Search NASA⌕ Search

SEARCH · Search NASA

Results for “Stack”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Porous transport electrodes for oxygen evolution reaction in proton exchange membrane water electrolysis -cells: Materials, designs, and diagnoses

H 2 production using proton exchange membrane (PEM) water electrolysis (PEMWE) cells has received considerable attention because of the high efficiencies of these cells and no harmful emissions from the related process. In PEMWE cells, porous transport electrodes (PTEs) composed of a catalyst layer (CL) comprising O 2 evolution reaction (OER) catalysts, porous transport layer (PTL), and PEM play key roles in the stack performance and lifetime. Herein, Ir-based and non-precious-metal OER catalysts that are highly active and stable at low pH values and high anodic potentials are reviewed to understand their OER mechanisms. Various strategies are proposed for engineering CLs and PTLs to improve the interfacial properties and mass transfers of reactants and products to and from the active sites. Additionally, diagnoses of PTEs is significantly crucial for interpreting electrochemical processes and addressing their current challenges. Therefore, half-cell analyses, including diffusion electrode (DE), floating electrode (FE), and modified rotating disk electrode (MRDE) techniques, are explored, and membrane electrode assembly (MEA)-based analyses, such as the polarization technique, electrochemical impedance spectroscopy, and magnetic field analysis, are established. In conclusion, this study aims to provide an overview of recent technologies used for the engineering and diagnostic tools of PEMWE cells and insights into the advanced components and systems to be developed in this field.

Diagnosis of PEMWE Cells↗
DOE OSTI2025

Hydrogen and water interactions with CrMnFeCoNi alloy from density functional theory calculations

High entropy alloys (HEAs) are a promising class of materials with remarkable mechanical and catalytic properties. Among these, the quinary CrMnFeCoNi alloy (also called “Cantor alloy”) has attracted considerable attention given its thermodynamic stability and remarkable mechanical properties under different temperatures. Given that various degradation mechanisms involve multiple contaminants, such as hydrogen and water in hydrogen embrittlement and surface poisoning, respectively, understanding their interactions with the Cantor alloy is critical for its practical applications as structural, nuclear, or hydrogen storage material. In this work, we perform first-principles calculations based on Density Functional Theory (DFT) to investigate such interactions when considering various microstructures, including bulk materials and those containing certain defects, such as grain boundaries, stacking faults, and vacancies. We also employ Global Sensitivity Analysis to identify the importance of different factors in the stability of the impurities. We find that the accuracy of the H formation energy is significantly affected by spin polarization and chemical short-range order. The study also identifies a strong tendency for hydrogen interstitials to segregate to Σ5(210)/[001] symmetric tilt grain boundary, even when H concentrations are high, suggesting that a certain type of grain boundaries acts as H sinks within the alloy. Further, this result is reinforced by the low formation energy of vacancy-hydrogen complexes, which can contain multiple hydrogen atoms. Finally, the surface reactivity analysis reveals that the adsorption energy of oxygen and hydroxyl groups is highly sensitive to the specific metal atom involved in the binding, with a clear preference for chromium atoms, which could have implications for the alloy’s oxidation and corrosion behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effects of interstitial oxygen on ω transformations and twin formation in bcc NbTaTiHf multi-principal element alloy from first-principles

Transformation- and twinning-induced plasticity (TRIP and TWIP) have been reported to contribute to the low-temperature deformation of some body-centered cubic (bcc) multi-principal element alloys (MPEAs) containing large fractions of group IV transition metals. The influence of interstitial solutes on the mechanisms underlying these forms of plasticity, however, remains unclear. Using first-principles calculations, we study the effects of interstitial O atoms on the relative stability of bcc and ω phases and on unstable and twin boundary stacking fault energy profiles in a representative bcc MPEA with high group-IV elemental fraction: NbTaTiHf. We find that O additions generally promote the relaxation of ω configurations back to their parent bcc structure, therefore inhibiting ω transformation. Calculations of the Rice parameter for bulk bcc and phases, as well as bcc-ω interfaces, further show that ω formation is a potent embrittlement factor, an effect that is enhanced by O additions, suggesting that the formation of bcc-ω interfaces is energetically preferred over the formation of the bulk ω phase. By contrast, the Rice parameter for twin boundaries indicates that these interfaces do not embrittle the material, even with O atoms at twin boundaries, providing a more favorable pathway for plastic deformation compared to ω transformation.

Density functional theory↗
DOE OSTI2025

Reverse martensitic transformation on the corrosion behavior of a 2304 lean duplex stainless steel

This study investigates the influence of the reverse martensitic transformation on the corrosion behavior of 2304 lean duplex stainless steel (LDSS) at low temperatures (400–600°C). The steel underwent cold rolling, followed by isochronal annealing for 0.5 h. Phase transformations and microstructures were analyzed using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Corrosion resistance was evaluated using a syringe cell with cyclic potentiodynamic polarization in 0.6 M NaCl solution and double-loop electrochemical potentiokinetic reactivation in 0.5 M H 2 SO 4 + 0.001 M KSCN solution. A bimodal breakdown potential behavior, associated with either pitting or transpassivity, was observed in the as-received (AR) and samples annealed at 400°C. Pitting in the AR sample was associated with Al–Mg–Si–Ca–O inclusions, while pitting on the samples annealed at 400°C was linked to local reduced passive film resistance. Severe cold rolling enhanced the pitting resistance of 2304 LDSS. Selective electrochemical etching revealed preferential corrosion at phase interfaces and within the austenite phase. Additionally, annealing at 500°C to 600°C led to a marked decrease in pitting resistance and increased sensitization. Electron backscatter diffraction analysis of the corroded pits in the annealed specimens indicated that pits tend to nucleate and grow in α'-martensite/austenite regions. Furthermore, the nanostructure formed during low-temperature reversion, characterized by dislocation-cell martensite with high dislocation density and stacking faults, may adversely affect corrosion resistance due to heterogeneities associated with alloying element redistribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2026

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work MFC 3.0 was made a published, documented, and open-source solver via Bryngelson et al. Comp. Phys. Comm. (2021) with numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. Here, we exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF Frontier and LLNL El Capitan. Combined with MFC’s single-accelerator performance, MFC achieves exascale computation in practice, and achieved the largest-to-date public CFD simulation at 200 trillion grid points as a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, N-fluid phase change, Euler–Euler and Euler–Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF Summit and Frontier and LLNL El Capitan achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-times increase in device count show parallel efficiencies over 90% on OLCF Frontier. MFC’s software stack has undergone further improvements, including continuous integration, which ensures code resilience and correctness through over 300 regression tests; metaprogramming, which reduces code length while maintaining performance portability; and code generation for computing chemical reactions

Computational fluid dynamics↗
DOE OSTI2025

A computational investigation of high-flux, plate-and-frame membrane modules for industrial carbon capture

In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO 2 capture system relies on high-performing membranes as well as effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO 2 capture from industrial flue gas due to its lower pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack through a combination of computational modeling and experimental investigations. The modeling approach is based on Computational Fluid Dynamics (CFD) to represent a multiphysics problem, including the fluid flow and diffusion processes within a membrane module. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest a good agreement between experiments and model outputs for the CO 2 recovery, CO 2 mole fraction in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights on the effects of fluid dynamics on mass transfer of CO 2 . We also carry out a sensitivity analysis to identify the effect of key parameters on the CO 2 recovery and the CO 2 purity of the outlet streams.

CFD simulation↗
DOE OSTI2025

Bioelectrochemical crossbar architecture screening platform for extracellular electron transfer

Electroactive microbes can serve as living components in bioelectronic devices, where their unique ability to transfer electrons enables applications in sensing, energy conversion, and synthesis, but they remain challenging to engineer because the bioelectrochemical systems (BESs) used for characterization are low throughput. Here, we present a bioelectrochemical crossbar architecture screening platform (BiCASP) that uses stacked and orthogonally arrayed electrodes to enable individual sample selection for characterization in arrayed formats. This device reports on the current generated by electroactive bacteria on the minute timescale, decreasing the time for data acquisition by several orders of magnitude compared to conventional BESs. This device increases the throughput of screening engineered biological components in cells, identifying mutants of the membrane protein wire MtrA in Shewanella oneidensis that retain the ability to support extracellular electron transfer (EET). BiCASP may be integrated with bioelectronics that need directed evolution of electroactive proteins.

Shewanella↗
DOE OSTI2025

Graphite crystals in catalytically-graphitized glass-like carbon

Catalytic graphitization of glass-like carbon leads to enhanced growth of micro-sized graphitic crystals with unusual shapes of wires, filaments, tubes, rods, whiskers, and spirals. Similar particles with axial symmetry are also found in pure glass-like carbon heat-treated at high temperatures. Nonetheless, the presence of a graphitization catalyst, Si in this case, in the heat-treatment process supports the transformation of porous, disordered carbon structure towards the graphitic atomic order and the formation of manifold peculiar polyhedral wires and particles of geometry distinct from the plate-like shape typical for conventional graphite. In contrast to conventional carbon nanotubes and fibers, the graphene layers are stacked perpendicular to the tube axis, while the size of the most common tube fibers can reach up to 10 μm in diameter and 100 μm in length. X-ray diffraction, Raman spectroscopy, scanning and transmission electron microscopy, small-angle X-ray scattering combined with complementary techniques have been used to characterize the structure of the glass-like carbon derived from furfuryl alcohol catalytically-graphitized using Si particles at 3000 °C. Finally, since control of graphite shape is vital to achieving the level of performance required in contemporary applications, the obtained results demonstrate that the catalytic graphitization method may be employed to produce filamentous graphite crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

TPCpp-10M: Simulated proton-proton collisions in a time projection chamber for AI foundation models

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.

Data Analysis, Statistics and Probability (physics↗
DOE OSTI2026

Intramolecular redox-site interplay effect on organic electrode for fast-charging and wide-temperature-range sodium-ion batteries

Organic electrode materials (OEMs) hold great promise for sodium-ion batteries (SIBs) due to their exceptional structural tunability and sustainability. However, the development of OEMs with fast redox kinetics and robust structural integrity remains challenging, especially over a wide temperature range. Herein, we propose an effective strategy to address both sluggish redox kinetics and insufficient structural stability in OEMs by constructing an intramolecular redox-site interplay effect. This effect is demonstrated by two hexaazatrinaphthylene-carboxylate isomers, namely HATN-m-COONa and HATN-o-COONa. Systematic experimental and computational results jointly reveal the intramolecular redox-site interplay effect in HATN-o-COONa decreases the rigid π-π stacking interactions and minimizes the skeleton structural distortion, offering faster redox kinetics and enhanced structural integrity in HATN-o-COONa compared to HATN-m-COONa (without intramolecular redox-site interplay effect). Consequently, HATN-o-COONa exhibits superior rate performance (258 mA h g−1 at 10 A g−1) and enhanced cycle stability (93% after 1000 cycles at 5 A g−1) compared to HATN-m-COONa. More importantly, HATN-o-COONa demonstrates exceptional wide-temperature adaptability, ranging from -40 °C (315 mA h g−1 at 0.1 A g−1) to 60 oC (343 mAh g-1 at 5 A g-1). This work establishes a promising design rationale for developing fast-charging and wide-temperature adaptable OEMs for energy storage systems.

Gao, Yawei [ORNL] (ORCID:0000000225672853)↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Oak Ridge Computing Academy: An HPC cluster deployment and management pilot

The High Performance Computing Technologies (HPCT) course is a hands-on High Performance Computing (HPC) cluster deployment and management training program offered as part of the International School for Advanced Studies (SISSA) and the International Center for Theoretical Physics (ICTP) Master in High Performance Computing (MHPC) specialization. Here, this training program introduces students to key concepts in cluster configuration. which include networking, software stack provisioning, job scheduling, and monitoring. The publicly available course materials feature several examples and underlying methods that are broadly applicable to cluster deployment and management. This paper discusses the design of a new workforce development program at the Oak Ridge National Laboratory that is based on HPCT, the Oak Ridge Computing Academy (ORCA). The ORCA pilot program was hosted by the Oak Ridge Leadership Computing Facility (OLCF) in Summer 2025. As a part of this discussion, HPCT and ORCA course contents and infrastructure are outlined, ORCA participant experiences are detailed, and potential opportunities for improvement are discussed.

Education↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2026

In-situ observations of cyclic deformation in an extruded Mg-2Nd-1Y-0.1Zr-0.1Ca alloy

In this study, the evolution of deformation mechanisms during cyclic loading in an extruded, solution-treated Mg–2Nd–1Y–0.1Zr–0.1Ca alloy was investigated using a combination of in-situ loading, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and focused ion beam (FIB) nanofabrication. The initial microstructure exhibited a random crystallographic texture with no preferred grain orientation. Flat, rectangular dog-bone specimens were subjected to load-controlled, fully reversed fatigue for 50 cycles, during which the same region was sequentially mapped to track microstructural changes. After 10 cycles of loading deformation twins were observed. During tensile reloading detwinning or narrowing of those twinned regions occurred. After 20 cycles, detwinning ceased and residual twins remained in the material. SEM imaging revealed numerous surface slip traces after cyclic loading. EBSD-assisted slip trace analysis identified the activation of prismatic and pyramidal < c+a> slip systems during low-cycle fatigue. Site-specific scanning transmission electron microscopy (STEM) further revealed that deformation was also accommodated by basal < a> slip and the dissociation of < c+a> dislocations. Center-of-symmetry (COS) analysis confirmed that the dissociation of < c+a> dislocations resulted in the formation of I₁ intrinsic stacking faults after cyclic loading. These findings provide new insights into the complex interplay of dislocation mechanisms governing fatigue deformation in rare-earth-containing Mg alloys.

Cyclic deformation↗
DOE OSTI2024

Insights from FEED studies for retrofitting existing fossil power plants with carbon capture technology

Recent United States Department of Energy (DOE) sponsored front-end engineering design (FEED) studies for retrofitting existing fossil-fueled power plants with state-of-the-art carbon capture technology contain previously overlooked real-world design considerations for near-term deployment of carbon capture. Insights from examining seven recently published FEED study reports are summarized in this paper. This includes a discussion of the design, performance, and cost implications associated with (1) location-specific considerations such as water availability, land availability, and accessibility; (2) host-plant-specific factors such as flue gas specifications, allowable degree of integration between the capture system and host plant, and operational mode; and (3) miscellaneous factors such as market conditions, permitting requirements, and business case incentives. In conclusion, this manuscript highlights (1) water availability as a key design and cost driver, with host plant steam extraction increasing capture system cooling water availability, (2) modularization and constructability impacts on the number of capture trains, (3) the impacts of host plant operational mode and capacity factor on the business case for installing capture, and (4) the merit of continued research, development, and demonstration efforts addressing steam extraction, host plant tie-in at the stack, solvent reclamation and air emissions control.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗