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DOE OSTI2022

The influence of cooling rate on condensation of iron, aluminum, and uranium oxide nanoparticles

Fundamental observations of particle size distributions are needed to develop models that predict the fate and transport of radioactive materials in the atmosphere following a nuclear incident. The extent of material transport is influenced by the time scales of particle formation processes (e.g., condensation, coagulation). In this study, we investigated the influence of cooling time scales on size distributions of uranium, aluminum, and iron oxide particles that are synthesized separately under identical run conditions inside the controlled environment of an argon plasma flow reactor. Two distinct temperature distributions are imposed along the flow reactor by varying the argon flow rate downstream of the plasma torch. The vaporized reactants of uranium, aluminum, and iron are cooled from about 5000K to 1000K before they are collected on silicon wafers for ex situ scanning electron microscope analysis. The microscope images show that the sizes of the largest aluminum and iron oxide particles heavily depend on the cooling time scales, whereas significant size variation with cooling rate is not observed for uranium oxide particles. In addition, the size distribution of aluminum oxide particles exhibits the broadest range among all three metal oxides studied. We performed simulations of particle size distributions using a kinetic model that couples gas phase oxidation chemistry with particle formation processes, including nucleation, condensation, and coagulation. The model results demonstrate the strong sensitivity of particle size distribution to different cooling histories (i.e., temperature vs residence time) along the flow reactor. In conclusion, the kinetic model also helps identify directions for future research to improve the predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Phase field-volumetric lattice Boltzmann model of ion uptake in porous nuclear waste form materials under continuous flow

The flow field within the mesopores of sorbent particles plays a crucial role in radionuclide diffusion and ion uptake kinetics, thus, impacting the overall performance of porous nuclear waste form materials. To fundamentally understand the influence of microstructures and material properties on the radionuclide absorption and retention processes requires a coupled multi-physics model that considers the advection and diffusion within the flow field, the reaction at liquid-solid interfaces, and finally, the solid-state diffusion within a complex nanoporous medium. Here, this study employs the volumetric lattice Boltzmann method (VLBM) to accurately and efficiently calculate the steady state velocity field inside the mesopores of sorbent particles. The obtained velocity field is then utilized to calculate the advection of ions in the steady flow. A phase field (PF) model of ion uptake is used to describe the reaction occurring at the solid-liquid interface and diffusion inside the porous medium. The integrated PF-VLBM model is verified in terms of the mass conservation and numerical efficiency and validated qualitatively with experimental observation data. Then, it is applied to study the influence of thermodynamic and kinetic properties, as well as flow field conditions on the ion uptake kinetics. The numerical results demonstrate that the ion uptake kinetics in porous particles has three distinct stages, which is in agreement with the observations in continuous flow experiments. In the first stage, the kinetics is predominantly controlled by the flow field and ion diffusivity in the liquid phase. The kinetics in the second stage is primarily governed by ion diffusivity in the solid phase. In the third stage the system reaches a dynamic equilibrium with a net zero uptake flux at the interface. It is also found that porous structures significantly affect the efficiency and capacity of ion uptake. The simulation results can help to understand the physics behind the observed ion uptake kinetics in experiments and to facilitate the development of constitutive equations that can account for heterogeneous microstructures in engineering performance codes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

NRAP-Open-IAM: Open-Source Integrated Assessment Model

NRAP Open IAM is the latest evolution in a series of quantitative models developed under the U.S. Department of Energy’s National Risk Assessment Partnership (NRAP) to address risk assessment, risk management and containment assurance questions of geological carbon storage (GCS). NRAP Open IAM allows a user to define a site specific GCS scenario, characterize important site features, events, and processes (considering uncertainty), couple together reduced order models of various system components to rapidly forecast response to CO 2 injection, and perform stochastic analysis to quantify containment effectiveness, leakage risk, and impact to receptors of concern.

Vasylkivska, Veronika S.↗

DOE OSTI2025

An international study on THM modelling of the full-scale heater experiment at Mont Terri laboratory

We present results from an international model comparison study of the Full-Scale Emplacement (FE) experiment in Opalinus Clay at the Mont Terri Laboratory, Switzerland. Based on a provided parameter set the teams decided which parameters they adopted for their models, whether they considered the excavation and the ventilation phase in addition to the heating phase and if they included technical features like the shotcrete or the EDZ. The teams were able to reproduce the measured parameters temperature, relative humidity and pore pressure. The modelled results for temperature agree very closely between the teams especially in the sensors in Opalinus Clay. All teams were able to reproduce the redistribution of water in the bentonite backfill due to heating. The evolution of the relative humidity showed similar trends with differences in the intensity of the dry out effect. To model the pore pressure evolution is more complex because it comprises the full interaction of the coupled THM processes. The spread between the pore pressure modelled by the teams was larger, with some teams overestimating the pressure increase due to heating and some teams overestimating the extent of drainage. The agreement of modelled results with measurements improves with larger distance to the heater. We conclude that the EDZ and the shotcrete potentially influence the behaviour of the rock causing higher differences closer to the heater. Further research is needed to better implement those influences into the models. Based on the calibrated models, the future evolution of temperature, relative humidity and pore pressure was predicted over the next 10 years following a change of the heat power applied in 2023 and 2024. Again, the predicted temperatures agree very closely between the teams. Most teams do not expect an increase in relative humidity during the next 10 years after the initial dry-out.

58 GEOSCIENCES↗

DOE OSTI2020

Contaminant Transport Modeling for Technology Evaluation and Long-Term Monitoring in the Tims Branch Testbed, SC - 20343

Studies conducted at U.S. DOE sites have shown the presence of heavy metals, radionuclides, and volatile organic compounds in surface water, groundwater, and soil as a result of nuclear activity in the Cold War era. Since the 1990's, innovative cleanup methods have been implemented in the Tims Branch watershed at Savannah River Site (SRS) to limit the contaminant flux to the stream that have reduced the contaminant concentrations to acceptable regulatory levels in the dissolved phase. A tin-based treatment which effectively eliminated all local anthropogenic mercury inputs to this ecosystem resulted in a known step function addition of inert tin oxide particles which now serve as a potential tracer for sedimentation and particle transport processes in the stream. The long-term effectiveness of this and other remediation techniques and the potential for remobilization of adsorbed contaminant in sediment during extreme hydrologic conditions however remains unclear. It is therefore important to understand not only the fate and transport of dissolved contaminants, but also the movement of sediment and the relevant interactions with dissolved contaminant. To narrow this knowledge gap, a study is being conducted using the Tims Branch watershed as a stream-scale ecosystem test-bed to identify the primary transport processes of major contaminants of concern (such as mercury, nickel and uranium) with an emphasis on interactions with sediment transport. This involves the development of a fully distributed hydrologic watershed model of the Tims Branch watershed to predict streamflow under extreme weather conditions, as well as the development of a comprehensive contaminant transport model that can properly account for coupled contaminant and sediment transport. Review of relevant research reports and peer-reviewed journals revealed that aside from advection-dispersion transport of dissolved contaminants, adsorption and desorption with suspended solids and bed sediment also play an important role in the transport of those contaminants of concern. To develop the fully distributed hydrologic watershed model, the MIKE SHE 2-dimensional (2D) land surface/3D groundwater model that simulates surface/subsurface hydrologic processes (such as overland flow, evapotranspiration, and infiltration) was coupled with a 1D streamflow model that accounts for stream water hydraulics (such as hydraulic structures, cross-sections, and network). To model contaminant transport, the MIKE 11 streamflow component was coupled with the MIKE 11 AD module that simulates solute transport through advection and dispersion, and MIKE ECO Lab module that accounts for both sediment transport and interactions with dissolved contaminant. At this stage, the development and optimization of the fully distributed hydrologic model has been completed, achieving satisfactory statistical results between observed and predicted discharge as indicated by a root mean square error (RMSE) of 0.039 cms and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.764. The ongoing development of the contaminant transport model has also yielded realistic results from preliminary tests. Results from this study are a key to evaluating the effectiveness of tin (II)-based mercury treatment of wetlands at the SRS site, and are also relevant to evaluating the potential of using this type of novel remediation technology in other mercury-contaminated stream systems. Knowledge acquired from this research will also support interpretation of historical data on the trends of contaminant concentration distribution in Tims Branch, particularly considering the effect of extreme hydrological events on the stream flow and pollutant transport. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

The electric field assisted sintering (EFAS) process involves tightly coupled physics that influence microstructural evolution in the particles being compacted. It is also an inherently multi-scale phenomenon, with the microstructure of the compact influencing the subsequent engineering-scale response of the sintering system. To improve understanding of how processing parameters influence microstructural evolution, we have developed a multi-scale modeling approach that couples a continuum-level model of the sintering system with a phase-field model for microstructural evolution of particles within the compact. The phase-field model couples the effect of chemical and electrical driving forces on microstructural evolution and includes the effect of charged defect segregation to surfaces and grain boundaries; this segregation leads to enhanced defect transport and heat generation at these interfaces in response to applied electric field. The effect of enhanced heat generation on particle neck growth and the influence of microstructural evolution on the engineering-scale model are demonstrated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Reactive transport modeling of the Aquifer Thermal Energy Storage (ATES) system at Stockton University, New Jersey during seasonal operations

Hydrogeochemical processes associated with Aquifer Thermal Energy Storage (ATES) operations can often impact the system performance owing to mineral precipitation either at the wellbore or in the aquifer owing to changes in temperature and fluid disequilibria. Although failure of ATES systems due to mineral precipitation ("fouling") is common, predictive reactive-transport models have rarely been applied to plan their design and operation. Here, the objective of this study is to develop a reactive-transport model by coupling thermal, hydrological, and chemical (THC) processes to evaluate effects of introduced atmospheric oxygen on water chemistry, mineral precipitation/dissolution, porosity, and permeability changes associated with an ATES system at Stockton University (New Jersey, USA). The THC model builds on a Thermal-Hydrological-Mechanical (THM) model of the site that evaluated system failure owing to possible fracturing in the caprock or around the wellbore. The causes of the system failure are not known – potential causes include hydraulic fracturing owing to elevated pump pressures that took place, a flow pathway created by one of the boreholes, or a pre-existing natural hydrologic connection between the upper unconfined aquifer and the ATES aquifer, any of which could have led to oxygenated water entering the reservoir and causing the observed Fe-oxide fouling on well screens. The THC model is used to evaluate some of the hypotheses and observations regarding system failure owing to geochemical processes. The reactive-transport code TOUGHREACT V4 was used to model the THC processes during seasonal heating and cooling operations at the Stockton ATES site over 6 years of operation. In the THC simulations, the primary effects on geochemistry were observed when the injection water is saturated with atmospheric oxygen. Simulations show greater precipitation of goethite near the cold wells as compared to the warm wells. Although volume fractions of Fe-hydroxides were relatively small, the model was aimed at processes in the aquifer at the scale of meters and larger rather than at the scale of mm or cm (i.e., a well screen). Kaolinite is the dominant precipitating phase, also around the cold wells. Illite dissolves near the cold wells and precipitates near the warm wells. There is a net decrease in the porosity near the cold wells and increase near the warm wells, although a slight amount of thermal contraction near the cold wells and expansion near the warm wells is responsible for a significant proportion of the porosity change. Owing to the coarse discretization of the numerical grid near the wells (compared to the screen thickness) the magnitude of permeability changes at the wellbore are likely underestimated. The reactive transport model in this study can be used for characterization of aquifers, optimizing the operational parameters (temperature, pressure, pH etc.), and planning of mitigation strategies for ATES systems.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Sea Ice and Cloud Processes Mediating Compensation between Atmospheric and Oceanic Meridional Heat Transports across the CMIP6 Preindustrial Control Experiment

Abstract Bjerknes compensation (BJC) refers to the anticorrelation observed between atmospheric and oceanic heat transport (AHT/OHT) variability, particularly on decadal to longer time scales that may be important to the predictability of the climate system. This study investigates the spread in BJC across fully coupled simulations of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and critical processes (particularly related to sea ice and clouds) that may contribute to that spread. BJC on decadal to longer time scales is confirmed across all the simulations evaluated, and it is strongest in the Northern Hemisphere (NH) between 60° and 70°N. At these latitudes, BJC appears to be primarily driven by the exchange of turbulent fluxes (sensible and latent) in the Greenland, Iceland, and Barents Seas. Metrics to break down how sea ice and clouds uniquely modify the radiative balance of the polar atmosphere during anomalous OHT events are presented. These metrics quantify the impacts of sea ice and clouds on surface and top of atmosphere (latent, sensible, longwave, and shortwave radiative) energy fluxes. Cloud responses tend to counter the clear sky impacts over the Marginal Ice Zone (MIZ). It is further shown that the degree of BJC present in a simulation at high latitudes is heavily influenced by the sensitivity of the sea ice to OHT, which is most influential over the MIZ. These results are qualitatively robust across models and explain the intermodel spread in NH BJC in the preindustrial control experiment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Assimilating Scanning Radar Data into High-Resolution Models

This report documents the findings of a project aiming to improve the initial conditions of km-scale simulations of deep convective storms by assimilating cloud-scale weather radar observations. More accurate numerical analyses will increase the effectiveness of research efforts using LES cloud models as tool to better understand land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development. Work reported on herein includes: i) assessment of radar data sets for use in data assimilation (‘DA’) experiments, ii) quality control of the radar data set and format conversion to one acceptable to DA schemes utilized by the Weather Research and Forecasting (WRF) model, and iii) examination of the sensitivity of the numerical representation of cloud-scale wind and microphysical features to a variety of tunable DA parameters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗

DOE OSTI2025

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

Abstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

Feng, Yuan↗

DOE OSTI2026

Model Sensitivities of Biomass-Burning Aerosol Chemical Aging, Sulfate Formation, and Cloud Droplet Activation in the Southeastern Atlantic Using CESM and E3SM

Biomass-burning smoke drives large uncertainty in climate projections of the Earth's radiative balance. This is due to the chemical and physical evolution of smoke and its impact on clouds and radiation. Here we focus on the southeastern Atlantic region and its inflow of African biomass-burning smoke during August 2017. We evaluate smoke properties and processes in two coupled earth-system models, the Energy Exascale Earth System Model (E3SM) and Community Earth System Model (CESM). These are compared against in situ aircraft observations from two field campaigns, ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) and CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017). Observations reveal an increase and subsequent decrease in smoke mean diameter, and a steady decrease in the mass ratio of organic aerosol (OA) to black carbon aerosol (BC) (OA:BC) over 4–12 days of aging, neither captured by the base models. Implementation of a photolytic loss scheme for secondary organic aerosol (SOA)—as a proxy for other heterogeneous volatilization chemistry—and a ∼1-day conversion for primary OA to SOA significantly improves the representation of this loss. In the boundary layer, both models show dimethyl sulfide driving a large increase in the sulfate aerosol mass fraction from the free troposphere, which is consistent with observations. Finally, models tend to underpredict cloud droplet number concentration partially due to weak modeled turbulent updraft strength, and model performance improves when the parameterized turbulent updraft strength is increased substantially. These results are expected to provide insights into future model development to reduce climate model uncertainties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method

Abstract. Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.

58 GEOSCIENCES↗

DOE OSTI2021

Toroidal modeling of runaway avalanche in DIII-D discharges

Abstract A toroidal modeling tool is developed to study the runaway electron (RE) avalanche production process in tokamak plasmas, by coupling the Rosenbluth–Putvinski avalanche model (Rosenbluth and Putvinski 1997 Nucl. Fusion 37 1355) with an n = 0 magneto-hydrodynamic (MHD) solver. Initial value numerical simulations are carried out for two DIII-D discharges with different plasma shapes (one near circular, and the other with high elongation). It is found that, assuming the same level of about 1% seed current level, the Rosenbluth–Putvinski model somewhat underestimates the RE plateau current for the circular-shaped plasma, as compared with that measured in DIII-D experiments. For an elongated, higher current plasma, simulations find strong runaway current avalanche production despite the lack of measured plateau RE current in experiments. A possible reason for this discrepancy is a lack of additional RE dissipation physics in the present two-dimensional model. Systematic scans of the plasma boundary shape, at fixed pre-disruption plasma current, find that the plasma elongation helps to reduce the RE avalanche production, confirming recent results obtained with an analytic model (Fülöp et al 2020 J. Plasma Phys. 86 474860101). The effect of the plasma triangularity (either positive or negative), on the other hand, has a minor effect. On the physics side, the avalanche process involves two competing mechanisms associated with the electric field. On the one hand, a stronger electric field produces a higher instantaneous avalanche growth rate. On the other hand, a fast growing RE current quickly reduces the fraction of the conduction current together with the electric field, and hence a faster dissipation of the poloidal flux. As a final result of these two dynamic processes, the runaway plateau current is not always the largest with the strongest initial electric field. These results lay the foundation for future self-consistent inclusion of the MHD dynamics and the RE amplification process.

Physics↗

DOE OSTI2020

Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain

Over-exploitation of groundwater (GW) in the North China Plain (NCP) since the 1960s has many environmental consequences. However, understanding of the dominant mechanisms remains limited, particularly at regional scale. In this study, the coupled ParFlow.CLM model representing subsurface and land-surface processes and their interactions was applied in the NCP at high spatio-temporal resolutions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the relative impacts of GW pumping and irrigation on the ground surface temperature (GST). Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges roughly between 0.01–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to increase the average WTD and hence, disturb GST, for about 20 years from the pre-pumping WTD in the NCP, before the WTD exceeds 10 m. The variations of GST are expected to be faster initially and gradually slow down due to the nonlinear behaviors of GST with WTD. The findings from this study in the NCP may also have implications for other regions with GW depletion.

Yang, Chen↗

DOE OSTI2022

Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance

Here, this paper is a byproduct of a line of research by the authors to analyze interrelationships of safety and financial performance of nuclear power plants (NPPs). The result of this line of research is summarized in three parts: Part 1 covers a categorical review of relevant literature and the theoretical bases that support the methodological developments in Part 2. Part 2 introduces an Integrated Enterprise Risk Management (I-ERM) methodological framework to quantify the interconnections of safety and financial performance with a focus on operation and maintenance (O&M) of NPPs. Part 2 has also demonstrated the applicability and values of the I-ERM methodology through an NPP case study. This paper is Part 3, where detailed development and implementation of one of the I-ERM modules, i.e., probabilistic physics-of-failure (PPoF) analysis, and its connection with safety and financial performance is reported. In this article, the physical failure modeling for hardware components is advanced by incorporating finite element analysis (FEA) into PPoF analysis and coupling the FEA-based PPoF with the maintenance performance through a renewal process model. This article covers two scientific contributions: (i) first-of-its-kind incorporation of FEA into the PPoF model of thermal fatigue for NPP components; and (ii) advancing the interface between the PPoF analysis and the renewal process model in order to deal with spatiotemporal FEA outputs and to efficiently estimate the physical transition rates even when the PPoF outputs are dominated by success data. Through the incorporation of FEA, the resolution of the PPoF analysis is enhanced as spatiotemporal conditions such as stress and temperature can be considered explicitly instead of relying on simplified assumptions or analytical models with reduced spatiotemporal dimensions. To demonstrate an application of the FEA-based PPoF analysis and its coupling with maintenance through the renewal process model, a case study is conducted using excess letdown elbow piping in the chemical and volume control system of a Pressurized Water Reactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗