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At least 469 records · Page 26

Nuclear data sensitivity and uncertainty study of copper-reflected integral experiments [Slides]

This presentation touches on reducing uncertainties in intermediate-energy actinide nuclear data and this continues to be a high priority for many applications. The goal of PARADIGM (PARallel Approach of Differential and InteGral Measurements): accelerate efforts to reduce biases and uncertainties in nuclear data through improvements to the nuclear data pipeline. This presentation includes integral experiments and a summarization of existing copper nuclear data.

Cu63↗

Aligning Ukrainian Building Energy Codes with the EPBD

The aim of this report is to support Ukraine in revising its building energy codes in line with regulations laid out by the Energy Performance in Buildings Directive (EPBD). The main objectives are to i) provide valuable information to help Ukraine align its building energy codes with EPBD requirements, ii) advance the capacity of key stakeholders in Ukraine to develop new regulations based on robust technical methodologies and analysis, iii) summarize building energy codes best practices using examples from the EU and elsewhere, and iv) recommend steps Ukraine can take to improve its building energy code regulations and implementation. This report summarizes EPBD requirements for new buildings, and buildings undergoing major renovations, related to cost-optimal and Nearly Zero Energy Building (NZEB) requirements. This report highlights Ukraine’s progress in aligning it’s building energy codes with the aforementioned requirements and best practices regarding those requirements from the EU and internationally. Based on best practices and Ukraine’s current progress on requirements, recommendations are presented. A brief overview of the EPBD and building energy codes in Ukraine is also provided. Implementation related to building energy codes is touched upon as well. Potential next steps for Ukraine are outlined based on provided recommendations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗

The Next Stage for Smart Cities: Metrics to Spur an Evolution from Technology for Efficiency to Technology for People's Needs

The Smart Cities movement is about a decade old, energized by the USDOT Smart Cities grant that sparked cities to dream big. Now, 10 years later, and multitudes of conferences and papers later, the biggest question regarding Smart Cities is whether any progress has been made? Smart Cities forums are typically led by the technology sector, either telecommunications, internet access, or most recently artificial intelligence (AI) concerns. Each of these areas have grown and found new applications in the context of Smart Cities, however, these technology applications are just tools toward ends. Do digital communications and assistance enable or foster improved access to real, tactile, non-virtual goods and services such as transportation, food, housing, trash removal, health care, education, social interaction? These and other topics are not 'big tech', but are each critical to quality of life outcomes and are day-to-day necessities, comprising many of the touch points through which citizens interact with municipal agencies. Regarding Smart Cities, relationships have not been fully refined as to how best to merge the emerging generation of technology and related trends with more mundane but essential requirements of tactile services. How should municipal managers and operators bring the range of new technology tools (e.g. on-demand transit, automated vehicles, digital connectivity) to bear to improve functions serving real- physical- non-virtual needs? How do we make cities more efficient in providing access to services and opportunities for improved quality of life? This paper examines the current Smart City movement, emerging trends, and the need to put metrics with respect to the delivery, access, and provision of real-life needs. Using a few case studies of past, present, and projected future scenarios, this paper attempts to bring Smart Cities into the real cities domain, perhaps pushing the moniker away from 'Smart' to 'Efficient' - something that can be measured, counted, and assessed. Through the consideration of the next stage of the evolution of cities from 'Smart' to 'Efficient', the combined efforts and experience of multiple researchers at the NREL and their network of collaborators highlights the need for updates metrics that can be used to start guiding efforts toward productive initiatives to improve quality of life.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Flow Forecasting on New England’s Great River Hydro (CRADA 549) (Final Report)

Great River Hydro (GRH) is New England's largest conventional hydropower generator and operates sustainable hydropower systems in Vermont, New Hampshire, and Massachusetts. Several of GRH’s hydropower projects on the Connecticut River are largely constrained to operate as run-of-river; however, though a new FERC license they have opportunity in the form of flex hours where they are allowed to deviate operations to capture favorable market conditions or emergency operations. To maximize value from these new operations, GRH is considering new sources of forecasted inflow. Pacific Northwest National Laboratory (PNNL) is evaluating the quality of two commercial inflow forecasts for GRH under DOE Water Power Technology Office’s Notice of Opportunity for Technical Assistance: Improving Hydropower’s Value through Informed Decision Making. This project will provide an unbiased evaluation of the accuracy of inflow forecasts available through commercial vendors. GRH will use this information to support their decision on which inflow forecast to use under their new FERC license and to assess the overall value of the available inflow forecasting tools and practices. In this talk we will discuss details of the near real-time forecast evaluation process, the metrics involved in the evaluation, and some preliminary results. In addition, we’ll touch on the second phase of the project which involves evaluating scheduling strategies that identify windows of opportunity to change operations in ways that increase profit through participation in day ahead and real time markets.

13 HYDRO ENERGY↗

Akiachak Energy Efficiency Retrofit Project

The goal of the project is to reduce the overall energy use of the Akiachak Native Community (ANC) by implementing energy efficiency measures in five high-use Tribal buildings. This project will have the following outcomes: Projected annual energy savings of $17,369; projected annual reduction in fuel oil #1 of 1,200 gallons and electricity of 17,751 kWh; annual reduction in carbon dioxide emissions of approximately 60,340 pounds/year. ANC will install energy efficiency measures in the Laundry, Tribal Indian Reorganization Act (IRA) Office, Clinic, Daycare, and Police Station. ANC obtained energy audits on these buildings in 2018, and this project will implement high-payback recommendations such as replacing lighting with LEDs, installing setback thermostats and occupancy sensors, replacing furnaces with more efficient models, replacing the circulation pumps with variable speed ones, air tightening, and adding insulation. Buildings will see energy cost reductions from 15% to 40%. These retrofits will help build ANC’s long-term vision for sustainable energy usage and address the first goal of the Tribal IRA Council’s Energy Efficiency and Conservation Strategy, to “create and maintain functionally appropriate, sustainable, accessible, high quality tribal infrastructure and facilities.” ANC intends to replicate this project by using the resulting energy savings to address audit recommendations in other buildings as well as to demonstrate the value of energy efficiency to community members. Other outcomes will include an increase in community resiliency, reduced dependence on outside shipments of fuel oil, training for maintenance staff, and no-touch control of building appliances to reduce transmission of diseases such as COVID-19.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Supercharge NY

The Energy Program for Innovation Clusters (EPIC) Supercharge NY enabled The Clean Fight to execute an inaugural accelerator program focused on enabling world-class, growth-stage energy storage companies to successfully scale deployments and establish domestic manufacturing pathways in New York State. The Clean Fight has developed and tested a successful accelerator model designed to reduce the barriers growth-stage climate tech companies face by collapsing the sales cycle to scaling solutions at speed. The Clean Fight’s Supercharge NY project allowed for the exploration into the understanding of New York-focused energy storage opportunities, challenges, and methods for enabling successful projects. The Clean Fight’s Supercharge NY project enabled high-touch matchmaking with industry leading customer and capital partners, technical assistance to facilitate scaling supply chain and manufacturing capacity, and access to non-dilutive funding for first-of-a-kind or demonstration projects in New York State. The results of the program provide a public benefit by accelerating deployments of energy storage to provide value-added energy services and sustainability benefits to communities while boosting economic opportunity and job creation for all.

25 ENERGY STORAGE↗

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗

Gate Set Tomography

Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is calibration-free . GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a gate set simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the pyGSTi implementation.

97 MATHEMATICS AND COMPUTING↗

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗

Effects of the evolving early Moon and Earth magnetospheres

Recently it has been identified that our Moon had an extensive magnetosphere for several hundred million years soon after it was formed when the Moon was within 20 Earth Radii (R E ) from the Earth. Some aspects of the interaction between the early Earth-Moon magnetospheres are investigated by mapping the interconnected field lines between the Earth and the Moon and investigating how the early lunar magnetosphere affects the magnetospheric dynamics within the coupled magnetospheres over time. So long as the magnetosphere of the Moon remains strong as it moves away from the Earth in the antialigned dipole configuration, the extent of the Earth’s open field lines decreases. As a result, at times it significantly changes the structure of the field-aligned current system, pushing the polar cusp significantly northward, and forcing magnetotail reconnection sites into the deeper tail region. In addition, the combined magnetospheres of the Earth and the Moon greatly extend the number of closed field lines enabling a much larger plasmasphere to exist and connecting the lunar polar cap with closed field lines to the Earth. That configuration supports the transfer of plasma between the Earth and the Moon potentially creating a time capsule of the evolution of volatiles with depth. This paper only touches on the evolution of the early Earth and Moon magnetospheres, which has been a largely neglected space physics problem and has great potential for complex follow-on studies using more advanced tools and due to the expected new lunar data coming in the next decade through the Artemis Program.

Astronomy & Astrophysics↗

Recent Progress in Transparent Conductive Materials for Photovoltaics

Transparent conducting materials (TCMs) are essential components for a variety of optoelectronic devices, such as photovoltaics, displays and touch screens. In recent years, extensive efforts have been made to develop TCMs with both high electrical conductivity and optical transmittance. Based on material types, they can be mainly categorized into the following classes: metal oxides, metal nanowire networks, carbon-material-based TCMs (graphene and carbon nanotube networks) and conjugated conductive polymers (PEDOT:PSS). This review will discuss the fundamental electrical and optical properties, typical fabrication methods and the applications in solar cells for each class of TCMs and highlight the current challenges and potential future research directions.

14 SOLAR ENERGY↗

Barriers to Balcony Solar and Plug-In Distributed Energy Resources in the United States

Plug-in distributed energy resources (DERs), such as balcony solar, backfeed power to the home through a standard plug. These systems may represent the future of residential solar and storage, particularly as recent net metering policies have reduced the economic appeal of rooftop solar. While plug-in DERs have seen widespread success in Europe, their U.S. market is stagnant. This paper reviews the technical, interconnection, and regulatory barriers hindering the adoption of plug-in DERs. We first discuss the technical barriers, which include touch safety, breaker masking, and bidirectional ground-fault circuit interrupters. We then examine utility perspectives on plug-in DERs and strategies for navigating interconnection challenges. Finally, we discuss regulatory hurdles related to UL standards and the National Electrical Code.

Gerber, Daniel L. (ORCID:0000000219720679)↗

Pressure-Induced Exciton Formation and Superconductivity in Platinum-Based Mineral Sperrylite

We report a comprehensive study of Sperrylite (PtAs 2 ), the main platinum source in natural minerals, as a function of applied pressures up to 150 GPa. While no structural phase transition is detected from pressure-dependent X-ray measurements, the unit cell volume shrinks monotonically with pressure following the third-order Birch–Murnaghan equation of state. The mildly semiconducting behavior found in pure synthesized crystals at ambient pressures becomes more insulating upon increasing the applied pressure before metalizing at higher pressures, giving way to the appearance of an abrupt decrease in resistance near 3 K at pressures above 92 GPa consistent with the onset of a superconducing phase. The pressure evolution of the calculated electronic band structure reveals the same physical trend as our transport measurements, with a non-monotonic evolution explained by a hole band that is pushed below the Fermi energy and an electron band that approaches it as a function of pressure, both reaching a touching point suggestive of an excitonic state. A Lifshitz transition of the electronic structure and an increase in the density of states may naturally explain the onset of superconductivity in this material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Trends in Microbial Community Composition and Function by Soil Depth

Microbial communities play important roles in soil health, contributing to processes such as the turnover of organic matter and nutrient cycling. As soil edaphic properties such as chemical composition and physical structure change from surface layers to deeper ones, the soil microbiome similarly exhibits substantial variability with depth, with respect to both community composition and functional profiles. However, soil microbiome studies often neglect deeper soils, instead focusing on the top layer of soil. Here, we provide a synthesis on how the soil and its resident microbiome change with depth. We touch upon soil physicochemical properties, microbial diversity, composition, and functional profiles, with a special emphasis on carbon cycling. In doing so, we seek to highlight the importance of incorporating analyses of deeper soils in soil studies.

59 BASIC BIOLOGICAL SCIENCES↗