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DOE OSTI2024

An explainable variational autoencoder model for three-dimensional acoustic emission source localization in hollow cylindrical structures

We introduce an explainable variational autoencoder for three-dimensional (3D) localization of acoustic emission sources in hollow cylindrical structures, with an unsupervised approach. This research capitalizes on multi-arrival waveforms generated by helical path propagation in cylindrical geometries to enable efficient two-receiver localization. By integrating the modal characteristics of Lamb modes under multi-path conditions, we demonstrate that two sets of time-of-arrival differences and peak amplitudes extracted from one receiver can serve as effective localization features. This initial approach identifies four potential source locations, highlighting the feasibility of two-receiver source localization using traditional feature extraction methods. However, direct extraction can be challenging when mode overlaps occur, complicating the localization process. To address this, our work proposes a novel waveform-based method. This method leverages the consistent dispersion characteristics within isotropic materials, where each unique combination of mode arrival times and peak amplitudes constructs a distinct waveform. This distinctiveness overcomes the ambiguities associated with mode overlaps, significantly enhancing the method’s precision and robustness. Our approach adopts a data-driven strategy for waveform-based localization using variational autoencoder (VAE). VAE discerns waveform patterns for localization, while also addressing data uncertainties. The VAE’s encoder and decoder networks capture the localization process and the source’s influence on waveform generation, respectively, guiding latent variables to segregate waveforms by source in the latent space. The design of the learning process focuses on specific localization characteristics to enhance result explainability. Localization predictions are generated by projecting test waveforms, not included in the training set, onto a trained latent space. The prediction is determined using a nearest-neighbor approach based on the closest latent representation of a source. Validation with pencil-lead-break tests on a metallic pipe confirmed our method’s effectiveness, achieving an averaged 3D localization accuracy of 0.84.

Lee, Guan-Wei↗

DOE OSTI2025

Sensitivity analysis of a parameterization of coagulation in an aqueous transport model

A three-dimensional aqueous transport model, ALGE, was developed at the Savannah River National Laboratory (SRNL) as a tool for emergency response. Recently, coagulation and break-up of sediment and particulate (contaminant adhered to sediment) matter has been added as a new model capability, representing both processes as functions of salinity and turbulence. A sensitivity analysis was performed by comparing time series of sediment and particulate concentrations at various locations when the model’s parameters (particle size and density) are altered. The results show significant differences between experiments in both freshwater and saltwater systems parameters vary (P-values < 0.05). From this sensitivity study, we have concluded the importance of incorporating both salinity and turbulent effects, as they have a large role in the physical process of coagulation and breakup of sediment. By accounting for these physical processes, we have made the ALGE model a more robust tool for emergency response.

Aqueous fate and transport↗

DOE OSTI2025

Three-dimensional core-collapse supernova models with phenomenological treatment of neutrino flavor conversions

Abstract We perform three-dimensional supernova simulations with a phenomenological treatment of neutrino flavor conversions. We show that the explosion energy can increase to as high as $\sim 10^{51}$ erg depending on the critical density for the onset of flavor conversions, due to a significant enhancement of the mean energy of electron antineutrinos. Our results confirm previous studies showing such energetic explosions, but for the first time in three-dimensional configurations. In addition, we predict neutrino and gravitational wave (GW) signals from a nearby supernova explosion aided by flavor conversions. We find that the neutrino event number decreases because of the reduced flux of heavy-lepton neutrinos. In order to detect GWs, next-generation GW telescopes such as Cosmic Explorer and the Einstein Telescope are needed even if the supernova event is located at the Galactic Center. These findings show that the neutrino flavor conversions can significantly change supernova dynamics and highlight the importance of further studies on the quantum kinetic equations to determine the conditions of the conversions and their asymptotic states.

Mori, Kanji↗

DOE OSTI2025

Initial steady-state core simulation capability or thermal and pool-type molten salt reactors, coupling reactor physics, thermal-hydraulics, and evolving chemistry

This report presents the development and validation of an initial steady-state multiphysics capability for molten salt reactors (MSRs) under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in Fiscal Year 2025. The framework integrates neutronics, thermal-hydraulics, species transport, and thermochemistry to capture the coupled dynamics of liquid-fueled systems. Implementation and testing were performed on two representative designs: the Molten Salt Reactor Experiment (MSRE), a thermal-spectrum, channeled-flow reactor, and the Lotus Molten Salt Reactor (L-MSR), a fast-spectrum, pool-type reactor. The modeling suite employs Griffin for reactor physics and depletion, Pronghorn and SAM for thermal-hydraulics, Thermochimica for chemistry, and Saline for thermophysical properties, with benchmarking and validation carried out against historical MSRE data, experimental flow-loop measurements, and reference depletion calculations from Monte Carlo codes. The framework demonstrated the ability to reproduce key reactor behaviors including temperature feedback, reactivity losses, delayed neutron precursor transport, xenon poisoning, and redox potential evolution. The results confirm the feasibility and accuracy of the coupled models in predicting steady-state and selected transient MSR behaviors. This latter ones are used in this report as a proxy indicating that the steady-state models from which the transient starts are accurate. For MSRE, validation showed good agreement with pump start-up and natural circulation tests, while for the L-MSR, benchmarking confirmed hydraulic calibration and consistency of neutronics–thermal coupling. The tools also provided new insights into species transport, noble metal deposition, and salt solidification dynamics. On the Xenon transport front, the code is validated against the steady state Xenon poisoining measurement and showed good agreement with the experimental value. Identified areas for future work include advanced void transport modeling, three-dimensional simulations, improved alloy corrosion models, and tighter integration with high-fidelity Monte Carlo codes. These developments provide a foundation for high-fidelity MSR simulations that can support reactor design optimization, safety assessments, and long-term operational strategies.

42 - ENGINEERING↗

DOE OSTI2024

Nonlinear solution of classical three-wave interaction via finite-dimensional quantum model

The quantum three-wave interaction, the lowest-order nonlinear interaction in plasma physics, describes energy–momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum–classical correspondence far from the fixed point dynamics. We discuss the Poincaré recurrence of the system and the mitigation of quantum scrambling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Data-driven global ocean modeling for seasonal to decadal prediction

Accurate modeling of ocean dynamics is crucial for enhancing our understanding of complex ocean circulation processes, predicting climate variability, and tackling challenges posed by climate change. Although great efforts have been made to improve traditional numerical models, predicting global ocean variability over multiyear scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), a data-driven three-dimensional ocean model for seasonal to decadal prediction of global ocean dynamics. ORCA-DL accurately simulates the three-dimensional structure of global ocean dynamics with high physical consistency and outperforms state-of-the-art numerical models in capturing extreme events, including El Niño–Southern Oscillation and upper ocean heat waves. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections. Our results demonstrate the high potential of data-driven models for providing efficient and accurate global ocean modeling and prediction.

Science & Technology - Other Topics↗

DOE OSTI2026

Alabama Carbon Storage: Data Sharing and Engagement (Final Report)

This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗

DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗

DOE OSTI2026

One Earth Energy Static and Dynamic Reservoir Modeling

This report presents the static and dynamic reservoir modeling conducted for the CarbonSAFE Phase III Illinois Storage Corridor project to assess the feasibility of commercial-scale CO 2 storage in the Mt. Simon Sandstone at the One Earth Energy (OEE) site in McLean County, Illinois. Three-dimensional geocellular models of the Mt. Simon storage complex were developed in Petrel ® by integrating petrophysical log data, core analyses, and seismic surveys from the OEE #1 stratigraphic test well and two nearby wells, with multiple model versions created as new data became available. Dynamic reservoir simulations, performed using Landmark's Nexus software, progressed through three phases (preliminary, sensitivity, and UIC Class VI permit studies) evaluating injection scenarios across varying rates, well orientations, permeability models, and multi-well configurations. Results demonstrate that commercial-scale storage is feasible: three injection wells spaced approximately one mile apart can store a total of 90 million tonnes of CO 2 over 20 years, producing a combined plume with an equivalent radius of 3.2 miles and a maximum pressure-front-defined Area of Review of 178 mi 2 at the end of injection that diminishes to 34 mi 2 after 50 years of post-injection monitoring. Sensitivity analyses indicate that a 20% change in porosity or permeability yields approximately a 7% change in AoR radius, and that perforating the high-permeability arkosic zone minimizes the pressure front compared to injection in the upper Mt. Simon Sandstone.

09 BIOMASS FUELS↗

DOE OSTI2024

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Tensor renormalization group approach to critical phenomena via symmetry-twisted partition functions

The locality of field theories strongly constrains the possible behaviors of symmetry-twisted partition functions, and thus they serve as order parameters to detect low-energy realizations of global symmetries, such as spontaneous symmetry breaking (SSB). We demonstrate that the tensor renormalization group (TRG) offers an efficient framework to compute the symmetry-twisted partition functions, which enables us to detect the symmetry-breaking transition and also to study associated critical phenomena. As concrete examples of SSB, we investigate the two-dimensional (2D) classical Ising model and the three-dimensional (3D) classical 𝑂⁡(2) nonlinear sigma model, and we identify their critical points solely from the twisted partition function. By employing the finite-size scaling argument, we find the critical temperature 𝑇 𝑐 = 2.2017⁢(2) with the critical exponent 𝜈 = 0.663⁢(33) for the 3D 𝑂⁡(2) model. In addition, we also study the Berezinskii–Kosterlitz–Thouless (BKT) criticality of the 2D classical 𝑂⁡(2) model by extracting the helicity modulus from the twisted partition functions, and we obtain the BKT transition temperature, 𝑇 BKT = 0.8928⁢(2).

lattice field theory↗

DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

DOE OSTI2024

Ion concentration polarization causes a nearly pore-length-independent conductance of nanopores

There has been a great amount of interest in nanopores as the basis for sensors and templates for preparation of biomimetic channels as well as model systems to understand transport properties at the nanoscale. The presence of surface charges on the pore walls has been shown to induce ion selectivity as well as enhance ionic conductance compared to uncharged pores. Here, using three-dimensional continuum modeling, we examine the role of the length of charged nanopores as well as applied voltage for controlling ion selectivity and ionic conductance of single nanopores and small nanopore arrays. First, we present conditions where the ion current and ion selectivity of nanopores with homogeneous surface charges remain unchanged, even if the pore length decreases by a factor of 6. This length-independent conductance is explained through the effect of ion concentration polarization (ICP), which modifies local ionic concentrations, not only at the pore entrances but also in the pore in a voltage-dependent manner. We describe how voltage controls the ion selectivity of nanopores with different lengths and present the conditions when charged nanopores conduct less current than uncharged pores of the same geometrical characteristics. The manuscript provides different measures of the extent of the depletion zone induced by ICP in single pores and nanopore arrays, including systems with ionic diodes. In conclusion, the modeling shown here will help design selective nanopores for a variety of applications where single nanopores and nanopore arrays are used.

Cain, DaVante↗

DOE OSTI2024

Efficient Dimension Reduction of Complex Three-dimensional CO2 Saturation using Deep Learning Models

In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.

Wang, Hongsheng↗

DOE OSTI2025

Compare low-gain 1D and 3D models toward a storage ring-based X-ray free-electron-laser oscillator

Fourth-generation diffraction-limited synchrotron light sources (DL-SLS) present a compelling platform for advanced light source applications due to their ultra-low emittance (around tens of picometers), sub-micrometer beam stability, and high repetition rates (greater than 1 MHz). Despite these advantages, fundamental limitations, such as long bunch durations (tens of picoseconds), large relative energy spread (~10⁻³), and low peak current (<300 A), have hindered their effectiveness in free-electron laser (FEL) applications. Using a low-gain three-dimensional (3D) model based on NSLS-II upgrade lattice parameters, we demonstrate that a 6% single-pass gain is achievable with an optimized transverse gradient undulator (TGU). Concurrently, recent advances in X-ray optics—specifically, a four-mirror bow-tie resonator utilizing diamond mirrors and beryllium compound refractive lenses (CRLs)—enable total round-trip losses below 4% and an output coupling efficiency near 1%, rendering a Storage Ring based X-ray FEL oscillator (XFELO) a realistic possibility. In the 3D framework, the TGU effectively mitigates the detrimental effects of large energy spread by spatially dispersing the beam, leading to enhanced gain relative to conventional one-dimensional (1D) models. We establish both analytical and numerical connections between the 1D and 3D descriptions, leveraging GENESIS simulations to map equivalent gain conditions. While 1D resonance is defined in the energy domain, the 3D TGU system shifts the resonance dependence to transverse gradient and dispersion parameters. Once optimized, this framework allows predictive modeling using 1D formulas, which can then be validated through full 3D simulations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Reaching the potential of electron diffraction

Microcrystal electron diffraction (MicroED) is an emerging structural technique in which submicron crystals are used to generate diffraction data for structural studies. Structures allow for the study of molecular-level architecture and drive hypotheses about modes of action, mechanisms, dynamics, and interactions with other molecules. Combining cryoelectron microscopy (cryo-EM) instrumentation with crystallographic techniques, MicroED has led to three-dimensional structural models of small molecules, peptides, and proteins and has generated tremendous interest due to its ability to use vanishingly small crystals. In this perspective, we describe the current state of the field for MicroED methodologies, including making and detecting crystals of the appropriate size for the technique, as well as ways to best handle and characterize these crystals. Our perspective provides insight into ways to unlock the full range of potential for MicroED to access previously intractable samples and describes areas of future development.

3D ED↗

DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗