Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Fundamental Frequency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-driven prediction of the output composition of an atmospheric pressure plasma jet

Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O 3 , N 2 O, and NO 2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. In conclusion, this methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Insights into the Promotion with Ru of Co/TiO 2 Fischer–Tropsch Catalysts: An In Situ Spectroscopic Study

Although ruthenium is a common promoter for cobalt-based Fischer–Tropsch synthesis (FTS) catalysts, the origin of Ru promotion and promoter concentration effects remains controversial. To gain a fundamental understanding of the Ru promotion effects, we herein performed an in situ spectroscopic study by X-ray absorption spectroscopy (XAS), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS)–Auger of working Co/TiO 2 catalysts (ca. 12 wt % Co, pure anatase TiO2 phase) promoted with different amounts of Ru (0.1–1.2 wt %). At typical FTS conditions (220 °C, 20 bar, H 2 /CO = 2), the activity in terms of both metal-time-yield (MTY) and initial turnover frequency (TOF) was maximized for the catalysts promoted with 0.1–0.2 wt % Ru, for which most of the Ru was alloyed with Co in bimetallic nanoparticles. The analysis of Auger electrons evidenced that the lower (apparent) TOF of accessible Co0 sites in the absence of Ru and at Ru concentrations beyond 0.2 wt % is bridged to the development, under reaction conditions, of FTS-inactive cobalt carbide (CoC x ) and cobalt oxide (CoO x ) patches, respectively, at the outermost surface of cobalt crystallites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Toward improved quantum simulations and sensing with trapped two-dimensional ion crystals via parametric amplification

Improving coherence is a fundamental challenge in quantum simulation and sensing experiments with trapped ions. Here we discuss, experimentally demonstrate, and estimate the potential impacts of two different protocols that enhance, through motional parametric excitation, the coherent spin-motion coupling of ions obtained with a spin-dependent force. The experiments are performed on two-dimensional crystal arrays of approximately 100 9 ⁢Be + ions confined in a Penning trap. By modulating the trapping potential at close to twice the center-of-mass mode frequency, we squeeze the motional mode and enhance the spin-motion coupling while maintaining spin coherence. With a stroboscopic protocol, we measure 5.4 ± 0.9 dB of motional squeezing below the ground-state motion, from which theory predicts a 10-dB enhancement in the sensitivity for measuring small displacements using a recently demonstrated protocol [K. A. Gilmore et al., Science 373, 673 (2021)]. With a continuous squeezing protocol, we measure and accurately calibrate the parametric coupling strength. Theory suggests this protocol can be used to improve quantum spin squeezing, limited in our system by off-resonant light scatter. Finally, we illustrate numerically the trade-offs between strong parametric amplification and motional dephasing in the form of center-of-mass frequency fluctuations for improving quantum spin squeezing in our setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2026

Constraints on the polarization angle oscillations of the Crab Nebula with the Simons Array and its applications to the search for axionlike particles

Here, we present a search for polarization oscillation of the Crab Nebula, also known as Tau A, at millimeter wavelengths using observations with the Simons Array, the successor experiment to POLARBEAR. We follow up on previous work by POLARBEAR using 90 GHz band data of the 2023 observing season of the Simons Array to evaluate the variability of Tau A’s polarization angle. Tau A is widely used as a polarization angle calibration source in millimeter-wave astronomy, and thus it is necessary to validate the stability. Additionally, an interesting application of the time-resolved polarimetry of Tau A is to search for axionlike particles (ALPs). We do not detect a global signal across the frequencies considered in this analysis and place a median 95% upper bound of polarization oscillation amplitude A < 0.12° over oscillation frequencies from 3.39 yr -1 to 1.50 day -1 . This constrains the ALP-photon coupling at a median 95% upper bound of g aγγ < 3.84 × 10 -12 × (m a /10 -21 eV) in the mass range from 4.4 × 10 -22 to 7.2 × 10 -20 eV, assuming the ALP constitutes all of dark matter, its field is a stochastic Gaussian field, and it is the sole source of Tau A’s polarization angle oscillation. Additionally, we do not detect signal at the frequencies where 2.5σ hints were previously reported by POLARBEAR, but we do not exclude these signals at the 95% confidence level.

Adkins, Tylor [University of California, Berkeley]↗
DOE OSTI2022

Active and tunable nanophotonic metamaterials

Abstract Metamaterials enable subwavelength tailoring of light–matter interactions, driving fundamental discoveries which fuel novel applications in areas ranging from compressed sensing to quantum engineering. Importantly, the metallic and dielectric resonators from which static metamaterials are comprised present an open architecture amenable to materials integration. Thus, incorporating responsive materials such as semiconductors, liquid crystals, phase-change materials, or quantum materials (e.g., superconductors, 2D materials, etc.) imbue metamaterials with dynamic properties, facilitating the development of active and tunable devices harboring enhanced or even entirely novel electromagnetic functionality. Ultimately, active control derives from the ability to craft the local electromagnetic fields; accomplished using a host of external stimuli to modify the electronic or optical properties of the responsive materials embedded into the active regions of the subwavelength resonators. We provide a broad overview of this frontier area of metamaterials research, introducing fundamental concepts and presenting control strategies that include electronic, optical, mechanical, thermal, and magnetic stimuli. The examples presented range from microwave to visible wavelengths, utilizing a wide range of materials to realize spatial light modulators, effective nonlinear media, on-demand optics, and polarimetric imaging as but a few examples. Often, active and tunable nanophotonic metamaterials yield an emergent electromagnetic response that is more than the sum of the parts, providing reconfigurable or real-time control of the amplitude, phase, wavevector, polarization, and frequency of light. The examples to date are impressive, setting the stage for future advances that are likely to impact holography, beyond 5G communications, imaging, and quantum sensing and transduction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measuring Black Hole Light Echoes with Very Long Baseline Interferometry

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this Letter, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Reproducibility in G 0 W 0 calculations for solids

Ab initio many-body perturbation theory within the GW approximation is a Green's function formalism widely used in the calculation of quasiparticle excitation energies of solids. In what has become an increasingly standard approach, Kohn–Sham eigenenergies, generated from a DFT calculation with a strategically-chosen exchange–correlation functional “starting point”, are used to construct G and W, and then perturbatively corrected by the resultant GW self-energy. In practice, there are several ways to construct the GW self-energy, and these can lead to variations in predicted quasiparticle energies. For example, for ZnO and TiO 2 , the GW fundamental gaps reported in the literature can vary by more than 1 eV depending on the GW code used. In this work, we calculate and analyze GW quasiparticle (QP) energies of these and other systems with three different GW codes: BERKELEYGW, ABINIT and YAMBO. Through a systematic analysis of the GW implementation of these three codes, we identify the primary origin of major discrepancies between codes reported in prior literature to be the different implementations the Coulomb divergence in the Fock exchange term and the frequency integration scheme of the GW self-energy. We then eliminate these discrepancies by using common numerical methods and algorithms, demonstrating that the same quasiparticle energies for a given material can be obtained with different codes, within numerical differences ascribable to the technical details of the underling implementations. This work will be important for users and developers in assessing the precision of future GW applications and methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sim-Situ: A Framework for the Faithful Simulation of in situ Processing

The amount of data generated by numerical simulations in various scientific domains led to a fundamental redesign of how the analysis and visualization of simulation outputs are performed. The throughput and capacity of storage subsystems have not evolved as fast as the computing power in extreme-scale supercomputers, making the classical post-hoc approach highly inefficient. In situ processing has then emerged as a solution in which simulation and data analysis/visualization are intertwined for better performance and greater interactivity.Determining the best allocation, i.e., how many resources to allocate to simulation and analysis respectively, mapping, i.e., where and at which frequency to run the analysis/visualization, and data transfer mode is a complex task whose performance assessment is crucial to the efficient execution of in situ processing. However, such a performance evaluation of different strategies usually relies either on directly running them on the targeted execution environments, which can rapidly become extremely time- and resource-consuming, or on resorting to simplified models of the components of an in situ application, which can lack of realism. In both cases, the validity of the performance evaluation is limited.In this paper, we present Sim-Situ, a simulation-based framework for the faithful performance evaluation of in situ processing strategies. We designed Sim-Situ to reflect the typical features of in situ processing systems. Thanks to its modular design, Sim-situ has the necessary flexibility to easily and faithfully evaluate the behavior and performance of various allocation, mapping, and data transfer strategies. We illustrate the simulation capabilities of Sim-Situ on a Molecular Dynamics use case. We study the impact of different strategies on performance and show how users can leverage Sim-Situ to determine interesting tradeoffs when adding analysis/visualization components to their application.

Honoré, Valentin↗
DOE OSTI2026

Self-Cooling Multiferroic Magnetic Devices

Increasing switching frequency reduces magnetic volume, but conventional ferrites, used from tens to hundreds of kilohertz, cannot sustain the temperature and frequency ranges demanded by current and emerging wide bandgap and ultrawide bandgap devices. Here, this work presents a novel magnetic material architecture combining nanocrystalline magnetic material and multiferroic layers for megahertz power conversion. The high saturation flux density of nanocrystalline alloys supports miniaturization but is traditionally constrained by excessive losses above 10 kHz. A revolutionary multiferroic material with solid-state cooling via caloric materials is defined that will enable the next generation of magnetic devices for wide-bandgap-integrated designs. This letter highlights the fundamental physics behind this capability alongside early development of a finite element analysis for the multiferroic-based magnetic device using ANSYS, showing that the core achieves more uniform thermal distribution and reduces peak temperature by 9 ° C compared to conventional ferrites.

Soft magnetic materials↗
DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Application of Accelerator Technology to Quantum Information Science

The intersection of accelerator and quantum information science (QIS) offers a unique platform to advance both fields through shared technology and infrastructure. This talk will discuss the synergies which exist between these two vastly different but complementary domains. We demonstrate how we leverage pre-existing infrastructure and knowledge to perform research and development which helps to realize dramatic improvements in both 10 km long accelerators and 10 cm large quantum processors. We will explore niobium superconducting radio-frequency (SRF) cavities, a highly advanced technology that excels in efficiently storing electromagnetic energy, enabling ultra-long photon lifetimes critical for quantum processors and facilitating the characterization of quantum materials with parts-per-billion precision. We will also discuss how advancements in superconducting materials, cryogenic systems, and control techniques help to reduce cost and improve performance for both quantum systems and particle accelerators. Moreover, we will discuss cross-disciplinary applications such as dark-matter searches and demonstrate the convergence of these fields in addressing fundamental scientific questions.

Bafia, Daniel [Fermilab]↗
DOE OSTI2022

Feasibility Study of Millimeter Wave Radars For Safeguards Applications

Containment and surveillance are fundamental measures in nuclear safeguards. Techniques such as video surveillance and laser curtain for containment provide effective monitoring in areas where maintaining continuity of knowledge is required. These systems, however, can be susceptible to loss of monitoring capabilities under certain environmental conditions such as poor visibility (i.e. low light conditions, smoke, fog, etc.) or extended power loss past the duration that the backup power system is designed for. Brookhaven National Laboratory has been investigating the feasibility of millimeter waves (mmWave) as a new perimeter seal in which radio frequency waves in the range of 60-64 GHz are used to detect and monitor objects of interest. Signals in this frequency range are not susceptible to environmental conditions. For proof-of-concept tests, mmWave sensors from Texas Instruments (TI), specificallyIWR6843, are used in a test bed at BNL's Waste Management facility to simulate the operations at nuclear facilities. The unique design of TI mmWave sensors requires less memory and power consumption compared to counterpart systems. These devices are capable of exporting 3D point-cloud data, which is visualized graphically and compared to videos recorded at the same time to validate the performance of the mmWave sensor. A set of experiments were planned to test the feasibility of the mmWave in this application, including monitoring static containers in a storage area and detecting intrusions at the boundaries of the area. In addition, the experiments also identify potential blind spots relative to sensor position and utilize multiple operating sensors simultaneously to reduce or eliminate such blind spots. The optimal positioning of multiple sensors was determined for the experimental room configuration. In this paper, we will discuss the details of this novel perimeter sealing concept and present the test results.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Light-Induced Charge Separation in Photosystem I from Different Biological Species Characterized by Multifrequency Electron Paramagnetic Resonance Spectroscopy

Photosystem I (PSI) serves as a model system for studying fundamental processes such as electron transfer (ET) and energy conversion, which are not only central to photosynthesis but also have broader implications for bioenergy production and biomimetic device design. In this study, we employed electron paramagnetic resonance (EPR) spectroscopy to investigate key light-induced charge separation steps in PSI isolated from several green algal and cyanobacterial species. Following photoexcitation, rapid sequential ET occurs through either of two quasi-symmetric branches of donor/acceptor cofactors embedded within the protein core, termed the A and B branches. Using high-frequency (130 GHz) time-resolved EPR (TR-EPR) and deuteration techniques to enhance spectral resolution, we observed that at low temperatures prokaryotic PSI exhibits reversible ET in the A branch and irreversible ET in the B branch, while PSI from eukaryotic counterparts displays either reversible ET in both branches or exclusively in the B branch. Furthermore, we observed a notable correlation between low-temperature charge separation to the terminal [4Fe-4S] clusters of PSI, termed F A and F B , as reflected in the measured F A /F B ratio. These findings enhance our understanding of the mechanistic diversity of PSI’s ET across different species and underscore the importance of experimental design in resolving these differences. Though further research is necessary to elucidate the underlying mechanisms and the evolutionary significance of these variations in PSI charge separation, this study sets the stage for future investigations into the complex interplay between protein structure, ET pathways, and the environmental adaptations of photosynthetic organisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

ETG turbulence in a tokamak pedestal

This paper explores the fundamental characteristics of electron-temperature-gradient (ETG)-driven turbulence in the tokamak pedestal. The extreme gradients in the pedestal produce linear instabilities and nonlinear turbulence that are distinct from the corresponding ETG phenomenology in the core plasma. The linear system exhibits multiple (greater than ten) unstable eigenmodes at each perpendicular wave vector, representing different toroidal and slab branches of the ETG instability. Proper orthogonal decomposition of the nonlinear fluctuations reveals no clear one-to-one correspondence between the linear and nonlinear modes for most wave vectors. Moreover, nonlinear frequencies deviate strongly from those of the linear instabilities, with spectra peaking at positive frequencies, which is opposite the sign of the ETG instability. The picture that emerges is one in which the linear properties are preserved only in a narrow range of k-space. Outside this range, nonlinear processes produce strong deviations from both the linear frequencies and eigenmode structures. This is interpreted in the context of critical balance, which enforces alignment between the parallel scales and fluctuation frequencies. We also investigate the nonlinear saturation processes. We observe a direct energy cascade from the injection scale to smaller scales in both perpendicular directions. However, in the bi-normal direction, there is also nonlocal inverse energy transfer to larger scales. Neither streamers nor zonal flows dominate the saturation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗