Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.

/pdf/help-or-hindrance-the-travel-energy-and-carbon-impacts-of-4552l0f6o0.pdf

Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles

Wind energy is the fastest growing alternate source of energy in the world since its purely economic potential is complemented by its great positive environmental impact. The wind turbine, whether it may be a Horizontal-Axis Wind Turbine (HAWT) or a Vertical-Axis Wind Turbine (VAWT), offers a practical way to convert the wind energy into electrical or mechanical energy. Although this book focuses on the aerodynamic design and performance of VAWTs based on the Darrieus concept, it also discusses the comparison between HAWTs and VAWTs, future trends in design and the inherent socio-economic and environmental friendly aspects of wind energy as an alternate source of energy.

Wind turbine design with emphasis on Darrieus concept Ion Paraschivoiu

The aerodynamic characteristics of train/vehicles under cross winds depend on not only the shapes of the vehicles but also those of infrastructures. Accordingly three kinds of wind tunnel tests were made to evaluate the aerodynamic characteristics of typical configurations of the vehicles on typical configurations of infrastructures such as bridges and embankments. The main results obtained from the wind tunnel tests are summarized below. 1. The aerodynamic side force coefficient of the vehicle increases more as the thickness of the bridge girder becomes larger. It also increases more as the roof of the vehicle becomes edgier. 2. The aerodynamic characteristics of the vehicle on the embankment depend on the distribution of the boundary layer on the ground. The aerodynamic side force coefficient of the vehicle on a high embankment is larger than that on a low embankment.

Aerodynamic Characteristics of Train/Vehicles under Cross Winds

Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes Here, we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in tra

A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from [Formula: see text] of the global energy mix in 2018 to [Formula: see text] by 2050 to keep global temperatures from rising 1.5°C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to [Formula: see text] for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant ([Formula: see text]) power increases of 7-[Formula: see text] for wind speeds near the site average and wind directions which occur during less than [Formula: see text] of nocturnal operation and 28-[Formula: see text] for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to [Formula: see text] Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.

https://www.pnas.org/content/pnas/116/29/14495.full.pdf

Paolo Schito

Papers

CFD study of Savonius wind turbine: 3D model validation and parametric analysis

Comparison between steady and moving railway vehicles subjected to crosswind by CFD analysis

Cross wind and rollover risk on lightweight railway vehicles

Numerical and Experimental Investigation on Vehicles in Platoon

Parametric numerical study of Savonius wind turbine interaction in a linear array