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DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Thermal deflection in neutron scattering sample environments at Oak Ridge National Laboratory

The neutron sources at Oak Ridge National Laboratory use a wide suite of sample environment equipment to deliver extreme conditions for a number of experiments. Much of this instrumentation focuses on extremes of temperature, such as cryostats, closed-cycle refrigerators in both low and high temperature configurations, and radiant heating furnaces. When the temperature is controlled across a large range, thermal deflection effects can notably move the sample and affect its alignment in the beam. Here, we combine these sample environments with neutron imaging and machine vision to determine the motion of a representative sample with respect to the neutron beam. We find vertical sample displacement on the order of 1–2 mm and horizontal displacement that varies from near-negligible to 1.2 mm. While these deflections are not relevant for some of the beamlines at the first target station at the spallation neutron source and the high flux isotope reactor, they will become critical for upcoming instrumentation at the second target station, as well as any instruments targeting sub-mm samples, as neutron sources and optics evolve to smaller and more focused beams. We discuss mitigation protocols and potential modifications to the environment to minimize the effect of misalignment due to thermal deflection.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

New generation bunch shape monitor for ion accelerators

Measuring longitudinal beam parameters is important for operation and development of high intensity linear accelerators, but it is notoriously difficult for proton and ion beams at non-relativistic energies. The Bunch Shape Monitor (BSM) is a device used for measuring the longitudinal bunch distribution in ion linacs. The existing BSM models have poor electron collection efficiency from the wire and are limited to one-dimensional measurements of the phase coordinate. In response to this problem, we have developed a new generation BSM with improved performance. The proposed design incorporates three major innovations: First, the collection efficiency was improved by adding a focusing field between the wire and the entrance slit, which will also allow measurements over a much wider dynamic range. Second, an improvement in the measurement speed was achieved by sampling longitudinal profiles of multiple energy slices simultaneously, where the BSM wire is placed at the exit of an ion spectrometer so that ions with different energies hit the wire at different horizontal coordinates along the wire. Finally, the design incorporates a motion system that can shift the wire and deflecting cavity together, enabling transverse profile measurements like a wire scanner. Here, in this paper, we will provide the design of the new BSM and report on its beam test results at the Spallation Neutron Source facility in Oak Ridge National Laboratory.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

NEUWAVE-12 at ESS: Workshop Series on Wavelength Dependent Neutron Imaging Back in Full Swing

The NEUWAVE workshop series, a cornerstone for advancements in energy- or wavelength-resolved neutron imaging, held its 12th meeting at the European Spallation Source (ESS) in Lund from September 1 to 5, 2024. The participants can be seen in Figure 1 during the site visit. The series focuses on developments in techniques using phenomena such as Bragg-edges, incoherent and inelastic scattering, or neutron absorption resonances as contrast mechanisms and is therefore far more specialized than more general conferences for neutron imaging such as the WCNR or ITMNR series [Citation1]. After a break during the pandemic and record attendance at NEUWAVE-11 in Tokyo in 2023, which underscored the immense interest in wavelength dependent neutron imaging, NEUWAVE returned to its roots with a focus on a smaller group of specialists meeting in a workshop format, allowing for in-depth discussions and the exchange of new ideas.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Adhesion Testing of Direct-Write Printed Ink on Metallic Structural Components

The successful adoption of additive manufacturing for the rapid prototyping of direct-write printed electronics requires the establishment of quantifiable metrics that can interrogate device performance. One measure of performance is the adhesion strength between the substrate and printed film interface, which is critical since the strength of this interface can dictate the accuracy and reliability of the printed device. In this work, a non-contact laser-induced spallation technique was used to quantify the adhesion of silver printed films on aluminum alloy substrates. The laser-based method was compared to a standardized pull-off adhesion test, which provided baseline measurements of adhesion strength. The sintering conditions were shown to play an important role in the film-substrate adhesion strength, as well as the cohesion and ductility of the film itself. The methods and results described in this work support the establishment of process control steps that are necessary for quickly verifying the reliability of printed devices prior to their deployment in critical experiments.

Adhesion test↗

DOE OSTI2025

Inelastic Neutron Scattering Data for Ba₃Zn(Ru₀.₁Sb₁.₉)O₉

This dataset contains inelastic neutron scattering measurements on the magnetically dilute compound Ba₃Zn(Ru₀.₁Sb₁.₉)O₉, collected at the Spallation Neutron Source using the Cold Neutron Chopper Spectrometer (CNCS) and the SEQUOIA Fine-Resolution Fermi Chopper Spectrometer. The sample consists of nominally 5% magnetic Ru⁵⁺ ions (S = 3/2) randomly substituted into a diamagnetic Sb⁵⁺ matrix. Measurements were performed at base temperature (2–5 K) and 300 K using multiple incident energies (1.00, 3.32, 12.0, and 100 meV) to resolve magnetic excitations from isolated monomers and small Ru clusters (dimers, trimers, etc.). The dataset includes raw event-mode files, momentum- and energy-resolved spectra, and supporting metadata for instrument configuration, sample environment, and reduction parameters.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗

DOE OSTI2024

Robustness and printed sensor qualification

A limiting factor of additive manufactured (AM) sensors for in-pile applications is the development of appropriate interconnection and packaging strategies that can maintain reliable performance in extreme environments. Failure mechanisms in these harsh conditions could include, but not limited to, materials interaction (i.e., intermetallic formation) and coefficient of thermal expansion (CTE) mismatch between the individual components of the sensors. To mitigate premature failure and enable the successful sustained operation of AM sensors, non-destructive qualification tests are used as an intermediate process control step used for verifying robustness and reliability of the sensor before they are deployed. A materials system of interest is the use of barium strontium titanium oxide (BST) films on stainless steel 316L (SS316L) substrate. Due to BST’s high dielectric constant and tunable dielectric properties, there is an interest for its application as an insulation/encapsulation layer for capacitive strain gauges when printed on structural materials. Previous work on the BST/SS316L material system, however, showed that the BST cracks when exposed to temperatures up to 600 °C due to CTE mismatch between the printed BST film and the metallic substrate. When comparing different fabrication techniques, less surface cracking was observed in the samples that deposited thinner (i.e., 2-20 μm) than samples that were thicker (i.e., 135 µm – 300 µm). The objective of this report is to establish a qualification process that will determine and address any challenges (i.e., mechanical failure of thick prints) of the AM sensor prior to its application in experimental tests. To demonstrate the qualification process in this report, laser spallation and uniaxial tensile tests using dynamic and quasi-static loading, respectively, will be discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. Further, the results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

An Overview of Experiments in The Entry Systems Modeling Project

The Entry Systems Modeling (ESM) project has invested in experiments designed to enhance understanding of salient interactions between thermal protection materials (TPS) and mission-relevant planetary entry environments. An arc-jet campaign has been carried out at the Aerodynamic Heating Facility (AHF) to investigate the spallation of fiber particles as a function of gas composition and to measure the effect of pyrolysis gas flow on the in-depth temperature response of phenolic impregnated carbon ablator (PICA). Moreover, fundamental insights into the pyrolysis and oxidation mechanisms of the phenolic binding agent of FiberForm will be discussed. Finally, two new apparatus developed at NASA will be presented that were designed to interrogate phenomena associated with the production of gases and particles generated under simulated flight conditions.

Material Response↗

NASA NTRSDate not supplied

An Overview of Experiments in The Entry Systems Modeling Project

Varcum↗

NASA NTRSDate not supplied

Updates to PATO’s Thermo-Mechanical Model

The solid mechanics module within the Porous material Analysis Toolbox (PATO) material response code was updated to model the stress contribution from both internal pressure and pyrolysis shrinkage as function of material’s decomposition. Incorporating these physical phenomena, alongside the previously included temperature dependent mechanical properties, thermal expansion, and aerodynamic loads, allows for a more accurate thermo-mechanical behavior modeling of Thermal Protection System (TPS) materials, which is critical to assess spallation risks during atmospheric entry. This poster outlines the workflow for obtaining the necessary PATO inputs, derived from dilatometry experiments, which are used to model the stress field in TPS materials due to thermal expansion and pyrolysis shrinkage. Furthermore, to analyze the relevance of the stress contribution from the internal pressure, a study was conducted using TACOT with different permeability values.

TPS↗

DOE OSTI2025

Advanced Manufactured Strain Sensors for Extreme Environments

Experimentation at irradiation test facilities are essential for reducing the innovation time of developmental fuels, fuel cladding, and structural materials employed in next-generation nuclear reactors. However, due to the harsh conditions generated in such reactors and limited instrumentation space, the evaluation of a material’s mechanical properties is often limited to characterization after the materials have been removed from reactor conditions, or post-irradiation examination. These experiments are costly, time-consuming, and fail to capture the critical time-evolving phenomena that occur during the irradiation experiments. Advanced manufactured digital image correlation patterns and strain sensor devices serve as two promising technologies that can be deployed in the confined and challenging orientations of these irradiation experiments while also providing key insight on salient materials phenomena (i.e., mechanical properties).To guide the development of the printed strain sensors prior to their deployment in critical experiments, the adhesion strength between the substrate and printed film interface is measured via tensile testing and a non-contact laser-induced spallation technique. The establishment of these process control steps helped guide the successful fabrication and testing of direct-write strain sensing devices discussed in this work. The fabrication process controls are necessary for enabling the sustained operation of these strain sensing device through experimentation and minimize the potential for premature failure.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Prospects for exploring non-standard neutrino properties with argon-based CEvNS experiments

Coherent elastic neutrino-nucleus scattering (CEvNS) provides a powerful frame-work for testing the Standard Model (SM) and searching for new physics at low energies. In this work, we examine the prospects for argon-based CEvNS experiments at stopped-pion sources to perform precision measurements of weak interactions and probe non-standard neutrino properties. Our study focused on the CENNS-10 and CENNS-750 detectors at the Spallation Neutron Source at Oak Ridge National Laboratory, the Coherent Captain Mills (CCM) detector at Los Alamos National Laboratory, and the proposed PIP2-BD detector at Fermilab’s Facility for Dark Matter Discovery (F2D2). Using realistic neutrino fluxes and detector configurations corresponding to these facilities, we evaluate event rates and sensitivities to a range of observables. Within the SM, argon-based CEvNS detectors enable precision tests of electroweak parameters, including the weak mixing angle, at momentum transfers well below the electroweak scale. We also investigate the sensitivity of these experiments to neutrino electromagnetic properties, such as the magnetic moment and effective charge radius, as well as to possible non-standard neutrino interactions with quarks. Together, these studies highlight the potential of argon-based CEvNS experiments as a clean and versatile platform for precision exploration of non-standard neutrino properties.

Carey, Sam [Wayne State U.] (ORCID:000900029607531↗

DOE OSTI2025

Characterization of SiO 2 Thermally Grown Oxide Stress Evolution of EBCs with Al-Containing Dopants

SiC/SiC ceramic matrix composites (CMCs) are desired for use in combustion environments to achieve higher turbine operating temperatures. However, CMCs require environmental barrier coatings (EBCs) for protection from the gas environment. EBC systems are known to primarily fail through coating delamination via growth of a thermally grown oxide (TGO) at the EBC—silicon bond coating interface when exposed to steam, which accelerates the TGO growth rate. The TGO undergoes a phase transformation during thermal cycling, which results in stresses that may encourage EBC spallation. Yb-silicate EBCs with mullite and yttrium aluminum garnet (YAG) dopant additions were deposited on SiC substrates with a Si intermediate bond coating and exposed to thermal cycling in steam at 1350 °C. The impact of Al dopant additions on the TGO growth rate and the SiO 2 phase transformation was assessed. Photo-stimulated luminescence spectroscopy (PSLS) was used to characterize the Al-containing phases and to measure stress evolution in the EBC following exposure using the stress-induced peak shift of the R-lines of mullite. Raman microscopy was used to map the stresses in the Si bond coating following exposure. It was found that the TGO phase transformation upon cooling increased compressive stress in the Si bond coating within 15 µm of the TGO.

Building Materials↗

DOE OSTI2024

New Opportunities for Neutrons in Environmental and Biological Sciences

The use of neutron methods in environmental and biological sciences is rapidly emerging and accelerating with the development of new instruments at neutron user facilities. This article, based on a workshop held at Oak Ridge National Laboratory (ORNL), offers insights into the application of neutron techniques in environmental and biological sciences. Here we highlight recent advances and identify key challenges and potential future research areas. These include soil and rhizosphere processes, root water dynamics, plant-microbe interactions, structure and dynamics of biological systems, applications in synthetic biology and enzyme engineering, next-generation bioproducts, biomaterials and bioenergy, nanoscale structure, and fluid dynamics of porous materials in geochemistry. We provide an outlook on emerging opportunities with an emphasis on new capabilities that will be enabled at the Spallation Neutron Source Second Target Station currently under design at ORNL. The mission of scientific neutron user facilities worldwide is to enable science using state-of-the-art neutron capabilities. We aim to encourage researchers in the environmental and biological research community to explore the unique capability afforded by neutrons at these facilities.

54 ENVIRONMENTAL SCIENCES↗