Development of a generic morphable motorcycle model for crash analysis

Sammanfattning

There is currently a lack of openly available finite element motorcycle (MC) modelssuitable for crash analysis. This thesis has aimed to address this problem by creatinga generic, morphable MC that can be adapted to geometrically represent a range ofcommonly encountered motorcycles on the road and reproduce their characteristicbehaviour during crash simulations. Additionally, the goal is to make the modelopen source, allowing users to access, morph, modify, and share it freely.As part of this project, a base model was morphed into four different MC presets:the Harley-Davidson Sport Glide, BMW GS1300, Honda CB125F, and KTMDuke 390. These models were calibrated against physical test data obtained fromfrontal impacts with a rigid wall (RW), this was done by tuning the structural stiffnessof the components and by tuning the spring-damper characteristics of the frontsuspension. In the physical tests, the RW was angled at 10° for the Harley-DavidsonSport Glide, BMW GS1300, and Honda CB125F, and at 0° for the KTM Duke 390.All MCs travelled at 40 km/h upon impact.After the calibration was completed, the KTM Duke 390 model was validated againsta physical test of a KTM Duke 390 with a post-mortem human subject (PMHS)impacting the side of a Honda Accord. This was replicated in the simulation usinga human body model (HBM), a Honda Accord car model (National Highway TrafficSafety Administration, n.d.), and the morphed KTM Duke 390 model. In the physicaltest, the car was angled at 30°, and the motorcycle had a velocity of 50 km/hupon impact (Carrol, Bolte, 2024).The calibration results show that, in many cases, the overall crash response of thebike is captured. Moreover, the validation shows good agreement with the physicaltest data, where the resultant behaviour of the MC matches the physical test.The results from the calibration and validation show that a simplified model, startingfrom a single generic model, can be morphed into specific MC models that replicateimportant behaviours crucial for crash outcomes and enable MC crash analysis usinghuman surrogates.