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DOE OSTI2025

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Large-scale calculations of 𝛽-decay rates and implications for 𝑟-process nucleosynthesis

Nuclear 𝛽 decay is a key element of the astrophysical rapid neutron capture process (𝑟 process). In this work, we present state-of-the-art global 𝛽-decay calculations based on the quantified relativistic nuclear energy density functional theory and the deformed proton-neutron quasiparticle random-phase approximation. Our analysis considers contributions from allowed and first-forbidden transitions. We used two point-coupling functionals with carefully calibrated time-odd terms and isoscalar pairing strength. The new calculations display consistent results for both employed functionals, especially near the neutron drip line, suggesting slower 𝛽 decays past the 𝑁=126 neutron shell closure than in commonly used 𝛽-decay models. The new rates, along with the existing rates based on the recent nonrelativistic global calculations, are found to slow down the synthesis of heavy elements in the 𝑟 process and significantly reduce the contribution of neutron-induced fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.

EPICS pvAccess↗

DOE OSTI2025

Real-time data processing for serial crystallography experiments

We report the use of streaming data interfaces to perform fully online data processing for serial crystallography experiments, without storing intermediate data on disk. The system produces Bragg reflection intensity measurements suitable for scaling and merging, with a latency of less than 1 s per frame. Our system uses the CrystFEL software in combination with the ASAP::O data framework. In a series of user experiments at PETRA III, frames from a 16 megapixel Dectris EIGER2 X detector were searched for peaks, indexed and integrated at the maximum full-frame readout speed of 133 frames per second. The computational resources required depend on various factors, most significantly the fraction of non-blank frames ('hits'). The average single-thread processing time per frame was 242 ms for blank frames and 455 ms for hits, meaning that a single 96-core computing node was sufficient to keep up with the data, with ample headroom for unexpected throughput reductions. Further significant improvements are expected, for example by binning pixel intensities together to reduce the pixel count. We discuss the implications of real-time data processing on the `data deluge' problem from recent and future photon-science experiments, in particular on calibration requirements, computing access patterns and the need for the preservation of raw data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Development of Real-Time High-Density Pulsar Data Transmission and Processing for Grid Synchronization

Taking advantage of the extreme stability of the pulsar period, it can serve as the timing source for grid synchronization to compensate for the timing drift instigated by the loss of GPS signal. Nevertheless, the real-time transmission and processing of the pulsar data suffer from its high-frequency data rate, varying from megahertz to gigahertz, resulting in reduced computing speed and increased time delay. To mitigate this issue, the hardware and software frameworks are implemented for the high-density pulsar data transmission and processing for grid synchronization in this research. Initially, the high-density pulsar data is transferred using open-source software. The complementary duty cycle timing module is designed to coordinate the operation of the dual-channel high-speed interface and software. Subsequently, the multiple-threading is applied to the receiving, parsing, and splicing pulsar data. Next, the pulsar signal extraction method is implemented based on the polyphase filterbank and time of arrival estimation. Ultimately, real-time performance verification experiments are carried out for different components under two hardware platforms. Finally, the results demonstrate that only 0.482 s is required for processing 4 Gigabyte data through multiple-threading, which is 3.8 times faster than the single thread. The pulsar signal extraction can also be executed within 707 ms for 4.8 seconds of data, thereby indicating that real-time requirements can be met.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

High-Throughput Data Processing at FRIB Using ESnet

Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.

Data processing↗

DOE OSTI2025

Process-Microstructure-Property Relationships in Low Heat Input Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Conference presentation on WAAM process optimization on Ni-based superalloy Haynes® 282®. This alloy is targeted for advanced power generation systems for its superior high temperature mechanical properties. Wire-arc additive manufacturing (WAAM) offers attractive cost and materials savings with high deposition rates. Based on initial build process optimization, WAAM blocks were made, and screened for defects using high-throughput CT scanning. The microstructural evolution in arc energy range of 250-700 J/mm was characterized in the as-built and aged conditions, using electron microscopy. Results showed self-consistent microstructure across conditions, with grain size dependence on arc energy, MC and M6C carbides in as-built, and blocky grain boundary M23C6 in as-heat-treated condition. Further reductions in as-built porosity were explored through 20+ combinations of H2 and CO2 additions to shielding gas. Initial results show improved wetting behavior, and bead overlap with up to 1% H2 and up to 0.25% CO2. Combination of experimentally determined datasets provided comprehensive understanding of process-structure-property relationships in WAAM Haynes 282.

Haynes 282↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats: Integration of Additive Manufacturing and Reinforced Polymer Composite

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Extracting the Breakout Distance from the ECOT Trajectories: Gaussian Process Regression Approach

Enhanced Corner Turning (ECOT) experiments provide an important metric of performance of high explosive (HE) formulations. The breakout distance is a single scalar value that characterizes the corner turning efficiency of an HE. Extracting the breakout distance from the raw ECOT results, whether experimental or simulated, is a conceptually straightforward procedure which, however, is non-unique, especially in the presence of noise. More specifically, this procedure involves numerical smoothing and selecting particular values for parameters of this smoothing introduces human bias. In this work, we propose to use the Gaussian process regression to analyze ECOT results. This analysis involves the effective smoothing of the data, thus allowing for accurate extraction of the breakout distance. Most importantly, the parameters of this smoothing can be inferred from the ECOT data itself, rendering the approach effectively parameter-free and thus diminishing the human bias. An additional benefit of the Gaussian process regression, being a statistical inference method, is that not just the value of the breakout distance, but also its confidence interval can be extracted from the data. This report introduces the Gaussian process regression, as applied to ECOT, and demonstrates its usefulness by extracting the breakout distances for a selection of experimental and simulated data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Time-resolved probing of laser-induced nanostructuring processes in liquids

Laser synthesis and processing of colloids (LSPC) in liquids has gained widespread applications in producing nanomaterials of different classes of solids. While the technical processes in different cases of ablation, fragmentation or colloidal fusion may look macroscopically different in each application, the underlying fundamental mechanisms are always the same cascade of laser interaction with matter, non-thermal or thermal energy deposition, phase transitions, and the subsequent structure formation processes. Disentangling these mechanisms represents a veritable challenge, as ultrafast and structurally sensitive experimental methods are required. This review presents a discussion of how state-of-the-art experimental protocols using ultrafast lasers and sensitive structural probes, such as electrons or X-rays are able to address this challenge. In particular, it is possible to investigate LSPC on single objects using single probe pulses and avoid accumulation effects in a heterogeneous sample. The presented results capture structure formation with femtosecond and atomic scale resolution. Ultrafast time-resolved probing approaches are key to revealing the transient states and pathways that govern material transformation in LSPC.

X-ray scattering↗

DOE OSTI2025

First Measurement of 87 Rb( α , xn ) Cross Sections at Weak r -process Energies in Supernova ν -driven Ejecta to Investigate Elemental Abundances in Low-metallicity Stars

Observed abundances of Z ∼ 40 elements in metal-poor stars vary from star to star, indicating that the rapid and slow neutron capture processes may not contribute alone to the synthesis of elements beyond iron. The weak r-process was proposed to produce Z ∼ 40 elements in a subset of old stars. Thought to occur in the ν-driven ejecta of a core-collapse supernova, ( α, xn ) reactions would drive the nuclear flow toward heavier masses at T = 2−5 GK. However, current comparisons between modeled and observed yields do not bring satisfactory insights into the stellar environment, mainly due to the uncertainties of the nuclear physics inputs where the dispersion in a given reaction rate often exceeds 1 order of magnitude. Involved rates are calculated with the statistical model where the choice of an α -optical-model potential ( α OMP) leads to such a poor precision. The first experiment on 87 Rb( α, xn ) reactions at weak r -process energies is reported here. Total inclusive cross sections were assessed at E c.m. = 8.1−13 MeV (3.7−7.6 GK) with the active target MUlti-Sampling Ionization Chamber. With an N = 50 seed nucleus, the measured values agree with statistical model estimates using the α OMP Atomki-V2. A reevaluated reaction rate was incorporated into new nucleosynthesis calculations, focusing on ν-driven ejecta conditions known to be sensitive to this specific rate. These conditions were found to fail to reproduce the lighter heavy element abundances in metal-poor stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Multimessengers from the Radioactive Decay of r -process Nuclei

The radioactive β-decay of nuclei synthesized in the rapid neutron capture process (r-process) releases a variety of particles, including electrons, γ-rays, neutrinos, and neutrons. These particles provide a rich set of multimessenger signals that carry information about the astrophysical environments where neutron-rich nucleosynthesis occurs. In this work, we calculate from first principles the emission spectra resulting from the β-decay of r-process nuclei. Our approach incorporates detailed nuclear structure and decay data to model the energy distributions of each particle species. We couple the spectra with a nuclear reaction network simulation to obtain the temporal evolution of these distributions. We find that the emission distributions vary significantly in time and are nonthermal, with substantial average energies. We investigate these nuclear signals as a direct probe of heavy element formation and show that they are complementary observables to kilonova.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Process Improvement For Pu-238 Production at Idaho National Laboratory

Idaho National Laboratory (INL) has supported the production of Pu-238 for future NASA deep space missions since 2017. Over this time, INL has worked to improve the qualification process of Pu-238 production targets as well as improve processes related to the shipping, storage, irradiation, and storage of Pu-238 production targets. Qualification of Pu-238 production targets began with flux measurements and scoping analysis to provide fundamental data to confirm the impacts on the operation of the Advanced Test Reactor (ATR), Fig1. Later, initial production targets were irradiated in ATR’s I-7 position, and then the South Flux Trap (SFT). A modified target design was then implemented which would use the full length of the ATR core and increase Pu-238 production. While working to improve and streamline the qualification of the Pu-238 production targets, INL worked to improve multiple operational aspects of the Pu-238 production process. These changes include updating procedures to streamline operations, supporting modification of shipping containers to contain five rather than one production target, reviewing target receipt procedures and changing work flow to provide flexibility in target receipt, and designing and fabricating support equipment for the storage and internal transfer of production targets

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Process Improvements For Pu-238 Production at Idaho National Laboratory

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2024

Approaches for the Simulation of Coupled Processes in Evolving Fractured Porous Media Enabled by Exascale Computing

Models have historically represented fractured porous media with continuum descriptions that characterize the media using bulk parameters. The impact of small-scale features is not captured in these models, although they may be controlling the performance of subsurface applications. Pore-scale models can simulate processes in small-scale features by representing the pore space geometry explicitly but are computationally expensive for large domains. The alternative multiscale approach entails the combination of pore-scale and continuum-scale descriptions in a single framework. We use Chombo-Crunch, a computational capability that discretizes complex geometries with an adaptive, embedded boundary method to contrast these two approaches. Chombo-Crunch takes advantage of recent computational performance and memory bandwidth improvements resulting from the emergence of exascale computing resources. These combined improvements enable the efficient simulation of reactive transport in fractured media with a high degree of fidelity and the ability to capture the control small-scale processes exert on the overall medium evolution.

42 ENGINEERING↗

DOE OSTI2026

Optimization of Processing, Microstructure, and Hardness of an Al–Ce–Ni–Mn–Zr Alloy With Laser Additive Manufacturing

Here, this study examines the processing behavior, microstructure, surface roughness, and hardness properties of an aluminum alloy containing 8.2 Ce, 4.5 Ni, 0.5 Mn, and 0.7 Zr (wt%) fabricated using laser powder bed fusion. Sixty samples were produced across a range of laser powers, scan speeds, and hatch spacings to evaluate their effect on porosity, hardness, and microstructural features. Porosity was measured using X-ray computed tomography, while microstructure and surface roughness were characterized by scanning electron (SEM) and laser confocal microscopy. High dense and cracking-free Al–Ni–Ce alloy was successfully manufactured. Porosity showed a U-shaped dependence on energy input, increasing under both insufficient and excessive melting conditions. Hardness increased with cooling rate due to finer cellular structures and solute redistribution. A general statistical model was developed to capture the relationships between processing parameters and material response. Results identify a narrow processing window defined by laser powers between 350 and 370 W, scan speeds from 1400 to 1800 mm/s, and hatch distances between 0.14 and 0.18 mm. Within this window, porosity is minimized (below 0.01%) and hardness is maximized (up to 160 HV), demonstrating that careful control of these parameters enables dense, high strength aluminum components suitable for demanding structural applications.

Aluminum alloys↗

DOE OSTI2025

Birefringent Glass‐Engraved Quasi‐Linear Nanograting Metasurface Based on Self‐Organizing Process for Large Aperture High Power Laser Applications

All-glass metasurface “nanograting” structures that exhibit birefringence in the formed layer are reported. The key enabler of this work is ion beam processing at an angle sufficiently off-normal incidence, inducing self-assembly of a deposited metal layer into quasi-linear metallic features that can function as an etching mask. As a result, a fused silica metasurface, monolithic to the underlying substrate, is demonstrated at 375 nm wavelength to exhibit a phase delay angle of 30° between the principal axes. The capability of an angled etch mask replenishment process is also demonstrated for achieving deeper etch depth and for increasing the grating period, another first – to the best of the knowledge. This is the first display of a technology capable of fabricating glass-engraved near-linear grating structure with a feature-to-feature period as small as 118.6 nm. Furthermore, this technology has the potential to generate grating-like structures with periods as small as 12.4 nm, as demonstrated here with reactive ion beam processing assisted mask assembly. Furthermore, these structures are shown to have reflectivity < 0.4% across the wavelength band 350 nm – 1000 nm. Such a technology can enable laser-durable grating structures for the deep-UV and even down to soft X-ray wavelengths.

Ray, Nathan J. [Lawrence Livermore National Labora↗