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DOE OSTI2019

Iodine Distribution During Evaporation of Hanford Waste Treatment Plant Direct Feed Low Activity Waste Effluent Management Facility Simulant

The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream from the off-gas system. The baseline plan for disposition of this stream during full WTP operations is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation, and recycled to the LAW vitrification facility. However, during the Direct Feed LAW (DFLAW) scenario, planned disposition of this stream involves concentrating the condensate in a new evaporator at the Effluent Management Facility (EMF) and returning it to the LAW melter. The LAW melter condensate stream will contain components, e.g. halides and sulfates, that are volatile at melter temperatures, have limited solubility in glass waste forms, and present a material corrosion concern. Further, some minor constituents in the melter condensate, such as volatile organic compounds and iodine, have been found to be at least partially volatile in the EMF. The condensate from the EMF will be sent to the Hanford Effluent Treatment Facility (ETF). The presence of some recalcitrant organics and iodine, as 129I, are expected to be problematic in the ETF. In order to better predict and prepare for the distribution of organics and 129I in the flowsheet, it is key to understand their chemistry and experimentally determine their behavior during evaporation. To do this, modeling and testing were performed to examine the speciation of iodine under relevant conditions, as well as determine partitioning of iodine and the organics in the EMF evaporator. This overall program examines the potential treatment and immobilization of the LAW melter condensate stream to enable alternative disposal. The objective of this task was to (1) perform modeling to determine the speciation and fate of iodine species, (2) prepare a simulant of the LAW Melter Off-gas Condensate expected during DFLAW operations, (3) spike in the key volatile organics and non-radioactive iodine, and (4) demonstrate evaporation in order to predict the final composition of the effluents from the EMF evaporator to aid in planning for their disposition. The results of the modeling of iodine speciation indicate that the iodine is expected to remain in the evaporator pot as iodide and possibly iodate ion if the pH is raised to 11.5-12, which is the planned condition for the EMF. Experimental testing confirmed that the iodide remained in the evaporator concentrate and was below detection limits in all condensate and knock-out pot samples, indicating it was not volatile. The organic nitrile compounds that were added to the simulant were absent from the evaporator feed solution, either lost through evaporation, or more likely, decomposition. The acetone and methylene chloride were below the target values in the feed and condensate samples, and were presumably lost to evaporation or decomposition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗

DOE OSTI2018

The impact of (n, γ ) reaction rate uncertainties of unstable isotopes near N = 50 on the i-process nucleosynthesis in He-shell flash white dwarfs

The first-peak s-process elements Rb, Sr, Y and Zr in the post-AGB star Sakurai's object (V4334 Sagittarii) have been proposed to be the result of i-process nucleosynthesis in a post-AGB very-late thermal pulse event. We estimate the nuclear physics uncertainties in the i-process model predictions to determine whether the remaining discrepancies with observations are significant and point to potential issues with the underlying astrophysical model. We find that the dominant source in the nuclear physics uncertainties are predictions of neutron capture rates on unstable neutron rich nuclei, which can have uncertainties of more than a factor 20 in the band of the i-process. We use a Monte Carlo variation of 52 neutron capture rates and a 1D multi-zone post-processing model for the i-process in Sakurai's object to determine the cumulative effect of these uncertainties on the final elemental abundance predictions. We find that the nuclear physics uncertainties are large and comparable to observational errors. Within these uncertainties the model predictions are consistent with observations. A correlation analysis of the results of our MC simulations reveals that the strongest impact on the predicted abundances of Rb, Sr, Y and Zr is made by the uncertainties in the (n, γ) reaction rates of 85 Br, 86 Br, 87 Kr, 88 Kr, 89 Kr, 89 Rb, 89 Sr, and 92 Sr. This conclusion is supported by a series of multi-zone simulations in which we increased and decreased to their maximum and minimum limits one or two reaction rates per run. We also show that simple and fast one-zone simulations should not be used instead of more realistic multi-zone stellar simulations for nuclear sensitivity and uncertainty studies of convective–reactive processes. Lastly, our findings apply more generally to any i-process site with similar neutron exposure, such as rapidly accreting white dwarfs with near-solar metallicities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation

In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of distribution system state estimation (DSSE) and provide observability with advanced metering infrastructure (AMI) against unobservable customers and missing data. The proposed technique is based on a game-theoretic expansion of relevance vector machines (RVMs). This platform is able to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets. To achieve this objective, the large training set is decomposed and distributed among multiple parallel learning entities. The resulting estimations from the parallel RVMs are then combined using a game-theoretic model based on the idea of repeated games with vector payoff. It is observed that through this approach and by exploiting the seasonal changes in customers’ behavior the accuracy of pseudo-measurements can be considerably improved, while introducing robustness against bad training data samples. The proposed pseudo-measurement generation model is integrated into a DSSE using a closed-loop information system, which takes advantage of a branch current state estimator (BCSE) to further improve the performance of the designed machine learning framework. This method has been tested on a practical distribution feeder model with smart meter data for verification.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Simulations of SITI Cookoff Experiments Carried Out with Different Lots of PBX 9502

A pressure dependent cookoff model for PBX 9502 was developed by Hobbs’ et. al. PBX 9502 is composed of 95% by mass triaminotrinitrobenzene (TATB) and a 5% by mass chlorotrifluoroethylene/vinylidine fluoride binder. The objective in this study is to implement this cookoff model in Aria to simulate Sandia Instrumented Thermal Ignition (SITI) experiments that were carried out with different manufacturing lots of PBX 9502. The SITI design consists of solid cylinders (1" diameter × 1" height) of insensitive high explosive (IHE) confined by a cylindrical aluminum case. An electric heater is wrapped around the lateral surface of the case. This heater produces a temperature heating ramp on the outer surface of the case. Internal thermocouples measure the IHE temperature rise from the center to locations close to the IHE-aluminum interface. The energetic material is heated until thermal ignition occurs. Pressure is measured with a static pressure transducer installed on top of the confinement case. Two–dimensional axisymmetric heat conduction finite element models were implemented to simulate these experiments using four options of the PBX 9502 cookoff model. In addition, the predictive ability of this thermal decomposition model is evaluated using Latin Hypercube Sampling (LHS) techniques.

42 ENGINEERING↗

DOE OSTI2014

The Coal-Seq III Consortium. Advancing the Science of CO 2 Sequestration in Coal Seam and Gas Shale Reservoirs

The Coal-Seq consortium is a government-industry collaborative that was initially launched in 2000 as a U.S. Department of Energy sponsored investigation into CO2 sequestration in deep, unmineable coal seams. The consortium’s objective aimed to advancing industry’s understanding of complex coalbed methane and gas shale reservoir behavior in the presence of multi-component gases via laboratory experiments, theoretical model development and field validation studies. Research from this collaborative effort was utilized to produce modules to enhance reservoir simulation and modeling capabilities to assess the technical and economic potential for CO2 storage and enhanced coalbed methane recovery in coal basins. Coal-Seq Phase 3 expands upon the learnings garnered from Phase 1 & 2, which has led to further investigation into refined model development related to multicomponent equations-of-state, sorption and diffusion behavior, geomechanical and permeability studies, technical and economic feasibility studies for major international coal basins the extension of the work to gas shale reservoirs, and continued global technology exchange. The first research objective assesses changes in coal and shale properties with exposure to CO2 under field replicated conditions. Results indicate that no significant weakening occurs when coal and shale were exposed to CO2, therefore, there was no need to account for mechanical weakening of coal due to the injection of CO2 for modeling. The second major research objective evaluates cleat, Cp, and matrix, Cm, swelling/shrinkage compressibility under field replicated conditions. The experimental studies found that both Cp and Cm vary due to changes in reservoir pressure during injection and depletion under field replicated conditions. Using laboratory data from this study, a compressibility model was developed to predict the pore-volume compressibility, Cp, and the matrix compressibility, Cm, of coal and shale, which was applied to modeling software to enhance model robustness. Research was also conducted to improve algorithms and generalized adsorption models to facilitate realistic simulation of CO2 sequestration in coal seams and shale gas reservoirs. The interaction among water and the adsorbed gases, carbon dioxide (CO2), methane (CH4), and nitrogen (N2) in coalbeds is examined using experimental in situ laboratory techniques to comprehensively model CBM production and CO2 sequestration in coals. An equation of state (EOS) module was developed which is capable of predicting the density of pure components and mixtures involving the wet CBM gases CH4, CO2, and N2 at typical reservoir condition, and is used to inform CO2 injection models. The final research objective examined the effects adsorbed CO2 has on coal strength and permeability. This research studied the weakening or failure of coal by the adsorption of CO2 from empirically derived gas production data to develop models for advanced modeling of permeability changes during CO2 sequestration. The results of this research effort have been used to construct a new and improved model for assessing changes in permeability of coal reservoirs due CO2 injection. The modules developed from these studies and knowledge learned are applied to field validation and basin assessment studies. These data were used to assess the flow and storage of CO2 in a shale reservoir, test newly developed code against large-scale projects, and conduct a basin-oriented review of coal storage potential in the San Juan Basin. The storage potential and flow of CO2 was modeled for shale sequestration of a proprietary Marcellus Shale horizontal gas production well using COMET3 simulation software. Simulation results from five model runs indicate that stored CO2 quantities are linked to the duration of primary production preceding injection. Matrix CO2 saturation is observed to increase in each shale zone after injection with an increase in primary production, and the size of the CO2 plume is also observed to increase in size the longer initial production is sustained. The simulation modules developed around the Coal-Seq experimental work are also incorporated into a pre-existing large-scale numerical simulation model of the Pump Canyon CO2-ECBM pilot in the San Juan Basin. The new model was applied to re-history match the data set to explore the improvements made in permeability prediction against previously published data sets and to validate this module. The assessment of the new data, however, indicates that the impact of the variable Cp is negligible on the overall behavior of the coal for CO2 storage purposes. Applying these new modules, the San Juan Basin and the Marcellus Shale are assessed for their technical ECBM/AGR and CO2 storage potential and the economic potential of these operations. The San Juan Basin was divided into 4 unique geographic zones based on production history, and the Marcellus was divided into nine. Each was assessed based upon each zone’s properties, and simulations were run to assess the potential of full Basin development. Models of a fully developed San Juan Basin suggest the potential for up to 104 Tcf of CO2 storage, and 12.3 Tcf of methane recovery. The Marcellus models suggest 1,248 Tcf of CO2 storage and 924 Tcf of AGR. The economics are deemed favorable where credits cover the cost of CO2 in the San Juan Basin, and in many cases in the Marcellus, but to maximize storage potential, credits need to extend to pay the operator to store CO2.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2017

LHC searches for dark sector showers

This paper proposes a new search program for dark sector parton showers at the Large Hadron Collider (LHC). These signatures arise in theories characterized by strong dynamics in a hidden sector, such as Hidden Valley models. A dark parton shower can be composed of both invisible dark matter particles as well as dark sector states that decay to Standard Model particles via a portal. The focus here is on the specific case of ‘semi-visible jets,’ jet-like collider objects where the visible states in the shower are Standard Model hadrons. We present a Simplified Model-like parametrization for the LHC observables and propose targeted search strategies for regions of parameter space that are not covered by existing analyses. Following the ‘mono-X’ literature, the portal is modeled using either an effective field theoretic contact operator approach or with one of two ultraviolet completions; sensitivity projections are provided for all three cases. We additionally highlight that the LHC has a unique advantage over direct detection experiments in the search for this class of dark matter theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Laying a Foundation for Performance Assessment (PA) Model Development - 19267

Performance Assessment (PA) models are abstractions of real-world conditions. As abstractions, modelers must make a number of decisions regarding model complexity and which features, events, and processes (FEPs) will be represented. To ensure that such decisions are transparent to stakeholders, modelers should document their approach and how this approach influences conceptual model development. This paper describes one such approach. Examples are used to describe an analysis of the potential FEPs. Through the FEPs analysis, all potentially relevant FEPs can be identified. These potentially relevant FEPs are then used to develop a detailed conceptual model known as the Central Scenario, which includes a description of the assessment context, the key features of the site and the closure system, and the expected processes of the system. From this Central Scenario conceptual model, mathematical models can be developed and implemented within modeling software and used to demonstrate compliance to PA performance objectives. FEPs which are beyond the scope of the Central Scenario are identified and used as the basis for developing alternative conceptual models (ACMs) or sensitivity analyses. Once these models and modeling cases are defined, the necessary inputs can be identified and defined. (author)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Developing Model Constraints on Northern Extra-Tropical Carbon Cycling Based on measurements of the Abundance and Isotopic Composition of Atmospheric CO 2

The objective of this project was to perform CO 2 data syntheses and modeling activities to address two central questions: 1) how much has the seasonal cycle in atmospheric CO 2 at northern high latitudes changed since the 1960s, and 2) how well do prognostic biospheric models represent these changes. This project also supported the continuation of the Scripps time series of CO 2 isotopes and concentration at ten baseline stations distributed globally.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Multi-objective Microgrid Assessment and Sizing Framework for Economic and Resilience Benefits

This paper presents an innovative multi-objective assessment and sizing framework for battery energy storage system paired with photovoltaics for a cost-effective and resilient microgrid. In the proposed framework, we simultaneously consider i) economic benefits from bundling grid services in grid-connected mode and ii) enhanced resilience by operating the microgrid in island mode upon outages of the main grid. Instead of simply treating resilience as another monetary value stream, this paper models resilience and economic benefits as two separate objectives. Multi-objective optimization is developed to explore different sizing and dispatch options and generate the Pareto front to assist decision-making in system design. Case studies on a real-world microgrid project in Puget Sound Energy system are presented to illustrate the proposed modeling and sizing methods.

Battery energy storage, Microgrid, Optimal Sizingp↗

DOE OSTI2015

Near-equipartition jets with log-parabola electron energy distribution and the blazar spectral-index diagrams

Fermi-LAT analyses show that the γ-ray photon spectral indices Γ{sub γ} of a large sample of blazars correlate with the νF{sub ν} peak synchrotron frequency ν{sub s} according to the relation Γ{sub γ}=d−klog ν{sub s}. The same function, with different constants d and k, also describes the relationship between Γ{sub γ} and peak Compton frequency ν{sub C}. This behavior is derived analytically using an equipartition blazar model with a log-parabola description of the electron energy distribution (EED). In the Thomson regime, k=k{sub EC}=3b/4 for external Compton (EC) processes and k=k{sub SSC}=9b/16 for synchrotron self-Compton (SSC) processes, where b is the log-parabola width parameter of the EED. The BL Lac object Mrk 501 is fit with a synchrotron/SSC model given by the log-parabola EED, and is best fit away from equipartition. Corrections are made to the spectral-index diagrams for a low-energy power-law EED and departures from equipartition, as constrained by absolute jet power. Analytic expressions are compared with numerical values derived from self-Compton and EC scattered γ-ray spectra from Lyα broad-line region and IR target photons. The Γ{sub γ} versus ν{sub s} behavior in the model depends strongly on b, with progressively and predictably weaker dependences on γ-ray detection range, variability time, and isotropic γ-ray luminosity. Implications for blazar unification and blazars as ultra-high energy cosmic-ray sources are discussed. Arguments by Ghisellini et al. that the jet power exceeds the accretion luminosity depend on the doubtful assumption that we are viewing at the Doppler angle.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

An analysis of the hybrid internal mass modeling approach in EnergyPlus

Accurate simulation of building system dynamics is particularly important for understanding building energy flexibility. Among all dynamics in a building, a zone temperature’s variation is especially important, as it significantly affects a building’s electricity load profile when its heating, ventilating, and air conditioning (HVAC) system is controlled with on/off cycles or setpoint reset strategies. To accurately simulate a zone temperature’s dynamics, internal mass needs to be modeled carefully. In this paper, we compare the two internal mass modeling approaches provided by EnergyPlus, i.e., internal mass object and zone air capacitance multiplier, to better understand their impacts on zone temperature simulation. Real building zone temperature dynamic data from small- and medium-sized office buildings are analyzed and compared with simulated data. In particular, we illustrate the effectiveness of a hybrid method of the two EnergyPlus modeling approaches, which yields more realistic zone temperature dynamics, especially when the zone is conditioned with heat pump systems with on/off cycling.

Chen, Zhelun↗

DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗

DOE OSTI2017

A Gibbs Energy Minimization Approach for Modeling of Chemical Reactions in a Basic Oxygen Furnace

In modern steelmaking, the decarburization of hot metal is converted into steel primarily in converter processes, such as the basic oxygen furnace. The objective of this work was to develop a new mathematical model for top blown steel converter, which accounts for the complex reaction equilibria in the impact zone, also known as the hot spot, as well as the associated mass and heat transport. An in-house computer code of the model has been developed in Matlab. The main assumption of the model is that all reactions take place in a specified reaction zone. The mass transfer between the reaction volume, bulk slag, and metal determine the reaction rates for the species. The thermodynamic equilibrium is calculated using the partitioning of Gibbs energy (PGE) method. The activity model for the liquid metal is the unified interaction parameter model and for the liquid slag the modified quasichemical model (MQM). The MQM was validated by calculating iso-activity lines for the liquid slag components. The PGE method together with the MQM was validated by calculating liquidus lines for solid components. The results were compared with measurements from literature. The full chemical reaction model was validated by comparing the metal and slag compositions to measurements from industrial scale converter. The predictions were found to be in good agreement with the measured values. Furthermore, the accuracy of the model was found to compare favorably with the models proposed in the literature. The real-time capability of the proposed model was confirmed in test calculations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis

The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.

36 MATERIALS SCIENCE↗