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DOE OSTI2022

Introduction to an MCGIDI Mini-App and Performance Comparisons with XSBench

In high-performance Monte Carlo (MC) radiation transport codes, cross section lookups often account for the majority of computational expens. In response to this observation, a number of nuclear data mini-apps have been developed to profile and optimize the lookup process. A review of these mini-apps can be found in Ref. In contrast to a fully fledged MC code, nuclear data mini-apps do not feature particle tracking; instead, the cross section lookup process is considered in isolation, a simplification that allows for easier implementation and evaluation of various lookup schemes and techniques. GIDI+, as described on its public repository1, is ...a collection of C++ libraries for accessing evaluated and processed nuclear data stored in the Generalized Nuclear Database Structure (GNDS). In addition to reading GNDS files, GIDI+ has functions to sum and collapse multi-group data as needed by deterministic transport codes, and to sample GNDS data as needed by Monte Carlo transport codes. Certain modes of Mercury, a LLNL-developed MC radiation transport code, utilize GIDI+ for cross section lookups. Specifically, Mercury uses the MCGIDI library contained within GIDI+ to perform lookups on GPUs. A mini-app exercising MCGIDI’s cross section lookup capabilities would prove useful for optimization, which in turn could improve the performance of client codes such as Mercury. Such a mini-app could also be used to assess the performance of MCGIDI compared to other nuclear data mini-apps. In this document, we introduce a nuclear data mini-app built with the MCGIDI library. The MCGIDI mini-app (MCGIDI-MA) was developed to closely resemble the methodology present in XSBench, a nuclear data mini-app developed at Argonne National Laboratory. The remainder of this section provides an overview on XSBench. Section 2 describes MCGIDI-MA and its usage. Section 3 contains performance results of MCGIDI-MA on LLNL’s Lassen and Quartz compute platforms, as well as comparisons to XSBench performance where appropriate. In Section 4, we present a summary of our findings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Developing novel electrodes with ultralow catalyst loading for high-efficiency hydrogen production in proton exchange membrane electrolyzer cells

Hydrogen plays more crucial roles for decarbonizing the planets and meeting the climate challenges because of its high energy density and zero-emission. It can be produced with proton exchange membrane electrolyzer cells (PEMECs) driven by sustainable and renewable energy resources. Although PEMECs have a number of advantages, including high purity production, quick response, and the ability to operate at high pressure facilitating the gas delivering, their performance and cost greatly hinder their commercial-scale applications. To achieve high-efficiency and cost-reduced hydrogen production in PEMECs, we proposed thin engineered liquid/gas diffusion layers (LGDLs) and associated electrodes, i.e., catalyst-coated LGDLs (CCLGDLs), over conventional porous transport layers (PTLs) and catalyst-coated membranes (CCMs). The research approaches in this project are based on material synthesis, in-situ and ex-situ characterizations, component design and treatment, numerical modeling, and cost analysis. The thin and tunable LGDLs (TT-LGDLs) and CCLGDLs were successfully developed with great performance improvement as demonstrated in lab-scale, bench-scale, and system-scale electrolyzer tests. The electrode thickness was reduced from 370 µm to less than 100 µm with simplified fabrication processes. With the catalytically enhanced Ir-based catalyst coating, the as-developed CCLGDLs with a catalyst loading of 0.34 mg Ir /cm 2 achieved a cell performance of 1.77 V at 2 A cm -2 , exhibiting the catalyst mass activity enhanced by >20 times with significant catalyst saving over conventional catalyst cell design. In-situ PEMEC characterizations, including the current distribution mapping and high-speed and multiscale visualizations, were conducted for a deeper understanding of mass transport and electrochemical reactions within an electrolyzer with LGDLs and CCLGDLs. A 2D cell model was developed and validated for the enhanced performance on TT-LGDL through reducing ohmic losses due to nonuniform hydration and water transport. Further, the cost analysis results have shown a path to move beyond equivalency and surpass costs associated with the project baseline. In this project, the design and fabrication of TT-LGDLs and CCLGDLs will contribute to the performance enhancement, manufacturing simplification, and cost reduction for PEMECs and other energy conversion devices, thus shortening their pathways towards commercialization. This project also provides a good foundation for furthering the in-situ reaction interface research.

08 HYDROGEN↗
DOE OSTI2022

Arctic Shrub Expansion, Plant Functional Trait Variation, and Effects on Belowground Carbon Cycling (Final Technical Report)

Terrestrial ecosystems are undergoing dramatic changes in response to climate warming, and these changes are expected to feedback to the atmosphere, potentially altering the trajectory of future climate change. Feedbacks from Arctic ecosystems are a major concern because the Arctic is projected to warm significantly in the 21 st century and because >50% of global belowground organic carbon is stored in permafrost and overlying soils. Warming-driven release of this carbon could drastically increase atmospheric greenhouse gas concentrations and accelerate climate warming. Plant communities are also responding to warming, as evidenced by the widely documented increase in woody-shrub growth and “greening” across much of the Arctic tundra biome. This vegetation shift may offset or amplify warming by altering carbon cycling. The direction and magnitude of shrub effects remain highly uncertain, however, due to limited understanding of the consequences of shrub expansion for belowground carbon cycling and simplification of these relationships in models. The major shrubs expanding in the Arctic (Betula, Salix, and Alnus) vary widely with respect to aboveground and belowground traits (e.g., tissue production and chemistry, rooting depth, microbial symbionts), and may also exhibit substantial intraspecific variation in these traits in response to environmental conditions. Such variation is likely to have profound implications for soil carbon cycling. The overarching goal of this project was to improve process-based understanding of the influence of shrub expansion on carbon cycling to enable improved representation of carbon dynamics in ecosystem and Earth system models. We investigated how plant functional traits vary among shrub genera, respond to environmental conditions, and affect belowground carbon and nutrient cycling by quantifying relationships among functional traits and biogeochemical cycling along edaphic gradients nested within a climate gradient in the Alaskan tundra. We found consistent differences in leaf and root traits among shrub genera and between shrubs and a widespread sedge species, indicating diverse nutrient acquisition strategies and belowground impacts among different arctic shrubs. We also found striking differences in trait values among individuals within the same species or genera within sites. Soil parameters were more important than climate parameters for predicting size and leaf trait variation, and root trait responses were less dependent on climate overall. For all but one root trait, including parameters representing aboveground traits improved the predictive ability of models. These results demonstrate that tundra shrub traits vary considerably at local scales and soil factors drive this variation, especially belowground. Furthermore, leveraging information about aboveground traits and soil conditions can improve predictions of how belowground traits will respond to climate change. Despite these differences, soil carbon and nitrogen pools in the active layer did not vary among plots dominated by different shrub or sedge genera. Instead, pool sizes generally decreased from warmer to colder sites, consistent with a productivity gradient. Patterns of isotopic N composition indicate that shrubs tighten nitrogen cycling via nitrogen resorption or immobilization of shrub litter. Overall, these results suggest that further identifying the specific shrub genera in the tundra landscape will ultimately provide better predictions of belowground dynamics across the changing arctic. We also performed simulation experiments with the Terrestrial Ecosystem Model (TEM) incorporated in the Predictive Ecosystem Analyzer (PEcAn) framework, treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We performed simulations across different types of tundra, including shrub tundra. One key finding was that both model sensitivity and uncertainty to a given parameter could vary within the same type of tundra, but in a different geographical location, such as over the climate gradient of shrub tundra described above. We organized a special session at the annual meeting of the Ecological Society of America in August 2019 to disseminate our results, refine recommendations for model improvement, and initiate collaborations to implement these recommendations in existing models of tundra carbon dynamics at ecosystem to Earth system scales. Our results support DOE near-term priorities by providing mechanistic insights into the role of vegetation change in the terrestrial carbon cycle in a region that is inadequately represented in Earth system models. Current models reduce the complexity of Arctic vegetation to a small number of plant functional types (PFTs). This approach implicitly assumes that each PFT represents the average ecological function of its constituent species, thus ignoring the effects of trait variation on biogeochemical cycling and potentially leading to large uncertainty in the sign and magnitude of ecosystem feedbacks to climate. By quantifying variation of plant functional traits across broad gradients of climatic and edaphic conditions and elucidating the linkages of such variation with carbon and nutrient cycling, our results illustrate the need and create a foundation for further developing trait-based modeling approaches that allow the traits of PFTs to vary as a function of environmental conditions. These approaches should improve the capacity of simulation models to offer insights into ecosystem carbon dynamics associated with novel plant communities in a rapidly changing Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High Frequency Transverse Instabilities in Low NOx Gas Turbines

This program addresses the topic of combustion instabilities under the goal of developing “Low-NOx Combustion Technology for ‘Air-Breathing’ Advanced Turbines”. With recent advances in high-temperature withstanding materials, the push towards higher efficiency has resulted in efforts to design high temperature, low NOx gas turbines. The development of such low NOx systems is often plagued with combustion instabilities. These instabilities manifest with acoustic modes that are longitudinal, transverse or both. A large body of literature and work exists addressing the underlying mechanisms and models for longitudinal instabilities and these have been successfully implemented in the design tools for low NOx gas turbines. The flame response aspect of the thermoacoustic feedback loop was often modeled in an acoustically compact framework since for low frequencies, the flame length scale was small compared to the acoustic wavelength. This enabled simplification where the modeling focused only on the overall flame response and not the local spatial distribution of the flame response. In addition, additional mechanisms such as the direct effect of pressure fluctuations on the flame response could be neglected. In contrast, high frequency instabilities which often are transverse in nature, have received relatively less attention in the literature. The flame is no longer acoustically compact and thus the local distribution of heat release must be accurately understood. The flame responds to velocity fluctuations, equivalence ratio fluctuations as before, but also to pressure fluctuations through kinetic effects. These aspects of high-frequency instabilities make it a challenging problem that requires a detailed elucidation of the underlying mechanisms and creating models for them. In this program we significantly improved the understanding of these instabilities through a combination of experiments (informed by interactions with OEMs), modeling of measured data and reduced order modeling of flame response. The results from the program can be used as basis for robust design tools that are essential for successfully developing gas turbines that operate in an environmentally acceptable manner with mitigation strategies for high-frequency instabilities.

08 HYDROGEN↗
DOE OSTI2022

Post-Marcus Theory and Simulation of Interfacial Charge Transfer Dynamics in Organic Semiconducting Materials (Final Report)

The research program developed, validated and applies predictive computational protocols for calculating charge transfer (CT) rates in complex molecular systems, including molecular dyads and triads in liquid solution and solid-state organic semiconducting (OSC) materials. We have established a transformative computational scheme that goes beyond widely used simplifications, to achieve realistic descriptions of CT processes. The approach properly addresses the contribution of molecular environment at ambient conditions to CT processes. Our approach achieves unique insight on investigated CT processes in relevant experimental efforts. The collaborative team included three principal investigators (PIs), with complimentary expertise in classical molecular dynamics simulations and data science (Cheung), state-of-the-art electronic structure calculations (Dunietz), and cutting-edge theory and simulation techniques for modeling energy, charge and coherence transfer dynamics in molecular systems (Geva).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Materials Science of the Interstitial Doping Process

Particle accelerators are an increasingly important tool for frontier science. Growing initial and operating costs are a significant barrier for upgrades and for new machines. While everything matters, the major cost contributor is the SRF cavities and their ancillary facilities (e.g., cryoplant). Accordingly, the accelerator science community devotes much R&D effort to improving their energy efficiency (increased Q o ) and gradient (E acc ). While improved gradient is at the forefront for certain machines (ILC), improved quality factor has broader impact, benefitting all SRF applications. An important opportunity for accelerator science and technology to move forward arose in the course of building the LCLS-II, the second generation Linac Coherent Light Source at SLAC. At more or less the same time, researchers at Fermilab discovered that introducing a small amount of nitrogen to the niobium surface could improve the mid-range quality factor as much as three-fold. A firm resolution of how nitrogen confers its benefit attracts much current research interest. The key elements of the “nitrogen doping” process were vacuum bake at 800 °C, brief exposure to mTorr of nitrogen at several hundred degrees followed by electropolish (EP) to remove several microns from the surface to eliminate unwanted nitrides: While the process as a whole was novel, the comprising unit operations are familiar to the accelerator community. It was judged reasonable to adopt it as a cost-reduction technology for LCLS-II. Researchers carried out a program of varying process parameters and measuring performance in single-cell cavities, leading to a consensus stable protocol for the project. The transition to vendor fabrication and multiple niobium sources has presented unforeseen challenges of performance variation evidently not connected to anything that could be incorporated in a purchase specification. Moving beyond the high temperature N-doping process above, researchers reported a simplified process consisting entirely of tens of hours anneal in a N atmosphere at low temperatures (~120°C – 160°C) after 800 °C UHV bake. These processes typically yield a few-nm doped layer while the high temperature process yields at least a few to many micron doped layer. Even more recently, oxygen has been used to dope instead of nitrogen which leaves a few-µm O-alloyed layer upon vacuum annealing for 300 °C for ~3 hours. The investigation of these materials is just beginning, but the process simplification they may offer is surely attractive. The very low quantity of material that appears to be significant in the “infusion” process indicates the need for very careful control of gas species available for diffusion into the surface during low temperature treatment, both for process control and research to characterize the underlying dynamics. Oxygen alloying offers the further opportunity to utilize the decomposition of the surface native oxide as the dopant source. It is necessary to understand and (thus) manage this process. We have been supported by the Department of Energy Offices of High Energy Physics and Nuclear Physics to pursue this goal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Primer on Nuclear "Recoil" Data

For many years the ENDF-6 format has existed to contain evaluated nuclear data. One primary driver for the format and the data is neutron transport calculations. Evaluated data can be processed by a code like NJOY into either continuous energy form (ACE format) for Monte Carlo codes like MCNP or into multi-group form (NDI tables) for deterministic codes like Partisn. The reaction cross sections are found in the MF 3 section of the ENDF-6 format. If the reaction produces one or more neutrons as outputs, then secondary neutron data must also be given in MF 4,5 or 6 sections of the format. Energy and angular distributions for the output neutrons must be given in one of several available formats. The most general formats are found in MF 6. MF 4 is for angular distributions, MF 5 is for Energy distributions, and MF 6 contains both. In recent years, more interest has developed in the other output particles (i.e., the “recoil” particles) from neutron induced reactions. This has been driven by interest in charged particle transport and in more specialized partial kermas. ( A separate total neutron kerma has been available for a long time.) Partial kermas are a breakdown of the Kinetic Energy Released into the MAterial by output particle or by neutron reaction. The sum of the partial kermas should be equal to the total kerma on a group-wise basis. The ENDF-6 format is general enough for these new data requirements. Ideally, evaluated data would exist for every output particle (including the secondary neutrons) from every neutron-induced reaction. In the MF 6 format section, data for multi-particle outputs may be entered using the LAW =1 option. This option explicitly allows energy and angular output distributions for each particle produced in the reaction. Such information should preserve the balance between partial kermas and the total kerma at the groupwise level as well as the individual particle averaged energies. For simpler 2-body reactions, LAW = 2 is available. This allows the evaluation to specify only enough data to specify the 2-body reaction fully. Such a simplification does not exist for 3 (or more) body breakups. A simplified form of LAW 2, i.e., LAW 3, also exists to generate approximate recoil output distributions in the absence of full data. However, evaluated data does not generally exist at this fine granularity for reactions involving more than 2 output particles ( e.g., the 3-body break-up reaction). When the multi-body detailed output distribution data is not available, LAW=6 may be employed in NJOY. LAW 6 produces approximate output distributions for all the particles produced in the reaction. Just like in the 2-body case, the smaller particles will generally carry off more energy. At any particular energy, the balance between the sum of the partial kermas and the total kerma will not necessarily be preserved. However, the average energy of each output particle from a reaction is preserved.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Composite PEMs from Electrospun Crosslinkable Poly(Phenylene Sulfonic Acid)s (Final Technical Report)

The main objective of this project is to fabricate and demonstrate superior fuel cell performance of an electrospun composite all-hydrocarbon, non-PFSA membrane. The project builds on two earlier, DOE-funded studies demonstrating high proton conductivity, even at very low humidity of crosslinkable poly(phenylenesulfonic acid), cPPSA, solution cast films, and high durability of electrospun perfluorosulfonic acid (PFSA) composite membranes. Most project milestones were fully met but the best membrane conductivity and fuel cell performance were obtained with membranes that did not contain electrospun sulfonated polyphenylene, as initially planned. Instead, an excellent performance was achieved with membranes fabricated by pore-filling, either with electrospun poly(phenyl sulfone) scaffolds or with commercial, expanded polytetrafluoroethylene scaffolds. Fuel cell testing demonstrated performance better than that of Nafion 211 or Nafion XL, particularly at lower cell humidification levels with significantly lower hydrogen crossover. No accelerated stress testing was performed, due to the initial numerous difficulties with controlling the copolymerization reaction, which took excessive amount of time to overcome. Potential scale-up of the proposed composite membrane technology can aid in boosting the fuel cell power output and simplification of the cell hydration system.

08 HYDROGEN↗
DOE OSTI2024

The Compelling Need for a Mid-Scale Stellarator Facility

In the pursuit of the goal of commercial fusion as an abundant and safe source of energy, the stellarator is a leading concept with compelling attractiveness and demonstrated performance. A new mid-size stellarator is needed to retire risks and innovate towards a high performance, economically attractive, stellarator Fusion Pilot Plant. In this presentation we, as a community of US researchers from Universities, National Laboratories, and Private Industry, involved in studying the stellarator concept, lay out the programmatic and technical motivation for a new and modern mid-size stellarator research facility. A new mid-scale stellarator is needed to realize the potential predicted by a solid body of theory and simulation along with advances in computational tools for optimization and non-linear turbulence modeling. Notably, it is possible to combine the advantages of the stellarator (steady state, no current drive, no disruptions) with the good confinement regularly achieved in tokamaks. The top priorities for experimental work, and the motivation for a mid-scale stellarator experiment are: turbulence control, non-resonant divertor, MHD stability at large beta, confinement of fast particles, and coil simplification. A new mid-size quasi-symmetric stellarator, built as a user facility, would complement existing research at Wendelstein 7-X and LHD and strongly augment private industry. It would provide a program of innovative research, concept validation, theoretical advancement, and workforce development. Growing support and interest for stellarators by the fusion community and private industry affirms this rationale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Bench-Scale Testing of Monolithic PPI Structured Contactors for Direct Air Capture of CO 2

The overall project objective was to develop, optimize and bench-scale test the integrated embodiment of a leading direct air capture (DAC) sorbent composition, linear-poly(propylenimine) (l-PPI), in a structured material system, specifically a monolithic contactor for achieving low pressure drop, to justify its further scale-up in a subsequent program. The project team was led by CORMETECH, a leading monolithic gas/solid contactor manufacturer for environmental applications, and included Global Thermostat, a well-known DAC start-up, and Georgia Tech, a prominent US academic institution in carbon capture and DAC. The monolithic contactor for l-PPI in this project employed an advanced, cost-effective fabrication technique from CORMETECH, namely a porous monolith substrate directly impregnated with the amine, an approach contrasting to the traditional wash-coating methods used for activating monoliths. The l-PPI monolithic contractor is targeted for use in DAC systems such as the Global Thermostat DAC process, which involves the cyclic operation of ambient air flow over a monolithic amine contactor followed by steam-mediated thermal desorption and CO 2 collection, maximizing volumetric productivity while reducing the auxiliary power required to capture CO 2 from air. The Global Thermostat DAC process currently utilizes poly(ethyleneimine) (PEI) materials as baseline sorbents, which are subject to oxidative degradation and slow capacity fade at the elevated temperature required for efficient CO 2 removal. Georgia Tech, with Global Thermostat, had discovered in lab-scale efforts that l-PPI had superior resistance to oxidative degradation compared to PEI, with similar CO 2 adsorption capacity. While the novel l-PPI sorbent offers significant advantages over PEI for DAC, including potential design simplification and increased process efficiency, it is not commercially available, and prior to this project, had not yet been evaluated on the bench-scale in a structured contactor.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DECOVALEX-2023: Task F1 Final Report

DECOVALEX-2023 Task F is a comparison of models and methods for post-closure performance assessment (PA) of a deep geologic repository for radioactive waste. The general aims of Task F are to build confidence in the models, methods, and software used for PA and to stimulate additional research and development in PA methodologies. The task objectives are to motivate development of PA modelling skills and capabilities, to examine the influence of model choices on calculated repository performance, and to compare the uncertainties introduced by model choices to other sources of uncertainty. Task F involves no actual experiment or site. It is a PA modelling exercise that requires the conceptual development of hypothetical repository designs and geologic settings. Because three of the teams were interested in salt and the rest of the teams were interested in crystalline rock, Task F was split into two branches: Task F1 for crystalline rock and Task F2 for salt. This report is for Task F1, crystalline rock. Teams from seven countries (Canada, Czech Republic, Germany, Korea, Sweden, Taiwan, and United States) participated in Task F1. The teams worked together to define the features, events, and processes of the reference case repository and established a set of performance measures. In addition, they defined a set of benchmark problems designed to test and compare modelling capabilities for fracture flow and transport at different scales. The repository design and benchmark problems are documented in a Task Specification that evolved over time as the group honed the specifications. The benchmark problems verified that each team can aptly model flow and transport in fractured media in 1-, 2-, and 3-dimensions. Two general approaches were used for the 3-dimensional benchmarks: discrete fracture network (DFN) and equivalent continuous porous medium (ECPM). DFN modelling involves explicit meshing of each fracture while ECPM modelling aims to capture the effective porosity and directional permeability of each cell in a space-filling mesh as affected by intersecting fractures. In some models, a combination of the two is used, i.e., DFN for large known fractures and ECPM for the rest of the domain. Transport is solved by using either the advection-dispersion equation or particle tracking. Although some variation is observed among model breakthrough curves in the benchmark problems, there is strong agreement in breakthrough behaviour up to at least the 75 th percentile for all benchmarks. At the 90 th percentile, breakthrough results show larger differences, suggesting several models retain substantially higher fractions of tracer in regions of slower moving water. In addition to the flow and transport benchmarks, several teams completed the source term benchmark, verifying capabilities for modelling radionuclide decay and ingrowth, waste package breach, instant release fractions, fuel matrix degradation rates, and radionuclide solubility limitations. The reference case is conceptualized as a generic spent fuel repository at a depth of 450 m in fractured crystalline rock. The repository has 50 parallel backfilled drifts, each with 50 deposition holes 6 m apart. Each deposition hole contains a 4-PWR waste package and bentonite buffer. The rock domain is 5 km in length, 2 km in width, and 1 km in depth. It has 6 deterministic fractured deformation zones and a multitude of stochastic fractures. Teams generally used the ECPM approach for the entire rock or a hybrid approach in which the deterministic fracture zones are modelled with a DFN and the rest of the rock is modelled by ECPM. Of the reference case problems specified, only the results of the initial reference case problem are compared in this report. The initial problem focuses on transport from the deposition holes to the surface, i.e., it neglects waste package performance. Tracers are released at all waste package locations at time zero and tracked for their releases to the near field and ground surface. The water fluxes calculated at the ground surface entry and exit regions of the domain are similar for all models except for two that have considerably lower fluxes. For tracer transport, large differences are observed among models in the magnitude of tracer transported. Much of the difference appears to be due to how the repository is implemented and hence the different degrees of repository simplification. Models that exclude the drifts, buffer, and backfill from the domain tend to show greater release of tracers and radionuclides from the repository. The initial study presented here indicates that major differences in modelling important processes within the repository (e.g., diffusion through buffer and backfill) can produce broadly different release and transport results, especially when those processes are excluded. Even for the models that included all specified features, events, and processes, the results show significant differences and demonstrate the importance of examining multiple modelling approaches in performance assessment. The differences in results observed in this study are expected to motivate teams to either increase complexity in future versions of the reference case models or to improve methods to account for the effects of simplified features and processes. Either way, future improvements in these models are expected to produce results that more closely agree.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Novel Carbon Storage Monitoring Methods

Conference presentation at American Institute of Chemical Engineers (AIChE) Annual Meeting, San Diego, California, October 27–31, 2024. We presented a high-level overview of many novel and sustainable carbon storage monitoring methods that are at various stages of planning or demonstration to accelerate the deployment of CCS technologies at future CCS sites across the United States. Our work impacts the general CCS industry by providing novel and more sustainable methods for tracking CO 2 plume migration and performing assurance monitoring. Our work primarily benefits three parties: 1) CCS community members through knowledge sharing of lessons learned; 2) CCS operators through commercialization of additional methods, including improvements on workflows and simplification of fieldwork; and 3) stakeholders of CCS projects through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗
DOE OSTI2025

Recent Collaborations and Innovations to Demonstrate Next-Generation Techniques for Monitoring Subsurface Carbon Storage

World Carbon Capture, Utilization, and Storage (CCUS) Conference, Bergen, Norway, September 1–4, 2025. This talk provides a high-level overview of many novel and sustainable carbon storage-monitoring methods to accelerate the deployment of CCUS technologies at future CCUS sites across the United States. The Energy & Environmental Research Center’s work impacts the general CCUS industry by providing novel low-impact methods for tracking the injected plume’s migration and more autonomous data collection and processing techniques for performing assurance monitoring. Specifically, the results benefit 1) CCUS community members through knowledge sharing of lessons learned; 2) CCUS operators through commercialization of additional methods, including improvements to workflows and simplification of fieldwork; and 3) CCUS project stakeholders through implementation of low-impact and more autonomous monitoring solutions.

02 PETROLEUM↗
DOE OSTI2025

Modeling moisture outgassing from silica-filled silicones with the generalized outgassing kinetics

Even though a generalized moisture outgassing modeling has been established and published in a peer-reviewed venue for as-received silica-filled silicones, it is still not yet intuitive for use in engineering/scientific applications. A simplification of the mathematics down to a practical level and illustration of how to do it would be beneficial to the practitioners in the field. In this report, simplified and more intuitive demonstrations of how to use the generalized outgassing kinetics for the prediction of H 2 O release from as-received and previously vacuum-baked silicones will be illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Manufacturing of Cathode Materials for Lithium-ion Batteries

Original objectives (per the Statement of Project Objectives) centered on developing and translating a continuous pilot process for cathode materials production while targeting (i) a 29% manufacturing cost reduction, (ii) an 82% energy consumption reduction for cathode production, and (iii) a doubling of manufacturing productivity. Planned validation included characterization at appropriate analytical facilities and electrochemical performance evaluation progressing from half-cells to pouch-cell formats, with a pathway to kilogram-scale production of finished cathode materials for representative testing. Scope evolution: During execution, the project team identified that the surfactant-enabled chemistry offered high selectivity for lithium precipitation from brine sources. A formal scope revision shifted the final project year from cathode manufacturing development to direct lithium extraction/lithium recovery, while maintaining the overarching intent of cost reduction and process simplification relevant to the battery supply chain.

25 ENERGY STORAGE↗