Multi-objective optimization method for Tesla valve

Abstract

The invention discloses a multi-objective optimization method for a Tesla valve. Establishing a three-dimensional model of the Tesla valve corresponding to the experiment, performing numerical simulation calculation, and verifying the feasibility of a numerical simulation scheme; after adjusting each parameter, establishing a new three-dimensional model of the Tesla valve, and screening main structural parameters of the Tesla valve; taking the performance value and the forward pressure drop of the Tesla valve as objective functions, taking the main structure parameters as influence factors, establishing a regression model of the main structure parameters of the Tesla valve and the performance value and the forward pressure drop of the Tesla valve, and performing multi-objective optimization on the Tesla valve based on a genetic algorithm; and comparing the performance of the optimized Tesla valve with the performance of a Tesla valve of an original structure, and verifying the feasibility of the multi-objective optimization method for the Tesla valve. According to the method, the structural parameters of the Tesla valve are improved, the performance of the Tesla valve is improved, meanwhile, the forward resistance of the Tesla valve is reduced, and the method is no longer limited to the influence of a single structural parameter on the performance of the Tesla valve.

Description

1. Technical Field

[0001] The invention belongs to the field of Tesla valve structure optimization, and in particular relates to a Tesla valve multi-objective optimization method. 2. Background technology

[0002] Tesla valve is a valve without moving parts and is a one-way valve. Its main structure consists of an inlet pipe, a shunt pipe, and an outlet pipe, wherein the shunt pipe includes a straight pipe and a curved pipe. When the fluid passes through the Tesla valve in the forward direction, most of the fluid will flow into the straight pipe in the shunt pipe, encountering less resistance and generating lower energy loss. Only a small part of the fluid will flow into the curved pipe, so the fluid will not generate a large pressure drop when passing through the Tesla valve in the forward direction. When the fluid passes through the Tesla valve in the reverse direction, most of the fluid will flow into the curved pipe in the shunt pipe. On the one hand, since most of the fluid will flow into the curved pipe and collide with the inner wall of the pipe, causing a large energy loss, on the other hand, after the fluid flows out of the curved pipe, it will collide with the fluid flowing through the straight pipe again to generate energy consumption. Therefore, the fluid will encounter greater resistance when passing through the Tesla valve in the reverse direction, which will generate a large pressure drop. Due to this characteristic, the Tesla valve has the function of making the fluid unidirectional and preventing backflow.

[0003] Tesla valve has the advantages of no moving parts, simple and stable structure, long service life, and easy mass production. It can not only effectively change the defects of traditional check valves such as unstable structure, easy wear, and short service life, but also can be used in small and micro fluid systems. At present, Tesla valves have been used in microfluidic control, microelectronic systems, microchannel heat transfer systems, biotechnology, analytical chemistry and other fields, and the application prospects of Tesla valves are very impressive. In recent years, a large number of studies are exploring its wider application and methods to improve the performance of Tesla valves. The working performance of Tesla valves can be improved by changing and optimizing their own structure, but how to ensure that the loss of the fluid in the forward flow of Tesla valves is small while its performance is improved has become a new problem. Therefore, it is urgent to propose a multi-objective optimization method for Tesla valves that can both improve the performance value of Tesla valves and reduce forward resistance. 3. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-objective optimization method for a Tesla valve, thereby improving the performance of the Tesla valve while reducing the loss caused by the fluid passing through the Tesla valve in the forward direction.

[0005] In order to achieve the above objectives, the following technical solutions are adopted:

[0006] A multi-objective optimization method for a Tesla valve, wherein the Tesla valve is a valve without movable parts, and the flow diversion pipeline thereof is composed of a straight pipeline and a curved pipeline. The multi-objective optimization method for the Tesla valve comprises the following steps:

[0007] Step 1: Based on the 3D modeling software, a 3D Tesla valve model with corresponding structural parameters is established according to the Tesla valve used in the experiment. The model is meshed using CFD software, and the simulation conditions are adjusted to the same conditions as the experiment. Numerical simulation calculations are performed, and the feasibility of the numerical simulation scheme is verified based on the calculation results.

[0008] Step 2: Select multiple adjustable structural parameters from the Tesla valve, specify the upper and lower limits of each structural parameter, establish a new Tesla valve three-dimensional model after adjusting each parameter within the range, perform numerical simulation calculations on it, and select the main structural parameters of the Tesla valve;

[0009] Step 3: Taking the performance value and forward pressure drop of Tesla valve as the objective function and the main structural parameters as the influencing factors, a regression model of the main structural parameters of Tesla valve and the performance value and forward pressure drop of Tesla valve is established, and multi-objective optimization of Tesla valve is performed based on genetic algorithm;

[0010] Step 4: Remodel the optimized solution and perform numerical calculations to compare the performance of the Tesla valve with that of the original structure to verify the feasibility of the multi-objective optimization method for the Tesla valve.

[0011] In step 1, the steps of verifying the experiment through the numerical simulation scheme are as follows:

[0012] (1) Determine the type to be optimized among different types of Tesla valves, select an experiment conducted on this type of Tesla valve, and perform 3D modeling based on the specific structural parameters of the Tesla valve used in the actual experiment using 3D modeling software;

[0013] (2) Based on CFD software, the three-dimensional model of the Tesla valve is meshed, a turbulence model that matches the experimental process is selected, the boundary conditions are set to be the same as the experimental conditions, and numerical simulation calculations are performed;

[0014] (3) Compare the simulation results with the actual experimental results. If the error is within 5%, it can be concluded that the numerical simulation scheme is feasible.

[0015] In step 2, the steps for screening the main structural parameters of the Tesla valve are as follows:

[0016] S1: Select multiple adjustable structural parameters according to the structure of the selected type of Tesla valve, increase or decrease one of the structural parameters at equal intervals within its adjustable range, and keep other structural parameters unchanged, and establish a three-dimensional model of the Tesla valve after adjustment;

[0017] S2: Grid each model and perform simulation calculations, organize the calculation results and draw the influence curve of single factors on performance values, analyze the change law of the adjusted structure relative to the original structure, where the performance value Diodicity of the Tesla valve, i.e. Di, is expressed as:

[0018]

[0019] Where ΔP f is the forward pressure drop of the Tesla valve; ΔP r Reverse voltage drop issued for Tesla;

[0020] S3: Carry out the above process on the remaining structural parameters in turn to obtain the influence of each structural parameter on the performance value, and then select several structural parameters that have a greater impact on the performance value of the Tesla valve as the main structural parameters.

[0021] In step 3, the steps of multi-objective optimization of the Tesla valve are as follows:

[0022] (1) Generate the sample space of multi-objective optimization design of Tesla valve based on the optimal Latin hypercube method;

[0023] (2) The main structural parameters, performance values, and forward pressure drop of each adjusted Tesla valve are taken as a data set, with the performance value and forward pressure drop as dependent variables and the main structural parameters as influencing factors. These data sets are imported into the neural network model for training, and a regression model of the main structural parameters of the Tesla valve and the performance value and forward pressure drop of the Tesla valve is established using the neural network algorithm;

[0024] (3) A genetic algorithm is used to perform multi-objective optimization on the regression model of Tesla valve performance value and forward pressure drop to determine the optimal structure of the liquid ring wheel.

[0025] In step 4, the steps for verifying the feasibility of the Tesla valve multi-objective optimization method are as follows:

[0026] S1: Establish an optimized Tesla valve three-dimensional model based on the optimal structural parameter group obtained after genetic algorithm optimization, and perform numerical calculations on it to obtain the performance value and forward pressure drop of the model;

[0027] S2: Compare the performance value and forward pressure drop of the Tesla valve after multi-objective optimization with the original structure. If the performance value is greater than the original structure and the forward pressure drop is less than the original structure, it means that the multi-objective optimization method of the Tesla valve is feasible.

[0028] The advantages of the invention are:

[0029] (1) The present invention proposes a Tesla valve multi-objective optimization method, by which the structural parameters of the Tesla valve are improved, thereby achieving the goal of improving the performance of the Tesla valve while reducing the forward resistance of the Tesla valve.

[0030] (2) The multi-objective optimization method for Tesla valve proposed in the present invention is no longer limited to the influence of a single structural parameter on the performance of Tesla valve, but can simultaneously consider the influence of multiple structural parameters on the performance after being adjusted simultaneously.

[0031] (3) The multi-objective optimization method for Tesla valve proposed in the present invention improves the process of many scholars exploring Tesla valve from all aspects, and will promote the optimization of the model and performance of Tesla valve.

[0032] (4) The multi-objective optimization method for Tesla valves proposed in the present invention will discover more applicable environments for Tesla valves, and the application areas of Tesla valves will gradually expand in the future. 4. Description of the drawings

[0033] Figure 1 This is a flowchart of a multi-objective optimization method for Tesla valve;

[0034] Figure 2 It is a data comparison diagram of simulation results and experimental results in a specific embodiment;

[0035] Figure 3 It is a structural diagram of a Tesla valve in a specific embodiment;

[0036] Figure 4 It is the fitting curve of the main structural parameters and performance values ​​of the Tesla valve in the specific embodiment;

[0037] Figure 5 It is the fitting curve of the main structural parameters and forward pressure drop of the Tesla valve in the specific embodiment;

[0038] Figure 6 This is a comparison curve of the forward pressure drop of the Tesla valve at different Reynolds numbers before and after optimization in a specific embodiment;

[0039] Figure 7 It is a comparison curve of the performance values ​​of the Tesla valve under different Reynolds numbers before and after optimization in a specific embodiment. 5. Specific implementation methods

[0040] The following is combined with Figure 1-7 The present invention is further described with reference to the specific embodiments, but the protection scope of the present invention is not limited thereto.

[0041] Example

[0042] A multi-objective optimization method for a Tesla valve is characterized in that the Tesla valve is a valve without movable parts, and its diversion pipeline consists of a straight pipeline and a curved pipeline.

[0043] The Tesla valve multi-objective optimization method includes the following steps:

[0044] (1) Experimental verification through numerical simulation scheme

[0045] ① Determine the type to be optimized among different types of Tesla valves. The Tesla valve verification experiment selected in this embodiment is the "Tesla valve performance value exploration experiment under different Reynolds numbers". Based on the three-dimensional modeling software, three-dimensional modeling is performed according to the specific structural parameters of the Tesla valve used in the actual experiment;

[0046] ② Based on ANSYS Fluent software, the three-dimensional model of the Tesla valve was meshed, the Realizable k-ε turbulence model that matched the experimental process was selected, the boundary conditions were set to be the same as the experimental conditions, and numerical simulation calculations were performed;

[0047] ③ Compare the simulation results with the actual experimental results (such as Figure 2 As shown in the figure, the error between simulation and experiment is within 5%, so it can be concluded that the numerical simulation scheme is feasible.

[0048] (2) Screening the main structural parameters of Tesla valve

[0049] ① Select the inlet pipe length, outlet pipe length, inlet and outlet pipe height, diversion angle, bend pipe reflux angle, straight pipe length before the bend, arc radius of the bend pipe, and outlet width of the bend pipe of the Tesla valve, increase or decrease it at equal intervals within the adjustable range of one of the structural parameters, and keep the other structural parameters unchanged, and establish a three-dimensional model of the Tesla valve after adjustment;

[0050] ② Mesh each model and perform simulation calculations, organize the calculation results and draw the influence curve of single factors on performance values, analyze the change law of the adjusted structure relative to the original structure, where the performance value Di of the Tesla valve is expressed as:

[0051]

[0052] Where ΔP f is the forward pressure drop of the Tesla valve; ΔP r Reverse voltage drop issued for Tesla;

[0053] ③ The remaining structural parameters are processed in the same way as above, and the influence of each structural parameter on the performance value is obtained. According to the calculation results, five structural parameters that have a significant impact on the performance of the Tesla valve are selected as the main structural parameters, namely, the inlet and outlet pipe height h, the diversion angle α, the bend pipe reflux angle β, the bend pipe inner arc radius R, and the bend pipe outlet width w (such as Figure 3 shown).

[0054] Table 1 Original data, upper and lower limits and adjustment values ​​of the main structural parameters of the Tesla valve

[0055]

[0056] (3) Multi-objective optimization of Tesla valve

[0057] ① Generate the sample space of multi-objective optimization design of Tesla valve based on the optimal Latin hypercube method;

[0058] ② Take each adjusted main structural parameter, performance value, and forward pressure drop of the Tesla valve as a data set, with the performance value and forward pressure drop as dependent variables and the main structural parameters as influencing factors. Import these data sets into the neural network model for training, and use the neural network algorithm to establish a regression model between the main structural parameters of the Tesla valve and the performance value and forward pressure drop of the Tesla valve (such as Figure 4 , Figure 5 shown);

[0059] ③The NSGAII genetic algorithm is used to perform multi-objective optimization on the regression model of Tesla valve performance value and forward pressure drop to determine the optimal structure of the liquid ring wheel.

[0060] Table 2 Sample space generated by the optimal Latin hypercube method

[0061] 0.2044 0.1009 0.4551 0.6080 0.3995 0.2044 0.4664 0.5439 0.2947 0.6723 0.9718 0.4664 0.9911 0.3476 0.7649 0.2760 0.6188 0.9911 0.5230 0.3065 0.0040 0.1193 0.7094 0.5230 0.1155 0.8353 0.9936 0.4683 0.4781 0.1155 0.6848 0.7268 0.1909 0.0305 0.2850 0.6848 0.7861 0.0773 0.3971 0.9697 0.5101 0.7861 0.3491 0.6041 0.6334 0.5494 0.2449 0.3491 0.0353 0.7641 0.1443 0.8859 0.7966 0.0353 0.8380 0.1731 0.5140 0.7943 0.0763 0.8380

[0062] (4) Verify the feasibility of the Tesla valve multi-objective optimization method

[0063] ① According to the optimal structural parameter group obtained after genetic algorithm optimization, the optimized Tesla valve three-dimensional model is established, and numerical calculation is performed on it to obtain the performance value and forward pressure drop of the model;

[0064] ② The performance value and forward pressure drop of the Tesla valve after multi-objective optimization are compared with the original structure. The performance value is greater than the original structure, and the forward pressure drop is less than the original structure, indicating that the optimization of the Tesla valve structure leads to an improvement in performance, that is, the multi-objective optimization method of the Tesla valve is feasible.

[0065] Table 3 Comparison of forward pressure drop of Tesla valve before and after optimization

[0066] Reynolds number 100 200 300 400 500 600 Before optimization / Pa 13.3166 33.73297 60.45683 93.13737 131.66425 176.00918 After optimization / Pa 13.06393 31.78802 56.33606 85.78499 125.15603 164.26391 Negative growth rate / % 1.9 5.77 6.82 7.89 4.94 6.67

[0067] Table 4 Comparison of Tesla valve performance values ​​before and after optimization

[0068] Reynolds number 100 200 300 400 500 600 Before optimization / Pa 1.063 1.302 1.521 1.625 1.913 2.079 After optimization / Pa 1.092 1.354 1.591 1.71 2.011 2.186 growth rate / % 2.73 3.99 4.60 3.57 4.14 5.15

[0069] Figure 6 The forward pressure drop comparison curves of the Tesla valve under different Reynolds numbers before and after optimization are given;

[0070] Figure 7 The performance comparison curves of the Tesla valve before and after optimization at different Reynolds numbers are given.

[0071] The contents described in the embodiments of this specification are merely an enumeration of implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the embodiments. The protection scope of the present invention also includes equivalent technical means that can be thought of by those skilled in the art based on the inventive concept.

Claims

1. A Tesla valve multi-objective optimization method, characterized in that: The Tesla valve is a valve without moving parts. Its diversion pipeline consists of a straight pipeline and a curved pipeline. The multi-objective optimization method of the Tesla valve includes the following steps: Step 1: Based on the 3D modeling software, a 3D Tesla valve model with corresponding structural parameters is established according to the Tesla valve used in the experiment. The model is meshed using CFD software, and the simulation conditions are adjusted to the same conditions as the experiment. Numerical simulation calculations are performed, and the feasibility of the numerical simulation scheme is verified based on the calculation results. Step 2: Select multiple adjustable structural parameters from the Tesla valve, specify the upper and lower limits of each structural parameter, establish a new Tesla valve three-dimensional model after adjusting each parameter within the range, perform numerical simulation calculations on it, and select the main structural parameters of the Tesla valve; Step 3: Taking the performance value and forward pressure drop of Tesla valve as the objective function and the main structural parameters as the influencing factors, a regression model of the main structural parameters of Tesla valve and the performance value and forward pressure drop of Tesla valve is established, and multi-objective optimization of Tesla valve is performed based on genetic algorithm; Step 4: Remodel the optimized solution and perform numerical calculations to compare the performance of the Tesla valve with that of the original structure to verify the feasibility of the multi-objective optimization method for the Tesla valve.

2. A Tesla valve multi-objective optimization method according to claim 1, characterized in that: In step 1, the steps of verifying the experiment through the numerical simulation scheme are: (1) Determine the type to be optimized among different types of Tesla valves, select an experiment conducted on this type of Tesla valve, and perform 3D modeling based on the specific structural parameters of the Tesla valve used in the actual experiment using 3D modeling software; (2) Based on CFD software, the three-dimensional model of the Tesla valve is meshed, a turbulence model that matches the experimental process is selected, the boundary conditions are set to be the same as the experimental conditions, and numerical simulation calculations are performed; (3) Compare the simulation results with the actual experimental results. If the error is within 5%, it can be concluded that the numerical simulation scheme is feasible.

3. A Tesla valve multi-objective optimization method according to claim 1, characterized in that: In step 2, the steps of screening the main structural parameters of the Tesla valve are: (1) Generate the sample space of multi-objective optimization design of Tesla valve based on the optimal Latin hypercube method; (2) selecting a plurality of adjustable structural parameters according to the structure of the selected type of Tesla valve, increasing or decreasing one of the structural parameters at equal intervals within its adjustable range, and keeping the other structural parameters unchanged, and establishing a three-dimensional model of the Tesla valve after adjustment; (3) Each model is meshed and simulated, the calculation results are sorted out and the influence curve of single factor on performance value is plotted, and the change law of the adjusted structure relative to the original structure is analyzed. The performance value Diodicity of the Tesla valve, i.e., Di, is expressed as: Where ΔP f is the forward pressure drop of the Tesla valve; ΔP r Reverse voltage drop issued for Tesla; (3) The remaining structural parameters are subjected to the above process in turn to obtain the influence of each structural parameter on the performance value, and then several structural parameters that have a greater impact on the performance value of the Tesla valve are selected as the main structural parameters.

4. A Tesla valve multi-objective optimization method according to claim 1, characterized in that: In step 3, the steps of multi-objective optimization of the Tesla valve are: (1) The main structural parameters, performance values, and forward pressure drop of each adjusted Tesla valve are taken as a data set, with the performance value and forward pressure drop as dependent variables and the main structural parameters as influencing factors. These data sets are imported into the neural network model for training, and a regression model of the main structural parameters of the Tesla valve and the performance value and forward pressure drop of the Tesla valve is established using the neural network algorithm; (2) A genetic algorithm is used to perform multi-objective optimization on the regression model of Tesla valve performance value and forward pressure drop to determine the optimal structure of the liquid ring wheel.

5. A Tesla valve multi-objective optimization method according to claim 1, characterized in that: In step 4, the steps to verify the feasibility of the Tesla valve multi-objective optimization method are: (1) Establishing an optimized Tesla valve three-dimensional model based on the optimal structural parameter group obtained after genetic algorithm optimization, and performing numerical calculations on it to obtain the performance value and forward pressure drop of the model; (2) The performance value and forward pressure drop of the Tesla valve after multi-objective optimization are compared with those of the original structure. If the performance value is greater than that of the original structure and the forward pressure drop is less than that of the original structure, it means that the multi-objective optimization method of the Tesla valve is feasible.

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