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At least 325 records · Page 18

Demonstration of OpenMC as a framework for atomic transport and plasma interaction

Modern tooling is demanded for predicting the transport and reaction characteristics of atoms and molecules, especially in the context of magnetic confinement fusion. DEGAS2, among the most common and capable tools currently in use, shares many fundamental similarities with the OpenMC framework, which was primarily developed for neutron and photon transport. In this work, we demonstrate that OpenMC is suitable for atomic transport calculations. The relative error between the models is small, and the performance of OpenMC is at least comparable to DEGAS2. This is the case even without taking advantage of heterogeneous computing architecture, which is only one of the several remarkable new capabilities that this demonstration heralds.

Monte Carlo↗

Sequential Bayesian Methods for Analyzing Computer Models

Efficient analysis of computer models is essential for the validation and uncertainty quantification of those models. Surrogate models, and Gaussian processes in particular, are a common and powerful approach to analyzing computer models that treat computer models as a black-box function. Gaussian processes form a Bayesian model over a space of functions that gives a measure of uncertainty about the computer model output at unobserved locations and a framework for sequential sampling. This tutorial will show how to fit Gaussian processes on a series of test functions and apply sequential design techniques to estimate extrema, level sets, and reliabilities of those test functions.

97 MATHEMATICS AND COMPUTING↗

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Neutrino-nucleus CC 0 π cross-section tuning in GENIE v3

This article summarizes the state of the art of v μ and $\bar{v}_μ$ CC0π cross-section measurements on carbon and argon and discusses the relevant nuclear models, parametrizations and uncertainties in GENIE v3. The CC0π event topology is common in experiments at a few-GeV energy range. Although its main contribution comes from quasi-elastic interactions, this topology is still not well understood. The GENIE global analysis framework is exploited to analyze CC0π datasets from MiniBooNE, T2K and MINERvA. A partial tune for each experiment is performed, providing a common base for the discussion of tensions between datasets. The results offer an improved description of nuclear CC0π datasets as well as data-driven uncertainties for each experiment. This work is a step towards a GENIE global tune that improves our understanding of neutrino interactions on nuclei. It follows from earlier GENIE work on the analysis of neutrino scattering datasets on hydrogen and deuterium.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

EFT triangles in the same-sign WW scattering process at the HL-LHC and HE-LHC

We investigate the Beyond Standard Model discovery potential in the framework of the Effective Field Theory (EFT) for the same-sign $WW$ scattering process in purely leptonic $W$ decay modes at the High-Luminosity and High-Energy phases of the Large Hadron Collider (LHC). The commonly applied procedure of studying the $WWWW$ quartic coupling by testing dimension-8 operators of the EFT one at a time is used in this work. In the considered process there is no experimental handle on the $WW$ invariant mass, and it has previously been shown that the discovery potential at 14 TeV is rather slim. In this paper we report the results calculated for a 27 TeV machine and compare them with the discovery potential obtained at 14 TeV. We find that while the respective discovery regions shift to lower values of the Wilson coefficients, the overall discovery potential of this procedure does not get significantly larger with a higher beam energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

MOOSE Framework Meshing Enhancements to Support Reactor Analysis

MOOSE-based physics codes require an input finite element mesh on which the physics solution is calculated, reported, and transferred to other physics codes. The use of difficult-touse, external licensed software is often required to generate high quality meshes for reactor geometries. High-fidelity geometry modeling also requires elaborate tracking of groups of elements for material property assignment and output reporting which can be considerably complex for the user to identify and maintain. Under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, several meshingrelated enhancements have been developed for the MOOSE framework to address user challenges in creating finite element meshes for advanced reactor geometries. MOOSE mesh generators have been developed to mesh hexagonal geometries (pins, ducted assemblies, and cores) commonly found in liquid-metal cooled fast reactor concepts. The mesh generator used for hexagonal pin cells is generic for regular polygons and therefore may also be used for Cartesian pin cells. Hexagonal pin cells can be stitched into ducted assemblies, and assemblies can be stitched together into a core. The user may specify region ids, region names, and other preferences on the mesh. This control is useful for later material mapping in the MOOSE-based physics codes input. A capability was also developed for meshing rotating control drums including determination of material volume fractions in each mesh element as a function of time. Control drum meshes may be stitched to other hexagonal assemblies to create a core configuration. Additional mesh generators were developed that wrap around the hexagonal meshing capabilities and utilize “extra element integer” ID values on each element. In regular Cartesian or hexagonal assemblies or cores, the bookkeeping of element groups for both material assignment and output reporting can now be automated through assignment of pin, assembly, core, axial and depletion id values stored as extra element integers. The extra element tags on the mesh greatly speed the reactor analyst’s efforts to map materials to meshes, track depletion zones, and parse output such as axial pin power distributions. At the highest level, pin, assembly, and core mesh generators (with this reactor terminology) have also been developed to easily generate regular Cartesian and hexagonal cores, including axial extrusion. These reactor geometry builders call upon the previously mentioned capabilities to produce analysis-ready 3D meshes including material assignments. Open source mesh triangulation capabilities were also investigated for integration into the MOOSE framework to address the need for meshing the core periphery region which extends from the irregular outer assembly border to a cylindrical boundary. Options are limited due to licensing constraints, and the recommendation is pursue building a native MOOSE Delaunay triangulator routine with full functionality. Finally, a series of verification problems were performed with NEAMS physics tools. All developed capabilities will be available in the new open-source “Reactor” module of the MOOSE framework, which is accessible to any MOOSE-based NEAMS physics tool.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NGEE Arctic Phase 4 Plant Functional Type Framework for Pan-Arctic Vegetation

The NGEE-Arctic research team identified a common set of hierarchical plant functional types (PFTs) for pan-arctic vegetation that we will use across our research activities. Interdisciplinary work within a large team requires agreement regarding levels of functional organization so that knowledge, data, and technologies can be shared and combined effectively. The team has identified plant functional types as a crucial area where such interoperability is needed. PFTs are used to represent plant pools and fluxes within models, summarize observational data, and map vegetation across the landscape. Within each of these applications, varying levels of PFT specificity are needed according to the specific scientific research goal, computational limitations, and data availability. By agreeing on a specific hierarchical framework for grouping variables in our vegetation data, we ensure the resulting research products will be robust, flexible, and scalable. In this document, we lay out the agreed upon PFT framework with definitions and references to existing literature. Table 1 included in the "NGA700_Phase4PFTFramework_about*" file outlines the relationship between NGEE-Arctic Phase 4, Tier 1 PFTs and the PFTs used within prominent arctic literature as well as publications by the NGEE-Arctic team during phases 1-3.This dataset consists of a table detailing a hierarchical PFT framework that spans 4 tiers with the most granular PFTs listed in tier 1 and the most general PFTs in tier 4. The PFTs within each tier has a single column in the dataset where the PFTs are named and a separate column where the characteristics used to define that PFT are listed. Grey fill of the cells is used to indicate where a given PFT starts to “lose” tier 1 details as you look from left to right. Note the excel file has merged cells to indicate grouping of PFTs across the Tiers- it will not translate into a delimited filetype (.csv, .txt, etc) without modification thus the hierarchical PFT framework table is available in three different file formats: 1) NGA700_Phase4PTS.xlsx – maintains the merged cells and grey fill; 2) NGA700_Phase4PTS.csv – merged cells are split, and grey fill is removed; 3) NGA700_Phase4PTS.pdf – image of the table with merged cells and grey fill. Metadata document included as a *.pdf and file-level metadata and data dictionary as *.csv files.

54 ENVIRONMENTAL SCIENCES↗

Integrating biomechanics in evolutionary studies, with examples from the amphidromous goby model system

The functional capacities of animals are a primary factor determining survival in nature. In this context, understanding the biomechanical performance of animals can provide insight into diverse aspects of their biology, ranging from ecological distributions across habitat gradients to the evolutionary diversification of lineages. To survive and reproduce in the face of environmental pressures, animals must perform a wide range of tasks, some of which entail tradeoffs between competing demands. Moreover, the demands encountered by animals can change through ontogeny as they grow, sexually mature or migrate across environmental gradients. To understand how mechanisms that underlie functional performance contribute to survival and diversification across challenging and variable habitats, we have pursued diverse studies of the comparative biomechanics of amphidromous goby fishes across functional requirements ranging from prey capture and fast-start swimming to adhesion and waterfall climbing. The pan-tropical distribution of these fishes has provided opportunities for repeated testing of evolutionary hypotheses. By synthesizing data from the lab and field, across approaches spanning high-speed kinematics, selection trials, suction pressure recordings, mechanical property testing, muscle fiber-type measurements and physical modeling of bioinspired designs, we have clarified how multiple axes of variation in biomechanical performance associate with the ecological and evolutionary diversity of these fishes. Here our studies of how these fishes meet both common and extreme functional demands add new, complementary perspectives to frameworks developed from other systems, and illustrate how integrating knowledge of the mechanical underpinnings of diverse aspects of performance can give critical insights into ecological and evolutionary questions.

59 BASIC BIOLOGICAL SCIENCES↗

Second-order closure models for rotating turbulent flows

The physical properties of the commonly used second-order closure models are examined theoretically for rotating turbulent flows. Comparisons are made with results which are a rigorous consequence of the Navier-Stokes equations for the problem of fully-developed turbulent channel flow in a rapidly rotating framework. It is demonstrated that all existing second-order closures yield spurious physical results for this test problem of rotating channel flow. In fact, the results obtained are shown to be substantially more unphysical than those obtained from the simpler k-epsilon and k-l models. Modifications in the basic structure of these second-order closure models are proposed which can alleviate this problem.

Speziale, Charles G.↗

Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200 at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for regulation flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof-of-concept was fabricated and the experimental results and lessons learned are discussed. This analysis presents a collection of CASMART collaborative best practices in order to allow readers to utilize the available design tools and understand their modeling principles. These design tools, which are based on engineering models, can provide first-order optimal designs and are a basic and efficient method for either demonstrating design feasibility or refining design parameters. Although the design and integration of an SMA-based actuation system always requires application- and environment-specific engineering considerations, common modeling tools can significantly reduce the investment required for actuation system development and provide valuable engineering insight.

Design tools↗

3D Cell-centered hydrodynamics with subscale closure model and multi-material remap

We extend a higher-order finite volume cell-centered hydrodynamic (CCH) formulation to include an interface-aware subscale closure model and a multi-material remap for simulating 3D compressible hydrodynamic problems within an arbitrary Lagrangian-Eulerian (ALE) framework. This CCH formulation involves a multidirectional approximate Riemann solution using quadratic polynomial reconstructions of the stress tensor and the velocity. At the subscale level, we determine pair-wise material interactions by solving a distinct approximate Riemann problem at the common interface, using the volume of fluids (VOF) method to find the interface. Material interactions are constrained to ensure smooth pressure equilibration among materials. The accuracy and robustness of the ALE method is demonstrated by simulating a suite of 3D Cartesian multi-material problems covering both gas and solid dynamics, where each test case has two or more materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Ten questions concerning low-cost indoor air quality sensors: Perspectives from research and practice

Low-cost indoor air quality (IAQ) sensors are increasingly being used in homes and commercial and public buildings, driven by growing concerns about the impact of air on health, cognitive performance, and occupant wellbeing. These sensors offer a potentially transformative opportunity to increase spatial and temporal coverage of IAQ monitoring at a fraction of the cost of conventional reference instruments. However, their widespread use raises questions around accuracy, calibration, placement, data handling and interpretation, and integration into existing standards and workflows. This paper presents ten critical questions concerning the use of low-cost IAQ sensors in buildings, drawing on the latest empirical research, field deployments, and emerging practice. It discusses potential frameworks for deployment and evaluation, examines current sensor capabilities for measuring common pollutants, identifies methodological gaps in validation and uncertainty quantification, and outlines the extent to which existing IAQ standards can accommodate sensor-based evidence. The paper also explores how monitoring needs and deployment models vary by building type, the potential of real-time IAQ data to support building operations, and the ethical and legal implications of widespread sensor use. While significant challenges remain in ensuring data quality and building stakeholder trust, new applications are emerging through open data initiatives and advances in analytics and visualization. As the technology, science, and standards co-evolve, low-cost IAQ sensors are poised to become integral to routine building operation, building science, and environmental health research.

Parkinson, Thomas↗

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks

Abstract As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

Zheng, Kai↗

Adaptation of Wallace's approach to the specific heat of elemental solids with significant intrinsic anharmonicity, particularly the light actinide metals

The quasiharmonic approximation is the most common method for modeling the specific heat of solids; however, it fails to capture the effects of intrinsic anharmonicity. In this study, we introduce the “elastic softening approximation,” an alternative approach to modeling intrinsic anharmonic effects on thermodynamic quantities, which is grounded in Wallace's thermodynamic framework that tracks entropy changes resulting from the continuous change (e.g., softening) of phonons as a function of temperature. A key finding of our study is a correlation between Poisson's ratio and the differential rate of phonon softening at finite frequencies, compared to lower frequencies relevant to elastic moduli measurements. We observe that elemental solids such as α -Be, diamond, Al, Cu, In, W, Au, and Pb, which span a wide range of Poisson's ratios and exhibit varying degrees of intrinsic anharmonicity, consistently follow this trend. When applied to α -U, α -Pu, and δ -Pu, our method reveals unusually large anharmonic phonon contributions at elevated temperatures across all three light actinide metals. These findings are attributed to the unique combination of enhanced covalency and softer elastic moduli inherent in the actinides, potentially influenced by their 5 f -electron bonding. Published by the American Physical Society 2025

36 MATERIALS SCIENCE↗

Second-order closure models for rotating turbulent flows

The physical properties of the commonly used second-order closure models are examined theoretically for rotating turbulent flows. Comparisons are made with results which are a rigorous consequence of the Navier-Stokes equations for the problem of a fully-developed turbulent channel flow in a rapidly rotating framework. It is demonstrated that all existing second-order closures yield spurious physical results for this test problem of rotating channel flow. In fact, the results obtained are shown to be substantially more unphysical than those obtained from the simpler K-epsilon and K-l models. Modifications in the basic structure of these second-order closure models are proposed which can alleviate this problem.

Speziale, C. G.↗

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗