Method and system for vortex suppression structure of servo valve flow passage based on cfd simulation

Abstract

The application discloses a method and system for vortex suppression structure of servo valve flow channel based on CFD simulation, and relates to the technical field of fluid simulation, and comprises the following steps: constructing a computational fluid dynamics simulation model of the servo valve flow channel; positioning the spatial position of the vortex in the flow channel and quantifying the harm degree of the vortex by using a multi-criteria fusion algorithm; optimizing the flow regulation structure of the inclination angle, the curvature radius and the porosity gradient change of the flow guide plate in the flow channel based on the vortex characteristics and the Reynolds number change; constructing a proxy model by the response surface method with the pressure drop, the flow coefficient and the flow uniformity as the targets, and combining the genetic algorithm to perform multi-objective collaborative optimization on the key parameters of the vortex suppression structure; and verifying the vortex suppression effect under the transient working condition by the dynamic grid technology. The beneficial effect is that the multi-objective balance of the pressure drop, the flow coefficient and the uniformity is realized by the systematic optimization design method, the response surface model (second-order polynomial) and the genetic algorithm, and the optimal vortex suppression effect is realized.

Description

Technical Field

[0001] This invention relates to the field of fluid simulation technology, and in particular to a method and system for suppressing vortex flow channels in servo valves based on CFD simulation. Background Technology

[0002] As a core component of fluid control systems, the flow characteristics within the servo valve's flow channel directly affect its performance indicators. The following problems often arise in the design of traditional servo valve flow channels:

[0003] (1) Vortex flow is easily generated inside the flow channel, resulting in increased energy loss.

[0004] (2) Vortexes cause flow instability, affecting the valve's response characteristics.

[0005] (3) Traditional design methods rely on empirical formulas and experimental verification, resulting in long development cycles and high costs.

[0006] In existing technologies, vortex suppression mainly employs structures such as guide vanes and rectifier grids, but lacks a systematic optimization design method, making it difficult to achieve the optimal vortex suppression effect. Summary of the Invention

[0007] The main objective of this invention is to propose a method and system for vortex suppression structure in servo valve flow channels based on CFD simulation, aiming to solve the technical problem of vortex suppression in servo valve flow channels.

[0008] To achieve the above objectives, the present invention proposes a method and system for vortex suppression structure of servo valve flow channel based on CFD simulation, comprising the following steps:

[0009] Construct a computational fluid dynamics simulation model of the servo valve flow channel;

[0010] A multi-criteria fusion algorithm was used to locate the spatial position of vortices within the flow channel and quantify their hazard level.

[0011] Based on vortex characteristics and Reynolds number variations, the flow rectification structure with optimized guide vane tilt angle, radius of curvature, and porosity gradient variations within the flow channel is optimized.

[0012] With pressure drop, flow coefficient, and flow uniformity as objectives, a surrogate model is constructed using the response surface methodology, and a genetic algorithm is combined to perform multi-objective collaborative optimization of the key parameters of the vortex suppression structure.

[0013] The vortex suppression effect under transient conditions was verified using dynamic mesh technology.

[0014] In one embodiment, the multiple criteria include the Q criterion, the λ² criterion, and a composite criterion:

[0015] The Q criterion is calculated through the symmetric / antisymmetric decomposition of the velocity gradient tensor (tensor A, B) and the Frobenius norm;

[0016] The λ² criterion is based on the symmetric tensor A. 2 +B 2 The negative eigenvalues ​​determine the vortex core region;

[0017] The composite criterion is defined by the composite tensor T = αB + βA. 2 +γ(AB-BA) (weights satisfy α+β+γ=1), and dynamically adjust the identification logic in combination with vorticity, curl and Reynolds number.

[0018] In one embodiment, the deflector tilt angle is adaptively adjusted according to Re (small tilt angle in laminar flow region, large tilt angle in turbulent flow region);

[0019] The radius of curvature is correlated with Re through an error function to match the vortex scale;

[0020] The rectifying structure is based on the Darcy-Forchheimer equation and is designed with a porosity gradient distribution to gradually dissipate vortex energy.

[0021] In one embodiment, response surface methodology and genetic algorithm are combined to determine the pressure drop ΔP(x) and flow coefficient C. v σ(x) and flow uniformity σ(x) are multiple objectives, and the optimal combination of geometric parameters is automatically searched.

[0022] In one embodiment, the region with the highest vortex density in a vortex is the vortex core, and the determination of the vortex core region requires the following conditions to be met simultaneously:

[0023]

[0024] Among them, C Q ∈[0.15,0.35];

[0025] ∈ λ =0.1·|A|;

[0026] k∈[0.01,0.1];

[0027] Q thresh The Q-criterion threshold is used to filter regions with strong rotation.

[0028] Turbulent kinetic energy is a physical quantity that describes the energy of turbulent motion.

[0029] λ2(T) is the second eigenvalue of tensor T;

[0030] ‖‖ represents the Frobenius norm;

[0031] W represents vorticity;

[0032] Let be the curl of the velocity field.

[0033] In one embodiment, turbulent kinetic energy reflects the kinetic energy of random pulsations in a fluid element and is directly related to the variance of the velocity components u′, v′, w′, calculated using the following formula:

[0034]

[0035] In one embodiment, the vortex core boundary is determined by the second derivative extremum point + Neumann boundary condition: Core location: Solving

[0036]

[0037] H(-λ2) is the Heaviside step function, which takes the value of 1 when its parameter -λ2 is greater than 0, and 0 otherwise.

[0038] Boundary conditions:

[0039]

[0040] This represents the partial derivative along the normal direction;

[0041] Indicates the boundary of the computation region.

[0042] In one embodiment, the weights of the composite criterion are α = 0.6, β = 0.3, and γ = 0.1, and the fluid rotation characteristics are constrained by the second eigenvalue λ2(T) of the composite tensor T.

[0043] In one embodiment, the multi-objective collaborative optimization method constructs a parameter correlation matrix by quantifying the influence weights of different combinations of geometric parameters on pressure drop, flow coefficient, and flow uniformity.

[0044] In one embodiment, it includes:

[0045] Flow field modeling module: used to build fluid simulation models of servo valve flow channels;

[0046] Vortex identification module: Employs a multi-criteria fusion algorithm (Q-criteria, λ2-criteria, composite criteria) to locate and quantify vortex features within the flow channel;

[0047] Structural design module: Based on vortex characteristics and Reynolds number variations, parametric design of guide vane tilt angle, radius of curvature, and porosity gradient rectification structure;

[0048] Optimization module: Response surface methodology and genetic algorithm are used to perform multi-objective optimization of the vortex suppression structure parameters;

[0049] Dynamic verification module: Simulates the eddy current suppression effect under transient conditions using dynamic mesh technology.

[0050] The technical solution of this invention suppresses vortexes through characteristic equations: turbulence intensity is quantified by Galilean invariants, and the eigenvalues ​​are dynamically adjusted to optimize the suppression structure;

[0051] Multi-objective collaborative optimization: Combining response surface model (second-order polynomial) with genetic algorithm to achieve a multi-objective balance of pressure drop, flow coefficient, and uniformity;

[0052] Dynamic mesh verification: Dynamic mesh technology is used to simulate the eddy current suppression effect under transient conditions. Through a systematic optimization design method, the optimal eddy current suppression effect is achieved. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0054] Figure 1 A flowchart of the method and system for suppressing vortex flow channels of servo valves based on CFD simulation provided by the present invention;

[0055] Figure 2 Simulation results of the method and system for vortex suppression structure of servo valve flow channel based on CFD simulation provided by the present invention.

[0056] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] It should be noted that if directional indicators (such as up, down, left, right, front, back, etc.) are involved in the embodiments of this invention, these directional indicators are only used to explain the relative positional relationships and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly. Unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0059] Furthermore, if the embodiments of the present invention involve descriptions using terms such as "first," "second," etc., these descriptions are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Furthermore, the use of "and / or" or "and / or" throughout the text includes three parallel options; for example, "A and / or B" includes option A, option B, or options where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0060] The present invention proposes a method and system for suppressing vortex flow channels in servo valves based on CFD simulation, comprising the following steps:

[0061] Construct a computational fluid dynamics simulation model of the servo valve flow channel;

[0062] A multi-criteria fusion algorithm was used to locate the spatial position of vortices within the flow channel and quantify their hazard level.

[0063] Based on vortex characteristics and Reynolds number variations, the flow rectification structure with optimized guide vane tilt angle, radius of curvature, and porosity gradient variations within the flow channel is optimized.

[0064] With pressure drop, flow coefficient, and flow uniformity as objectives, a surrogate model is constructed using the response surface methodology, and a genetic algorithm is combined to perform multi-objective collaborative optimization of the key parameters of the vortex suppression structure.

[0065] The vortex suppression effect under transient conditions was verified using dynamic mesh technology.

[0066] Using composite criteria to find vortices within the flow channel is more efficient. By optimizing the structural parameters within the flow channel, based on vortex characteristics and Reynolds number variations, and targeting pressure drop, flow coefficient, and flow uniformity, an algorithm is used to screen for superior vortex suppression structures. Finally, dynamic mesh technology is used to verify the optimization effect. This achieves a complete process from identifying vortices to optimizing them and finally selecting the best among the best, resulting in optimal vortex suppression.

[0067] Specifically, this invention provides a systematic solution for eliminating vortices within a flow channel in a simulation path.

[0068] In one embodiment, the multiple criteria include the Q criterion, the λ² criterion, and a composite criterion:

[0069] The Q criterion is calculated through the symmetric / antisymmetric decomposition of the velocity gradient tensor (tensor A, B) and the Frobenius norm;

[0070] The λ² criterion is based on the symmetric tensor A. 2 +B 2 The negative eigenvalues ​​determine the vortex core region;

[0071] The composite criterion is defined by the composite tensor T = αB + βA. 2 +γ(AB-BA) (weights satisfy α+β+γ=1), and dynamically adjust the identification logic in combination with vorticity, curl and Reynolds number.

[0072] In one embodiment, the deflector tilt angle is adaptively adjusted according to Re (small tilt angle in laminar flow region, large tilt angle in turbulent flow region);

[0073] The radius of curvature is correlated with Re through an error function to match the vortex scale;

[0074] The rectifying structure is based on the Darcy-Forchheimer equation and is designed with a porosity gradient distribution to gradually dissipate vortex energy.

[0075] In one embodiment, response surface methodology and genetic algorithm are combined to determine the pressure drop ΔP(x) and flow coefficient C. v σ(x) and flow uniformity σ(x) are multiple objectives, and the optimal combination of geometric parameters is automatically searched.

[0076] In one embodiment, the region with the highest vortex density in a vortex is the vortex core, and the determination of the vortex core region requires the following conditions to be met simultaneously:

[0077]

[0078] Among them, C Q ∈[0.15,0.35];

[0079]

[0080] Tanh(x) characteristics: x=0, tanh(x)=0; x=∞, tanh(x)=1.

[0081] When Re≈8000, C Q =0.2 (transition zone);

[0082] When Re = ∞, C Q =0.25 (fully developed turbulent region);

[0083] ΔRe least squares method fitting of experimental data yielded 2000

[0084]

[0085] In a typical servo valve flow path (Re∈[2000,15000], the measured range of this ratio is [0.15,0.35].

[0086] ∈ λ =0.1·|a|;

[0087] k∈[0.01,0.1];

[0088] Q thresh The Q-criterion threshold is used to filter regions with strong rotation.

[0089] Turbulent kinetic energy is a physical quantity that describes the energy of turbulent motion.

[0090] λ2(T) is the second eigenvalue of tensor T;

[0091] ‖‖ represents the Frobenius norm;

[0092] W represents vorticity;

[0093] Let be the curl of the velocity field.

[0094] In one embodiment, turbulent kinetic energy reflects the kinetic energy of random pulsations in a fluid element and is directly related to the variance of the velocity components u′, v′, w′, calculated using the following formula:

[0095]

[0096] In one embodiment, the vortex core boundary is determined by the second derivative extremum point + Neumann boundary condition: Core location: Solving

[0097]

[0098] H(-λ2) is the Heaviside step function, which takes the value of 1 when its parameter -λ2 is greater than 0, and 0 otherwise.

[0099] Boundary conditions:

[0100]

[0101] This represents the partial derivative along the normal direction;

[0102] Indicates the boundary of the computation region.

[0103] In one embodiment, the weights of the composite criterion are α = 0.6, β = 0.3, and γ = 0.1, and the fluid rotation characteristics are constrained by the second eigenvalue λ2(T) of the composite tensor T.

[0104] In one embodiment, the multi-objective collaborative optimization method constructs a parameter correlation matrix by quantifying the influence weights of different combinations of geometric parameters on pressure drop, flow coefficient, and flow uniformity.

[0105] In one embodiment, it includes:

[0106] Flow field modeling module: used to build fluid simulation models of servo valve flow channels;

[0107] Vortex identification module: Employs a multi-criteria fusion algorithm (Q-criteria, λ2-criteria, composite criteria) to locate and quantify vortex features within the flow channel;

[0108] Structural design module: Based on vortex characteristics and Reynolds number variations, parametric design of guide vane tilt angle, radius of curvature, and porosity gradient rectification structure;

[0109] Optimization module: Response surface methodology and genetic algorithm are used to perform multi-objective optimization of the vortex suppression structure parameters;

[0110] Dynamic verification module: Simulates the eddy current suppression effect under transient conditions using dynamic mesh technology.

[0111] The following description, using a preferred embodiment, illustrates the content related to the above embodiments:

[0112] 1. Vortex Identification and Quantification Method: (Multi-criteria fusion identification algorithm: to achieve accurate vortex location and quantification of hazard level, providing data support for subsequent optimization)

[0113] 1.1 Basic Eddy Recognition Method

[0114] Vortex identification methods are all based on the eigenvalues ​​of the velocity gradient tensor.

[0115]

[0116] For the Laplace operator;

[0117] (u, v, w): These represent the components of the velocity vector in the V direction. That is, u is the component of the velocity in the x-direction.

[0118] v is the component of velocity in the y direction;

[0119] w is the component of the velocity in the z-direction.

[0120] u, v, w are the same as u, v, ω.

[0121] Its characteristic equation is:

[0122]

[0123] The above equation is rewritten using the three Galilean invariants P, Q, and R of the characteristic equation, where P is pressure.

[0124] Q represents flow rate;

[0125] R represents the radius of curvature;

[0126] It can be written as:

[0127] (λ-P)(λ-Q)(λ-R)=0

[0128] λ 3 +Pλ 2 +Qλ+R=0

[0129] in,

[0130] P = -(λ1 + λ2 + λ3)

[0131] Q=λ1λ2+λ2λ3+λ3λ1

[0132] R=λ1λ2λ3

[0133] Where λ1, λ2, and λ3 are respectively represented as The three eigenvalues.

[0134] The Galilean invariance is not derived by formula; it is an assumed law. The Galilean transformation describes the transformation relationship between different inertial reference frames. For example, when observing the same physical process from a stationary reference frame to a uniformly moving reference frame, the physical laws should remain unchanged.

[0135] Here, P, Q, and R represent the invariance. P, pressure, is a scalar that remains constant across inertial reference frames; R, radius of curvature, is a geometric quantity that also does not depend on the choice of reference frame; Q, flow rate, can also be invariant if defined relative to a boundary or control surface. Therefore, P, Q, and R can, in a sense, be chosen as Galilean invariants to construct physical models that hold true in different inertial reference frames. This is primarily to eliminate the influence of reference frame motion on vortex core identification, making it suitable for dynamic operating conditions.

[0136] The Q criterion uses Q > 0, and its specific calculation can be obtained through decomposition:

[0137]

[0138] Q can be written as:

[0139]

[0140] |||| represents the Frobenius norm.

[0141] λ² criterion:

[0142]

[0143] Where P represents pressure and ρ is density. When the symmetric tensor A... 2 +B 2 When two negative eigenvalues ​​exist, the pressure is minimized in the plane formed by the eigenvectors corresponding to these two negative eigenvalues. Assuming the eigenvalue indices are arranged in descending order of magnitude (λ1>λ2>λ3), then the existence of two eigenvalues ​​is equivalent to λ2<0; therefore, this method is called the λ2 method. That is, the location of the vortex is the region where λ2<0.

[0144] vorticity W: the curl of the velocity field

[0145]

[0146] 1.2 Unified Derivation of Composite Criteria

[0147] Tensor Invariant Reconstruction: Defining Composite Tensors

[0148] T = αB + βA 2 +γ(AB-BA)

[0149] The weighting coefficients satisfy α + β + γ = 1 (the preferred range in the patent is α = 0.6, β = 0.3, γ = 0.1).

[0150] Composite characteristic conditions:

[0151] 1.2.1 The vortex core region must simultaneously satisfy the following:

[0152] These three conditions work together to identify and determine vortex structures or specific flow characteristics in a fluid. Specifically:

[0153] The first condition is to determine whether the vortex intensity is large enough by comparing Q with its threshold.

[0154] The second condition is to determine whether the fluid deformation or rotation characteristics meet the requirements by limiting the eigenvalue λ2(T);

[0155] The third condition is to determine whether the degree of fluid rotation is significant by comparing the curl of the velocity field with the vortex intensity.

[0156]

[0157] Among them, C Q =0.25, ∈ λ =0.1·‖A‖, κ∈[0.01,0.1];

[0158] Q thresh The Q-criterion threshold is used to filter regions with strong rotation.

[0159] Turbulent kinetic energy is a physical quantity describing the kinetic energy of turbulent motion. It reflects the kinetic energy of random fluctuations in a fluid element and is directly related to the variance of its velocity components u′, v′, w′. The calculation formula is as follows:

[0160]

[0161] The higher the value, the more intense the turbulent pulsation.

[0162] λ2(T) is the second eigenvalue of tensor T.

[0163] 1.2.2 Accurate determination of vortex core boundary: Identifying and locating vortex structures in fluids. Specifically, it determines the position and intensity of vortices by combining the second derivative extreme point location method with Neumann boundary conditions.

[0164] Location via second derivative extrema: By solving for points where the second derivative equals zero, the core location of the vortex structure can be found. These points typically correspond to the vortex center or the location of maximum vortex intensity.

[0165]

[0166] H(-λ2) is the Heaviside step function, which takes the value 1 when its parameter -λ2 is greater than 0, and 0 otherwise.

[0167] Combined with Neumann boundary conditions: ensure that the rate of change of vortex intensity (along the normal direction) at the boundary of the computational domain is zero, prevent discontinuities or abnormal changes in the vortex structure at the boundary, and ensure the rationality and stability of the calculation results.

[0168]

[0169] This represents the partial derivative along the normal direction;

[0170] Indicates the boundary of the computation region.

[0171] 1.3 Theoretical basis for dynamic parameters:

[0172] 1.3.1 Reynolds number adaptive threshold:

[0173] Based on the assumption of local equilibrium in turbulence: In turbulence, the local equilibrium assumption holds that, within a certain range, the generation, transport, and dissipation of turbulent energy are in a dynamic equilibrium state. Therefore, changes in the Reynolds number can reflect changes in turbulence intensity.

[0174]

[0175] Q thresh (Re) represents the adaptive threshold that varies with the Reynolds number Re;

[0176] Q ∞ The limit value for high Reynolds numbers is taken as 0.2;

[0177] Re: Reynolds number, is a dimensionless number used to determine the flow state (laminar or turbulent).

[0178] Re c Critical Reynolds number: When the Reynolds number reaches this value, the flow begins to transition from laminar to turbulent. It is taken as 3000.

[0179] The formula adaptively adjusts the threshold Q using the Reynolds number Re. thresh This reflects the variation of turbulence intensity with Reynolds number. In practical applications, corresponding thresholds can be calculated based on different Reynolds numbers to identify and analyze turbulent structures.

[0180] 1.3.2 Vortex Intensity Weighting Factor: Defines the contribution of vortex intensity, which combines the effects of vorticity, Q criterion and Reynolds number, and can comprehensively evaluate the contribution of vortex intensity at a certain point in the fluid.

[0181]

[0182] 2. Parametric suppression structure design:

[0183] By parametrically defining parameters such as tilt angle θ and radius of curvature R (ratio of R / H to channel height), the vortex evolution at different Reynolds numbers is defined to optimize the channel structure.

[0184] 2.1 According to boundary layer theory, the relationship between the characteristic length scale of vortices in the near-wall region and Re is:

[0185]

[0186] σ v D is the characteristic length of the vortex core. h Where ρ is the hydraulic diameter, C1 is an empirical coefficient taken as 0.5, ρ is the fluid density, μ is the fluid dynamic viscosity, and u is the average flow velocity.

[0187] 2.2 The effect of the guide vane tilt angle on flow separation:

[0188]

[0189] ΔRe is the turbulent transition range; θ0 is the reference angle; Δθ is the adjustment range.

[0190] When Re <Re c At this angle, a small tilt angle (≈30°) reduces flow separation; when Re > Re c At this time, a large tilt angle (≈60°) enhances the guiding effect.

[0191] 2.3 Empirical relationship between radius of curvature and vortex strength:

[0192]

[0193] When Re is 8000, erf(0) = 0.

[0194] C2 directly corresponds to the ratio of the radius of curvature to the hydraulic diameter at a Reynolds number of 8000, and is the benchmark reference point for the model, approximately 0.15.

[0195]

[0196] This formula ensures that:

[0197] At low Re values ​​(Re < 5000): small curvature (R / D) h ≈0.1) Enhanced vortex dissipation;

[0198] At high Re values ​​(Re>10000): large curvature (R / D) h ≈0.3) Reduce secondary flow losses.

[0199] 2.4 A rectification structure with a porosity gradient is set at the point of abrupt change in the flow channel to gradually dissipate vortex energy.

[0200] Based on the Darcy-Forchheimer equations, the flow field control equations are modified as follows:

[0201]

[0202] Where: ε is porosity; V is fluid velocity; ρ is fluid density; P is pressure; α(ε) represents the velocity tensor; μ is the hydrodynamic viscosity; α(ε) is the shape factor related to porosity; β(ε) is the inertia factor related to porosity. dp is the particle diameter; T is the viscous stress tensor.

[0203] Define eddy energy density Its dissipation rate is:

[0204]

[0205] C d C is a constant related to the properties of the medium. f is a turbulence characteristic constant.

[0206] Optimal porosity distribution: to make vortex energy decrease exponentially along the flow channel E v (x)=E v0 e -λx Solving for:

[0207]

[0208] ε(x) is the porosity at position x; ε0 is the porosity at the initial position; ε L denoted as porosity at the end of the flow channel; L is the total length of the flow channel; and x is the distance along the flow channel.

[0209] To achieve a smooth transition, the aperture varies according to a quadratic law:

[0210]

[0211] d p (x) is the aperture at position x.

[0212] 3. Intelligent optimization process:

[0213] Combining the response surface methodology (RSM) and the genetic algorithm (NSGA-II), the pressure drop ΔP(x) and flow coefficient C were used to determine the optimal parameters. v σ(x) and flow uniformity σ(x) are multiple objectives, and the optimal combination of geometric parameters is automatically searched.

[0214] Multi-objective function definition:

[0215]

[0216] The goal of f1(x) is to reduce pressure drop, f2(x) is to improve flow performance, and f3(x) is to increase the uniformity of velocity distribution.

[0217] Constructing a response surface model (second-order polynomial)

[0218]

[0219] The predicted target response value is represented by ΔP(x) and C as described above. v (x), σ(x);

[0220] β0, β i β ii β ij , representing the model's response value;

[0221] x i These are the geometric parameters (tilt angle θ, radius of curvature R (ratio of R / H to channel height), and porosity ε).

[0222] The Pareto front is obtained by running the NSGA-II algorithm with the goal of minimizing ΔP and σ and maximizing Cv.

[0223] Pareto Frontier Analysis:

[0224]

[0225] The TOPSIS method is used to select the optimal solution from the Pareto solution set.

[0226] 1) Standardized Pareto Frontier Data

[0227] The objective values ​​of the Pareto solution set are normalized: the evaluation matrix X of the m-th solution and the n-th objective is normalized.

[0228]

[0229] 2) Weighted Standardization Matrix

[0230] v ij =w j ·r ij

[0231] w j This is a weight vector, and the weights are dynamically adjusted according to the operating conditions.

[0232]

[0233] 3) Determine the ideal solution and the negative ideal solution

[0234] Ideal solution A + ={minv i1 ,maxv i2 ,minv i3}

[0235] Negative ideal solution A - ={maxv i1 ,maxv i2 ,maxv i3}

[0236] 4) Calculate distance and proximity

[0237]

[0238] Choose proximity level C i The largest solution is considered the optimal solution.

[0239] Dynamic mesh simulation verification: For the transient working condition of the servo valve core movement, dynamic mesh technology is used to simulate the suppression effect of the suppression structure on transient vortices.

[0240] like Figure 2 As shown, based on the comparison of different vortex identification methods, the vortex false negative rate is reduced by approximately 62% when comparing the identification performance of the traditional single criterion (Q / λ2) with the composite criterion of this invention.

[0241] The following are explanations of some terms:

[0242] The Laplacian operator is an important second-order differential operator in mathematics and physics, used to describe the smoothness or "uniformity" of a function in space.

[0243] Galilean invariants: Galilean invariants are physical quantities that describe the motion of a fluid and are not affected by the uniform motion of the observer (i.e., quantities that remain unchanged under Galilean transformations).

[0244] Turbulent kinetic energy (TKE) is a core parameter in fluid mechanics that describes the energy of turbulent motion. It is defined as half the sum of the root-mean-square squares of the pulsating velocity components of a fluid element.

[0245] Pareto front: A core concept in the field of multi-objective optimization, referring to the boundary or surface formed by the set of all optimal trade-off solutions among multiple conflicting objectives.

[0246] TOPSIS method: The method ranks each solution by calculating the combined distance between each solution and the ideal optimal solution (positive ideal solution) and the ideal worst solution (negative ideal solution), and selects the solution that is closest to the optimal solution and furthest from the worst solution.

[0247] It should be understood that the terms "one embodiment" or "one example" throughout the specification mean that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in one example" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the invention.

[0248] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0249] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It is particularly important to note that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0250] The above description is merely an exemplary embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention specification and drawings under the technical concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for vortex suppression structure of servo valve flow channel based on CFD simulation, characterized in that, Includes the following steps: Construct a computational fluid dynamics simulation model of the servo valve flow channel; A multi-criteria fusion algorithm is used to locate the spatial position of vortices within the flow channel and quantify their hazard level. The multi-criteria include the Q-criteria, the λ²-criteria, and a composite criterion. The Q criterion is obtained by decomposing the velocity gradient tensor into a symmetric part tensor A and an antisymmetric part tensor B, and then performing the Frobenius norm calculation. The λ² criterion is based on the symmetric tensor A. 2 +B 2 The negative eigenvalues ​​determine the vortex core region; The composite criterion is defined by the composite tensor T = αB + βA. 2 +γ(AB-BA), where the weights satisfy α+β+γ=1, and the identification logic is dynamically adjusted in combination with vorticity, curl and Reynolds number; Based on vortex characteristics and Reynolds number variations, the flow rectification structure with optimized guide vane tilt angle, radius of curvature, and porosity gradient variations within the flow channel is optimized. With pressure drop, flow coefficient, and flow uniformity as objectives, a second-order polynomial surrogate model between geometric parameters and optimization objectives is constructed using the response surface methodology. The geometric parameters include guide vane tilt angle, radius of curvature, and porosity. The Pareto front is obtained through a genetic algorithm, and the TOPSIS method is used to select the comprehensive optimal solution for multiple objectives from the Pareto solution set, thus completing the multi-objective collaborative optimization of vortex suppression structural parameters. The vortex suppression effect under transient conditions was verified using dynamic mesh technology.

2. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 1, characterized in that: The guide vane tilt angle is adaptively adjusted according to Re. When the fluid flow is laminar, the guide vane tilt angle is set to a small angle; when the fluid flow is turbulent, the guide vane tilt angle is set to a large angle. Re is the Reynolds number, which is used to determine the fluid flow state. The radius of curvature is correlated with Re through an error function to match the vortex scale; The rectifying structure is based on the Darcy-Forchheimer equation and is designed with a porosity gradient distribution to gradually dissipate vortex energy.

3. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 1, characterized in that: Combining response surface methodology and genetic algorithm, with pressure drop ΔP(x) and flow coefficient C... v σ(x) and flow uniformity σ(x) are multiple objectives, and the optimal combination of geometric parameters is automatically searched.

4. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 1, characterized in that: The region with the highest vortex density in a vortex is the vortex core. The determination of the vortex core region requires the following simultaneous conditions: Among them, C Q ∈[0.15,0.35]; e λ =0.1·||A||; κ∈[0.01,0.1]; Q thresh The Q-criterion threshold is used to filter regions with strong rotation. Turbulentkinetic energy is a physical quantity that describes the energy of turbulent motion. λ2(T) is the second eigenvalue of tensor T; A is the symmetric part of the velocity gradient tensor, and ||A|| represents the Frobenius norm of A; |||| represents the Frobenius norm; W represents vorticity; Let be the curl of the velocity field.

5. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 4, characterized in that: Turbulent kinetic energy reflects the kinetic energy of random fluctuations in a fluid element, and is directly related to the variances of the velocity components u′, v′, and w′. The calculation formula is as follows:

6. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 4, characterized in that: The boundary of the vortex core is determined by the second derivative extremum point + Neumann boundary condition: Core location: Solution The Laplace operator is used to describe the smoothness of a function in space; H(-λ2) is the Heaviside step function, which takes the value of 1 when its parameter -λ2 is greater than 0, and 0 otherwise. Boundary conditions: This represents the partial derivative along the normal direction; Indicates the boundary of the computation region.

7. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 1, characterized in that: The weights of the composite criterion are α = 0.6, β = 0.3, and γ = 0.

1. The fluid rotation characteristics are constrained by the second eigenvalue λ2(T) of the composite tensor T.

8. The method for vortex suppression structure of servo valve flow channel based on CFD simulation as described in claim 1, characterized in that: The multi-objective collaborative optimization constructs a parameter correlation matrix by quantifying the influence weights of different combinations of geometric parameters on pressure drop, flow coefficient, and flow uniformity.

9. A system for suppressing vortex flow channels in a servo valve based on CFD simulation, characterized in that, include: Flow field modeling module: used to build fluid simulation models of servo valve flow channels; Vortex identification module: Employs a multi-criteria fusion algorithm to locate and quantify vortex features within the flow channel; The multiple criteria include the Q criterion, the λ² criterion, and a composite criterion. The Q criterion is calculated by decomposing the velocity gradient tensor into a symmetric / antisymmetric tensor A and an antisymmetric tensor B, and using the Frobenius norm. The λ² criterion is based on the symmetric tensor A. 2 +B 2 The negative eigenvalues ​​determine the vortex core region; the composite criterion is defined by the composite tensor T = αB + βA. 2 +γ(AB-BA), where the weights α, β, and γ satisfy α+β+γ=1, and the identification logic is dynamically adjusted in combination with vorticity, curl and Reynolds number; Structural design module: Based on vortex characteristics and Reynolds number variations, parametric design of guide vane tilt angle, radius of curvature, and porosity gradient rectification structure; Optimization module: With pressure drop, flow coefficient and flow uniformity as objectives, a second-order polynomial surrogate model between geometric parameters and optimization objectives is constructed using response surface methodology. The geometric parameters include guide vane tilt angle, radius of curvature and porosity. The Pareto front is obtained through genetic algorithm, and the TOPSIS method is used to select the comprehensive optimal solution of multiple objectives from the Pareto solution set to complete the multi-objective collaborative optimization of vortex suppression structural parameters. Dynamic verification module: Simulates the eddy current suppression effect under transient conditions using dynamic mesh technology.

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