Mehdi Noori

A macro-level decision analysis of wind power as a solution for sustainable energy in the USA

This study aims to quantify the socio-economic and environmental impacts of producing electricity by wind power plants for the US electricity mix. To accomplish this goal, all direct and supply chain-related impacts of different onshore and offshore wind turbines are quantified using a hybrid economic input-output-based triple bottom line (TBL) life cycle assessment model. Furthermore, considering TBL sustainability implications of each onshore and offshore wind energy technology, a multi-criteria decision-making tool which is coupled with Monte Carlo simulation is utilised to find the optimal choice of onshore and offshore wind energy. The analysis results indicate that V90-3.0 MW wind turbines have lower impacts than V80-3.0 MW for both socio-economic and environmental indicators. The Monte Carlo simulation results reveal that when environmental issues are more important than socio-economic impacts, V90-3.0 MW offshore is selected among the alternatives.

Economic Input–Output Based Sustainability Analysis of Onshore and Offshore Wind Energy Systems

According to the U.S. Department of Energy’s wind energy scenario, 20% share of the U.S. energy portfolio is to come in from wind power plants by the year 2030. This research aims to quantify the direct and supply chain related indirect environmental impacts of onshore and offshore wind energy technologies in the United States. To accomplish this goal, a hybrid life cycle assessment (LCA) model is developed. On average, offshore wind turbines produce 48% less greenhouse gas emissions per kWh produced electricity than onshore wind turbines. It is also found that the more the capacity of the wind turbine, the less the environmental impact when the turbine generates per kWh electricity.

Evaluation of Conditional Transit Signal Priority Technology for Regional Implementation

This research evaluated the implementation of transit signal priority (TSP) on a test corridor along International Drive in Orlando, Florida, to see whether the implementation was successful and justified expansion to a regional implementation of TSP for bus tie-ins to the new regional SunRail commuter rail in Central Florida. TSP is a technology that provides preferential treatment to buses at signalized intersections. This research demonstrated the effectiveness of TSP in improving bus corridor travel time in a simulated environment by using real-world data for the International Drive corridor. Evaluation was conducted with microsimulation to compare unconditional and conditional TSP with the no TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles), including average speed profiles, average travel times, average number of stops, and crossing street delay. Different conditional TSP scenarios of activating TSP when a bus is 3 or 5 min behind schedule were considered. The simulation demonstrated that conditional TSP significantly improved bus travel times with little effect on crossing street delays. Unconditional TSP resulted in significant crossing street delays at some intersections with only minor improvement to bus travel time compared with both conditional TSP scenarios. The results also showed that using TSP technology reduced the environmental emissions in the International Drive corridor. With a benefit–cost ratio of 7.92 in the International Drive corridor, conditional TSP 3 min behind schedule was determined to be the most beneficial and practical TSP scenario for real-world implementation at corridor and regional levels.

Environmental footprint analysis of on-shore and off-shore wind energy technologies

Wind energy technologies have gained a tremendous interest worldwide to mitigate the environmental impacts related to power generation. In this study, we assess on-shore and off-shore by tracing all of the economy-wide supply chain requirements of wind power plants. The wind turbines consist of two onshore (V80-2.0 MW and V90-3.0 MW) and two offshore (V80-2.0 MW and V90-3.0 MW) turbines, all manufactured by the Vestas Wind Systems A/S.

An optimum selection strategy of reflective cracking mitigation methods for an asphalt concrete overlay over flexible pavements

Abstract Reflective Cracking (RC) has been a daunting challenge in pavement maintenance and rehabilitation (M&R), yet, still, after several decades of research, no exclusive solution prevails. Moreover, RC mitigation methods have shown significant variation in in situ performance. Therefore, a technique tailored to select an effective RC mitigation method is essential for the success of pavement M&R. In this study, a life cycle cost (LCC) and multi-criteria decision-making (MCDM) analyses were conducted to evaluate the effectiveness of currently available RC mitigation methods and to select the optimal method for an asphalt concrete overlay above flexible pavements. The MCDM includes three components: LCC, performance, and materials (recyclability). These criteria determine the selection ranking of each RC mitigation method. In addition, the effects of the priority level including cost, performance, and recyclability on the final decision were evaluated by conducting a series of sensitivity analysis under multiple scenarios; therefore, weight combination of the three criteria were recorded to define the measurements affecting the final decision.