Updated Fuel Tank Puncture Survey: 1995-2020

The Federal Railroad Administration’s Office of Research, Development and Technology has been conducting research into passenger fuel tank crashworthiness. The occurrence of a fuel tank puncture during passenger rail collisions and derailments increases the potential of serious injury and fatality for crew and passengers due to the possibility of fire. The purpose of the FRA research is to help support regulatory and standard development with technical data. In the last decade, the research has focused on understanding how fuel tanks are punctured during an impact and how various tank designs respond to common types of loading in collisions, derailments and general operation. Throughout the research, surveys have been conducted to determine the most likely scenarios that are causing fuel tank punctures. A previous FRA survey found that fuel tank punctures occur under two types of loading conditions: a blunt impact or a raking impact. A limited number of accident/incidents were evaluated in this survey. These incidents showed that fuel tanks are punctured on any side that is not protected or shielded. The purpose of this paper is to report on a recently conducted fuel tank puncture survey updated to include the last decade. This paper identifies and describes accidents and incidents that led to breached fuel tanks in freight and passenger trains traveling on the general railroad system in the U.S. between 2008 and 2020. The results include data from the FRA’s Railroad Accident/Incident Reporting System (RAIRS), queried from 1995 to 2020. This data include the number of recorded accidents/incidents and other trends like fuel spillage, operating authority and cause of accident/incident. RAIRS data showed accidents/incidents with fuel tank puncture ranging from 10 to 55 accidents/incidents per year. Additionally, more detailed results are shared from field investigations recently conducted by the FRA or Volpe Center. These more detailed investigations provide additional insight into the types of loading that may lead to a fuel tank puncture. This survey supplements the RAIRS data with more detailed information from field investigations. The paper finally discusses the conditions that lead to fire and the associated hazards.

Secondary Impact Protection for Locomotive Engineers – Improved Airbag Design

Under the locomotive cab occupant protection research program sponsored by the Federal Railroad Administration (FRA), Sharma & Associates, Inc. (SA) developed a Secondary Impact Protection System (SIPS) for locomotive engineers. The system uses a large, automotive-style, passenger airbag in combination with a deformable knee bolster to provide the level of protection needed for the locomotive engineer, without compromising the normal operating environment and egress. A prior version of the system [1] was prototyped and tested in a dynamic sled test with a 23g crash pulse and was shown to meet most limiting human injury criteria defined in the Department of Transportation (DOT)’s Federal Motor Vehicle Safety Standards (FMVSS 208) [2] for the head, chest, neck, and femur. The system also showed marginal performance for the chest injury index and indicated potential for an improved airbag design to fully meet all requirements. In the current study, simulations with an optimized airbag and higher capacity inflator system showed that SIPS can provide excellent occupant protection for an unbelted locomotive occupant in a frontal crash. Sled testing of SIPS confirmed the performance, and the system successfully met all eleven (11) criteria of the FMVSS 208 standard [2]. The shape and position of the airbag module and its attachments to the desk were generally the same as those presented in previous research. The key changes that helped meet all criteria were the higher capacity inflators, knee bolster system brackets moved forward, thicker knee plate, higher volume airbag and additional vents.

Multimodal Mobility Framework: Towards Seamless Mobility Experience

Mobility is no longer just a necessity for travelers, but choices among several possible routes and transportation modes. Urban passenger rail transport plays an essential role because it is affordable, convenient, safe, and fast. On the other hand, rail lines are limited to high passenger density corridors. Inevitably, rail has to be placed together with different transport modes, forming a multimodal network. However, to enable this integration with other modes of transport, numerous practical problems remain, such as making a smooth transition from the existing siloed, mode specific operational structure towards an interconnected system of transportation modes and business models for a seamless connected journey. The current isolated operational structure lacks a single truth and accurate visibility, which further discourages participation from augmenting transportation modes and leads to the extended reaction time for new technology integration. This research article introduces a Multimodal Mobility (MMM) solution framework that provides a functional interface to integrate and synchronize the railroad operations with other public transit networks (including train-bus-rapid transits) and micro-mobility services. The known approach to addressing the users’ seamless mobility experience entails a centralized, prearranged, a priori knowledge and mechanism for operating intermodal transport systems. In contrast, the method defined in this paper focuses on a market-driven demand-responsive system that allows for dis-intermediation in a network of peer-level transportation modes operations. The framework facilitates blockchain-based decentralized and multi-organizational engagement. The focus here is the role of railroad in the multimodal ecosystem and its performance advancements in this integrated solutions framework. Leveraging a combination of graph analytics and machine learning algorithms, we provide methods to address challenges in encoding spatial and temporal dependencies of multimodal transit networks and handle complex optimization problems such as mixed time window and volume variation for resource allocation and transit operational analytics. This enables operation of different transit modes with varied resolution and flexibility for operational parameters like time, capacity, ridership, revenue management, etc. The analytics enable solutions for recommendations on synchronizing and integrating operations of transportation systems. Further, the network’s decentralization and modular handling enable market-driven co-optimization of operational resources across various transportation modes to ensure seamless transit experience for users.

Progress in Railway Mechanical Engineering 2019 – 2020 Survey – Locomotives

This survey reports on motive power developments for the previous year ending in August 2020. Invitations to collaborate were sent to several locomotive builders and railroads. The survey looks at the scope of new and rebuilt motive power during the past year. During this time, several Class 1 railroads have heavily adopted the rebuilding and modernization of existing motive power. The survey also looks at how the needs of the railroads are changing in terms of motive power both due to Precision Scheduled Railroading (PSR) and also due to the novel Coronavirus pandemic. Finally, this survey will conclude with a look back at the high horsepower race following the retirement of the EMD SD80MAC locomotives from Class 1 ownership.

Multiscale Simulation-Based Mixed Train Derailment Analysis: A Case Study

The makeup of mixed-manifest freight trains is often determined through rigid operating rules designed based on steady-state in-train forces under nominal vehicle and track conditions. Despite compliance with these rules, excessive forces experienced during exceptional situations still hold significant potential to result in derailments. This paper presents a case study involving a mixed train derailment in a sharp curve following an undesired brake release. The operating conditions and sequence of events are modelled using a novel multiscale simulation approach to combine industry-established longitudinal and multi-body dynamics simulation packages. The root cause of the derailment in this case study is assessed, along with the effectiveness of subsequent rule changes in mitigating the corresponding risk. The case study and results are further discussed in the broader context of a research initiative to utilize near real-time data collection from wayside and vehicle-mounted measurement systems, together with robust multiscale simulation approaches, to improve the area of mixed train marshalling.

Rail Neutral Temperature Estimation Using Field Data, Numerical Models, and Machine Learning

Effective Rail Neutral Temperature (RNT) management is needed for continuous welded rail (CWR). RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Mismanagement of RNT can lead to rail fracture or buckling when thermal stresses exceed the limits of rail steel. In this work, we propose an effective RNT estimation method structured around four hypotheses. The work leverages field-collected vibration test data, high-fidelity numerical models, and machine learning techniques. First, a contactless non-destructive and non-disruptive sensing technology was developed to collect real-world rail vibrational data. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict rail vibration behavior under the influence of temperature and longitudinal load. Excellent agreement between model and experimental results were obtained using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT estimation accuracy when measurement or model noise is low.

Estimation of Pre-COVID19 Daily Ridership Patterns From Paper and Electronic Ticket Sales Data With Origin-Destination, Time-Of-Day, and Train-Start Detail on a Commuter Railroad: Quick-Response Big Data Analytics in a World Steeped With Tradition

Our niche method independently estimates hourly commuter rail station-to-station origin-destination (OD) matrix data each day from ticket sales and activation data from four sales channels (paper/mobile tickets, mail order, and onboard sales) by extending well-established transportation modelling methodologies. This algorithm’s features include: (1) handles multi-pack pay-per-ride fare instruments not requiring electronic validation, like ten-trip paper tickets “punched” onboard by railroad conductors; (2) correctly infers directionality for direction-agnostic ticket-types; (3) estimates unlimited ride ticket utilization patterns sufficiently precisely to inform vehicle assignment/scheduling; (4) provides integer outputs without allowing rounding to affect control totals nor introduce artifacts; (5) deals gracefully with cliff-edge changes in demand, like the COVID19 related lockdown; and (6) allocates hourly traffic to each train-start based on passenger choice. Our core idea is that the time of ticket usage is ultimately a function of the time of sale and ticket type, and mutual transformation is made via probability density functions (“patterns”) given sufficient distribution data. We generated pre-COVID daily OD matrices and will eventually extend this work to post-COVID inputs. Results were provided to operations planners using visual and tabular interfaces. These matrices represent data never previously available by any method; prior OD surveys required 100,000 respondents, and even then could neither provide daily nor hourly levels of detail, and could not monitor special event ridership nor specific seasonal travel such as summer Friday afternoons.

Stabilization of Railroad Ballast Using Polyurethane Under Various Coal Fouling Conditions

This paper presents a comparative study on the shear behavior of coal fouled polyurethane-stabilized and polyurethane-stabilized coal fouled ballast. Fresh ballast and coal fines of mean particle sizes (D50) of 42 mm and 545 microns, and Elastan polyurethane polymer with a density of 1100 kg/m3 were used in the current study. Tests were conducted at normal stress (σn) varying from 60–120 kPa and at rate of shearing (Sr) 3 mm/min. To mimic the effect of coal fouling, a predetermined amount of coal was added that signifies a fouling level, of 30% void contamination index (VCI), in the present study. Test results highlighted that polyurethane stabilization technique enhanced shear stress of ballast. However, the coal fouling reduced the shear stress of polyurethane-stabilized and unstabilized ballast. The friction (φ) and dilation (ψ) angles of polyurethane-stabilized and unstabilized ballast were found to reduce non-linearly with an increase in σn. Furthermore, the values of φ and ψ of unstabilized ballast reduced from 65° to 60° and 21° to 16°, respectively due to coal fouling. The stabilization efficiency factor (Sef), stated as the ratio of the shear stress of stabilized ballast to the unstabilized ballast, differs from 1.70 to 1.75 for polyurethane-stabilized ballast as σn varies from 60 to 120 kPa. Moreover, it is observed in coal fouling conditions that the coal fouled polyurethane-stabilized ballast (FPSB) shown better performance when compared to polyurethane-stabilized coal fouled ballast (PSFB).

Intermediate Distance Testing of Optical Top-of-Rail (TOR) Lubricity Sensors on a Remote-Controlled Rail Cart

The primary intent of this study is to evaluate the effectiveness and utility of a laser-based measurement unit for qualitative assessment of the presence and amount of Top-of-Rail Friction Modifier (TOR or TORFM), at reasonably high speeds and over long distances in the field. As a capstone to this phase of development, a series of field tests were conducted on revenue service track in partnership with a local Class 1 railroad. For these tests, the Third Generation Rail Lubricity Sensor was mounted on a Remote-Controlled Rail Cart and tested continuously over several miles of track. This longer window is able to cover the domain of multiple wayside applicators over a distance of more than 3 miles, the expected carry distance of TORFM. The results of this testing demonstrate the capacity of optical sensors to measure and evaluate track lubricity. The signal characteristics at or near wayside applicators demonstrate a clear impulse from the heavy lubricant close to the applicator. Further, by collecting continuous data down track from a wayside applicator it is possible to observe several novel ways in which the TORFM and flange grease carries beyond the point of application. One such example is a clear spike in track lubricity when entering or exiting curves caused by the lateral shift of the wheelsets drawing fresh lubricant previously out of contact with the rail into contact creating a “phantom applicator” effect. These observations are crucial to understanding in detail the way the TORFM and flange grease is carried down track. They are also essential to creating predictive models for most effective application of friction modifiers to specific track geometries.

A Systems Approach for the Evaluation and Rebuilding of the Rogers Pass Systems on Canadian Pacific

In the 1980’s, Canadian Pacific (CP) constructed one of the most ambitious projects since the original completion of the railway in 1885. The Rogers Pass project was initiated at CP in the early 1980’s to allow for increased capacity and efficiency by installing a second main track within the Rogers Pass area. Completed in 1988, the Rogers Pass Project included the construction of a new line with significantly lower westbound grades and two tunnels with a combined length of over ten miles. Several other systems were required to complete the project that will be discussed I this paper. Recently, CP has started a new Multi-Year Plan to rebuild virtually all of the tunnel systems infrastructure that will not only prolong the life of these systems, but will introduce technology not known at the time of construction. These new systems will enable CP to greatly reduce maintenance cost while improving reliability. These systems include a high voltage transmission line that feed the ventilation house, a sophisticated ventilation system that allows fresh combustion air to reach the locomotives working the uphill grades, as well as process controllers that automate all of these systems. As all of the systems are reaching the end of their useful life, CP’s rebuilding will also increase overall system capacity.