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At least 577 records · Page 32

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DOE OSTI2023

Developing a small participant framework: An investigation of mode choice influential factors

An in-depth comprehension of the changing impact of mode choice influencing factors is essential in planning for behavior-adaptive mode shift policies. This study developed a framework to investigate a method of evaluation of the changes in mode choice using a small participation pool. A field application in Pima County, Arizona was developed to test the developed framework to evaluate the impact of a real transit experience. Twenty-two participants were recruited and repeated measurements were taken before and after their transit trip. The results revealed that people are less susceptible to their mental biases after a transit trip and consider more objective elements such as walking time. The developed framework resulted in successful evaluation methods that are reproducible for other application study designs. Thus, the results further present that low participant recruitment in application-based field studies can be accomplished using this developed methodology, for other regions and studies.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro- Waveform Measurements

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. Here, the outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.

High-fidelity synchro-waveform measurements↗

DOE OSTI2020

The Transition From Rebound to Bonding in High-Velocity Metallic Microparticle Impacts: Jetting-Associated Power-Law Divergence

A metallic microparticle impacting a metallic substrate with sufficiently high velocity will adhere, assisted by the emergence of jetting—the splash-like extrusion of solid matter at the periphery of the impact. In this work, we compare real-time observations of high-velocity single-microparticle impacts to an elastic–plastic model to develop a more thorough understanding of the transition between the regimes of rebound and bonding. We first extract an effective dynamic yield strength for copper from prior experiments impacting alumina spheres onto copper substrates. We then use this dynamic yield strength to analyze impacts of copper particles on copper substrates. We find that up to moderate impact velocities, impacts and rebound velocities follow a power-law behavior well-predicted on the basis of elastic-perfectly plastic analysis and can be captured well with a single value for the dynamic strength that subsumes many details not explicitly modeled (rate and hardening effects and adiabatic heating). However, the rebound behavior diverges from the power-law at higher impact velocities approaching bonding, where jetting sets on. This divergence is associated with additional lost kinetic energy, which goes into the ejection of the material associated with jetting and into breaking incipient bonds between the particle and substrate. These results further support and develop the idea that jetting facilitates bonding where a critical amount of bond formation is required to effect permanent particle deposition and prevent the particle from rebounding.

36 MATERIALS SCIENCE↗

DOE OSTI2026

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. To accelerate DUNE’s physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. At its core is a Real-Time Beam Integrity Monitor using a physics-informed Digital Twin. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5% to 1%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US–Japan R&D effort will deploy a LAPPD-based muon monitor in the NuMI beamline. ToF measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong response to these effects. ML models predict beam quality and horn current to sub-percent accuracy from muon data, enhancing anomaly detection and stability. This scalable, AI-enabled strategy improves beam fidelity, reduces systematics, and sets a new standard for high-power accelerator operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

DOE OSTI2025

EUREICA: Efficient UltRa Endpoint IoT-enabled Coordinated Architecture

The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) that include renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. The purpose of this project was to develop a framework ((Efficient, Ultra-REsilient, IoT-Coordinated Assets, or EUREICA)for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices, and a local electricity market (LEM) to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. Experiments conducted during this project demonstrated that, with this SA, a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. The demonstrations were carried out using a variety of high-fidelity co-simulation platforms, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

14 SOLAR ENERGY↗

DOE OSTI2025

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Implementing Online Oscillation Monitoring and Forced Oscillation Source Locating at Peak Reliability

This paper introduces a framework of online oscillation monitoring systems and forced oscillation detection & source location tools that Peak Reliability ('PEAK') has implemented for the Reliability Coordinator (RC) function of the Western Interconnection. The framework consists of four main components: (1) Montana Tech's Modal Analysis Software (MAS) engine; (2) Washington State University's Oscillation Monitor System (OMS) software; (3) Forced Oscillation Detection and Source Location Algorithms (FODSL); and (4) PEAK in-house visualization tool and alarming logic built in PI Processbook. The framework has been validated and applied for real system oscillation studies successfully. Implementation experience and lessons learned will be discussed in the paper.

14 SOLAR ENERGY↗

DOE OSTI2026

Data driven drift correction for complex optical systems

To exploit the thousand-fold increase in spectral brightness of modern light sources, increasingly intricate experiments are being conducted that demand extremely precise beam trajectory. Maintaining the optimal trajectory over several hours of an experiment with the needed precision necessitates active drift control. Here, we outline time varying Bayesian optimization (TVBO) as a data driven approach for robust drift correction, and illustrate its application for a split and delay optical system composed of six crystals and twelve input dimensions. Using numerical simulations, we exhibit the application of TVBO for linear drift, non-smooth temporal drift as well as constrained TVBO for multi-objective control settings, representing real-life operating conditions. This approach can be easily adapted to other X-ray beam conditioning and guidance systems, including multi-crystal monochromators and grazing-incidence mirrors, to maintain sub-micrometer and nanoradian beam stability over the course of an experiment spanning several hours.

Bayesian optimization↗

DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗

DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗

DOE OSTI2024

Real-space atomic dynamics in metallic liquids investigated by inelastic neutron scattering

Understanding the dynamics of liquids at the atomic level remains a major challenge. Even though viscosity is one of the most fundamental properties of liquids, its atomistic origin is not fully elucidated. Through inelastic neutron scattering experiment on levitated metallic liquid droplets, the time-dependent pair correlation function, the Van Hove function, was determined for Z⁢r 50 ⁢C⁢u 50 and Z⁢r 80 ⁢P⁢t 20 liquids at various temperatures. The time for change in local atomic connectivity, τ LC , which is the timescale of atomic bond cutting and forming, is estimated based on the exponential decay of the nearest neighbor peak of the Van Hove function. At high temperatures above the crossover temperature T A , τ LC is equal to the Maxwell relaxation time, τ M =η/G ∞ , where η is the macroscopic shear viscosity and G ∞ is the high-frequency shear modulus. Below T A the ratio of τ M /τ LC increases with decreasing temperature, indicating increased atomic cooperativity as predicted by molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

ASPIRES: Airport Shuttle Planning and Improved Routing Event-driven Simulation

Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. Here, the simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗

DOE OSTI2023

Ni/NiO Reference Electrode Potential Measurements in Molten CaCl 2 –CaO

A direct oxide reduction (DOR) process is used to reduce PuO 2 to Pu metal via Ca metal. Molten CaCl 2 is commonly used as the electrolyte for this reaction. As the DOR reaction proceeds, CaO accumulates in the salt and must be measured in real-time to know when the reaction has gone to completion. Open circuit potential (OCP) was measured in this report between a reference electrode consisting of Ni/NiO contained in a MgO tube and a tungsten working electrode. Two methods of CaO additions were performed over several experiments, and both methods correlated to a 0.11 to 0.23 V decrease in OCP up to 14.4 wt% CaO. CaO concentrations were separately measured via sampling and acid-base titration. A plot of OCP versus concentration of CaO fits the Nernst equation with the CaO powder additions up to 5.2 wt% and also at the higher end of the CaO pellet concentration range. While the MgO tubes were observed to be structurally stable in the molten salt, scanning electron microscopy and energy dispersive X-ray spectroscopy showed that Mg in the tubes had been partially replaced with Ca.

36 MATERIALS SCIENCE↗

DOE OSTI2020

New Beginnings: Optimising Waste and End-state at UK Nuclear Sites to Enable Environmental Permit surrender (the GRR) - 20193

The decommissioning of nuclear sites involves the management of large volumes of radioactive waste, other conventional waste, and the clean-up of land and groundwater contamination. In the UK, the disposal of radioactive waste from nuclear sites is regulated by the Environment Agency (England), Natural Resources Wales (Wales) and the Scottish Environment Protection Agency (Scotland). All three environment agencies seek to make sure that the generation and disposal of radioactive waste is optimised: ensuring impacts to people are 'as low as reasonably achievable' (ALARA). With a large proportion of UK nuclear sites embarking on decommissioning, the environment agencies have collaboratively developed new guidance: the 'Management of radioactive waste from the decommissioning of nuclear sites: Guidance on the Requirements for Release from radioactive substances regulation' (referred to as the GRR)[1]. The environment agencies worked in liaison with the Office for Nuclear Regulation (ONR), the UK nuclear safety regulator, and the UK government body responsible, the Nuclear Decommissioning Authority (NDA). Prior to finalising the guidance, a 12 month trial was completed at three of the NDA's nuclear sites, enabling the environment agencies to test their draft GRR guidance against 'real world scenarios' and the challenge of incorporating its requirements into decommissioning plans. Experience gained during the trial was incorporated into the current version of the GRR, published in July 2018. The GRR represents the 'new beginning' of this paper's title because for the first time, the UK environmental regulators have expressed in detail their requirements for surrender of environmental permits for nuclear sites. The GRR provides a framework for a UK nuclear site to plan, assess and justify its management of radioactive waste and deliver through decommissioning, an optimal, environmentally safe and sustainable end-state. This paper will describe the requirements and technical standards within the GRR, the establishment of defensible clean-up criteria and the production of a Waste Management Plan (WMP), supported by a Site-Wide Environmental Safety Case (SWESC). It will describe the experience gleaned by operators and regulators during the trial and it will highlight current work by regulators to implement the new approach proportionately. Finally, this paper will look forward and outline planned amendments to legislation in the UK affecting nuclear sites that should enable the full benefits of the GRR to be realised in the future. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Active Lithium Injection for a Real Time Control of the Divertor Heat Flux for Fusion Devices

When the local heat flux exceeds specified flux limit, tungsten PFC surfaces can be damaged, which is not acceptable for a reliable reactor operations. The divertor PFCs are typically designed for a specific heat flux limit usually assuming an average steady-state heat flux which is typically 5–10 MW/m 2 . However, in addition to steady-state heat flux, fusion reactor divertor PFCs could experience transient heat fluxes such as ELMs and/or other magnetic reconnection events which can deposit large transient heat fluxes onto the divertor PFCs. The transient divertor heat flux could be significantly larger than the steady-state heat flux which could damage the solid PFC surfaces. The divertor heat flux can be subjected to additional complications such as the uncertainties in the the divertor strike point heat flux projection. Moreover, there are additional experimental observations of non-axisymmetric power flux which can occur under non-axisymmetric magnetic perturbations. The liquid lithium (LL) PFCs is more resilient against such transient heat fluxes as they could evaporate LL as needed and the lost LL can be then replenished afterward. In this paper, we analyze a case for a transient divertor heat pulse of 1 MJ in 10 ms for a ITER-size reactor. This is a small perturbation (~ 0.1%) to the expected plasma stored energy compared to the previously analyzed case of 20 MJ heat pulse. Even with this relatively modest heat pulse, the LL surface undergoes ~ 100 °C temperature rise. However, the resulting LL surface heating without rapid cooldown mechanism could lead to excessive LL evaporation continuing well after the transient heat flux resulting in a significant Li injection of ~ 0.6 mol in about a 200 ms period. This amount of Li injection could cause plasma dilution and performance degradation. On the other hand, an active Li injection capability if optimized could prevent the LL surface temperature rise and thus reducing subsequent Li evaporation into the plasma by a factor of 7 compared to the passive LL PFC case. A crucial tool of active Li injection is a rapid response pellet injector which could inject light impurity pellets before the excessive heat flux could reach the divertor plate causing serious damage. A simple pellet ablation model suggests a favorable pellet deposition profile for smaller ~ 0.1 mm radius pellet with ~ 10–20 m/s velocity. Moreover, if it is possible to inject from the private flux region, the pellet injection efficiency into the high heat flux strike point region can be as high as 80% compared to ~ 50% for the injection from outer radius region. The pellet deposition efficiency can be further improved by designing a shell-pellet which can burst when a certain ablation fraction is reached. Finally, a possible implementation technique using an inductive pellet injector with a rapid time response of a few msec is proposed here which can be tested in NSTX-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Dynamic study of direct CO 2 capture from indoor air using poly(ethylenimine)-impregnated fiber sorbents

Supported amine adsorbents are promising materials for direct air capture (DAC) of CO 2 due to their high CO 2 capacity and relatively low energy requirement for regeneration. For a DAC process, it is essential to properly define operating parameters to achieve high sorbent productivity (amount of CO 2 captured per unit quantity of sorbent material over unit time). It is furthermore essential to understand the kinetic behavior of the process under the influence of various operating conditions such as the inlet air velocity, sorbent composition, and humidity to select an effective range of operating conditions to maximize sorbent productivity. Here, the dynamic behavior of a DAC process is probed using a fixed fiber sorbent contactor containing poly(ethylenimine) (PEI)-impregnated composite silica/cellulose acetate (CA) fibers. Throughout this study, experiments are conducted using both simulated air (398 ppm CO 2 balanced by N 2 ) and real indoor air (~400–500 ppm CO 2 ). The experimental behavior of the fibers using simulated air and indoor air is compared, and the influence of the inlet air velocity on the breakthrough behavior is assessed. By changing operating conditions, the impact on the fiber sorbent productivity (mmol CO 2 g fiber –1 h –1 ) is quantified to identify conditions that could favor high rates of CO 2 removal. The kinetics of steam-assisted CO 2 desorption are studied, identifying achievable desorption times. Productivities of 1.2 mmol CO 2 g fiber –1 h –1 are obtained using an inlet air velocity of 1.1 m s –1 . Performance trends show that further increasing the inlet air velocity will likely lead to even higher productivities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗