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At least 775 records · Page 43

Adaptation of existing facilities to isentropic compression experiments

We demonstrate that the established pulsed power infrastructure at the National High Magnetic Field Laboratory - Pulsed Field Facility (NHMFL-PFF) at the Los Alamos National Laboratory can be adapted to obtain high quality isentropic compression experiment (ICE) data on materials in extreme conditions of dynamic high pressure. Experiments utilized a single-turn magnet pulsed power system at the NHMFL-PFF that was originally designed to measure actinide samples in extremes of high magnetic field (to 300 Tesla). A simple modification to the single-turn magnet has converted it to a fast turnaround dynamic high pressure measurement system. This paper details the work done including important background details that indicate that much more can be accomplished with optimization of the load characteristics in terms of ultimate peak pressures. To match the rise time of the NHMFL capacitor bank ({approx}2 {mu}s versus {approx}0.5 {mu}s for the Sandia Z-machine) the sample dimensions can be relatively large, i.e., up to 5 mm thickness. The maximum stresses are {approx}50GPa (0.5 Mbar) at the maximum bank voltage (60 kV) and higher pressures may be possible if the sample is tamped. For the design and predictions of performance of the NHMFL-ICE experiment it is important to have good predictive models. A SPICE code simulation was chosen to model all aspects of the experiment, electrical and physical. To this end, accurate dynamic load models were developed to simulate the compression and expansion of the dynamic load at high pressures using shock physics principles. A series experiments have been performed which demonstrated the feasibility of the NHMFL-ICE technique. The results will be shown and discussed. The NHMFL-ICE technique is an excellent method for measuring equations of state (EOS) at megabar pressures. Because a complete EOS can be obtained in one experiment from zero to the peak pressure, and because many shots can be fired in one day, the technique promises to provide high quality EOS data at relatively low cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Effects of Photovoltaic Module Materials and Design on Module Deformation Under Load

Static structural finite element models of an aluminum-framed crystalline silicon (c-Si) photovoltaic (PV) module and a glass-glass thin film PV module were constructed and validated against experimental measurements of deflection under uniform pressure loading. Parametric analyses using Latin Hypercube Sampling (LHS) were performed to propagate simulation input uncertainties related to module material properties, dimensions, and manufacturing tolerances into expected uncertainties in simulated deflection predictions. This exercise verifies the applicability and validity of finite element modeling for predicting mechanical behavior of solar modules across architectures and enables computational models to be used with greater confidence in assessment of module mechanical stressors and design for reliability. Sensitivity analyses were also performed on the uncertainty quantification data sets using linear correlation coefficients to elucidate the key parameters influencing module deformation. This information has implications on which materials or parameters may be optimized to best increase module stiffness and reliability, whether the key optimization parameters change with module architecture or loading magnitudes, and whether parameters such as frame design and racking must be replicated in reduced-scale reliability studies to adequately capture full module mechanical behavior.

14 SOLAR ENERGY↗

Simulation methodology for fuel assembly drop accident during handling

Fresh and irradiated fuel assembly handling is a routine part of Nuclear Power Plant activities. Fuel Assembly handling occurs during fresh fuel receipt, core refueling, spent fuel assemblies transfer from spent fuel pool to the storage containers, etc. Westinghouse, ENUSA and KEPCO-NF have collaborated in the technical area of Fuel Assembly Drop Accident (FADA) Simulation to update the approach for estimating the number of failed fuel rods as a consequence of a fresh or irradiated fuel assembly drop, which might occur during a handling accident in a PWR-type reactor. The proposed FADA simulation methodology starts with an assessment of fuel assembly impact velocity at the final stage of the drop in water or air. The vertical, horizontal or inclined drop directions are considered. This velocity is an input to determine a fuel assembly structural response during impact which defines the loading function for the fuel assembly components including spacer grids. The dynamic load on the fuel rod is calculated from the detailed spacer grid model and the corresponding loading function. Finally, the load from the spacer grid spring and dimples are applied to the fuel rod model to determine the cladding parameters for use in compliance to fuel rod cladding failure criteria. A combination of first principal, finite element analysis (FEA) and computational fluid dynamic (CFD) methods has been utilized to create a technically sound methodology. A number of specific and traditional tests for the fuel assembly and its components has been used to confirm that the proposed approaches which describe the physical phenomena related to the fuel assembly drop accident are reasonably conservative.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Multi-objective surrogate-assisted calibration of CPFEM models using macroscopic response and in situ EBSD measurements of grain reorientation trajectories

Crystal plasticity finite element method (CPFEM) models are widely used to simulate the deformation behaviour of polycrystalline materials, but their calibration is often limited by their high computational cost and the non-convexity of the optimisation landscape. Here, this study develops a multi-objective surrogate-assisted calibration workflow that couples a multi-objective genetic algorithm (MOGA) with an adaptively trained deep neural network (DNN) surrogate model to efficiently identify CPFEM parameters from experimental data. The workflow is demonstrated on three crystal plasticity (CP) formulations of increasing complexity — Voce hardening (VH), two-coefficient latent hardening (LH2), and six-coefficient latent hardening (LH6) — using in situ electron backscatter diffraction (EBSD) measurements of Alloy 617 under uniaxial tensile loading. The CPFEM models are calibrated against the experimentally observed stress–strain response and reorientation trajectories of eight grains, then validated against eight additional trajectories and overall texture evolution. Across the CP formulations, the macroscopic response was reproduced reliably, while differences emerged in the robustness and accuracy of the grain-scale predictions. Including grain reorientation trajectories in the multi-objective calibration improved texture evolution predictions and filtered out physically inconsistent parameter sets that can arise from calibrating against only the stress–strain data. The workflow also demonstrates good transferability of calibrated parameters from a low- to a high-fidelity microstructural model. These results provide practical guidance for integrating in situ microstructural data into CPFEM through efficient, repeatable, and physically meaningful multi-objective calibration.

Crystal plasticity finite element method↗

Load Aggregation Methods for Quasi-Static Power Flow Analysis on High PV Penetration Feeders

This paper focuses on developing load aggregation methodologies for analyzing quasi-static power flows on high photovoltaic penetrated distribution feeders so statistics of the transformer loading levels, voltage ramping events, and voltage violation events can be properly quantified. A load profile aggregation algorithm is presented to construct 24-hour, minute-by-minute load profiles at each load node on the test feeder so the aggregation of those load profiles matches the load profile measured at the feeder head. A quasi-static power flow analyses are conducted to obtain minute-by-minute power flow results that can be used to quantify transformer overloads and voltage issues in distribution systems. To demonstrate the effectiveness of the developed methodology, the IEEE-123 test feeder model and residential load profiles collected from the PECAN street project are used in the case studies. Simulation results show that the method is effective in producing the required operation statistics.

Load aggregaton, PV integration, quasi-static powe↗

Effect of occupant behavior on peak cooling and dehumidification loads in typical and high-efficiency homes

Residential building codes and voluntary labeling programs are continually increasing the energy efficiency requirements of residential buildings. Improving a building's thermal enclosure, installing the ductwork in conditioned space, and improving the building's airtightness results in significant reductions in externally-driven sensible and latent cooling loads. As a building's efficiency is improved, occupant-related internal gains become a larger portion of the building sensible and latent loads. Additionally, internal gains are highly uncertain compared to other load components. In this study, we use a stochastic approach to simulate occupant-related internal gains and compare the internal gains to other sensible and latent heat sources in four house efficiency levels in 10 U.S. climates using whole-building energy simulation software. We compare the expected range in occupant-related internal gains to other building characteristics such as cooling set point, air infiltration rate, and mechanical ventilation rate. We show that in high-efficiency homes, sensible internal gains vary from less than 10% to greater than 40% of the building sensible load under peak total cooling conditions depending on climate and internal gain profile. Likewise, latent internal gains vary from less than 10% to more than 60% of the building latent load under peak total cooling and peak dehumidification conditions depending on climate and internal gain profile.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A path-dependent fatigue crack propagation model under non-proportional modes I and III loading conditions

It has been well established that fatigue damage process is load-path dependent under non-proportional multi-axial loading conditions. Most of studies to date have been focusing on interpretation of S-N based test data by constructing a path-dependent fatigue damage model. Our paper presents a two-parameter mixed-mode fatigue crack growth model which takes into account of crack growth dependency on both load path traversed and a maximum effective stress intensity attained in a stress intensity factor plane (e.g.,KI-KIII plane). Furthermore, by taking advantage of a path-dependent maximum range (PDMR) cycle definition (Dong et al., 2010; Wei and Dong, 2010), the two parameters are formulated by introducing a moment of load path (MLP) based equivalent stress intensity factor range (ΔKNP) and a maximum effective stress intensity parameter KMax incorporating an interaction term KI·KIII. To examine the effectiveness of the proposed model, two sets of crack growth rate test data are considered. The first set is obtained as a part of this study using 304 stainless steel disk specimens subjected to three combined non-proportional modes I and III loading conditions (i.e., with a phase angle of 0°, 90°, and 180°). The second set was obtained by Feng et al. (2007) using 1070 steel disk specimens subjected to similar types of non-proportional mixed-mode conditions. Once the proposed two-parameter non-proportional mixed-mode crack growth model is used, it is shown that a good correlation can be achieved for both sets of the crack growth rate test data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Examining the Impacts of Microphysical-Dynamical Feedbacks on Convective Clouds in Different Aerosol Environments Using Enhanced Observational and Modeling Strategies

This project investigated how microphysical–dynamical feedbacks influence deep convective clouds across a range of aerosol conditions, storm lifecycles, and meteorological regimes, and how these sensitivities depend on the modeling framework used to represent convection and microphysics. Using the Aerosol, Cloud, Precipitation, and Climate (ACPC) Working Group model intercomparison simulations of isolated deep convection in the Houston region, we found a robust warm-phase aerosol response in most cloud-resolving models. Increased aerosol loading tended to suppress warm-rain production, increase cloud water, and reduce surface rainfall and near-surface evaporation. These impacts were typically strongest early in the convective lifecycle, with the largest aerosol-driven differences occurring during the first half of storm evolution. The ice-phase response, on the other hand, varied widely across modeling frameworks, and differed in sign, timing, and vertical structure. Ice microphysical pathways and parameterizations are therefore leading sources of uncertainty in aerosol–deep convection interactions. Theoretical analyses further suggested that aerosol-driven invigoration through cold-phase processes was much weaker than previously hypothesized for cold-based storms, and in warm-based storms could even reduce updraft strength. These results imply that any invigoration signal is more likely linked to warm-phase processes and peaks early in storm development.

54 ENVIRONMENTAL SCIENCES↗

Revealing the unusual rate-dependent mechanical behaviors of nematic liquid crystal elastomers

Liquid crystal elastomers (LCEs) exhibit unique mechanical properties of soft elasticity and enhanced energy dissipation with rate dependency. They are potentially transformative materials for applications in mechanical impact mitigation and vibration isolation. However, previous studies have primarily focused on the mechanics of LCEs under equilibrium and quasistatic loading conditions. Critical knowledge gaps exist in understanding their rate-dependent behaviors, which are a complex mixture of traditional network viscoelasticity and the soft elastic behaviors with changes in the mesogen orientation and order parameter. Together, these inelastic mechanisms lead to unusual rate-dependent energy absorption responses of LCEs. In this work, we developed a viscoelastic constitutive theory for monodomain nematic LCEs to investigate how multiple underlying sources of inelasticity manifest in the rate-dependent and dissipative behaviors of monodomain LCEs. Here, the theoretical modeling framework combines the neo-classical network theory with evolution rules for the mesogen orientation and order parameter with conventional viscoelasticity. The model is calibrated with uniaxial tension and compression data spanning six decades of strain rates. The established 3D constitutive model enables general loading predictions taking the initial mesogen orientation and order parameter as inputs. Additionally, parametric studies were performed to further understand the rate dependence of monodomain LCEs in relation to their energy absorption characteristics. Based on the parametric studies, particularly loading scenarios are identified as conditions where LCEs outperform conventional elastomers regarding energy absorption.

42 ENGINEERING↗

Zika virus dynamics: Effects of inoculum dose, the innate immune response and viral interference

Experimental Zika virus infection in non-human primates results in acute viral load dynamics that can be well-described by mathematical models. The inoculum dose that would be received in a natural infection setting is likely lower than the experimental infections and how this difference affects the viral dynamics and immune response is unclear. Here we study a dataset of experimental infection of non-human primates with a range of doses of Zika virus. We develop new models of infection incorporating both an innate immune response and viral interference with that response. We find that such a model explains the data better than models with no interaction between virus and the immune response. We also find that larger inoculum doses lead to faster dynamics of infection, but approximately the same total amount of viral production.

59 BASIC BIOLOGICAL SCIENCES↗

Mathematical Modeling of Fracture Processes in Plates with Systems of Cracks Under the Action of Long-Term Loads, High Temperatures, and Corrosive Media

We formulate mathematical models for the evaluation of the lifetime of plates with systems of cracks under the action of long-term force loads in the presence of high temperatures and corrosive media. These models are based on the use of the well-known main mechanisms of propagation of creep cracks, corrosion fracture, and the first law of thermodynamics (balance of energy components and rates of their changes) for a metal plate containing a system of macrocracks and subjected to the action of long-term tension and corrosive media at high temperatures. We consider the case of a double-periodic system of cracks.

42 ENGINEERING↗

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads in extreme idling conditions

Abstract. Floating offshore wind turbine (FOWT) systems are subject to complex environmental loads, with significant potential for damage in extreme storm conditions. Design simulations in these conditions are required to assess the survivability of the device with some level of confidence. Aero-hydro-servo-elastic engineering tools can be used with a reasonable balance of accuracy and computational efficiency. The models require many input parameters to describe the air and water conditions, the system properties, and the load calculations. Each of these parameters has some possible range, due to either statistical uncertainty or variations with time. Variation in the input parameters can have important effects on the uncertainty in the resulting loads, but it is not practical to perform detailed assessments of the impact of this uncertainty for every input parameter. This work demonstrates a method to identify the input parameters that have the most impact on the loads to focus further inspection. The process is done specifically for extreme storm load cases defined in the International Electrotechnical Commission design requirements for floating offshore wind turbines. The analysis was performed using the International Energy Agency Wind 15 MW offshore reference wind turbine atop the University of Maine VolturnUS-S reference platform in two US offshore wind regions, the Gulf of Maine and Humboldt Bay. It was found that the direction of incident waves and current, yaw misalignment, and the length of mooring line sections were among the primary sensitivities.

17 WIND ENERGY↗

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗

Elimination of metabolic pathways to all traditional fermentation products increases ethanol yields in Clostridium thermocellum

Clostridium thermocellum has the natural ability to convert cellulose to ethanol, making it a promising candidate for consolidated bioprocessing (CBP) of cellulosic biomass to biofuels. To further improve its CBP capabilities, we study a mutant strain of C. thermocellum that was constructed (strain AG553; C. thermocellum Δhpt ΔhydG Δldh Δpfl Δpta-ack ) to increase flux to ethanol by removing side product formation. Strain AG553 showed a two- to threefold increase in ethanol yield relative to the wild type on all substrates tested. On defined medium, strain AG553 exceeded 70% of theoretical ethanol yield on lower loadings of the model crystalline cellulose Avicel, effectively eliminating formate, acetate, and lactate production and reducing H 2 production by fivefold. On 5 g/L Avicel, strain AG553 reached an ethanol yield of 63.5% of the theoretical maximum compared with 19.9% by the wild type, and it showed similar yields on pretreated switchgrass and poplar. The elimination of organic acid production suggested that the strain might be capable of growth under higher substrate loadings in the absence of pH control. Final ethanol titer peaked at 73.4 mM in mutant AG553 on 20 g/L Avicel, at which point the pH decreased to a level that does not allow growth of C. thermocellum , likely due to CO 2 accumulation. In comparison, the maximum titer of wild type C. thermocellum was 14.1 mM ethanol on 10 g/L Avicel. In conclusion, with the elimination of the metabolic pathways to all traditional fermentation products other than ethanol, AG553 is the best ethanol-yielding CBP strain to date and will serve as a platform strain for further metabolic engineering for the bioconversion of lignocellulosic biomass.

09 BIOMASS FUELS↗

Three-Stage Production Cost Modeling Approach for Evaluating the Benefits of Intra-Hour Scheduling Between Balancing Authorities

This paper introduces a production cost modeling approach for evaluating the benefits of intra-hour scheduling among Balancing Authorities (BAs). System operation is modeled in a three-stage sequential manner: day ahead (DA)-hour ahead (HA) real time (RT). In addition to contingency reserve, each BA will need to carry out 'up' and 'down' load following and regulation reserve capacity requirements in the DA and HA time frames. In the RT simulation, only contingency and regulation reserves are carried out as load following is deployed. To model current RT operation with hourly schedules, a new constraint was introduced to force each BA net exchange schedule deviation from HA schedules to be within North American Electric Reliability Corporation (NERC) area control error (ACE) limits. Case studies that investigate the benefits of moving from hourly exchange schedules between Western Electricity Coordinating Council (WECC) BAs into 10-minute exchange schedules under two different levels of wind and solar penetration (11% and 33%) are presented.

17 WIND ENERGY↗

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Optimal Load Ensemble Control in Chance-Constrained Optimal Power Flow

Distribution system operators (DSOs) world-wide foresee a rapid roll-out of distributed energy resources. From the system perspective, their reliable and cost effective integration requires accounting for their physical properties in operating tools used by the DSO. This work describes an decomposable approach to leverage the dispatch flexibility of thermostatically controlled loads (TCLs) for operating distribution systems with a high penetration level of photovoltaic resources. Each TCL ensemble is modeled using the Markov Decision Process (MDP). The MDP model is then integrated with a chance constrained optimal power flow that accounts for the uncertainty of PV resources. Since the integrated optimization model cannot be solved efficiently by existing dynamic programming methods or off-the-shelf solvers, this paper proposes an iterative Spatio-Temporal Dual Decomposition algorithm (ST-D2). Finally, we demonstrate the merits of the proposed integrated optimization and ST-D2 algorithm on the IEEE 33-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗