Quantifying the Effect of Weather on Advanced Air Mobility Operations

We quantify and analyze the potential number of flyable hours for an advanced air mobility (AAM) vehicle over the contiguous United States. We use Meteorological Aerodrome Reports (METARs) from 2019, covering 91 airports in the US. By filtering the METARs based on Federal Aviation Administration mandated flight conditions and the vehicle’s physical capabilities, our analysis shows nearly double the amount of annual acceptable flying time between the most flyable and least flyable locations in the country and identifies the largest cause of non-flyable hours as cloud cover. Our work can be used to understand the viability of AAM vehicles in a geographic location

In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery

We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, are varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones.Comment: 13 pages, 11 figures, submitted to Scientific Dat

Energy and the Military: Leading by Example

Economic and national security require reliable sources of energy, and militaries lead with innovations that quickly find civilian purposes. Energy plays a role in all facets of war, and innovations boost security, explain Constantine Samaras, Morgan D. Bazilian and William J. Nuttall. Britain had an advantage during World War I after its fleet converted from Welsh coal to foreign oil, increasing speed while minimizing logistical burdens and smoky trails that revealed positions. A century later, the United States relies on portable solar-powered systems to reduce reliance on diesel fuel in Afghanistan and flexible microgrids. The writers conclude, “The world is on the cusp of an energy transition, with dramatically shifting technology priorities.” National security requires developing renewable energy as well as addressing energy efficiency and climate change. – YaleGloba

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Abstract Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change

Fuel Economy Testing of Autonomous Vehicles

<p>Environmental pollution and energy use in the light-duty transportation sector are currently regulated through fuel economy and emissions standards, which typically assess quantity of pollutants emitted and volume of fuel used per distance driven. In the United States, fuel economy testing consists of a vehicle on a treadmill, while a trained driver follows a fixed drive cycle. By design, the current standardized fuel economy testing system neglects differences in how individuals drive their vehicles on the road. As autonomous vehicle (AV) technology is introduced, more aspects of driving are shifted into functions of decisions made by the vehicle, rather than the human driver. Yet the current fuel economy testing procedure does not have a mechanism to evaluate the impacts of AV technology on fuel economy ratings, and subsequent regulations such as Corporate Average Fuel Economy targets. This paper develops a method to incorporate the impacts of AV technology within the bounds of current fuel economy test, and simulates a range of automated following drive cycles to estimate changes in fuel economy. The results show that AV following algorithms designed without considering efficiency can degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results, by up to 10%. This suggests the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles. As AV technology improves and adoption increases in the future, a further reimagining of drive cycles and testing is required.</p

Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves

To improve the resiliency of designs, particularly for long-lived infrastructure, current engineering practice must be updated to incorporate a range of future climate conditions that are likely to be different from the past. However, a considerable mismatch exists between climate model outputs and the data inputs needed for engineering designs. The present work provides a framework for incorporating climate trends into design standards and applications, including: selecting the appropriate climate model source based on the intended application, understanding model performance and uncertainties, addressing differences in temporal and spatial scales, and interpreting results for engineering design. The framework is illustrated through an application to depth-duration-frequency curves, which are commonly used in stormwater design. A change factor method is used to update the curves in a case study of Pittsburgh, PA. Extreme precipitation depth is expected to increase in the future for Pittsburgh for all return periods and durations examined, requiring revised standards and designs. Doubling the return period and using historical, stationary values may enable adequate design for short duration storms; however, this method is shown to be insufficient to enable protective designs for larger duration storms

Decarbonizing US passenger vehicle transport under electrification and automation uncertainty has a travel budget

The transportation sector is at the beginning of a transition represented by electrification, shared mobility, and automation, which could lead to either increases or decreases in total travel and energy use. Understanding the factors enabling deep decarbonization of the passenger vehicle sector is essential for planning the required infrastructure investments and technology adoption policies. We examine the requirements for meeting carbon reduction targets of 80% and higher for passenger vehicle transport in the United States (US) by midcentury under uncertainty. We model the changes needed in vehicle electrification, electricity carbon intensity, and travel demand. Since growth in fleet penetration of electric vehicles (EVs) is constrained by fleet stock turnover, we estimate the EV penetration rates needed to meet climate targets. We find for a base case level of passenger vehicle travel, midcentury deep decarbonization of US passenger transport is conditional on reducing the electricity generation carbon intensity to close to zero along with electrification of about 67% or 84% of vehicle travel to meet decarbonization targets of 80% or 90%, respectively. Higher electricity generation carbon intensity and degraded EV fuel economy due to automation would require higher levels of fleet electrification and/or further constrain the total vehicle travel allowable. Transportation deep decarbonization not only depends on electricity decarbonization, but also has a total travel budget, representing a maximum total vehicle travel threshold that still enables meeting a midcentury climate target. This makes encouraging ride sharing, reducing total vehicle travel, and increasing fuel economy in both human-driven and future automated vehicles increasingly important to deep decarbonization