Derailment Analysis for Prediction of Damage and Probability of Release for Novel Railroad Tank Car Designs

Steven W. Kirkpatrick, Chen Yu Lin*, Leandro Iannacone, Paul Gharzouzi, Todd Treichel, Christopher P.L. Barkan, Paolo Gardoni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Over the past 15 years, there has been substantial research performed to improve the safety of tank cars carrying hazardous materials. This has included a series of full-scale tank car impact tests and the development of impact/puncture models capable of evaluating the performance of existing and novel tank car designs under specific impact conditions. In parallel, statistical information was developed on tank car performance based on data from past accidents. However, such data cannot predict the performance of novel tank car designs involving new materials and configurations. This paper presents an innovative analytical framework to address this gap by simulating railroad tank cars behaviors under various train accident and rolling stock features. The finite element method-based simulation model is developed to generate impact forces on railcars after the initiation of train derailments. The results of the modeling were compared, validated, and calibrated with the real-world train derailment to assure the practicality of the analytical tool. This research is novel as it is the first quantitative, analytical methodology that can accurately and practically assess the physical behaviors of railcar movements and impact forces in train derailments. The results of this study provide new and important information for tank car performance in train derailments, which contributes to the literature in structural modeling of rail tank cars. It also specifies an approach to building a bridge between analytical methods and real-world scenarios and data for estimating tank car conditional probability of release (a frequently used metric for hazardous materials transportation safety).

Original languageEnglish
Pages (from-to)812-828
Number of pages17
JournalTransportation Research Record
Volume2677
Issue number5
Early online date30 Nov 2022
DOIs
StatePublished - May 2023

Keywords

  • computer models
  • modeling and forecasting
  • rail
  • rail
  • safety
  • train

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