143] (5) Noise optimization objective function (used to adjust acoustic noise):

[0144] Structural vibration will cause sound radiation, so the impact of structural vibration on sound radiation needs to be considered. In the sound radiation simulation optimization design, the selection of the objective function needs to be determined according to the specific design requirements and application scenarios.

[0145] In scenarios where local noise control is required, such as air conditioning duct outlets or surrounding sensitive areas, consider minimizing the sound pressure level (SPL) at specific locations or areas to reduce noise. Sound pressure level is a physical quantity that measures the sound pressure intensity of a sound wave at a certain point, usually measured in decibels (dB):

[0146] min SPL=20log 10 (p / p0)

[0147] Where p is the sound pressure and p0 is the reference sound pressure, which is usually 20 μPa in air.

[0148] In scenarios where the overall noise radiation of a sound source needs to be controlled, such as the overall noise output of air conditioning pipes and air conditioning outdoor units, consider minimizing sound power to reduce the overall noise radiation. Sound power is the total amount of sound energy radiated by a sound source, indicating the total energy output of the sound source:

[0149] min SWL = 10log 10 (P / P0)

[0150] Where P is the sound power, P0 is the reference sound power, usually set to 10 -12 W.

[0151] Step S130 , performing finite element analysis based on the identified compressor load and the acquired constraint conditions and objective function in combination with a preset multi-objective optimization algorithm to obtain a parameter value combination solution set of pipeline parameters.

[0152] Optimize and analyze the piping system. Based on the piping structure, model the piping. Draw the three-dimensional structure based on the air conditioner outdoor unit (for example, structural design software such as ProE or CreoE). Then, parameterize the structural dimensions that need to be optimized.

[0153] The multi-objective optimization algorithm may specifically include: a non-dominated sorting genetic algorithm (NGSA-II) and / or a multi-objective genetic optimization algorithm (MOGA). A multi-objective optimization algorithm can optimize multiple objective functions simultaneously and is applicable to complex problems with conflicting objectives.

[0154] Figure 4 This is a schematic diagram of a specific implementation of the steps of performing finite element analysis based on the identified compressor load and the obtained constraints and objective function in combination with a preset multi-objective optimization algorithm to obtain a parameter value combination solution set of pipeline parameters. Figure 4 As shown, step S130 includes step S131, step S132 and step S133.

[0155] Step S131: establishing a finite element analysis model of the air conditioner.

[0156] Specifically, a finite element analysis model can be established using 3D structural design software and finite element simulation software. For example, a structural geometry model can be created and imported using finite element modeling software or CAD tools, and the geometry model can be discretized into finite elements.

[0157] Step S132 : applying the identified compressor load to the finite element analysis model, and integrating the constraint conditions and the objective function.

[0158] Specifically, in the finite element analysis model, a center of mass point is set at the center of mass of the compressor, the identified compressor load is applied at the center of mass point, and the objective function, design variables (pipeline parameters that need to be optimized) and constraints are integrated into the finite element analysis model. It can rely on specific software (such as HyperStudy) or programming language to call the model data file (such as ANSYS's APDL) for integration.

[0159] Step S133 , running finite element analysis according to the constraint conditions and the objective function, and performing iterative calculations through the preset multi-objective optimization algorithm to obtain a combined solution set of pipeline parameter values.

[0160] Specifically, a preset number of pipeline parameter value combinations (i.e., design variable combinations) are randomly generated, for example, based on the first constraint (i.e., the value range of the pipeline parameter). For each pipeline parameter value combination, a finite element analysis is performed to calculate the response corresponding to the objective function to evaluate the fitness of the pipeline parameter value combination. The multi-objective optimization algorithm is then repeatedly iterated until a termination condition is met, thereby obtaining a Pareto optimal solution set.

[0161] The finite element analysis process specifically involves creating and importing a structural geometry model, discretizing the geometry into finite elements, defining the element's material properties, and applying loads and boundary conditions. Then, based on the structural geometry, materials, and loads, the element stiffness matrix is ​​established. The stiffness matrices of all elements are assembled into a global stiffness matrix. Loads are applied to the structural nodes, and the displacement matrix is ​​calculated by solving KU=F. The element stresses and strains are then calculated using the node displacements.

[0162] Multi-objective optimization is an optimization method that seeks a balance between multiple objectives. The goal is to find a set of solutions that achieve the optimal balance between these different objectives, including the pipeline parameters to be designed. Simply put, it involves finding the best compromise between multiple conflicting or contradictory objectives. These optimal solutions are generally referred to as Pareto optimal solutions.

[0163] Multi-objective optimization problems can be expressed through mathematical formulas from three aspects: optimization objectives, design parameter variables, and constraints. The following is the specific mathematical expression:

[0164] parameter:x=(x1,x2,...x n )

[0165] object:min / max f(x)={f1(x),f2(x),...f k (x)}

[0166] subject to:

[0167] g j (x)≤0,j=1,2,...,m

[0168] h l (x)=0,l=1,2,...,p

[0169] x∈X

[0170] Where x is the design parameter variable, that is, the variable that can be adjusted during the optimization process. f(x) is the objective function vector, including k objective functions f k (x). g j (x) is the jth inequality constraint, h l (x) is the lth equality constraint, m and p are the number of inequality constraints and equality constraints, respectively. Constraints are used to limit the range of design variables to ensure that the optimization results meet actual engineering requirements.

[0171] Taking the multi-objective genetic optimization algorithm as an example, repeated iterations of the multi-objective optimization algorithm can include the following steps: initializing a certain number of individuals (i.e., management parameter value combinations) randomly generated; evaluating the fitness of each individual according to the objective function and constraints, and the constraints (i.e., the second constraint) include: structural strength constraint σ stress, vibration control constraint A, and at least one of the fluid performance constraints. The objective function includes: adjusting at least one of the pipeline natural frequency max|Δw|, the pipeline vibration amplitude min A, the pipeline stress min F, the pipeline weight min V, and the pipeline acoustic noise min SWL; selecting individuals to advance to the next generation based on fitness; generating new individuals to increase population diversity; forming a new population, and repeating the above steps until the iteration termination condition is met, the multi-objective genetic optimization algorithm terminates, and a set of Pareto optimal solutions is output. A Pareto optimal solution is one in which no other solution is inferior to it in all objectives, and at least one solution is superior to it in all objectives.

[0172] For the Pareto solution set obtained in one iteration of multi-objective optimization, if a solution is not inferior to another solution in all objectives and is better in at least one objective, then it dominates the other solution. One solution corresponds to one scheme (i.e., a combination of pipeline parameter values). The Pareto dominance relationship can be used to sort the pipeline design schemes to help select the better pipeline design scheme. Among them, the fitness of the scheme with a higher ranking is higher.

[0173] The response of the finite element analysis is the response of the objective function. In other words, the objective function values ​​are all responses calculated by the finite element analysis method. Different combinations of pipeline parameter values ​​(corresponding to the solutions in the Pareto solution set, and also corresponding to the pipeline design scheme) will obtain different objective function values. If the objective function value of the current pipeline design scheme (pipeline parameter value combination) has a higher fitness in the Pareto dominance relationship, then the design scheme will be retained and enter the next iteration. The fitness will be compared with the objective function values ​​of other pipeline design schemes (pipeline parameter value combinations) calculated in the next iteration. If it is better than the next pipeline design scheme, it will be retained; otherwise, it will be eliminated.

[0174] Step S140 , determining a pipeline design solution for the air conditioner based on the obtained pipeline parameter value combination solution set.

[0175] Each solution in the Pareto optimal solution set represents a compromise between various objectives, and no solution is superior to another solution in all objectives.

[0176] In a specific embodiment, according to preset requirements, an optimal pipeline parameter value combination that meets the preset requirements can be determined from the obtained pipeline parameter value combination solution set as the pipeline design solution for the air conditioner.

[0177] Specifically, it can be implemented through programming languages ​​(such as Matlab, Python, etc.). For example, the preset requirement is to reduce the bend angle. First, all solutions in the Pareto solution set are used as original data, and the bend angle and straight pipe length are the input parameters in each solution data. The programming language inputs the code for finding the minimum bend angle, and the pipeline optimization solution corresponding to the minimum bend angle can be obtained. If it is necessary to take into account both the reduction of the bend angle and the straight pipe length, an interval range n can be set, and n solutions can be found from the pipeline design solutions sorted from small to large for the bend angle, and n solutions can be found from the pipeline design solutions sorted from large to small for the straight pipe length. All solutions found for the two goals are displayed as a reference.

[0178] In a specific embodiment, the obtained pipeline parameter value combination solution set is displayed through a chart, so that the pipeline design scheme of the air conditioner can be determined based on the chart.

[0179] Figure 5 An example of visualizing the solution set of pipeline parameter value combinations is shown in Figure 1. Figure 5 As shown in the figure, the horizontal and vertical axes represent the thickness of the two pipe sections, respectively, in centimeters. The positions of the scattered points in the figure correspond to the corresponding horizontal and vertical coordinate values ​​of the thickness of the two pipe sections at that time. At this time, the pipeline optimization solution satisfies the pre-set constraints and completes the optimization design. It should be noted that this is only an example using the thickness of the two pipe sections as the input parameters. Other input parameters can be selected for visualization.

[0180] like Figure 5 As shown, the Pareto optimal solution set is visualized in a multidimensional target space through charts (such as scatter plots and radar charts), helping to understand the strengths and weaknesses of each solution. The most satisfactory design solution can be directly selected from the solution set based on the emphasis of various parameters in actual air conditioning piping. For example, in air conditioning piping design, if the designer prefers to reduce bend angles and increase straight pipe lengths, the optimal solution with the smallest bend angle can be found in the solution set. Figure 5 The horizontal and vertical coordinates represent the thickness of the two pipe sections, measured in centimeters. The locations of the scattered points in the graph correspond to the optimal design solution for the pipe section at the time the corresponding horizontal and vertical coordinate values ​​are taken. The horizontal and vertical coordinate values ​​corresponding to these points are the optimal solutions for the current horizontal and vertical coordinates. The specific optimal solution selected depends on the bias towards the specific parameters.

[0181] For the objective function involved in the present invention, a balance is achieved among multiple objectives such as the pipeline natural frequency and the pipeline vibration amplitude, and it is impossible to further improve any one objective without sacrificing other objectives. Therefore, n pipeline design schemes that take into account multiple objective functions are finally obtained. The number of schemes here corresponds to the number of solutions of Pareto optimal solutions (i.e., combinations of pipeline parameter values). In practical applications, the most appropriate scheme can be selected from the Pareto optimal solutions based on the actual situation of the pipeline parameters and the bias towards multiple objectives (for example, more emphasis on cost control or vibration control, etc.).

[0182] The present invention also provides a storage medium corresponding to the air-conditioning pipeline design method, on which a computer program is stored, and when the program is executed by a processor, the steps of any of the aforementioned methods are implemented.

[0183] The present invention also provides a computing device corresponding to the air conditioning pipeline design method, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the program.

[0184] The present invention also provides a computer program product corresponding to the air conditioning pipeline design method, comprising a computer program, which implements the steps of any of the aforementioned methods when executed by a processor.

[0185] Based on this, the solution provided by the present invention uses a relatively simple compressor load identification method as the vibration source to optimize the design of air-conditioning pipelines, thereby improving the accuracy of pipeline optimization, while considering the three optimization goals of frequency modulation, amplitude modulation, and weight modulation to achieve a balance between design margin and economy. It provides a Pareto optimal solution in the multi-objective optimization method and explains the relative emphasis of the solution under multiple indicators.

[0186] The technical solution of the present invention automatically searches for the optimal design solution in pipeline design through the actual load of the variable frequency air conditioner operation, while optimizing pipeline vibration, noise, and cost, and incorporating pipeline design specifications. It can perform real-time detection of pipeline parameters during pipeline modeling or model updating, and correct pipeline parameters that do not meet the pipeline design specifications, so that the pipeline with automatically optimized parameters meets actual use requirements, thereby improving the practicality of the method, assisting in pipeline optimization design, and improving design efficiency and design accuracy.

[0187] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and implementations are within the scope and spirit of the present invention and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or a combination of any of these. Furthermore, each functional unit may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0188] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0189] The units described as separate components may or may not be physically separate, and the components of the control device may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0191] The foregoing description is merely an embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of the claims.

Claims

1. A method for designing air conditioning pipelines, characterized in that: include: identifying a compressor load of the air conditioner; Obtaining the constraints of the pipeline parameters to be optimized and the objective function of the pipeline optimization, wherein the constraints include: a first constraint determined according to a preset pipeline design specification; Based on the identified compressor load and the obtained constraint conditions and objective function, a finite element analysis is performed in combination with a preset multi-objective optimization algorithm to obtain a combined solution set of pipeline parameter values; A pipeline design scheme for the air conditioner is determined based on the obtained pipeline parameter value combination solution set.

2. The method according to claim 1, characterized in that Identifying a compressor load of the air conditioner, comprising: Acquiring vibration acceleration data of the compressor surface obtained through modal testing; The load data at the center of mass of the compressor is calculated based on the acquired vibration acceleration data.

3. The method according to claim 2, characterized in that Calculating the load data at the center of mass of the compressor based on the acquired vibration acceleration data includes: Based on the acquired vibration acceleration data, the load data at the center of mass of the compressor is calculated using the following load identification formula: Where M is the equivalent mass matrix of the compressor center of mass, K is the equivalent stiffness matrix of the compressor, and T 0p is the acceleration transfer matrix between the compressor surface and the compressor mass center, is the vibration acceleration data of the compressor surface, and the vibration acceleration data includes acceleration data of two points on the compressor surface.

4. The method according to claim 1, wherein The constraint conditions further include: a second constraint condition; The second constraint condition includes at least one of a structural strength constraint, a vibration control constraint, and a fluid performance constraint.

5. The method according to claim 1, wherein The objective function of pipeline optimization includes: at least one of a pipeline natural frequency optimization objective function, a pipeline vibration amplitude optimization objective function, a pipeline stress value optimization objective function, a pipeline weight optimization objective function and a noise optimization objective function.

6. The method according to any one of claims 1 to 5, characterized in that Based on the identified compressor load and the obtained constraint conditions and objective function, a finite element analysis is performed in combination with a preset multi-objective optimization algorithm to obtain a pipeline parameter solution set, including: Establishing a finite element analysis model of the air conditioner; applying the identified compressor load in the finite element analysis model and integrating the constraints and the objective function; According to the constraints and the objective function, a finite element analysis is run, and an iterative calculation is performed using the preset multi-objective optimization algorithm to obtain a combined solution set of pipeline parameter values.

7. The method according to any one of claims 1 to 5, characterized in that Determining a pipeline design scheme for the air conditioner based on the obtained pipeline parameter value combination solution set includes: An optimal pipeline parameter value combination that meets preset requirements is determined from the obtained pipeline parameter value combination solution set as the pipeline design solution for the air conditioner.

8. A storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computing device, characterized in that The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the steps of the method according to any one of claims 1 to 7 are implemented when the processor executes the program.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed by a processor.

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