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DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A paradigm shift - the key to optimal, defensible and transparent waste management, disposal and remediation decisions - 15236

Current methods for structuring nuclear waste management, disposal, and remediation decisions are subject to three major limitations. First, they are based on a series of extraordinarily conservative assumptions. The dominant way of thinking is that, for example, conservative models yield better, safer decisions. Second, they lack methods to meaningfully address and incorporate the 'human dimension'. The dominant way of thinking is that the current approach is sufficient to bring together the values and interests of both internal and external stakeholders in making a decision. And third, they lack a formal method through which both internal and external stakeholders are encouraged collectively to 'think through' complex problems that contain multiple competing objectives so that wise, socially acceptable, and scientifically-defensible decisions are made. To overcome these limitations, a paradigm shift is needed towards a holistic, integrated approach to structure nuclear waste management, disposal and remediation decisions. The first requirement of this paradigm shift is the realization and recognition that the problems faced are decision problems, and that there is a formal theory of decision analysis that can, and should, be used to support finding the best solutions. Decision analysis, very simply, is formalized common sense. The reason it is needed for complex problems is that there are too many moving parts (variables) for humans to manage with out the formalism. Decision analysis leads to addressing problem solving using a combination of a science-based model and a costs/values model. Expectation ('realism') and uncertainty are addressed in the science-based models, whereas stakeholder values, preferences, and desires are addressed when modeling costs and value judgments. Science-based modeling tools have been developed, improved, and used since the current regulatory setting was introduced about 30 years ago. However, modeling of costs and value judgments has not often been addressed using any formal approach, despite the openings that exist in regulations (e.g., CERCLA's modifying criteria and the concept of 'as low as reasonably achievable' referenced in DOE Orders). A discussion of the need and rationale for this paradigm shift is presented, followed by a description of the technical basics of this approach to decision modeling, and ending with an explanation of the steps that are taken in this approach to structured decision-making. A software framework program called Guided Interactive Statistical Decision Tools (GiSdT - pronounced 'gist') is introduced that supports this paradigm shift. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Zhu, Guang‐Li↗

DOE OSTI2023

A Design of Activity-Based Mobility Intervention

Human mobility influences our society and vice versa. During the COVID-19 pandemic, non-pharmaceutical intervention that alters activity-based mobility such as work-from-home greatly impacted human mobility patterns. Many studies on developing mitigation strategies have employed or implemented their own mobility intervention within their model assumption. For fair evaluation between intervention strategies across models, it is significant to set up compatible experimental environments. However, it is difficult to apply the identical intervention to different kinds of models and compare their effectiveness because each model might have different assumptions, capabilities, and implementations. Even if one can apply intervention to heterogeneous models, it may produce undesirable artifacts due to difference of models and integration with intervention. Therefore, minimizing undesirable artifacts and facilitating intervention experiments across heterogeneous models are substantial. Taking this into account, this paper investigates a design of activity-based mobility intervention (ABMI). We define ABMI together with related concepts and develop an extensible data model and schema of ABMI based on the 5W1H method that can be used in different models. As a case study, we apply the ABMI model to a micro-simulation to demonstrate the usability of the proposed model. We expect that standardized ABMI and interfaces may help to streamline development and experiments of intervention strategies across heterogeneous models.

Kim, Joon-Seok↗

DOE OSTI2012

Dynamic modeling and control of a solid-sorbent CO{sub 2} capture process with two-stage bubbling fluidized bed adsorber reactor

Solid-sorbent-based CO{sub 2} capture processes have strong potential for reducing the overall energy penalty for post-combustion capture from the flue gas of a conventional pulverized coal power plant. However, the commercial success of this technology is contingent upon it operating over a wide range of capture rates, transient events, malfunctions, and disturbances, as well as under uncertainties. To study these operational aspects, a dynamic model of a solid-sorbent-based CO{sub 2} capture process has been developed. In this work, a one-dimensional (1D), non-isothermal, dynamic model of a two-stage bubbling fluidized bed (BFB) adsorber-reactor system with overflow-type weir configuration has been developed in Aspen Custom Modeler (ACM). The physical and chemical properties of the sorbent used in this study are based on a sorbent (32D) developed at National Energy Technology Laboratory (NETL). Each BFB is divided into bubble, emulsion, and cloud-wake regions with the assumptions that the bubble region is free of solids while both gas and solid phases coexist in the emulsion and cloud-wake regions. The BFB dynamic model includes 1D partial differential equations (PDEs) for mass and energy balances, along with comprehensive reaction kinetics. In addition to the two BFB models, the adsorber-reactor system includes 1D PDE-based dynamic models of the downcomer and outlet hopper, as well as models of distributors, control valves, and other pressure-drop devices. Consistent boundary and initial conditions are considered for simulating the dynamic model. Equipment items are sized and appropriate heat transfer options, wherever needed, are provided. Finally, a valid pressure-flow network is developed and a lower-level control system is designed. Using ACM, the transient responses of various process variables such as flue gas and sorbent temperatures, overall CO{sub 2} capture, level of solids in the downcomer and hopper have been studied by simulating typical disturbances such as change in the temperature, flowrate, and composition of the flue gas. To maintain the overall CO{sub 2} capture at a desired level in face of the typical disturbances, two control strategies were considered–a proportional-integral-derivative (PID)-based feedback control strategy and a feedforward-augmented feedback control strategy. Dynamic simulation results show that both the strategies result in unacceptable overshoot/undershoot and a long settling time. To improve the control system performance, a linear model predictive controller (LMPC) is designed. In summary, the overall results illustrate how optimizing the operation and control of carbon capture systems can have a significant impact on the extent and the rate at which commercial-scale capture processes will be scaled-up, deployed, and used in the years to come.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2018

A superstructure optimization approach for process synthesis under complex reaction networks

Here in this work, we present mixed integer linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams in systems where the composition of the reactor inlet streams can vary substantially, thereby making the determination of the limiting component as well as the calculation of the stream heating/cooling and power requirements challenging. First, towards the modeling of reactors, we develop an extent-based method which detects the limiting reactant of each reaction occurring in parallel with others, based on the inlet flows of the reactants. Second, we develop a computationally tractable method for the calculation of the work and heating/cooling duty needed to condition any stream of a process based on simple calculations that can be performed offline. Finally, we present how the two aforementioned components can be integrated in an optimization model generated based on a process superstructure. We demonstrate the application of the developed methods for the synthesis of a biorefinery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Kinetic Modeling of Microbiological Processes

Kinetic description of microbiological processes is vital for the design and control of microbe-based biotechnologies such as waste water treatment, petroleum oil recovery, and contaminant attenuation and remediation. Various models have been proposed to describe microbiological processes. This editorial article discusses the advantages and limiation of these modeling approaches in cluding tranditional, Monod-type models and derivatives, and recently developed constraint-based approaches. The article also offers the future direction of modeling researches that best suit for petroleum and environmental biotechnologies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

TU-CD-BRA-05: Atlas Selection for Multi-Atlas-Based Image Segmentation Using Surrogate Modeling

Purpose: The growing size and heterogeneity in training atlas necessitates sophisticated schemes to identify only the most relevant atlases for the specific multi-atlas-based image segmentation problem. This study aims to develop a model to infer the inaccessible oracle geometric relevance metric from surrogate image similarity metrics, and based on such model, provide guidance to atlas selection in multi-atlas-based image segmentation. Methods: We relate the oracle geometric relevance metric in label space to the surrogate metric in image space, by a monotonically non-decreasing function with additive random perturbations. Subsequently, a surrogate’s ability to prognosticate the oracle order for atlas subset selection is quantified probabilistically. Finally, important insights and guidance are provided for the design of fusion set size, balancing the competing demands to include the most relevant atlases and to exclude the most irrelevant ones. A systematic solution is derived based on an optimization framework. Model verification and performance assessment is performed based on clinical prostate MR images. Results: The proposed surrogate model was exemplified by a linear map with normally distributed perturbation, and verified with several commonly-used surrogates, including MSD, NCC and (N)MI. The derived behaviors of different surrogates in atlas selection and their corresponding performance in ultimate label estimate were validated. The performance of NCC and (N)MI was similarly superior to MSD, with a 10% higher atlas selection probability and a segmentation performance increase in DSC by 0.10 with the first and third quartiles of (0.83, 0.89), compared to (0.81, 0.89). The derived optimal fusion set size, valued at 7/8/8/7 for MSD/NCC/MI/NMI, agreed well with the appropriate range [4, 9] from empirical observation. Conclusion: This work has developed an efficacious probabilistic model to characterize the image-based surrogate metric on atlas selection. Analytical insights lead to valid guiding principles on fusion set size design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

Forward treatment planning for modulated electron radiotherapy (MERT) employing Monte Carlo methods

Purpose: This paper describes the development of a forward planning process for modulated electron radiotherapy (MERT). The approach is based on a previously developed electron beam model used to calculate dose distributions of electron beams shaped by a photon multi leaf collimator (pMLC). Methods: As the electron beam model has already been implemented into the Swiss Monte Carlo Plan environment, the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA) can be included in the planning process for MERT. In a first step, CT data are imported into Eclipse and a pMLC shaped electron beam is set up. This initial electron beam is then divided into segments, with the electron energy in each segment chosen according to the distal depth of the planning target volume (PTV) in beam direction. In order to improve the homogeneity of the dose distribution in the PTV, a feathering process (Gaussian edge feathering) is launched, which results in a number of feathered segments. For each of these segments a dose calculation is performed employing the in-house developed electron beam model along with the macro Monte Carlo dose calculation algorithm. Finally, an automated weight optimization of all segments is carried out and the total dose distribution is read back into Eclipse for display and evaluation. One academic and two clinical situations are investigated for possible benefits of MERT treatment compared to standard treatments performed in our clinics and treatment with a bolus electron conformal (BolusECT) method. Results: The MERT treatment plan of the academic case was superior to the standard single segment electron treatment plan in terms of organs at risk (OAR) sparing. Further, a comparison between an unfeathered and a feathered MERT plan showed better PTV coverage and homogeneity for the feathered plan, with V{sub 95%} increased from 90% to 96% and V{sub 107%} decreased from 8% to nearly 0%. For a clinical breast boost irradiation, the MERT plan led to a similar homogeneity in the PTV compared to the standard treatment plan while the mean body dose was lower for the MERT plan. Regarding the second clinical case, a whole breast treatment, MERT resulted in a reduction of the lung volume receiving more than 45% of the prescribed dose when compared to the standard plan. On the other hand, the MERT plan leads to a larger low-dose lung volume and a degraded dose homogeneity in the PTV. For the clinical cases evaluated in this work, treatment plans using the BolusECT technique resulted in a more homogenous PTV and CTV coverage but higher doses to the OARs than the MERT plans. Conclusions: MERT treatments were successfully planned for phantom and clinical cases, applying a newly developed intuitive and efficient forward planning strategy that employs a MC based electron beam model for pMLC shaped electron beams. It is shown that MERT can lead to a dose reduction in OARs compared to other methods. The process of feathering MERT segments results in an improvement of the dose homogeneity in the PTV.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around the location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support by a grant from NCI 1R01CA140413.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Stochastic Simulation of Complex Fluid Flows (Progress Report for period 07/01/2016 - 06/30/2018)

At a microscopic scale, fluids are composed of molecules whose positions and velocities are random. This gives rise to thermal fluctuations that span the whole range of scales from the microscopic through the mesoscopic, and even the macroscopic. The inclusion of thermal fluctuations is crucial in multi-scale models, which are an important theme in the research program of the DOE Office of Science, and in particular the ASCR Applied Mathematics program's priority focus area on modeling of complex systems involving processes that span vastly different time and/or length scales. In this five-year Early Career project, the PI Aleksandar Donev and collaborators developed computational algorithms for modeling complex fluid mixtures at small scales using a formulation based on fluctuating hydrodynamics. Novel computational methods were developed to model complex fluids with increasing physical complexity, starting from binary miscible and immiscible mixtures, going through multispecies non-reactive and reactive mixtures, and culminating with reactive electrolytes mixtures of neutral molecules and ions. In close collaboration with the group of John Bell at Lawrence Berkeley National Laboratory, the methods were implemented in a scalable computational framework suitable for modern parallel supercomputers, and made publicly available on github. A number of physical examples in which giant nonequilibrium fluctuations are improtant were studied, with a special focus on instabilities at a liquid-liquid interface driven by gravity, diffusion, reactions, and/or electric fields. The methods and codes developed in this project are expected to enable other novel applications in the DOE Basic Energy Sciences program, and engineering sciences more broadly.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2012

ENTHALPY-BASED THERMAL EVOLUTION OF LOOPS. II. IMPROVEMENTS TO THE MODEL

This paper develops the zero-dimensional (0D) hydrodynamic coronal loop model 'Enthalpy-based Thermal Evolution of Loops' (EBTEL) proposed by Klimchuk et al., which studies the plasma response to evolving coronal heating, especially impulsive heating events. The basis of EBTEL is the modeling of mass exchange between the corona and transition region (TR) and chromosphere in response to heating variations, with the key parameter being the ratio of the TR to coronal radiation. We develop new models for this parameter that now include gravitational stratification and a physically motivated approach to radiative cooling. A number of examples are presented, including nanoflares in short and long loops, and a small flare. The new features in EBTEL are important for accurate tracking of, in particular, the density. The 0D results are compared to a 1D hydro code (Hydrad) with generally good agreement. EBTEL is suitable for general use as a tool for (1) quick-look results of loop evolution in response to a given heating function, (2) extensive parameter surveys, and (3) situations where the modeling of hundreds or thousands of elemental loops is needed. A single run takes a few seconds on a contemporary laptop.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Transmission-Distribution Dynamic Co-Simulation of Electric Vehicles Providing Grid Frequency Response

This paper investigates the impacts of electric vehicles (EVs) on power system frequency regulation based on an open-source transmission-and-distribution (T&D) dynamic co-simulation framework. The development of a EV dynamic model based on appropriate WECC dynamic model is introduced first, then the T&D dynamic co-simulation platform is described. The advantage of the overall platform is that DERs such as distributed photovoltaic (DPV) and EV are modeled explicitly in both transmission and distribution simulators for frequency dynamics and voltage, respectively. In the case studies, EVs frequency responses (i.e., primary and/or secondary) are simulated after the system is exposed to N-1 contingency such as a generation trip. Various EV frequency regulation participation strategies are investigated as well to study their impacts on system frequency response. The studies shows that EVs have potential capability to provide effective frequency regulation services.

electric vehicle↗

DOE OSTI2024

Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED (IC Report) [Slides]

Modeling nonlinear effects of laser plasma instabilities (LPI) is critically important for ICF/HED experiments. Our project has the following goals: • Develop physics-based nonlinear LPI models using PIC simulations • Couple the nonlinear LPI effects to macroscopic modeling of ICF/HED exp. through laser ray-tracing (LRT).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Transmission-Distribution Dynamic Co-Simulation of Electric Vehicles Providing Grid Frequency Response: Preprint

electric vehicle↗

DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE cor- relations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. This model opens a possibility to use charge density as a descriptor for predicting SFE in alloys. Furthermore, a descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development of a Gibbs Energy Minimiser for the MOOSE-based Corrosion Modelling App Yellowjacket and Validation of MSTDB

Nuclear materials are highly complex multiscale, multiphysics systems,and an effective prediction of nuclear reactor performance and safety requires simulation capabilities that tightly couple different physical phenomena. The Idaho National Laboratory’s Multiphysics Object Oriented Simulation Environment (MOOSE) provides the computational foundation for performing such simulations. With the move towards advanced reactors, such as the Molten Salt Reactor (MSR), that employ high temperature fluids compared to conventional reactors, corrosion has become a problem of great interest. A new application called Yellowjacket is currently under development to directly couple thermodynamic equilibrium and kinetics with phase field models in order to model corrosion in MSRs. As part of Yellowjacket, a Gibbs energy minimiser is being developed to perform thermochemical equilibrium calculations for a range of different materials, which is currently in its infancy. This report describes the further progress towards the development of Yellowjacket Gibbs energy minimiser. Ontario Tech University is developing a new Gibbs energy minimiser for Yellowjacket which is the primary contribution of this work. The aim to develop a thermochemistry solver for the MOOSE framework following the same development philosophy and using the same tools and libraries. A special focus is on performance, documentation and SQA. Furthermore through a scope extension partway through the fiscal year a thorough assessment of the MSTDB-TC v1.3 was performed at Ontario Tech University in the context of continuous improvement and quality assurance. The objective of the work was to have an arm’s length review of the database to give confidence that the database is performing as it was intended while assessing its current state to give recommendations to future developments. This assessment involved two parts: A) a quantitative assessment, and B) a qualitative assessment. Part A involved developing an automated test-suite that would compute values from the database using Thermochimica with comparisons to experimental measurements for validation purposes, which gives confidence to the database’s stakeholders that its functioning properly. Part B involved reviewing all binary systems in the database and making a qualitative assessment with two performance indicators: comprehensiveness and overall confidence. It is important to note that the models in the database are empirical, which is to say that the quality of any model is highly dependent on the experimental data used to inform its development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗