Optimal Placement of PV Smart Inverters With Volt-VAr Control in Electric Distribution Systems
Not provided.
Computer Science↗
SEARCH · Engineering Papers
Results for “Engineering science”
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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An Adaptive Virtual Oscillator Control Structure for Grid-Forming Inverters
The electrical grid is facing unprecedented challenges due to the increasing penetration of inverter-based resources. Grid forming inverters (GFMIs) are a promising technology to address these challenges. Recently, the virtual oscillator based GFMI control is attracting more attention due to its superior performance over other control strategies. In this article, an adaptive control strategy is proposed to provide flexible operation and transition between grid-connected and islanded modes. The controller adapts the virtual oscillator's parameter values depending on the operation mode. It also provides a feedback signal using a measured frequency to account for any steady-state errors and to allow a seamless transition from grid-connected to islanded mode. Finally, to show the feasibility of the proposed controller, this article discusses the simulation results from the implementation of the controller on a single inverter system and on a group of inverters on a large practical utility feeder, the IEEE 13 node feeder, using the DIgSILENT simulation environment.
adaptive control↗
The Impact of Nonzenith Elevation Angles on Ground-Based Infrared Thermodynamic Retrievals
Observing thermodynamic profiles within the planetary boundary layer is essential to understanding and predicting atmospheric phenomena because of the significant exchange of sensible and latent heat between the land and atmosphere within that layer. The Atmospheric Emitted Radiance Interferometer (AERI) is a ground-based infrared spectrometer used to obtain the vertical profiles of temperature and water vapor mixing ratio. Most AERIs are only capable of zenith views, although the Marine AERI (M-AERI) has a design that allows it to view various elevation angles. In this study, we quantify the improvement in the information content and accuracy of the retrieved profiles when nonzenith angles are included, as is common with microwave radiometer profilers. The impacts of the additional scan angles are quantified through both a synthetic study and with M-AERI observations from the ARM Cloud Aerosol Precipitation Experiment (ACAPEX) campaign. Furthermore, the simulation study shows that low elevation angles contain more information content for temperature whereas high elevation angles have more information content for water vapor. Outside of very humid environments, the addition of low elevation angles also results in lower root-mean-square errors when compared with high angles for both temperature and water vapor mixing ratio, although this is primarily a result of averaging multiple observations together to reduce instrument noise. Real-world results from the ACAPEX dataset indicate similar results as were found for the simulation study, although not all predicted benefits are realized because of the small sample size and observational uncertainties.
54 ENVIRONMENTAL SCIENCES↗
American Chemical Society's summer school in nuclear and radiochemistry. Final report
The ACS Summer Schools in Nuclear and Radiochemistry (herein called “Summer Schools”) were held each year at San Jose State University (SJSU) and Brookhaven National Laboratory (BNL). The Summer Schools are an intensive, six-week program that includes both a lecture component covering fundamental principles of nuclear chemistry and radiochemistry and a laboratory component providing hands-on experience for the students to observe and test many of the basic principles that they learn about in lecture and in the special seminars. Each site hosts 12 undergraduate students annually. Participants are selected from a nation-wide pool of applicants who are currently enrolled as undergraduates seeking bachelors’ degrees, preferably in disciplines of physical sciences or engineering. To broaden the students’ perspectives on nuclear science, prominent research scientists active in nuclear and/or radiochemical research participate in a Guest Lecture Series. Symposia emphasizing nuclear security, nuclear medicine, and career opportunities are conducted as a part of the program.
07 ISOTOPE AND RADIATION SOURCES↗
Engineering Microbial Communities: Frontier Science for the Bioeconomy Workshop Series
In nature, biological systems are shaped by complex interactions of diverse microorganisms such as bacteria, archaea, fungi, and viruses living within communities called microbiomes (Berg et al. 2020; Prescott 2017). These collective interactions result in emergent community properties that can be leveraged for beneficial purposes such as bioenergy and biomolecule production. Given this potential and the immensity of microbial genomic diversity, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program has long invested in research to better understand the biology of environmental microbes and microbiomes.
59 BASIC BIOLOGICAL SCIENCES↗
Building Bridges: Establishing a Dialogue Between Software Engineering Research and Computational Science.
Abstract not provided.
Milewicz, Reed↗
Los Alamos National Laboratory R&D Intern
This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.
36 MATERIALS SCIENCE↗
A Detonator Design Guide and Knowledge Capture System [Slides]
Engineering design--good design--is a blend of engineering and science; this is especially true for of explosive detonator design. Detonators are complex components with interrelated mechanical, electrical, and chemical attributes, all of which must be balanced to achieve a robust and reliable, working whole. This design guide presents an integrated, lifecycle approach to the design process, bringing together engineering design with science, emphasizing elegance and subtlety in the design process.
42 ENGINEERING↗
Sensitivity Analysis and Design Optimization of Ceramic Breeders project report
Explore the source record for details and available documents.
Engineering↗
The U.S. Department of Energy Computational Science Graduate Fellowship, 1991-2021: Follow-Up Study Shows Major Impact on Recipients and the Scientific Workforce
Since 1991, the U.S. Department of Energy Computational Science Graduate Fellowship (DOE CSGF) has addressed DOE National Laboratory needs as well as demands in the national workforce for trained professionals in computational science and engineering. Sponsored by the Department of Energy's Office of Science and the National Nuclear Security Administration, the DOE CSGF supports doctoral students in the pursuit of novel scientific or engineering discoveries using high-performance computing (HPC) resources. To meet the program’s core requirements, recipients participate in multidisciplinary studies, carry out at least one 12-week DOE laboratory research practicum, and contribute to an annual program review where the fellows present their research for sponsor review. The Krell Institute, which as managed the fellowship on behalf of the DOE since 1997, has commissioned several follow-up studies to examine the DOE CSGF recipients’ characteristics, fellows’ outcomes and professional accomplishments, alumni’s career paths and achievements, and recipients’ impact on national priorities through research and education.
97 MATHEMATICS AND COMPUTING↗
The U.S. Department of Energy Computational Science Graduate Fellowship, 1991-2021: Follow-Up Study Shows Major Impact on Recipients and the Scientific Workforce
Since 1991, the U.S. Department of Energy Computational Science Graduate Fellowship (DOE CSGF) has addressed DOE National Laboratory needs as well as demands in the national workforce for trained professionals in computational science and engineering. Sponsored by the Department of Energy's Office of Science and the National Nuclear Security Administration, the DOE CSGF supports doctoral students in the pursuit of novel scientific or engineering discoveries using high-performance computing (HPC) resources. To meet the program’s core requirements, recipients participate in multidisciplinary studies, carry out at least one 12-week DOE laboratory research practicum, and contribute to an annual program review where the fellows present their research for sponsor review. The Krell Institute, which as managed the fellowship on behalf of the DOE since 1997, has commissioned several follow-up studies to examine the DOE CSGF recipients’ characteristics, fellows’ outcomes and professional accomplishments, alumni’s career paths and achievements, and recipients’ impact on national priorities through research and education.
97 MATHEMATICS AND COMPUTING↗
Experimental and DFT Calculated IR Spectra of Guests in Zeolites: Acyclic Olefins and Host-Guest Interactions
We performed experimental and periodic density functional theory (DFT) IR spectroscopy to investigate the adsorption of acyclic olefins over both acidic and non-acidic zeolites. Two conjugated polyenes, 2,4-dimethyl-1,3-pentadiene (I) and 2,6-dimethyl-2,4,6-octatriene (II) were studied to probe organic intermediates that can form during methanol conversion, and that can lead to deactivating species known collectively as “coke.” We computed vibrational spectra using zeolite-adsorbed and gas-phase models for both neutral and protonated forms of I and II, and compared these DFT results to diffuse reflectance IR Fourier transform (DRIFT) spectra of zeolite-guest systems. Our experimental and computational results are precise enough to pinpoint that the gauche s-cis conformation of species I predominates during adsorption over de-aluminated zeolite beta. Computed zeolite-adsorbed spectra of the protonated species I and II best represent the DRIFT spectra obtained after the adsorption of the olefins on HMOR at 20 ?, with computed bands at 1543 and 1562 cm–1 for molecules I+ and II+, respectively, attributed to the allylic stretching mode, ?(C=C–C+). These computed band frequencies are within 6 cm–1 of experimental data and confirm that the interaction between neutral acyclic olefins and acidic zeolites leads to protonation of the olefin. Comparison of computed spectra of the protonated species in the gas phase to those in the zeolite indicates that the electrostatic interaction between alkenyl and alkadienyl cations and negative zeolite framework does not significantly impact the position of the allylic stretching bands. These results highlight that computed spectroscopy and thermodynamics coupled with experimental spectra can be used for elucidating complex mixtures in zeolites, and certain spectral features of adsorbed olefins can be accurately modeled by gas-phase calculations. ACKNOWLEDGMENTS BM and SMA were supported by the National Science Foundation under Award # CBET-1512442. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support (or partial support) of this research (#589978-ND5, EDH and FCJ). MDB was supported by the US Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Division of Materials Science and Engineering. CJM was supported by the DOE, Office of Science, BES, Division of Chemical Sciences, Geosciences, and Biosciences. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated by Battelle for the U.S. DOE. Computing resources were generously provided through PNNL’s institutional computing (PIC).
Manookian, Babgen↗
Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities
Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.
97 MATHEMATICS AND COMPUTING↗
Advancing our understanding of instabilities in high-energy-density systems through the study of benchmark datasets with advanced modeling tools
Numerous types of pulsed power driven inertial confinement fusion (ICF) and high energy density (HED) systems rely on implosion stability to achieve desired temperatures, pressures, and densities. Sandia National Laboratories Pulsed Power Sciences Center’s main ICF platform, Magnetized Liner Inertial Fusion (MagLIF), suffers from implosion instabilities which limit attainable fuel conditions and can compromise fuel confinement. This Truman Fellowship research primarily focused on computationally exploring (a) methods for improving our understanding of hydrodynamic and magnetohydrodynamic instabilities that form during cylindrical liner implosions, (b) methods for mitigating implosion instabilities, particularly those that degrade performance of MagLIF targets, and (c) novel MagLIF target designs intended to improve target performance primarily via enhanced implosion stability. Several multi-dimensional computational tools were used, including the magnetohydrodynamics code ALEGRA, the radiation-magnetohydrodynamics code HYDRA, and the magnetohydrodynamics code KRAKEN. This research succeeded in executing and analyzing simulations of automagnetizing liner implosions, shockless MagLIF implosions, dynamic screw pinch driven cylindrical liner implosions, and cylindrically convergent HED instability studies. The methods and tools explored and developed in this Truman Fellowship research have been published in several peer-reviewed journal articles and will serve as useful contributions to the fields of pulsed power science and engineering, particularly pertaining to pulsed power ICF and HED science.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Polyphenol-Sensitized Atomic Layer Deposition for Membrane Interface Hydrophilization
Improvements in energy-water systems will necessitate fabrication of high-performance separation membranes. To this end, interface engineering is a powerful tool for tailoring properties, and atomic layer deposition (ALD) has recently emerged as a promising and versatile approach. However, most non-polar polymeric membranes are not amenable to ALD processing due to the absence of nucleation sites. Here, a sensitization strategy for ALD-coating is presented, illustrated by membrane interface hydrophilization. Facile dip-coating with polyphenols effectively sensitizes hydrophobic polymer membranes to TiO2 ALD coating. Tannic acid-sensitized ALD-coated membranes exhibit outstanding underwater crude oil repulsion and rigorous mechanical stability through bending and rinsing tests. As a result, these membranes demonstrate outstanding crude oil-in-water separation and reusability compared to untreated membranes or those treated with ALD without polyphenol pretreatment. A possible polyphenol-sensitized ALD mechanism is proposed involving initial island nucleation followed by film intergrowth. This polyphenol sensitization strategy enriches the functionalization toolbox in material science, interface engineering, and environmental science.
36 MATERIALS SCIENCE↗
2022 Annual Report Laboratory Directed Research & Development
Idaho National Laboratory’s (INL’s) mission is “to discover, demonstrate and secure innovative nuclear energy solutions, other clean energy options and critical infrastructure.” INL executes this mission through research and development across the continuum from basic science to applied science to engineering demonstration and then deployment. The Department of Energy (DOE) Laboratory Directed Research and Development (LDRD) program enables INL to conduct high-risk, impactful research that enriches the laboratory capabilities in order to further its missions. INL’s LDRD portfolio specifically advances the core capabilities of the laboratory aligned with its five science and technology initiatives: 1) nuclear reactor sustainment and expanded deployment, 2) integrated fuel cycle solutions, 3) integrated energy systems, 4) advanced design and manufacturing for extreme environments, and 5) secure and resilient cyber-physical systems. The 45 projects that ended in fiscal year 2022 and highlighted in this report are just a small sample of the impressive breadth and depth of cutting-edge science, technology, and engineering ongoing at INL.
99 GENERAL AND MISCELLANEOUS↗
LLMs for Mfg.—On the State of Large Language Models and Applications to Manufacturing
Additive Manufacturing (AM), referred to as 3D printing, has emerged as a key pillar of Industry 4.0 enabling layer-by-layer fabrication of intricate geometries from CAD models. In parallel, Large Language Models (LLMs), deep learning models for natural language generation trained on vast text corpora, have demonstrated unprecedented capabilities in understanding and generating human-like text. The convergence of these trends opens new opportunities at the intersection of AM and AI/ML, where LLMs can assist engineers and researchers in design, manufacture planning, and knowledge discovery. Recent academic work has begun to explore LLM applications in AM and adjacent fields, such as material science, mechanical engineering, and design for additive manufacturing. This exploration ranges from intelligent process planning to domain-specific knowledge retrieval. This survey provides a comprehensive review of current developments, focusing on peer-reviewed literature contributions that apply, adapt, and advance LLMs in general and domain-specific domains. We analyze state-of-the-art (SOTA) techniques, such as fine-tuning foundational models for specific domains, retrieval-augmented generation (RAG) pipelines, knowledge graph integration, and delve into the architectures and evaluation methods employed. The goal of this survey is to inform researchers and practitioners of the current capabilities and limitations of LLMs in general and in domain-specific applications, and to outline how these models are being tailored to meet the requirements of these applications.
36 MATERIALS SCIENCE↗
Using real-time data analysis to conduct next-generation synchrotron fatigue studies
Next-generation experimental techniques, like high energy X-ray diffraction microscopy (HEDM), usher in new opportunities to collect the grain-scale data necessary for understanding the evolving processes that drive fatigue failure. In this study, we present a framework for monitoring the evolution of a deforming polycrystal, in real-time, by applying principal component analysis (PCA) to raw X-ray diffraction image data. We applied this framework to inform in-situ HEDM measurements of a cyclically loaded Inconel-718 superalloy. Further, we discovered correlations between PCA of the diffraction data and the physical processes in the polycrystal. Lastly, we discuss extending this framework in future HEDM fatigue studies.
36 MATERIALS SCIENCE↗