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DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗

DOE OSTI2024

Direct comparison of using a Z -transformation instead of the traditional $Q^2$ for extraction of the proton radius from $e-p$ scattering data

A discrepancy in the determination of the proton’s charge radius, r p , between muonic hydrogen spectroscopy versus classic atomic spectroscopy and electron scattering data has become known as the proton radius puzzle. Extractions of r p from electron scattering data require determination of the slope of the proton’s charge form factor, $G$$^{p}_{E}$, in the limit of Q 2 → 0 through fitting and extrapolation. Some works have presented the Z-transformation fitting technique as the best choice for this type of extraction due to the true functional form of $G$$^{p}_{E}$ being mathematically guaranteed to exist within the parameter-space of the fit function. In this work, we test this claim by examining the mathematical bias and variances introduced by this technique as compared to the more traditional Q 2 fits using statistically sampled $G$$^{p}_{E}$ parameterizations with known input radii. Furthermore, our tests conclude that the quality of the Z-transformation technique depends on the range of data used. In the case of new experiments, the fit function and technique should be selected in advance by generating realistic pseudodata and assessing the power of different techniques.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset for Blueprinting Electrified Transit System Implementation

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

A Preliminary Feasibility Study on Multi-Cavity Cryomodule Integration for the Electron Ion Collider Energy Recover Linac Cooler

The Electron-Ion Collider (EIC) is a state-of-the-art accelerator designed for collisions between highly polarized electrons and ions. To achieve optimal luminosity, the ion beam is cooled using an electron beam sourced from an energy recovery linac (ERL). In the current ERL baseline design, one BNL type RF cavity is used per cryomodule, leading to spatial and cost challenges. This study examines the feasibility of using more compact PERLE (Powerful Energy Recovery Linac for Experiments) type cavities, which can house multiple cavities within a single cryomodule, by evaluating their Beam Breakup (BBU) instability performance. Higher Order Modes (HOM) parameters were obtained through frequency scaling, assuming constant quality factor Q_L, R/Q, and linearly scaling HOM frequencies. To predict the BBU threshold current and ensure accuracy, two different BBU tracking simulations are used for cross-verification. Although the reduced footprint of PERLE-type cavities is advantageous, maintaining sufficient damping of HOMs remains crucial. Finally, we compare the HOM damping efficiency of both cavity types and suggest a pathway forward.

Deitrick, K.↗

DOE OSTI2021

PVInsight (Final Technical Report)

Data generated from real-world photovoltaic (PV) systems represent significant opportunities for the industry–from digital operations and maintenance to real-time planning and forecasting. However, these data also come with substantial, unique challenges. A particular challenge is the analysis of unlabeled PV performance data, which we define as time-series measurements of real power production (or sometimes current or voltage) that do not have corresponding meteorological measurements (irradiance, temperature, etc.) or system configuration information (sometimes called system metadata). These challenges are amplified in the distributed rooftop sector, in which data quality, completeness, and metadata can be very poor.

14 SOLAR ENERGY↗

DOE OSTI2025

High-Quality Factor Microwave Resonators using Rhenium

Coplanar waveguide resonators are a perfect tool to evaluate the losses induced by defects and interfaces in superconducting devices. Even if niobium is the most used superconductor for resonators and qubits, its native oxide at the metal-air (MA) interface limits the device results. Tantalum has recently significantly improved qubit performances due to a thinner and less disordered oxide layer compared to Nb. To further improve the MA interface, we used rhenium, a superconducting material with 1.7 K critical temperature resistant to oxidation: it forms an oxide layer thinner than 1 nm. In this study, we will present a thorough investigation of rhenium CPW resonator measurements with internal quality factors at the single photon level exceeding 2 million. The devices have been fabricated on a sapphire substrate while the processing parameters have been varying and optimized. The measurements have been performed as a function of power and temperature to disentangle different sources of losses, such as two-level systems (TLS) and quasi-particles. A peculiar TLS temperature dependence has been measured and analyzed. In this work, we also vary the participation ratio of the devices to extract the losses introduced by the involved interfaces with higher fidelity and precision. We carried on a deep material characterization effort to link the results to the differences in the fabrication steps, and we will present material characterization measurements performed via AFM, ToF-SIMS, XPS, and TEM.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

What Is the Limit of Quantification for the Minor Phase in Time-of-Flight Neutron Diffraction? A Case Study on Fe and Ni Powder Mixtures at VULCAN

A phase present in small quantities within materials may not simply serve as a secondary component; it can play a crucial role in determining the integrity, properties, and performance of the material. These minor but important phases usually draw attention in material design and processing for fundamental understanding as well as material quality control. Accurately quantifying a minor phase amid a majority phase, especially at extremely low fractions, remains a challenging task. Time-of-flight neutron diffraction, coupled with advanced pattern analysis techniques like Rietveld refinement, is a powerful tool for crystal structure identification and phase quantification. The deep penetrating capability of neutrons enables the detection and quantification of trace phases within materials. In this study, the quantification limits of time-of-flight neutron diffraction were explored using the VULCAN diffractometer at the Spallation Neutron Source, using Fe–Ni powder mixtures as a sample system. By comparing the refinement results to the known weighed values, it was determined that the reliable quantification of a minor Ni phase is achievable down to about 0.1 wt% while a Ni fraction as low as 0.02 wt% is difficult to trace. Effective control of the refinement parameters, especially the profile function parameters, are found to significantly influence the convergence of fittings and the accuracy of phase quantification.

Rietveld refinement↗

DOE OSTI2020

Preparing Magnesium Alloys for Electron Backscatter Diffraction

Electron Backscatter Diffraction (EBSD) is a powerful characterization tool that is commonly employed to study grain size, grain morphology, crystallographic texture and other important microstructure parameters. However, its relatively low atomic number and propensity to corrode can make it difficult to get high quality EBSD patterns from magnesium specimens. This work will cover some common issues encountered during sample prep and suggestions for avoiding them. Microscope and EBSD detector settings will also be covered, as well as methods for optimizing instrument time. Lastly, comments will be made on how to “clean” and present EBSD data.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Electronic properties of corundum-like Ir 2 O 3 and Ir 2 O 3 -Ga 2 O 3 alloys

In the hexagonal, corundum-like structure, α-Ga 2 O 3 has a bandgap of ~5.1 eV, which, combined with its relatively small electron effective mass, high Baliga's figure of merit, and high breakdown field, makes it a promising candidate for power electronics. Ga 2 O 3 is easy to dope n-type, but impossible to dope p-type, impeding the realization of some electronic device designs. Developing a lattice-matched p-type material that forms a high-quality heterojunction with n-type Ga 2 O 3 would open new opportunities in electronics and perhaps optoelectronic devices. In this work, we studied Ir 2 O 3 as a candidate for that purpose. Using hybrid density functional theory calculations we predict the electronic band structure of α-Ir 2 O 3 and compare that to α-Ga 2 O 3 , and study the stability and electronic properties of α-(Ir x Ga 1–x ) 2 O 3 alloys. We discuss the band offset between the two materials and compare it with recently available experimental data. We find that the Ir d bands that compose the top of the valence band in α-Ir 2 O 3 are much higher in energy than O p bands in α-Ga 2 O 3 , possibly enabling effective p-type doping. Finally, our results provide an insight into using the Ir 2 O 3 or Ir 2 O 3 -Ga 2 O 3 alloys as p-type material lattice-matched to α-Ga 2 O 3 for the realization of p–n heterojunctions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A review of tidal energy—Resource, feedbacks, and environmental interactions

The ocean contains a variety of renewable energy resources, little of which has been exploited. Here, we review both tidal range and tidal stream energy, with a focus on the resource, feedbacks, and environmental interactions. The review covers a wide range of timescales of relevance to tidal energy, from fortnightly (spring-neap) and semi-diurnal variability, down to array, and device-scale turbulence. When simulating the regional tidal energy resource, and to assess environmental impacts, it is necessary to account for feedbacks between the tidal array and the resource itself. We critically review various methods for simulating energy extraction, from insights gained through theoretical studies of “tidal fences” in idealized channels, to realistic three-dimensional model studies with complex geometry and arrays of turbines represented by momentum sinks and additional turbulence due to the presence of rotors and support structures. We discuss how variability can be reduced by developing multiple (aggregated) sites with a consideration of the enhanced phase diversity offered by exploiting less energetic tidal currents. This leads to future research questions that have not yet been explored in depth at first-generation tidal sites in relatively sheltered channels (e.g., the interaction of waves with currents). Such enhanced understanding of real sea conditions, including the effects of wind and waves, leads to our other identified primary future research direction—reduced uncertainties in turbulence predictions, including the development of realistic models that simulate the interaction between ambient turbulence and the turbulence resulting from multiple wakes, and changes to system-wide hydrodynamics, water quality, and sedimentation.

Environmental impacts↗

DOE OSTI2022

Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search

The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient --- the sequential version can generate topologies with the same quality while being up to 67% faster. Here, the parallelization schemes can further achieve high speedups compared to the sequential version.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Capillary-Enhanced Two-Phase Micro-Cooler Using Copper-Inverse-Opal Wick with Silicon Microchannel Manifold for High-Heat-Flux Cooling Application

In this work, we demonstrate a two-phase capillary-fed boiling micro-cooler that consists of a ~ 25-..mu..m-thick copper inverse opal (CIO) porous wicking structure for high-heat-flux boiling and a silicon 3D-manifold for distributed liquid delivery and vapor extraction across a 0.5 cm x 0.5 cm heated area. At low inlet water mass flow rates of 1.5 to 1.9 g(min)-1, the micro-cooler displays nearly two-phase boiling with exit vapor quality ~ 1 and a high critical heat flux (CHF) of 253 to 320 W cm-2 with low superheat of ~ 10 degrees C resulting in a thermal resistance of boiling ~ 0.025 cm2 degrees C W-1 or heat transfer coefficient of 0.4 MW m-2 degrees C-1. For higher flow rates of 5, 10, and 15 g(min)-1, the micro-cooler exhibits a hybrid single-phase and two-phase cooling regime where the contribution of the sensible heat (single-phase) cooling is linearly added to that of the two-phase cooling. For the highest flow rate of 15 g(min)-1, the CHF is increased to ~ 500 W cm-2 resulting in an overall thermal resistance of ~ 0.18 cm2 degrees C W-1. However, the two-phase heat transfer effectiveness, which estimates the utilization level of the inlet mass flow rate for two-phase boiling, is reduced to ~ 0.11. To achieve the best cooling system performances, the micro-cooler must operate entirely within the two-phase boiling regime (exit vapor quality or two-phase heat transfer effectiveness ~ 1). Ideally, the "coolant" should be delivered near its saturation temperature (~ 100 degrees C for water), which provides significant advantages for the energy-efficient operation of data centers and power electronics. We present detailed analysis with Infrared and high speed camera images at various inlet flow rates and heat fluxes to understand complex heat transfer in the micro-cooler. Furthermore, a conjugate thermofluidic simulation model, which incorporates the physics of capillary-fed boiling in a porous copper layer, agrees well with the experimental data.

capillary-enhanced boiling↗

DOE OSTI2022

Preface to special topic: The High Repetition Rate Frontier in High-Energy-Density Physics

High-repetition-rate (HRR) experiments can collect large datasets with high temporal, spatial, and/or parametric resolution or large numbers of repeat measurements for statistics. HRR experiments also enable new experimental designs, including active feedback control loops and novel diagnostics, that can improve the reproducibility as well as the quantity of measurements. Together, these attributes make HRR experiments ideal for performing high-quality repeatable science. Until recently, these techniques have not been applied to high-energy-density–physics (HEDP) experiments, which are typically restricted to repetition rates of a few per day. However, recent advancements in lasers, pulsed-power drivers, target fabrication, and diagnostics are starting to change this fact, opening an exciting new frontier of HRR HEDP experiments. A mini-conference on this subject at the 2021 meeting of the American Physical Society Division of Plasma Physics brought together members of this growing community. As a result, the “High Repetition Rate Frontier in High-Energy-Density Physics” special topic in Physics of Plasmas highlights current progress in this exciting area.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A WISER way for simulating the performance of gratings

Soft X-ray monochromators for synchrotron radiation sources have been continuously developed over the years, improving energy resolution and stability. Great effort has been made in improving the surface quality of the optics involved, reaching values permitting diffraction-limited images. Still, one problem has not been solved, nor fully understood, yet: groove placing errors. Nowadays, these are one of the major factors responsible for resolving the power reduction of diffraction-gratings-based X-ray monochromators. Despite decades of use of gratings, there is not yet a universally established method for predicting and simulating the effect of groove placing errors on monochromator performance. This is especially important in the new scenario of high-coherent X-ray sources, i.e. diffraction-limited storage rings and free-electron lasers. To address this problem, in this article an approach based on WISER (Wavefront propagatIon Simulation codE libRary) is presented. WISER is a physical optics simulation package, also available in the user-friendly Orange Synchrotron Radiation Suite – OASYS. Even though it was originally conceived to assess the focusing performance of X-ray mirrors in the presence of height defects, it perfectly simulates the performance of a periodic (or quasi-periodic) structure like a diffraction grating. In this article, the way to use WISER and its application to a specific case, e.g. the design of a monochromator for the upgrade of a beamline at the Advanced Light Source, are shown. A simple rule for estimating how well the grooves are placed on a grating, based on calculation of the Strehl ratio, is also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Structure and Dynamics of Domains in Ferroelectric Nanostructures – Phase-Field Modeling (Final Technical Report)

Domain pattern formation is one of the most common phenomena in nature and is a topic of immense interest in many fields ranging from materials science and physics to chemistry and biology. This DOE sponsored research project explored the basic science concerning the thermodynamic stability of mesoscale polarization domain patterns and their temporal evolution mechanisms during formation and subsequent switching in ferroelectric nanostructures and heterostructures. The project employed the computational phase-field method in combination of microelasticity and electrostatic theories. The research was carried out in close collaboration with a number of experimental groups who used High Resolution Transmission Electron Microscopy (HRTEM), in situ TEM with Scanning Probe Microscopy (SPM), or Piezoresponse Force Microscopy (PFM) to characterize the domain structures and dynamics of ferroelectric thin films and heterostructures and who grow high-quality ferroelectric and multiferroic thin films using advanced growth techniques such as Molecular Beam Epitaxy (MBE), Pulsed Laser Deposition (PLLD), and sputtering. The research efforts of the project help establish the phase-field method as the most powerful method for understanding and predicting domain structures in ferroelectric thin films and nanostructures. The findings of the project led to the basic understanding of stability and switching mechanisms of ferroelectric domains under different mechanical boundary conditions and under either homogeneous capacitor configurations or local fields using metallic probes as electrodes and the emergence of charged domain walls during domain switching. The project predicted the spatial length scales, temperature ranges, and electromechanical conditions for different polar states in heterostructures and guided the discovery of both transient and stable novel polarization states containing vortex lattices in oxide superlattices. The project resulted in 134 journal publications and 6 PhD theses with all the PhD graduates currently working in either academia or industry within the United States. The basic understanding on the stability of mesoscale polar states and pattern evolution achieved by the project improved our ability to control and engineer properties of ferroelectric thin films and heterostructures for potential applications in nanoscale electronic devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗