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DOE OSTI2020

Hysteretic temperature sensitivity of wetland CH 4 fluxes explained by substrate availability and microbial activity

Methane (CH 4 ) emissions from wetlands are likely increasing and important in global climate change assessments. However, contemporary terrestrial biogeochemical model predictions of CH 4 emissions are very uncertain, at least in part due to prescribed temperature sensitivity of CH 4 production and emission. While statistically consistent apparent CH 4 emission temperature dependencies have been inferred from meta-analyses across microbial to ecosystem scales, year-round ecosystem-scale observations have contradicted that finding. Here, we show that apparent CH 4 emission temperature dependencies inferred from year-round chamber measurements exhibit substantial intra-seasonal variability, suggesting that using static temperature relations to predict CH 4 emissions is mechanistically flawed. Our model results indicate that such intra-seasonal variability is driven by substrate-mediated microbial and abiotic interactions: seasonal cycles in substrate availability favors CH 4 production later in the season, leading to hysteretic temperature sensitivity of CH 4 production and emission. Our findings demonstrate the uncertainty of inferring CH 4 emission or production rates from temperature alone and highlight the need to represent microbial and abiotic interactions in wetland biogeochemical models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Historical Contingency in Microbial Resilience to Hydrologic Perturbations

Development of reliable biogeochemical models requires a mechanistic consideration of microbial interactions with hydrology. Microbial response to and its recovery after hydrologic perturbations (i.e., resilience) is a critical component to understand in this regard, but generally difficult to predict because the impacts of future events can be dependent on the history of perturbations (i.e., historical contingency). Fundamental issues underlying this phenomenon include how microbial resilience to hydrologic perturbations is influenced by historical contingency and how their relationships vary depending on the characteristics of microbial functions. To answer these questions, we considered a simple microbial community composed of two species that redundantly consume a common substrate but specialize in producing distinct products and developed a continuous flow reactor model where the two species grow with trade-offs along the flow rate. Simulations of this model revealed that (1) the history of hydrologic perturbations can lead to the shifts in microbial populations, which consequently affect the community's functional dynamics, and (2) while historical contingency in resilience was consistently predicted for all microbial functions, it was more pronounced for specialized functions, compared to the redundant function. As a signature of historical contingency, our model also predicted the emergence of hysteresis in the transitions across conditions, a critical aspect that can affect transient formation of intermediate compounds in biogeochemistry. This work presents microbial growth traits and their functional redundancy or specialization as fundamental factors that control historical contingencies in resilience.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Materials and Approaches for Mitigation of SOFC Cathode Degradation in SOFC Power Systems

Perovskites such as doped lanthanum manganite and cobaltite have been used successfully as a cathode material in SOFC power systems operating between 700-1000°C. The electrodes, however, remain prone to degradation in presence of Cr containing gas atmosphere under systems operating conditions. Cathode in SOFCs is considered as one of the largest contributors of electrochemical performance degradations, leading to significant increase in ohmic and non-ohmic losses as well as interfacial compound formation. These degradations are mostly irreversible due to permanent changes to chemical and morphological alterations in the electrodes, resulting in the blockage of triple phase boundary (TPB) sites and decrease in oxygen reduction reactions (ORR). Fundamental mechanistic understanding has been developed to elucidate the reaction pathways of electrode poisoning in electronically conducting electrodes (LSM) as well as mixed ionic and electronically conducting electrodes (LSCF). The overall goal of this program has been identification and development of cost effective solutions to mitigate electrode degradations under “real world” systems operating conditions. The overall technological innovations and impact from this work are: The research provides an understanding of the degradation mechanism and cost-effective approaches for implementation in SOFC systems; Develop mitigation processes utilizing low cost getters using conventional synthesis and fabrication route; Develop tailored high surface area powders and coatings for high Cr capture capacity under systems operating conditions; >Developed computational models to understand mechanistic behavior; Experimentally measure rate of Cr evaporation from chromia and alumina forming alloys under SOFC operating conditions; Developed conditions to minimize Cr evaporation from balance-of-plant alloys; This innovation will support the development of getter platform to capture both intrinsic and extrinsic impurities present in air; and, Applications under wide operating temperature range (500-900°C) as well for wide high-temperature electrochemical systems (SOFC, SOEC, and OTM). The work has been successfully developed, validated experimentally and implemented. Successful technology transfer of this knowledge with industrial partners has been accomplished and the getters have been independently tested and validated under their systems operating conditions. The innovation will also find application in related high temperature electrochemical systems such as OTM and SOEC for the prevention of Cr assisted performance degradation. The proposed approach for Cr capture can also be applied to oxy-combustion and other advanced combustion techniques for the reduction of Cr vapor in the exhaust gas stream. This fundamental knowledge gained also has led to the path forward for developing advanced getters for capturing more than one contaminant in air.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

EF-Hand Battle Royale: Hetero-ion Complexation in Lanmodulin

The lanmodulin (LanM) protein has emerged as an effective means for rare earth element (REE) extraction and separation from complex feedstocks without the use of organic solvents. Whereas the binding of LanM to individual REEs has been well characterized, little is known about the thermodynamics of mixed metal binding complexes (i.e., heterogeneous ion complexes), which limits the ability to accurately predict separation performance for a given metal ion mixture. In this paper, we employ the law of mass action to establish a theory of perfect cooperativity for LanM-REE complexation at the two highest-affinity binding sites. The theory is then used to derive an equation that explains the nonintuitive REE binding behavior of LanM, where separation factors for binary pairs of ions vary widely based on the ratio of ions in the aqueous phase, a phenomenon that is distinct from single-ion-binding chemical chelators. We then experimentally validate this theory and perform the first quantitative characterization of LanM complexation with heterogeneous ion pairs using resin-immobilized LanM. Importantly, the resulting homogeneous and heterogeneous constants enable accurate prediction of the equilibrium state of LanM in the presence of mixtures of up to 10 REEs, confirming that the perfect cooperativity model is an accurate mechanistic description of REE complexation by LanM. We further employ the model to simulate separation performance over a range of homogeneous and heterogeneous binding constants, revealing important insights into how mixed binding differentially impacts REE separations based on the relative positioning of the ion pairs within the lanthanide series. In addition to informing REE separation process optimization, these results provide mathematical and experimental insight into competition dynamics in other ubiquitous and medically relevant, cooperative binding proteins, such as calmodulin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

When can we detect lianas from space? Toward a mechanistic understanding of liana‐infested forest optics

Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals—reflectance across wavelengths—from liana‐infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive database, including leaf reflectance spectra from 5424 leaves, fine‐scale airborne reflectance data from 999 liana‐infested canopies, and coarse‐scale satellite reflectance data covering 775 ha of liana‐infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we corroborate with field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and short‐wave infrared regions, linked to lianas' less costly leaf construction compared with trees on average. The existence of a consistent global spectral signal for lianas suggests that large‐scale quantification of liana infestation is feasible. However, because the traits responsible for the liana canopy‐reflectance signal are not exclusive to lianas, accurate large‐scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross‐site patterns also prompt ecological questions about lianas' adaptive similarities in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.

Environmental Sciences & Ecology↗

DOE OSTI2022

Spontaneous Lithiation of Binary Oxides during Epitaxial Growth on LiCoO 2

Epitaxial growth is a powerful tool for synthesizing heterostructures and integrating multiple functionalities. Interfacial mixing can readily occur at temperatures required for complete film crystallization and can significantly modify the properties of layered structures, particularly for those containing energy storage materials with smaller cations. Here we show a two-step sequence involving the growth of an epitaxial LiCoO 2 cathode layer followed by the deposition of a binary transition metal oxide (WO 3 , TiO 2 , and others) in which controlled lithiation of the binary oxide occurs. Orientation-controlled epitaxial synthesis of the model solid-state-electrolyte Li 2 WO 4 and model anode material Li 4 Ti 5 O 12 occurs as WO 3 and TiO 2 nucleate their respective host lattices and attract Li ions from the underlying cathode. The cathode layer readily provides a tunable amount of Li ions for the controlled lithiation of the subsequent layer. We demonstrate that this approach can be used for energy materials discovery and exploring different combinations of epitaxial interfaces that can serve as well-defined model systems for mechanistic studies of energy storage and conversion processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Nanoscale Surface Structure of Nanometer-Thick Ferroelectric BaTiO 3 Films Revealed by Synchrotron X-ray Scanning Tunneling Microscopy: Implications for Catalytic Adsorption Reactions

Ferroelectric nanomaterials are of interest in catalysis, non-volatile memory, and neuromorphic computing among other applications because of their switchable structure that can alter electronic and interface properties of a single material. The investigation of the role of polarization on the surface structure and chemistry of ferroelectric nanomaterials is a longstanding challenge, as it ideally requires a combination of both nanoscale imaging and chemical spectroscopy. In this work we study a model ferroelectric BaTiO 3 thin film by synchrotron X-ray scanning tunneling microscopy (SX-STM), a unique method which integrates nanoscale surface imaging and chemically sensitive spectroscopy. We find that polarization switching from downward to upward in (001)single crystalline BaTiO 3 thin films increases the intensity of X-ray absorption across Ba M, Ti L and O K edges. Chemical mapping of nanometer sized domains further demonstrates the modulation of surface structures upon polarization switching, as well as confirming the trends observed in single point experiments across the surface. Here, we complement these measurements with ab-initio computational absorption spectroscopy to elucidate the effect of polarization switching on the core-hole excitations using the Bethe-Salpeter equation approach. Our experimental and theoretical results thus confirm a stronger binding strength for the upward polarized surface with molecular O 2 as a model reactant, offering mechanistic evidence that supports previous reports. This work advances the understanding of the surface chemistry and electronic structure of ferroelectrics, which can ultimately aid strategies to design interfaces with tailored properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Redox regulation of m 6 A methyltransferase METTL3 in β-cells controls the innate immune response in type 1 diabetes

Type 1 Diabetes (T1D) is characterized by the destruction of pancreatic ß-cells. Several observations have renewed the interest in ß-cell RNA sensors and editors. Here, in this study, we report that N 6 -methyladenosine (m 6 A) is an adaptive ß-cell safeguard mechanism that controls the amplitude and duration of the antiviral innate immune response at T1D onset. m 6 A writer methyltransferase 3 (METTL3) levels increase drastically in ß-cells at T1D onset but rapidly decline with disease progression. m 6 A-sequencing revealed the m 6 A hypermethylation of several key innate immune mediators including OAS1, OAS2, OAS3, and ADAR1 in human islets and EndoC-ßH1 cells at T1D onset. METTL3 silencing enhanced OAS levels by increasing its mRNA stability. Consistently, in vivo gene therapy, to prolong Mettl3 overexpression specifically in ß-cells, delayed diabetes progression in the non-obese diabetic (NOD) mouse model of T1D. Mechanistically, the accumulation of reactive oxygen species blocked upregulation of METTL3 in response to cytokines, while physiological levels of nitric oxide enhanced its expression. Furthermore, for the first time to our knowledge, we report that the cysteines in position C276 and C326 in the zinc finger domains of the METTL3 protein are sensitive to S-nitrosylation (SNO) and are significant for the METTL3-mediated regulation of OAS mRNA stability in human ß-cells. Collectively, we report that m 6 A regulates ß-cells to control the innate immune response during the onset of T1D in humans.

2'-5'-oligoadenylate synthetase↗

DOE OSTI2021

An efficient and durable anode for ammonia protonic ceramic fuel cells

Ammonia protonic ceramic fuel cells (PCFCs) have the potential to be a highly efficient power source with high energy density. However, the inadequate catalytic activity of the existing anodes for utilization of ammonia greatly limits the performance of PCFCs. In this study, we report an Fe-modified state-of-the-art Ni cermet anode with greatly enhanced activity and durability toward utilization of ammonia. Cells with an Fe-decorated Ni-BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3 (Ni–BZCYYb) anode demonstrate an excellent performance, achieving peak power densities of 0.360, 0.723, 1.257, and 1.609 W cm –2 at 550, 600, 650, and 700 °C, respectively, which reveal the highest performance of solid oxide fuel cells fueled on ammonia. In addition, the cells show an excellent durability when operated at a constant current density of 0.5 A cm –2 (or a power density of ~0.435 W cm –2 ) at 650 °C. The superior activity and durability of the Fe-modified Ni/BZCYYb anode are attributed to the alternation of NH 3 adsorption strength and N 2 desorption barrier heights, as confirmed by first-principles based mechanistic and microkinetic modeling. Our research provides a valuable guidance for the development of efficient electro-catalysts for ammonia PCFCs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Stimulation of isoprene emissions and electron transport rates are a key mechanism of thermal tolerance in the tropical species Vismia guianensis

Tropical forests absorb large amounts of atmospheric CO 2 through photosynthesis, but high surface temperatures suppress this absorption while promoting isoprene emissions. While mechanistic isoprene emission models predict a tight coupling to photosynthetic electron transport (ETR) as a function of temperature, direct field observations of these phenomenon are lacking in the tropics and are necessary to assess the impact of a warming climate on global isoprene emissions. In this paper, we demonstrate that in the early successional species Vismia guianensis in the central Amazon, ETR rates increased with temperature in concert with isoprene emissions, even as stomatal conductance ($g _s$) and net photosynthetic carbon fixation ($P_n$) declined. We observed the highest temperatures of continually increasing isoprene emissions yet reported (50°C). While $P_n$ showed an optimum value of 32.6 ± 0.4°C, isoprene emissions, ETR, and the oxidation state of PSII reaction centers (q L ) increased with leaf temperature with strong linear correlations for ETR (ρ = 0.98) and q L (ρ= 0.99) with leaf isoprene emissions. In contrast, other photoprotective mechanisms, such as non-photochemical quenching (NPQ), were not activated at elevated temperatures. Inhibition of isoprenoid biosynthesis repressed $P_n$ at high temperatures through a mechanism that was independent of stomatal closure. While extreme warming will decrease $g_s$ and $P_n$ in tropical species, our observations support a thermal tolerance mechanism where the maintenance of high photosynthetic capacity under extreme warming is assisted by the simultaneous stimulation of ETR and metabolic pathways that consume the direct products of ETR including photorespiration and the biosynthesis of thermoprotective isoprenoids. Our results confirm that models which link isoprene emissions to the rate of ETR hold true in tropical species and provide necessary "ground-truthing" for simulations of the large predicted increases in tropical isoprene emissions with climate warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Atomic-scale polarization switching in wurtzite ferroelectrics

Ferroelectric wurtzites have the potential to revolutionize modern microelectronics because they are easily integrated with multiple mainstream semiconductor platforms. However, the electric fields required to reverse their polarization direction and unlock electronic and optical functions need substantial reduction for operational compatibility with complementary metal-oxide semiconductor (CMOS) electronics. To understand this process, we observed and quantified real-time polarization switching of a representative ferroelectric wurtzite (Al 0.94 B 0.06 N) at the atomic scale with scanning transmission electron microscopy. The analysis revealed a polarization reversal model in which puckered aluminum/boron nitride rings in the wurtzite basal planes gradually flatten and adopt a transient nonpolar geometry. Independent first-principles simulations reveal the details and energetics of the reversal process through an antipolar phase. Furthermore, this model and local mechanistic understanding are a critical initial step for property engineering efforts in this emerging material class.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Isolation and characterization of 24 phages infecting the plant growth-promoting rhizobacterium Klebsiella sp. M5al

Bacteriophages largely impact bacterial communities via lysis, gene transfer, and metabolic reprogramming and thus are increasingly thought to alter nutrient and energy cycling across many of Earth’s ecosystems. However, there are few model systems to mechanistically and quantitatively study phage-bacteria interactions, especially in soil systems. Here, we isolated, sequenced, and genomically characterized 24 novel phages infectingKlebsiellasp. M5al, a plant growth-promoting, nonencapsulated rhizosphere-associated bacterium, and compared many of their features against all 565 sequenced, dsDNAKlebsiellaphage genomes. Taxonomic analyses revealed that theseKlebsiellaphages belong to three known phage families (Autographiviridae,Drexlerviridae, andStraboviridae) and two newly proposed phage families (CandidatusMavericviridaeand Ca.Rivulusviridae). At the phage family level, we found that core genes were often phage-centric proteins, such as structural proteins for the phage head and tail and DNA packaging proteins. In contrast, genes involved in transcription, translation, or hypothetical proteins were commonly not shared or flexible genes. Ecologically, we assessed the phages’ ubiquity in recent large-scale metagenomic datasets, which revealed they were not widespread, as well as a possible direct role in reprogramming specific metabolisms during infection by screening their genomes for phage-encoded auxiliary metabolic genes (AMGs). Even though AMGs are common in the environmental literature, only one of our phage families,Straboviridae, contained AMGs, and the types of AMGs were correlated at the genus level. Host range phenotyping revealed the phages had a wide range of infectivity, infecting between 1–14 of our 22 bacterial strain panel that included pathogenicKlebsiellaandRaoultellastrains. This indicates that not all capsule-independent Klebsiella phages have broad host ranges. Together, these isolates, with corresponding genome, AMG, and host range analyses, help build theKlebsiellamodel system for studying phage-host interactions of rhizosphere-associated bacteria.

Science & Technology - Other Topics↗

DOE OSTI2024

The Release of the Simplified Radionuclide Transport (SRT) Code (V.2.1)

Reactor licensing centers on the protection of the public and environment from the inadvertent release of radioactive material. Therefore, mechanistic source term analysis, or the realistic evaluation of radionuclide transport from the source to the environment for specific transient scenarios, is vital to reactor licensing efforts. Developed to resolve a gap in mechanistic source term modeling capabilities for sodium fast reactors (SFRs), the Simplified Radionuclide Transport (SRT) code created by Argonne National Laboratory (Argonne) is now utilized by advanced reactor vendors, universities, and research institutions to support a multitude of design, licensing, and research efforts. Recently, SRT version 2.1 was released, which includes improvements to code models and verification and validation (V&V) suite to support the SRT user community. The following work provides an overview of the improvements made as part of the release of SRT version 2.1. This effort is supported by the U.S. Department of Energy Office of Nuclear Energy (DOE:NE) Advanced Reactor Technologies (ART) Fast Reactor Program (FRP), as part of the program’s support of national laboratory design and safety analysis computer codes utilized by the fast reactor industry. The expansion of SRT code capabilities and improvements to code V&V associated with version 2.1 are in response to user requests and lessons learned from recent source term analyses performed by Argonne and advanced reactor vendors. They also align with the evolving role of SRT, from research and development tool to software utilized for reactor licensing calculations. The report is structured in alignment with the code improvements, as outlined in Figure 1-1. Section 2 provides background information on SRT, including its history, capabilities, and utilization. Section 3 details new code capabilities as part of version 2.1, while Section 4 focuses on the expansion of the code’s V&V suite. Lastly, Section 5 provides a summary and discussion of next steps.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

The effect of heat treatment on the defect evolution in LPBF 316H stainless steel

This work investigates the effect of processing and heat treatment on defect evolution and irradiation response of laser powder bed fusion (LPBF) 316H stainless steel, with comparisons to LPBF 316L and wrought 316L/316H. Using in-situ and ex-situ ion irradiations across a wide parameter space—temperature (300–675 °C), dose (0.2–25 dpa), dose rate (10 -3 –10 -5 dpa/s), and helium co-implantation (20–2500 appm)—we correlated void swelling, dislocation loop evolution, and segregation behavior with pre-irradiation microstructures (as-built, stress-relieved, solution-annealed, and cold-worked). Results show that swelling is strongly controlled by dislocation density: intermediate densities maximize swelling, while solution annealing or cold working reduce susceptibility to levels comparable to wrought alloys. Low dose rates and helium both promote cavity nucleation and lower the incubation barrier, with helium suppressing the role of dislocation density and driving swelling behavior toward wrought-like response. Loop evolution in SA LPBF 316H resembles wrought 316L but shows localized denuded zones near low-angle grain boundaries. STEM-EDS mapping further revealed Ni segregation at voids and sparse Al-rich oxides, without evidence of Ni–Si precipitates. Collectively, these findings identify dislocation density, helium content, and dose rate as the factors governing swelling in LPBF 316H and provide mechanistic datasets for model validation, supporting LAIN and the qualification of AM austenitic steels for nuclear service.

316 stainless steel↗

DOE OSTI2025

Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide

Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments – including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas – via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes (Creamean et al., 2024, 2020b; https://doi.org/10.5439/1770816).

Creamean, Jessie M. [Colorado State Univ., Fort Co↗

DOE OSTI2022

Digital Twins for Predicting Nitrifier Population and Kinetics

Digital twins can be mechanistic biological process models that when regularly calibrated give an indication of nitrifier kinetics and nitrifier population. Having such information can inform waste rates and dissolved oxygen setpoints for ammonia-based aeration control.

Sparks, Jeff↗

DOE OSTI2020

RELAP-7 Application and Enhancement for FLEX Strategies and ATF Behavior under Extended Loss of AC Power Conditions

This report summarizes the results of a three-year research project sponsored by the U.S. Department of Energy (DOE) Nuclear Energy University Program (NEUP) to enhance and apply the RELAP-7 code by adding and improving several important components (e.g., a mechanistic Reactor Core Isolation Cooling (RCIC) system model) for thermal hydraulic studies of LWRs under ELAP conditions and evaluating the time available for transition to portable FLEX equipment. The project team included University of Massachusetts–Lowell, The Ohio State University, Texas A&M University, Idaho National Laboratory and Oak Ridge National Laboratory. In the Fukushima accident, it was found that the RCIC system played a crucial role in delaying core meltdown by almost three days in Fukushima Daiichi Unit 2, because of self-regulated operation of the steam driven RCIC turbine-pump injection system. Steam flow in the convergent-divergent nozzles of the RCIC Terry turbine is two-phase non-equilibrium transonic flow with homogenous nucleation condensation. To more accurately predict the dynamic process and behavior of the transonic compressible steam flow, a one-dimensional transient two-phase analytical model is presented. A simplified four-fluid model was employed in the present work with the consideration of four separate fluid fields: vapor, liquid film, entrained droplets and condensed droplets. The mass, momentum and energy interactions between the fluids were considered and modeled. An extended seven-equation non-equilibrium critical flow model was developed to obtain the critical pressure and velocities of each phase at the nozzle throat. To predict the wetness in the divergent section, a mechanistic nucleation condensation model was integrated in the nozzle analysis model, considering the generation and consequent growth of droplets. The governing differential equations on a staggered grid were discretized using the second-order Lax-Wendroff scheme with a flux limiter, and the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE) algorithm was employed to solve the discrete linear system. To demonstrate the predictability and reliability of the physical models and the numerical method proposed in the present work, three representative nozzles were modeled and simulated. The results show good agreement with the available experimental data, even for condensation shock. Then, the 1D nozzle model was employed to obtain nozzle flow tables of the Terry turbine nozzle for different working pressures which can cover the operation pressure range of the RCIC system. A mechanistic RCIC turbine-pump system model was developed and implemented in the system code TRACE to simulate dynamic responses of the RCIC system under Beyond Design Basis Accident (BDBA) conditions. The turbine-pump governing equations are based on the control volume approach of the angular momentum balance. The physics based mechanistic RCIC model was developed using the TRACE control system components (i.e., signal variables, control blocks, and tables), and incorporated into a TRACE boiling water reactor (BWR) model. The TRACE model in this report has a detailed nodalization of the reactor pressure vessel (RPV), and all of the major flow paths and system components, including the safety relief valves (SRVs) and the containment suppression pool and drywell. Based on the nozzle flow tables generated from the 1D nozzle model developed, the turbine drive torque can be calculated from table lookup. Since the detailed specifications of the RCIC pump are unavailable, the homologous curves for a Bingham pump were used in the current pump component. A station black-out (SBO) accident test problem was selected to demonstrate the TRACE RCIC model. The short-term SBO simulations were performed for two cladding materials: Zircaloy and FeCrAl, to demonstrate the effect of the accident tolerant fuel cladding on fuel heat-up under BDBA accident conditions. The wetwell plays a vital safety role in SBO and other BWR accident scenarios in that it can reduce containment pressure and supply additional core make-up water. The suppression pool temperature distribution has a very large impact on both RPV and containment pressure. Thus, another novel contribution of the project comes mainly from an improved, systems-level wetwell model which can capture buoyancy-induced thermal stratification effects due to steam injection and condensation. A two-zone stratified wetwell model has been implemented in RELAP-7 and some results from that model are presented. This wetwell model is capable of simulating thermal stratification due to a low steam mass injection rate. With a low mass flow rate, the model assumes that all the steam condenses within the pipe and the resulting plume can be approximated with a purely buoyant, heat-source driven model. The wetwell model developed with these assumptions is adequate to simulate slow transients such as extended SBO transients.

42 ENGINEERING↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗