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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Bayesian Entropy Neural Networks for physics-aware prediction

This article addresses the need for deep learning models to integrate well-defined constraints into their outputs, driven by their application in surrogate models, learning with limited data and partial information, and scenarios requiring flexible model behavior to incorporate non-data sample information. We introduce Bayesian Entropy Neural Networks (BENN), a framework grounded in Maximum Entropy (MaxEnt) principles, designed to impose constraints on Bayesian Neural Network (BNN) predictions. BENN is capable of constraining not only the predicted values but also their derivatives and variances, ensuring a more robust and reliable model output. To achieve simultaneous uncertainty quantification and constraint satisfaction, we employ the method of multipliers approach. This allows for the concurrent estimation of neural network parameters and the Lagrangian multipliers associated with the constraints. Our experiments, spanning diverse applications such as beam deflection modeling and microstructure generation, demonstrate the effectiveness of BENN. The results highlight significant improvements over traditional BNNs and showcase competitive performance relative to contemporary constrained deep learning methods.

14 SOLAR ENERGY↗
DOE OSTI2025

Particle signal considerations for isotope ratio analysis with single particle multi-collector inductively coupled plasma mass spectrometry

Particle analysis has benefitted from the advent of single particle inductively coupled plasma-mass spectrometry (spICP-MS) due to its robustness, sensitivity, and high-throughput nature. Previous methods of spICP-MS have typically utilized quadrupole or time-of-flight mass analyzers and therefore employ electron multiplier-based detectors (such as secondary electron multipliers or microchannel plates). However, to obtain precise measurements on elemental or isotopic ratios within individual particles, multi-collector ICP-MS (MC-ICP-MS) can be used. Here, we investigate Ce isotope ratios, specifically 142 Ce/ 140 Ce, by spMC-ICP-MS using an all-Faraday cup collector array. Using 1 μm (diameter) cerium dioxide particles, integration times of the Faraday cup detectors were varied from 50–500 ms. The signal from the cerium isotopes in the particles was used to determine isotope ratios, which closely matched the expected natural isotopic abundances. Due to the signal decay response from the Faraday cups, the signal from particles lasts much longer than the expected 1–2 ms (up to 100 s of ms). To explore this effect on isotope ratio analysis, multiple ratio analysis methods were used to determine how to obtain optimal precision and accuracy. Relative differences were around 2% for methods that calculated isotope ratios from summing the total signal of an individual particle before calculating the ratio (rather than using every data point individually). It was found that summing all data points per particle, or integrating under the signal peak, yielded both accurate and precise isotope ratios within the particle population. Particles were also sampled off a solid substrate via microextraction, and isotope ratios were determined with relative differences of 0.13% to 9%. This demonstrates the ability to use spMC-ICP-MS to obtain isotope ratios on particles, with little to no relative difference in comparison to the expected ratio, even when operating Faraday detectors at fast 50 ms integration times.

Szakas, Sarah E. [Oak Ridge National Laboratory (O↗
DOE OSTI2025

Effect of self-generated magnetic fields on x-ray emission in Kr-filled targets at the National Ignition Facility

We examine the effects of self-generated magnetic fields in a Kr gas pipe x-ray source platform. X-ray emission from Kr plasma is dependent on the plasma conditions, as the ionization state is largely a function of temperature. Magnetic fields are known to limit heat conduction, which increases temperature. We show that the emission in simulations of the gas pipe x-ray source is dependent on how self-generated magnetic fields are modeled. The inclusion of self-generated magnetic fields in simulations more accurately captures the emission of lower energy x-ray emission (L-shell), bringing results closer to experiments. The modeled x-ray emission and self-generated magnetic fields are shown to be particularly sensitive to the inclusion of the Nernst effect in simulations. Severely limiting the Nernst effect leads to a hotter Kr plasma, which can account for the discrepancy seen in earlier studies. By modifying the Nernst effect multiplier, we can achieve better experimental agreement in x-ray emission from gas pipes; the value of the multiplier that leads to the best agreement is dependent on the laser power of the drive. Currently, the suppression factor of the Nernst effect needed for high power drives (PL>200 TW) is more restrictive than what is currently put forward by non-local models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Constraining hydrodynamic models of inertial confinement fusion implosions using capsule surrogate experiments

We conduct capsule surrogate experiments at the National Ignition Facility to calibrate radiation hydrodynamic simulations to infer hydrodynamic conditions that are not observable in indirect drive ignition implosions. We tune the simulations by applying laser power and cross beam energy transfer (CBET) saturation multipliers to match the observables from capsule surrogate experiments. Shock timing, velocity, and symmetry are measured in liquid D 2 filled Keyhole capsule surrogate experiments and implosion trajectory, stagnation time, and shape time history are measured in in-flight 2D backlit x-ray radiography experiments (“2DConA”) of D 2 gas filled capsule implosions. Calibrated simulations suggest that the N210808 ignition implosion (fusion target gain = 0.7) had a shell mass remaining at stagnation of less than the nominal %5 (3.8%) and resulted in less confinement. For N221204, the shell was made 5.75 μm thicker to trade implosion velocity for increased confinement and resulted in a target gain = 1.5 with a shell mass remaining of 5.7%. Furthermore, a single adjusted model can reproduce all shock timing data as changes are made to shell thickness (79–85 μm) and laser wavelength separation (1.8–4.0 Å). However, for the 2DConA implosions, a 5% variation in the peak power laser multipliers and a 30% variation in late-time CBET between experiments are needed to match the observed stagnation times, in-flight $P_2$ shape, and hot-spot $P_2$ shape. While progress is being made to improve the models in simulations using focused experiments, capsule surrogate experiments will continue to be needed to optimize future ignition designs.

Lasers↗
DOE OSTI2024

Frequency-domain computing using nonlinear acoustic-wave device on lithium niobate

Abstract Multiply-accumulation are crucial computing operations in signal processing, numerical simulations, and machine learning. In recent years, optical analog approaches have demonstrated higher computing performance and better power efficiency than their digital counterparts. However, analog computing chips usually need large areas and complex structures for parallel computing, as a single device element only executes one computing operation at a single time. Here, we demonstrate frequency-domain computing using the nonlinear acoustic-wave devices on lithium niobate, featuring a normalized external second-harmonic generation conversion efficiency of ~ 5.7 × 10-4 W-1. The second-order sum-frequency nonlinear process of lithium niobate enables multiplication of inputs encoded in the frequency domain. Compared to the analog schemes, our device features a notably simpler design, and nanofabrication requires only one lift-off. Using a single acoustic-wave device within an area of 0.03 mm2, we can simultaneously conduct over 130,000 multiply-accumulation operations. Our acoustic-wave device shows applications in real and complex vector convolutions and image processing. This demonstration sets the stage for experimental realizations into frequency-domain integrated nonlinear acoustic computing systems, potentially shaping future developments in acoustic neural networks and quantum computing.

chai, mingzhao (ORCID:0009000466226341)↗
DOE OSTI2024

SIMS Data Correction Procedure for Quasi‐Simultaneous Arrival ( QSA ) Under‐counting and Ramifications of Misapplication

Isotope geochemistry requires isotope ratios measured using secondary ion mass spectrometry (SIMS) to be made with optimal precision and accuracy. Under some analytical conditions when using electron multiplier detectors, secondary ions may be under‐counted because of quasi‐simultaneous arrival (QSA) at the first dynode. The relative magnitude of the associated QSA correction to raw measured isotopic ratios can be up to seventy permil or more. Therefore, not applying the correction, or misapplication of it could lead to significant inaccuracies in published isotope ratio data. Examples and ramifications of the latter are described in addition to a straightforward procedure for QSA under‐counting correction.

Geochemistry & Geophysics↗
DOE OSTI2026

The NEXT-100 Detector

The NEXT collaboration is dedicated to the study of double beta decays of 136 Xe using a high-pressure gas electroluminescent time projection chamber. This advanced technology combines exceptional energy resolution (≤ 1% FWHM at the Q ββ value of the neutrinoless double beta decay) and powerful topological event discrimination. Building on the achievements of the NEXT-White detector, the NEXT-100 detector started taking data at the Laboratorio Subterráneo de Canfranc (LSC) in May of 2024. Designed to operate with xenon gas at 13.5 bar, NEXT-100 consists of a time projection chamber where the energy and the spatial pattern of the ionising particles in the detector are precisely retrieved using two sensor planes (one with photo-multiplier tubes and the other with silicon photo-multipliers). The detector has been operating at stable conditions using argon and xenon gases at ~4 bar and drift fields of 74 V cm –1 and 118 V cm –1 , respectively. Alpha decays from the 222 Rn chain have been used to test and monitor the stability of the detector, showing a constant electron lifetime in the drift volume. In this paper, in addition to reporting the results of the commissioning run, we provide a detailed description of the NEXT-100 detector, describe its assembly, and present the current estimation of the radiopurity budget.

Adams, C. [Argonne National Laboratory; NVIDIA] (O↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The DESI Single Fiber Lens Search. I. Four Thousand Spectroscopically Selected Galaxy–Galaxy Gravitational Lens Candidates

We present 4110 strong gravitational lens candidates, 3887 of which are new discoveries, selected from a sample of 5,837,154 luminous red galaxies (LRGs) observed with the Dark Energy Spectroscopic Instrument (DESI). Candidates are identified via the presence of background ionized oxygen [O II ] nebular emission lines in the foreground LRG spectra, which may originate from the lensing of higher-redshift star-forming galaxies. Using the measured foreground redshift, background redshift, and integrated flux of the background [O II ] doublet, we integrate over impact parameters to compute the probability that each candidate is a lens. We expect 53% of candidates to be true lenses with Einstein radii ranging from 0$^{''}_.$1–4", which can be confirmed with high-resolution imaging. Confirmed strong lenses from this sample will form a valuable cosmological data set, as strong gravitational lensing is the only method to directly measure dark matter halo substructure at cosmological distances. We independently recover the host of the multiply imaged gravitationally lensed type Ia supernova iPTF16geu. Monitoring these lenses for future multiply lensed transients will enable (a) H 0 measurements via time-delay cosmography and (b) substructure measurements via flux ratios.

Karp, Juliana S. M. [Univ. of Washington, Seattle,↗
DOE OSTI2026

Using Filter Methods to Guide Convergence for ADMM, with Applications to Nonnegative Matrix Factorization Problems

Nonconvex, nonlinear optimization problems arise naturally in parameter fitting and machine learning. While augmented Lagrangian methods have demonstrated robust convergence for classes of these problems, their convergence for block updates has been relatively unexplored outside of the context of the alternating direction method of multipliers (ADMM). ADMM has seen extensive use in these applications, but may exhibit uncertain convergence behavior in many practical nonconvex settings, and struggles with general nonlinear constraints. In contrast, filter methods have proved effective in enforcing convergence for sequential quadratic programming methods and interior point methods with feasibility criteria. We develop an ADMM-filter method for highly nonlinear and nonconvex problems. Here, we show convergence under mild assumptions for several types of coordinate descent schemes, and demonstrate our algorithm on nonnegative matrix factorization and completion problems in imaging and chemical spectrum analysis.

Nonconvex optimization↗
DOE OSTI2025

Construction of Porous Tetrazine-Functionalized Networks: From Two- to Three-Dimensional Counterparts with Tunable Properties

Porous organic networks (PONs) linked by aza-fused rings are extensively studied and demonstrated wide applications in diverse fields. It is a long-term attractive and challenging subject to explore novel PONs functionalized by unexplored aza-fused moieties. Herein, a series of tetrazine-linked PONs (Tz-PONs) were constructed from two-dimensional to three-dimensional counterparts with tunable properties. The reaction pathway composed of the amidrazone intermediates formation using multiply substituted aromatic nitrile monomers was catalyzed by zinc salts with Lewis super acidity in the presence of hydrazine, and the subsequent tetrazine formation was assisted by sodium nitrite solution. Textural property of the as-constructed scaffolds could be tuned by the acidity of the zinc salts, as well as the organic solvents. As an initial assessment, those Tz-PONs displayed different photo absorption behavior, which may influence the corresponding photocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Fundamentally New Coupled Approach to Contact Mechanics via the Dirichlet‐Neumann Schwarz Alternating Method

Contact phenomena are crucial for understanding the behavior of mechanical systems. However, existing computational approaches for simulating mechanical contact often face numerical challenges, such as inaccurate physical predictions, energy conservation errors, and unwanted oscillations. Here, we introduce an alternative technique for simulating dynamic contact based on the non‐overlapping Schwarz alternating method, originally developed for domain decomposition. In multibody contact scenarios, this method treats each body as a separate, non‐overlapping domain and prevents interpenetration using an alternating Dirichlet–Neumann iterative process. This approach has a strong theoretical foundation, eliminates the need for contact constraints, and offers flexibility, making it ideal for multiscale and multiphysics applications. We conducted a numerical comparison between the Schwarz method and traditional methods, such as the Lagrange multiplier and penalty methods, focusing on a benchmark impact problem. Our results indicate that the Schwarz alternating method outperforms traditional methods in several key areas: it provides more accurate predictions for various measurable quantities and demonstrates exceptional energy conservation capabilities. To address unwanted oscillations in contact velocities and forces, we explored various algorithms and stabilization techniques, ultimately opting for the naïve‐stabilized Newmark scheme for its simplicity and effectiveness. Additionally, we validated the efficiency of the Schwarz method in a three‐dimensional impact problem, highlighting its inherent capacity to accommodate different mesh topologies, time‐integration schemes, and time steps for each interacting body.

Schwarz alternating method↗
DOE OSTI2025

Measurements of the branching fractions of ${\Xi }_{c}^{+}\to {\Sigma }^{+}{K}_{S}^{0}$, ${\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+}$, and ${\Xi }_{c}^{+}\to {\Xi }^{0}{K}+$ at Belle and Belle II

Using 983.0 fb −1 and 427.9 fb −1 data samples collected with the Belle and Belle II detectors at the KEKB and SuperKEKB asymmetric energy e + e − colliders, respectively, we present studies of the Cabibbo-favored ${\Xi }_{c}^{+}$ decays ${\Xi }_{c}^{+}\to {\Sigma }^{+}{K}_{S}^{0}$ and ${\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+}$, and the singly Cabibbo-suppressed decay ${\Xi }_{c}^{+}\to {\Xi }^{0}{K}^{+}$. The ratios of branching fractions of ${\Xi }_{c}^{+}\to {\Sigma }^{+}{K}_{S}^{0}$ and ${\Xi }_{c}^{+}\to {\Xi }^{0}{K}^{+}$ relative to that of ${\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+}$ are measured for the first time, while the ratio $\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+})/\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+})$ is also determined and improved by an order of magnitude in precision. The measured branching fraction ratios are $\begin{array}{c}\frac{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Sigma }^{+}{K}_{S}^{0}\right)}{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+}\right)}=0.067\pm 0.007\pm 0.003,\\ \frac{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+}\right)}{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+}\right)}=0.251\pm 0.005\pm 0.010,\\ \frac{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Xi }^{0}{K}^{+}\right)}{\mathcal{B}\left({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+}\right)}=0.017\pm 0.003\pm 0.001.\end{array}$ Additionally, the ratio $\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{0}{K}^{+})/\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+})$ is measured to be 0.068 ± 0.010 ± 0.004. Here, the first and second uncertainties are statistical and systematic, respectively. Multiplying the ratios by the branching fraction of the normalization mode, $\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+})=(2.9\pm 1.3)%$, we obtain the following absolute branching fractions $\begin{array}{c}\mathcal{B}({\Xi }_{c}^{+}\to {\Sigma }^{+}{K}_{S}^{0})=(0.194\pm 0.021\pm 0.009\pm 0.087)\text{%},\\ \mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{0}{\pi }^{+})=(0.728\pm 0.014\pm 0.027\pm 0.326)\text{%},\\ \mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{0}{K}^{+})=(0.049\pm 0.007\pm 0.003\pm 0.022)\text{%},\end{array}$ where the third uncertainties are from $\mathcal{B}({\Xi }_{c}^{+}\to {\Xi }^{-}{\pi }^{+}{\pi }^{+})$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Normalization of ZZ instanton amplitudes in type 0B minimal superstring theory

Abstract We study ZZ instanton corrections in the (2,4k)$$ \mathcal{N} $$ N = 1 minimal superstring theory with the type 0B GSO projection, which becomes the type 0B$$ \mathcal{N} $$ N = 1 super-JT gravity in thek→ ∞ limit. Each member of the (2,4k) family of theories has two phases distinguished by the sign of the Liouville bulk cosmological constant. The worldsheet method for computing the one-loop normalization constant multiplying the instanton corrections gives an ill-defined answer in both phases. We fix these divergences using insights from string field theory and find finite, unambiguous results. Each member of the (2,4k) family of theories is dual to a double-scaled one-matrix integral, where the double-scaling limit can be obtained starting either from a unitary matrix integral with a leading one-cut saddle point, or from a hermitian matrix integral with a leading two-cut saddle point. The matrix integral exhibits a gap-closing transition, which is the same as the double-scaled Gross-Witten-Wadia transition whenk= 1. We also compute instanton corrections in the double-scaled matrix integral for allkand in both phases, and find perfect agreement with the string theory results.

Physics↗
DOE OSTI2025

Opportunities in multiscale modeling of mosquito-borne flaviviruses

Mosquito-borne flaviviruses, such as Zika, dengue, West Nile, and yellow fever virus, represent a growing public health concern due to their widespread distribution and the severe diseases they cause. These viruses are difficult to control as climate change and urbanization help mosquitoes expand into new areas, increasing the risk of outbreaks. Mathematical models play a key role in understanding their spread, providing insights at every level—from how the virus multiplies inside cells to how it circulates through entire populations. This review examines various approaches used in modeling arboviruses, including microscale models that focus on cellular and molecular dynamics, mesoscale models that address within-host processes, and macroscale models that capture population-level transmission. We briefly summarize the methodology used for models at each scale, which primarily consists of sets of differential equations with parameters that represent physical rates of change for different subprocesses. We particularly highlight how temperature affects virus transmission, which is key to understanding the impact of climate change. We also show how multiscale models can connect viral replication, immune response, and the spread of infection at a larger scale. This is essential for developing better vaccines and treatments, evaluating disease control measures, predicting the impact of climate change, and improving public health responses to outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2026

Link statistics of dislocation network during strain hardening

Dislocations are line defects in crystals that multiply and self-organize into a complex network during strain hardening. The length of dislocation links, connecting neighboring nodes within this network, contains crucial information about the evolving dislocation microstructure. By analyzing data from Discrete Dislocation Dynamics (DDD) simulations in face-centered cubic (fcc) Cu, we characterize the statistical distribution of link lengths of dislocation networks during strain hardening on individual slip systems. Here, our analysis reveals that link lengths on active slip systems follow a double-exponential distribution, while those on inactive slip systems conform to a single-exponential distribution. The distinctive long tail observed in the double-exponential distribution is attributed to the stress-induced bowing out of long links on active slip systems, a feature that disappears upon removal of the applied stress. We further demonstrate that both observed link length distributions can be explained by extending a one-dimensional Poisson process to include different growth functions. Specifically, the double-exponential distribution emerges when the growth rate for links exceeding a critical length becomes super-linear, which aligns with the physical phenomenon of long links bowing out under stress. This work advances our understanding of dislocation microstructure evolution during strain hardening and elucidates the underlying physical mechanisms governing its formation.

Crystal plasticity↗