Rail Temperature Prediction Model and Heat Slow Order Management

2014 ◽  
Author(s):  
Radim Bruzek ◽  
Larry Biess ◽  
Leopold Kreisel ◽  
Leith Al-Nazer
Keyword(s):  
Prediction Model ◽  
Weather Forecast ◽  
Ambient Air ◽  
Detection Theory ◽  
Model Verification ◽  
Temperature Prediction ◽  
Order Management ◽  
Wide Range ◽  
Forecast Data ◽  
The Impact

Track buckling due to excessive rail temperature may cause derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order management process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX) and FRA, ENSCO Inc. conducted a comprehensive model verification study by comparing actual rail temperatures measured by wayside sensors installed at 23 measurement sites located across the CSX network with the rail temperatures predicted by the model based on weather forecast data over the course of spring and summer 2012. In addition to the correlation analysis, detection theory was used to evaluate the model’s ability to correctly identify instances when rail temperatures are elevated above a wide range of thresholds. Detection theory provides a good way of comparing the performance of the model to the performance of the current industry practice of estimating rail temperature based on constant offsets above predicted daily peak ambient air temperatures. As a next step in order to quantify the impact of implementation of the model on CSX operations, heat slow orders issued by CSX in 2012 on 10 selected subdivisions were compared to theoretical heat slow orders generated by the model. The paper outlines the analysis approach together with correlation, detection theory and slow order comparison results. The analysis results along with investigation of past heat related track buckle derailments indicate that the railroad would benefit from adopting the rail temperature prediction model along with flexible rail temperature thresholds. The implementation of the model will have a positive impact on safety by allowing for issuing of advance heat slow orders in more accurate, effective and targeted way.

Development of Rail Temperature Predictions to Minimize Risk of Track Buckle Derailments

2013 ◽  
Author(s):  
Radim Bruzek ◽  
Larry Biess ◽  
Leith Al-Nazer
Keyword(s):  
Weather Forecast ◽  
Model Verification ◽  
Order Process ◽  
Railroad Track ◽  
Significant Cost ◽  
Web Based ◽  
Increased Risk ◽  
Forecast Data ◽  
Verification Process ◽  
Web Based System

Track buckling due to excessive rail temperature is a major cause of derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While the slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX), ENSCO Inc. conducted a model verification study by comparing actual rail temperatures measured by wayside sensors installed on railroad track near Folkston, GA, with the rail temperatures predicted by the model based on weather forecast data over the course of summer 2011. The paper outlines the procedure of the verification process together with correlation results, which are favorable. The paper also presents results of several case studies conducted on derailments attributed to track buckling. These investigations improve our understanding of conditions and temperature patterns leading to increased risk of rail buckles and validate further use of the Rail Temperature Prediction Model as track buckling prediction tool and as an aid to the railroads in making more informed decisions on slow order issuing process.

scholarly journals Spatio-Temporal Variation in the Concentration of Inhalable Particulate Matter (PM10) in Uganda

2019 ◽  
Vol 16 (10) ◽  
pp. 1752 ◽  
Author(s):  
Silver Onyango ◽  
Beth Parks ◽  
Simon Anguma ◽  
Qingyu Meng
Keyword(s):  
Particulate Matter ◽  
Sampling Period ◽  
Ambient Air ◽  
Average Density ◽  
Diurnal Variations ◽  
Time Data ◽  
Seasonal Differences ◽  
Wide Range ◽  
The Impact

Long-term particulate matter (PM10) measurements were conducted during the period January 2016 to September 2017 at three sites in Uganda (Mbarara, Kyebando, and Rubindi) representing a wide range of urbanization. Spatial, temporal and diurnal variations are assessed in this paper. Particulate matter (PM10) samples were collected for 24-h periods on PTFE filters using a calibrated pump and analyzed gravimetrically to determine the average density. Particulate levels were monitored simultaneously using a light scattering instrument to acquire real time data from which diurnal variations were assessed. The PM10 levels averaged over the sampling period at Mbarara, Kyebando, and Rubindi were 5.8, 8.4, and 6.5 times higher than the WHO annual air quality guideline of 20 µg·m−3, and values exceeded the 24-h mean PM10 guideline of 50 µg·m−3 on 83, 100, and 86% of the sampling days. Higher concentrations were observed during dry seasons at all sites. Seasonal differences were statistically significant at Rubindi and Kyebando. Bimodal peaks were observed in the diurnal analysis with higher morning peaks at Mbarara and Kyebando, which points to the impact of traffic sources, while the higher evening peak at Rubindi points to the influence of dust suspension, roadside cooking and open-air waste burning. Long-term measurement showed unhealthy ambient air in all three locations tested in Uganda, with significant spatial and seasonal differences.

The Challenges of Turbocharger Matching for North American Freight Locomotives

2019 ◽  
Author(s):  
Thomas Lavertu ◽  
Matthew Hart ◽  
Christopher Homison ◽  
Preeti Vaidya
Keyword(s):  
North American ◽  
Engine Performance ◽  
Ambient Air ◽  
Ambient Conditions ◽  
Efficient Operation ◽  
Engine Efficiency ◽  
Air Temperatures ◽  
Wide Range ◽  
The Impact ◽  
Operating Points

Abstract Engine development is centered on developing a solution for best performance while meeting emissions and operational requirements. This will lead to a tradeoff between engine efficiency and emissions across a wide range of load and ambient operating points. Proper airflow to the engine through turbocharger matching is critical to ensure efficient operation and to meet emissions. This study addresses the challenges of turbocharger matching for vehicle advanced emissions control using a North American freight locomotive application as an example. The airflow trends in moving across the various operating points will be shown along with the impact on both the turbocharger and engine performance. First, the airflow trends across the locomotive load set points will be discussed along with the performance and emissions tradeoffs to meet required airflows. Results on the impact on turbocharger performance such as speed will be shown along with the engine efficiency and emissions implications. Next, the ambient operating requirements for a locomotive will be reviewed and the impact on turbocharger matching. Locomotives operate in a wide range of ambient conditions, including altitudes up to 3,050 meters and across ambient air temperatures ranging from −40 °C to well over 38 °C (including higher temperature operation). This thermal swing provides stress on the turbocharger to efficiently deliver the necessary airflow across all conditions. Trends in turbocharger performance will be reviewed and discussed across this range of ambient conditions. In addition, challenges unique to locomotive applications, such as unventilated tunnel operation and vibrational loading, will be reviewed. Finally, potential for advanced technologies such as variable geometry turbines and their applicability to locomotive operation will be discussed.

scholarly journals Dynamic Electro-Thermal PV Temperature and Power Output Prediction Model for Any PV Geometries in Free-Standing and BIPV Systems Operating under Any Environmental Conditions

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4743
Author(s):  
Eleni Kaplani ◽  
Socrates Kaplanis
Keyword(s):  
Prediction Model ◽  
Environmental Conditions ◽  
Power Output ◽  
Prediction Models ◽  
Tracking System ◽  
The Sun ◽  
Free Standing ◽  
Temperature Prediction ◽  
Fixed Angle ◽  
Wide Range

PV temperature significantly affects the module’s power output and final system yield, and its accurate prediction can serve the forecasting of PV power output, smart grid operations, online PV diagnostics and dynamic predictive management of Building Integrated Photovoltaic (BIPV) systems. This paper presents a dynamic PV temperature prediction model based on transient Energy Balance Equations, incorporating theoretical expressions for all heat transfer processes, natural convection, forced convection, conduction and radiation exchanges between both module sides and the environment. The algorithmic approach predicts PV temperature at the centre of the cell, the back and the front glass cover with fast convergence and serves the PV power output prediction. The simulation model is robust, predicting PV temperature with high accuracy at any environmental conditions, PV inclination, orientation, wind speed and direction, and mounting configurations, free-standing and BIPV. These, alongside its theoretical basis, ensure the model’s wide applicability and clear advantage over existing PV temperature prediction models. The model is validated for a wide range of environmental conditions, PV geometries and mounting configurations with experimental data from a sun-tracking, a fixed angle PV and a BIPV system. The deviation between predicted and measured power output for the fixed-angle and the sun-tracking PV systems was estimated at −1.4% and 1.9%, respectively. The median of the temperature difference between predicted and measured values was as low as 0.5 °C for the sun-tracking system, and for all cases, the predicted temperature profiles were closely matching the measured profiles. The PV temperature and power output predicted by this model are compared to the results produced by other well-known PV temperature models, illustrating its high predictive capacity, accuracy and robustness.

scholarly journals Sources of Airborne Endotoxins in Ambient Air and Exposure of Nearby Communities—A Review

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 375 ◽  
Author(s):  
Catherine Rolph ◽  
Ceri Gwyther ◽  
Sean Tyrrel ◽  
Zaheer Nasir ◽  
Gillian Drew ◽  
...  
Keyword(s):  
Urban Areas ◽  
Wastewater Treatment Plants ◽  
Ambient Air ◽  
Assessment Process ◽  
Rural And Urban Areas ◽  
Rural And Urban ◽  
Wide Range ◽  
Reported Data ◽  
Airborne Endotoxins ◽  
The Impact

Endotoxin is a bioaerosol component that is known to cause respiratory effects in exposed populations. To date, most research focused on occupational exposure, whilst much less is known about the impact of emissions from industrial operations on downwind endotoxin concentrations. A review of the literature was undertaken, identifying studies that reported endotoxin concentrations in both ambient environments and around sources with high endotoxin emissions. Ambient endotoxin concentrations in both rural and urban areas are generally below 10 endotoxin units (EU) m−3; however, around significant sources such as compost facilities, farms, and wastewater treatment plants, endotoxin concentrations regularly exceeded 100 EU m−3. However, this is affected by a range of factors including sampling approach, equipment, and duration. Reported downwind measurements of endotoxin demonstrate that endotoxin concentrations can remain above upwind concentrations. The evaluation of reported data is complicated due to a wide range of different parameters including sampling approaches, temperature, and site activity, demonstrating the need for a standardised methodology and improved guidance. Thorough characterisation of ambient endotoxin levels and modelling of endotoxin from pollution sources is needed to help inform future policy and support a robust health-based risk assessment process.

THE IMPACT OF LEADERSHIP PRACTICES TO TEAM MEMBERS’ SATISFACTION

2009 ◽  
Vol 8 (1) ◽  
Author(s):  
Chalimah .
Keyword(s):  
Conflict Resolution ◽  
Leadership Practices ◽  
Hotel Industry ◽  
Leader Behavior ◽  
Team Leader ◽  
Shop Floor ◽  
Team Members ◽  
Top Executives ◽  
Wide Range ◽  
The Impact

eamwork is becoming increasingly important to wide range of operations. It applies to all levels of the company. It is just as important for top executives as it is to middle management, supervisors and shop floor workers. Poor teamwork at any level or between levels can seriously damage organizational effectiveness. The focus of this paper was therefore to examine whether leadership practices consist of team leader behavior, conflict resolution style and openness in communication significantly influenced the team member’s satisfaction in hotel industry. Result indicates that team leader behavior and the conflict resolution style significantly influenced team member satisfaction. It was surprising that openness in communication did not affect significantly to the team members’ satisfaction.

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