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At least 685 records · Page 38

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DOE OSTI2021

Valorisation of technical lignin in rigid polyurethane foam: A critical evaluation on trends, guidelines and future perspectives

Lignin is one of the most abundant aromatic natural polymers. Produced as a by-product from the biomass refinery industries, lignin remains largely underutilised in high-value industrial applications. The incorporation of lignin in rigid polyurethane foam (RPUF) has been the focus of much research, due to its potential to replace fossil fuel-based components of RPUF. However, the overall sustainability of RPUF depends on numerous factors including processability, cost-effectiveness, and retention of performance throughout the service life. To date, the incorporation of lignin has been explored either as filler particles (through direct incorporation) or as a blendable liquid polyol introduced after chemical modifications (such as oxyalkylation, functionalisation, or depolymerisation). However, the production of lignin incorporated foam with high performance through cost-effective processing is still an ongoing challenge. Herein, this review critically appraises the progress on the effective incorporation of lignin in RPUF. Firstly, this review briefly covers the essential raw materials, formulation, important properties, and sustainability aspects of RPUF for industrial applications. Secondly, it provides insights on the key parameters of lignin of relevance to incorporation into RPUF. Thirdly, it benchmarks the reported studies on incorporation of lignin in RPUF systems by evaluating their important properties and proposes potential strategies for addressing the key challenges in the incorporation of lignin in RPUF. By bridging the gaps that exist in the literature on the utilisation of lignin in RPUF this account will serve as a resource for both beginners and professionals in the polyurethane and biorefinery industries towards the successful development of lignin incorporated RPUF for industrial applications

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Resilient Control of Networked Microgrids Using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations

Improving system-level resiliency of networked microgrids against adversarial cyber-attacks is an important aspect in the current regime of increased inverter-based resources (IBRs). To achieve that, this paper contributes in designing a hierarchical control layer, in conjunction with the existing control layers, resilient to adversarial attack signals. Considering model complexities, unknown dynamical behaviors of IBRs, and privacy issues regarding data sharing in multi-party-owned microgrids, designing such a control layer is non-trivial. Here, to tackle these issues, a novel federated reinforcement learning (Fed-RL) method is proposed. To grasp the interconnected dynamics of networked microgrids, the paper develops Federated Soft Actor-Critic (FedSAC) algorithm following the vertical structure of implementing Fed-RL. Next, utilizing the OpenAI Gym interface, we built a custom set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with IEEE 123-bus benchmark comprising 3 interconnected microgrids. Finally, the learned policies in the simulation are transferred to the real-time hardware-in-the-loop (HIL) test-bed developed using the high-fidelity Hypersim platform. Finally, experiments show that the simulator-trained RL controllers achieve desirable performance with the test-bed platform, validating the minimization of the sim-to-real gap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO 2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

ICSBEP Benchmark Template

The purpose of this document is to demonstrate the ability of LATEX to create an ICSBEP benchmark evaluation. Creating a large document in LATEX offers many benefits, such as automatic numbering of items such as tables and figures, and easier placement of objects in the page. Additionally, it is easier to have multiple styles of pages in the same document, and in general the handling of large documents (which evaluations very commonly are) is not problematic. The formatting of this document is meant to conform to both the standards of the document and formatting guide and established practices in order to make the creation of new evaluations a more streamlined process. This will also help reduce the benchmark-to-benchmark variation in style.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

IER: 121 Updates on the NeSO Benchmark [Slides]

NeSO stands for Neptunium Subcritical Observation. The presentation covers an overview, details of the sphere composition, decay, and modeling and initial results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Real-time interfacial electron dynamics revealed through temporal correlations in x-ray photoelectron spectroscopy

We present a novel technique to monitor dynamics in interfacial systems through temporal correlations in x-ray photoelectron spectroscopy (XPS) signals. To date, the vast majority of time-resolved x-ray spectroscopy techniques rely on pump–probe schemes, in which the sample is excited out of equilibrium by a pump pulse, and the subsequent dynamics are monitored by probe pulses arriving at a series of well-defined delays relative to the excitation. By definition, this approach is restricted to processes that can either directly or indirectly be initiated by light. It cannot access spontaneous dynamics or the microscopic fluctuations of ensembles in chemical or thermal equilibrium. Enabling this capability requires measurements to be performed in real (laboratory) time with high temporal resolution and, ultimately, without the need for a well-defined trigger event. The time-correlation XPS technique presented here is a first step toward this goal. The correlation-based technique is implemented by extending an existing optical-laser pump/multiple x-ray probe setup by the capability to record the kinetic energy and absolute time of arrival of every detected photoelectron. The method is benchmarked by monitoring energy-dependent, periodic signal modulations in a prototypical time-resolved XPS experiment on photoinduced surface-photovoltage dynamics in silicon, using both conventional pump–probe data acquisition, and the new technique based on laboratory time. The two measurements lead to the same result. The findings provide a critical milestone toward the overarching goal of studying equilibrium dynamics at surfaces and interfaces through time correlation-based XPS measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2022

Evaluating the X2 initial core zero power physics tests with Serpent-Ants

The validation of the Ants nodal neutronics code for VVER applications is started by modelling the zero power physics tests for the initial core of the Khmelnitsky 2 nuclear power plant as described in the X2 benchmark using the Serpent-Ants two step neutronics calculation chain. The Ants prediction compare favorably against an earlier continuous energy Monte-Carlo reference solution as well as the measured data, except for discrepancies in the SCRAM worth between predicted and measured values. Similar discrepancies have been previously reported for VVER-1000 reactors, and the Ants predicted SCRAM worths match well with the Serpent results. Additionally, the assembly power distribution and axial power distribution predicted by Ants for the critical HZP state of the reactor are also compared to a reference Serpent solution with very good results. Lastly, an investigation into the effects of the few-group structure used in the nodal calculations and the use or lack of leakage correction for group constants on the results shows that the best accuracy is reached with seven or eight energy groups without leakage correction, although a reasonable accuracy can also be obtained with two or three energy groups using fundamental mode leakage correction. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

RX-ADS: Interpretable Anomaly Detection Using Adversarial ML for Electric Vehicle CAN Data

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/ communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) window based feature extraction method; 2) deep Autoencoder based anomaly detection method; and 3) adversarial machine learning based explanation generation methodology. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach presented performance comparable to the HIDS approach (OTIDS dataset) and has outperformed HIDS and GIDS approaches (Car Hacking dataset). Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. Furthermore, these explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

42 ENGINEERING↗

DOE OSTI2025

Single-Bunch Instabilities at the Fermilab Recycler Ring

Understanding and characterizing collective instabilities is critical for high-intensity operation at the Fermilab Recycler Ring. This work presents an application of the the Nested Head-Tail (NHT) formalism for modeling single-bunch transverse instabilities, incorporating analytical solutions to the resistive wall impedances in the absence of space charge. Predicted growth rates and mode structures are benchmarked against PyHEADTAIL simulations and ongoing experimental measurements. The experimental program includes studies of bare-machine instabilities at different machine parameters. These results support the understanding of naturally-occurring instabilities in the Recycler and contribute to the development of predictive tools for beam stability in future high-intensity configurations.

Gonzalez-Ortiz, C. [Fermilab]↗

DOE OSTI2025

Single-Bunch Instabilities at the Fermilab Recycler Ring

Gonzalez-Ortiz, Cristhian [Fermilab]↗

DOE OSTI2024

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect of risk awareness involves modelling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. These infrequent occurrences present a formidable challenge for data-driven methods aiming to capture such risky events accurately. While risk-aware RL techniques do exist, they suffer from high variance estimation due to the inherent data scarcity. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value distribution. To achieve this, we formulate the extreme values of the state-action value function distribution as parameterized distributions, drawing inspiration from the principles of extreme value theory (EVT). We propose an extreme value theory based actor-critic approach, namely, Extreme Valued Actor-Critic (EVAC) which effectively addresses the issue of infrequent occurrence by leveraging EVT-based parameterization. Importantly, we theoretically demonstrate the advantages of employing these parameterized distributions in contrast to other risk-averse algorithms. Our evaluations show that the proposed method outperforms other risk averse RL algorithms on a diverse range of benchmark tasks, each encompassing distinct risk scenarios.

Wang, Yu↗

DOE OSTI2020

Energy Intensity Baselining and Tracking Guidance

Each company joining the U.S. Department of Energy’s (DOE’s) Better Buildings, Better Plants Program (Better Plants) commits to establishing an energy consumption and energy intensity (EI) baseline and to tracking its energy performance over a 10-year period against that baseline. The baseline must reflect a company’s energy consumption over a 12-month period, covering all its U.S.-based operations. Energy consumption is calculated by fuel type in terms of primary energy (also known as source energy). EI is broadly defined as the amount of energy consumed per unit of output produced. For this guidance document and for the program, the term energy performance represents an evaluation of a facility’s capacity to use energy efficiently. Metrics used to assess a facility’s energy performance can include EI, energy consumption, improvements in EI, etc. Establishing an energy baseline and tracking system is a critical first step in effectively managing energy use. Developing a baseline can help a company understand energy use within the corporation and give it a point of comparison to evaluate future efforts to improve energy performance. It can also support efforts to validate a company’s energy management activities, improve comparative analyses when using benchmarks, and help in predicting future energy needs. In addition, a company that normalizes its performance data can determine highly defensible measures of energy savings generated through implemented energy efficiency projects. Establishing a baseline and tracking energy performance is also a requirement for ISO 50001 certification. Although basic energy data can be collected through utility bills, most manufacturers will have to perform additional analyses to develop accurate and robust energy baselines and tracking systems. Energy is consumed in many ways within the manufacturing sector and can come from multiple sources. Energy is sometimes generated and sold to other parties or captured and reused on-site. External events can exert a significant impact on a facility or company’s energy use independent of any purposeful efforts to improve energy efficiency. Operational changes, such as production shifts—which may be inevitable for some companies over the 10-year period covered by the program—can also make a big difference in energy use. Since Better Plants asks companies to account for all their U.S.-based operations, mergers, acquisitions, and divestitures can also have significant implications for a company’s energy metrics. This document aims to demystify the sometimes complex baselining process. It devotes special attention to the task of normalizing and adjusting energy consumption to account for external factors, such as weather and production changes. A key recommendation is that companies use regression analysis to normalize their energy consumption data whenever possible. Regression analysis is a statistical technique that estimates the dependence of a variable (i.e., energy use in the context of Better Plants) on one or more independent variables such as ambient temperature, while controlling for the influence of other variables at the same time. A properly developed regression analysis can provide a reliable estimate of energy savings resulting from energy improvement strategies and projects by accounting for the effects of variables such as annual production levels and weather. DOE has developed a companion Energy Performance Indicator software tool (EnPI) to simplify the baselining process. This tool can run regression models, calculate changes in EI at the facility level, and automatically compile facility-level data into a corporate-wide metric. Note that although the relevant equations used to calculate EI are provided in this document, the EnPI tool will automatically perform most calculations for the user. Additionally, Better Plants Partners (Partners) can call on their Technical Account Manager (TAM) to help them establish a baseline and assist with the necessary calculations to track progress.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Updates and Validation for the n+ 63,65 Cu Cross Sections [Slides]

The RRR and URR parameters for 63,65 Cu have been optimized to transmission and capture data. Several data sets have been newly incorporated in this evaluation. The angular distribution parameters have been adopted from differential measurements, as was done in ENDF/B-V. The performance of this evaluation in the integral benchmark calculations is favorable, and the validation is ongoing.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Godiva 4 CAAS Shielding Benchmark: Pre CED-3A Overview [Slides]

Thiis presentation shows the character of an integral experiment. It covers the question why Godiva 4? Additionally, this presentation touches on the IER 498 benchmark quantity, activities facilitated by IER 498, proposed CED-2 test matrix, and what happened during the IER 498’s gap year.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Best Practices for Nuclear Experiment Data Preservation at Idaho National Laboratory: A Guide for Researchers and Reactor Operators

Preserving experimental data is essential for supporting advancements in nuclear science and ensuring the longevity of Idaho National Laboratory's contributions to reactor technology and safety. This report provides a comprehensive guide to best practices for experimental data management and preservation, focusing on standardized data formats, redundancy in storage, metadata documentation, and alignment with international standards. By following these recommendations, experimentalists and reactor operators can enhance the accessibility, reproducibility, and utility of critical datasets for regulatory review, validation computational methods, and future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

A reproducible study design for the MIMIC-IV in-hospital mortality task

Open, tabular electronic health record (EHR) datasets such as MIMIC-III and MIMIC-IV have become critical resources for developing machine learning (ML) models addressing clinical prediction tasks, including hospital readmission, length of stay, and in-hospital mortality (IHM). While MIMIC-III has benefited from well-established preprocessing pipelines and standardized feature sets, MIMIC-IV remains comparatively challenging to work with because there are no standardized benchmarks to support reproducibility and comparability across studies. To address this limitation, we present a rigorously curated MIMIC-IV custom feature set optimized for IHM prediction, constructed through a reproducible preprocessing pipeline and feature selection strategy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Summary of GFL Inverter Capability and Performance: Malawi [Slides]

This presentation provides a comprehensive comparison of inverter-based resource (IBR) technical requirements across multiple international grid codes - including IEEE 2800, ESO, EirGrid, and Malawi's evolving regulatory framework - to support capacity building for electricity-sector institutions in Malawi. It highlights key performance expectations for grid-following (GFL) inverters across critical domains such as primary and fast frequency response, ramping behavior, frequency and voltage ride-through capabilities, reactive power and voltage control, ROCOF withstand, phase jump tolerance, system strength considerations, protection schemes, and dynamic modeling requirements. Emphasizing a "Do no harm, do something, do more" philosophy, the document synthesizes best practices and operational benchmarks to guide the integration and stability of inverter-based resources within Malawi's power system. The presentation forms part of the SABESS Center of Excellence initiative supported by the Global Energy Alliance for People and the Planet.

24 POWER TRANSMISSION AND DISTRIBUTION↗