Examines multi-point wheel-rail contact dynamics, suggesting improved simulation accuracy.
Accurate identification of wheel–rail contact state is critical to the establishment of vehicle–track dynamics models. Existing quasi-elastic contact methods can improve the smoothness and continuity of the wheel–rail contact position under single-point contact conditions. However, their global weighting strategies may generate erroneous contact points under multi-point contact conditions, thereby preventing effective quasi-elastic correction in complex wheel–rail contact scenarios. To address this issue, a wheel–rail multi-point quasi-elastic contact method (MQEC) is proposed. Based on the wheel–rail gap function, the proposed method determines multi-point contact regions by combining normal and lateral criteria and introduces an exponential weighting strategy within each effective contact region to perform quasi-elastic correction of rigid multi-point contact positions. Taking Chinese No. 18 high-speed railway turnout and LMB10 worn wheel profile as the research objects, vehicle–turnout system dynamics models are established based on MQEC and wheel–rail multi-point rigid contact method (MRC), respectively, and the differences in dynamic responses obtained using the two methods are comparatively analyzed. The results show that MQEC can identify the complex variation characteristics of wheel–rail contact points in the turnout zone, and the resulting contact point position curves exhibit better smoothness and continuity. In particular, at locations where contact point jumps or local oscillations occur in MRC, MQEC can identify the wheel-load transition process, achieve smooth migration of multipoint contact positions, and reduce unreasonable fluctuations in wheel–rail force. The proposed method provides a quasi-elastic correction approach for wheel–rail multi-point contact calculation, improves the stability of wheel–rail contact identification under complex conditions, and provides smoother and more continuous contact-geometry parameters for vehicle–track dynamics simulation.
No takes yet. Share an insight, caveat, or question.
Qi et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: