Development of a Detailed Finite Element Model of the BIPED and Verification of Fidelity in Two Cases of Blunt Impact

Abstract

Physical surrogates of the human head are commonly used to model cranial impacts and assess head injuries. The Brain Injury Protection Evaluation Device (BIPED mk2) is a head form that contains a brain simulant, fluid layer, connective membranes, a skull, and a skin layer and can measure kinematics, intracranial pressures, and strains. Finite element (FE) models can play a significant role in the development of new head form digital design iterations that better mimic the biological response of the head during impact by allowing researchers to modify material properties and geometries without fully redesigning and manufacturing the head form. This requires digitizing precise geometry, developing accurate material models and implementing realistic boundary conditions within the model. This study aims to create a digital model of the BIPED, perform a comparison of the model using supplied experimental data for both node displacement and pressure, complete a sensitivity study that ascertains whether the location of

experimentally instrumented locations affected model outputs, and determine the effectiveness of experimental pressure sensors at capturing the coup and contrecoup phenomenon.

The model was developed in ABAQUS based on Computer-Aided Design (CAD) geometry supplied by Defense Research and Development Canada (DRDC). Two different types of BIPED experimental test data were simulated with the developed finite element model: displacement tests and pressure tests. Pressure and displacement time series responses were compared to the experimental data using CORrelation and Analysis (CORA).

The CORA values for the pressure comparison indicate an excellent correlation (>0.7) at the front sensor, while the back sensor was not considered just below a good correlation (<0.5). CORA ratings for the x (anterior-posterior) and z (superior-inferior) displacements of the 18 nodes tested resulted in a 0.554 average value, indicating a good correlation to the experimental data (> 0.5).

Model simulations and helmeted experimental impacts were used to understand the sensitivity of the pressure sensor locations within the BIPED. Kinematics from helmeted drop tower experiments were input into the model to determine the sensitivity of the simulation output location. One element removed (approximately 5 mm) in the x (anterior-posterior), y (medial-lateral), and z (superior-inferior) directions were compared to the center element (sensor location). A directional bias was observed in the direction parallel to impact, with the average percent difference from the center element being 11.7%. Nodal percent differences were then compared for the displacement tests in the x and z directions. This resulted in a 14.6%, unbiased, percent difference. These large sensitivities indicate that pressure and displacements in a finite element model brain are highly dependant on location. The helmeted impacts were used to determine the effectiveness of the pressure sensor locations at correctly identifying the coup and contrecoup pressures. This was done by extracting the pressure gradient along the line of impact and comparing the values that the sensor locations

read. It was determined that the sensors successfully characterised the coup and contrecoup pressure for impacts along its line of action but failed to do so for off-line impacts.

Based upon CORA scores, this study demonstrates successful development of a digital twin FE model for the BIPED head surrogate and comparison against experimental pressure and nodal displacement data with both kinematic and force inputs. Additionally, this study underlines the importance of knowing the correct location of the physical sensors while choosing output locations in finite element simulations. Lastly, this thesis helps identify that the locations chosen for pressure sensors in physical surrogate models adequately represented the coup and contrecoup pressures.

In order to get material properties for the Sylgard 527 brain, material testing was undertaken. Brain material is largely considered hyperelastic and viscoelastic, and a sample of Sylgard 527 has been characterized using an Ogden hyperelastic model previously. In order to further characterise the material, a stress relaxation compression test, and a quasi-static compression test were performed to obtain a single term Ogden model and a Prony viscoelastic model. A second reason was to ensure that the Sylgard material used in the material test was prepared and cured in the same manner as the experimental brains. Even though the literature had done a test on Sylgard 527, it may have been prepared slightly differently. A Biomomentum, Mach-1 material tester was used for both experiments.