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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 937 records · Page 52

A Clarke Transformation-Based DFT Phasor and Frequency Algorithm for Wide Frequency Range

Despite its wide applications in power grid monitoring, the classic discrete Fourier transform (DFT)-based synchrophasor estimation algorithms suffer from significant errors when the power system operates under off-nominal frequency conditions. This phenomenon is caused by spectral leakage of DFT and becomes even more severe for single-phase synchrophasor estimation. To address this issue, a theory to eliminate the spectral leakage-caused errors is proposed and a Clarke transformation-based DFT synchrophasor estimation algorithm is proposed to implement the theory in this paper. The Clarke transformation constructs a second signal that has exactly 90° phase angle difference from the original single-phase input signal and helps eliminate the estimation errors for a wide frequency range. The proposed algorithm is tested under the conditions required in the phasor measurement unit standard C37.118.1-2011 and C37.118.1a-2014, as well as the harmonic and noise conditions not required in the standard to verify its performance. More importantly, the idea of using Clarke transformation can be used for other DFT-based synchrophasor algorithms in order to achieve higher synchrophasor measurement accuracy under dynamic conditions. An example is presented at last to demonstrate the expandability of the proposed idea.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Cascading Transformer Failure Probability Model Under Geomagnetic Disturbances

This paper develops a probabilistic model to assess the cascading failure of transformers in an electric power grid experiencing geomagnetic disturbances caused by a solar storm. We propose a model in which the probability of failure is a function of the intensity of the solar storm, the physical properties of the transformer, the geographical location of the transformer, and the flow of electrical power. We demonstrate the proposed model using the IEEE 14-bus system and several notional solar storms. The model quickly computes the initial and cascading failure probabilities of the transformers in the system as a first step towards quantifying the risks posed by future solar storms.

Shukla, Pratishtha↗

Soft crystal martensites: An in situ resonant soft x-ray scattering study of a liquid crystal martensitic transformation

Liquid crystal blue phases (BPs) are three-dimensional soft crystals with unit cell sizes orders of magnitude larger than those of classic, atomic crystals. The directed self-assembly of BPs on chemically patterned surfaces uniquely enables detailed in situ resonant soft x-ray scattering measurements of martensitic phase transformations in these systems. The formation of twin lamellae is explicitly identified during the BPII-to-BPI transformation, further corroborating the martensitic nature of this transformation and broadening the analogy between soft and atomic crystal diffusionless phase transformations to include their strain-release mechanisms.

36 MATERIALS SCIENCE↗

P3DFFT: A Framework for Parallel Computations of Fourier Transforms in Three Dimensions

Fourier and related transforms are a family of algorithms widely employed in diverse areas of computational science, notoriously difficult to scale on high-performance parallel computers with a large number of processing elements (cores). This paper introduces a popular software package called P3DFFT which implements fast Fourier transforms (FFTs) in three dimensions in a highly efficient and scalable way. It overcomes a well-known scalability bottleneck of three-dimensional (3D) FFT implementations by using two-dimensional domain decomposition. Designed for portable performance, P3DFFT achieves excellent timings for a number of systems and problem sizes. On a Cray XT5 system P3DFFT attains 45% efficiency in weak scaling from 128 to 65,536 computational cores. Library features include Fourier and Chebyshev transforms, Fortran and C interfaces, in- and out-of-place transforms, uneven data grids, and single and double precision. P3DFFT is available as open source at http://code.google.com/p/p3dfft/. This paper discusses P3DFFT implementation and performance in a way that helps guide the user in making optimal choices for parameters of their runs. Read More: https://epubs.siam.org/doi/10.1137/11082748X

Mathematics↗

Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration library is intended to aid in the explorability and explainability of transformer classification networks, or transformer language models with sequence classification heads. The basic function of this library is to take a trained transformer and test/train dataset and produce an ipywidget dashboard which can be displayed in a jupyter notebook or in jupyter lab.

Martindale, Nathan [Oak Ridge National Lab. (ORNL)↗

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an offthe-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from wellcurated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

60 APPLIED LIFE SCIENCES↗

Exactly unitary discrete representations of the metaplectic transform for linear-time algorithms

The metaplectic transform (MT), a generalization of the Fourier transform sometimes called the linear canonical transform, is a tool used ubiquitously in modern optics, for example, when calculating the transformations of light beams in paraxial optical systems. The MT is also an essential ingredient of the geometrical-optics modeling of caustics that we recently proposed. In particular, this application relies on the near-identity MT (NIMT); however, the NIMT approximation used so far is not exactly unitary and leads to numerical instability. Here, we develop a discrete MT that is exactly unitary, and approximate it to obtain a discrete NIMT that is also unitary and can be computed in linear time. We prove that the discrete NIMT converges to the discrete MT when iterated, thereby allowing the NIMT to compute MTs that are not necessarily near-identity. Finally, we then demonstrate the new algorithms with a series of examples.

47 OTHER INSTRUMENTATION↗

Transition of dislocation glide to shear transformation in shocked tantalum

A TEM study of pure tantalum and tantalum-tungsten alloys explosively shocked at a peak pressure of 30 GPa (strain rate: ~1 x 10 4 sec -1 ) is presented. While no ω (hexagonal) phase was found in shock-recovered pure Ta and Ta-5W that contain mainly a low-energy cellular dislocation structure, shock-induced ω phase was found to form in Ta-10W that contains evenly distributed dislocations with a stored dislocation density higher than 1 x 10 12 cm -2 . The TEM results clearly reveal that shock-induced α (bcc) → ω (hexagonal) shear transformation occurs when dynamic recovery reactions which lead the formation low-energy cellular dislocation structure become largely suppressed in Ta-10W shocked under dynamic (i.e., high strain-rate and high-pressure) conditions. A novel dislocation-based mechanism is proposed to rationalize the transition of dislocation glide to twinning and/or shear transformation in shock-deformed tantalum. Lastly, twinning and/or shear transformation take place as an alternative deformation mechanism to accommodate high-strain-rate straining when the shear stress required for dislocation multiplication exceeds the threshold shear stresses for twinning and/or shear transformation.

36 MATERIALS SCIENCE↗

TX$^2$: Transformer eXplainability and eXploration

The Transformer eXplainability and eXploration (Martindale & Stewart, 2021), or TX 2 software package, is a library designed for artificial intelligence researchers to better understand the performance of transformer models (Vaswani et al., 2017) used for sequence classification. The tool is capable of integrating with a trained transformer model and a dataset split into training and testing populations to produce an ipywidget (Project Jupyter Contributors, 2021) dashboard with a number of visualizations to understand model performance with an emphasis on explainability and interpretability. The TX 2 package is primarily intended to integrate into a workflow centered around Jupyter Notebooks (Kluyver et al., 2016), and currently assumes the use of PyTorch (Paszke et al., 2019) and Hugging Face transformers library (Wolf et al., 2020). The dashboard includes visualization and data exploration features to aid researchers, including an interactive UMAP embedding graph (McInnes et al., 2018) to understand classification clusters, a word salience map that can be updated as researchers alter textual entries in near real time, a set of tools to understand word frequency and importance based on the clusters in the UMAP embedding graph, and a set of traditional confusion matrix analysis tools.

97 MATHEMATICS AND COMPUTING↗

High Voltage Modeling and Testing of Transformer, Line Interface Devices, and Bulk System Components Under Electromagnetic Pulse, Geomagnetic Disturbance, and other Abnormal Transients

This project aims at quantitatively analyzing possible knowledge gaps related to the risk of Electromagnetic Pulse (EMP) impact on high-voltage, power transformers (transmission-class), that represent the most critical power grid assets. In relation to the electric infrastructure, some past studies indicate that the grid is overall fairly robust and resilient against most EMP threats. This assessment stems, in part, from estimates of the peak voltage that can be induced on a transmission power line for a nominal EMP fast-transient, compared with the typically very high basic insulation level that is featured by the high-voltage infrastructure. The project provides additional insight toward a more comprehensive risk evaluation of the grid EMP and Geomagnetic Disturbances (GMD) vulnerabilities. This work is aligned with the U.S. Dept. of Energy current research thrust in support of the development of a highly secure and resilient power infrastructure. Project milestones have been devised to address the fundamental technical issues in support of protection of large power transformer against EMP threats. More specifically, this project included parallel research tasks focused on the following topics: analysis of EMP-induced, fast-transient, current and voltages on a variety of power line configurations and EMP impact scenarios, detailed characterization of EMP damage potential due to the voltage surge, from the impact on the power line, to the transformer windings, GMD and EMP slow transients and impact from harmonics generation and reactive power flow on a complex grid layout, and identification of risk mitigation paths, with emphasis on GIC damage prevention for large power transformers, and on impact protection options from a grid stability perspective.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Electrochemically-Driven Phase Transformations in Battery Compounds (Final Technical Report)

Ion-insertion compounds for advanced batteries frequently exhibit phase transformations as the concentration of the working ion varies. Under the large electrochemical driving forces inherent to practical use, systems are often driven far from equilibrium and exhibit phase transition behavior not seen in other materials. An improved understanding of phase transformation pathways in electrode materials upon cycling will lead to new materials design concepts and electrochemical duty cycle management strategies that improve capacity utilization at high charge/discharge rates, reduce voltage and capacity hysteresis, and extend battery life. The main goal of this project is to develop a predictive understanding of the phase transition behavior of battery compounds when electrochemically driven far from equilibrium through a combined experimental- theoretical approach. Research focuses on elucidating how the phase transition behaviors in a diverse group of selected model systems (olivines, lithium metal, etc.) are regulated by various factors including transformation strains, plasticity, metastable transformation pathways, electrode microstructure, surface reaction and ion diffusion kinetics, and what are the unique features of phase transitions in battery compounds. To this end, operando X-ray-based characterization techniques are applied to interrogate the structure and composition evolution of electrode materials and interfaces at nano- and meso-scales. Mesoscale modeling techniques such as phase-field simulation are employed to shed light on the experimental observations and establish a unified framework for describing phase transition behaviors in battery compounds.

25 ENERGY STORAGE↗

Basic Research Needs for Transformative Manufacturing (Brochure)

Manufacturing is central to the nation’s prosperity and security. Manufacturing currently represents about 12% of the gross domestic product, provides nearly 13 million jobs, and accounts for about 25% of energy use. The nation’s economy relies heavily on wide-ranging manufacturing sectors - all of which share common challenges including data issues, lack of physics and chemistry-based models across scales, and resource constraints in a global environment. Furthermore, there are many hurdles that must be overcome to move basic science innovations to market. Addressing broad-ranging challenges demands a basic-science strategy that underpins applied research activities. This strategy would accelerate innovation and transform manufacturing. A Basic Research Needs workshop for Transformative Manufacturing was held in March 2020. The focus of the workshop was to identify the basic science research priorities that could accelerate innovation to transform manufacturing in the future. This was the first workshop of its kind to examine how basic energy science can drive manufacturing forward and innovate new ways to manufacture goods. Five Priority Research Directions were identified that address these science challenges: (1) innovative synthetic approaches to enable scalable assembly of matter, (2) computational methods and theoretical models to transform how manufacturing processes are controlled, (3) new characterization tools that can handle the necessary complexity, scales, and processing speeds to meet manufacturing needs, (4) new science to address opportunities relevant to sustainable and energy-efficient manufacturing, and (5) foundational approaches to co-design of materials, process, and products.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modeling Failure of Electrical Transformers due to Effects of a HEMP Event

Understanding the effect of a high-altitude electromagnetic pulse (HEMP) on the equipment in the United States electrical power grid is important to national security. A present challenge to this understanding is evaluating the vulnerability of transformers to a HEMP. Evaluating vulnerability by direct testing is cost-prohibitive, due to the wide variation in transformers, their high cost, and the large number of tests required to establish vulnerability with confidence. Alternatively, material and component testing can be performed to quantify a model for transformer failure, and the model can be used to assess vulnerability of a wide variety of transformers. This project develops a model of the probability of equipment failure due to effects of a HEMP. Potential failure modes are cataloged, and a model structure is presented which can be quantified by the results of small-scale coupon tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗

HEMP Transformer Defense Through Power Electronics

High altitude electromagnetic pulses and geo-magnetic disturbances have the potential to severely impact the electric power grid by damaging large power transformers and causing severe power quality issues. This impact comes as a result of a quasi-static bias induced on transmission lines by geomagnetically induced currents which saturate magnetic components in the electric power system. This paper introduces the concept of utilizing a h-bridge inverter on the neutral of a LPT to inject a DC bias equivalent voltage onto the neutral side of the transformer windings. This biasing floats the transformer windings, eliminating the effect of the DC current and keeping the transformer from saturating. Schematic diagrams will be presented, along with simulation model data using Typhoon and PLECS, and finally test results from a benchtop hardware test.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy (Workshop Report)

The US Department of Energy’s (DOE’s) Advanced Manufacturing Office held the virtual workshop entitled “Thermal Process Intensification: Transforming the Way Industry Uses Thermal Process Energy” in November and December 2020. The workshop brought together participants from universities/laboratories, industries, equipment manufacturers, technology vendors, nongovernmental organizations, and subject-matter experts to discuss transformative technologies and strategies to substantially improve the performance (e.g., energy productivity, thermal efficiency, reduced greenhouse gas [GHG] emissions, reduced number of process steps) of thermal processing systems in the industrial sector. The US industrial sector accounts for 32% of the nation’s primary energy use (including feedstocks), and refining, chemicals, pulp and paper, iron and steel, and food products represent the top energy-consuming sectors. Thermal processing or process heating represents the largest energy use category; it accounts for 63% of all energy use in manufacturing. Additionally, thermal processing is the largest contributor of carbon dioxide (CO2) generation, resulting from combustion of fuels and process related chemical reactions, such as in the case of cement and lime production. The challenge of achieving net-zero industrial GHG emissions is colossal considering the established industrial base that depends mainly on carbon-based processes and energy sources; the time frame and cost to replace carbon-based energy sources and feedstocks; and the long-term outlook for development with large-scale adaptation of alternative non carbon–based technologies. The goals of the DOE Advanced Manufacturing Office Thermal Process Intensification Workshop were as follows: (1) Identify R&D gaps and opportunities to facilitate transformative improvement in industrial thermal processes beyond current technologies and allow for entirely new methods for processing materials; (2) Gain insight into new and innovative approaches to thermally intensify processes, reduce heat demand, harness waste heat, and use fuels and hydrocarbon feedstocks more efficiently; (3) Identify the R&D pathways to thermal process intensification (TPI) with the highest potential for impact and adoption by the industrial sector; (4) Define areas of research, development, and demonstration (RD&D) activities to accelerate development and application of emerging and transformative technologies to intensify thermal processes in industry. The scope and focus of the workshop were defined to meet these goals. Based on the available data for energy use and GHG emissions, the industries that collectively use more than 80% of the total process heating energy consumption were selected as primary focus areas. The chosen industries were combined into the following four groups based on similarities in their thermal processes: high-temperature metal processing (iron and steel industry, alumina-aluminum industry); high-temperature nonmetal and mineral processing (cement and glass industry); medium- to low-temperature thermal processing (food processing and pulp and paper industry as part of forest products sector); and hydrocarbon processing (petroleum refining and chemical industry). Furthermore, all potential TPI technologies associated with the processes defined were considered as part of the workshop. Different types of TPI technologies possible in industrial in the document.

42 ENGINEERING↗

Vision Transformers Explained Series

Since their introduction in 2017 with Attention is All You Need¹, transformers have established themselves as the state of the art for natural language processing (NLP). In 2021, An Image is Worth 16x16 Words successfully adapted transformers for computer vision tasks. Since then, numerous transformer-based architectures have been proposed for computer vision. This article walks through the Vision Transformer (ViT) as laid out in An Image is Worth 16x16 Words.

97 MATHEMATICS AND COMPUTING↗

Overcoming Barriers in Plant Transformation: A Focus on Bioenergy Crops

Although 2023 marked the 40th anniversary of the first transgenic plant, routine transformation of most plant genotypes remains elusive. Rapid systems to overexpress, interfere, or knock out genes— collectively defined in this report as “transformation and editing technologies”—are needed to understand plant gene function. This understanding in turn is crucial for efficiently developing new, sustainable, high-yielding, and climate-resilient crops to meet the growing demand for food, feed, fiber, and fuel. In particular, the ability to apply transformation and editing technologies to bioenergy crops has remained largely unrealized. To address this opportunity, the U.S. Department of Energy (DOE) Biological and Environmental Research Program convened a workshop on September 18–20, 2023, to define transformation and editing needs and barriers focused on bioenergy crops. The main conclusions are summarized below.

09 BIOMASS FUELS↗

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗