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DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor

Focused Electron Beam Induced Processing (FEBIP) is a powerful tool for the “direct-write” of nanomaterials with the possibility of atomistic control on suspended 2D material substrates. FEBIP capabilities have been significantly expanded by using a localized jet-based delivery of precursors, and especially when a thermally energized supersonic micro-jet enhances the delivery of mass flux along with controlling the far-from-equilibrium thermodynamic state of adsorbed adatoms. The possibilities of growing nanomaterials with “dialed-in” composition with ultra-high growth rates and aspect ratios up to 100:1 have been demonstrated using the supersonic micro-jet-FEBIP. Bringing this scientific discovery to the level of maturity required for practical applications in additive nanomanufacturing requires simulation tools that are capable of capturing the complex flow physics of micro-jet-substrate interactions that bridge a wide range of flow regimes from the high-density gas micro-jet expanding into the vacuum environment of FEBIP. To address this significant computational challenge, a new approach has been developed and described in this work for a multiscale adaptive DSMC (Direct Simulation Monte Carlo) algorithm to predict the micro-jet gas dynamics in an FEBIP environment, spanning the full range of flow regimes from low Knudsen (Kn) number O(0.01) continuum flow to high Kn of O(10) for the molecular flow within a unified computational framework. Here, the fundamental principles of the adaptive DSMC algorithm are described, its viability as a DSMC technique is demonstrated, and computational improvements for the benchmark cases are discussed in the context of advancing 3D nanofabrication with FEBIP. Ultimately, combining the first principle simulations via adaptive DSMC with the complementary experimental data will enable the creation of the powerful CAD tools for in silico design and optimal operation of micro-jet-FEBIP.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Robust Real-Time Modeling of Distribution Systems with Data-Driven Grid-Wise Observability (Final Technical Report)

The overall objective of this project is to leverage existing and emerging sensor measurements to develop data-driven observability enhancement algorithms as well as robust state estimation and parameter identification techniques to enable real-time grid-wise monitoring and modeling of loads and distributed energy resources (DERs). The project resulted in a holistic framework with the following key components: 1) data-driven and machine learning-based grid-edge visibility enhancement, 2) robust branch-current-based state estimation (BCSE), and 3) robust real-time steady-state and dynamic-state modeling of loads and DERs. The research outcomes have provided utility companies better network visibility, higher-fidelity load/DER models, and more accurate assessment of DERs’ impacts, thus, facilitating the integration of renewable energy sources. The synergistic collaborative project among Iowa State University (ISU), Argonne National Laboratory (ANL), Electric Power Research Center (EPRC), SIEMENS Industry, Alliant Energy, Cedar Falls Utilities (CFU), and Maquoketa Valley Electric Cooperative (MVEC) to leverage the team’s extensive expertise and experience in power distribution systems, state estimation, online modeling and identification, and DER integrations. The proposed frameworks have been verified with industry adopted software and attempted integrated with existing tool wherever possible

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Dominant Role of Hole Transport Pathway in Achieving Record High Photoconductivity in Two‐Dimensional Metal–Organic Frameworks

Abstract Metal–organic frameworks (MOFs) with mobile charges have attracted significant attention due to their potential applications in photoelectric devices, chemical resistance sensors, and catalysis. However, fundamental understanding of the charge transport pathway within the framework and the key properties that determine the performance of conductive MOFs in photoelectric devices remain underexplored. Herein, we report the mechanisms of photoinduced charge transport and electron dynamics in the conductive 2D M−HHTP (M=Cu, Zn or Cu/Zn mixed; HHTP=2,3,6,7,10,11‐hexahydroxytriphenylene) MOFs and their correlation with photoconductivity using the combination of time‐resolved terahertz spectroscopy, optical transient absorption spectroscopy, X‐ray transient absorption spectroscopy, and density functional theory (DFT) calculations. We identify the through‐space hole transport mechanism through the interlayer sheet π–π interaction, where photoinduced hole state resides in HHTP ligand and electronic state is localized at the metal center. Moreover, the photoconductivity of the Cu−HHTP MOF is found to be 65.5 S m −1 , which represents the record high photoconductivity for porous MOF materials based on catecholate ligands.

Wang, Denan↗

DOE OSTI2023

Dominant Role of Hole Transport Pathway in Achieving Record High Photoconductivity in Two‐Dimensional Metal–Organic Frameworks

14 SOLAR ENERGY↗

DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2026

Confinement-Controlled Rearrangements in Dioxolane Upgrading on H-ZSM-5 Revealed by Periodic DFT

Zeolitic Brønsted acid sites catalyze carbocation rearrangements central to upgrading biomass-derived oxygenates. Here we elucidate the mechanism of dioxolane conversion to methyl ethyl ketone and isobutanal on H-ZSM-5 using periodic density functional theory on the MFI model, complemented by ab initio molecular dynamics to probe confinement effects. Dioxolane adsorption at the Brønsted site is followed by protonation-assisted ring opening to form an oxocarbenium intermediate stabilized by the deprotonated framework. From this common intermediate, selectivity is governed by two competing rearrangements, namely, the 1,2-hydride shift with a free-energy barrier of 18.05 kcal mol –1 at 498 K leading toward MEK, and the 1,2-methyl shift with a higher barrier of 25.40 kcal mol –1 leading toward isobutanal. The hydride-shift channel is kinetically preferred over the methyl-shift channel, lowering the isobutanal/MEK ratio below the 3:1 limit expected for equal branching. Adsorption thermodynamics further indicate stronger stabilization of MEK than isobutanal within ZSM-5 channels, suggesting that confinement-controlled binding can bias product distributions in addition to intrinsic rearrangement barriers. These results highlight how Brønsted acidity and pore confinement jointly shape the rearrangement landscape in MFI zeolites.

adsorption↗

DOE OSTI2026

Conformational Isomerization of Imide Anions Governs Solvation and Transport in Water-in-Salt Electrolytes

The behavior of highly concentrated electrolytes departs radically from the dilute-solution theory, yet the molecular origin of this transformation remains unresolved. Here, we identify the conformational isomerization of molecular ions as a decisive, previously unrecognized control parameter governing structure and transport in crowded aqueous electrolytes. Across a series of fluorosulfonimide anions, we show that increasing concentration drives a collective shift from extended transoid to compact cisoid conformers, revealed by small-angle X-ray scattering, vibrational spectroscopy, pulsed-field gradient NMR, and molecular dynamics simulations. This conformational transition triggers a collapse of the hydrogen-bonded water network and the emergence of densely packed ionic domains with confined water, producing a qualitative change in Li+ transport from solvent-mediated diffusion to network-confined hopping. Anion size and asymmetry systematically tune the onset of this transition, demonstrating that molecular geometry dictates mesoscale organization and dynamics in the ion-rich regime. Our results establish ion conformation, not merely composition or coordination, as a fundamental thermodynamic variable in concentrated solutions, providing a chemical framework that unifies solvation structure and transport in water-in-salt electrolytes and suggesting new principles for designing dense ionic media.

Nguyen, Huong TD↗

DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach

Abstract Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li 7- x La 3 Zr 2 O 12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.

25 ENERGY STORAGE↗

DOE OSTI2026

Excitons in van der Waals magnetic materials

Two-dimensional magnetic semiconductors provide a unique platform where long-range magnetic order coexists with strongly bound excitons. Because excitonic states and magnetic moments originate from the same electronic orbitals and couple via intrinsic exchange interactions, optical excitations in these systems exhibit pronounced sensitivity to magnetic order. Recent experiments show unusually strong magneto-optical responses and direct exciton–magnon coupling, establishing new routes for controlling light–matter interactions with spin degrees of freedom. Here, this Review surveys key developments, focusing on representative material systems, experimental signatures, and theoretical frameworks used to describe these phenomena. We conclude with perspectives on how this rapidly evolving field could enable next-generation optoelectronic and quantum technologies leveraging the coupled dynamics of light, charge and spin.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Reaction processes at step edges on S-decorated Cu(111) and Ag(111) surfaces: MD analysis utilizing machine learning derived potentials

We report a variety of complexation, reconstruction, and sulfide formation processes can occur at step edges on the {111} surfaces of coinage metals (M) in the presence of adsorbed S under ultra-high vacuum conditions. Given the cooperative many-atom nature of these reaction processes, Molecular Dynamics (MD) simulation of the associated dynamics is instructive. However, only quite restricted Density Functional Theory (DFT)-level ab initio MD is viable. Thus, for M = Ag and Cu, we instead utilize the DeePMD framework to develop machine-learning derived potentials, retaining near-DFT accuracy for the M–S systems, which should have broad applicability. These potentials are validated by comparison with DFT predictions for various key quantities related to the energetics of S on M(111) surfaces. The potentials are then utilized to perform extensive MD simulations elucidating the above diverse restructuring and reaction processes at step edges. Key observations from MD simulations include the formation of small metal–sulfur complexes, especially MS 2 ; development of a local reconstruction at A-steps featuring an S-decorated {100} motif; and 3D sulfide formation. Additional analysis yields further information on the kinetics for metal–sulfur complex formation, where these complexes can strongly enhance surface mass transport, and on the propensity for sulfide formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Simulation Framework to Support the Deployment of Vanadium Redox Flow Batteries

This work presents a simulation framework to support Vanadium Redox Flow Batteries deployment. First, an electrical equivalent model of a commercial three-stack, 20 kW VRFB is implemented to capture the battery dynamics. Next, a topology of a power electronics-based solution to integrate a VRFB is introduced, and the effectiveness of the control system is evaluated through battery charging and discharging operation under varying conditions. The results provide insights into power and voltage levels, demonstrating that the series-connected architecture can alleviate the voltage boost on the interface power electronics converter. Additionally, efficiency and temperature-critical factors for VRFB system deployment-are also analyzed.

Rezende Da Costa Reis Kimpara, Renata [ORNL] (ORCI↗

DOE OSTI2021

Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts During Natural and Human-made Disaster

Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as "Acute Phase" by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.

Thakur, Gautam Malviya↗

DOE OSTI2020

Coupling of CTF and TRANSFORM using the Functional Mockup Interface

An in-memory coupling between the sub-channel thermal hydraulics code COBRA-TF (CTF), which is included in the Virtual Environment for Reactor Applications (VERA), and the systems code Transient Simulation Framework of Reconfigurable Models (TRANSFORM) was developed in this work. Data exchange is accomplished by using the Functional Mock-Up Interface (FMI), an open standard for coupling dynamic systems models together. The FMI-based coupling necessitated the development of a novel FORTRAN wrapper for communicating with Functional Mock-Up Units. This wrapper will facilitate future FMI-based code couplings with other FORTRAN-based programs. CTF-TRANSFORM is exercised on a simplified Molten Salt Reactor Experiment (MSRE) model in steady-state and transient configurations. The coupled CTF-TRANSFORM model is shown to predict core temperature deltas similar to those available in historical MSRE operational data and is shown to be robust to fast power transients.The coupling paves the way to performing sensitivity and design studies impossible with VERA/CTF alone, in which secondary and tertiary loop operating conditions and design parameters may be perturbed and their impact on the core operating conditions studied.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Decoding Zeolite Crystallization and Stage III in Nuclear Waste Glasses by Coupled Modeling and Experiments

Under specific conditions of pH and temperature, nuclear waste immobilization borosilicate glasses may exhibit a sudden acceleration in their corrosion kinetics (stage III)—a behavior that has been associated with the formation of zeolite crystals. Such accelerated dissolution may compromise the integrity of nuclear wasteforms placed in geological depositories. However, thus far, none of the available models is able to predict the thermodynamic propensity and kinetics of zeolite precipitation as a function of the solution conditions due to (i) a lack of fundamental knowledge regarding the nucleation & growth mechanisms of zeolitic phases, (ii) uncertainty regarding the compositions (types) of zeolites that may form and the rate-limiting step in their precipitation as a function of the solution conditions, and (iii) the complexities that arise due to the vast parametric space (i.e., solution chemistry, temperature, number of secondary phases, etc.) that encompass these systems under conditions of environmental exposure. To resolve these challenges, this project aimed to unambiguously identify the thermodynamic propensity for zeolite precipitation and the kinetics thereof as a function of the solution conditions (composition, pH, and temperature). To achieve this goal: 1) We identified the solution conditions and zeolite phases relevant to nuclear glass dissolution. 2) We performed a series of ab initio molecular dynamics (AIMD) simulations to compute the thermodynamic properties of a group of characteristic zeolites that features a large range of compositions, various hydration levels, a wide range of framework structures, and partial atomic site occupancies. 3) We released a first-of-a-kind self-consistent thermodynamic database that can be used to assess the kinetics and the stability fields of zeolitic phases within a Gibbs energy minimization (GEM) framework. 4) We developed a robust geochemical modeling method allowing us to predict the stability of secondary phases (including zeolites, calcium–silicate–hydrate gels, and clays) upon the dissolution of nuclear waste immobilization glasses. 5) We introduced a model that predicts the dissolution kinetics of a series of borosilicate nuclear waste immobilization glasses in terms of the topology of their atomic network. 6) We investigated the roles of the solution composition on the crystallization kinetics of phillipsite zeolites and tobermorite silicate hydrates. Via PNNL’s collaboration and engagement, this project directly supports DOE’s nuclear waste immobilization activities by offering a technical, science-based foundation that will (i) facilitate predictions of the long-term corrosion rates and extents of existing nuclear waste immobilization glasses to help ensure safe and successful vitrification operations, (ii) inform the development of advanced glass formulations with enhanced durability, and, (iii) enable cost-savings that result from making more decisive and hence less conservative predictions while offering higher levels of nuclear waste embedment in smaller, more compact glass volumes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗

DOE OSTI2025

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

16 TIDAL AND WAVE POWER↗