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DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

MANIPULATE-2020: An Interface Tool for Nuclear Data Processing

This report provides basic background data on the Manipulate-2020 code. This code is used for processing and "manipulation" of nuclear data in support of radiation metrology applications. The code is made available on the open GitHub repository and is available to the general nuclear data community.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Data on Cu- and Ni-Si-Mn-rich solute clustering in a neutron irradiated austenitic stainless steel

The data presented in this article is supplementary to the research article “Phase instabilities in austenitic steels during particle bombardment at high and low dose rates” (Levine et al.). Needle-shaped samples were prepared with focused ion beam milling from a 304L stainless steel that was irradiated with fast neutrons (E 0.1 MeV) in the BOR-60 reactor at 318 °C to 47.5 dpa. Atom probe tomography (APT) experiments in voltage mode were then conducted on a Cameca LEAP 5000X HR. Atom position, range, and mass spectrum files after reconstruction with Cameca’s IVAS software are included. Cu- and Ni-Si-Mn-rich solute nanoclusters were identified and analyzed using the Open Source Characterization of APT Reconstructions (OSCAR) program. Python code for OSCAR, information on the program’s underlying algorithm, and sample output files are provided. A proximity histogram of a Ni-Si-Mn-rich cluster and a 1D density/solute concentration profile of a Cu-rich cluster are given to demonstrate OSCAR’s analytical functionalities. The provided APT dataset is valuable for benchmarking phase instabilities in neutron-irradiated austenitic stainless steels that occur at high doses. The OSCAR program can be reused to process other APT data sets where solute nanoclustering is of interest.

42 ENGINEERING↗

DOE OSTI2021

The effect of substrate temperature on the critical velocity in microparticle impact bonding

A number of nascent coating and micromanufacturing processes involve particles sprayed at a high velocity, which impact a substrate, deform, and adhere. Successful bonding between the particles and the substrate requires impact velocities higher than the so-called critical adhesion velocity. This critical velocity is influenced by the temperature of the substrate, a variable that we isolate in this work by conducting single-particle impacts on a variable-temperature substrate for three systems (Al–Al, Sn–Sn, and Ti–Ti) with particles individually selected within a narrow size distribution. Our results quantitatively connect the increase in substrate temperature to a significant lowering of the critical velocity, which we attribute to the lower dynamic strength of the thermally softened substrate. The data are generally consistent with expectations for bonding being controlled by a hydrodynamic process of jetting upon impact.

36 MATERIALS SCIENCE↗

DOE OSTI2023

ENDF/B-VIII.1 and AMPX [Slides]

This presentation is on ENDF/B-VIII.1 and AMPX. This presentation begins with a brief introduction to the AMPX nuclear data processing code. The lecture then touches on nuclear data libraries that ship with the SCALE code system. Finally, this lecture concludes on preparations for the ENDF/B-VIII.1 libraries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

The MicroBooNE Single-Photon Low-Energy Excess Search

MicroBooNE is a short baseline neutrino experiment at Fermilab designed to address the low energy excess observed by the MiniBooNE experiment. This note describes and presents preliminary results for the MicroBooNE analysis developed to address this excess as a single photon plus one or zero protons in the final state. The analysis assumes neutrino neutral current Δ resonance production followed by Δ radiative decay on argon (NC Δ → $N_γ$) as the “signal model”; event reconstruction and selection have been developed and optimized in order to maximize efficiency and reduce cosmogenic and other beam-related backgrounds to the NC Δ → $N_γ$ signal. We present the analysis methodology and validation checks performed on limited-statistics open data sets, corresponding to 5×10 19 protons on target (POT), following a blind analysis, as well as the projected sensitivities for testing the Standard Model (SM) predicted rate for the NC Δ → $N_γ$ process and for testing the interpretation of the previously observed MiniBooNE low energy excess as NC Δ → $N_γ$ events, using the full anticipated MicroBooNE data set of 12.25×10 20 POT.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Spatially Resolved Performance Analysis for Perovskite Solar Cells

This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.

14 SOLAR ENERGY↗

DOE OSTI2020

A small-volume, high-throughput approach for surface tension and viscosity measurements of liquid fuels

An ongoing effort looking at bioblendstocks to function as drop-in replacements or blending components for gasoline has identified a large number of candidate fuels. This work documents an approach for rapid screening of candidate fuels using relatively small sample sizes ($\mathcal{O}(\mu\,{\rm l})$) that targets two key physical properties of liquid fuels—surface tension and viscosity. The approach utilizes shape oscillation dynamics of single droplets generated by a piezo-electric device and their decay over time. Strobed imaging of the oscillation process is used along with image processing, edge detection, and data analysis to capture the decay of the oscillation over time. The time constant of the decay process along with oscillation frequency are then used to estimate viscosity and surface tension using a theory for small amplitude droplet oscillations. Measurements are obtained for primary reference fuels (isooctane and n-heptane) as well as candidate fuels from four bio-derived functional groups of interest. Measurement results for surface tension and viscosity are correlated with literature data as well as measurements from standard reference instruments. The measurement results show that the droplet oscillation based approach is capable of reproducing surface tension and viscosity values for the tested fuels within deviations of 7% and 13% respectively from literature data. Results are obtained using an average of 5 µl per fuel within about 20 s, thus demonstrating a small-volume, high-throughput approach that can be used for screening of candidate bioblendstock fuels.

09 BIOMASS FUELS↗

DOE OSTI2025

Hydropower Biological Evaluation Toolset (HBET) Version 3.0: User Guide

The Hydropower Biological Evaluation Tools (HBET) software package, developed by Pacific Northwest National Laboratory (PNNL), is designed to assemble, organize, and process data collected by Sensor Fish and live fish. HBET enable users to characterize the hydraulic conditions of hydropower structures and estimate fish injury and mortality rates from various stressors. Future updates of the software may support other technologies, such as bead tracking in physical models and computational fluid dynamics. The HBET program can be customized to analyze different hydraulic applications, including turbines, spillways, weirs, pumped storage, and other user-defined functions, and therefore, help researchers, turbine designers, hydropower operators, and regulators better evaluate hydropower structures regarding their environmental sustainability and cost-effectiveness. Added content to the user guide about the new feature for predicting absolute injury rates.

13 HYDRO ENERGY↗

DOE OSTI2023

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗

DOE OSTI2022

CLAS Database for Studies of the Structure of Hadrons in Electromagnetic Processes

The CLAS Physics Database (CLAS DB) has been developed in collaboration between SINP at MSU and Hall B at Jefferson Lab. CLAS DB contains most experimental results on inclusive, semi-inclusive, and exclusive reactions on proton and nuclei obtained with the CLAS detector. The substantial part of these experimental results represents the only data sets available in the world. The results and analysis tools provided by the CLAS DB are of particular importance for studies of the structure of the ground and excited hadron states, offering insight into strong nonperturbative QCD dynamics, which underlie the generation of hadrons from quarks and gluons.

Chesnokov, V. V.↗

DOE OSTI2022

Regimes of evaporation and mixing behaviors of nanodroplets at transcritical conditions

The objective of this paper is to examine the fundamental mechanisms responsible for the transition between subcritical evaporation and supercritical dense-fluid-mixing in the absence of convection effects, specifically focusing on the liquid–vapor interfacial dynamics. To isolate the dynamics of this transition process, we characterize the different physical behaviors exhibited by an -dodecane nanoscale droplet placed in different nitrogen ambient conditions across the fuel’s critical point. We employ a continuum-based interface-resolving diffuse-interface method to explore the underlying phase-exchange mechanisms that bring about such distinct dynamics. Following the comparison against molecular dynamics simulations and experiments of evaporating droplets and experimental data for vapor–liquid equilibria, a parametric study at various ambient conditions and droplet sizing is performed to identify four regimes of evaporation/mixing behaviors: sub- and supercritical droplet evaporation, and sub- and supercritical dense-fluid-mixing. It is shown that the distinction in the phase-exchange mechanisms in these four regimes are brought about by the different thermodynamic phases the droplet center can exhibit during the evaporation/mixing process: subcritical liquid, supercritical liquid-like, subcritical gaseous, and supercritical gas-like, respectively. It is shown that the subcritical dense-fluid-mixing behavior is a direct result of nanoconfinement of the liquid–vapor interfacial structure and thus is not present for large droplet sizes. Finally, the present study also shows that the supercritical phase-exchange dynamics can follow two different pathways: supercritical droplet-like evaporation and supercritical dense-fluid-mixing. Furthermore, promoting the early transition to supercritical dense-fluid-mixing can significantly expedite the phase-exchange process through the disintegration of the liquid-like droplet core.

42 ENGINEERING↗

DOE OSTI2024

Toward Accelerated Nuclear-physics Parameter Estimation from Binary Neutron Star Mergers: Emulators for the Tolman–Oppenheimer–Volkoff Equations

Abstract Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman–Oppenheimer–Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: multilayer perceptrons (MLPs), Gaussian processes, and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms, whereas the RBM results in the largest speedup with respect to the full high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Performance of the CMS Level-1 trigger in proton-proton collisions at $\sqrt{s} =$ 13 TeV

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13\TeV. During Run 2 (years 2015–2018) the LHC eventually reached a luminosity of 2.1× 1034 cm-2s-1, almost three times that reached during Run 1 (2009–2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016–2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Application of On-Line Monitoring and Real-Time Characterization of Low Level Waste Samples from Hanford Tanks

Waste and treatment stream characterization is an essential part of safe, efficient, and cost-effective processing of Hanford materials. The demand for materials characterization, including chemical, radionuclide, and physical attributes during waste management, transfer, and staging operations in the Hanford tank farms, is expected to significantly increase as the U.S. Department of Energy Office of River Protection (ORP) progresses toward Hanford waste treatment and immobilization. The application of on-line monitoring can greatly improve operation timeframes and reduce costs by providing real-time data and supporting the reduction of necessary grab sample collection. Recently, our team applied Raman online monitoring and chemometric modeling to the supernate of Hanford tank 241-AP-105, and demonstrated the quantitative measurement of nine analytes within this waste. Application of this method has been expanded to additional Hanford tank wastes, AW-102 and AP-107. The performance of the process monitoring instruments and the accuracy of the chemometric models will be assessed. On-line monitoring results will be compared to anion results determined from the current standard, ion-chromatographic and inductively coupled plasma mass spectrometry methods. Comparisons of both determined values and uncertainties of results will be presented. Results will be shared with personnel working on flowsheet interfaces for the Direct-Feed Low Activity Waste mission at Hanford.

Lines, Amanda M.↗

DOE OSTI2021

Characterizing major and trace element compositions in fallout melt glass from a near-surface nuclear test

We report the chemical and isotopic compositions of fallout melt glasses from nuclear tests contain a range of information constraining the physical conditions within the fireball and the mechanisms of fallout formation but historic studies tended to exclude the behavior of stable major and trace elements. Here, we present a large study specifically focused on major and trace element relationships within a population of macroscale fallout samples from a single event. We interpret these data to better constrain how fallout melt glass formation in near surface environments is influenced by that environment and demonstrate how major and trace element abundances can provide useful insights into chemical processes within the fireball. Data confirm that the uranium in the fallout glass population derives from two isotopically distinct endmembers: isotopically enriched uranium (presumably from the weapon), and natural composition uranium that may be a combination of anthropogenic and environmental materials from within the blast zone. The similarity between major and trace element concentrations in fallout and corresponding local soils from the event site confirm the local soils as the most probable source of entrained material into the fireball and the source of carrier material into which the bomb vapor was incorporated. The lack of correlation between major and trace element abundances with size indicates that volatility driven processes, such as condensation from the fireball, do not control the composition of macroscale fallout melt glass. Although the fallout has major and trace element chemical characteristics broadly similar to those of the local, associated soils, some systematic differences are observed between the two populations. Fallout melt glass is depleted in volatile elements such as K, Na, Tl and Pb, consistent with heating to temperatures above ~1000 °C for 3-10 s. This is supported by the results of laser heating experiments performed on rhyolitic soil at temperatures (1600-2200 °C) and timescales (1-120 s) that are broadly relevant to fallout formation conditions. Relative enrichments of metals such as Cu and Co do not correlate with the abundance of uranium, suggesting that fallout also records input of near field anthropogenic materials. Our observations suggest that major chemical features can be related to processing in the fireball and used to inform the thermal-chemical evolution of the system. Ultimately, these data are consistent with a fallout formation mechanism that involves rapid melting of surface materials to form carrier material melts with minor incorporation of bomb vapor and a degree of volumetric volatile loss due to heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗