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At least 73 records · Page 4

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)

Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control

This paper validates the efficacy of an artificial intelligence (AI)-based photovoltaic (PV) plant control and optimization approach in enabling PV plants as accountable grid reliability service providers. The validation is performed in a realistic laboratory controller-hardware-in-the-loop environment, leveraging accurate PV plant modeling and standard industrial communication protocols. Through simulations that account for diverse weather conditions and active control scenarios, the results highlight the superior performance of the AI-based solution in comparison to a state-of-the-art reference-control grouping-based approach. Such a finding contributes to mitigating the risk of overcurtailment and uninstructed deviations of active PV plant controls, and offers practical guidance for its field deployment. Furthermore, it establishes a standardized testing framework for comparing various PV active control strategies.

hardware-in-the-loop

A Distributionally Robust Optimization Framework for Stochastic Assessment of Power System Flexibility in Economic Dispatch

Given the complexity of power systems, particularly the high-dimensional variability of net loads, accurately depicting the entire operational range of net loads poses a challenge. To address this, recent methodologies have sought to gauge the maximum range of net load uncertainty across all buses. In this paper, we consider the stochastic nature of the net load and introduce a distributionally robust optimization framework that assesses system flexibility stochastically, accommodating a minimal extent of system violations. We verify the proposed method by solving the flexibility of the economic dispatch problem on four distinct IEEE standard test systems. Compared to traditional deterministic flexibility evaluations, our approach consistently yields less conservative flexibility outcomes.

distributionally robust optimization

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING

Application of Peracetic Acid in Poultry Processing: Effects of Treatment Dynamics and Emerging Risk of Resistance Development in Salmonella spp.

Nontyphoidal Salmonella is a leading cause of foodborne illness, with poultry representing a major source. Peracetic acid (PAA), a widely adopted antimicrobial in poultry processing, offers advantages over traditional disinfectants but has sparked interest in its combined use with other antimicrobials and potential resistance. This review evaluates the efficacy of PAA in mitigating Salmonella in combination with other food-grade antimicrobials, explores possible synergism, efficacy under varying treatment parameters, resistance development against PAA, and its role in resistance evolution. While PAA demonstrates broad-spectrum efficacy, its performance varies with environmental parameters; higher temperatures generally enhance antimicrobial action but also accelerate PAA degradation. Organic matter diminishes PAA efficacy by reactive quenching. Variability in concentration and contact time further influences outcomes. Despite its oxidative mode of action and presumed low risk for resistance, emerging studies indicate that Salmonella can develop adaptive tolerance and potential cross/coresistance following repeated or sublethal exposure to PAA. These adaptations may involve genetic upregulation of oxidative stress response pathways, efflux systems, and modifications in cell membrane integrity, raising concerns about the long-term sustainability of PAA use. Additionally, combinatorial treatments (e.g., PAA with UV-C, enzymes, or organic acids) show promise in enhancing efficacy while mitigating resistance risks. Despite recognition of PAA's safety and effectiveness, knowledge gaps remain regarding standardized resistance definitions, serotype-specific tolerance, and optimal intervention strategies in commercial settings. Therefore, there is a need for standardized testing protocols, robust studies on potential resistance, and further exploration of synergistic PAA applications to ensure sustained poultry product safety and public health protection.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C

PV Lifetime Project (2024 NREL Annual Report)

DOE’s PV Lifetime project was initiated in 2016 with the goal of accurately characterizing the early-life evolution of photovoltaic (PV) field performance. Different PV cell and module technologies result in different initial degradation rates due to effects like light-induced degradation (LID) and light & elevated temperature-induced degradation (LeTID). To accurately characterize the initial field degradation of maximum power (Pmp) requires the use of high-accuracy indoor IV curve measurements at standard test conditions. Therefore, PV modules involved in this study are removed from the field once or twice per year and brought indoors for measurement under constant temperature and irradiance conditions.

14 SOLAR ENERGY

Detection of Critical Surface Features in PTLs and GDLs for Improved Device Performance and Manufacturing Reliability

High points, or features that protrude above the surface of the material, on porous transport layers (PTLs) and gas diffusion layers (GDLs) can be critical features that may affect the manufacturing process and the performance of the device containing the feature. High points on PTLs and GDLs may stress the membrane of a polymer electrolyte membrane (PEM) during lamination and cell operation of a PEM electrolyzer or fuel cell. Additionally, high points on GDLs may impact the reliability of the manufacturing process. Thus, understanding these critical features and developing procedures to detect them are a key part of developing quality control techniques for PTLs and GDLs. This work evaluates the effectiveness of the Keyence VR6200 benchtop-scale structured light optical profilometer for detection of surface protrusions on PTLs and GDLs. Standard testing procedures for detecting and measuring high points were created for use on both material types. These procedures were evaluated using Gage Repeatability and Reproducibility (Gage R&R), where the repeatability, reproducibility, and effectiveness of the system to detect and measure high points were quantified. We have shown with high statistical power that the system is very effective in detection and measurement of high points, with Gage R&R contributions measured to be 2.2% and 3.6% for PTLs and GDLs, respectively.

36 MATERIALS SCIENCE

PV Lifetime Project - 2025 NLR Annual Report

DOE's PV Lifetime project was initiated in 2016 with the goal of accurately characterizing the early-life evolution of photovoltaic (PV) field performance. Different PV cell and module technologies result in different initial degradation rates due to effects like light-induced degradation (LID) and light and elevated temperature-induced degradation (LeTID). To accurately characterize the initial field degradation of maximum power (Pmp) requires the use of high-accuracy indoor IV curve measurements at standard test conditions. Therefore, PV modules involved in this study are removed from the field once or twice per year and brought indoors for measurement under constant temperature and irradiance conditions. Overall annual degradation rates are as follows: our first modules to be deployed (Jinko, Trina, QCells) have annual median degradation rate between -0.4%/yr and -0.5%/yr mainly concentrated in the first year. Mission Solar, LG and Panasonic modules are all displaying modest degradation, better than -0.3% / year. Indeed, Mission Solar fielded modules degraded less than their control modules which remain indoors and un-exposed. This is also true for the LONGi monofacial modules, which had some field degradation, but not as much as the degradation of the indoor control modules. The LONGi bifacial modules on the other hand have degraded more in the field than their monofacial counterparts, although still a modest amount (-0.4 %/yr). Of the four newest module types in the study, only one has had better than average degradation. REC360NP2 (N-type TOPCon) had a slight performance increase over the first year and a half of field deployment. For the other three new module types (plus one older module type), degradation was more rapid. In our study of 16 module types, four have demonstrated degradation faster than -1%/yr: two N-type Heterojunction, one PERC bifacial and one PERC shingled module. The two heterojunction modules in our study are degrading the most rapidly. Sunpreme n-HIT bifacial modules are showing a loss rate around -1.5%/yr, for over -10% total to date. This is largely attributed to loss in front-side Isc. This is distinct from the REC 405AA-Pure modules which have degraded -6.8% in only a year and a half, for an annualized decline of -3.9 %/yr. For this module type, the decline is roughly half in Voc, with the remaining split between FF and Isc. Of the remaining two module types, Prism Solar PERC bifacial has declined -5% total since 2019, although this loss appears to have stabilized in the most recent measurement. The Solaria PowerX-400R Shingled module type has also lost around -3.2% in the first 1.5 years of field deployment. It remains to be seen if these losses will continue with time.

14 SOLAR ENERGY

Performance-portable Binary Neutron Star Mergers with AthenaK

We introduce an extension to the AthenaK code for general-relativistic magnetohydrodynamics (GRMHD) in dynamical spacetimes using a 3+1 conservative Eulerian formulation. Like the fixed-spacetime GRMHD solver, we use standard finite-volume methods to evolve the fluid and a constrained-transport scheme to preserve the divergence-free constraint for the magnetic field. We also utilize a first-order flux correction (FOFC) scheme to reduce the need for an artificial atmosphere and optionally enforce a maximum principle to improve robustness. We demonstrate the accuracy of AthenaK using a set of standard tests in flat and curved spacetimes. Using a SANE accretion disk around a Kerr black hole, we compare the new solver to the existing solver for stationary spacetimes using the so-called "HARM-like" formulation. We find that both formulations converge to similar results. We also include the first published binary neutron star (BNS) mergers performed on graphical processing units (GPUs). Thanks to the FOFC scheme, our BNS mergers maintain a relative error of $\mathcal{O}$(10 –11 ) or better in baryon mass conservation up to collapse. Finally, we perform scaling tests of AthenaK on OLCF Frontier, where we show excellent weak scaling of ≥80% efficiency up to 32,768 GPUs and 74% up to 65,536 GPUs for a GRMHD problem in dynamical spacetimes with six levels of mesh refinement. AthenaK achieves an order-of-magnitude speedup using GPUs compared to CPUs, demonstrating that it is suitable for performing numerical relativity problems on modern exascale resources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Medium Voltage Testbed for Comparing Advanced Power Line Sensors vs. Transformers with Electrical Grid Events

Electrical utilities have relied upon potential transformers (PTs) and current transformers (CTs) for very accurate metering and to provide reliable signals for protective relays. These devices measure phase voltages and currents and are commissioned by electrical engineers. PTs/CTs can detect and react to various electrical anomalies that could adversely affect electrical grid operations. Less expensive alternative sensing technologies offer the possibility of wider deployment, particularly in grids that employ distributed energy resources. In this work, the performance of an advanced medium-voltage sensor is compared with that of a reference PT and a CT and experimentally evaluated for different power grid scenarios on an advanced outdoor power line sensor testbed at the U.S. Department of Energy’s Oak Ridge National Laboratory. The sensor is based on a capacitive divider for voltage monitoring and a Rogowski coil with an integrator for current monitoring. This study simulated a power grid model based on a utility circuit at the Riverside EPB of Chattanooga. The simulation circuit was created with MATLAB/Simulink software and was integrated into an RT-LAB project to run with the OP4510 real-time simulator at the OPLST. During the tests, the real-time simulations were run for 40 s, and the signal to record the test event with the power meter was set at 30 s for the event-trigger circuit. The advanced outdoor power line sensor testbed has a real-time simulator that is used to generate transient scenarios (e.g., electrical faults, capacitor bank operation, and service restoration), while the analog signals are recorded by the same high-resolution power meter. The behaviors of analog signals, harmonic components, total harmonic distortion, and crest factors are assessed for this power line sensor and compared with those of the reference PT/CT because of the absence of testing standards for advanced outdoor power line sensors. The results showed that this OPLS technology responded identically to the PT and CT under all conditions.

Piesciorovsky, Emilio [ORNL]

Transient Stability Enhancement via a Scalable RL Method with VSG Parameter Tuning

This paper presents a reinforcement learning (RL)-driven strategy to improve the transient stability of power systems via tuning parameters of multiple virtual synchronous generators (VSGs). We proposed a scalable method to support RL training convergence probability and speed, even when a large number of contingencies are considered. The proposed scalable RL framework first decomposes the large number of contingencies into multiple groups and then conducts parallel training for each group, decreasing the state space and complexity of each training. Additionally, we propose a contingency grouping algorithm to streamline the RL action space and facilitate the training. The proposed method is validated across various standard test systems.

Huang, Xiaoge

Dynamically Learning Incentives for Load Control

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.

demand response

Toward Standardized Microscale Tensile Testing for Two‐Photon Polymerization‐Fabricated Materials in Liquid

Two-photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP-fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate-based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross-sectional areas ranging from 1 to 36 μm 2 . Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

mechanical characterization

Comparison of the performance of TLD, OSL and RPL personal dosimetry systems to the American National Standard Institute (ANSI) test categories

The Laboratory Accreditation Program (LAP) tests the capability and performance of the United States Department of Energy's (USDOE) facilities to accurately measure and quantify the whole-body and extremity radiation equivalent doses to the occupational workers. The dosimetry methods used in personal dosimetry can be Thermoluminescence (TL), Optically Stimulated Luminescence (OSL) and Radiophotoluminescence (RPL), among other techniques available. Currently, the TL (LiF:Mg,Ti) and OSL (Al 2 O 3 :C) dosimetry systems are DOELAP-accredited for occupational dose measurements and regulatory reporting. In this study, the performance of a beryllium oxide (BeO) OSL dosimetry system and of a silver-doped phosphate glass RPL dosimetry system is compared against the performance of an accredited LiF:Mg,Ti TL dosimetry system. The bias and standard deviation in each of the performance test categories are compared between these three systems for exposures to photons, beta, and mixed radiation fields with the criteria established by the American National Standard Institute (ANSI) and Health Physics Society (HPS) N13.11-2022 standard. The practical implementation of the dosimetry program and its equivalency to the occupational personal equivalent dose H p (0.07) and H p (10) measurements were evaluated. The TL, OSL and RPL dosimetry systems passed the ANSI performance test criteria for H p (0.07) and H p (10) occupational dose measurements and met the DOELAP accreditation requirements.

61 RADIATION PROTECTION AND DOSIMETRY

An International Round-Robin Study on Thermoelectric Module Testing and Development of Standard Power Generation Modules

An international round-robin study on thermoelectric power generation modules was conducted with nine participating laboratories. Two types of commercially available bismuth telluride modules, 30 mm × 30 mm and 40 mm × 40 mm, were used. A test protocol was followed with five temperature set points from 50°C to 150°C. Graphite sheets were used as thermal interface materials with test pressure at 100 psi (0.69 MPa). The results showed large lab-to-lab variations and the key source of uncertainty for module efficiency was identified as the heat flux measurement. In the meantime, significant uncertainty was also found in maximum electrical power (P max ) measurements. As a result of the round-robin, a “standard module” with 4 × 4 legs on a 20 mm × 20 mm platform was suggested. A skutterudite module and a half-Heusler module were produced with identical geometry and 4 mm × 4 mm × 8 mm legs. All transport properties to calculate the figure-of-merit, zT, were measured from ambient temperature to 500°C. Module performance was measured by two laboratories. Two finite-element-analysis (FEA)-based models were developed independently to simulate and predict the module performance. In conclusion, the standard modules eliminated significant test uncertainties and are aimed at assisting device design and achieving more accurate performance predictions.

Round-robin

POTATO: A Unified Assessment System for HL-LHC Outer Tracker Module Productions

The CMS detector s silicon tracker will be replaced in preparation for the upcoming High-Luminosity LHC (HL LHC) upgrade. The inner tracker will include a new pixel detector while the outer tracker (OT) will feature 5,592 pixel strip (PS) and 7,608 strip strip (2S) sensor modules with production distributed across 7 sites worldwide, requiring standardized grading and testing. One testing apparatus the burn-in box replicates the temperature cycling conditions that successful modules must withstand. The Phase-II Outer Tracker Analyzer of Test Outputs (POTATO) standardizes module evaluation and grading among testing sites. Given burn-in box test output, POTATO analyzes the results and assigns a letter grade to a module and its sub-components. POTATO's grading process must adapt to evolving standards for module evaluation; hence, we detail work on POTATO's database and grading functionality that will facilitate the unified assessment of Phase-II OT module assembly and testing worldwide. Once completed, POTATO will allow technicians to interact with a database of all graded modules and efficiently assess the global module assembly effort.

Clark, Olivia J. [U. Tennessee, Knoxville]

Femto-second Laser’s Enabling New Length Scale Fabrications for Rapid Post Irradiation Examination of Materials: Concluding LDRD Project Poster

Mechanical testing campaigns are required to qualify materials for advanced reactor conditions, yet economical and safety limitations restrict the number of standardized mechanical tests that can be performed. Reducing the size of the sample is one approach to addressing these challenges and to accelerating testing. Previous research has shown that smaller mechanical test samples produce higher yield and ultimate stress values compared to values measured from standard sample sizes: the “smaller is stronger” effect. Specimens used in accelerated material testing campaigns must reflect bulk material performance to enable engineering scale material property measurement. The objective of this research project was to determine if engineering scale mechanical behavior—the yield stress—could be measured with micro-tensile test samples smaller than traditional standard testing geometries. The relationship between yield stress and sample size was explored with two different nuclear-relevant structural materials: Zircaloy-4 and tungsten. Mechanical testing of both metals demonstrated decreasing yield stress values with increasing sample gauge size across three different sizes. Yield stress values from the largest gauge size, fabricated with a femto-second laser ablation system, approach bulk material yield stress values reported in published literature. Preliminary analysis of the tungsten samples indicates the yield stress value depends on the grain characteristics within the gauge section, in addition to the gauge size. Accompanying modeling efforts, including response surface generation and crystal plasticity approaches, further demonstrated that the size of the sample gauge section alone cannot explain the change in yield stress values.

36 MATERIALS SCIENCE