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At least 127 records · Page 7

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DOE OSTI2026

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗

DOE OSTI2024

Advances in laser-plasma interactions using intense vortex laser beams

Low-intensity light beams carrying orbital angular momentum (OAM), commonly known as vortex beams, have garnered significant attention due to promising applications in areas ranging from optical trapping to communication. In recent years, there has been a surge in global research exploring the potential of high-intensity vortex laser beams and specifically their interactions with plasmas. Here, this paper provides a comprehensive review of recent advances in this area. Compared with conventional laser beams, intense vortex beams exhibit unique properties such as twisted phase fronts, OAM delivery, hollow intensity distribution, and spatially isolated longitudinal fields. These distinct characteristics give rise to a multitude of rich phenomena, profoundly influencing laser-plasma interactions and offering diverse applications. The paper also discusses future prospects and identifies promising general research areas involving vortex beams. These areas include low-divergence particle acceleration, instability suppression, high-energy photon delivery with OAM, and the generation of strong magnetic fields. With growing scientific interest and application potential, the study of intense vortex lasers is poised for rapid development in the coming years.

high energy density science↗

DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗

DOE OSTI2025

Microtron Data Log

The Microtron at Los Alamos National Laboratory (LANL) is a versatile electron accelerator originally designed for medical therapy. Since 2001, it has been used for non-destructive radiographic imaging and research and development applications. Operating at four different energy levels—6, 10, 15, and 20 MeV—the Microtron produces dose rates of approximately 780, 1800, 2700, and 2800 R/min at a distance of one meter from the source, respectively. This high-energy X-ray source enables detailed internal examination of dense and thick objects without causing damage, making it invaluable for various scientific and industrial applications. For instance, LANL’s Microtron has been utilized to study the performance of large-panel cerium-doped lutetium yttrium silicon oxide (LYSO) scintillators, which are essential components in advanced imaging systems.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

LLM integration into EPICS

The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.

Adams, Ethan↗

DOE OSTI2025

Exascale workflow applications and middleware: An ExaWorks retrospective

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. Here, we present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗

DOE OSTI2025

Energy recovery proton linear accelerator

High-power proton linear accelerators have important applications in both scientific research and industry. However, the operation of such accelerators with megawatt-level beam power is expensive and limits the broad availability of these facilities. In this Letter, we propose a novel energy recovery proton linear accelerator in which the final GeV-level proton beam is reinjected into the linear accelerator from the accelerator exit and is decelerated in the same accelerator down to about 2 MeV, close to the initial beam energy. This substantially reduces the power consumption of the proton linear accelerator and also avoids the need for a high-power beam dump. We demonstrate this concept through self-consistent simulations and show that the beam-beam effect between the forward-accelerating beam and the backward-decelerating beam would not be a limiting factor for the proposed concept. A potential application of this concept to an electron-ion collider based on energy recovery electron and ion accelerators is discussed.

Energy recovery linacs↗

DOE OSTI2025

Pulse duration dependent effects of ultrafast laser induced damage on a 1030 nm multi-layer dielectric mirror for high repetition rate, high average power laser systems

High repetition rate, high peak, and average power laser systems are crucial for next-generation particle accelerators, inertial confinement fusion, and secondary particle sources. These applications demand durable laser optics, particularly interference coatings on optics lasting millions of shots at high fluence. This study focuses on designing, testing, and simulating multi-layer dielectric (MLD) mirrors for pulse durations of 260 fs, 77 fs, and 25 fs at 1030 nm wavelength and 45-degree incidence angle with p -polarization. S-on-1 laser-induced damage thresholds (LIDT) for varying pulse numbers were determined, with single-shot LIDT values of 0.98 Jcm -2 , 1.63 Jcm -2 , and 2.3 Jcm -2 for 25 fs, 77 fs, and 260 fs respectively. A strong correlation between blister shape and local fluence was observed, implying that the layer expansion in a blister depends on local fluence. We have also examined mechanisms responsible for laser-induced stress generation and energy release rates in blister formation. Damage mechanisms are further explored by finite-difference time-domain (FDTD) simulations, incorporating Keldysh strong field ionization, whose predictions were in excellent agreement with the onset of damage determined experimentally. These findings offer insights for enhancing MLD coating technology, promising more efficient and resilient laser systems for diverse scientific and industrial applications.

Noor, Mohamed Yaseen (ORCID:000000021036644X)↗

DOE OSTI2025

Precise half-life determination of 131 Ba and 125 Xe

Barium-131 and Xenon-125 are valuable radioisotopes with numerous scientific and medical applications. Here, we produced both isotopes via proton irradiation of a cesium iodide (CsI) target at the Brookhaven Tandem Van de Graaff accelerator (BTVG). Following irradiation, we conducted systematic γ-ray spectroscopy measurements at the National Nuclear Data Center (NNDC) decay station using a calibrated High-Purity Germanium (HPGe) detector, collecting data over a 2-month period (spanning approximately 4.5 half-lives for 131 Ba). Through careful analysis of the characteristic γ-ray emissions from both isotopes, we determined the half-life of 131 Ba to be 11.55(6) days, which agrees with the previously evaluated value. For 125 Xe, we measured a half-life of 16.56(8) hours, slightly lower than the evaluated value.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Distinctive features of fluorescence and waveguides in magnesium aluminate spinel crystals driven by structural discrepancy

Transparent polycrystalline ceramics are of significant importance for a wide range of scientific and industrial applications. Developing a deeper understanding of their thermodynamic behavior is essential for achieving their maximum output performance in technological applications. This study provides a systematic investigation into the thermal excitation-induced fluorescence kinetics and waveguide characteristics of Magnesium Aluminate Spinel (MgAl 2 O 4 ) single crystals, with a focus on the intricate relationship between structure and properties. High-temperature extreme environments through irradiation with swift heavy ions 645.0 MeV Xe and 352.8 MeV Fe ions were created; the atomic deposition energy threshold (E th ) associated with disorder morphologies was assessed between 0.91 and 0.99 eV atom –1 . The track prediction model was developed to support the theoretical prediction of track formation. Electronic energy loss (E ele ) disrupts the balance of the initial structure through the thermal spike effect, leading to the formation of absorption-related F and F + color centers. These defects enhance photoluminescence in the visible spectrum and result in an effective modulation of the intrinsic bandgap. Moreover, as ion beams penetrate into the material, the uneven damage distribution induces the formation of waveguide structures. In conclusion, these findings provide valuable insights into the fabrication of functional devices through irradiation technologies and the structural changes of MgAl 2 O 4 at high temperatures within extreme environments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Characterizing the combined impact of nucleation-driven precipitation and secondary passivation on carbon mineralization

The evolution of mineral reactive surface area is one of the primary phenomena controlling the progression and extent of mineral carbonation. The CO 2 mineralization begins with nucleation of crystals that provide initial surface area for subsequent growth of the mineral. However, many reactive transport models (RTMs) for CO 2 mineralization do not include the nucleation process. The few RTMs that do include it are yet to be validated against experimental data. Similarly, many RTMs ignore passivating effects of the secondary mineral, which coats the surface of the dissolving mineral, slow down the reaction process, and reduce the total extent of carbonation. Furthermore, the combined impact of nucleation and passivation on carbon mineralization is yet to be properly characterized. In this study, we consider the coupled effects of passivation and nucleation on the mineralization extent. The nucleation-driven precipitation model relies on the formation of nuclei to provide a surface area for crystal growth, while a new model is proposed to account for passivation effects. Our analysis shows that (i) omission of nucleation leads to overestimation of extent of mineralization, and (ii) omission of passivation leads to overestimation of host rock reactivity. The model was evaluated via comparison with CO 2 mineralization data from the literature and models that ignore these processes. We observed that including nucleation and passivation lead to closer predictions of the CO 2 mineralization extent. Therefore, this study highlights the importance of including the coupled nucleation-driven precipitation and secondary passivation in RTMs. Here, the findings from the study can be applied in various scientific and engineering applications such as petroleum production, cement carbonation, CO 2 sequestration, chemical weathering, and concrete degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Characteristics of oxide-dispersion strengthened alloys produced by high-temperature severe deformation

This study is to explore an economically attractive and technically feasible processing method for oxide-nanoparticle strengthened alloys for fusion reactor application. Despite many scientific merits of the advanced oxide-dispersion strengthened (ODS) alloys, such as the nanostructured ferritic alloy (NFA) 14YWT, the only viable production path for a high-quality NFA is the high-power mechanical alloying process. This process is often a multi-day high-speed ball milling of alloy powder with a small quantity of yttria (Y 2 O 3 ) powder, followed by the milled-powder consolidation using extrusion or other methods and additional thermomechanical processing (TMP) for property control. This complex production path, including the low-temperature mechanical alloying in particular, has limited technical advancement toward the cost-effective and scale-up production of ODS alloy components. To overcome such a practical limitation, we proposed to explore alternative low-cost processing routes using traditional thermomechanical processing (TMP) method only. Further, a series of continuous TMP cycles, which were designed to impose high-temperature severe plastic deformation (HT-SPD) conditions to the consolidated powder mixtures, were applied to achieve the effective distribution of oxide particles in nanograin structure and thus desirable mechanical properties. Since the reduced-activation ferritic-martensitic (RAFM) alloy powders (Fe-10Cr and Fe-14Cr alloys with various Y contents) are available in our inventory, we focused to utilize the new solid-state synthesis approach for controlling oxide (oxygen source) dissolution and nanoscale clustering in nanograin structure in those alloys. A combination of powder consolidation at 900 °C and continuous thermomechanical activation at 600 °C yielded two essential ODS alloy microstructure contents–nanograin structure and nanoparticle distribution–and thus demonstrated a good combination of strength and ductility.

36 MATERIALS SCIENCE↗

DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

PSCF+: An Extended and Improved Open-Source Software Package for Polymer Self-Consistent Field Calculations

This work introduces PSCF+, a freely available open‑source software tool for modeling how block copolymers self‑assemble into ordered nanoscale structures. The software allows researchers to study a wide range of polymer models and interaction types, making it possible to directly compare theoretical predictions with molecular and simulation‑based studies in a consistent way. PSCF+ incorporates several recently developed numerical methods that significantly reduce computer memory requirements and speed up calculations, enabling efficient use of modern GPU hardware. Here, it also provides automated tools to calculate free‑energy landscapes and phase boundaries, which are essential for understanding and predicting material behavior. Together, these advances make PSCF+ a powerful and flexible platform for studying polymer self‑assembly and support the design of new polymeric materials for scientific and technological applications.

Chemical calculations↗

DOE OSTI2025

Electric Field’s Dueling Effects through Dehydration and Ion Separation in Driving NaCl Nucleation at Charged Nanoconfined Interfaces

Investigating nucleation in charged nanoconfined environments under electric fields is crucial for many scientific and engineering applications. Here we study the nucleation of NaCl from aqueous solution near charged surfaces using machine-learning-augmented enhanced sampling molecular dynamics simulations. Our simulations successfully drive phase transitions between the liquid and solid phases of NaCl. The solid phase is stabilized under electric fields, particularly at an intermediate surface charge density. We examine which physical characteristics drive the nucleation of NaCl from aqueous solutions and find that the removal of solvent water from Cl– at the solid precursor surface plays a more critical role than the accumulation of ions. Our simulations reveal the competing effects of electric fields on nucleation processes: they facilitate the removal of water, promoting nucleation, but also promote the separation of ion pairs, thereby hindering nucleation. Here, this work provides a framework for studying nucleation processes in nanoconfined environments under electric fields and provides physical insights for the design of electrochemistry materials.

Electric fields↗

DOE OSTI2025

Kβ X-ray Emission Spectra Analysis Using Bayesian Optimization

The Kβ X-ray emission spectrum of 3 d transition metals is rich with electronic and structural information due to strong exchange interactions with the valence shell of the metal, and has become crucial for understanding their spin and oxidation states. The spectrum is commonly treated using crystal-field multiplet theory, a semi-empirical theory that uses tunable parameters to control the strength of the effects present in X-ray emission spectroscopy (XES). However, determining the experimental values of these parameters remains a challenge. We present a methodology that applies Bayesian optimization to crystal-field multiplet theory to determine parameter values. The algorithm is tested on the X-ray emission spectra of a collection of Mn, Co, and Ni oxides. We are able to find optimal values for the four most impactful parameters: Slater−Condon reduction factors F dd , F pd , and G pd , and crystal field splitting 10 Dq . The algorithm produces significantly improved accuracy compared to current analysis methods, and probes interparameter dependencies by modeling the error landscape. This advancement enhances XES analysis by offering an approach of obtaining quantitative electronic structural information on 3 d transition metal valence shells, facilitating applications across various scientific fields.

Bayesian optimization↗

DOE OSTI2024

Direct observation of ultrafast cluster dynamics in supercritical carbon dioxide using X-ray Photon Correlation Spectroscopy

Supercritical fluids exhibit distinct thermodynamic and transport properties, making them of particular interest for a wide range of scientific and engineering applications. These anomalous properties emerge from structural heterogeneities due to the formation of molecular clusters at conditions above the critical point. While the static behavior of these clusters and their effects on the thermodynamic response functions have been recognized, the relation between the ultrafast cluster dynamics and transport properties remains elusive. By measuring the intermediate scattering function in carbon dioxide at conditions near the critical point with X-ray photon correlation spectroscopy, we directly capture the cross-over dynamics between 4 and 13 picoseconds, revealing the transition between ballistic and diffusive motion. Complementary analysis using large-scale molecular dynamics simulations reveals that this behavior arises from collisions between unbound molecules and clusters. This study provides direct evidence of the ultrafast momentum exchange between clusters, which has significant impact on transport properties, solvation processes, and reaction kinetics in supercritical fluids.

carbon capture and storage↗