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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
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DOE OSTI2026

Unveiling the Interplay: Cold Work, Recrystallization, and Flux Expulsion in SRF Cavities

The fabrication of SRF cavities from sheet materials includes deep-drawing, electron beam welding, chemical and mechanical polishing, high-temperature heat treatment, and material diffusion. The performance of these cavities is frequently limited by magnetic flux trapping giving rise to additional rf loss. In this presentation, we thoroughly examine how recrystallization influences flux expulsion in SRF cavities, using cold-worked niobium sheets from various suppliers. Our findings reveal that cold-worked sheets enhance flux expulsion, especially at lower heat treatment temperatures, by promoting improved recrystallization. In particular, a traditionally fabricated Nb cavity half-cell from an annealed poly-crystalline Nb sheet after an 800 C heat treatment leads to a bi-modal microstructure that ties in with flux trapping and inefficient flux expulsion. This non-uniform microstructure is related to varying strain profiles along the cavity shape. A novel approach to prevent this non-uniform microstructure is presented by fabricating a 1.3 GHz single cell Nb cavities with a cold-worked sheet and subsequent heat treatment leading to better flux expulsion after 800 C/3h

Dhakal, P. [Old Dominion Univ., Norfolk, VA (Unite↗
DOE OSTI2025

Advancements in HF-free bipolar pulsed electropolishing for next-generation superconducting cavities

Hydrofluoric acid (HF)-free bipolar pulsed electropolishing (BPEP) offers an environmentally sustainable alternative to conventional Buffered Chemical Polishing (BCP) and Electropolishing (EP) techniques for superconducting radiofrequency (SRF) cavities. Recent studies at Jefferson Lab have demonstrated that a single-cell 1.3 GHz niobium cavity processed using HF-free BPEP achieved an accelerating gradient Eacc of 35 MV/m with a quality factor Q0 of 1E10 at 2 K, following extensive research and optimization. This talk will highlight the challenges encountered in developing this technique, key insights gained from experimental studies, and ongoing efforts to enhance its capabilities. In particular, we will explore its potential for refining Nb3Sn-coated niobium cavities via vapor diffusion techniques and for electroplating Nb3Sn films onto various cavity substrates, contributing to the advancement of high-performance SRF systems.

Tian, Hui [Thomas Jefferson National Accelerator F↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗
DOE OSTI2026

Topographic Characterization of Niobium Films on Copper for SRF Applications

Niobium films on copper substrates are being actively developed as a low-cost alternative to bulk niobium for superconducting radio-frequency (SRF) cavities, offering superior thermal conductivity, reduced material cost, and the potential for scalable cavity fabrication. The performance of such films can be limited by imperfect surfaces that reduce the peak achievable magnetic field. Nb films deposited under varied electron cyclotron resonance conditions were characterized via atomic force microscopy. The films exhibit a corrugated surface morphology which can be characterized by the depth of the corrugation, slope angle, and standard roughness parameters. The influence of this surface roughness on field limitations is examined within the framework of London theory. These measurements and models aid in understanding how ECR deposition conditions influence the nanometer-scale roughness of Nb films on Cu and its impact on high-field SRF cavity performance.

Redman, Brandi [Old Dominion Univ., Norfolk, VA (U↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

DAOS Benchmarks and Findings

We benchmark DAOS on a 127-node, 4,064-target pool using fio, IOR, IO500, and mdtest, comparing the DFS API against DFuse+POSIX. Single-client fio sweeps show larger block sizes and writes yield higher bandwidth, with similar random/sequential performance. Multi-node IOR (1–32 nodes, 6–48 tasks/node) shows bandwidth saturating around 32 tasks/node, with large transfer sizes hurting read throughput at high concurrency and write latency growing faster than read latency. An 8-node IO500 run shows DFS achieving ~5x higher bandwidth and ~190x higher IOPS than POSIX. We also detail operational issues: SCM target-filling under mdtest, intermittent NA_HOSTUNREACH errors, fio CPU-affinity failures, and a pool error storm resolved by recreation.

George, Rebecca [Thomas Jefferson National Acceler↗
DOE OSTI2026

Benchmarking DAOS Filesystem on Aurora

We benchmark the DAOS filesystem on Argonne's Aurora supercomputer (127 nodes, 4,064 targets) using fio, IOR, mdtest, and IO500 to characterize I/O and metadata performance across the DFS API and DFuse+POSIX. Single-client fio shows POSIX bandwidth saturating at 1–2 MiB I/O sizes, with write-heavy workloads outperforming reads. Multi-node IOR shows DFS bandwidth scaling well up to ~32 tasks/node, with write latency growing faster than read latency. An 8-node IO500 evaluation shows DFS achieving ~5x higher bandwidth and ~190x higher IOPS than POSIX. Results indicate DAOS is well-suited to read-heavy workloads like AI training data loading, given appropriately sized transfers and concurrency.

George, Rebecca [College of William and Mary, Will↗
DOE OSTI2025

Robotic Assembly of SRF Cavity Pair

Superconducting Radio Frequency (SRF) cavities for particle accelerators require tight tolerances, ultrahigh vacuum, and strict cleanliness during assembly. As in the semiconductor industry, defects such as particles and residues are deleterious to performance, possibly rendering a cavity unfit for use. This problem is addressed primarily through cleanroom assembly during sensitive steps and rigorous chemical processing to prevent and remove such defects. Human workers are often the largest source of contamination, even with proper gowning and practices. The semiconductor industry has long integrated robots in cleanroom operation, but this has not occurred for SRF cavity production; SRF cavities, unlike wafers, are complex shapes, require more hands-on mechanical assembly, and are low-volume production items. At Jefferson Laboratory, a co-operative robot (cobot) has been setup to overcome these problems. Cobots are safe for use alongside human workers and can integrate new tools such as a 3D camera part detection and gripper for item manipulation. Therefore, cobots represent a promising avenue for reducing particulate generated during a variety of assembly tasks. A mockup of a cavity pair and coupler was setup and the cobot programmed to automatically pick up the coupler and place on the mating cavity flange. Particle counting methods were setup to measure human vs cobot assembly particulate generation inside a cavity mockup. Other potential uses will be discussed for further improving SRF cavity assembly steps, where a cobot can replace or assist a human operator, and what potential gains are expected.

Duzik, Adam [Thomas Jefferson National Accelerator↗
DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Robust extraction of power corrections and nuclear dynamics from DIS at large $x$

We present recent updates from the CTEQ-JLab (CJ) global PDF analysis, focusing on the interplay and implementation systematics of the HT and offshell correction (CJ22ht). We also discuss preliminary results of the CJ25 global analysis, showing the impact of the full JLab 6 GeV datasets, that we recently collected in a comprehensive DIS database, and having a first look at early JLab 12 GeV measurements. We finally offer a few thoughts on how future data may help unraveling the nuclear and partonic dynamics in light nuclei.

Accardi, Alberto [Thomas Jefferson National Accele↗
DOE OSTI2025

Photo-Induced Hydrogen Production from Formic Acid Using a Palladium Catalyst

Liquid organic hydrogen carriers (LOHCs) are recognized as promising sustainable hydrogen (H 2 ) carriers due to their high volumetric capacity and ability to store H 2 at ambient conditions, eliminating the need for energy-intensive liquefaction or compression processes associated with H 2 or ammonia gas. One of the main current drawbacks, however, is LOHCs’ high energy demand for H 2 release. This work presents the photo-induced liberation of H 2 from formic acid (FA) as a liquid H 2 carrier, using visible light and well-established 5 wt% palladium nanoparticles supported over carbon (Pd/C). We show that low-power light-emitting diodes (LEDs) produced higher gas flow than their thermal baseline (35 °C), with 27.2 mL/min and 7.72 mL/min, respectively. Further, the rate of gas evolved with light intensity, catalyst loading, and the concentration of FA. Light-induced dehydrogenation shows similar deactivation as the known thermal mechanisms, such as the decreased Pd 2+ /Pd 0 ratio and Pd nanoparticle agglomeration. Hence, these observations suggest a photothermal mechanism, whereby the LED provides heat efficiently absorbed by the Pd/C catalyst and enhanced by Pd’s ability to absorb light, thereby driving the FA dehydrogenation reaction at ambient conditions.

08 HYDROGEN↗
DOE OSTI2026

The Roper Resonance and Kin

The properties of the Roper resonance N(1440) 1/ 2 + are reviewed. Quark models have long struggled to reproduce its mass relative to its negative parity partner N(1535) 1/ 2 − . This discrepancy motivated interpretations of the Roper as a dynamically generated meson–baryon state. Including its isospin partners ∆(1600) 3/ 2 + and ∆(1700) 3/ 2 − further accentuates the tension between quark-model predictions and experiment. Recent develop ments based on AdS/QCD and functional methods achieve much improved agreement, identifying the Roper as an ordinary three-quark excitation. Electroproduction experiments at Jefferson Lab have now resolved this long-standing question, revealing the Roper as a qqq core dressed by a substantial meson cloud. The Roper resonance belongs to a family of four N ∗ states with J P = 1/ 2 + ; the highest-mass member, N(2100) 1/ 2 + , likely represents a Roper-like excitation in the fourth shell.

Burkert, Volker [Thomas Jefferson National Acceler↗
DOE OSTI2026

Roper Resonance Structure and Exploration of Emergent Hadron Mass from CLAS Electroproduction Data

The N(1440)1/2 + nucleon resonance, first identified in 1964 by Roper and collaborators in analyses of πN hadroproduction data, has continued to provide pivotal insights that serve to advance our understanding of nucleon excited states. In this contribution, we present results from studies of the structure of the Roper resonance based on exclusive πN and π + π − p electro production data measured with the CLAS detector at Jefferson Lab. These analyses have revealed the Roper resonance as a complex interplay between an inner core of three dressed quarks and an external meson–baryon cloud. Analyses of the CLAS results on the evolution of the Roper resonance electroexcitation amplitudes with photon virtuality Q 2 , within the framework of the Continuum Schwinger Method, have conclusively demonstrated the capability to gain insight into the strong interaction dynamics responsible for generating more than 98% of hadron mass. Further extension of such studies to higher Q 2 , through experiments currently underway with the CLAS12 detector and in the future with a potential CEBAF energy upgrade to 22 GeV, offers the only foreseeable opportunity to explore the full range of distances where the dominant portion of hadron mass and resonance structure emerge.

Mokeev, Victor I. [Thomas Jefferson National Accel↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗