Abstract
Powder coating is a finishing process used in various industries where electrostatically charged particles are sprayed onto metal surfaces. High-fidelity simulation is becoming increasingly popular to optimize this technique. However, the current standard approaches based on modeling the individual particles require substantial computational effort, which limits their practical application. To alleviate this drawback, the multiscale pseudo-direct numerical simulation method (P-DNS) is extended for the study of the turbulent flow of charged particles. In an offline process, representative volume elements (RVEs) are simulated considering the particles and their interactions with the surrounding gas and electrostatic field, and the dynamic response of the mixture is homogenized. On the global scale, where a continuous field for the particulate phase is used, the effects of turbulence and coupling forces are obtained by evaluating synthetic models of the RVEs with local dimensionless parameters. Additionally, a coat film model is presented to quantify the coating layer thickness on the target surface. The numerical tool is first validated with experimental data from the literature. Then, it is applied to the coating of a surface of complex geometry, analyzing the impact of particle diameter, applied voltage, and spray gun trajectory. The reliable results obtained with modest resources confirm the potential of the methodology as a rapid predictive tool in processes involving this coating technology.
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Abbreviations
- \(\textrm{H}\) :
-
Length [m]
- \(\textrm{r}\) :
-
Particle to fluid density ratio [−]
- \(\epsilon \) :
-
Volume fraction of the carrier phase [−]
- \(\epsilon _{\textrm{p}}\) :
-
Volume fraction of the particulate phase [−]
- \(\textbf{u}\) :
-
Velocity of the carrier phase [m/s]
- \({\textbf{u}}_{\textrm{p}}\) :
-
Velocity of the particulate phase [m/s]
- \(\textbf{u}_{\textrm{r}}\) :
-
Relative velocity between phases [m/s]
- \(\textbf{u}^{\textrm{M}}\) :
-
Velocity of the moving frame [m/s]
- \(\textbf{v}_\textrm{p}\) :
-
Velocity of a particle [m/s]
- \(\mathrm p\) :
-
Pressure [kg/(m s\(^2\))] = [Pa]
- \(\rho \) :
-
Density of the carrier phase [kg/m\(^3\)]
- \(\rho _{\textrm{p}}\) :
-
Density of the particulate phase [kg/m\(^3\)]
- \(\mu \) :
-
Dynamic viscosity of the carrier phase [kg/(m s\(^2\))]
- \(\mathrm g\) :
-
Gravitational acceleration magnitude [m/s\(^2\)]
- \({\textbf {T}}^\mu \) :
-
Viscous stress tensor [m\(^2\)/s\(^2\)]
- \({\textbf {T}}^\rho \) :
-
Fine inertial stress tensor from the fine scale [m\(^2\)/s\(^2\)]
- \({\textbf {s}}\) :
-
Fine-scale dispersion field [m/s]
- \(\textbf{f}\) :
-
Momentum transfer between phases by mass unit [m/s\(^2\)]
- \(\textrm{V}_{\textrm{p}}\) :
-
Volume of a particle [m\(^3\)]
- \({{\varvec{f}}}_{\textrm{d}}\) :
-
Drag force acting over a particle [kg m/s\(^2\)] = [N]
- \({{\varvec{f}}}_{\textrm{b}}\) :
-
Mass-dependent forces acting over a particle [kg m/s\(^2\)] = [N]
- \({{\varvec{f}}}_{\textrm{g}}\) :
-
Gravitational force acting over a particle [kg m/s\(^2\)] = [N]
- \({{\varvec{f}}}_{\textrm{e}}\) :
-
Electric force acting over a particle [kg m/s\(^2\)] = [N]
- \({\textbf {G}}\) :
-
Coarse velocity symmetric gradient [m\(^2\)/s\(^2\)]
- \({\textbf {b}}\) :
-
Body acceleration [m/s\(^2\)]
- \(\varphi \) :
-
Electrostatic potential [kg m\(^2\)/(s\(^3\) A)] = [kV]
- \(\textbf{e}\) :
-
Electric field [kg m/(s\(^3\) A)] = [kV/m]
- \(\textrm{q}_{\textrm{p}}\) :
-
Specific charge of a particle [A s/kg]
- \(\rho _{\textrm{e}}\) :
-
Density of charge [A s/m\(^3\)]
- \(\epsilon _0\) :
-
Permittivity of void [A\(^2\) s\(^4\)/(kg m\(^3\))]
- \(\textrm{Id}_1\) :
-
Dimensionless instability index 1 [−]
- \(\textrm{Id}_2\) :
-
Dimensionless instability index 2 [−]
- \(\textrm{b}\) :
-
Body acceleration magnitude [m/s\(^2\)]
- \(\theta \) :
-
Polar angle of the body acceleration [−]
- \(\psi \) :
-
Azimuth angle of the body acceleration [−]
- \(\textrm{d}_{\textrm{p}}\) :
-
Mean of particle diameters [−]
- \(\eta \) :
-
Dispersion of particle diameters [−]
- \(\textrm{r}\) :
-
Particle to fluid density ratio [−]
- \({\textrm{k}}\) :
-
Turbulent kinetic energy [m\(^2\)/s\(^2\)]
- \(\textbf{K}\) :
-
Turbulent diffusivity of mass [m\(^2\)/s]
- \(\textbf{D}\) :
-
Turbulent diffusivity of momentum [m\(^2\)/s]
- \(\delta _{\textrm{p}}\) :
-
Coating thickness [m]
- \(\delta \) :
-
First-cell height [m]
- \(\textbf{n}\) :
-
Surface normal [−]
- Normal:
-
Scalar
- Bold-lowercase:
-
Vector
- Bold-uppercase:
-
Tensor
- \({(.)}^{\textrm{c}}\) :
-
Coarse-scale field
- \({(.)}^{\textrm{f}}\) :
-
Fine-scale field
- \(\mathrm {(.)}^{\textrm{R}}\) :
-
RVE field
- \(\widetilde{(.)}\) :
-
Dimensionless field
References
Pendar MR, Rodrigues F, Páscoa JC, Lima R (2022) Review of coating and curing processes: evaluation in automotive industry. Phys Fluids 34(10)
Janardhanan S, Zvonkina I, Soucek MD (2000) Powder coating technology. Kirk-Othmer encyclopedia of chemical technology, pp 1–16
Du Z, Wen S, Wang J, Yin C, Yu D, Luo J (2016) The review of powder coatings. J Mater Sci Chem Eng 4(3):54
Barringer SA, Sumonsiri N (2015) Electrostatic coating technologies for food processing. Annu Rev Food Sci Technol 6(1):157. https://doi.org/10.1146/annurev-food-022814-015526. (PMID: 25648420)
Yang Q, Ma Y, Zhu J, Chow K, Shi K (2017) An update on electrostatic powder coating for pharmaceuticals. Particuology 31:1
Yang Q, Yuan F, Ma Y, Shi K, Yang G, Zhu J (2020) Electrostatic powder coated osmotic pump tablets: influence factors of coating powder adhesion and film formation. Powder Technol 360:444
Ayrilmis N (2022) A review on electrostatic powder coatings for the furniture industry. Int J Adhes Adhes 113:103062
Kafui K, Thornton C, Adams M (2002) Discrete particle-continuum fluid modelling of gas-solid fluidised beds. Chem Eng Sci 57(13):2395. https://doi.org/10.1016/S0009-2509(02)00140-9
Balachandar S, Eaton J (2010) Turbulent dispersed multiphase flow. Annu Rev Fluid Mech 42(1):111. https://doi.org/10.1146/annurev.fluid.010908.165243
Ye Q, Steigleder T, Scheibe A, Domnick J (2002) Numerical simulation of the electrostatic powder coating process with a corona spray gun. J Electrostat 54(2):189
Shah U, Zhang C, Zhu J, Wang F, Martinuzzi R (2007) Validation of a numerical model for the simulation of an electrostatic powder coating process. Int J Multiph Flow 33(5):557
Toljic N, Adamiak K, Castle G, Kuo HHH, Fan HTC (2011) Three-dimensional numerical studies on the effect of the particle charge to mass ratio distribution in the electrostatic coating process. J Electrostat 69(3):189
Li Z, Zhang C, Zhu J (2006) Numerical study of air-particle two-phase flows inside a powder coating booth. Int J Comput Methods Eng Sci Mech 7(3):141
Toljic N, Adamiak K, Castle G, Kuo HHH, Fan HTC (2013) A full 3d numerical model of the industrial electrostatic coating process for moving targets. J Electrostat 71(3):299
Soma T, Amemiya S, Katayama T, Saito Y, Matsushita Y, Aoki H, Inamura T, Daikoku M, Fukuno J (2017) Numerical simulations of particle-laden turbulent flows to characterize the two different types of paint spray; bell-cup atomizer and powder spray gun. J Chem Eng Jpn 50(4):254
Yang Q, Chen J, Zhou X, Zhou H, Yang G, Zhu J (2022) Modeling of inter-tablet coating uniformity of electrostatic dry powder coating by discrete element method. Powder Technol 411:117929
Ludwig W, Ligus G, Korman P, Skedlak A, Zajkac D (2022) CFD modelling of a powder spraying nozzle used for dry coating. Chem Eng Res Des 178:550
Boiger G, Boldrini M, Lienhard V, Siyahhan B, Khawaja H, Moatamedi M (2020) Multiphysics Eulerian-Lagrangian electrostatic particle spray-and deposition model for openfoam® and kaleidosim® cloud-platform. Int J Multiphys 14(1):1
Pendar MR, Páscoa JC (2019) Numerical modeling of electrostatic spray painting transfer processes in rotary bell cup for automotive painting. Int J Heat Fluid Flow 80:108499
Pendar MR, Páscoa JC (2020) Atomization and spray characteristics around an erbs using various operational models and conditions: Numerical investigation. Int J Heat Mass Transf 161:120243
Pendar MR, Páscoa JC (2021) Numerical analysis of charged droplets size distribution in the electrostatic coating process: effect of different operational conditions. Phys Fluids 33(3)
Pendar MR, Cândido S, Páscoa JC (2023) Optimization of painting efficiency applying unique techniques of high-voltage conductors and nitrotherm spray: developing deep learning models using computational fluid dynamics dataset. Phys Fluids 35(7)
Drew DA (1983) Mathematical modeling of two-phase flow. Annu Rev Fluid Mech 15(1):261
Ma J, Oberai AA, Hyman MC, Drew DA, Lahey RT Jr (2011) Two-fluid modeling of bubbly flows around surface ships using a phenomenological subgrid air entrainment model. Comput Fluids 52:50
Ma J, Oberai AA, Lahey RT, Drew DA (2011) Modeling air entrainment and transport in a hydraulic jump using two-fluid rans and des turbulence models. Heat Mass Transf 47(8):911
Dastourani H, Jahannama M, Eslami-Majd A (2018) A physical insight into electrospray process in cone-jet mode: Role of operating parameters. Int J Heat Fluid Flow 70:315
Zeybek M, Grosshans H (2021) Eulerian formulation for the triboelectric charging of polydisperse powder flows. Phys Fluids 33(6):063304
Idelsohn SR, Nigro N, Larreteguy A, Gimenez JM, Ryzhakov P (2020) A pseudo-DNS method for the simulation of incompressible fluid flows with instabilities at different scales. Comput Particle Mech 7(1):19. https://doi.org/10.1007/s40571-019-00264-x
Gimenez JM, Idelsohn SR, Oñate E, Löhner R (2021) A multiscale approach for the numerical simulation of turbulent flows with droplets. Arch Comput Methods Eng 28(6):4185
Idelsohn SR, Gimenez JM, Löhner R, Oñate E (2022) A multiscale approach for the study of particle-laden flows using a continuous model. Comput Methods Appl Mech Eng 401:115174
Idelsohn SR, Gimenez JM, Nigro N, Oñate E (2021) The pseudo-direct numerical simulation method for multi-scale problems in mechanics. Comput Methods Appl Mech Eng 380:113774. https://doi.org/10.1016/j.cma.2021.113774
Idelsohn SR, Gimenez JM, Nigro NM (2022) The pseudo-direct numerical simulation method considered as a reduced order model. Adv Model Simul Eng Sci 9(1):1
Larreteguy AE, Gimenez JM, Nigro NM, Sívori FM, Idelsohn SR (2023) A data-driven memory model for solving turbulent flows with the pseudo-direct numerical simulation method. Int J Numer Meth Fluids 95(1):44
Armenio V, Fiorotto V (2001) The importance of the forces acting on particles in turbulent flows. Phys Fluids 13(8):2437. https://doi.org/10.1063/1.1385390
Zannetti P (1990) Air pollution modeling: theories, computational methods and available software. Computational Mechanics Publication (Springer US). https://books.google.es/books?id=FyxSAAAAMAAJ
Schmitt FG (2007) About boussinesq’s turbulent viscosity hypothesis: historical remarks and a direct evaluation of its validity. Comptes Rendus Mécanique 335(9–10):617
Gimenez JM, Aguerre H, Idelsohn SR, Nigro N (2019) A second-order in time and space particle-based method to solve flow problems on arbitrary meshes. J Comput Phys 380:295. https://doi.org/10.1016/j.jcp.2018.11.034
OpenFOAM.org. https://www.openfoam.org. Accessed: August 08, 2023
Sigmond R (1982) Simple approximate treatment of unipolar space-charge-dominated coronas: The warburg law and the saturation current. J Appl Phys 53(2):891
Wang F, Martinuzzi R et al (2005) Experimental study of particle trajectory in electrostatics powder coating process. Powder Technol 150(1):20
OpenFOAM.com. https://www.openfoam.com. Accessed 08 Aug 2023
Bre F, Gimenez JM (2022) A cloud-based platform to predict wind pressure coefficients on buildings. Build Simul 15(8):1507
Jasak H (2009) in 47th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition
Bachler G, Schiffermüller H, Bregant A (2001) A parallel fully implicit sliding mesh method for industrial CFD applications. Parallel Comput. Fluid Dyn, pp 501–508
Farrell PE, Piggott MD, Pain CC, Gorman GJ, Wilson CR (2009) Conservative interpolation between unstructured meshes via supermesh construction. Comput Methods Appl Mech Eng 198(33–36):2632
Aguerre H, Márquez Damián S, Gimenez JM, Nigro N (2017) Conservative handling of arbitrary non-conformal interfaces using an efficient supermesh. J Comput Phys 335:21
Salvatore F, Alves Pereira F, Felli M, Calcagni D, Di Felice F (2006) Description of the INSEAN E779A propeller experimental dataset. https://doi.org/10.5281/zenodo.6077997. This report is also classified as INSEAN Technical Report 2006-085
Cabral B, Leedom LC (1993) Proceedings of the 20th annual conference on Computer graphics and interactive techniques, pp 263–270
Acknowledgements
This work was supported by the CERCA program of the Generalitat de Catalunya, and the Spanish Ministry of Economy and Competitiveness through the "Severo Ochoa Programme for Centres of Excellence in R &D" (CEX2018-000797-S). Also, the author acknowledges MCIN / AEI and FEDER Una manera de hacer Europa for funding this work via project PID2021-122676NB-I00. by CIENCIA FEDER Una manera de hacer Europa via project PID2021-122676NB-I00. Last but not least, the author kindly thanks Prof. Sergio Idelsohn for the fruitful meetings and discussions. His distinct viewpoint was helpful in crafting this work.
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Gimenez, J.M. Multiscale simulation of electrostatic powder coating sprays. Comp. Part. Mech. (2024). https://doi.org/10.1007/s40571-023-00703-w
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DOI: https://doi.org/10.1007/s40571-023-00703-w