A Study on the Optimization of Water Jet Decontamination Performance Parameters Based on the Response Surface Method
Abstract
:1. Introduction
2. Methods
2.1. CFD Method
2.1.1. Geometric Model
2.1.2. Governing Equation
2.1.3. Boundary Conditions and Solution Method Setup
2.1.4. Mesh Delineation and Mesh-Independence Verification
2.2. Response Surface Method
3. Results and Discussion
3.1. Regression Equation
3.2. Analysis of Variance (ANOVA)
3.3. 3D Diagrams
3.4. One-Dimensional Diagram
3.5. Optimum Point
4. Conclusions
- (1)
- The experimental scheme and response values were obtained under different parameter conditions through response surface design and numerical simulation methods. The experimental results were statistically analyzed using Design Expert software. Additionally, a numerical model describing the influence of the affected parameters on the pressure of the jet striking track teeth was successfully established using the theory of least squares.
- (2)
- The ANOVA results and response surface plots indicate that the three factors have varying degrees of impact on the cleaning and decontamination performance of high-pressure water jets. Specifically, the jet pressure and jet target distance have a highly significant effect on the pressure of the jet’s striking track teeth, while the jet angle also has a significant effect. Furthermore, there is an interaction between the factors, particularly between jet pressure and jet target distance, as well as between jet pressure and jet angle, which has a substantial impact on the pressure exerted by the jet on the track teeth.
- (3)
- The optimal test parameters for evaluating the efficacy of water jet cleaning and decontamination performance on the track are identified as an injection pressure of 0.983 MPa, a target distance of 0.14 m, and an impact angle of 89.5°.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, J.; Yin, B.; Chen, B.; Wu, H.; Li, J.; Liu, W. Study on reducing sediment adhesion of the tracks in deep-sea mining vehicle by submerged water jet. Min. Res. Dev. 2020, 40, 570–585. [Google Scholar] [CrossRef]
- Hein, J.R.; Mizell, K.; Koschinsky, A.; Conrad, T.A. Deep-ocean mineral deposits as a source of critical metals for high- and green-technology applications: Comparison with land-based resources. Ore Geol. Rev. 2013, 51, 1–14. [Google Scholar] [CrossRef]
- Cho S-g Park, S.; Oh, J.; Min, C.; Kim, H.; Hong, S.; Jang, J.; Lee, T.H. Design optimization of deep-seabed pilot miner system with coupled relations between constraints. J. Terramech. 2019, 83, 25–34. [Google Scholar] [CrossRef]
- Sun, P.; Lu, H.; Yang, J.; Liu, M.; Li, S.; Zhang BJ, O.E. Numerical study on shear interaction between the track plate of deep-sea mining vehicle and the seafloor sediment based on CEL method. Ocean Eng. 2022, 266, 112785. [Google Scholar] [CrossRef]
- You, S. Research on Dynamics and Trajectory Tracking of Multi-Section Articulated Crawler Seabed Mining. Available online: https://kns.cnki.net/kcms2/article/abstract?v=v-1aSqfKcSW7wFPoWRYmOTmtDV8zkVY7nHou1f53DMcwuib1wvsIJ1fEtFXI4MCMmIWIo9oquJTQbmK65nRCo15BPd2jLzHcWJontrJWVC4THlZ_va17SGfDGbzsV_tPuzEOHD4plEXMc_xek51T2rcJOJkFS-wUXmLlcWSJAi_eVBIqXXSrNpD5h5wn50ooMdswUvcLpXQTI7Mknd80mW6YKvyt1urv&uniplatform=NZKPT&language=CHS (accessed on 15 January 2024).
- Dai, Y.; Su, Q.; Zhang, Y. A new dynamic model and trajectory tracking control strategy for deep ocean mining vehicle. Ocean Eng. 2020, 216, 108162. [Google Scholar] [CrossRef]
- Liu, S.; Dai, Y.; Li, Y.; Li, L.; Cao, X. Dynamic Analysis of the Complete Integrated Deep-Ocean Mining Pilot System Based on Single-Body Tracked Miner and Discrete Element Model of Pipe. In ISOPE Pacific/Asia Offshore Mechanics Symposium; ISOPE: Mountain View, CA, USA, 2008. [Google Scholar]
- Love, A.H.; Bailey, C.G.; Hanna, M.L.; Hok, S.; Vu, A.K.; Reutter, D.J.; Raber, E. Efficacy of liquid and foam decontamination technologies for chemical warfare agents on indoor surfaces. J. Hazard. Mater. 2011, 196, 115–122. [Google Scholar] [CrossRef]
- Wang, L.; Chen, X.; Wang, L.; Li, Z.; Yang WJ, O.E. Mechanical properties and soil failure process of interface between grouser of tracked mining vehicle and deep-sea sediment. Ocean Eng. 2023, 285, 115336. [Google Scholar] [CrossRef]
- Fu, Z.; Li, K.; Pang, Y.; Ma, L.; Wang, Z.; Jiang BJ, S. Study on Water Jet Characteristics of Square Nozzle Based on CFD and Particle Image Velocimetry. Symmetry 2022, 14, 2392. [Google Scholar] [CrossRef]
- Zhang, Z.; Zhao, J.; Tang, X.; Sun, C.; Li, F. Simulation and optimization of lotus root digging machine sprinkler system based on EDEM-Fluent. J. Shandong Univ. (Eng. Sci.) 2022, 52, 86–93+99. [Google Scholar] [CrossRef]
- Xiong, T.; Kang, D.; Fang, Z. Impact characteristics analysis of high-speed water jet against hull surface based on ALE algorithm. J. Vib. Shock 2023, 42, 17–24+60. [Google Scholar] [CrossRef]
- Zhong, L.; Lei, J.; Li, X.; Lei, Z.; Deng, J.; Lei, L. Numerical analysis of cleaning in-situ leaching well filter by submerged high-pressure waterjet. Min. Metall. Eng. 2022, 42, 18–22+28. [Google Scholar] [CrossRef]
- Matthes, R.; Jablonowski, L.; Pitchika, V.; Holtfreter, B.; Eberhard, C.; Seifert, L.; Kocher, T. Efficiency of biofilm removal by combination of water jet and cold plasma: An in-vitro study. BMC Oral Health 2022, 22, 157. [Google Scholar] [CrossRef]
- Deng, R.; Jing, B.; Zhang, W. Application of response surface method to structural optimization of self-excited pulse nozzles. Mech. Sci. Technol. Aerosp. Eng. 2019, 38, 1366–1372. [Google Scholar] [CrossRef]
- Basson, N.; Peng, C.-H.S.; Geoghegan, P.; van der Lecq, T.; Steven, D.; Williams, S.; Ho, W.H. A computational fluid dynamics investigation of endothelial cell damage from glaucoma drainage devices. Sci. Rep. 2024, 14, 3777. [Google Scholar] [CrossRef]
- Li, L.; Zhang, S.; He, Q.; Hu, X. Application of response surface methodology in experiment design and optimization. Res. Explor. Lab. 2015, 34, 41–45. [Google Scholar] [CrossRef]
- Li, D.; Feng, G.; Guo, Y.; Qi, T.; Jia, X.; Feng, J.; Li, Z. Analysis on the strength increase law of filling material based on response surface method. J. China Coal Soc. 2016, 41, 392–398. [Google Scholar] [CrossRef]
- Wang, X.; Wu, Y.; Jia, P.; Liu, H.; Yun, F.; Li, Z.; Wang, L. Orthogonal experimental design based nozzle geometry optimization for the underwater abrasive water jet. Machines 2022, 10, 1243. [Google Scholar] [CrossRef]
- Liu, Y.; Zhu, H.; Huang SJ IJ, H.T. Effect of structural parameters of high-pressure water jet nozzles on flow field features. Int. J. Heat Technol. 2017, 4, 707–712. [Google Scholar] [CrossRef]
- Sun, H.; Luan, F.; Feng, L.; Zhao, P.; Chen, X.; Zhang, J. Structure optimization of high pressure water cleaning nozzle for aluminum alloy car body based on orthogonal experiment. Chin. Hydraul. Pneum. 2024, 48, 122–130. [Google Scholar] [CrossRef]
- Wen, J.; Qi, Z.; Behbahani, S.S.; Pei, X.; Iseley, T. Engineering. Research on the structures and hydraulic performances of the typical direct jet nozzles for water jet technology. J. Braz. Soc. Mech. Sci. 2019, 41, 570. [Google Scholar] [CrossRef]
- Wang, Y.; Li, F.; Huo, Y.; Zhang, S. Numerical simulation of angular cavitation nozzle. Autom. Appl. 2022, 9, 169–172. [Google Scholar] [CrossRef]
- Guan, T.; Zhang, J.; Shan, Y. Investigation of convective heat transfer on a wedge-shaped concave surface subjected to a row of tab-excited hot jets. J. Propuls. Technol. 2016, 37, 119–127. [Google Scholar] [CrossRef]
- Su, H.; Li, Z.; Wei, X.; Yu, J.; Zhao, X. Study on heat transfer and flow characteristics of single tube in open rack vaporizer. Chem. Eng. Mach. 2022, 49, 282–288. [Google Scholar] [CrossRef]
- Mi, G.; Huang, Z.; Hu, S.; An, Q.; Li, H. Research on simulation of flow field in press section of screw press based on Fluent. Food Mach. 2021, 37, 85–88. [Google Scholar] [CrossRef]
- Yang, Y.; Qiu, T. Numerical simulation of fan nozzle of guide board type cleaning jet flow field. J. Drain. Irrig. Mach. Eng. 2017, 35, 243–247. [Google Scholar] [CrossRef]
- Wu, X.; Zhang, Y.; Zhao, S.; Li, G. Optimization design and vortex characteristics analysis of swirling cavitating nozzle. J. Cent. South Univ. (Sci. Technol.) 2023, 54, 1500–1517. [Google Scholar] [CrossRef]
- Gao, A.; Zhang, Y.; Han, X.; Wang, K.; Fan, X.; Wang, Y. Optimization of Fenton oxidation for cephalosporin pharmaceutical wastewater treatment by response surface methodology. Jiangsu Agric. Sci. 2013, 41, 357–359. [Google Scholar] [CrossRef]
- Xu, Y.; Li, M.; Zhao, X.; Lu, F. The response curved surface regression analysis technique-the application of a new regression analysis technique in materials research. Rare Met. Mater. Eng. 2001, 6, 428–432. [Google Scholar] [CrossRef]
- Chai, X.; Hou, L.; Wang, Z.; Tang, Z. Bird strike model calibration for an aero engine wide-chord fan blade. J. Aerosp. Power 2016, 31, 1032–1038. [Google Scholar] [CrossRef]
- Wang, X.; Ma, X.; Sun, Y.; Zhao, L. Multi-factor coupling analysis on flocculation and sedimentation effect of whole tailings based on RSM-BBD. Min. Res. Dev. 2023, 43, 62–67. [Google Scholar] [CrossRef]
- Wu, J. Research on Tooth Profile Modification of Helical Gear Based on Response Surface Analysis and Analysis of Its Dynamic Characteristics Hefei University of Technology. Available online: https://kns.cnki.net/kcms2/article/abstract?v=v-1aSqfKcSXp0mtSjpMhjc1KO5gyfQhgUPkM_4vRyNqodjvu8UchDX8ESiSRbc3fwympkiLuSNW9qHXJuc6Qz5EcDHH9S_aaLmMfmXJWxQa6tFcCsqlLA5wOK0ZlMKx425VUyV-T3I0rpVEy31-cTd7iDH3JCrdtMmtFqZScZLHANYHnkVSM_N_-TiSu0PMafT5ZfKBztY4=&uniplatform=NZKPT&language=CHS (accessed on 15 May 2023).
- Xing, Y.; Wang, Z.; Hong, C.; Si, Y.; Liu, M.; Li, Y.; Tian, X. Parameter optimization of sludge co-conditioning based on RSM model. China Environ. Sci. 2014, 34, 2866–2873. [Google Scholar]
Mesh | Mesh Size (mm) | Velocity (m/s) | Dynamic Pressure (Pa) |
---|---|---|---|
Mesh 1 | 1 | 45.21 | 1,020,234.4 |
Mesh 2 | 2 | 45.22 | 1,020,450.1 |
Mesh 3 | 4 | 45.3 | 1,024,043.9 |
Mesh 4 | 8 | 45.22 | 1,020,485.9 |
Mesh 5 | 16 | 44.97 | 1,009,342.6 |
Mesh 6 | 32 | 43.47 | 943,026.4 |
Mesh 7 | 64 | 43 | 923,008.8 |
Input Parameters | Variables Coded | Units | Level 1 | Level 2 | Level 3 |
---|---|---|---|---|---|
Injection pressure | MPa | 0.4 | 0.7 | 1 | |
Target distance | m | 0.14 | 0.16 | 0.18 | |
Impact angle | ° | 60 | 75 | 90 | |
Constant parameters | |||||
Nozzle type | Linear nozzle | ||||
Nozzle diameter | 30 mm | ||||
Ambient pressure | 50 MPa |
Run | /MPa | /m | /° | Y/Pa |
---|---|---|---|---|
1 | 0.7 | 0.18 | 90 | 413,480 |
2 | 0.7 | 0.14 | 90 | 492,057 |
3 | 0.4 | 0.14 | 75 | 286,069 |
4 | 1 | 0.16 | 60 | 565,882 |
5 | 0.4 | 0.16 | 60 | 238,407 |
6 | 0.7 | 0.18 | 60 | 413,718 |
7 | 1 | 0.14 | 75 | 664,159 |
8 | 0.4 | 0.18 | 75 | 231,040 |
9 | 0.7 | 0.16 | 75 | 404,380 |
10 | 0.7 | 0.16 | 75 | 404,380 |
11 | 0.7 | 0.16 | 75 | 404,380 |
12 | 1 | 0.16 | 90 | 647,506 |
13 | 1 | 0.18 | 75 | 558,866 |
14 | 0.7 | 0.16 | 75 | 404,380 |
15 | 0.4 | 0.16 | 90 | 261,442 |
16 | 0.7 | 0.14 | 60 | 451,204 |
17 | 0.7 | 0.16 | 75 | 404,380 |
Source | Sum of Squares | df | Mean Squares | F-Value | p-Value |
---|---|---|---|---|---|
Model | 2.7 × 1011 | 9 | 3 × 1010 | 274.31 | <0.0001 |
2.52 × 1011 | 1 | 2.52 × 1011 | 2305.10 | <0.0001 | |
9.55 × 109 | 1 | 9.55 × 109 | 87.39 | <0.0001 | |
2.64 × 109 | 1 | 2.64 × 109 | 24.14 | 0.0017 | |
6.32 × 108 | 1 | 6.32 × 108 | 5.78 | 0.0472 | |
8.58 × 108 | 1 | 8.58 × 108 | 7.85 | 0.0264 | |
4.22 × 108 | 1 | 4.22 × 108 | 3.86 | 0.0901 | |
2 | 2.81 × 108 | 1 | 2.81 × 108 | 2.57 | 0.1526 |
2 | 2.13 × 109 | 1 | 2.13 × 109 | 19.47 | 0.0031 |
2 | 1.05 × 109 | 1 | 1.05 × 109 | 9.57 | 0.0175 |
Residual | 7.65 × 108 | 7 | 1.09 × 108 | ||
Lack of Fit | 7.65 × 108 | 3 | 2.55 × 108 | ||
Pure Error | 0 | 4 | 0 | ||
Cor. Total | 2.71 × 1011 | 16 |
R2 | Adjusted R2adj | Predicted R2 | C.V. | Adeq Precision |
---|---|---|---|---|
0.9972 | 0.9935 | 0.9548 | 2.45% | 52.8 |
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Qiu, X.; Wang, M.; Chen, B.; Ai, Y. A Study on the Optimization of Water Jet Decontamination Performance Parameters Based on the Response Surface Method. Appl. Sci. 2024, 14, 7409. https://doi.org/10.3390/app14167409
Qiu X, Wang M, Chen B, Ai Y. A Study on the Optimization of Water Jet Decontamination Performance Parameters Based on the Response Surface Method. Applied Sciences. 2024; 14(16):7409. https://doi.org/10.3390/app14167409
Chicago/Turabian StyleQiu, Xianyan, Mengkun Wang, Bingzheng Chen, and Yang Ai. 2024. "A Study on the Optimization of Water Jet Decontamination Performance Parameters Based on the Response Surface Method" Applied Sciences 14, no. 16: 7409. https://doi.org/10.3390/app14167409