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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Grand Teton National Park Federal Fleet Tiger Team EVSE Site Assessment

The U.S. Department of Energy Federal Energy Management Program (FEMP) helps federal agencies reduce petroleum consumption and increase alternative fuel use through its resources for sustainable federal fleets. A key element of this assistance involves supporting agencies in the transition to zero-emission vehicles (ZEVs). Fleet electrification is part of a federal policy to achieve net-zero emissions economy-wide and a carbon pollution-free electricity sector, established through two executive orders (EOs) - EO 14008: Tackling the Climate Crisis at Home and Abroad and EO 14057: Catalyzing America's Clean Energy Industries and Jobs through Federal Sustainability. This site report supports the development of a ZEV deployment plan for the Grand Teton National Park (GRTE) that can ultimately be incorporated into the overall Department of the Interior ZEV fleet strategy.

33 ADVANCED PROPULSION SYSTEMS↗

Nonlinear Elimination Applied to Radiation Diffusion

We apply a nonlinearly preconditioned, quasi-Newton framework to accelerate the numerical solution of the thermal radiative transfer (TRT) equations. This framework was inspired by the unpublished method that has existed for years in Teton, Lawrence Livermore National Laboratory’s deterministic TRT code. In this paper, we cast this iteration scheme within a formal nonlinear preconditioning framework and compare its performance against other iteration schemes in the framework. With proper choices of iteration controls for the various levels of the solver, we can recover the standard linearized one-step method, a full nonlinear Newton scheme, as well as the method in Teton. In brief, the nonlinear preconditioning TRT scheme formally eliminates the material temperature equation from the nonlinear system in a nonlinear analog of a Schur complement. This nonlinear elimination step involves solving a decoupled nonlinear equation for each spatial degree of freedom and is therefore inexpensive. By applying a quasi-Newton iteration scheme on the new system, we obtain a three-level iteration scheme that is at least as efficient as commonly used TRT schemes. The new method allows full convergence to the nonlinear backward Euler time-discretized system, increasing accuracy and robustness, while using a similar number of linear iterations as the more common linearized one-step methods Eq. (4).

77 NANOSCIENCE AND NANOTECHNOLOGY↗

UMT

UMT is a Sn deterministic thermal radiative transport Fortran proxy application. Its primary purpose is to research how data layout, programming paradigms and architectures effect the implementation and performance of Sn transport in Fortran on unstructured meshes. It is based on the LLNL Teton Sn deterministic thermal radiative transport code.

Yee, BenC.↗

Request for Funding for the 2016 Air & Waste Management Association Visibility Conference on Atmospheric Optics:Aerosols, Visibility, and the Radiative Balance

This international conference will provide a technical forum on advances in the scientific understanding of the effects of aerosols on urban, regional, continental, and global-scale haze and the radiative balance. The conference will take a multipronged approach and address scientific topics (e.g., related to measurements, modeling, etc.) as well as regulatory and policy issues. There will be sessions on black and brown carbon, as recent research has shown the importance of these particles for radiative forcing. In addition, there will be sessions related to the synergistic and increasing concerns of the effects of atmospheric nitrogen and carbonaceous material on haze, climate change, and nitrogen deposition on ecosystems. Conference learning will be enhanced with a half day excursion and hikes in Grand Teton National Park and a Night Sky Program.

54 ENVIRONMENTAL SCIENCES↗

Economics of Solar PV and Stationary Storage for Electric Bus Charging in Missoula, Montana [Slides]

This analysis was conducted under the first round of the Solar Energy Innovation Network (SEIN), a program led by the National Renewable Energy Laboratory (NREL) that assembles diverse teams of stakeholders to research solutions to real-world challenges associated with solar energy adoption. This analysis supported the efforts of the Montana Solar Powered Community Transportation Initiative, a team consisting of the Montana Renewable Energy Association, Montana Energy Office at the Department of Environmental Quality, City of Missoula, City of Bozeman, City of Whitefish, Climate Smart Missoula, and Yellowstone-Teton Clean Cities. The team is investigating the synergies between solar generation and electric vehicle (EV) charging, including the potential opportunities and challenges related to co-locating solar energy and charging infrastructure to reduce costs and enhance co-benefits of the electrification of transportation. The City of Missoula's transit agency has purchased six electric buses. This analysis supports the team's efforts to understand the potential costs and specific options for using solar and storage to reduce EV bus charging costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Mixing effects on geothermometric calculations of the Newdale geothermal area in the Eastern Snake River Plain, Idaho

The Newdale geothermal area in Madison and Fremont Counties in Idaho is a known geothermal resource area whose thermal anomaly is expressed by high thermal gradients and numerous wells producing warm water (up to 51 °C). Geologically, the Newdale geothermal area is located within the Eastern Snake River Plain (ESRP) that has a time-transgressive history of sustained volcanic activities associated with the passage of Yellowstone Hotspot from the southwestern part of Idaho to its current position underneath Yellowstone National Park in Wyoming. Locally, the Newdale geothermal area is located within an area that was subjected to several overlapping and nested caldera complexes. The Tertiary caldera forming volcanic activities and associated rocks have been buried underneath Quaternary flood basalts and felsic volcanic rocks. Two southeast dipping young faults (Teton dam fault and an unnamed fault) in the area provide the structural control for this localized thermal anomaly zone. Geochemically, water samples from numerous wells in the area can be divided into two broad groups – Na-HCO3 and Ca-(Mg)-HCO3 type waters and are considered to be the product of water-rhyolite and water-basalt interactions, respectively. Each type of water can further be subdivided into two groups depending on their degree of mixing with other water types or interaction with other rocks. For example, some bivariate plots indicate that some Ca-(Mg)-HCO3 water samples have interacted only with basalts whereas some samples of this water type also show limited interaction with rhyolite or mixing with Na-HCO3 type water. Traditional geothermometers [e.g., silica variants, Na-K-Ca (Mg-corrected)] indicate lower temperatures for this area; however, a traditional silica-enthalpy mixing model results in higher reservoir temperatures. We applied a new multicomponent equilibrium geothermometry tool (e.g., Reservoir Temperature Estimator, RTEst) that is based on inverse geochemical modeling which explicitly accounts for boiling, mixing, and CO2 degassing. RTEst modeling results indicate that the well water samples are mixed with up to 75% of the near surface groundwater. Relatively, Ca-(Mg)-HCO3 type water samples are more diluted than the Na-HCO3 type water samples. However, both water types result in similar reservoir temperatures, up to 150 °C. Samples in the vicinity of faults produced higher reservoir temperatures than samples away from the faults. Although both the silica-enthalpy mixing and RTEst models indicated promising geothermal reservoir temperatures, evaluation of the subsurface permeability and extent of the thermal anomaly is needed to define the hydrothermal potential of the Newdale geothermal resource.

Eastern Snake River Plain, Newdale, RTEst, geother↗

Machine learning to classify animal species in camera trap images: Applications in ecology

Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an r package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our r package makes these methods accessible to ecologists.

54 ENVIRONMENTAL SCIENCES↗