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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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DOE OSTI2021

Water Resource Opportunities at Lake Gazivode/Ujmani

The U.S. Department of Energy’s (DOE’s) Pacific Northwest National Laboratory (PNNL) has been tasked by DOE’s Office of International Affairs to assess the use of water resources for power generation needs on Lake Gazivode/Ujmani in Kosovo, and provide recommendations for improved coordination and efficiency. Lake Gazivode/Ujmani is a 15-mile long man-made reservoir that straddles the Serbian-Kosovo border. Lake Gazivode/Ujmani is currently managed without a transboundary cooperation agreement. Kosovo is profoundly dependent on the lake’s waters, which provide one-third of Kosovo’s drinking water and cool two coal plants that provide 95 percent of Kosovo’s energy production. After conducting a scoping visit to Pristina, Kosovo, Lake Gazivode/Ujmani, and Belgrade, Serbia, in October 2020, PNNL staff compiled hydrometeorological, water management operations, and power grid operations data. They analyzed the data and existing literature to provide third-party observations about and recommendations for the use of the lake. Their recommendations aim to promote regional water and energy security.

13 HYDRO ENERGY↗
DOE OSTI2021

Stove Solutions: Improving Health, Safety, and the Environment in Darfur with Fuel-Efficient Cookstoves

Nearly three billion people across the globe cook every day using open, three-stone fires, or rudimentary stoves that burn biomass such as wood, agricultural waste, animal dung, and charcoal. Cooking with these traditional cookstoves is inefficient and grossly polluting, harming health and the environment, and contributing to global warming. In many places worldwide, women must walk for hours to collect firewood, risking their safety and sacrificing energy and time that could be used to earn a living. While often overlooked as a major contributor to the global burden of disease, cooking over open fires indoors is the largest environmental health risk in developing countries, and exposes women (and the young children near them) to amounts of smoke equivalent to burning 1,000 cigarettes inside the home. In Darfur, Sudan, where about 2.7 million people have been displaced from their homes by conflict, the situation is particularly dire. Each day, Darfuri women face the difficult choice between risking sexual assault during treks to collect firewood or selling a portion of their family’s meager food rations for cash to purchase wood. Here, the Berkeley-Darfur Stove, developed by scientists at the Lawrence Berkeley National Laboratory (LBNL) and volunteers from UC Berkeley and Engineers Without Borders, is a metal stove that reduces the need for firewood by more than half, owing to its improved combustion and heat transfer efficiencies. LBNL has partnered with a nonprofit organization, Potential Energy, and a number of aid organizations to disseminate more than 22,000 Berkeley-Darfur Stoves to Darfuri women. Several hundred thousand more are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Energy Snapshot - Belize

This profile provides a snapshot of the energy landscape of Belize, a Central American country bordering Mexico to the north, Guatemala to the west and south, and the Caribbean Sea to the east. Although not an island nation, Belize is included in this energy snapshot series because it is a member of the Caribbean Community (CARICOM), an alliance of 15 Caribbean nations in the region.

44 EE - Strategic Programs, Energy Transition Init↗
DOE OSTI2021

Report on Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods

The emergence of artificial intelligence (AI) and machine learning (ML) in the modern world has impacted nearly every application imaginable. This includes nuclear proliferation detection, which offers the potential to improve existing capabilities as well as create new ones. Proliferation detection seeks to detect and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology, materials, or knowledge. Such a mission is vitally important for global stability and security but is notoriously difficult. By leveraging advances in AI, exciting opportunities exist to enhance the proliferation detection regime. The Data Science and AI portfolio within the National Nuclear Security Administration’s Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) seeks to leverage the capabilities of the Department of Energy’s (DOE’s) national laboratories and other partners to develop AI systems that can accomplish otherwise impossible tasks in support of proliferation detection. As part of its efforts, the portfolio has created a series of workshops on Next-Gen AI for Proliferation Detection to help define the requirements for suitable AI systems, share successful research and best practices, and foster connection and understanding between the relevant parties including researchers and end-users. Each workshop in the series focuses on a specific and critical aspect of AI to enable it to accomplish proliferation detection objectives. The first workshop focused on explainability techniques; the second workshop and the topic of this report, covers methods for incorporating domain awareness into AI. The Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods took place virtually over two days in February 2021 and included four keynote presentations, 22 technical presentations, and a concluding panel. The presentations, discussions, and workshop findings are summarized in this report.

97 MATHEMATICS AND COMPUTING↗