Skip to main content
SpringerLink
Log in

Component-Level Finite Element Model and Validation for a Modern American Football Helmet

  • Published:
Journal of Dynamic Behavior of Materials Aims and scope Submit manuscript

A Correction to this article was published on 05 March 2019

This article has been updated

Abstract

Epidemiological data and measurement of biomechanical data collected through physical experiments have contributed to the understanding of potential head injury risk in contact sports such as football. Further improvement of protective helmets requires repeatable conditions and detailed parametric studies that may be addressed in part using the Finite Element (FE) method. However, such an approach requires thorough experimentation and validation of the helmet materials and subcomponents, which previous studies do not fully address, prior to implementing these subcomponents in a full helmet model. Development of a modern football helmet FE model requires a bottom-up approach, focusing on material testing, constitutive models and subcomponent-level validation, which has not been fully addressed in the literature to date. Material samples were extracted from a modern football helmet including the helmet shell, energy-absorbing structures, comfort foam, and strap system. The subcomponents were tested across a range of quasi-static and high deformation rates relevant to football impact scenarios. FE models of the subcomponents were developed and validated, focusing on verification of material behaviour, representative loading conditions, and mesh convergence to achieve realistic responses and increase computational efficiency. Evaluation of the FE models comparing experimental with simulated responses, yielded good to excellent CORrelation and Analysis (CORA) ratings of 0.954–0.985, 0.931, 0.937–0.974, and 0.847–0.958 for the helmet shell, facemask, energy-absorbing structures, and comfort pads, respectively. The resulting validated subcomponent-level models provide an important foundation for integration into a full helmet model to ultimately optimize performance and reduce head injury.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Change history

  • 05 March 2019

    M.A. Corrales’s name appeared incorrectly on the original publication of this article. It is corrected here.

References

  1. Langlois JA, Rutland-Brown W, Wald MM (2006) The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil 21:375–378

    Article  Google Scholar 

  2. Harmon KG, Drezner JA, Gammons M et al (2013) American medical society for sports medicine position statement: concussion in sport. Br J Sports Med 47:15–26. https://doi.org/10.1136/bjsports-2012-091941

    Article  Google Scholar 

  3. Gessel LM, Fields SK, Collins CL et al (2007) Concussions among United States high school and collegiate athletes. J Athl Train 42:495–503

    Google Scholar 

  4. Marar M, McIlvain NM, Fields SK, Comstock RD (2012) Epidemiology of concussions among united states high school athletes in 20 sports. Am J Sports Med 40:747–755. https://doi.org/10.1177/0363546511435626

    Article  Google Scholar 

  5. Rowson S, Duma SM, Beckwith JG et al (2012) Rotational head kinematics in football impacts: an injury risk function for concussion. Ann Biomed Eng 40:1–13. https://doi.org/10.1007/s10439-011-0392-4

    Article  Google Scholar 

  6. Slobounov SM, Sebastianelli WJ (2014) Concussions in athletics. Springer, New York

    Book  Google Scholar 

  7. Moss WC, King MJ (2011) Impact response of US Army and National Football League helmet pad systems. Livermore

  8. Johnston JM, Ning H, Kim JE et al (2015) Simulation, fabrication and impact testing of a novel football helmet padding system that decreases rotational acceleration. Sport Eng 18:11–20. https://doi.org/10.1007/s12283-014-0160-4

    Article  Google Scholar 

  9. Tan LB, Tse KM, Lee HP et al (2012) Performance of an advanced combat helmet with different interior cushioning systems in ballistic impact: Experiments and finite element simulations. Int J Impact Eng 50:99–112. https://doi.org/10.1016/j.ijimpeng.2012.06.003

    Article  Google Scholar 

  10. Darling T, Muthuswamy J, Rajan SD (2016) Finite element modeling of human brain response to football helmet impacts. Comput Methods Biomech Biomed Engin 19:1432–1442. https://doi.org/10.1080/10255842.2016.1149574

    Article  Google Scholar 

  11. Pellman EJ, Viano DC, Withnall C et al (2006) Concussion in professional football: helmet testing to assess impact performance—part 11. Neurosurgery 58:78–95. https://doi.org/10.1227/01.NEU.0000196265.35238.7C

    Article  Google Scholar 

  12. Viano DC, Withnall C, Halstead D (2012) Impact performance of modern football helmets. Ann Biomed Eng 40:160–174. https://doi.org/10.1007/s10439-011-0384-4

    Article  Google Scholar 

  13. Jankowski M (2010) Dynamic compression tests of a polyurethane flexible foam as a step in modelling impact of the head to the vehicle seat head restraint. FME Trans 38:121–127

    Google Scholar 

  14. Whyte T, Gibson T, Eager D, Milthorpe B (2016) Response of a full-face motorcycle helmet FE model to the UNECE 22.05 chin bar impact test. Int J Crashworthiness 21:555–565. https://doi.org/10.1080/13588265.2016.1199007

    Article  Google Scholar 

  15. Krzeminski DE, Fernando BMD, Curtzwiler G et al (2016) Repetitive impact exposure and characterization of stress-whitening of an American football helmet outer shell material. Polym Test 55:190–203. https://doi.org/10.1016/j.polymertesting.2016.08.019

    Article  Google Scholar 

  16. Yin ZN, Wang TJ (2012) Investigation of tensile deformation behavior of PC, ABS, and PC/ABS blends from low to high strain rates. Appl Math Mech (English Ed 33:455–464. https://doi.org/10.1007/s10483-012-1563-x

    Article  Google Scholar 

  17. Lamb L, Post A, Hoshizaki TB et al (2009) Development of a new methodology capable of characterizing the contribution of a controlled venting system to impact attenuation in chamber structures for head protection. J ASTM Int 6:101884. https://doi.org/10.1520/JAI101884

    Article  Google Scholar 

  18. Qi HJ, Boyce MC (2005) Stress-strain behavior of thermoplastic polyurethane. Mech Mater 37:817–839. https://doi.org/10.1016/j.mechmat.2004.08.001

    Article  Google Scholar 

  19. Sarva SS, Deschanel S, Boyce MC, Chen W (2007) Stress-strain behavior of a polyurea and a polyurethane from low to high strain rates. Polymer 48:2208–2213. https://doi.org/10.1016/j.polymer.2007.02.058

    Article  Google Scholar 

  20. Yi J, Boyce MC, Lee GF, Balizer E (2005) Strain rate dependence of the stress-strain behavior of polyurethane. 889–898

  21. Hibbeler R (2016) Mechanics of Materials, 10th edn. Pearson, Boston

    Google Scholar 

  22. Trimiño LF, Cronin DS (2016) Evaluation of numerical methods to model structural adhesive response and failure in tension and shear loading. J Dyn Behav Mater 2:122–137. https://doi.org/10.1007/s40870-016-0045-7

    Article  Google Scholar 

  23. Salisbury C, Cronin D, Lien F-S (2015) Deformation Mechanics of a non-linear hyper-viscoelastic porous material, part i: testing and constitutive modeling of non-porous polychloroprene. J Dyn Behav Mater 1:237–248. https://doi.org/10.1007/s40870-015-0026-2

    Article  Google Scholar 

  24. Bustamante M, Cronin DS, Singh D (2018) Experimental Testing and Computational Analysis of Viscoelastic Wave Propagation in Polymeric Split Hopkinson Pressure Bar. In: Dyn. Behav. Mater. Vol. 1 Proc. 2017 Annu. Conf. Exp. Appl. Mech. Dynamic Behavior of Materials, Volume 1: Proceedings of the 2017 Annual Conference on Experimental and Applied Mechanics, pp 67–72

  25. Salisbury C, Cronin D, Lien F-S (2015) Deformation Mechanics of a Non-Linear Hyper-Viscoelastic Porous Material, Part II: Porous Material Micro-Scale Model. J Dyn Behav Mater 1:249–258. https://doi.org/10.1007/s40870-015-0027-1

    Article  Google Scholar 

  26. Giudice JS, Kong K, Caudillo A et al (2018) User Manual: Finite Element Models of Helmet Assessment Tools (Hybrid III Head-Neck, NOCSAE Headform, Linear Impact, Pendulum Impact, Drop Impact) Version 1.0 for LS-DYNA. Charlottesville

  27. Roache PJ (1994) Perspective: A Method for Uniform Reporting of Grid Refinement Studies. J Fluids Eng 116:405–413. https://doi.org/10.1115/1.2910291

    Article  Google Scholar 

  28. Gehre C, Gades H, Wernicke P (2009) Objective rating of signals using test and simulation responses. Enhanc. Saf. Veh

  29. Vavalle NA, Davis ML, Stitzel JD, Gayzik FS (2015) Quantitative Validation of a Human Body Finite Element Model Using Rigid Body Impacts. Ann Biomed Eng 43:2163–2174. https://doi.org/10.1007/s10439-015-1286-7

    Article  Google Scholar 

  30. Miller LE, Urban JE, Stitzel JD (2016) Development and validation of an atlas-based finite element brain model. Biomech Model Mechanobiol 15:1201–1214. https://doi.org/10.1007/s10237-015-0754-1

    Article  Google Scholar 

  31. Barker JB, Cronin DS, Nightingale RW (2017) Lower Cervical Spine Motion segment computational model validation: kinematic and kinetic response for quasi-static and dynamic loading. J Biomech Eng 139:061009. https://doi.org/10.1115/1.4036464

    Article  Google Scholar 

  32. Thunert C (2012) CORA Release 3.6 User’s Manual. Ingolstadt, Germany

    Google Scholar 

  33. Lu J, Ravi-Chandar K (1999) Inelastic deformation and localization in polycarbonate under tension. Int J Solids Struct 36:391–425. https://doi.org/10.1016/S0020-7683(98)00004-3

    Article  Google Scholar 

  34. Shah QH, Abakr YA (2008) Effect of distance from the support on the penetration mechanism of clamped circular polycarbonate armor plates. Int J Impact Eng 35:1244–1250. https://doi.org/10.1016/j.ijimpeng.2007.07.012

    Article  Google Scholar 

  35. Du Bois P (2003) A simplified approach to the simulation of rubber-like materials under dynamic loading. 4th Eur LS-DYNA Users Conf 31–46

  36. Serifi E, Hirth A, Matthaei S, Mullerschon H (2003) Modelling of foams using MAT83—preparation and evaluation of eperimental data. 4th Eur LSDYNA Users Conf 59–72

  37. Chang F, Song Y, Lu DX (1998) Unified constitutive equations of foam materials. J Eng Mater Technol 120:212–217. https://doi.org/10.1115/1.2812345

    Article  Google Scholar 

  38. Cronin DS, Ouellet S (2016) Low density polyethylene, expanded polystyrene and expanded polypropylene: strain rate and size effects on mechanical properties. Polym Test 53:40–50. https://doi.org/10.1016/j.polymertesting.2016.04.018

    Article  Google Scholar 

  39. Davallo M, Pasdar H (2012) The Influence of a variety of plasticisers on properties of poly (vinyl chloride). Adv Appl Sci Res 3:1900–1904

    Google Scholar 

  40. Uhlemann J, Stranghöner N, Saxe K (2015) Comparison of stiffness properties of common coated fabrics. Steel Constr 8:222–229. https://doi.org/10.1002/stco.201510030

    Article  Google Scholar 

  41. Tsukinovsky D, Zaretsky E, Rutkevich I (1997) Material Behavior in plane polyurethane-polyurethane impact with velocities from 10 to 400 m/sec. Le J Phys IV 07:C3-335-C3-339. https://doi.org/10.1051/jp4:1997359

  42. Cronin D, Barker J, Gierczycka D et al (2018) User Manual: Finite Element Model of 2016 Xenith X2E (Safety Equipment Institute model X2E) Version 1.0 for LS-DYNA. Charlottesville

  43. Lamb L, Hoshizaki TB (2009) Deformation mechanisms and impact attenuation characteristics of thin-walled collapsible air chambers used in head protection. Proc Inst Mech Eng Part H J Eng Med 223:1021–1031. https://doi.org/10.1243/09544119JEIM573

    Article  Google Scholar 

Download references

Acknowledgements

The research presented in this paper was made possible by a Grant from Football Research, Inc. (FRI.) The views expressed are solely those of the authors and do not represent those of FRI or any of its affiliates or funding sources.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    M. C. Bustamante, D. Bruneau, J. B. Barker, D. Gierczycka, M. A. Coralles & D. S. Cronin

Authors
  1. M. C. Bustamante
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Bruneau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. B. Barker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. D. Gierczycka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. A. Coralles
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. D. S. Cronin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. S. Cronin.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bustamante, M.C., Bruneau, D., Barker, J.B. et al. Component-Level Finite Element Model and Validation for a Modern American Football Helmet. J. dynamic behavior mater. 5, 117–131 (2019). https://doi.org/10.1007/s40870-019-00189-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40870-019-00189-9

Keywords

Search

Navigation