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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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At least 19 records

Filter Adapters for BI-PAP and CPAP Equipment

Researchers at Los Alamos National Laboratory are researching and designing technology intended to help reduce the exposure of patients and medical staff to pathogens, such as SARS-CoV-2, while providing medical staff the dexterity and flexible work conditions necessary to conduct routine and complex patient care on infected patients. This investigative technology would potentially enable hospitals to adapt, utilize, and repurpose existing Bilevel Positive Airway Pressure (BiPAP) or Continuous Positive Airway Pressure (C-PAP) equipment in the fight against COVID-19, which would potentially save hospitals significant money, reduce exposure to staff and patients, and increase the supply of critically needed devices at a time when supplies are scarce in the market place. A provisional patent has been filed on the technology and we are seeking a commercialization partner to license and further develop the product for commercial purposes, including processing the device through the FDA emergency use authorization or clearance process. One hospital system has indicated that upon FDA authorization or clearance, it would have interest in the product.

42 ENGINEERING↗

Insulating Nanofluids and Solids to Upgrade our Large Aging Transformer Equipment (Advanced Research Projects Agency – Energy Research Final Report)

This project developed a highly promising, minimally invasive pathway for transformers with up to double the thermal life that is achieved today. Thermal life is important because it is occasionally necessary to overload transformers in service and this overload raises the internal temperature, accelerating aging2. A seminal initial finding in the research was that a likely achievable increase in the thermal conductivity of the transformer paper could double the thermal life. Oil-impregnated paper is used to wrap the electrical conductors in a transformer to provide a good balance of electrical insulation and thermal conductivity. The novel approach to increase the thermal conductivity was to add micrometer boron nitride particles to the paper during manufacturing. This is encouraging because it provides benefits with minimal impact on the manufacturing processes of the paper or of a transformer.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Fueling Nuclear Research at MFC: The Environmental Backbone of Nuclear Operations

The National Environmental Policy Act (NEPA) review guidelines are an essential part of the approval process of any construction or modification being done at Idaho National Laboratory (INL). The Material and Fuels Complex (MFC) Environmental team is responsible for writing and reviewing Environmental Impact Statements (EISs), Environmental Assessments (EAs), Environmental Compliance Permits (ECPs), Air Permitting Applicability Determinations (APADs), and other NEPA compliance documentation. This documentation allows for innovation in MFC research by permitting the introduction of new laboratory equipment, research facilities, and materials for researchers to use in their experiments. Without the work of the Environmental team, nuclear research and operations at MFC would not be possible.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

An Overview of Argonne’s Advanced Mobility Technology Laboratory Vehicle Systems Instrumentation and Evaluation Methodology

This report will provide a general overview of the testing facilities, research equipment, and general testing methodologies Argonne utilizes to conduct vehicle technology evaluations of advanced technology research vehicles. Data is captured from vehicle evaluations that provide powertrain operation and corresponding energy consumption based on a combination of in-depth instrumentation and focused testing. This resulting dataset of hundreds of time-resolved vehicle signals provide a basis for direct analysis and model validation of vehicle specific technologies. Argonne has attempted to standardize the approach to vehicle instrumentation and testing at Argonne, but it should be noted that each vehicle, and the corresponding assessment, remains unique. It is suggested that the reader reference vehicle specific testing reports for an overview of the unique aspects for each test vehicle. Additionally, the datasets for research vehicles are made publicly available through the Advanced Mobility Technology Laboratory’s Downloadable Driving Database (D3) at www.anl.gov/d3.

33 ADVANCED PROPULSION SYSTEMS↗

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

59 BASIC BIOLOGICAL SCIENCES↗

The TRANSCEND University Consortium: An Overview - 20106

The UK government is committed to nuclear energy having an important role in delivering a secure, low-carbon and affordable energy future. Projections indicate that between now and 2035, ∼14 GW of new nuclear generating capacity may be built. Successful delivery of this strategy recognises the need for research and development, skills development and international collaboration as key enablers. One key area is the decommissioning and clean-up of all civil nuclear licensed sites and the implementation of geological disposal of highly active wastes, with a central component being a joined-up approach to nuclear R and D across government, industry and academia which benefits the UK economy and ensures the security of supply. Essential to public acceptance of this strategy remains a demonstrable ability to safely manage and dispose of waste, with acceptance directly linked to having scientifically robust routes for its safe clean-up and disposal. There therefore remains an ongoing need for further research in the areas of decommissioning, immobilisation and management of nuclear waste, and there is significant government and stakeholder support for such research. Given the needs of both new nuclear power generation and legacy wastes, there is a requirement to develop innovative approaches to waste management and decommissioning. Following on from earlier, highly successful projects supported by UK Research and Innovation's Energy Programme, the work of the TRANSCEND (Transformative Science and Engineering for Nuclear Decommissioning) consortium is part of the response to these needs, providing support to a strong and internationally leading group of researchers in this key technology area. The consortium is a collaboration between 11 universities, supported by 8 key industry partners, with a total funding of UK Pounds 9.5 M from the UK Engineering and Physical Research Council, the universities and key project partners. The research being carried out is multidisciplinary and covers both fundamental and applied topics, with 40 research projects clustered in four technical themes: integrated waste management; site decommissioning and remediation; spent fuels; and nuclear materials. Key aims of the consortium are to extend and further develop a relevant academic skill base in the UK associated with the needs of nuclear waste management and decommissioning, to train the next generation of UK researchers, equipping them with skills and experiences relevant to nuclear waste management and decommissioning issues, and to carry out cutting-edge research that has the potential to provide new and innovative pathways to better management of both legacy and future nuclear wastes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Are you using the right probe molecules for assessing the textural properties of metal–organic frameworks?

Textural properties—such as the surface area, pore size distribution, and pore volume—are at the forefront of characterization for porous materials. Therefore, it is essential to accurately and reproducibly report a material's textural properties as they could ultimately dictate its applicability. This work aims to provide insightful and comprehensive studies of textural properties for a set of metal–organic frameworks (MOFs), a class of porous materials, using various gases to equip researchers in the field with a helpful guide and reference. Here, we selected a series of nine MOFs with different surface areas, pore sizes, shapes, and chemical environments to represent a wide range of materials. We probed the textural properties of these MOFs using traditional and distinctive gases: N 2 , Kr and O 2 at 77 K, Ar at 87 K, and CO 2 at 195 and 273 K. With regard to surface area, we discuss the validity and challenges associated with the current BET method, the importance of utilizing the Rouquerol et al. consistency criteria to ensure accuracy and reproducibility, and the recommended gas probes for certain materials. For pore size distribution, we discuss the efficacy of each probe for determining the pore sizes within a porous material relative to the calculated distribution from its crystal structure, the limitations of current computational kernels used to calculate pore size distributions, and the need for advanced kernels to envelope the diversity of porous materials. Finally, for pore volume, we discuss the use of the Gurvich rule to obtain the total pore volume in comparison with calculated values from crystal structures and its consistency as a metric for porous materials. Ultimately, we hope that this article will aid researchers in characterizing the textural properties of porous materials and encourage the development of new kernels capable of encompassing the complexity of MOFs and other porous materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

Precursor Analysis Report: Cyber Attack on Thyssenkrupp Blast Furnace 2014

The Cyber Attack on Thyssenkrupp Blast Furnace 2014 Precursor Analysis Report leverages publicly available information about the Thyssenkrupp Steel Mill cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. In December 2014, the German Government’s Federal Office for Information Security (BSI) released a report detailing a cyber attack on a German steel mill that occurred earlier that year, though exact dates and details of the attack were not revealed. While the report did not specify the name of the company, multiple sources identified the victim as one of Europe’s largest steel manufacturers, Thyssenkrupp AG. Further, Thyssenkrupp announced on 16 May of that year that Europe’s largest blast furnace, “Schwelgern 2,” located at its facility in Duisburg, Germany, would be offline for several weeks for repairs and upgrades, suggesting Schwelgern 2 was likely the target of the attack. The attack began in early 2014, when adversaries infiltrated the victim steel mill’s Information Technology (IT) network via a spearphishing campaign, then worked their way into the Operational Technology (OT) environment, where they executed software that caused denial of service, denial of control, and eventually a loss of control. This led to the blast furnace shutting down without proper safety procedures, resulting in catastrophic physical damage. No lives were lost in the incident, but ThyssenKrupp suffered $4 million in damage to the blast furnace and an additional $6 million in lost revenue. The adversaries required specialized knowledge and expertise in steel production, which enabled them to compromise a variety of internal systems and components across both IT and OT networks. The attack also demonstrated detailed knowledge of the industrial control systems (ICS) and production processes being used. This combination resulted in one of the earliest known publicly reported cybersecurity incidents resulting in physical damage to ICS equipment. Researchers and analysts identified 19 unique techniques (used in a sequence of 20 steps) utilized during the attack with a total of 454 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Fifteen of the identified techniques used during the Thyssenkrupp cyber attack were precursors to the triggering event. Analysis identified 369 observables associated with these precursor techniques, 316 of which were assessed to have an increased likelihood of being perceived in the 120 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

An Open-Source Decarbonization Analytics Framework: Designing for Low-Carbon Emission Districts and Communities: Preprint

This paper introduces an open-source analytics framework designed to assist in creating low or net-zero carbon buildings and urban districts. Integrated within URBANopt, an open-source platform for energy analysis in districts and communities, this framework equips researchers, architects, engineers, and other stakeholders with tools to evaluate the carbon footprint implications of their design choices. The framework enables the analysis of various scenarios, incorporating both historical and future emission factors, and can span across different climate zones, each with distinct grid and emissions characteristics. The results showcase the framework's capability to evaluate the impact of design upgrades and control strategies on carbon emissions in districts and communities. An illustrative analysis using a hypothetical district in Denver, Colorado, shows reduced emissions from energy efficiency upgrades and control strategies, highlighting the sensitivity in their effects on emissions and energy use.

buildings energy efficiency↗

Hardware Selection and Performance of Low-Cost Fluorometers

Access to and extensive use of fluorometric analyses is limited, despite its extensive utility in environmental transport and fate. Wide-spread application of fluorescent tracers has been limited by the prohibitive costs of research-grade equipment and logistical constraints of sampling, due to the need for high spatial resolutions and access to remote locations over long timescales. Recently, low-cost alternatives to research-grade equipment have been found to produce comparable data at a small fraction of the price for commercial equipment. Here, we prototyped and benchmarked performance of a variety of fluorometer components against commercial units, including performance as a function of tracer concentration, turbidity, and temperature, all of which are known to impact fluorometer performance. While component performance was found to be comparable to the commercial units tested, the best configuration tested obtained a functional resolution of 0.1 ppb, a working concentration range of 0.1 to >300 ppb, and a cost of USD 59.13.

42 ENGINEERING↗

Theoretical Particle Physics in the Data-Driven Era (Final Report)

DOE award DE-SC0021447 supports theoretical research in high energy physics. PI and students are not a part of any experimental collaboration, even if some works were done in collaboration with experimental colleagues. All research products are in the form of research articles and they are publicly available under inspirehep.net and arxiv.org, which are typical publication methods in high energy physics. No invention or patent is involved with this research. No equipment or hardware is involved with the research under this award. Research outcomes are used by colleagues in high energy physics, including both theorists and experimentalists. Therefore the outcome of research topics under this award supported the High Energy Physics experimental research program, both in understanding the data and in finding new directions for experimental exploration. The research materials are used to train undergraduate students, graduate students and postdoctoral scholars. We summarize research activity under this award in the following section and major products are listed in section 3. Section 4 contains professional presentations given by PI. Other products developed during the award period are in section 5. Students’ activities are summarized in section 6.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Electrical Shock During Phone Assisted Troubleshooting of Laboratory Equipment

We report researchers have the potential to be exposed to a wide variety of hazards inherent to the equipment they use and maintain. When equipment does not function as expected, researchers sometimes reach out to their vendors for assistance. Early diagnostic or troubleshooting interactions between researcher and vendor are often conducted over the telephone and can lead to researchers performing work outside of their area of expertise and exposure to unknown hazards. This type of interaction significantly contributed to an incident where during diagnostic activities a researcher accidentally contacted, and discharged, a capacitor in an X-ray diffraction instrument. While this incident did not produce a serious injury, if the capacitor discharge path had occurred hand-to-hand across the heart, a serious injury may have been possible.

47 OTHER INSTRUMENTATION↗

Multiple injection for improving knock, gaseous and particulate matter emissions in direct injection SI engines

Advances in fuel injector technology have enabled research and development on a large variety of direct injection spark ignition (DISI) engine fueling strategies targeted to improve engine performance and reduce engine-out emissions. This study explores the effect of multiple injections on knock, engine efficiency and stability, and particulate number and gaseous emissions on a single-cylinder DISI research engine. Work to date on multiple injections in the literature is reviewed, and then two aspects of multiple injection strategies are experimentally investigated: the number of injections (up to five times in a cycle); and the timing of the injections (classified relative to the timing of intake valve opening and closing). A boosted, single-cylinder research engine equipped with a state-of-the-art piezoelectric hollow cone spray direct injector and research-grade E10 gasoline was used for the study. The results show multiple injections maintain torque and combustion stability compared with single injection and slightly increase the knock limits and indicated thermal efficiencies (maximum 0.7% absolute, 2.8% relative compared with single-injection baseline) due to improved heat release phasing, especially with an additional late injection during the intake valve closed (compression stroke) period. The gaseous pollutant emissions including nitrogen oxides and unburned hydrocarbons were reduced by 25% with multiple injections, particularly with injection during the compression stroke. In contrast, carbon monoxide emissions increased with multiple injections for all conditions. Increasing the number of injection events significantly reduced particulate number emissions (half with each additional injection), and the decrease in particulate number was not sensitive to the injection timing.

42 ENGINEERING↗

Optimal control to handle variations in moisture content and reactor in-feed rate

The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%-2.24%. Furthermore, a biorefinery can adapt HPC to achieve its design capacity.

09 BIOMASS FUELS↗

Automating the interpretation of PM 2.5 time–resolved measurements using a data–driven approach

The rapid development of automated measurement equipment enables researchers to collect greater quantities of time-resolved data from indoor and outdoor environments. While significant, the interpretation of the resulting data can be a time-consuming effort. This paper introduces an automated process of interpreting PM 2.5 time-resolved data and differentiating PM 2.5 emissions resulting from indoor and outdoor sources. Here, we use Random Forest (RF), a machine learning approach, to study a dataset of 836 indoor emission events that occurred over a 2-week period in 18 apartments in California. In this paper, we show model development and evaluate its performance as the sample size and source vary. We discuss the characteristics of the dataset that tended to help the source identification and why. For example, we show that data from many events and from different apartments are essential for the model to be suitable for analyzing a new separate dataset. We also show that longitudinal data appear to be more helpful than the time frequency of measurements within a given apartment. We use the resulting RF model to analyze PM 2.5 data of an entirely separate dataset collected from 65 new homes in California. The RF model identifies 442 indoor emission events, with only a few misidentifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Extreme Miller cycle with high intake boost for improved efficiency and emissions in heavy-duty diesel engines

This study experimentally investigates the impact of extreme Miller cycle strategies paired with high intake manifold pressures on the combustion process, emissions, and thermal efficiency of heavy-duty diesel engines. Well-controlled experiments isolating the effect of Miller cycle strategies on the combustion process were conducted at constant engine speed and load (1160 rpm, 1.76 MPa net IMEP) on a single cylinder research engine equipped with a fully-flexible hydraulic valve train system. Late intake valve closing (LIVC) timing strategies were compared to a conventional intake valve profile under either constant cylinder composition, constant engine-out NO x emission, or constant overall turbocharger efficiency (η TC ) to investigate the operating constraints that favor Miller cycle operation over the baseline strategy. Furthermore, utilizing high boost with conventional intake valve closing timing resulted in improved fuel consumption at the expense of sharp increases in peak cylinder pressures, engine-out NO x emissions, and reduced exhaust temperatures. Miller cycle without EGR at constant λ demonstrated LIVC strategies effectively reduce engine-out NO x emissions by up to 35%. However, Miller cycle associated with very aggressive LIVC timings led to fuel consumption penalties due to increased pumping work and exhaust enthalpy. LIVC strategies allowed for increased charge dilution at the baseline NO x constraint of 3.2 g/kWh, resulting in significant fuel consumption benefits over the baseline case without compromising exhaust temperatures or peak cylinder pressures. As Miller cycle implementation was shown to affect the boundary conditions dictating η TC , the LIVC and conventional IVC cases were studied at an equivalent η TC point representative of high boost operation. With high boost, LIVC yielded reduced NO x emissions, reduced peak cylinder pressures, and elevated exhaust temperatures compared to the conventional IVC case without compromising fuel consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Quarterly Management Document – FY20, 1st Quarter, Multi-pass Hybrid Laser Arc Welding of Alloy 740H

During the first quarter of FY20, work focused on implementing a subcontract with Penn State University to collaborate on the project. Significant delays prevented the subcontract from being placed until the end of December. As a result only limited progress was made on the project. Required equipment was researched and ordered during the first quarter but will not be delivered until the middle of the second quarter of FY20. Delays are not expected to adversely impact the schedule of the project.

36 MATERIALS SCIENCE↗