The Educational Value of Modelling Complex Thermodynamic Systems with System Dynamics Software

The solution of problems involving complex thermodynamic systems often occupies much of a students\u27 time and can be a distraction from them developing a clear understanding of system components, interaction of subsystems, modelling simplifications and assumptions, and design optimization. Refocusing students on the fundamental concepts of thermal systems design and analysis is possible with the introduction of system modelling software that carries some of the load of repetitive calculation required for complex systems. Models of thermodynamic systems encountered in an advanced undergraduate thermodynamics course were developed by students (some provided to students) to solve homework problems of complex steam power plants, internal combustion engines, gas turbine power plants, refrigeration, and building energy systems. Computer modelling systems used included two commercial modelling programs, an open source program, and systems developed by the authors. Use of the modelling software forced students to setup problems in the same way as if solved on paper but allowed them to identify common components and processes that could be modeled by common blocks and used in multiple thermal systems. One example presented is a simple process block that gives the state for any location in a converging/diverging supersonic nozzle with a normal shock. The initial implementation has resulted in positive feedback from students and an improved self-efficacy in understanding and modelling complex thermodynamic systems not presented in class

Radiative Heat Transfer Analysis of Railroad Bearings for Wayside Hot-Box Detector Optimization

The railroad industry utilizes wayside detection systems to monitor the temperature of freight railcar bearings in service. The wayside hot-box detector (HBD) is a device that sits on the side of the tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. Various factors can affect the temperature measurements of these wayside detection systems. The class of the railroad bearing and its position on the axle relative to the position of the wayside detector can affect the temperature measurement. That is, the location on the bearing cup where the wayside infrared sensor reads the temperature varies depending on the bearing class (e.g., class K, F, G, E). Furthermore, environmental factors can also affect these temperature readings. The abovementioned factors can lead to measured temperatures that are significantly different than the actual operating temperatures of the bearings. In some cases, temperature readings collected by wayside detection systems did not indicate potential problems with some bearings, which led to costly derailments. Attempts by certain railroads to optimize the use of the temperature data acquired by these wayside detection systems has led to removal of bearings that were not problematic (about 40% of bearings removed were non-verified), resulting in costly delays and inefficiencies. To this end, the study presented here aims to investigate the efficacy of the wayside detection systems in measuring the railroad bearing operating temperature in order to optimize the use of these detection systems. A specialized single bearing dynamic test rig with a configuration that closely simulates the operating conditions of railroad bearings in service was designed and built by the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) for the purpose of this study. The test rig is equipped with a system that closely mimics the wayside detection system functionality and compares the infrared sensor temperature reading to contact thermocouple and bayonet temperature sensors fixed to the outside surface of the bearing cup. This direct comparison of the temperature data will provide a better understanding of the correlation between these temperatures under various loading levels, operating speeds, and bearing conditions (i.e. healthy versus defective), which will allow for an optimization of the wayside detectors. The impact on railway safety will be realized through optimized usage of current wayside detection systems and fewer nonverified bearings removed from service, which translates into fewer costly train stoppages and delays

Developing and Testing an Electronic Homework System to Improve Student Engagement and Learning in Engineering Thermodynamics

An electronic homework delivery system was developed for an advanced undergraduate engineering thermodynamics course due to limitations in available electronic homework systems. A commercially available system was effectively used in the introductory course for simple problems. The complexity of problems in the advanced course made adoption of the same system problematic as students needed feedback in the problem solving process. A system was devised that delivers individual problems to each student, provides feedback throughout the process, and records results for assessment. The system has helped students become more active in homework assignments in both completing assignments and in doing original work. Over time, students would share general solutions from previous semesters and shortcut a deeper understanding of the problems. A recent addition to the system has been the introduction of problems that are based on fundamental concepts rather than using the right equation to get the correct numerical answer. A successful example gives students a randomly generated 4-6 state heat engine cycle (from 200 developed cycles) that does not correlate with any cycle in literature. Students are forced to work through the basic steps of evaluating a new process which is theirs alone without being aided by a Google search or a classmate. The overall impact of the system and the latest addition on student learning is presented

Defect detection in freight railcar tapered-roller bearings using vibration techniques

Currently, there are two types of defect detection systems used to monitor the health of freight railcar bearings in service: wayside hot-box detection systems and trackside acoustic detection systems. These systems have proven to be inefficient in accurately determining bearing health, especially in the early stages of defect development. To that end, a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center, Inc. in Pueblo, CO. The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective tapered-roller bearing components with defect areas smaller than 12.9 cm2 while in service

Vibration-Based Defect Detection for Freight Railcar Tapered-Roller Bearings

The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high risk defects. Many defective bearings may never be detected by TADS™ due to the fact that a high risk defect is considered a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. The accuracy and reliability of the temperature readings from this wayside detection system have been concluded to be inconsistent when comparing several laboratory and field studies. The measured temperatures can be significantly different from the actual operating temperature of the bearings due to several factors such as the class of railroad bearing and its position on the axle relative to the position of the wayside detector. Over the last two decades, a number of severely defective bearings were not identified by several wayside detectors, some of which led to costly catastrophic derailments. In response, certain railroads have attempted to optimize the use of the temperature data acquired by the HBDs. However, this latter action has led to a significant increase in the number of non-verified bearings removed from service. In fact, about 40% of the bearings removed from service in the period from 2001 to 2007 were found to have no discernible defects. The removal of non-verified (defect-free) bearings has resulted in costly delays and inefficiencies. Driven by the need for more dependable and efficient condition monitoring systems, the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure. The developed system currently utilizes temperature and vibration signatures to monitor the true condition of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here provides concrete evidence that the use of vibration signatures of a bearing is a more effective method to assess the bearing condition than monitoring temperature alone. The prototype bearing condition monitoring system is capable of identifying a defective bearing with a defect size of less than 6.45 cm2 (1 in2) using the vibration signature, whereas, the temperature profile of that same bearing will indicate a healthy bearing that is operating normally

Impact of Hysteresis Heating of Railroad Bearing Thermoplastic Elastomer Suspension Pad on Railroad Bearing Thermal Management

It is a known fact that polymers and all other materials develop hysteresis heating due to the viscoelastic response or internal friction. The hysteresis or phase lag occurs when cyclic loading is applied leading to the dissipation of mechanical energy. The hysteresis heating is induced by the internal heat generation of the material, which occurs at the molecular level as it is being disturbed cyclically. Understanding the hysteresis heating of the railroad bearing elastomer suspension element during operation is essential to predict its dynamic response and structural integrity, as well as to predict the thermal behavior of the railroad bearing assembly. The main purpose of this ongoing study is to investigate the effect of the internal heat generation in the thermoplastic elastomer suspension element on the thermal behavior of the railroad bearing assembly. This paper presents an experimentally validated finite element thermal model that can be used to obtain temperature distribution maps of complete bearing assemblies in service conditions. The commercial software package ALGOR 20.3™ is used to conduct the thermal finite element analysis. Different internal heating scenarios are simulated with the purpose of determining the bearing suspension element and bearing assembly temperature distributions during normal and abnormal operation conditions. Preliminary results show that a combination of the ambient temperature, bearing temperature, and frequency of loading can produce elastomer pad temperature increases above ambient of up to 125°C when no thermal runway is present. The higher temperature increase occurs at higher loading frequencies such as 50 Hz, thus, allowing the internal heat generation to significantly impact the temperature distribution of the suspension pad. This paper provides several thermal maps depicting normal and abnormal operation conditions and discusses the overall thermal management of the railroad bearing assembly

Estimating the Inner Ring Defect Size and Residual Service Life of Freight Railcar Bearings Using Vibration Signatures

There are currently two primary wayside detection systems for monitoring the health of freight railcar bearings in the railroad industry: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the train operator of high-risk defects. However, many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans about 90% of a bearing’s raceway, and there are less than 30 systems in operation throughout the United States and Canada. HBDs sit on the side of the rail-tracks and use non-contact infrared sensors to acquire temperatures of bearings as they roll over the detector. These wayside bearing detection systems are reactive in nature and often require emergency stops in order to replace the wheelset containing the identified defective bearing. Train stoppages are inefficient and can be very costly. Unnecessary train stoppages can be avoided if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. Using a proactive approach, railcars with defective bearings could be allowed to remain in service operation safely until reaching scheduled maintenance. The University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been working on developing a proactive bearing condition monitoring system which can reliably detect the onset of bearing failure. Unlike wayside detection systems, the onboard condition monitoring system can continuously assess the railcar bearing health and can provide accurate temperature and vibration profiles to alert of defect initiation. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). The system first uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Then, an analysis of the frequency domain of the acquired vibration signature determines if the bearing has a defective inner ring (cone) and the RMS value is used to estimate the defect size. This estimated size is then used to predict the residual life of the bearing. The methodology proposed in this paper can assist railroads and railcar owners in the development of a proactive and cost-efficient maintenance cycle for their rolling stock

An Analysis of the Efficacy of Wayside Hot-Box Detector Data

Wayside hot-box detectors (HBDs) are devices that are currently used to monitor bearing, axle, and brake temperatures as a way of assessing railcar component health and to indicate any possible overheating or abnormal operating conditions. Conventional hot-box detectors are set to alarm whenever a bearing is operating at a temperature that is 94.4°C (170°F) above ambient, or when there is a 52.8°C (95°F) temperature difference between two bearings that share an axle. These detectors are placed adjacent to the railway and utilize an infrared sensor in order to obtain temperature measurements. Bearings that trigger HBDs or display temperature trending behavior are removed from service for disassembly and inspection. Upon teardown, bearings that do not exhibit any discernible defects are labeled as “non-verified”. The latter may be due to the many factors that can affect the measurement of HBDs such as location of the infrared sensor and the class of the bearing among other environmental factors. A field test was performed along a route that is more than 483 km (300 mi) of track containing 21 wayside hot-box detectors. Two freight cars, one fully-loaded and one empty, and one instrumentation car pulled by a locomotive were used in this field test. A total of 16 bearings (14 Class F and 2 Class K) were instrumented with K-type bayonet thermocouples to provide continuous temperature measurement. The data collected from this field test were used to perform a systematic study in which the HBD IR sensor data were compared directly to the onboard thermocouple data. The analyses determined that, in general, HBDs tend to overestimate Class K bearing temperatures more frequently than Class F bearing temperatures. Additionally, the temperatures of some bearings were underestimated by as much as 47°C (85°F). Furthermore, the HBD data exhibited some false trending events that were not seen in temperature histories recorded by the bayonet thermocouples. The findings from the field test suggest that HBDs may inaccurately report bearing temperatures, which may contribute to the increased percentage of non-verified bearing removals. To further investigate the accuracy of the wayside detection systems, a dynamic test rig was designed and fabricated by the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV). A mobile infrared sensor was developed and installed on the dynamic tester in order to mimic the measurement behavior of a HBD. The infrared temperature measurements were compared to contact thermocouple and bayonet temperature measurements taken on the bearing cup surface. The laboratory-acquired data were compared to actual field test data, and the analysis reveals that the trends are in close agreement. The large majority of temperature measurements taken using the IR sensor have been underestimated with a similar distribution to that of the data collected by the HBDs in field service

Energy Harvesting Potential of Terfenol-D for On-Board Bearing Health Monitoring Applications

One of the limiting factors in on-board bearing health monitoring systems is the life of the batteries used to power the system. Thus, any device that can extend the life of the battery, or entirely replace it, is a notable improvement on any currently available systems. Existing on-board monitoring systems, not optimized for low power, are designed to run on approximately 300 mW of power. Current bearing health monitoring systems have proven effective with as few as one reading every four minutes. The environment under which railroad bearings operate is a harsh one, making most forms of energy harvesting very hard to implement. Terfenol-D is a novel and sustainable solution for this problem due to its durable characteristics and strong magnetostriction. A fixture is designed using multiple magnets of ranging magnetization to properly characterize energy harvesting using Terfenol-D. The maximum available power observed during these experiments is about 77 mW under ideal conditions. The generated power is sufficient to run low-power bearing health monitoring systems

Fatigue Life Estimation of Modified Railroad Bearing Adapters for Onboard Monitoring Applications

This paper presents a study of the fatigue life (i.e. number of stress cycles before failure) of Class K cast iron conventional and modified railroad bearing adapters for onboard monitoring applications under different operational conditions based on experimentally validated Finite Element Analysis (FEA) stress results. Currently, freight railcars rely heavily on wayside hot-box detectors (HBDs) at strategic intervals to record bearing cup temperatures as the train passes at specified velocities. Hence, most temperature measurements are limited to certain physical railroad locations. This limitation gave way for an optimized sensor that could potentially deliver significant insight on continuous bearing temperature conditions. Bearing adapter modifications (i.e. cut-outs) were required to house the developed temperature sensor which will be used for onboard monitoring applications. Therefore, it is necessary to determine the reliability of the modified railroad bearing adapter. Previous work done at the University Transportation Center for Railway Safety (UTCRS) led to the development of finite element model with experimentally validated boundary conditions which was utilized to obtain stress distribution maps of conventional and modified railroad bearing adapters under different service conditions. These maps were useful for identifying areas of interest for an eventual inspection of railroad bearing adapters in the field. Upon further examination of the previously acquired results, it was determined that one possible mode of adapter failure would be by fatigue due to the cyclic loading and the range of stresses in the railroad bearing adapters. In this study, the authors experimentally validate the FEA stress results and investigate the fatigue life of the adapters under different extreme case scenarios for the bearing adapters including the effect of a railroad flat wheel. In this case, the flat wheel translates into a periodic impact load on the bearing adapter. The Stress-Life approach is used to calculate the life of the railroad bearing adapters made out of cast iron and subjected to cyclic loading. From the known material properties of the adapter (cast iron), the operational life is estimated with a mathematical relationship. The Goodman correction factor is used in these life prediction calculations in order to take into account the mean stresses experienced by these adapters. The work shows that the adapters have infinite life in all studied cases