Multi-objective parameters optimization design of self-excited oscillation pulsed atomizing nozzle
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Abstract
The self-excited oscillation pulsed atomizing nozzle can effectively and evenly spray the high-speed solid cone jet without any extra power. The primary atomization of the jet at the outlet of nozzle directly affects the final spray quality, and the turbulence and cavitation at the outlet of the atomizing nozzle are the other two main factors affecting the atomization. In this work, multi-objective optimization depending on nozzle parameters was established by using mathematical optimization techniques and computational fluid dynamics to improve the jet atomization quality at the outlet of the nozzle. The central composite design method and the response surface method were used to obtain the approximate mathematical model of the primary atomization quality of the jet at the outlet of nozzle. Finally, the non-dominated sorting genetic algorithm with elitist strategy (NSGA-II) and the grey theory were used in combination to optimize the nozzle parameters. Through combining with the NSGA-II and the grey theory, the nozzle parameters were optimized in order to obtain the best primary atomization at the outlet area of nozzle. The optimization results verified the nozzle design with multi-objective optimization method. The optimized values of the turbulent kinetic energy F_{1} and the vapor volume fraction F_{2} increased by 28.26% and 5.56%, respectively, and the corresponding nozzle parameters of the chamber diameter D, the lower nozzle diameter d_{2} and the upper nozzle inlet pressure P_{in} were, respectively, optimized to 28.056 mm, 5.472 mm and 3.999 Mpa.
Keywords
Self-excited oscillation pulsed atomizing nozzle Primary atomization Multi-objective optimization Modified cavitation model1 Introduction
Liquid atomization is a physical process in which the liquid is driven by the additional energy to form atomizing droplets in the gas environment. Atomization technology is applied in almost all areas such as industrial manufacturing, agricultural production and daily life, in addition to all kinds of fuel combustion. Atomization technology is also widely used in the combustion fields such as spraying, dusting, humidification and shower. The liquid atomization technology is closely related to industrial production; therefore, it plays an important role in many industrial sectors. However, new challenges shall be posed. On the one hand, increasing the injection pressure can improve the aerodynamic effect of the jet and enhance the spray performances. However, higher injection pressure also increases the energy consumption [1]. On the other hand, the jet shape obtained by the general pressure atomization method is hollow and results in uneven spray and large size of atomized droplets, which is harmful to the quality of spray jet [2]. Therefore, it is urgent to develop a new mechanism and liquid atomization technology to realize the uniform spray without extra energy consumption and break through these two bottlenecks.
The self-excited oscillation pulsed cavitation nozzle is a rotor with a hollow structure. The special nozzle structure and specific boundary conditions generate the self-exited oscillation pulse and transform the continuous jet to the pulsed jet by increasing the pressure peak at the nozzle outlet without the external energy, which improve the spray quality [3, 4]. Using the pulsed jet with self-excited oscillation to improve the atomizing quality is a new spray technology that can effectively address the challenges of hollow jet and uneven spray [5, 6, 7]. Therefore, the use of self-excited oscillation pulsed jet technology on liquid atomization and the optimization for qualifying the design the atomizing nozzle possess an exciting prospect for development.
A number of scholars studied the influences of the geometrical and operation parameters on atomization performances by experiments and numerical simulations. Wang et al. [8] established a spray model coupled with nozzle cavitating flow to discover the influence of diesel nozzle geometric parameters including the ratio of nozzle hole length to diameter, entrance curvature radius of nozzle hole and sac volume structure on spray characteristics, which were significant parameters for the structure optimization of the fuel system. Shervani-Tabar et al. [9] studied the influences of injection pressure on the atomizing performances like the spray penetration, the Sauter mean diameter (SMD) and the evaporation of the fuel via numerical simulation. He pointed out the spray penetration and the spray atomization quality would improve with the increase in injection pressure, though SMD would decrease. Park et al. [10] investigated the effects of various design parameters of the twin-fluid nozzle used in wafer cleaning, such as the liquid nozzle diameter, gas slit angle and the number of nozzles on the spray behavior in the experiment. Alexandr et al. [11] studied the effects of the number of tangential passages on the spray cone angle, mass distribution and propellant mixture ratio of a bipropellant atomizer, and three atomizers were designed, manufactured and evaluated experimentally. The results showed that number of tangential passages affects more on the circumferential mass distribution. Yao et al. [12] adopted the single-hole cylindrical injectors with different orifice diameters and lengths to analyze the influences of the nozzle geometry on the distribution of the spray droplet size and corresponding combustion characteristics. They provided important parameters for injector manufacture although lots of time and cost will be spent on those experiments and simulations due to insufficient samples for accuracy improvement. Therefore, simply using experiments or simulation means to optimize the parameters of the self-oscillating pulse (SOP) atomizing nozzle hardly improves the primary atomization quality of the nozzle jet. It is necessary to find another method to optimize the nozzle.
Considering the technology of self-excited oscillation pulsed cavitation jet nozzle, the atomization process can be divided into the primary atomization and the secondary atomization. The primary atomization is the first disintegration of the coherent liquid into big droplets and ligaments near the outlet of nozzle, which strongly depends on the flow conditions inside the injection holes and the starting conditions for the spray breakup [13]. Tamaki et al. [14] and Geng et al. [15] pointed out that the cavitation effect and turbulence effect are the incentive of the primary atomization of the jet near the nozzle. Therefore, the optimization of the primary atomization quality near the nozzle can be transformed into an optimization issue on the cavitation effect and the turbulent effect at the outlet of nozzle, which is obviously a multi-objective optimization problem.
At present, the computational fluid dynamics (CFD)-based multi-objective parameters optimization has been widely used. Uebel et al. [16] used a CFD-based optimization of a quench conversion concept to improve the system performances and obtained the feasible design parameters for the quench reactor. Tahara et al. [17] developed a CFD-based multi-objective parameters optimization for ship design on the optimizer modules of a computer-assisted design (CAD) and CFD. And the multi-objective optimization results appear to be meaningful and prospective. Manshadi et al. [18] used CFD to optimize the three-dimensional wing defined by four design variables and improved approximately aerodynamic efficiency by 15%. Darvish et al. [19] studied the multi-objective optimization of airflow within a ribbed channel to achieve the maximum heat transfer and minimum pressure drop. Through using CFD simulations in conjunction with the neural network and the NSGA-II optimization, valuable and useful results are obtained. Actually, it is hard to get the satisfying results before if no use of the combined method. Paul et al. [20] present the multi-objective optimization on ratio analysis (MOORA) method coupled with the principal component analysis (PCA) in order to achieve the optimal combination of EDM parameters. Safikhani et al. [9] performed a multi-objective optimization on Al_{2}O_{3}–water nanofluid parameters in flat tubes using CFD techniques, artificial neural networks (ANN) and NSGA-II. It was shown that the obtained Pareto-optimal solutions are valuable for the nanofluid parameter design in flat tubes. In this paper, the self-excited oscillation pulsed cavitation jet nozzle will be optimized as the objective to improve the primary atomization quality at the outlet of the nozzle. Since the optimization of the primary atomization quality is a problem of multi-objective optimization and lots of time and cost shall be taken into account by using experiments and simulations, the CFD-based multi-objective parameter optimization will be an effective method for solving this kind of primary atomization optimization problems.
The self-excited oscillation pulsed cavitation nozzle makes use of their structural characteristics and specific boundary conditions of the self-excited oscillation pulse and consequently produces the cavitation effect and the solid cavitation jet with high-speed pulse to achieve excellent spraying quality. The performances of the self-excited oscillation pulsed atomization nozzle is imperfect, especially near the nozzle area of atomization jet. Meanwhile, jet atomization is the basis of the secondary atomization, and the quality of jet atomization will directly affect the jet spray characteristics. Therefore, this article mainly focuses on the jet primary atomization region close to the nozzle for optimization. Turbulence and cavitation are the two main causes resulting in the primary jet atomization that happens in the area close to the self-exited oscillation pulsed nozzle. By optimizing the nozzle parameters, the effects of these two factors on better primary atomization quality will increase. Based on this idea, this paper proposes an approach based on the mathematical optimization and CFD methods for designing the parameters of the self-excited oscillation pulsed atomizer. Aiming at the optimal objectives of nozzle outlet turbulence and cavitation, the jet atomization quality in the area near the jet nozzle is improved firstly.
2 Numerical calculation model
2.1 Geometric model
2.2 Cavitation model
Considering the cavitation in the jet nozzle is not only related to the local saturated vapor pressure, but also related to the shear stress as well as the pressure pulsations induced by the turbulent kinetic energy. The cavitation model in the nozzle was proposed by Wang Z et al. [6] as follows:
At this point, the cavitation occurs, and the cavitation pressure threshold is \(P_{\text{th}} = {\text{Min}}\left( {P_{\text{v}} ' + \tau '} \right)\). The cavitation model is established in the Fluent to define the vapor pressure property through the user-defined function (UDF).
2.3 Turbulence model
The large eddy simulation (LES) is usually used as the turbulence model for simulating the turbulent flow in the jet nozzle. However, a sufficiently fine mesh especially near the solid wall will be required to adopt the large eddy simulation, which will lead to high computational costs [22]. In recent years, combining with a Reynolds-Averaged Navier–Stokes (RANS) equation solver considering the effect of turbulence, some compromise methods called hybrid RANS/LES are presented to balance the computational cost and accuracy. Therefore, the hybrid RANS/LES method is adopted as the turbulence model in the paper.
2.4 Numerical methods
The numerical simulation is performed for solving the cavitation flow in the self-excited oscillation pulsed cavitation jet nozzle by using the finite volume element method of Fluent. Considering the convergence and calculation speed, a second-order upwind method is used for the convective and diffusive terms. The pressure implicit with splitting of operator (PISO) algorithm is employed to solve the coupling problem between the velocity and pressure fields. Boundary conditions are listed as follows: the inlet boundary condition is set as the pressure at inlet; the outlet boundary condition uses the pressure at outlet set to 1 atm. In addition, it is assumed that liquid phase on the wall surface satisfies the no-slip condition and the wall function is used to deal with the near-wall region.
The structure parameters and operating parameters of the nozzles are listed as follows: the diameter of the upper nozzle is 6 mm, the diameter of the lower nozzle is 10.8 mm, the length of the cavity is 36 mm, and the cavity diameter is 30 mm. The inlet boundary condition adopts pressure inlet set to 2 MPa, and the outlet boundary condition adopts outlet pressure set to a standard atmospheric pressure. The other is wall boundary condition. The PRNS turbulence model and the modified Schnerr and Sauer model are used to solve the multi-phase flow. The time step is 0.0001 s, and the number of time steps is 3000.
3 Response surface method design
3.1 Key design parameters
Experimental design factor levels
Code level | Factor | ||
---|---|---|---|
D/(mm) | d_{2}/(mm) | P_{in}/(MPa) | |
− 1 | 28 | 5 | 2 |
0 | 30 | 5.4 | 3 |
1 | 32 | 5.8 | 4 |
3.2 Evaluation indexes for the primary atomization quality
3.3 Response surface approximation model
Experimental design and results
Number | Design parameters | Response parameters | |||
---|---|---|---|---|---|
D/(mm) | d_{2}/(mm) | P_{in}/(MPa) | F_{1} | F_{2} | |
1 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
2 | 0 | 1 | 0 | − 0.1294 | 0.504 |
3 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
4 | 0 | 0 | − 1 | − 0.7289 | − 0.5868 |
5 | 1 | − 1 | 1 | 0.7241 | − 0.3087 |
6 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
7 | − 1 | 1 | − 1 | − 1 | − 0.4312 |
8 | − 1 | − 1 | − 1 | − 0.8909 | − 1 |
9 | 0 | 0 | 1 | 0.6072 | 0.4892 |
10 | 1 | 0 | 0 | 0.1247 | 0.2777 |
11 | 1 | − 1 | − 1 | − 0.8501 | − 0.971 |
12 | 1 | 1 | − 1 | − 0.5868 | 0.3007 |
13 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
14 | − 1 | − 1 | 1 | 1 | 0.2157 |
15 | − 1 | 0 | 0 | − 0.1519 | 0.2647 |
16 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
17 | − 1 | 1 | 1 | 0.5407 | 1 |
18 | 0 | 0 | 0 | − 0.1361 | 0.2875 |
19 | 1 | 1 | 1 | 0.5479 | 0.9452 |
20 | 0 | − 1 | 0 | − 0.0473 | − 0.151 |
- 1.
Complex correlation coefficient R^{2}
- 2.
Modified complex correlation coefficient R_{adj}^{2}
Mean variance analysis table of response surface regression model
Parameter | Response indicators | |
---|---|---|
F_{1} | F_{2} | |
R^{2} | 0.9883 | 0.9893 |
R_{adj}^{2} | 0.9829 | 0.9796 |
3.4 Calculation results
Analysis of variance in the experimental effect of design parameters on F_{1}
Source | Sum of squares | df | Mean square | F | p > F | Significance |
---|---|---|---|---|---|---|
Model | 5.84 | 6 | 0.97 | 183.39 | < 0.0001 | Significant |
D | 0.021 | 1 | 0.021 | 4.02 | 0.0662 | |
d_{2} | 0.032 | 1 | 0.032 | 5.98 | 0.0295 | |
P_{in} | 5.59 | 1 | 5.59 | 1053.2 | < 0.0001 | |
Dd_{2} | 0.054 | 1 | 0.054 | 10.12 | 0.0072 | |
DP_{in} | 0.065 | 1 | 0.065 | 12.3 | 0.0039 | |
d_{2}P_{in} | 0.078 | 1 | 0.078 | 14.69 | 0.0021 | |
Residual | 0.069 | 13 | 0.0053 | |||
Lack of fit | 0.069 | 8 | 0.0086 | |||
Pure error | 0 | 5 | 0 | |||
Cor total | 5.91 | 19 |
Analysis of variance in the experimental effect of design parameters on F_{2}
Source | Sum of squares | df | Mean square | F | p > F | Significance |
---|---|---|---|---|---|---|
Model | 5.44 | 9 | 0.6 | 102.34 | < 0.0001 | Significant |
D | 0.00379 | 1 | 0.0037 | 0.64 | 0.4417 | |
d_{2} | 2.06 | 1 | 2.06 | 347.99 | < 0.0001 | |
P_{in} | 2.53 | 1 | 2.53 | 428.29 | < 0.0001 | |
Dd_{2} | 0.17 | 1 | 0.17 | 29.09 | 0.0003 | |
DP_{in} | 0.22 | 1 | 0.22 | 38.01 | 0.0001 | |
d_{2}P_{in} | 0.00488 | 1 | 0.0048 | 0.83 | 0.3845 | |
D^{2} | 0.0036 | 1 | 0.0036 | 0.62 | 0.4504 | |
d_{2}^{2} | 0.0093 | 1 | 0.0093 | 1.58 | 0.237 | |
P_{in}^{2} | 0.22 | 1 | 0.22 | 37.44 | 0.0001 | |
Residual | 0.059 | 10 | 0.0059 | |||
Lack of fit | 0.059 | 5 | 0.012 | |||
Pure error | 0 | 5 | 0 | |||
Cor total | 5.5 | 19 |
4 Multi-objective optimization
4.1 Multi-objective optimization model
In the paper, NSGA-II by Deb for solving multi-objective optimization problem is proposed in order to obtain Pareto-optimal solutions with objective functions F_{1} and F_{2} [26]. The NSGA-II is implemented with an effective sorting method based on non-dominated individual sorting. The method is based on non-dominated personal ranking and crowding distance metric rankings that can assess the overall density of solutions of the same level. It is known that the NSGA-II has a good search performance for widely distributing the Pareto-optimal solutions.
The grey theory [27] is a new strategy of the multi-objective optimization. The main idea of the grey theory is that when a multi-objective optimization is performed, the optimal solution of each single-objective optimization is as a reference sequence and the sequence is optimized as an objective sequence. The correlation degree between the reference sequence and the objective sequence is generated by using the grey relation of the grey theory. The greater the correlation degree is, the better the corresponding solution is. Therefore, the multi-objective optimization problem is converted into a single-objective optimization problem. The grey theory is an effective way to solve the optimal solution of the multi-objective optimization problem. In the paper, the grey theory is adopted as the compromising method to seek the optimal solution of F_{1} and F_{2}.
Correlation data and correlation degree
Number | Correlation data | Correlation degree | |
---|---|---|---|
Target sequence | Reference sequence | ||
1 | 0.5102 | 0.9496 | 0.6894 |
2 | 0.5875 | 0.8259 | 0.8087 |
3 | 0.5481 | 0.9001 | 0.7328 |
4 | 0.7974 | 0.5421 | 0.7814 |
5 | 0.8624 | 0.4266 | 0.7183 |
6 | 0.7129 | 0.6644 | 0.9154 |
7 | 0.8849 | 0.3878 | 0.7022 |
8 | 0.5515 | 0.8811 | 0.7494 |
9 | 0.4612 | 0.9643 | 0.6771 |
10 | 0.7481 | 0.6050 | 0.8446 |
11 | 0.9076 | 0.3552 | 0.6882 |
12 | 0.5600 | 0.8456 | 0.7817 |
13 | 0.9309 | 0.3065 | 0.6751 |
14 | 0.6409 | 0.7517 | 0.9139 |
15 | 0.3765 | 0.9785 | 0.6672 |
16 | 0.6673 | 0.7275 | 0.9659 |
17 | 0.9237 | 0.3294 | 0.6791 |
18 | 0.6182 | 0.7895 | 0.8587 |
19 | 0.8353 | 0.4718 | 0.7407 |
20 | 0.8059 | 0.5281 | 0.7716 |
21 | 0.8509 | 0.4464 | 0.7274 |
22 | 0.4150 | 0.9738 | 0.6702 |
23 | 0.7354 | 0.6308 | 0.8699 |
24 | 0.9173 | 0.3412 | 0.6827 |
25 | 0.5419 | 0.9146 | 0.7199 |
26 | 0.5334 | 0.9335 | 0.7037 |
27 | 0.9456 | 0.2910 | 0.6678 |
28 | 0.7830 | 0.5591 | 0.7975 |
29 | 0.6922 | 0.6908 | 0.962 |
30 | 0.4906 | 0.9574 | 0.6828 |
4.2 Results analysis and discussion
5 Conclusions
- 1.
Depending on the response surface analysis, the turbulent flow energy F_{1} at the outlet increases with the nozzle chamber diameter D and the inlet pressure P_{in}, though it decreases firstly and then increases with the enlargement of the lower nozzle diameter d_{2}; the vapor phase volume fraction at the outlet F_{2} gets higher as the nozzle chamber diameter D and the lower nozzle diameter d_{2} increase, though it increases firstly and then decreases as the nozzle inlet pressure P_{in} is amplified. The approximate mathematical model of the primary atomization quality of the jet near the outlet nozzle was established by the response surface method.
- 2.
Using the combined algorithm with NSGA-II and grey theory, the optimized set of nozzle parameters is determined relied on the optimal primary atomization near the nozzle area. The difference between the analytical and the numerical solutions is minor. The atomization model established for the nozzle is correct; therefore, the model can approximatively take place of the simulation results.
- 3.
It is feasible to improve the primary atomization quality of the jet flow near the nozzle area by the multi-objective optimization. The optimized values of the turbulent kinetic energy F_{1} and the vapor volume fraction F_{2} increase by 28.26% and 5.56%. Depending on the analysis of the distribution of turbulent kinetic energy and the vapor phase volume fraction in the nozzle outlet section, the optimized turbulent kinetic energy and the vapor phase volume fraction in the nozzle outlet section are generally higher than the original values.
Notes
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 51875419 and 51405352.
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