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DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗

DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

System and technoeconomic analysis of solar thermochemical hydrogen production

Hydrogen is a promising energy carrier that can be obtained from various feedstocks using renewable energy sources. Direct solar thermochemical hydrogen (STCH) production by water splitting can utilize the full spectrum of solar radiation and has the potential to achieve high solar energy conversion efficiencies. Currently STCH research areas focus on material discovery. This paper evaluates the performance of various STCH materials in the context of a system platform to assess techno-economic benefits and gaps in the path to STCH scale-up. Additionally, to analyze the hydrogen production cost, a concentrating solar thermal (CST) system is introduced as a platform for integrating STCH materials and accommodating generalized thermochemical processes. The thermochemical process is based on a two-step STCH cycle using metal oxide that consists of a high temperature step for metal oxide reduction, followed by an oxidation step for water splitting at a lower temperature. A preferred configuration is to have the high temperature step occurring in a directly irradiated solar receiver reactor. To this end, we conceptualized a receiver design and associated solar field layout and investigated STCH operational boundaries, component costs and sensitivity parameters on the $2/kgH 2 goal of hydrogen production. The study explored system-related variables and factors associated with scaling up. The CST platform allows more comprehensive studies that encompass aspects of STCH materials and systems such as cost, hydrogen productivity and replacement frequency, alongside other system components like heliostat field, tower, and potential receiver costs.

08 HYDROGEN↗

DOE OSTI2024

Elucidation of Ce/Zr ratio effects on the physical properties and catalytic performance of CuO x /Ce y Zr 1− y O 2 catalysts

Although cerium oxide (CeO 2 ) is widely used as a catalyst support, its limited defect sites and surface oxygen vacancy/mobility should be improved. The incorporation of zirconium (Zr) in the cerium (Ce) lattice is shown to increase the number of oxygen vacancies and improve catalytic activity. Using a fixed surface density (SD) of copper (∼2.3 Cu atoms per nm 2 ) as a surface species, the role of the support (Ce y Zr 1−y O 2 (y = 1.0, 0.9, 0.6, 0.5, and 0.0)) and defect site effects in the CO oxidation reaction was investigated. Spectroscopic (e.g., Raman, XRD, XPS) and microscopic (e.g., SEM-EDX, HR-TEM) characterization techniques were applied to evaluate the defect sites, crystallite size, lattice parameters, chemical composition, oxidation states of elements and microstructure of the catalysts. Here, the CO oxidation reaction with varied CO : O 2 ratios (1 : 5, 1 : 1, and 1 : 0.5 (stoichiometric)) was used as a model reaction to describe the relationship between the structure and the catalytic performance of each catalyst. Based on the characterization results of Ce y Zr 1−y O 2 materials, the addition of Zr causes physical and chemical changes to the overall material. The inclusion of Zr into the structure of CeO 2 decreased the overall lattice parameter of the catalyst and increased the number of defect sites. The prepared catalysts were able to reach complete CO conversion (∼100%) at low temperature conditions (<200 °C), each showing varied reaction activity. The difference in CO oxidation activity was then analyzed and related to the structure, wherein Cu loading, surface oxygen vacancies, reduction–oxidation ability, CuO x –support interaction and oxygen mobility in the catalyst were the crucial descriptors.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Variations in lithium vapor cave performance predictions due to radial transport and recycling assumptions

The lithium vapor cave is a detached divertor design that uses a single private flux region baffle to contain a dense cloud of lithium vapor to dissipate heat flux. Plasma flows are created via fuel gas puffing in order to minimize lithium contamination of the main plasma. Significant modeling using the 2D edge code SOLPS-ITER has already been performed, predicting that sufficient target heat flux reductions (q$^{max}_{Target}$ MW m −2 ) with ( $n$ Li /$n$ e ) LCFS < 0.05 is possible in a case with 90 MW m −2 unmitigated heat flux in NSTX-U. Low heat flux and low upstream concentration was found with a variety of combinations of divertor geometries, target recycling coefficients, upstream plasma parameters, lithium evaporation locations and deuterium fueling locations, with variations in performance found for each design choice. However, the most universal uncertainty of SOLPS-ITER simulations has until now remained unaddressed systematically, namely the cross-field anomalous particle and heat diffusivities. This article aims to bound the uncertainty in lithium concentration prediction as a result of the assumed deuterium cross-field transport and recycling. For the simulations presented here, a factor of 2.1 increase in the upstream lithium density prediction across a factor of four decrease to the assumed deuterium radial particle diffusivity is found. This result is compared across different assumed deuterium recycling coefficients, known to reduce with lithium injection. Upstream lithium density is found to vary by a factor of 2.4 across a feasible range of recycling coefficients at similar $n$$^{OMP,sep}_{e}$.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Data for Solvent-Free Enzymatic Esterification of Free Fatty Acids with Glycerol for Biodiesel Application: Optimized Using the Taguchi Experimental Method

Presence of free fatty acids along with glycerides poses a technical difficulty for biodiesel production. This work used a Taguchi L9 design to optimize the solvent-free enzymatic process to result in the esterification of oleic acid with glycerol. Under optimal conditions the esterification reaction temperature of 60°C, enzyme dose of 5 wt%, glycerol: oleic acid molar ratio of 5:1, and reaction time of 3 h, a 75.235 ± 2.19% conversion of oleic acid to esters was achieved. With the addition of molecular sieves, the conversion increased to 86.73% ± 1.09%. However, using the parameters predicted by Taguchi design (60°C, 5 wt%, 5:1, and 4.5 h), 88.5% ± 1.11% of oleic acid could be converted to esters derivative. Diglycerides were the major product, and the reaction equilibrium was attained after 4 h. The immobilized enzyme could be used up to seven times with only a 10% reduction in the conversion. Thus, the process can efficiently reduce the free fatty acid content of oil to make it suitable for biodiesel production.

Conversion↗

DOE OSTI2024

Site Occupancy Preference and Magnetic Properties in Nd 2 (Fe,Co) 14 B

Partial replacement of Fe by Co is an effective method to increase Curie temperature (T C ) , which improves the thermal stability of magnetic properties in Nd 2 Fe 14 B-based permanent magnets. The correlation between Fe substitution and magnetic properties has been studied in Nd 2 (Fe,Co) 14 B via a first-principles calculation. The calculated Fe substitution energies indicate that the Co atoms avoid the 8 j 2 site, which agrees with the experiments. The Co atoms are ferromagnetically coupled with Fe sublattice and show magnetic moments of about 1.2 to 1.7 μ B at different crystallographic sites, less than that of Fe (2.1–2.7 μ B ), resulting in the decrease in total magnetization at ground state (0 K) with increasing Co content. The effective exchange interaction parameter, derived from the energy difference between varied magnetic structures, increases from 7.8 meV to 17.0 meV with increasing Co content from x = 0 to x = 14 in Nd 2 Fe 14–x Co x B. This change in the effective exchange interaction parameter is responsible for the enhancement of T C in Nd 2 (Fe,Co) 14 B. The total magnetization at 300 K, derived from mean-field theory, shows a peak maximum value at x = 1 in Nd 2 Fe 14–x Co x B. The phenomenon results from the interplay between the reduction of the magnetic moment in the Fe(Co) sublattice and the enhancement of T C with increasing Co content.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Identifying Critical Electrode Metrics for Efficient, Selective CO 2 Electrochemical Conversion

Low-temperature electrochemical CO 2 reduction (CO 2 R) in zero-gap membrane electrode assembly (MEA) reactors presents a scalable route to fuels and carbon utilization. However, performance at industrially relevant current densities hinges on mesoscale catalyst layer integration, particularly at the ionomer|catalyst interface. Here, we demonstrate a generalizable in situ electrochemical impedance spectroscopy (EIS) method. We utilize this technique to decouple electrode-level parameters that are correlated to the overall MEA performance. By performing this ex situ EIS method on CO 2 -to-CO catalyst-coated membranes with systematically varied ionomer-to-catalyst (I:C) ratios, we reveal a pronounced dependence of performance, ion transport resistance, and catalyst utilization on the I:C ratio as well as the electrode conditioning. We demonstrate that an optimal I:C ratio exists at which ion transport resistance is minimized and Faradaic efficiency for CO production is maximized. Beyond the electrodes examined, here we compare ion transport resistance to MEA selectivity/Faradaic efficiency obtained in prior studies, revealing a clear correlation between the two. These results suggest that ion transport resistance within the catalyst layer may be a quantitative predictor of MEA performance which underscores the importance of mesoscale integration in achieving scalable CO 2 R technologies.

08 HYDROGEN↗

DOE OSTI2020

Nature of the spin resonance mode in CeCoIn 5

Spin-fluctuation-mediated unconventional superconductivity can emerge at the border of magnetism, featuring a superconducting order parameter that changes sign in momentum space. Detection of such a sign-change is experimentally challenging, since most probes are not phase-sensitive. The observation of a spin resonance mode (SRM) from inelastic neutron scattering is often seen as strong phase-sensitive evidence for a sign-changing superconducting order parameter, by assuming the SRM is a spin-excitonic bound state. Here we show that for the heavy fermion superconductor CeCoIn 5 , its SRM defies expectations for a spin-excitonic bound state, and is not a manifestation of sign-changing superconductivity. Instead, the SRM in CeCoIn 5 likely arises from a reduction of damping to a magnon-like mode in the superconducting state, due to its proximity to magnetic quantum criticality. Our findings emphasize the need for more stringent tests of whether SRMs are spin-excitonic, when using their presence to evidence sign-changing superconductivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Information content of higher order galaxy correlation functions

The shapes of galaxy N-point correlation functions can be used as standard rulers to constrain the distance–redshift relationship. The cosmological density fields traced by late-time galaxy formation are initially nearly Gaussian, and hence, all the cosmological information can be extracted from their two-point correlation function. Subsequent non-linear evolution under gravity, as well as halo and then galaxy formation, generates higher order correlation functions. Since the mapping of the initial to the final density field is, on large scales, invertible, it is often claimed that the information content of the initial field’s power spectrum is equal to that of all the higher order functions of the final, non-linear field. This claim implies that reconstruction of the initial density field from the non-linear field renders analysis of higher order correlation functions of the latter superfluous. We show that this claim is false when the N-point functions are used as standard rulers. Constraints available from joint analysis of the two and three-point correlation functions can, in some cases, exceed those offered by the initial power spectrum. We provide a mathematical justification for this claim and demonstrate it using a large suite of N-body simulations. In particular, we show that for the z = 0 real-space matter field in the limit of vanishing shot-noise, taking modes up to k max = 0.2 h Mpc −1 , using the bispectrum alone offers a factor of 2 reduction in the variance on the cosmic distance scale relative to that available from the linear power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

A Redox-Electrodialysis Model with Zero Fitting Parameters: Insights into Process Limitations, Design, and Material Interventions

Redox-Electrodialysis (r-ED) is an electrochemical desalination cell architecture that has recently received considerable interest, due to its low energy demand relative to electrochemical desalination technologies that rely on electrode-based ion removal. To further improve the energy efficiency of r-ED, we developed a lumped mathematical model with no adjustable parameters to investigate the various sources of overpotential within the cell. Existing models of electrodialysis and r-ED cells either do not accurately incorporate all phenomena contributing to the overpotential or utilize empirical fitting parameters. The model developed here indicates that ohmic overpotentials, especially in the diluate chamber, are the most significant contributors to energy losses. Based on this insight, we hypothesized that adding an ion exchange resin wafer in the diluate compartment would increase the ionic conductivity and decrease the energy demand. Experimental results showed an 18% reduction in specific energy use while achieving the same degree of salt removal (20 mM to 12 mM). Furthermore, the resin wafer enabled complete desalination to potable drinking levels at a current density previously unachievable within practical operating voltage limits (4.93 mA cm -2 ). We also expanded the model to explore differences in r-ED energy use between configurations using multiple cells and a single cell with increased area.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Wind Turbine Rotor Design Using High-Fidelity Aerostructural Optimization

Large wind turbines yield more energy but demand careful aeroelastic blade design. Coupled multiphysics design strategies can reduce wind energy costs by exploiting fluid-structure interactions. This work presents the first high-fidelity aerostructural optimization study of a large wind turbine rotor. We use blade-resolved fluid dynamics and structural solvers in a monolithic gradient-based optimization framework to explore steady-state torque and blade mass tradeoffs. The coupled-adjoint approach computes gradients efficiently, enabling the optimization of over 100 structural and geometric parameters simultaneously. Our optimization study modifies a DTU 10 MW benchmark with a simplified structure and isotropic material properties. The tightly coupled optimizations increase torque by 14% while reducing rotor mass by 9% or reduce blade mass by 27% while maintaining torque. Blade-resolved models provide greater design freedom, enabling 5% higher mass reductions than conventional parameterizations at equal torque. This framework paves the way for more detailed high-fidelity optimization studies to complement conventional design approaches.

17 WIND ENERGY↗

DOE OSTI2024

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Technoeconomic Benefits of Film-Forming Amine Products Applied to Steam Surface Condensers

In a conventional Rankine cycle, the majority of power plants employ surface condensers that use pumped cooling water to reject heat from the cycle. In such cases, heat rejection occurs in a shell and tube heat exchanger by film-wise condensation of low-pressure steam on stainless steel, titanium, brass, or copper-nickel tubing. To improve the thermal performance of steam surface condensers, a replenishable film-forming substance (FFS) can be ap-plied to the condenser tubing to promote efficient dropwise condensation. Conventionally, film-forming amine prod-uct (FFAP) coatings protect boiler surfaces from oxidative corrosion, which substantially reduces the operation and maintenance costs. To quantify the technical and economic benefits of FFAP coatings applied to condenser tubing due to the promotion of dropwise condensation, a thermal resistance network model was established. Using a rep-resentative steam surface condenser, the improvements in thermal performance (overall heat transfer coefficient) and process parameters (net plant efficiency, cooling water flowrate, and turbine backpressure) were determined due to the enhancement in the condensation heat transfer coefficient. Experimentally measured condensation heat transfer coefficients for common condenser materials were compared with the modeling results and were found to be within attainable bounds. Lastly, the trend in total heat exchanger cost reduction is generalized to understand the trade-off between reduced surface area for heat rejection and increase in coating application costs for a re-plenishable coating system.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2024

Parameter development and characterization of laser powder directed energy deposition of Nb – Alloy C103 for thin wall geometries

This work focuses on the parameter development and microstructural characterization of Nb-based alloy C103 for thin wall structures produced via laser powder – directed energy deposition (LP – DED). Laser power and scanning speeds were varied as part of a design of experiments to identify adequate print parameters. Combinations were evaluated for relative density, porosity, and geometrical accuracy. A combination of a laser power of 1420 W and scanning speed of 14 mm/s resulted in a relative density >99%, exhibited a consistent weld bead profile and was used for further microstructural evaluation. A mix of optical and scanning electron microscopy (SEM) revealed small, slightly elongated grains along the edges and large epitaxial grains in the central region along the Build – Transverse view. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis revealed Hf rich columnar cells in the center that transition into an evenly spaced cellular structure towards the edges of the built sample. Electron backscatter diffraction (EBSD) scans show a sharp [001] texture when looking at the cross section of the sample along the build direction. The Build – Scan view revealed a zig – zag pattern that follows the back – and – forth deposition strategy that was used. Finally, microhardness measurements were taken in the as – built (AB) and stress relieved (SR) conditions to baseline preliminary mechanical properties. The AB condition exhibited a large amount of scatter in the data and averages up to 11% larger than the SR condition. The reduction in scatter upon applying the SR cycle are indicative of a large concertation of dislocations present in the AB condition.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators

Abstract Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters; this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $$10^3{\times }$$ 10 3 × in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs. The domain of applications include both parabolic and hyperbolic PDEs, regardless of the dimension of full-order models (FOMs).

42 ENGINEERING↗

DOE OSTI2023

Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms

Abstract Operations and maintenance (O&M) of floating offshore wind farms (FOWFs) poses various challenges in terms of greater distances from the shore, harsher weather conditions, and restricted mobility options. Robotic systems have the potential to automate some parts of the O&M leading to continuous feature‐rich data acquisition, operational efficiency, along with health and safety improvements. There remains a gap in assessing the techno‐economic feasibility of robotics in the FOWF sector. This paper investigates the costs and benefits of incorporating robotics into the O&M of a FOWF. A bottom‐up cost model is used to estimate the costs for a proposed multi‐robot platform (MRP). The MRP houses unmanned aerial vehicle (UAV) and remotely operated vehicle (ROV) to conduct the inspection of specific FOWF components. Emphasis is laid on the most conducive O&M activities for robotization and the associated technical and cost aspects. The simulation is conducted in Windfarm Operations and Maintenance cost‐Benefit Analysis Tool (WOMBAT), where the metrics of incurred operational expenditure (OPEX) and the inspection time are calculated and compared with those of a baseline case consisting of crew transfer vessels, rope‐access technicians, and divers. Results show that the MRP can reduce the inspection time incurred, but this reduction has dependency on the efficacy of the robotic system and the associated parameterization e.g., cost elements and the inspection rates. Conversely, the increased MRP day rate results in a higher annualized OPEX. Residual risk is calculated to assess the net benefit of incorporating the MRP. Furthermore, sensitivity analysis is conducted to find the key parameters influencing the OPEX and the inspection time variation. A key output of this work is a robust and realistic framework which can be used for the cost‐benefit assessment of future MRP systems for specific FOWF activities.

17 WIND ENERGY↗

DOE OSTI2023

An experiment-informed discrete element modelling study of knife milling for flexural biomass feedstocks

A discrete element method (DEM) based approach is used to study the relationships between material attributes (MAs), processing parameters (PPs), and quality attributes (QAs) for the knife milling of maize stalks. An approximate DEM shape model was conceptualized based on real maize stalks and calibrated based on experimental bending test data for flexural properties (elastic bending stiffness, elastic bending angle limit, elastoplastic ratio, etc.). DEM simulations of maize stalk comminution in a Jordan Reduction Solutions (“JRS”) knife mill were performed to investigate the relationships between the MAs (maize stalk size and breakage stress limit), PPs (impeller rotational speed), and QAs (mass throughput and output particle size distribution (PSD)). The DEM results suggest that stalk length has little influence on mass throughput and PSD, whilst stalks with larger cross sections tend to generate larger sizes of milled particles given the same breakage stress limit. Both the DEM and experimental results show that faster impeller rotation (or higher power) does not necessarily generate higher throughput or smaller output PSD, especially for maize stalks of higher breakage stress limit. The correlations between these MAs, PPs and QAs are found highly stochastic, though breakage stress limit dictates mass throughput, regardless of stalk size. The DEM-predicted output particle size tended to match the experimental data with coarse PSDs based on sieve size but showed weakened fidelity with finer material, indicating the potential for further model improvement.

09 BIOMASS FUELS↗