Low-pressure atomizing nozzle structure parameter optimization method based on digital twinning and response surface method

Abstract

A low-pressure atomizing nozzle is a key equipment core component in fog drop irrigation. However, an existing low-pressure atomizing nozzle is large in energy consumption, complex in structure and poor in low-pressure operation quality performance, directly affects the irrigation efficiency of a sprinkling irrigation system and the deposition effect of fog drops on a target, and even causes drift loss outside the target. The invention provides a low-pressure atomizing nozzle structure parameter optimization method based on a digital twinning and response surface method, and the method comprises the steps: researching the influence of the interaction among the working medium water pressure, the nozzle diameter, the distance between a thimble and a nozzle outlet, and the thimble taper angle of a low-pressure atomizing nozzle on the hydraulic performance of the nozzle; the structure parameters of the nozzle diameter, the distance between the ejector pin and the nozzle outlet and the distance between the ejector pin and the ejector pin taper angle are optimized, the problems are solved, the reliability of the low-pressure atomization nozzle structure optimization model is improved, the optimization efficiency and the hydraulic performance quality effect are improved, meanwhile, the working intensity of operators is reduced, the farmer income is increased, and the economic benefit is increased. The environment is protected, and sustainable ecological development is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural machinery, and in particular relates to a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology. Technical Background

[0002] Digital twin technology refers to the use of technologies such as the Internet of Things, artificial intelligence, big data, and cloud computing to create corresponding digital models in the digital world that can reflect the working status and behavioral environment changes of physical objects in real time, and perform simulation, analysis, and optimization. It has been widely used in agriculture, industry, and the military, and has played an important role in many aspects.

[0003] Mist irrigation is one of the most common water-saving irrigation technologies in China, with advantages such as high water resource utilization rate, high crop absorption rate and low soil compaction rate. As the terminal spraying device of the sprinkler irrigation system, the low-pressure mist nozzle is a crucial core component of the equipment in the drip irrigation.

[0004] However, the types and structures of the existing low-pressure atomizing nozzles are relatively extensive, and there are no relevant professional special nozzles, which results in high energy consumption and complex structure of low-pressure atomizing nozzles, and poor quality performance when operating at low pressure. This not only increases the labor intensity of the operator, but also directly affects the irrigation efficiency of the sprinkler system and the deposition effect of droplets on the target, and even causes drift loss outside the target. The level of its atomization performance directly affects the quality effect of the atomizing irrigation system. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for optimizing the structural parameters of a low-pressure atomizing nozzle based on digital twins and response surface methodology. By studying the influence of the interaction between the working fluid water pressure, nozzle diameter, distance between the ejector pin and the nozzle outlet, and ejector pin cone angle of the low-pressure atomizing nozzle on the hydraulic performance of the nozzle, the structural parameters of the nozzle diameter, distance between the ejector pin and the nozzle outlet, and the ejector pin cone angle are optimized to achieve the purpose of optimal hydraulic performance, solve the problems of high energy consumption, complex structure, and poor quality performance of low-pressure atomizing nozzles during low-pressure operation, and reduce the labor intensity of operators. The present invention mainly relates to a nozzle atomization model, which is specifically applied to agricultural and forestry irrigation, urban gardening, plant protection spraying, foliar fertilization, and dust removal spraying.

[0006] The present invention achieves the above technical objectives through the following technical means.

[0007] A method for optimizing the structural parameters of low-pressure atomizing nozzles based on digital twin and response surface methodology is proposed. Based on the data of single-factor experimental research on low-pressure atomizing nozzles based on digital twin technology, the Sauter Mean Diameter (SMD) and the Relative Span (RS) are used as response quantities. The mathematical model of key structural parameters is established by response surface regression analysis to optimize the key structural parameters that affect the hydraulic performance of low-pressure atomizing nozzles. The optimal structural combination parameters of low-pressure atomizing nozzles are obtained through optimization design calculation.

[0008] To achieve the above object, the present invention comprises the following steps:

[0009] Step 1: Determine the response parameters of the low-pressure atomizing nozzle optimization; the response parameters include the average diameter of Sauter and the width of the droplet spectrum;

[0010] The Sauter Mean Diameter (SMD) is the "area mean diameter", which represents the weighted average of the droplet diameter to its own surface area, reflecting the ratio of the volume occupied by the liquid to the total surface area of ​​the liquid, and is usually used as a measure of the quality of the nozzle atomization effect. The calculation method in the present invention is simplified to D 32 , the optimization calculation model is shown in (1):

[0011]

[0012] Where: D 32 It refers to the average particle size, where "3" refers to the sum of the product of the number of particles in a certain particle size and the cube of the median of the two boundaries of the particle size among all particle sizes; "2" refers to the sum of the product of the number of particles in a certain particle size and the square of the median of the two boundaries of the particle size among all particle sizes; D i Refers to the diameter of droplet size class i; N i Refers to the selected droplet size class number; n i is the number of droplets in each size class; N is the total number of droplets.

[0013] The relative span (RS) is a structural parameter that measures the width of the droplet size distribution. Its optimization calculation model is shown in (2):

[0014]

[0015] Where: Dvm (m is 0.1-0.9) refers to the value of the median diameter of the droplet volume that accounts for m×100% of the total volume when all droplets are sorted by size. v0.1 Indicates the droplet diameter at which the cumulative distribution of droplets is 10%; D v0.5 Indicates the droplet diameter at which the cumulative distribution of droplets is 50%; Dv0.9 Indicates the droplet diameter at which the cumulative distribution of droplets is 90%.

[0016] Step 2: Determine the structural parameters that need to be optimized for the low-pressure atomizing nozzle: nozzle outlet diameter, distance between the ejector pin and the nozzle outlet diameter, and ejector pin cone angle.

[0017] Step 3: Based on the digital twin technology, the single factor control variable method is used to determine the test plan, and the test is carried out to obtain the atomization parameters, and then the atomization parameters are recorded to form an experimental database.

[0018] Step 4: Input the single-factor test results based on digital twin technology into the response surface software Design-Expert13, and use the Box-Behnken surface response method to analyze the test results. According to the analysis results, the optimization calculation model between the structural parameters of the sprinkler and the response value is obtained:

[0019] The optimization calculation model between the droplet Sottel mean diameter (SMD) of the low-pressure atomizing nozzle and the optimizable parameters is as follows (3):

[0020] As shown:

[0021] SMD=146.73792-261.89667P-85.10278d+38.03733h+1.97507θ-661P*d+123.7P*h

[0022] -1.11P*θ-99.3d*h-0.533611d*θ-0.089833h*θ+1902.7P 2 +135.03611d 2 +49.42300h 2

[0023] -0.005898θ 2 (3) Where: P is the working fluid water pressure, KPa; d is the nozzle outlet diameter, mm; h is the distance between the ejector pin and the nozzle outlet, mm; θ is the ejector cone angle, in degrees.

[0024] The optimization calculation model between the droplet spectrum width (RS) of the low-pressure atomizing nozzle and the optimizable parameters is shown in (4):

[0025] RS=3.605+15.60833P+0.997222d-0.715h-0.082417θ-8.25P*d+0.15P*h+0.013333P*θ

[0026] -0.5d*h+0.02d*θ+0.007833h*θ-6.95833P 2 -0.314815d2 +0.261667h 2 +0.000224θ 2

[0027] (4) Where: P is the working fluid water pressure, KPa; d is the nozzle outlet diameter, mm; h is the distance between the ejector pin and the nozzle outlet, mm; θ is the ejector cone angle, in degrees.

[0028] Step 5: Based on the test data based on digital twin technology and the response surface optimization model, the combination parameters of different structures when the hydraulic performance of the sprinkler is optimal can be obtained.

[0029] The beneficial effects of the low-pressure atomizing nozzle structural parameter optimization method based on digital twin and response surface methodology of the present invention are as follows:

[0030] (1) The present invention uses single-factor experimental data to construct a mathematical model based on digital twin technology and response surface methodology, thereby improving the reliability of the structural optimization model and its results;

[0031] (2) The present invention can quickly screen out the parameters with greater influence from the numerous parameters that affect the hydraulic performance of the low-pressure atomizing nozzle, and can also determine the single influence of each parameter on the hydraulic performance of the low-pressure atomizing nozzle and the comprehensive cross-influence of the interaction of each parameter on the hydraulic performance of the low-pressure atomizing nozzle, thereby improving the directionality of the optimization target and the optimization efficiency;

[0032] (3) The present invention uses Box-Behnken analysis to test the comprehensive cross-effect of the interaction of various nozzle parameters on the hydraulic performance of the low-pressure atomizing nozzle, and establishes a regression model based on digital twin technology and response surface method to calculate the optimal combination parameters of the hydraulic performance of the low-pressure atomizing nozzle, so as to solve the current problems of high energy consumption, complex structure, and poor quality performance of low-pressure atomizing nozzles during low-pressure operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the optimization steps of a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology of the present invention.

[0034] Figure 2 It is a schematic diagram of the main part of the low-pressure atomizing nozzle of the present invention and the optimized structure involved.

[0035] Figure 3 It is the main effect diagram of the average diameter of the droplets of the present invention.

[0036] Figure 4 It is the main effect diagram of the droplet spectrum width of the present invention.

[0037] Figure 5 It is an interactive response diagram of the working fluid water pressure and the nozzle outlet diameter, in which the response quantity of the present invention is the average diameter of the droplet Sotell.

[0038] Figure 6 It is an interactive response diagram of the nozzle outlet diameter and the distance between the ejector pin and the nozzle outlet, in which the response quantity of the present invention is the droplet spectrum width.

[0039] Figure 7 It is an interactive response diagram of the working fluid water pressure and the nozzle outlet diameter, in which the response quantity of the present invention is the average diameter of the droplet Sotell.

[0040] Figure 8 It is an interactive response diagram of the nozzle outlet diameter and the ejector cone angle, in which the response quantity of the present invention is the droplet spectrum width.

[0041] The following are the descriptions of the reference numerals:

[0042] 1. Thread, 2. Ejector pin holder, 3. Round hole, 4. Adjustable gap, 5. Ejector cone. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0044] The specific implementation steps of the low-pressure atomizing nozzle structural parameter optimization method based on digital twin and response surface methodology of the present invention are as follows:

[0045] (1) Determine the response parameters for optimizing the low-pressure atomizing nozzle; the response parameters include the Sauter Mean Diameter and the droplet spectrum width. SMD (Sauter Mean Diameter) represents the ratio of the volume occupied by the liquid to the total surface area of ​​the liquid, and is usually used as a measure of the quality of the nozzle atomization effect. When the volume is the same, the smaller the SMD, the more atomized particles, the smaller the average particle diameter, and the better the atomization effect. The calculation method is simplified to D32, and the optimization calculation model is as follows:

[0046]

[0047] Where D 32 It refers to the average particle size, where "3" refers to the sum of the product of the number of particles in a certain particle size and the cube of the median of the two boundaries of the particle size among all particle sizes; "2" refers to the sum of the product of the number of particles in a certain particle size and the square of the median of the two boundaries of the particle size among all particle sizes; D i Refers to the diameter of droplet size class i; N i Refers to the selected droplet size class number; n i is the number of droplets in each size class; N is the total number of droplets.

[0048] RS (Relative Span, droplet spectrum width) is a structural parameter that measures the width of droplet size distribution. The larger the RS, the lower the droplet uniformity. The optimization calculation model is as follows:

[0049]

[0050] Where: Dvm (m is 0.1-0.9) refers to the value of the median diameter of the droplet volume that accounts for m×100% of the total volume when all droplets are sorted by size. v0.1 Indicates the droplet diameter at which the cumulative distribution of droplets is 10%; D v0.5 Indicates the droplet diameter at which the cumulative distribution of droplets is 50%; D v0.9 Indicates the droplet diameter at which the cumulative distribution of droplets is 90%.

[0051] (2) Determine the optimized structural parameters of the low-pressure atomizing nozzle: nozzle outlet diameter, distance between the ejector pin and the nozzle outlet diameter, and ejector pin cone angle; This atomization performance test adopts a single-factor control variable method based on digital twin technology, as shown in Table 1. When the nozzle outlet diameter changes, ensure that other conditions do not change, the test pressure is 0.2MPa, the ejector pin cone angle is designed to be 120°, and the distance between the nozzle outlet and the ejector pin is 2mm for the experiment. When the ejector pin cone angle changes, the test pressure is 0.2MPa, the nozzle outlet diameter is designed to be 1.0mm, and the nozzle outlet is 1.5mm away from the ejector pin. Similarly, when the distance between the nozzle outlet and the ejector pin changes, the test pressure is 0.2MPa, the nozzle outlet diameter is designed to be 1.0mm, and the ejector pin cone angle is 120°.

[0052] Table 1 Experimental design of single factor control variable method based on digital twin technology

[0053]

[0054] The single factor control variable method based on digital twin technology was used to determine the test plan. The nozzle was subjected to atomization test to obtain its atomization parameters. The atomization parameters were recorded to form an experimental database. The database data was used to carry out experimental design based on digital twin technology and response surface methodology, as shown in Table 2.

[0055] Table 2 Experimental design and results based on digital twin technology and response surface methodology

[0056]

[0057]

[0058] The single-factor test results based on digital twin technology were input into the response surface software Design-Expert 13, and the test results were analyzed using the Box-Behnken surface response method. According to the analysis results, the polynomial relationship between the structural parameters of the sprinkler and the response value and the three-dimensional graph of the interactive effect of each factor on the response were obtained as follows:

[0059] Figure 5 This is the effect of working fluid water pressure and outlet diameter on the average diameter of Sauter; Figure 6 This is the effect of the outlet diameter and ejector distance on the average diameter of Sauter; Figure 7 This is the effect of working fluid water pressure and outlet diameter on droplet spectrum width; Figure 8 This is a diagram showing the influence of outlet diameter and outlet cone angle on droplet spectrum width.

[0060] The test scheme and test results based on digital twin technology and response surface method were imported into Design-Expert13, and the Box-Behnken surface response method was used to establish the response surface optimization model. Table 3 shows two response surface optimization models with the response quantities of the average diameter of droplets and the width of droplet spectrum, where: P is the working fluid water pressure, MPa; d is the nozzle outlet diameter, mm; h is the distance between the ejector and the nozzle outlet, mm; θ is the ejector cone angle, unit is °; SMD is the average diameter of droplets, mm; RS is the width of nozzle droplet spectrum. Among them, R2 (R-squared) is called the coefficient of determination of the equation, which indicates the degree of explanation of the independent variable in the equation to the dependent variable. The coefficients of determination of the two response surface optimization models are 0.9396 and 0.8288, respectively, indicating that 93.96% and 82.88% of the changes in the response values ​​come from the selected variables, indicating that the response surface optimization model is generally consistent with the digital twin test value. From the response surface optimization model, it can be seen that the adjustment coefficients of determination are 0.8730 and 0.6577, respectively, indicating that the response surface optimization model established can explain 87.3% and 65.77% of the changes in the average diameter of Sauter and the width of the droplet spectrum, respectively, indicating that the response surface optimization model has a good degree of fit. When the signal-to-noise ratio is greater than 4, it means that the model can be used for optimization. From Table 3, it can be seen that the values ​​of the signal-to-noise ratios of the two response surface optimization models are 16.1714 and 9.1581, respectively, indicating that the two response surface optimization models can be used for the optimization of their corresponding response quantities. Tables 4 and 5 are the variance analysis results of the response quantities being the average diameter of Sauter and the width of the droplet spectrum, respectively, and the regression coefficient of the average diameter of Sauter model <0.0001 can be obtained, and the difference is extremely significant, and the regression coefficient of the width of the droplet spectrum model <0.01, and the difference is significant. Among them, the p values ​​of the lack of fit coefficients (Lack of fit) are 0.2315 and 0.292, respectively, both greater than 0.05, and both insignificant, indicating that the response surface optimization model has a good fit. The analysis shows that the influence of various factors on the average diameter of droplets and the width of droplet spectrum are P (working fluid water pressure) > d (nozzle outlet diameter) > h (distance between the ejector pin and the nozzle outlet diameter) > θ (ejector cone angle) from large to small.

[0061] Table 3 Response surface regression analysis table

[0062]

[0063] Table 4 Analysis of variance of average diameter of droplet Sotel

[0064]

[0065]

[0066] Table 5 Analysis of variance of droplet spectrum width

[0067]

[0068]

[0069] The optimal structural combination parameters of the low-pressure atomizing nozzle were obtained by optimizing the design based on digital twin and response surface method. Based on the analysis of test results and the fitting of optimization model, the optimization model was analyzed by Box-Behnken to obtain the working fluid water pressure, nozzle outlet diameter, distance between the ejector and the nozzle outlet, and ejector cone angle when the average Sotter diameter and the droplet spectrum width were the smallest. Some analysis results are shown in Table 6.

[0070] Table 6 Low pressure atomizing nozzle performance test results:

[0071]

[0072] According to the target optimization results, when the average Sotell diameter and droplet spectrum width are the smallest, the adjustable structural parameters are pressure of 0.3MPa, nozzle outlet diameter of 1.8mm, distance between the ejector and the nozzle outlet of 0.9mm, and ejector cone angle of 90°. The optimization model calculates that the average Sotell diameter of the droplets at this time is 118.315μm and the droplet spectrum width is 1.412.

[0073] In order to verify the reliability of the optimization model based on digital twin and response surface method, the test was repeated three times according to the best combination parameters of the above different structural parameters, and the test results were averaged. The obtained test value was compared with the predicted value of the prediction regression model. If the two values ​​are close, it can be considered that the optimization model based on digital twin and response surface method has high reliability. Table 7 is a comparison table of test results and optimization model predictions. The experimental values ​​of the two response targets in the three experiments are very close to the predicted values ​​of their optimization models, with a minimum error of 1.56% and a maximum error of 9.35%, indicating that the optimization model based on digital twin and response surface method is reliable.

[0074] Table 7 Comparison of test results and predictions of optimization model

[0075]

[0076]

[0077] The optimized design of the low-pressure atomizing nozzle can be achieved according to the above method and steps.

[0078] The embodiments are preferred implementations of the present invention, but the present invention is not limited to the above-mentioned implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essential content of the present invention belong to the protection scope of the present invention.

Claims

1. A method for optimizing the structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology, characterized in that: The optimization method comprises the following steps: Step 1: Determine the response parameters of the low-pressure atomizing nozzle optimization, wherein the response parameters include the average diameter of the droplets and the spectrum width of the droplets; Step 2: Determine the structural parameters that need to be optimized for the low-pressure atomizing nozzle: nozzle outlet diameter, distance between the ejector pin and the nozzle outlet diameter, and ejector pin cone angle; Step 3: Based on the digital twin technology, a single factor control variable method is used to determine the test plan, and the atomization parameters are obtained by conducting the test, and then the atomization parameters are recorded to form an experimental database; Step 4: Input the single-factor test result data based on digital twin technology into the response surface software Design-Expert13, use the Box-Behnken surface response method to analyze the test results, and fit the various structural parameters and response parameters of the sprinkler according to the analysis results. Step 5: Based on the test data based on digital twin technology and the response surface optimization model, the combination parameters of different structures when the hydraulic performance of the sprinkler is optimal are obtained.

2. According to claim 1, a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology is characterized in that: The calculation method of the droplet Sotel mean diameter is simplified to D32, and the optimized calculation model is shown in (1): Where: D 32 Refers to the average particle size; D i Refers to the diameter of droplet size class i; N i Refers to the selected droplet size class number; n i is the number of droplets in each size class; N is the total number of droplets.

3. According to claim 1, a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology is characterized in that: The optimization calculation model of the droplet spectrum width is shown in (2): Where: D v0.1 Indicates the droplet diameter at which the cumulative distribution of droplets is 10%; D v0.5 Indicates the droplet diameter at which the cumulative distribution of droplets is 50%; D v0.9 Indicates the droplet diameter at which the cumulative distribution of droplets is 90%.

4. According to claim 1, a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology is characterized in that: The optimization calculation model of the droplet mean diameter SMD and the optimizable parameters of the low-pressure atomizing nozzle is shown in (3): SMD=146.73792-261.89667P-85.10278d+38.03733h+1.97507θ-661P*d+123.7P*h -1.11P*θ-99.3d*h-0.533611d*θ-0.089833h*θ+1902.7P 2 +135.03611d 2 +49.42300h 2 -0.005898θ 2 (3) Where: P is the working fluid water pressure, KPa; d is the nozzle outlet diameter, mm; h is the distance between the ejector pin and the nozzle outlet, mm; θ is the ejector cone angle, in degrees.

5. According to claim 5, a method for optimizing structural parameters of a low-pressure atomizing nozzle based on digital twin and response surface methodology, It is characterized in that The optimization calculation model between the droplet spectrum width (RS) of the low-pressure atomizing nozzle and the optimizable parameters is shown in (4): RS=3.605+15.60833P+0.997222d-0.715h-0.082417θ-8.25P*d+0.15P*h+0.013333P*θ -0.5d*h+0.02d*θ+0.007833h*θ-6.95833P 2 -0.314815d 2 +0.261667h 2 +0.000224θ 2 (4) In the formula: P is the working fluid water pressure, Kpa; d is the nozzle outlet diameter, mm; h is the distance between the ejector pin and the nozzle outlet, mm; θ is the ejector cone angle, in degrees.

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