Relevant bibliographies by topics / Rail temperature forecast

Academic literature on the topic 'Rail temperature forecast'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Rail temperature forecast"

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Li, Xin Can. "Organizational Field Forecast during Heavy Rail Quenching Process by Air Cooling." Applied Mechanics and Materials 494-495 (February 2014): 697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.697.

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Organizational field is a vital parameter in quenching process. the phase changing temperature of steel U71Mn was got based on its CCT curves. Through cooling curves of several key points, the cooling rate at phase transition point was calculated. By comparing with every microstructures critical cooling rate, the final cooling microstructure was predicted. Relative tests showed that the prediction was reasonable.
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Liu, Zhong Min, Jin Long Yan, and Jian Feng Li. "Research on NOx Emission Characteristics of Medium Speed Diesel Engine." Advanced Materials Research 744 (August 2013): 215–18. http://dx.doi.org/10.4028/www.scientific.net/amr.744.215.

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In order to research the NOx emission characteristics of the medium speed diesel engine DN8340, a finite element model is built. Calculate the influence of changing the intake charge temperature and injection timing on NOx emission, and forecast how it works to decrease the intake charge temperature and delay injection timing by the methods of Miller-cycle, oscillating cooling piston and electronic controlled high pressure common rail. The calculation result shows that Decreasing the intake charge temperature and delaying the injection timing can reduce the NOx emission to 2.22g/kWh effectively which meets the requirement of IMO TierIII.
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Zhang, Yan Qin, Li Guo Fan, Yao Chen, Rui Li, Tian Zheng Wu, and Xiao Dong Yu. "Deformation Analysis of Hydrostatic Thrust Bearing under Different Load." Applied Mechanics and Materials 494-495 (February 2014): 583–86. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.583.

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In order to solve deformation problem of large size hydrostatic thrust bearing under different load. Simulation model of fan cavity hydrostatic thrust bearing, three-dimensional mathematical model in guide rail is set up. Using CFD principle, FIUENT and ANSYS Workbench software, temperature field, pressure field and deformation field of hydrostatic bearing is simulated. Hydrostatic bearing deformation under different load conditions can be simulated and the lubrication characteristic is forecast in advance through this method. It provides valuable theoretical basis for hydrostatic bearing structure parameter optimization of oil cavity and worktable.
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Feng, Guoce, Lei Zhang, Feifan Ai, Yirui Zhang, and Yupeng Hou. "An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages." Entropy 24, no. 8 (August 12, 2022): 1111. http://dx.doi.org/10.3390/e24081111.

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A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed.
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Gómez, I., S. Molina, J. Olcina, and J. J. Galiana-Merino. "Perceptions, Uses, and Interpretations of Uncertainty in Current Weather Forecasts by Spanish Undergraduate Students." Weather, Climate, and Society 13, no. 1 (January 2021): 83–94. http://dx.doi.org/10.1175/wcas-d-20-0048.1.

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AbstractThis quantitative study evaluates how 71 Spanish undergraduate students perceive and interpret the uncertainty inherent to deterministic forecasts. It is based on several questions that asked participants what they expect given a forecast presented under the deterministic paradigm for a specific lead time and a particular weather parameter. In this regard, both normal and extreme weather conditions were studied. Students’ responses to the temperature forecast as it is usually presented in the media expect an uncertainty range of ±1°–2°C. For wind speed, uncertainty shows a deviation of ±5–10 km h−1, and the uncertainty range assigned to the precipitation amount shows a deviation of ±30 mm from the specific value provided in a deterministic format. Participants perceive the minimum night temperatures as the least-biased parameter from the deterministic forecast, while the amount of rain is perceived as the most-biased one. In addition, participants were then asked about their probabilistic threshold for taking appropriate precautionary action under distinct decision-making scenarios of temperature, wind speed, and rain. Results indicate that participants have different probabilistic thresholds for taking protective action and that context and presentation influence forecast use. Participants were also asked about the meaning of the probability-of-precipitation (PoP) forecast. Around 40% of responses reformulated the default options, and around 20% selected the correct answer, following previous studies related to this research topic. As a general result, it has been found that participants infer uncertainty into deterministic forecasts, and they are mostly used to take action in the presence of decision-making scenarios. In contrast, more difficulties were found when interpreting probabilistic forecasts.
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Scheuerer, Michael, Scott Gregory, Thomas M. Hamill, and Phillip E. Shafer. "Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles." Monthly Weather Review 145, no. 4 (March 21, 2017): 1401–12. http://dx.doi.org/10.1175/mwr-d-16-0321.1.

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Abstract A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.
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DeGaetano, Arthur T., Brian N. Belcher, and Pamela L. Spier. "Short-Term Ice Accretion Forecasts for Electric Utilities Using the Weather Research and Forecasting Model and a Modified Precipitation-Type Algorithm." Weather and Forecasting 23, no. 5 (October 1, 2008): 838–53. http://dx.doi.org/10.1175/2008waf2006106.1.

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Abstract The Weather Research and Forecasting model (WRF) is used to provide 6–12-h forecasts of the necessary input parameters to a separate algorithm that determines the most likely precipitation type at each model grid point. In instances where freezing rain is indicated, an ice accretion model allows forecasts of radial ice thickness to be developed. The resulting forecasts are evaluated for 38 icing events of varying magnitude that occurred in the eastern United States using National Weather Service storm impact reports and observed data from Automated Surface Observing Systems (ASOS). Ice accretion hindcasts, using the WRF, allow the development of climatologies based on archived model initialization data. Ice accretion forecasts, based on the Ramer precipitation-type algorithm, consistently underestimated the maximum observed ice accretion amounts by between 10 and 20 mm. Ice accretion at ASOS sites was also underestimated. Applying a modification to the Ramer precipitation-type algorithm, and focusing on the thermal profile below the lowest 0°C isotherm, improved the ice accretion forecasts, but still underestimated the maximum ice thickness. Little bias was evident in ice accretion forecasts for the ASOS sites. Using previous observations from outside the forecast window to account for WRF and precipitation-type algorithm biases in precipitation amount, wind speed, temperature, and precipitation type provided some forecast improvement. The forecast procedure using the modified Ramer precipitation algorithm captures both the magnitude and extent of icing in both widespread severe icing events and localized storms. Minimal icing is indicated in events and at locations where precipitation fell as rain or snow.
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Lopez, Philippe. "Experimental 4D-Var Assimilation of SYNOP Rain Gauge Data at ECMWF." Monthly Weather Review 141, no. 5 (May 1, 2013): 1527–44. http://dx.doi.org/10.1175/mwr-d-12-00024.1.

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Abstract Four-dimensional variational data assimilation (4D-Var) experiments with 6-hourly rain gauge accumulations observed at synoptic stations (SYNOP) around the globe have been run over several months, both at high resolution in an ECMWF operations-like framework and at lower resolution with the reference observational coverage reduced to surface pressure data only, as would be expected in early twentieth-century periods. The key aspects of the technical implementation of rain gauge data assimilation in 4D-Var are described, which include the specification of observation errors, bias correction procedures, screening, and quality control. Results from experiments indicate that the positive impact of rain gauges on forecast scores remains limited in the operations-like context because of their competition with all other observations already available. In contrast, when only synoptic station surface pressure observations are assimilated in the data-poor control experiment, the additional assimilation of rain gauge measurements substantially improves not only surface precipitation scores, but also analysis and forecast scores of temperature, geopotential, wind, and humidity at most atmospheric levels and for forecast ranges up to 10 days. The verification against Meteosat infrared imagery also shows a slight improvement in the spatial distribution of clouds. This suggests that assimilating rain gauge data available during data-sparse periods of the past might help to improve the quality of future reanalyses and subsequent forecasts.
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Al-Jiboori, Monim, Mahmoud Jawad Abu Al-Shaeer, and Ahemd S. Hassan. "Statistical Forecast of Daily Maximum Air Temperature in Arid Areas at Summertime." Journal of Mathematical and Fundamental Sciences 52, no. 3 (December 31, 2020): 353–65. http://dx.doi.org/10.5614/j.math.fund.sci.2020.52.3.8.

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Based on historical observations of summers for the period from 2004 to 2018 with a focus on daily maximum and minimum air temperatures and wind speed recorded at 0600 GMT, a non-linear regression hypothesis is developed for forecasting daily maximum air temperature (Tmax) in arid areas such as Baghdad International airport station, which has a hot climate with no cloud cover or rain. Observations with dust storm events were excluded, thus this hypothesis could be used to predict daily Tmax on any day during summers characterized by fair weather. Using mean annual daily temperature range, daily minimum temperature, and the trend of maximum temperature with wind speed, Tmax was forecasted and then compared to those recorded by meteorological instruments. To improve the accuracy of the hypothesis, daily forecast errors, bias, and mean absolute error were analyzed to detect their characteristics through calculating relative frequencies of occurrence. At the end of this analysis, a value of (-0.45ºC) was added to the hypothesis as a bias term.
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Hung, N. Q., M. S. Babel, S. Weesakul, and N. K. Tripathi. "An artificial neural network model for rainfall forecasting in Bangkok, Thailand." Hydrology and Earth System Sciences 13, no. 8 (August 7, 2009): 1413–25. http://dx.doi.org/10.5194/hess-13-1413-2009.

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Abstract. This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.
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Dissertations / Theses on the topic "Rail temperature forecast"

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(13966684), Ying M. Wu. "Development of rail temperature prediction model and software." Thesis, 2011. https://figshare.com/articles/thesis/Development_of_rail_temperature_prediction_model_and_software/21344169.

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The railway track buckling occurs all over the world due to inadequate rail stress adjustment, which is greatly influenced by the variation in weather induced rail temperature and the rigidity of the track structure. Climate change and the ever increase in extreme changes in temperatures have made buckling an ever more prevalent problem in the railway industry. The ultimate goal of any research in the area of track stability management is to comprehensively manage rail buckling and the subsequent procedures that follow after buckling. The first step to have a clear understanding of how the temperature change of the rail track is influenced by the environmental conditions. The second step is to have an accurate prediction of what the environmental conditions will be in the next day so that management procedure can be put into place.

This study aims to develop a model and software that is capable of predicting rail temperature 24 hours in advance that is as accurate for use in the rail buckling management. Two distinct and separate mathematical manipulations are performed to achieve this goal.

One method used weather forecasts from the Australian Bureau of Meteorology (BoM) and forecasts the weather for the location that the rail is situated. This involves using 3-dimensional cubic interpolation that is the weather parameters are interpolated in 2-dimensions geographically and then 1-dimensionally through time. An interactive software is written in MATLAB to convert the BoM raw data into a rail temperature forecast for this study. The result is a 15 -minute forecast for every 3.06 km. The second method used multivariate linear regression, to predict the rail temperatures 24 to 48 hours in advance.

To validate the rail temperature predications, 3 months field test spanning June, July and August 2010, is conducted on Queensland Rail's (QR) coal network, this involved erecting an automated weather station (AWS) and adhering temperature sensors on to a section of track. The guidelines of World Meteorological Organization's (WMO) were followed for implementation of the AWS on site (WMO 2008). The AWS model WXT520 , produced by Vaisala (Vaisala 2009) was used in this study which an off the shelf product that is similar to what some rail compaies are already using for continues monitoring of critical sites.

The temperature sensors (surface thermocouples) and an off the shelf product Salient system's rail -stress modules are used to measure rail surface temperatures on both rails of the track (Salient Systems Inc 2009). The sensors were attached to the surface of the rail track to directly measure temperature change of the rail profile throughout the diurnal cycle. Statistical correlations between the different measured points of the rail profile are evaluated in relation to the diurnal cycle to assess the accuracy of current rail temperature measuring practices.

Statistical evaluation of how well the BoM predictions compare with weather parameters at the field experimentation site are performed, so too is a statistical evaluation of the accuracy of the rail temperature model developed. The prediction model is compared with the existing empirical methods as found in the literature review and an assessment of track conditions. This is a flag ship study in Australia; the main purpose of this study is to prove in a test case scenario that a rail temperature forecast without use of weather instrumentation is possible and the accuracy of the prediction is as good if not better than the instrumentation calculation.

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Book chapters on the topic "Rail temperature forecast"

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Bianchi, Thomas S. "Hydrodynamics." In Biogeochemistry of Estuaries. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195160826.003.0009.

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The hydrologic cycle has received considerable attention in recent years with particular interest in the dynamics of land–atmosphere exchanges as it relates to global climate change and the need for more accurate numbers in global circulation models (GCMs). Recent advance in remote sensing and operational weather forecasts have significantly improved the ability to monitor the hydrologic cycle over broad regions (Vörösmarty and Peterson, 2000). The application of hydrologic models in understanding interactions between the watersheds and estuaries is critical when examining seasonal changes in the biogeochemical cycles of estuaries. Water is the most abundant substance on the Earth’s surface with liquid water covering approximately 70% of the Earth. Most of the water (96%) in the reservoir on the Earth’s surface is in the global ocean. The remaining water, predominantly stored in the form of ice in polar regions, is distributed throughout the continents and atmosphere—estuaries represent a very small fraction of this total reservoir as a subcomponent of rivers. Water is moving continuously through these reservoirs. For example, there is a greater amount of evaporation than precipitation over the oceans; this imbalance is compensated by inputs from continental runoff. The most prolific surface runoff to the oceans is from rivers which discharge approximately 37,500 km3 y−1 (Shiklomanov and Sokolov, 1983). The 10 most significant rivers, in rank of water discharge, account for approximately 30% of the total discharge to the oceans (Milliman and Meade, 1983; Meade, 1996). The most significant source of evaporation to the global hydrologic cycle occurs over the oceans; this occurs nonuniformly and is well correlated with latitudinal gradients of incident radiation and temperature. The flow of water from the atmosphere to the ocean and continents occurs in the form of rain, snow, and ice. Average turnover times of water in these reservoirs can range from 2640 y in the oceans to 8.2 d (days) in the atmosphere (Henshaw et al., 2000; table 3.1). The aqueous constituents of organic materials, such as overall biomass, have an even shorter turnover time (5.3 d). These differences in turnover rate are critical in controlling rates of biogeochemical processes in aquatic systems.
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Conference papers on the topic "Rail temperature forecast"

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Bruzek, Radim, Larry Biess, and Leith Al-Nazer. "Development of Rail Temperature Predictions to Minimize Risk of Track Buckle Derailments." In 2013 Joint Rail Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/jrc2013-2451.

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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.
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Bruzek, Radim, Larry Biess, Leopold Kreisel, and Leith Al-Nazer. "Rail Temperature Prediction Model and Heat Slow Order Management." In 2014 Joint Rail Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/jrc2014-3767.

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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.
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Girardi, L., D. Boulanger, E. Laurans, P. Pouligny, Y. Xu, and J. Colibri. "Rail temperature forecasts over different time-ranges for track applications." In 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011). IET, 2011. http://dx.doi.org/10.1049/cp.2011.0609.

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Barbosa, Fábio C. "Battery Electric Rail Technology Review - A Technical and Operational Assessment. Current Status, Challenges and Perspectives." In 2022 Joint Rail Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/jrc2022-78133.

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Abstract The transport industry is currently responsible for around a quarter of GHG emissions worldwide, with a forecasted increased share for the next coming years. The rail industry is one of the most energy efficient and less polluting transport mode. In European Union (E.U.), for instance, the rail sector currently accounts for 1.5% of E.U. transport GHG emissions, with over 8.5% of total market share, while on the United States (U.S.), it accounts for 1.9% of transport GHG emissions (0.6% for freight railroads, with a share of around 40% of long distance freight volumes, in a ton-mile basis). Despite this remarkable environmental and energetic performance, the rail sector is also strongly committed with the compromise of a continuous reduction of its specific GHG emissions, for both the passenger (passenger-km) and freight (tone-km) services, to comply with the world effort, to keep the overall increase in average global temperature below 2 °C, compared to pre-industrial levels. One of the main strategies to reach these ambitious targets is to increase the rail electrification share, with the massive use of electric powertrains, with their inherent improved efficiency and zero local emission performance, compared to the internal combustion engines powered counterparts. The conventional rail electrification, based on a third-rail or overhead line, might be cost and environmental effective for city and inter-city intensively used routes. However, secondary lines and regional routes, lack the required traffic density for conventional electrification, given the high infrastructure costs required. In this context, alternative rail electrification routes, such as the use of Battery Electric Rail (BER) powertrains, have been seen as a promising electrification alternative to comply with rail industry compromises, to reduce its GHG share and efforts to improve energy efficiency, especially in the low density traffic rail segments. It is noteworthy that alternative rail powertrains have launched the debate around traction technology selection for rail fleet renewal, given the average 30 year lifetime of rail equipment, which ultimately require immediate policies and actions to guarantee the long term rail environmental and efficiency targets. In the E.U., BER has been evaluated as alternatives for non electrified low density rail segments (mainly for inter-regional passenger transport), with extensions in the 40–80 km range, given the medium term strategic decision to ban the diesel powertrains, currently used on Diesel Multiple Units (DMU). In the rail freight segment, there are some U.S. preliminary BER initiatives, focused on both shunt/switch locomotives (given their low power and range requirements, compared to road locomotives), and even line-haul locomotives, the later with a limited potential, given their large power and range requirements. However, prior to the widespread use of BER, there are some challenges to be taken up, such as battery technologies improvement (basically the battery chemistry, to meet the harsh rail operational requirements), charging infrastructure, sustainable electricity cost and availability, as well as operational impacts (such as the required charging intervals and the use of battery tender cars on rail productivity). This work presents an unbiased assessment of battery rail technology (for both the passenger and freight sectors), based on the state of the art technical sources, with a focus on battery technology and infrastructure topics, weighted against rail operational requirements. Finally, there are presented some case studies, with BER experiences and prototypes, showing preliminary BER outcomes, followed by the technological and operational challenges to be faced, prior to its commercial use in specific rail niche segments.
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