Force Reduction in a Finite Element Cycling Helmet Model with Liquid Shock Absorbers

Introduction: : Cycling is the leading cause of sports- and recreation-related traumatic brain injuries (TBIs) in the United States, with over 500,000 emergency department visits between 2009-2018 [1]. Although concussion is a frequently reported outcome, incidence between helmeted and unhelmeted users is nearly identical [2]. This is because cycling helmets are designed to prevent skull fracture and severe TBI, which occur at higher accelerations than concussion [3,4]. Certification protocols evaluate cycling helmet performance using vertical drop tests and typically assign pass/fail thresholds based on peak linear accelerations (PLAs) between 150-300g [5]. As a result, helmets can pass certification while offering limited protection against concussion.

Our recent instrumented mouthguard data show that PLA is a strong predictor of concussion even though it was previously associated with focal injuries like skull fracture. This suggests targeting linear acceleration attenuation may improve protection against concussion under existing certification conditions.

Liquid shock absorbers have reduced PLA in both physical helmet testing and finite element (FE) simulations [6,7]. During impact, the absorber compresses axially and forces fluid through an internal orifice, generating a velocity-dependent pressure drop that resists deformation and improves energy absorption across a range of impact velocities. For example, liquid shock absorbers integrated into an American football helmet model reduced the Head Acceleration Response Metric by 33% compared to commercial helmets, suggesting a similar approach could improve cycling helmet performance [7]. In this study, we evaluate a novel cycling helmet FE model featuring liquid shock absorbers and assess its ability to reduce linear acceleration-associated concussion risk.

Materials and

Methods: : A medium-sized Giro Caden cycling helmet was used as the geometric basis and baseline helmet for comparison. The helmet was 3D scanned and meshed to convert the physical helmet into a high-fidelity FE model [8]. The original expanded polystyrene liner (mass = 214.7 g) was removed and replaced with five cylindrical liquid shock absorbers positioned at the top, front, back, and lateral regions (Fig. 1). The shock absorber walls were modeled as *MAT_FABRIC for the plain-weave high-strength fabric material (E = 76 GPa, 𝜌 = 777 kg/m³) with carbon fiber endcaps and an internal water chamber. The orifice area for fluid ejection was set to 165 mm². Absorber heights ranged from 24-28 mm, with individual masses between 37–43 g, totaling 195.9 g for all five absorbers. The outer helmet shell was changed from 3 mm polycarbonate (E = 2.5 GPa, 𝜌 = 1200 kg/m³) to a 0.5 mm carbon fiber composite (CF T700/2510, E = 55 GPa, 𝜌 =1500 kg/m³). Nodal constraints tied the top surface of each absorber to the shell’s inner surface.

Both helmet models were fitted to a 50th percentile Hybrid III male headform and subjected to drop impact simulations at the top location based on the EN1078 certification standard [9]. Initial velocities were 5.24 m/s and 4.48 m/s for flat and curbstone anvils, respectively. Linear acceleration was extracted from the head center of gravity and filtered using a fourth order low-pass Butterworth filter with a 200 Hz frequency cutoff.

Results, Conclusions, and Discussions:: In Figure 2, we present the concussion risk function for PLA derived from our instrumented mouthguard data, which we applied to estimate concussion risk during simulated helmet impacts. The PLA curve is shown in comparison to published injury risk functions from collegiate football and cycling [10,11]. In curbstone impacts at 4.48 m/s, the liquid helmet reduced PLA to 75 g, well below the EN1078 certification threshold of 250 g (Fig. 3b), compared to 125 g for the baseline helmet. For flat anvil impacts at 5.24 m/s, the liquid helmet achieved a PLA of 88 g, whereas the baseline helmet reached 198 g. Consequently, the liquid helmet reduced predicted concussion risk from 99% to 22% for curbstone impacts, and from 96% to 50% for flat anvil impacts (Fig. 3d).

The improved performance of the liquid helmet is due in part to greater compression of the liquid shock absorbers, which enables more stroke to be utilized as the head decelerates. As the shock absorbers compress, fluid is forced through a fixed orifice, and pressure builds in proportion to the flow rate and orifice area. This creates a regulated resistive force that remains steady throughout the stroke and reduces the sharp force spike observed in the baseline helmet, as shown in Figure 4c. The result is an extended impulse duration over which kinetic energy is dissipated, ultimately reducing estimated concussion risk.

Currently, few helmet or automotive safety standards evaluate peak linear acceleration as a concussion metric, as it is primarily associated with skull fracture. The Fédération Équestre Internationale recently proposed a 150 g PLA threshold for concussion, yet our risk model indicates this corresponds to greater than 99% probability of injury. A lower threshold of 100 g (with 200 Hz-filtered kinematics) would more accurately capture concussion risk, and our cycling helmet prototype illustrates the practical benefit of applying such a criterion. These results suggest that translational impact mitigation technologies could reduce concussion risk within existing certification frameworks. Updating helmet standards to reflect concussion-relevant PLA limits could therefore enhance brain injury prevention and incentivize more effective helmet designs.

Acknowledgements and/or References (Optional)::

References:
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Acknowledgements:
This work was supported by the Taube Stanford Children’s Concussion Initiative, Pac-12 Conference’s Student-Athlete Health and Well-Being Initiative, the National Institutes of Health (R24NS098518), the Maternal and Child Health Research Institute, the Office of Naval Research, and the National Science Foundation Graduate Research Fellowship Program. This work was also supported by Stanford’s Department of Bioengineering.