The Future Automotive Systems Technology Simulator (FASTSim) is a high-level advanced vehicle powertrain systems analysis tool supported by the U.S. Department of Energy's Vehicle Technologies Office. FASTSim provides a quick and simple approach to compare powertrains and estimate the impact of technology improvements on light-and heavy-duty vehicle efficiency, performance, cost, and battery life. The input data for most light-duty vehicles can be automatically imported. Those inputs can be modified to represent variations of the vehicle or powertrain. The vehicle and its components are then simulated through speed-versus-time drive cycles. At each time step, FASTSim accounts for drag, acceleration, ascent, rolling resistance, each powertrain component's efficiency and power limits, and regenerative braking. Conventional vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, all-electric vehicles, compressed natural gas vehicles, and fuel cell vehicles are included. Powertrains with electric-traction drive can optionally be simulated using electric roadway technologies such as dynamic wireless power transfer. FASTSim also has an interface for running large batches of real-world drive cycles. FASTSim's calculation framework and balance among detail, accuracy, and speed enable it to simulate thousands of driven miles in minutes. The key components and vehicle outputs have been validated by comparing the model outputs to test data for many different vehicles to provide confidence in the results. A graphical user interface makes FASTSim easy and efficient to use. FASTSim is freely available for download from the National Renewable Energy Laboratory's website (see www.nrel.gov/fastsim). Powertrain Components FASTSim captures the key inputs for most high-level vehicle powertrain modeling. They include parameters that define the vehicle, including the fuel storage, fuel converter, motor, traction battery, wheel, and energy management strategy.

The Automotive Deployment Options Projection Tool (ADOPT) is a light-duty vehicle consumer choice and stock model supported by the U.S. Department of Energy's Vehicle Technologies Office. It estimates technology improvement impacts on future U.S. light-duty vehicles sales, petroleum use, and greenhouse gas emissions. ADOPT uses techniques from the multinomial logit method and the mixed logit method to estimate vehicle sales. Specifically, it estimate sales based on the weighted value of key attributes including vehicle price, fuel cost, acceleration, range and usable volume. The average importance of several attributes changes nonlinearly across its range and changes with income. For several attributes, a distribution of importance around the average value is used to represent consumer heterogeneity. The majority of existing vehicle makes, models, and trims are included to fully represent the market. The Corporate Average Fuel Economy regulations are enforced. The sales feed into the ADOPT stock model. It captures key aspects for summing petroleum use and greenhouse gas emissions. This includes capturing the change in vehicle miles traveled by vehicle age, the creation of new model options based on the success of existing vehicles, new vehicle option introduction rate limits, and survival rates by vehicle age. ADOPT has been extensively validated with historical sales data. It matches in key dimensions including sales by fuel economy, acceleration, price, vehicle size class, and powertrain across multiple years. A graphical user interface provides easy and efficient use. It manages the inputs, simulation, and results.

Nocturnal human sleep is composed of cycles between rapid eye movement (REM) sleep and non-REM (NREM) sleep. In adults, the structure of ultradian cycles between NREM and REM sleep is well characterized; however, less is known about the developmental trajectories of ultradian sleep cycles across early childhood. Cross-sectional studies indicate that the rapid ultradian cycling of active-quiet sleep in infancy shifts to a more adult-like pattern of NREM-REM sleep cycling by the school-age years, yet longitudinal studies elucidating the details of this transition are scarce. To address this gap, we examined ultradian cycling during nocturnal sleep following 13 h of prior wakefulness in 8 healthy children at 3 longitudinal points: 2Y (2.5-3.0 years of age), 3Y (3.5-4.0 years of age), and 5Y (5.5-6.0 years of age). We found that the length of ultradian cycles increased with age as a result of increased NREM sleep episode duration. In addition, we observed a significant decrease in the number of NREM sleep episodes as well as a nonsignificant trend for a decrease in the number of cycles with increasing age. Together, these findings suggest a concurrent change in which cycle duration increases and the number of cycles decreases across development. We also found that, consistent with data from adolescents and adults, the duration of NREM sleep episodes decreased with time since lights-off whereas the duration of REM sleep episodes increased over this time period. These results indicate the presence of circadian modulation of nocturnal sleep in preschool children. In addition to characterizing changes in ultradian cycling in healthy children ages 2 to 5 years, this work describes a developmental model that may provide insights into the emergence of normal adult REM sleep regulatory circuitry as well as potential trajectories of dysregulated ultradian cycles such as those associated with affective disorders.

The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software.

Commercial vehicle fuel economy is known to vary significantly with both positive and negative road grade. Medium-and heavy-duty vehicles operating at highway speeds require incrementally larger amounts of energy to pull heavy payloads up inclines as road grade increases. Non-hybrid vehicles are unable to recapture energy on descent and lose energy through friction braking. While the on-road effects of road grade are well understood, the majority of standard commercial vehicle drive cycles feature no road grade requirements. Additionally, the existing literature offers a limited number of sources that attempt to estimate the on-road energy implications of road grade in the medium-and heavy-duty space. This study uses real-world commercial vehicle drive cycles from the National Renewable Energy Laboratory's Fleet DNA database to simulate the effects of road grade on fuel economy across a range of vocations, operating conditions, and locations. Road grade data is appended to real-world drive cycles using the United States Geological Survey's 1/3 arc-second digital elevation model. Realworld drive cycles are then paired with vocation-specific vehicle models and simulated with and without grade. Percentage fuel use increase due to grade is presented, and variation in fuel consumption due to drive cycle and vehicle characteristics is explored through graphical and statistical comparison. The results of this study suggest that road grade accounts for 1%-9% of fuel use in commercial vehicles on average and up to 40% on select real-world drive cycles.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.