Capacity optimization of a prestressed concrete railroad tie

Today the use of concrete ties is on the rise in North America as they become an economically competitive alternative to the historical industry standard wood ties, while providing performance which exceeds its competition in terms of durability and capacity. Similarly, in response to rising energy costs, there is increased demand for efficient and sustainable transportation of people and goods. One source of such transportation is the railroad. To accommodate the increased demand, railroads are constructing new track and upgrading existing track. This update to the track system will increase its capacity while making it a more reliable means of transportation compared to other alternatives. In addition to increasing the track system capacity, railroads are considering an increase in the size of the typical freight rail car to allow larger tonnage. An increase in rail car loads will in turn affect the performance requirements of the track. Due to the increased loads heavy haul railroads are considering applying to their tracks, current designs of prestressed concrete railroad ties for heavy haul applications may be undersized. In an effort to maximize tie capacity while maintaining tie geometry, fastening systems and installation equipment, a parametric study to optimize the existing designs was completed. The optimization focused on maximizing the capacity of an existing tie design through an investigation of prestressing quantity, configuration, stress levels and other material properties. The results of the parametric optimization indicate that the capacity of an existing tie can be increased most efficiently by increasing the diameter of the prestressing and concrete strength. However, researchers also found that current design specifications and procedures do not include consideration of tie behavior beyond the current tie capacity limit of cracking to the first layer of prestressing. In addition to limiting analysis to the cracking limit, failure mechanisms such as shear in deep beams at the rail seat or pullout failure of the prestressing due to lack of development length were absent from specified design procedures, but discussed in this project.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.