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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Crystallization and assembly at interfaces: Celebrating the achievements of a vibrant research community

Crystallization is one of the cornerstones of modern materials science and engineering and plays a critical role in industries ranging from petroleum derivative manufacturing to microstructural engineering of structural materials and the defect-free growth of silicon single crystals for integrated chip technology. Also, in the realm of environmental and biological processes, the mineralization of diverse compounds has shaped the vast array of ecosystems we observe today. Conversely, understanding the crystallization and assembly of building blocks of various sizes at interfaces has broader impacts on materials synthesis, performance of energy storage devices, optimized processing conditions, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Design, Construction and Testing of the Straw Tracking Detectors for the E989 Muon g-2 Experiment at Fermilab.

The requirement of science, to stretch the boundaries and our understanding of the standard model,is the driving force for the continuous development of our knowledge of the building blocks of theUniverse. Experiments are designed, built and tested to support the theories of the mathematiciansof the physics community and the theorists help interpret the experimental results, looking foravenues of new physics. However, as is known in both science and human nature, nothing is everperfect - there is always room for improvement.The E989 Muon g-2 Collaboration experiment at Fermi National Accelerator Laboratory (otherwiseknown as Fermilab), aims to quantify the muon anomalous magnetic moment to unprecedentedprecision and looks to increase the accuracy of the measurement by fourfold of the predecessorexperiment - E821 at Brookhaven. The discrepancy between the theoretical prediction of theanomalous magnetic moment of the muon and the experimental results, has given tantalisingindication of new physics, prompting an ”upgrade” for systematic accuracy on the E821.The particular focus of this thesis details the design, construction and quality testing of the strawtracking modules for the E989 experiment. The author was one of the dedicated technicians whobuilt and tested the tracker modules at the University of Liverpool High Energy Physics (HEP)department, she built and quality tested the modules, wrote the procedural document for the buildand subsequently was involved in the Run I startup, shut down and data taking shifts (for Run Iand Run II) which contributed to the recent release of the first unblinded data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

MAGNETIC FIELD MAPPING OF 1.3 GHz SUPERCONDUCTING RADIO FREQUENCY NIOBIUM CAVITIES

Niobium is the material of choice to build superconducting radio frequency (SRF) cavities, which are fundamental building blocks of modern particle accelerators. These cavities require a cryogenic cool-down to ~2 - 4 K for optimum performance minimizing RF losses on the inner cavity surface. However, temperature-independent residual losses in SRF cavities cannot be prevented entirely. One of the significant contributor to residual losses is trapped magnetic flux. The flux trapping mechanism depends on different factors, such as surface preparations and cool-down conditions. We have developed a diagnostic magnetic field scanning system (MFSS) using Hall probes and anisotropic magneto-resistance sensors to study the spatial distribution of trapped flux in 1.3 GHz single-cell cavities. The first result from this newly commissioned system revealed that the trapped flux on the cavity surface might redistribute with increasing RF power. The MFSS was also able to capture significant magnetic field enhancement at specific cavity locations after a quench.

Parajuli, I. P.↗
DOE OSTI2022

Scientific Computational Imaging Code (SCICO)

Scientific Computational Imaging Code (SCICO) is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗
DOE OSTI2020

Tandem and Bimetallic Catalysts for Oxidative Dehydrogenation of Light Hydrocarbons with Renewable Feedstock

One of the grand challenges for national energy security is the establishment of sustainable chemical transformations. The chemical industry relies on naphtha feedstock from nonrenewable crude oil, and uses energy intense processes to make useful building block molecules such as ethylene through steam cracking, which generates significant greenhouse carbon dioxide emissions. Sustainable chemistry through selective catalysis should enable chemical transformations that use renewable feedstock such as nonfood biomass, minimize energy consumption, and do not emit greenhouse gases or other waste byproducts. In this research, we studied fundamental chemistry that couples the removal of oxygen atoms from nonfood biomass derived polyols and diols with oxidative dehydrogenation (ODH) of petroleum based alcohols. Heterobimetallic Re-Ir complexes were constructed and their reaction chemistry established. Typical substrates are glycerol, sugar alcohols and pentane to produce allyl alcohol, pentene, and water. The reaction mechanism of a recyclable rhenium nano-particle catalyst was established for deoxydehydration of sugar alcohols without the use of organic solvents.

09 BIOMASS FUELS↗
DOE OSTI2020

Long-Term Monitoring & Stewardship at Los Alamos National Laboratory [Slides]

Development and implementation of the Institutional Control Evaluation Project will include: Evaluate institutional surface water controls and infrastructure installed by LANL. Implement pilot project in the Los Alamos Canyon Watershed Spring, 2020. Initial evaluations will include canyon bottom controls with plan to expand program as funding allows. This will be a building block for successful Long-Term Monitoring and Stewardship (LTM&S) Program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Nuclei Of Atoms In The Third Period Of The Periodic Table Of Elements (Monograph No.3)

Nuclei are shown for the elements sodium, magnesium, aluminum, silicon, phosphorus, sulphur, chlorine, and argon. Individual nucleons are not identified as protons or neutrons. Nucleons are illustrated using a sphere schematic of the wave functions of singular, linear, and tetrahedral nucleon arrangements. Nucleon arrangements in protium, deuteron, triton, helion, and alpha are shown as building blocks in the nuclei of elements in the third period of the periodic table of elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

PETSc Users Manual (Rev. 3.13)

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear solvers, nonlinear solvers, and time integrators that may be used in application codes written in Fortran, C, C++, and Python. PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than \rolling them" yourself.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

2020 State of the Science Report, Chapter 2: Marine Renewable Energy: Environmental Effects and Monitoring Strategies

As we learn more about interactions between marine renewable energy (MRE) devices, the animals and habitats near them, and the oceanographic processes with which they interact, we need to clarify the language used to discuss those interactions. For example, if an MRE device or system negatively affected a number of animals, we could say that the device or the system of foundations, anchors, and mooring lines had an impact on the population, and take steps to avoid the impact or, in some cases, mitigate the impact. However, at this stage in MRE development, there are few, if any, cases in which a negative impact has been observed or measured. Instead, we are developing the building blocks that support investigations of interactions and potential impacts. https://tethys.pnnl.gov/publications/state-of-the-science-2020-chapter-2-environmental-effects

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Bioprocessing Separations Consortium Three-Year Overview: Technical Advances, Process Economics Influence, and State of the Science

The Bioprocessing Separations Consortium solves separations challenges that are unique to bioprocessing. To do this, the Consortium uses two methods: applying existing technologies to new challenges and developing new technologies that may address these unique challenges better than today’s commercial technologies. In its research and development, the Consortium leverages its three core capabilities: materials development and evaluation, process development, and analysis and computation. In its first three years, from 2016 to 2019, the Consortium addressed four critical challenges in biochemical and thermochemical processing. First, the Consortium targeted the fractionation of lignin to enable valorization of this portion of biomass that offers diverse, complex, and valuable building blocks and products. Second, Consortium researchers developed technologies to enable process intensification, which can reduce equipment needs, energy consumption, and waste generation, thereby cutting bioprocessing capital costs and rendering processes more efficient. Third, the Consortium sought to recover carbon from dilute aqueous streams that are common to bioprocessing; recovering dilute carbon can improve process efficiency and economics. Finally, Consortium researchers designed and developed new materials and catalysts to reduce targeted foulants and poisons in bioprocessing streams that can limit the lifetime of downstream catalysts or fermenting microorganisms. Table 1 lists the technologies that were examined and places them in the context of the Consortium’s capabilities, types of bioprocesses, and critical challenges in bioprocessing separations. Overall, across these projects, we have developed separations technologies for 10 bioprocesses, addressed 9 target compounds in bioprocessing, developed 10 materials, and evaluated 4 processes for cost and sustainability.

09 BIOMASS FUELS↗
DOE OSTI2020

Bioprocessing Separations Consortium Three-Year Overview: Technical Advances Process Economics Influence, and State of the Science

The Bioprocessing Separations Consortium solves separations challenges that are unique to bioprocessing. To do this, the Consortium uses two methods: applying existing technologies to new challenges and developing new technologies that may address these unique challenges better than today’s commercial technologies. In its research and development, the Consortium leverages its three core capabilities: materials development and evaluation, process development, and analysis and computation. In its first three years, from 2016 to 2019, the Consortium addressed four critical challenges in biochemical and thermochemical processing. First, the Consortium targeted the fractionation of lignin to enable valorization of this portion of biomass that offers diverse, complex, and valuable building blocks and products. Second, Consortium researchers developed technologies to enable process intensification, which can reduce equipment needs, energy consumption, and waste generation, thereby cutting bioprocessing capital costs and rendering processes more efficient. Third, the Consortium sought to recover carbon from dilute aqueous streams that are common to bioprocessing; recovering dilute carbon can improve process efficiency and economics. Finally, Consortium researchers designed and developed new materials and catalysts to reduce targeted foulants and poisons in bioprocessing streams that can limit the lifetime of downstream catalysts or fermenting microorganisms. Table 1 lists the technologies that were examined and places them in the context of the Consortium’s capabilities, types of bioprocesses, and critical challenges in bioprocessing separations. Overall, across these projects, we have developed separations technologies for 10 bioprocesses, addressed 9 target compounds in bioprocessing, developed 10 materials, and evaluated 4 processes for cost and sustainability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Human-System Interface Design Review Guidelines

The U.S. Nuclear Regulatory Commission (NRC) staff reviews the human factors engineering (HFE) aspects of nuclear power plants in accordance with the Standard Review Plan (NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition). The Human Factors Engineering Program Review Model (NUREG-0711, Revision 3, issued November 2012) contains detailed design review procedures. As part of the review process, the interfaces between plant personnel and the plant's systems and components are evaluated for conformance with HFE guidelines. This document, Human-System Interface Design Review Guidelines (NUREG-0700, Revision 3), provides the guidelines necessary to perform this evaluation. The review guidelines address the physical and functional characteristics of human-system interfaces (HSIs). Because these guidelines only address the HFE aspects of design and not other related considerations, such as instrumentation and control and structural design, they are referred to as HFE guidelines. In addition to the review of actual HSIs, the NRC staff can use the NUREG-0700 guidelines to evaluate a design-specific HFE guidelines document or style guide. The HFE guidelines are organized into four basic parts, which are divided into sections. Part I contains guidelines for the basic HSI elements: information displays, user-interface interaction and management, and analog displays and controls. These elements are used as building blocks to develop HSI systems to serve specific functions. Part II contains the guidelines for reviewing the following HSI systems: alarm system, safety parameter display system, group-view display system, soft control system, computer-based procedure system, automation system, and communication system. Part III provides guidelines for the review of workstations and workplaces. Part IV provides guidelines for the review of HSI support (i.e., maintainability of digital systems and degraded HSI and instrumentation and control conditions).

42 ENGINEERING↗
DOE OSTI2020

FLC Notable Technology Award Oleo-Furan Surfactant

It’s a well-kept secret that laundry detergents are harmful to the environment. The important chemical in laundry detergent, called a surfactant. Surfactants are the workhorses of laundry detergent and soaps, and they're attracted to things like grease on your hands and dirt in your clothes to wash them away. Surfactants are used in everything from shampoo, hand soap, laundry detergent, and cosmetics. Many current surfactants are made from petroleum feedstock. These petroleum-derived building blocks come with a big carbon footprint. As the need for greener feedstock alternatives for synthesizing surfactants became obvious, manufacturers turned to natural oils, such as palm kernel and coconut oil. However, these oils are costly, particularly in areas where they are not domestically produced, and using them for surfactant synthesis competes with the food supply chain. Surfactants deactivate and do not work in hard water. Hard water is common - 85% of the U.S. water supply is considered hard because of the presence of small metal ions. These metal ions cover the surfactants making them useless. To solve this problem, many manufacturers add chemicals called chelators to bind the metal ions and make the water less hard, enhancing the effectiveness of the laundry detergent. However, this solution leads to more problems, including increased cost of detergent production and a large output of environmentally harmful chemicals. There remains a strong need for effective laundry detergents that do not negatively impact the environment and are available to consumers at an affordable cost. The American Cleaning Institute (ACI), a surfactant manufacturer organizing body, is emphasizing commitment to developing bio-renewable alternatives to current surfactants in laundry detergents. That’s why chemists at SIRONIX Renewables partnered with Los Alamos National Laboratory to further develop Oleo-furan surfactants (OFS). OFS are a new class of non-toxic, non-irritating surfactants for laundry detergent. It is the only surfactant that performs effectively in cold and hard water without chelators to bind the metal ions in hard water. OFS can be produced readily from sustainable, bio-derived molecules. They are multi-functional molecules - performing the role of a surfactant and a chelator in one - and are completely bio-degradable. OFS does not rely on petroleum feedstock and is synthesized from non-food, bio-renewable cellulosic feedstock. OFS solves each of the pertinent problems facing detergents and our environment today.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Noise Erasure in Quantum-Limited Current Amplifiers

Superconducting quantum interference devices (SQUIDs) are extraordinarily sensitive to magnetic flux and thus make excellent current amplifiers for cryogenic applications. One such application of high interest to Sandia is the set-up and state read-out of quantum dot based qubits, where a qubit state is read out from a short current pulse (microseconds to milliseconds long) of approximately 100 pA, a signal that is easily corrupted by noise in the environment. A Parametric SQUID Amplifier can be high bandwidth (in the GHz range), low power dissipation (less than 1pW), and can be easily incorporated into multi-qubit systems. In this SAIL LDRD, we will characterize the noise performance of the parametric amplifier front end -- the SQUID -- in an architecture specific to current readout for spin qubits. Noise is a key metric in amplification, and identifying noise sources will allow us to optimize the system to reduce its effects, resulting in higher fidelity readout. This effort represents a critical step in creating the building blocks of a high speed, low power, parametric SQUID current amplifier that will be needed in the near term as quantum systems with many qubits begin to come on line in the next few years.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Predictive Skill of Deep Learning Models Trained on Limited Sequence Data

In this report we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, especially variants of recurrent neural networks (RNNs) have been studied for influenza forecasting, and have achieved higher forecasting skill compared to conventional models such as ARIMA models. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block temporal convolutional networks and simple neural attentive meta-learner for epidemiological forecasting and test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗