Experimental and simulative investigation of the oil distribution during a deep-hole drilling process and comparing of the RANS kω-SST and RANS hybrid SAS-SST turbulence model

Ekrem Oezkaya

Article ID: 874
Vol 1, Issue 4, 2018, Article identifier:

VIEWS - 339 (Abstract) 271 (PDF)


Helical deep hole drilling is a process frequently used in industrial applications to produce bores with a large length to diameter ratio. For better cooling and lubrication, the deep drilling oil is fed directly into the bore hole via two internal cooling channels. Due to the inaccessibility of the cutting area, experimental investigations that provide information on the actual machining and cooling behavior are difficult to carry out. In this paper, the distribution of the deep drilling oil is investigated both experimentally and simulatively and the results are evaluated. For the Computational Fluid Dynamics (CFD) simulation, two different turbulence models, i.e. the RANS k-ω-SST and hybrid SAS-SST model, are used and compared. Thereby, the actual used deep drilling oil is modelled instead of using fluid dynamic parameters of water, as is often the case. With the hybrid SAS-SST model, the flow could be analyzed much better than with the RANS k-ω-SST model and thus the processes that take place during helical deep drilling could be  simulated with realistic details. Both the experimental and the simulative results show that the deep drilling oil movement is almost exclusively generated by the tool rotation. At the tool’s cutting edges and in the flute, the flow velocity drops to zero for the most part, so that no efficient cooling and lubrication could take place there. In addition, cavitation bubbles form and implode, concluding in the assumption that the process heat is not adequately dissipated and the removal of chips is adversely affected, which in turn can affect the service life of the tool and the bore quality. The carried out investigations show that the application of CFD simulation is an important research instrument in machining technology and that there is still great potential in the area of tool and process optimization.


Deep-hole drilling; twist drill, CFD simulation; RANS k-ω-SST turbulence model; RANS hybrid SAS-SST turbulence model

Full Text:



. Abele E; Ellermeier A; Hohenstein J; Tschannerl M: Tool length influence on wear behaviour of twisted carbide drills. Production Engineering, 1 (2007) 1, pp. 51–56

. Biermann D; Iovkov I; Blum H; Rademacher A, et al.: Thermal Aspects in Deep Hole Drilling of Aluminium Cast Alloy Using Twist Drills and MQL. Procedia CIRP, 3 (2012), pp. 245–250, https://doi.org/ 10.1016/j.procir.2012.07.043

. Uhlmann E; Richarz S: Twisted deep hole drilling tools for hard machining. Journal of Manufacturing Processes, 24 (2016), pp. 225–230, https://doi.org/10.1016/j.jmapro.2016.09.013

. Eichler R: Prozeßsicherheit beim Einlippenbohren mit kleinsten Durchmessern. Dissertation Universität Stuttgart, 1996, ISBN 0943-3821

. Fallenstein F; Aurich JC.: CFD based Investigation on Internal Cooling of Twist Drills. Procedia CIRP, 14 (2014), pp. 293–298, https://doi.org/10.1016/j.procir.2014.03.112

. Beer N; Oezkaya E; Biermann D: Drilling of Inconel 718 with geometry-modified twist drills. Procedia CIRP, 9 (2014), pp. 49–55, https://doi.org/10.1016/j.procir.2014.07.124

. Oezkaya E; Beer N; Biermann D: Experimental studies and CFD simulation of the internal cooling conditions when drilling Inconel 718. International Journal of Machine Tools and Manufacture, 108 (2016), pp. 52–65, https://doi.org/10.1016/j.ijmachtools.2016.06.003

. Biermann D; Oezkaya E: CFD simulation for internal coolant channel design of tapping tools to reduce tool wear. CIRP Annals, 66 (2017) 1, pp. 109–112, https://doi.org/10.1016/j.cirp.2017.04.024

. Schnabel D; Özkaya E; Biermann D; Eberhard P: Modeling the motion of the cooling lubricant in drilling processesusing the finite volume and the smoothed particle hydrodynamics methods. Computer Methods in Applied Mechanics and Engineering, 329 (2017), pp. 369–395, https://doi.org/10.1016/j.cma.2017.09.015

. Oezkaya E; Biermann D: Decreasing Drill Damage. ANSYS ADVANTAGE MAGAZINE, XI (2017) 1, pp. 24–27

. Rextroth Bosch Group [Accessed 12 May 2018]. URL: https://www.boschrexroth.com

. Supertechperformance [Accessed 12 May 2018]. URL: https://www.supertechperformance.com/

. DePuy Synthes Deutschland [Accessed 12 May 2018]. URL: https://emea.depuysynthes.com

. Normed [Accessed 13 May 2018]. URL: http://www.normed-online.com/

. Himpe [Accessed 13 May 2018]. URL: http://www.himpe.de/

. ABB [Accessed 13 May 2018]. URL: http://new.abb.com/de

. Biermann D.; Kirschner M.; Eberhardt D: A novel method for chip formation analyses in deep hole drilling with small diameters. Production Engineering, 8 (2014) 4, pp. 491–497, https://doi.org/10.1007/s11740-014-0566-7

. Oezkaya E; Michel S; Biermann D: Experimental studies and FEM simulation of helical-shaped deep hole twist drills. Production Engineering, 12 (2018) 1, pp. 11–23, https://doi.org/10.1007/s11740-017-0779-7

. Versteeg HK; Malalasekera W: An introduction to computational fluid dynamics. Pearson Education Ltd., Harlow, England, 2007, ISBN 978-0131274983

. Menter FR; Kuntz M; Langtry, R.: Ten Years of Industrial Experience with the SST Turbulence Model. Turbulence, Heat and Mass Transfer 4 (2003), pp.625–632

. Menter FR: A scale-adaptive simulation model using two-equation models. AIAA paper 2003-0767, Reno, NV (2003)

. Kármán T: Mechanische Ähnlichkeit und Turbulenz. 3. Internationaler Kongress für Technische Mechanik , pp. 85–93

. Menter FR; Egorov Y: The Scale-Adaptive Simulation Method for Unsteady Turbulent Flow Predictions. Part 1: Theory and Model Description. Flow, Turbulence and Combustion, 85 (2010) 1, pp. 113–138

. Domnick CB: Untersuchung des strömungs- und strukturdynamischen Verhaltens von Dampfturbineneinlassventilen im Teillastbetrieb. Dissertation, Universität Duisburg – Essen, 2016

. David Lee Davidson: The Role of Computational Fluid Dynamics in Process Industries. The Bridge, 32 (2002) 4, pp. 9–14

. Menter FR: Turbulence Modeling for Engineering Flows. ANSYS, A Technical Paper (2011), pp. 1–25

. Batten P; Goldberg U; Chakravarthy S: Interfacing Statistical Turbulence Closures with Large-Eddy Simulation. AIAA Journal, 42 (2004) 3, pp. 485–492, https://doi.org/10.2514/1.3496

. Oezkaya E; Biermann D: A new reverse engineering method to combine FEM and CFD simulation three-dimensional insight into the chipping zone during the drilling of Inconel 718 with internal cooling. Machining Science and Technology, 51 (2018), S. 1–18, DOI: 10.1080/10910344.2017.1415933

. Salim MS; Cheah SC: Wall y+ Strategy for Dealing with Wall-bounded Turbulent Flows. Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol II IMECS, March 18 - 20, 2009, Hong Kong

. Jeong J; Hussain F: On the identification of a vortex. Journal of Fluid Mechanics, 285 (1995), pp. 69–94, https://doi.org/10.1017/S0022112095000462

. Cucitore R; Quadrio M; Baron A: On the effectiveness and limitations of local criteria for the identification of a vortex. European Journal of Mechanics – B/Fluids, 18 (1999) 2, pp. 261–282, https://doi.org/10.1016/S0997-7546(99)80026-0

. Wu J-Z.; Ma H-Y; Zhou M-D: Vorticity and Vortex Dynamics. Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, 2006, ISBN 978-3-54029-027-8

. Oezkaya E: FEM-basiertes Softwaresystem für die effiziente 3D-Gewindebohrsimulation und Werkzeugoptimierung mittels CFD-Simulation, Vulkan Verlag, 2016, ISBN 978-3-8027-8793-5

. Kolář V: Vortex identification. International Journal of Heat and Fluid Flow, 28 (2007) 4, pp. 638–652, https://doi.org/10.1016/j.ijheatfluidflow.2007.03.004

DOI: http://dx.doi.org/10.24294/tse.v1i4.874


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License

This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.