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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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On June 30, 2020, a 0.87 gram PETN charge being pressed in the Rapid Prototyping Facility (RPF), unexpectedly initiated, resulting in destruction of the pressing fixture but no injuries or facility damage. In response, the Safety Review Board (SRB) met on Aug. 13, 2020 and Oct. 1, 2020 to review information collected following the incident, consider likely direct causes, and form recommendations.
A collaborative literature review between Idaho National Laboratory (INL) and Pacific National Laboratory (PNNL) was conducted to research alternative metals/materials for xenon adsorption in gaseous effluents generated from used nuclear fuel (UNF) reprocessing. The review was conducted to research sorbents that could outperform silver mordenite (AgZ) at ambient temperature. The use of silver requires complex and expensive waste disposal processes due to it being an RCRA metal, therefore alternative materials need to be researched and evaluated. The report details two different types of sorbent material: alternative zeolite materials and non-zeolite-based materials (i.e., MOFs, COFs, etc.). This report documents a brief history of the materials, what testing has been conducted on them, and what results suggest the possibility of outperforming the silver-based materials. Sorbent performance characteristics such as durability, capacity, and operating temperatures were evaluated. The simplicity of the sorbents was also evaluated, meaning how they are acquired (synthesized vs. naturally abundant). Recommendations were made for the most promising sorbents based on available research in the subject area and these recommended sorbents will be tested in the upcoming fiscal year. The recommended sorbents that are zeolite based are CHA zeolites, ZIF-69, and Na-ETS-10. The recommended sorbents that are non-zeolite based are SBMOF-1 and CC3.
A collaborative literature review between Idaho National Laboratory (INL) and Pacific National Laboratory (PNNL) was conducted to research alternative metals/materials for xenon adsorption in gaseous effluents generated from used nuclear fuel (UNF) reprocessing. The review was conducted to research sorbents that could outperform silver mordenite (AgZ) at ambient temperature. The use of silver requires complex and expensive waste disposal processes due to it being an RCRA metal, therefore alternative materials need to be researched and evaluated. The report details two different types of sorbent material: alternative zeolite materials and non-zeolite-based materials (i.e., MOFs, COFs, etc.). This report documents a brief history of the materials, what testing has been conducted on them, and what results suggest the possibility of outperforming the silver-based materials. Sorbent performance characteristics such as durability, capacity, and operating temperatures were evaluated. The simplicity of the sorbents was also evaluated, meaning how they are acquired (synthesized vs. naturally abundant). Recommendations were made for the most promising sorbents based on available research in the subject area and these recommended sorbents will be tested in the upcoming fiscal year. The recommended sorbents that are zeolite based are CHA zeolites, ZIF-69, and Na-ETS-10. The recommended sorbents that are non-zeolite based are SBMOF-1 and CC3.
The 2013 Immune Risk Standing Review Panel (from here on referred to as the SRP) met for a site visit in Houston, TX on February 3-4, 2014. The SRP reviewed the new Evidence Report for the Risk of Crew Adverse Health Event Due to Altered Immune Response (from here on referred to as the 2013 Immune Evidence Report), as well as the Research Plan for this Risk that is in the current version of the Human Research Program’s (HRP) Integrated Research Plan (IRP).
The Iodine Satellite (iSAT) is a 12U cubesat with a primary mission to demonstrate the iodine fueled Hall Effect Thruster (HET) propulsion system. The spacecraft (SC) will operate throughout a one year mission in an effort to mature the propulsion system for use in future applications. The benefit of the HET is that it uses a propellant, iodine, which is easy to store and provides a high thrust-to-mass ratio. This paper will describe the thermal analysis and design of the SC between Preliminary Design Review (PDR) and Critical Design Review (CDR). The design of the satellite has undergone many changes due to a variety of challenges, both before PDR and during the time period discussed in this paper. Thermal challenges associated with the system include a high power density, small amounts of available radiative surface area, localized temperature requirements of the propulsion components, and unknown orbital parameters. The thermal control system is implemented to maintain component temperatures within their respective operational limits throughout the mission, while also maintaining propulsion components at the high temperatures needed to allow gaseous iodine propellant to flow. The design includes heaters, insulation, radiators, coatings, and thermal straps. Currently, the maximum temperatures for several components are near to their maximum operation limit, and the battery is close to its minimum operation limit. Mitigation strategies and planned work to solve these challenges will be discussed.
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Abstract 316L stainless steel (316L SS) is a flagship material for structural applications in corrosive environments, having been extensively studied for decades for its favorable balance between mechanical and corrosion properties. More recently, 316L SS has also proven to have excellent printability when parts are produced with additive manufacturing techniques, notably laser powder bed fusion (LPBF). Because of the harsh thermo-mechanical cycles experienced during rapid solidification and cooling, LPBF processing tends to generate unique microstructures. Strong heterogeneities can be found inside grains, including trapped elements, nano-inclusions, and a high density of dislocations that form the so-called cellular structure. Interestingly, LPBF 316L SS not only exhibits better mechanical properties than its conventionally processed counterpart, but it also usually offers much higher resistance to pitting in chloride solutions. Unfortunately, the complexity of the LPBF microstructures, in addition to process-induced defects, such as porosity and surface roughness, have slowed progress toward linking specific microstructural features to corrosion susceptibility and complicated the development of calibrated simulations of pitting phenomena. The first part of this article is dedicated to an in-depth review of the microstructures found in LPBF 316L SS and their potential effects on the corrosion properties, with an emphasis on pitting resistance. The second part offers a perspective of some relevant modeling techniques available to simulate the corrosion of LPBF 316L SS, including current challenges that should be overcome.
Occupants are the core of the built environment. Traditional Heating, Ventilation, and Air-Conditioning (HVAC) systems operate with predefined schedules and maximum occupancy assumptions with no consideration of specific occupant information. These generalized assumptions usually do not align with the actual demand and result in over-conditioning and occupant discomfort. In recent years, with the aid of Information & Communication Technology (ICT) and Computer Science (CS), it is possible to acquire real-time and accurate occupant information to satisfy the exact thermal requirement through specific HVAC control in one particular built environment. This mechanism is called HVAC “Occupant-centric Control (OCC).” HVAC OCC strategy starts with collecting the occupant’s information (e.g., presence/absence) and then applies it to meet the occupant’s requirement (e.g., thermal comfort). However, even though some research studies and field pilot demonstrations have been devoted to the field of OCC, there is a lack of systematic knowledge about occupant data, which is the principal component of OCC for HVAC researchers and practitioners. To fill this gap, this review paper discusses OCC with a particular emphasis on occupant information and investigates how this information can assist HVAC operation in providing an acceptable built environment in required spaces during the required time. Finally, we provide a fine-grained, comprehensive picture of occupant information, discuss its features, the modalities of information feed-in into the HVAC control, and the application of commonly utilized occupant information for OCC.
Here, the conventional approaches for handling food waste has been incineration, composting, landfilling, and anaerobic digestion for producing biogas. In light of organic waste bans and newly discovered presence of per- and polyfluorinated substances (PFAS) in food, food packaging materials, and compost, this review provides a critical summary of what has been investigated and reported and what needs to be considered when choosing suitable pathways for food waste. In addition to the fundamental principles inherent to anaerobic digestion and hydrothermal liquefaction, challenges for each process are identified followed by discussion of potential solutions to resolve the bottlenecks.
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.
Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing the performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on accurate building energy models, which are primarily physics-based and heavily dependent on detailed building information, expert knowledge, and case-by-case model calibrations, significantly limiting their scalability. With the development of sensing technology and the increased availability of data, there is growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have begun integrating physics priors into data-driven models, a methodology known as physics-informed machine learning (PIML). PIML is an emerging field where its definitions, methodologies, evaluation criteria, application scenarios, and future directions remain open. To bridge those gaps, this study systematically reviews the state-of-the-art PIML for BPS, offering a comprehensive definition of PIML and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance, and computational cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages, and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.
Black carbon (BC) has emerged as an important short-lived climate forcer. Due to its light absorption properties, BC can darken the snow/ice surface, affect the energy balance, and further lead to acceleration of the cryospheric melting. By reviewing the recent published literatures, we present an overview of the historical changes, spatial distribution of BC in snow/ice, and how these changes are related to the melting of the cryosphere (e.g., glaciers, snow cover, and sea ice). Ice core records show an abrupt increase of BC concentrations that began in the 1850s and continued throughout the 20th century, which is consistent with an increase in BC emissions owing to industrialization. A decrease in the amount of BC since 1970s in Arctic and European ice cores has been partially attributed to the Clean Air Act. However, in the Himalayas, BC records show a continuous increase during this period. BC concentrations in snow and ice in the mid-latitude regions are one to two orders of magnitude higher than those in the polar regions. In particular, BC concentrations in aged snow and granular ice in ablation areas of mountain glaciers are one to three orders of magnitude higher than those in fresh snow or snowpits in the glacier accumulation areas due to BC accumulation during melting season. BC in the surface snow/ice is responsible for about 20% of the albedo reduction in the Tibetan Plateau during glacier melt season. Globally, observations and modeling results indicate that radiative forcing (RF) induced by BC in snow and ice is highest in the mid-latitudes, ranging from several W m–2 in fresh snow to hundreds of W m–2 in aged snow and granular ice in glacier ablation areas. The large BC-in-snow RF and associated snow albedo feedback lead to an acceleration in the total glacier melt (approximately 20%) and/or a reduction in the duration of the snow cover by several days, resulting in an increase of glacier discharge. Given our limited understanding of the role of BC in cryospheric melting, it is important to synthesize the existing research on the multi-scale processes related to BC in snow and ice to identify the gaps in our understanding of these processes and to propose a path forward to improve the quality of our observations of the aforementioned phenomena to fill these gaps.
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Imagine being able to predict — with unprecedented accuracy and precision — the structure of the proton and neutron, and the forces between them, directly from the dynamics of quarks and gluons, and then using this information in calculations of the structure and reactions of atomic nuclei and of the properties of dense neutron stars (NSs). Also imagine discovering new and exotic states of matter, and new laws of nature, by being able to collect more experimental data than we dream possible today, analyzing it in real time to feed back into an experiment, and curating the data with full tracking capabilities and with fully distributed data mining capabilities. Making this vision a reality would improve basic scientific understanding, enabling us to precisely calculate, for example, the spectrum of gravity waves emitted during NS coalescence, and would have important societal applications in nuclear energy research, stockpile stewardship, and other areas. This review presents the components and characteristics of the exascale computing ecosystems necessary to realize this vision.