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DOE OSTI2021

Report on High-Fidelity Dynamic Modeling of a Coal-Fired Steam Power Plant

As a result of the growth of renewables including solar and wind energy with fluctuating production, fossil fuel power plants are being required to cycle between high and low power production. This cycling is both at a greater frequency and over a wider range than in the past. In many cases, power plants are not designed for this type of cycling operation but can nonetheless endure these challenging operation requirements under the right conditions. In this project, optimal solution for enhanced flexible operations are being investigated using model based estimation and control techniques. To support the development of model-based estimator and model-based controls at GE Global Research, GE Steam Power configured a dynamic model using a reference steam plant design including the boiler, turbine, and water/steam conditioning systems as well as the controls needed for plant cycling with stability and reliability. The dynamic model was built using the APROS® software from VTT, and then calibrated to multiple load conditions from full load (100%TMCR) to partial loads (75%TMCR, 50%TMCR and 25%TMCR) based on internally developed steady state heat balance models at the unit level. These internal heat balance models are based on first principles and extensive engineering experiences from GE Steam Power as an OEM and a services provider. This topical report presents the structure of the unit level dynamic model, the tuning process, and representative simulation results from typical load cycling simulations using the dynamic model.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2024

Observation and Modeling of Dynamic Fracture Behaviors of Battery Cell Under Impact Loading Using Enhanced Representative Volume Element Concept

The burgeoning electric automobile industry has increased interest in battery safety. Battery cells experience significant mechanical stress during operation, including the impact of accidents and vibrations from driving. The potential for thermal runaway reactions in battery cells raises safety concerns. Although numerous researchers have defined the dynamic behavior of battery cells and proposed numerical models to describe it, few studies have focused on the high-strain rate mechanical impact phase correlated with the onset of fracture. In this study, we describe the dynamic behavior of pouch battery cells and propose a modeling method to study their mechanical failure under impact situations. Impact tests are conducted at various velocities and heights. To overcome numerical issues commonly encountered under rapid deformation scenarios, a new finite element model is developed based on the representative volume element model. The proposed approach efficiently simulates continuous crack propagation and brittleness behavior during impact by permitting the individual behavior of the cell components. Therefore, engineers can reliably design safer electric vehicle battery cells by measuring the properties of the cell components.

ENERGY STORAGE↗

DOE OSTI2020

The role of chemical potential in compensation control in Si:AlGaN

Reduction in compensation in Si-doped Al-rich AlGaN is demonstrated via chemical potential control (CPC). The chemical potentials and the resulting formation energies of carbon on the nitrogen site (C N ) and cation vacancy complex with Si (V III + nSi III ) were related to growth variables through a thermodynamic supersaturation model, which quantitatively predicted the incorporation of C N and the generation of the V III + nSi III complex. The compensation “knee” behavior, i.e., decreasing conductivity with increasing Si incorporation beyond a certain concentration, was successfully controlled. The maximum free carrier concentration was improved by impeding the formation of V III + nSIi III complexes under III-richer conditions, while the impurity compensation by C N was reduced by making the growth environment N-richer. The results of Hall effect measurement and photoluminescence agreed well with quantitative theoretical predictions of the CPC model. Based on the developed model, the highest conductivity of 160 Ω -1 cm -1 with free carrier concentration of 3 × 10 19 cm -3 in Al 0.7 Ga 0.3 N ever reported was achieved on single crystal AlN substrates. The demonstrated predictive power of the CPC model should greatly reduce the empirical analysis or iterative experimentation that would otherwise be necessary.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Assessment of effective elastic constants of U-10Mo fuel microstructures

Monolithic U-10Mo fuel undergoes significant microstructural changes during fuel burnup which degrades its mechanical properties. In this talk, we present results form a numerical method to assess the impact of the various microstructural features--grains, intragranular and intergranular Xe gas bubbles--on the elastic stiffness tensor. Using the Multiphysics Object-Oriented Simulation Environment (MOOSE), phase-field-based microstructures are combined with asymptotic expansion homogenization method to obtain effective elastic constants as a function of porosity and fission density. The results are verified and compared against analytical homogenization models. With this approach, elastic degradation in operating nuclear fuels can be quantified when the distributions of microstructural features are known from experimental characterization or rate-theory based models. We further develop an evolution model based on the virial equation of state for Xe gas and investigate the effect of growth and coalescence of the bubbles at the grain boundary faces and triple junctions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2012

Model of Procedure Usage – Results from a Qualitative Study to Inform Design of Computer-Based Procedures

The nuclear industry is constantly trying to find ways to decrease the human error rate, especially the human errors associated with procedure use. As a step toward the goal of improving procedure use performance, researchers, together with the nuclear industry, have been looking at replacing the current paper-based procedures with computer-based procedure systems. The concept of computer-based procedures is not new by any means; however most research has focused on procedures used in the main control room. Procedures reviewed in these efforts are mainly emergency operating procedures and normal operating procedures. Based on lessons learned for these previous efforts we are now exploring a more unknown application for computer based procedures - field procedures, i.e. procedures used by nuclear equipment operators and maintenance technicians. The Idaho National Laboratory, the Institute for Energy Technology, and participants from the U.S. commercial nuclear industry are collaborating in an applied research effort with the objective of developing requirements and specifications for a computer-based procedure system to be used by field operators. The goal is to identify the types of human errors that can be mitigated by using computer-based procedures and how to best design the computer-based procedures to do this. The underlying philosophy in the research effort is “Stop – Start – Continue”, i.e. what features from the use of paper-based procedures should we not incorporate (Stop), what should we keep (Continue), and what new features or work processes should be added (Start). One step in identifying the Stop – Start – Continue was to conduct a baseline study where affordances related to the current usage of paper-based procedures were identified. The purpose of the study was to develop a model of paper based procedure use which will help to identify desirable features for computer based procedure prototypes. Affordances such as note taking, markups, sharing procedures between fellow coworkers, the use of multiple procedures at once, etc. were considered. The model describes which affordances associated with paper based procedures should be transferred to computer-based procedures as well as what features should not be incorporated. The model also provides a means to identify what new features not present in paper based procedures need to be added to the computer-based procedures to further enhance performance. The next step is to use the requirements and specifications to develop concepts and prototypes of computer-based procedures. User tests and other data collection efforts will be conducted to ensure that the real issues with field procedures and their usage are being addressed and solved in the best manner possible. This paper describes the baseline study, the construction of the model of procedure use, and the requirements and specifications for computer-based procedures that were developed based on the model. It also addresses how the model and the insights gained from it were used to develop concepts and prototypes for computer based procedures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A Generalization of Threshold-Based and Probability-Based Models of Information Diffusion

Diffusion of information through complex networks is of interest in studies such as propagation prediction and influence maximization, both of which have applications in viral marketing and rumor controlling. There are a variety of information diffusion models, all of which simulate the adoption and spread of information over time. However, there is a lack of understanding of whether, despite their conceptual differences, these models represent the same underlying generative structures. For instance, if two different models utilize different conceptual mechanisms, but generate the same results, does the choice of model matter? A classification of diffusion of information models is developed based on the neighbor knowledge of the model infection requirement and the stochasticity of the model. This classification allows for the identification of models that fall into each respective category. The study involves the analysis of the following agent-based models on directed scale-free networks: (1) a linear absolute threshold model (LATM), (2) a linear fractional threshold model (LTFM), (3) the independent cascade model (ICM), (4) Bass-Rand-Rust model (BRRM) (5) a stochastic linear absolute threshold model (SLATM) (6) a stochastic fractional threshold model (SLFTM), and (7) Dodds–Watts model (DWM). Through the execution of simulations and analysis of the experimental results, the distinctive properties of each model are identified. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

A consistent and conservative model and its scheme for N -phase- M -component incompressible flows

Here, we propose a consistent and conservative model for multiphase and multicomponent incompressible flows, where there can be arbitrary numbers of phases and components. Each phase has a background fluid called the pure phase, each pair of phases is immiscible, and components are dissolvable in some specific phases. The model is developed based on the multiphase Phase-Field model including the contact angle boundary condition, the diffuse domain approach, and the analyses on the proposed consistency conditions for multiphase and multicomponent flows. The model conserves the mass of individual pure phases, the amount of each component in its dissolvable region, and thus the mass of the fluid mixture, and the momentum of the flow. It ensures that no fictitious phases or components can be generated and that the summation of the volume fractions from the Phase-Field model is unity everywhere so that there is no local void or overfilling. It satisfies a physical energy law and it is Galilean invariant. A corresponding numerical scheme is developed for the proposed model, whose formal accuracy is 2nd-order in both time and space. It is shown to be consistent and conservative and its solution is demonstrated to preserve the Galilean invariance and energy law. Numerical tests indicate that the proposed model and scheme are effective and robust to study various challenging multiphase and multicomponent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Tensile and Fatigue Testing and Material Hardening Model Development for 508 LAS Base Metal and 316 SS Similar Metal Weld under In-air and PWR Primary Loop Water Conditions

This report provides an update on an assessment of environmentally assisted fatigue for light water reactor components under extended service conditions. This report is a deliverable in September 2015 under the work package for environmentally assisted fatigue under DOE’s Light Water Reactor Sustainability program. In an April 2015 report we presented a baseline mechanistic finite element model of a two-loop pressurized water reactor (PWR) for systemlevel heat transfer analysis and subsequent thermal-mechanical stress analysis and fatigue life estimation under reactor thermal-mechanical cycles. In the present report, we provide tensile and fatigue test data for 508 low-alloy steel (LAS) base metal, 508 LAS heat-affected zone metal in 508 LAS–316 stainless steel (SS) dissimilar metal welds, and 316 SS-316 SS similar metal welds. The test was conducted under different conditions such as in air at room temperature, in air at 300 oC, and under PWR primary loop water conditions. Data are provided on materials properties related to time-independent tensile tests and time-dependent cyclic tests, such as elastic modulus, elastic and offset strain yield limit stress, and linear and nonlinear kinematic hardening model parameters. The overall objective of this report is to provide guidance to estimate tensile/fatigue hardening parameters from test data. Also, the material models and parameters reported here can directly be used in commercially available finite element codes for fatigue and ratcheting evaluation of reactor components under in-air and PWR water conditions.

316 Stainless Steel↗

DOE OSTI2021

Improved Control Strategy of Grid Interface for EV High-Power Dynamic Wireless Charging

This paper proposes a control strategy for the grid interface converter in high-power dynamic wireless charging system (DWCS) to address two issues on distribution network integration. Due to the unique pulsating load profile of DWCS, load transient response capability is critical for the grid interface to maintain the dc-bus voltage stable. Besides, the inherent unbalanced situation of distribution network would lead to 2nd-order oscillations on the dc-bus voltage, which would further affect the stable operation of the entire system. In this paper, the DWCS model is developed, and the relationship between the dc-bus voltage and the input/output power is analyzed. Based on the developed model, a control strategy based on direct power control is presented. Both simulation results and hardware-in-the-loop (HIL) results demonstrate that the proposed control strategy not only improves load transient response capability, but also eliminates the 2nd-order oscillations on the dc-bus voltage under imbalanced distribution network conditions.

Zeng, Rong↗

DOE OSTI2017

Clinical Implementation of a Model-Based In Vivo Dose Verification System for Stereotactic Body Radiation Therapy–Volumetric Modulated Arc Therapy Treatments Using the Electronic Portal Imaging Device

Purpose: To report findings from an in vivo dosimetry program implemented for all stereotactic body radiation therapy patients over a 31-month period and discuss the value and challenges of utilizing in vivo electronic portal imaging device (EPID) dosimetry clinically. Methods and Materials: From December 2013 to July 2016, 117 stereotactic body radiation therapy–volumetric modulated arc therapy patients (100 lung, 15 spine, and 2 liver) underwent 602 EPID-based in vivo dose verification events. A developed model-based dose reconstruction algorithm calculates the 3-dimensional dose distribution to the patient by back-projecting the primary fluence measured by the EPID during treatment. The EPID frame-averaging was optimized in June 2015. For each treatment, a 3%/3-mm γ comparison between our EPID-derived dose and the Eclipse AcurosXB–predicted dose to the planning target volume (PTV) and the ≥20% isodose volume were performed. Alert levels were defined as γ pass rates <85% (lung and liver) and <80% (spine). Investigations were carried out for all fractions exceeding the alert level and were classified as follows: EPID-related, algorithmic, patient setup, anatomic change, or unknown/unidentified errors. Results: The percentages of fractions exceeding the alert levels were 22.6% for lung before frame-average optimization and 8.0% for lung, 20.0% for spine, and 10.0% for liver after frame-average optimization. Overall, mean (± standard deviation) planning target volume γ pass rates were 90.7% ± 9.2%, 87.0% ± 9.3%, and 91.2% ± 3.4% for the lung, spine, and liver patients, respectively. Conclusions: Results from the clinical implementation of our model-based in vivo dose verification method using on-treatment EPID images is reported. The method is demonstrated to be valuable for routine clinical use for verifying delivered dose as well as for detecting errors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Development of Predictive Model for Accurate Rupture Time from Multi-Axial Creep in Alloy 709 with Physics-Based Simulations

A physics-based model is developed to predict multiaxial creep behavior in Alloy 709 (A709), an advanced austenitic stainless steel intended for high-temperature applications such as Sodium Fast Reactors (SFRs). Compared to conventional stainless steels like 316H, A709 offers superior high-temperature performance; however, comprehensive data on its multiaxial creep response remain limited. To address this gap, a crystal plasticity finite element (CPFE) framework is used to simulate the deformation and failure mechanisms of A709 under multiaxial loading conditions. The model incorporates an extended Hu-Cocks dislocation creep formulation that accounts for precipitation effects, along with the Sham–Needleman model to capture grain boundary cavitation-driven failure. These advanced constitutive models enable a detailed understanding of the interplay between microstructural evolution and macroscopic creep response. Furthermore, the study evaluates the predictive accuracy of various effective stress measures in estimating creep rupture life, leveraging simulated multiaxial creep data. The findings provide critical insights into the applicability of different stress measures for engineering design and life prediction of A709 components operating under complex loading conditions. This work contributes to improving the reliability of high-temperature structural components by advancing predictive modeling capabilities for advanced austenitic steels.

Alloy 709↗

DOE OSTI2018

Quality control for community-based sea-ice model development

A new collaborative organization for sea-ice model development, the CICE Consortium, has devised quality control procedures to maintain the integrity of its numerical codes' physical representations, enabling broad participation from the scientific community in the Consortium's open software development environment. Using output from five coupled and uncoupled configurations of the Los Alamos Sea Ice Model, CICE, we formulate quality control methods that exploit common statistical properties of sea-ice thickness, and test for significant changes in model results in a computationally efficient manner. New additions and changes to CICE are graded into four categories, ranging from bit-for-bit amendments to significant, answer-changing upgrades. These modifications are assessed using criteria that account for the high level of autocorrelation in sea-ice time series, along with a quadratic skill metric that searches for hemispheric changes in model answers across an array of different CICE configurations. These metrics also provide objective guidance for assessing new physical representations and code functionality.

58 GEOSCIENCES↗

DOE OSTI2022

Developments in Physics-Based Modeling and Machine Learning for Environmental Effects in Nuclear Materials [Slides]

This presentation is part of an annual review for the new Advanced Materials and Manufacturing Technologies program under the Office of Nuclear Energy within the Department of Energy. This presentation covers advances in physics-based modeling and machine learning for environmental effects in nuclear materials. It discusses efforts in combining density functional theory with machine learning to predict material properties, studying irradiation defect recombination, and improving MOOSE-based tools for crystal plasticity modeling and the Stochastic Tools Module.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Flex Fuel Optimized SI and HCCI Engine

The central objective of the proposed work is to demonstrate an HCCI (homogeneous charge compression ignition) capable SI (spark ignited) engine that is capable of fast and smooth mode transition between SI and HCCI combustion modes. The model-based control technique was used to develop and validate the proposed control strategy for the fast and smooth combustion mode transition based upon the developed control-oriented engine; and an HCCI capable SI engine was designed and constructed using production ready two-step valve-train with electrical variable valve timing actuating system. Finally, smooth combustion mode transition was demonstrated on a metal engine within eight engine cycles. The Chrysler turbocharged 2.0L I4 direct injection engine was selected as the base engine for the project and the engine was modified to fit the two-step valve with electrical variable valve timing actuating system. To develop the model-based control strategy for stable HCCI combustion and smooth combustion mode transition between SI and HCCI combustion, a control-oriented real-time engine model was developed and implemented into the MSU HIL (hardware-in-the-loop) simulation environment. The developed model was used to study the engine actuating system requirement for the smooth and fast combustion mode transition and to develop the proposed mode transition control strategy. Finally, a single cylinder optical engine was designed and fabricated for studying the HCCI combustion characteristics. Optical engine combustion tests were conducted in both SI and HCCI combustion modes and the test results were used to calibrate the developed control-oriented engine model. Intensive GT-Power simulations were conducted to determine the optimal valve lift (high and low) and the cam phasing range. Delphi was selected to be the supplier for the two-step valve-train and Denso to be the electrical variable valve timing system supplier. A test bench was constructed to develop control strategies for the electrical variable valve timing (VVT) actuating system and satisfactory electrical VVT responses were obtained. Target engine control system was designed and fabricated at MSU for both single-cylinder optical and multi-cylinder metal engines. Finally, the developed control-oriented engine model was successfully implemented into the HIL simulation environment. The Chrysler 2.0L I4 DI engine was modified to fit the two-step vale with electrical variable valve timing actuating system. A used prototype engine was used as the base engine and the cylinder head was modified for the two-step valve with electrical VVT actuating system. Engine validation tests indicated that cylinder #3 has very high blow-by and it cannot be reduced with new pistons and rings. Due to the time constraint, it was decided to convert the four-cylinder engine into a single cylinder engine by blocking both intake and exhaust ports of the unused cylinders. The model-based combustion mode transition control algorithm was developed in the MSU HIL simulation environment and the Simulink based control strategy was implemented into the target engine controller. With both single-cylinder metal engine and control strategy ready, stable HCCI combustion was achived with COV of 2.1% Motoring tests were conducted to validate the actuator transient operations including valve lift, electrical variable valve timing, electronic throttle, multiple spark and injection controls. After the actuator operations were confirmed, 15-cycle smooth combustion mode transition from SI to HCCI combustion was achieved; and fast 8-cycle smooth combustion mode transition followed. With a fast electrical variable valve timing actuator, the number of engine cycles required for mode transition can be reduced down to five. It was also found that the combustion mode transition is sensitive to the charge air and engine coolant temperatures and regulating the corresponding temperatures to the target levels during the combustion mode transition is the key for a smooth combustion mode transition. As a summary, the proposed combustion mode transition strategy using the hybrid combustion mode that starts with the SI combustion and ends with the HCCI combustion was experimentally validated on a metal engine. The proposed model-based control approach made it possible to complete the SI-HCCI combustion mode transition within eight engine cycles utilizing the well controlled hybrid combustion mode. Without intensive control-oriented engine modeling and HIL simulation study of using the hybrid combustion mode during the mode transition, it would be impossible to validate the proposed combustion mode transition strategy in a very short period.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2012

Development of a Mechanistic-Based Healing Model for Self-Healing Glass Seals

Self-healing glass, a recent development of hermetic sealant materials, has the ability to effectively repair damage when heated to elevated temperatures; thus, able to extend its service life. Since crack healing morphological changes in the glass material are usually temperature and stress dependent, quantitative studies to determine the effects of thermo-mechanical conditions on the healing behavior of the self-healing glass sealants are extremely useful to accommodate the design and optimization of the sealing systems within SOFCs. The goal of this task is to develop a mechanistic-based healing model to quantify the stress and temperature dependent healing behavior. A two-step healing mechanism was developed and implemented into finite element (FE) models through user-subroutines. Integrated experimental/kinetic Monte Carlo (kMC) simulation methodology was taken to calibrate the model parameters. The crack healing model is able to investigate the effects of various thermo-mechanical factors; therefore, able to determine the critical conditions under which the healing mechanism will be activated. Furthermore, the predicted results can be used to formulate the continuum damage-healing model and to assist the SOFC stack level simulations in predicting and evaluating the effectiveness and the performance of various engineering seal designs.

Fuel Cell↗

DOE OSTI2015

Systems and Photosystems: Cellular Limits of Autotrophic Productivity in Cyanobacteria

Recent advances in the modeling of microbial growth and metabolism have shown that growth rate critically depends upon the optimal allocation of finite proteomic resources among different cellular functions and that modeling growth rates becomes more realistic with the explicit accounting for the costs of macromolecular synthesis, most importantly, protein expression. The “proteomic constraint” is considered together with its application to understanding photosynthetic microbial growth. The central hypothesis is that physical limits of cellular space (and corresponding solvation capacity) in conjunction with cell surface-to-volume ratios represent the underlying constraints on the maximal rate of autotrophic microbial growth. The limitation of cellular space thus constrains the size the total complement of macromolecules, dissolved ions, and metabolites. To a first approximation, the upper limit in the cellular amount of the total proteome is bounded this space limit. This predicts that adaptation to osmotic stress will result in lower maximal growth rates due to decreased cellular concentrations of core metabolic proteins necessary for cell growth owing the accumulation of compatible osmolytes, as surmised previously. The finite capacity of membrane and cytoplasmic space also leads to the hypothesis that the species-specific differences in maximal growth rates likely reflect differences in the allocation of space to niche-specific proteins with the corresponding diminution of space devoted to other functions including proteins of core autotrophic metabolism, which drive cell reproduction. An optimization model for autotrophic microbial growth, the autotrophic replicator model, was developed based upon previous work investigating heterotrophic growth. The present model describes autotrophic growth in terms of the allocation protein resources among core functional groups including the photosynthetic electron transport chain, light-harvesting antennae, and the ribosome groups.

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