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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2026

Dual-RF Phase Control for Adiabatic Energy Ramping in the EIC Rapid Cycling Synchrotron

The Electron-Ion Collider (EIC) Rapid Cycling Synchrotron (RCS) accelerates electrons from 750 MeV to 10 GeV over approximately 64.7 ms. While the baseline design simultaneously ramps both RF voltage and phase, this study evaluates an alternative where RF cavities are divided into two groups, each operating at a fixed 6.5 MV, with the effective voltage synthesized through phase control. In the symmetric scheme (Option A), both groups vary symmetrically about the synchronous phase to provide exact matching of energy gain and synchrotron tune throughout the ramp. Conversely, the asymmetric scheme (Option B) pins one group at the final operating phase while the other varies to supply the required energy gain. The feasibility of Option B is determined by the adiabaticity of the resulting synchrotron tune evolution, with ramp profiles, phase trajectories, and adiabaticity metrics analyzed for both implementations.

Gamage, B. [Thomas Jefferson National Accelerator ↗
DOE OSTI2026

Ion Trapping Studies and Mitigation Strategies for the EIC ERL-Based Strong Hadron Cooler

An Energy Recovery Linac based strong hadron cooler was previously considered for the Electron-Ion Collider. The required electron beam parameters for variable-energy strong hadron cooling place significant constraints on ion trapping and collective effects. This paper presents initial studies of these constraints through a combination of analytical modelling and numerical simulations of ion production, trapping behaviour, and mitigation strategies. A multi-bunch tracking framework based on ELEGANT with the ionEffects module is used to simulate machine operation over millisecond time scales, corresponding to more than 3 × 10^5 electron bunches. The simulations include modelling of ionisation processes together with transverse electron–ion dynamics, allowing the evolution and accumulation of ions to be investigated. Analytical expressions based on Gaussian beam distributions are used to estimate ion trapping conditions and benchmark the simulation results. A bi-periodic bunch spacing scheme is also investigated as a possible mitigation method by detuning the ion oscillation frequency. These studies provide an initial assessment of ion trapping in the strong hadron cooler and demonstrate possible approaches for reducing beam–ion effects.

Bi, R. [Lancaster University, Cockcroft Institute]↗
DOE OSTI2025

Point cloud-based diffusion models for the Electron-Ion Collider

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

Araz, Jack Y. [Stony Brook Univ., NY (United State↗
DOE OSTI2025

UNCERTAINTY-AWARE DEEP LEARNING FRAMEWORK FOR FORECASTING COASTAL WATER LEVEL IN VIRGINIA BEACH

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and examined the performance of three different model architectures. In addition to exclusively including historical data, we investigated the improvement wind forecasts provide to the accuracy of 72-hour-ahead water level predictions. The results show a twelvefold improvement in the flood forecast for a real flooding event.

Hasan, Mahmud [Thomas Jefferson National Accelerat↗
DOE OSTI2026

Electropolishing-induced topographic defects in niobium: Insights and Implications for SRF

Electropolishing (EP) is the premier surface preparation method for high-Q, high-gradient superconducting RF cavities made of Nb. This leaves behind an apparently smooth surface, yet the achievable peak magnetic fields fall well below the superheating field of Nb, in most cases. In this work, the ultimate surface finish of EP was investigated by studying its effect on highly polished Nb samples. EP introduces high slope angle sloped-steps at grain boundaries. The magnetic field enhancement and superheating field suppression factors associated with such a geometry are calculated in the London theory. Despite the by-eye smoothness of electropolished Nb, such defects compromise the stability of the low-loss Meissner state, likely limiting the achievable peak accelerating fields in superconducting RF cavities. Finally, the impact of surface roughness on impurity diffusion is investigated which can link surface roughness to the effectiveness of heat treatments like low-temperature baking or nitrogen infusion in the vortex nucleation or hydride hypotheses. Surface roughness tends to decrease the effective dose of impurities as a result of the expansion of impurities into regions with greater internal angle. The effective dose of impurities can be protected by minimizing slope angles and step heights, ensuring uniformity.

Lechner, Eric [Thomas Jefferson National Accelerat↗
DOE OSTI2025

Potential for tensor polarized deuterons in Hall D at Jefferson lab

Hall D at Jefferson lab is an ideal place to install a polarized deuteron target which can be “tensor polarized”, allowing the separation of the spin states m = 0, ±1 or the measurement of tensor asymmetries. The bremsstrahlung photon beam with 3 - 12 GeV endpoint provides very little heating or radiation damage compared to an electron beam, allowing the target to be run in frozen spin mode. Adiabatic fast passage spin manipulations can then be used to greatly enhance the population of the m = 0 spin state of the deuteron. Coherent photoproduction of ρ mesons from deuterium is sensitive to double-scattering at high momentum transfer and in the m = 0 spin state an additional sensitivity at intermediate momentum transfer opens up. In conclusion, we propose a frozen spin target for Hall D and the measurement of ρ photoproduced coherently from the deuteron as a flagship measurement.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reconstruction framework advancements to support streaming for the ePIC detector at the EIC

The ePIC collaboration adopted the JANA2 framework to manage its reconstruction algorithms. This framework has since evolved substantially in response to ePIC’s needs. There have been three main design drivers: integrating cleanly with the Podio-based data models and other layers of the key4hep stack, enabling external configuration of existing components, and supporting timeframe splitting for streaming readout. The result is a unified component model featuring a new declarative interface for specifying inputs, outputs, parameters, services, and resources. This interface enables the user to instantiate, configure, and wire components via an external file. One critical new addition to the component model is a hierarchical decomposition of data boundaries into levels such as Run, Timeframe, PhysicsEvent, and Subevent. Two new component abstractions, Folder and Unfolder, are introduced in order to traverse this hierarchy, e.g. by splitting or merging. The pre-existing components can now operate at different event levels, and JANA2 will automatically construct the corresponding parallel processing topology. This means that a user may write an algorithm once, and configure it at runtime to operate on timeframes or on physics events. Overall, these changes mean that the user requires less knowledge about the framework internals, obtains greater flexibility with configuration, and gains the ability to reuse the existing abstractions in new streaming contexts.

Brei, Nathan [Thomas Jefferson National Accelerato↗
DOE OSTI2025

Strangeness in the proton from $W+$ charm production and SIDIS data

We perform a global QCD analysis of unpolarized parton distribution functions (PDFs) in the proton, including new 𝑊+⁢ charm production data from 𝑝⁢𝑝 collisions at the LHC and semi-inclusive pion and kaon production data in lepton-nucleon deep-inelastic scattering, both of which have been suggested for constraining the strange quark PDF. Compared with a baseline global fit that does not include these datasets, the new analysis reduces the uncertainty on the strange quark distribution over the range 0.01 < 𝑥 < 0.3, and provides a consistent description of processes sensitive to strangeness in the proton. Including the new datasets, the ratio of strange to nonstrange sea quark distributions is $R_s = (s + \bar{s})/(\bar{u} +\bar{d})$ $=$ {$0.7⁢2^{+0.52}_{−0.34}, 0.4⁢6^{+0.30}_{−0.20}, 0.3⁢2^{+0.23}_{−0.15}$} for 𝑥 ={$0.01, 0.04, 0.1$} at 𝑄 2 $=$ 4 GeV 2 . The data place more stringent constraints on the strange asymmetry $(s - \bar{s})$, which is found to be consistent with zero in this range.

Anderson, Trey [College of William and Mary, Willi↗
DOE OSTI2025

Polarizing 3 He via metastability exchange optical pumping using a 1.2 mbar sealed cell at magnetic fields up to 5 T

Here, we report high nuclear polarization of 1.2 mbar 3 He gas in a sealed cell in magnetic fields up to 5 T using Metastability Exchange Optical Pumping (MEOP). The creation of a highly polarized 3 He gas target for use in the 5 T field of Jefferson Lab’s CLAS12 spectrometer would enable new studies of spin-dependent asymmetries on the neutron. We describe a systematic study which was conducted for the first time up to the full 5 T field required for the final target design to evaluate the effects of discharge intensity, pump laser power, and optical pumping transition schemes on nuclear polarization and pumping rates. Steady-state polarizations up to 86% in magnetic fields between 2 and 5 T were achieved, with a discharge-on relaxation time of 898 s at 5 T. These results underscore the potential of MEOP for high-field applications in nuclear physics experiments.

3He polarization↗
DOE OSTI2025

Scintillating glass for precision calorimetry in nuclear physics

High-performance scintillator materials are needed for particle identification and measurements of energy and momentum of electromagnetic particles in modern nuclear physics experiments. As an example, the US Electron-Ion Collider, a unique collider with diverse physics topics, requires electromagnetic calorimetry enabling high-quality electron identification and detection in the momentum range of 0.3 to tens of GeV. The highest resolution in electromagnetic calorimeters can be provided by homogeneous materials, e.g., lead tungstate crystals. Inorganic glass scintillators have been investigated as an attractive and cost-effective alternative to crystals, that is also easier and faster to manufacture in mass production. In this paper, we discuss progress in the fabrication and characterization of recent scintillating glass samples on both test bench and beam tests. Further, the results are well-reproduced by simulation and are discussed in the context of the Electron-Ion Collider experimental requirements and bench-marked against lead tungstate crystals.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

One-to-one mapping between the electromagnetic modes of cylindrical and coaxial half-wave cavities

Design of radio frequency (RF) couplers and diagnostics require a good understanding of the electromagnetic mode patterns of RF cavities. This study investigates the adiabatic transformation of transverse magnetic (TM) modes in a cylindrical cavity into transverse electromagnetic (TEM) modes of a coaxial cavity by gradually introducing an inner conductor. Using CST Studio Suite, we simulate the eigenmode evolution as the geometry transforms from a pure cylindrical to a coaxial configuration. We track the behavior of TM010 through TM014 modes to observe the continuous evolution into the corresponding TEM0 through TEM4 modes of the coaxial cavity. The process is governed by the evolution of the electric field orientation as the geometry shifts, enabling the axial TM fields to reorient into the radial electric field configuration of TEM modes. Field patterns, eigen-frequencies, and mode indentities are analyzed throughtout the transition. The results provide simulation-based evidence that TM to TEM conversion occurs without generation of newer eigenmodes, offering a valuable insight into the design of transition regions in superconducting RF (SRF) systems and provides a foundation for experimental validation.

Accelerator Physics↗
DOE OSTI2026

An SF 6 gas-insulated −500 kV DC conical insulator vacuum feedthrough

A test electrode was biased to −500 kV direct current (DC) by means of a novel SF 6 gas-insulated conical alumina insulator feedthrough. The design enables the extreme high vacuum conditions required by spin-polarized electron sources by reducing the photogun vacuum chamber volume compared to coaxial cylindrical insulators. In contrast to conventional inverted-insulator photogun designs that rely on solid dielectric interfaces for high-voltage terminations, the present design employs a pressurized SF 6 intervening volume between a conical alumina insulator and a modified commercial epoxy high-voltage cable receptacle. Here, this approach enables the use of commercially available cable hardware while extending the demonstrated operating voltage of solid feedthroughs. A maximum voltage of −500 kV was applied to a test electrode attached to the narrow end of the feedthrough inside a chamber filled with SF 6 at 170.3 kPa (10 psig) and later under vacuum to −259 kV, limited by field emission. The results demonstrate that a commercial high-voltage cable combined with a custom-designed SF 6 gas-insulated vacuum feedthrough can be used to bias photogun electrodes to −500 kV DC.

Palacios-Serrano, Gabriel [Thomas Jefferson Nation↗
DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗