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DOE OSTI2024

Development of a rate-based ENRTL-RK process model for a water-lean solvent

Advanced water-lean solvents (WLS) for post-combustion CO2 capture offer several advantages over the aqueous amine solvents . WLS have lower parasitic energy penalty, lower corrosion, lower temperature and high-pressure CO2 regeneration leading to lower cost of CO2 capture. RTI International, with funding from the US Department of Energy, has been developing its novel water-lean solvent, that has shown specific reboiler duty of 2.3 GJ/t-CO2 at the 60-kWe pilot testing unit (Tiller Plant, SINTEF, Norway) and 2.6 GJ/t-CO2 at the engineering scale testing system (12 MWe) at the Technology Centre Mongstad (TCM) in Norway. All heat duties, including the one from TCM testing, were consistent with Aspen Plus modeling of the specific configuration of each test plant. This work focuses on the development of a detailed process model using in-house laboratory measurements and process data at pilot scale. The eNTRL-RK model used in this work is based on an unsymmetric activity coefficient model with the reference states chosen to be pure liquids for solvents and ideal dilute solution at unit solute molality (resulting in activity coefficient of unity at infinite dilution) for electrolytes. It uses the Redlich-Kwong equation of state for vapor phase properties and Henry’s law for solubility of supercritical gases. The model was validated using process data from the pilot-scale campaign at the Tiller plant, and the engineering scale test campaign at TCM. Data on CO2 capture rate, absorber, and regenerator temperature profiles and specific reboiler duties from two different test campaigns at Tiller and TCM, were used to further refine and validate the model and the model compares favorably to experimental data. The validation results against TCM campaign will be presented in this work.

CO2 capture↗

DOE OSTI2026

Assessing Geospatial and Seasonal Influences on Energy and Cost-Efficiency of Drayage Trucks

The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2019

Progress in disruption prevention for ITER

Key plasma physics and real-time control elements needed for robustly stable operation of high fusion power discharges in ITER have been shown in recent research worldwide. Recent analysis has identified the current density profile as the main drive for disruptive instabilities in discharges simulating ITER's baseline scenario with high and low external torque. Ongoing development of model-based profile control and active control of magnetohydrodynamic instabilities is improving the stability of multiple scenarios. Significant advances have been made toward real-time physics-based prediction of instabilities, including path-oriented analysis, active sensing, and machine learning techniques for prediction that are beginning to go beyond simple disruption mitigation trigger applications. Active intervention adds to prevention of disruptions, including forced rotation of magnetic islands to prevent wall locking, and localized heating/current drive to shrink the islands. Stable discharge rampdowns have been achieved with the fastest ITER-like scaled current ramp rates, while maintaining an X-point configuration. Moreover, these elements are being integrated into stable operating scenarios and new event-handling systems for off-normal events in order to develop the physics basis and techniques for robust control in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Model-based Co-Simulation of Heat Pump Water Heater with Phase Change Materials Thermal Energy Storage

The study analysis the behavior of a new developed heat pump water heater technology which integrates a phase change materials storage with a standard heat pump water heater to maximize the performance parameters of the Unified Energy Factor (UEF) and First Hour Rating (FHR). A model-based control development co-simulation platform is developed to include equipment models, such as heat pump, standard water tank, phase change materials storage tank, and integrate them with control model to support controls design, analysis, verification, and validation. Simulation results are compared with lab test results to validate the accuracy of developed co-simulation platform

Sun, Jian↗

DOE OSTI2014

Leveraging a Fundamental Understanding of Fracture Flow, Dynamic Permeability Enhancement, and Induced Seismicity to Improve Geothermal Energy Production

This project focused on assessment and discovery of fluid-rock interaction in geothermal reservoirs. We accomplished work in four main areas: 1) fracture formation and the relationship between fluid flow and shear failure, 2) assessment of fracture geometry and fluid permeability using novel acoustic measurements, 3) an improved understanding of how drilling, injection and geothermal production influence local seismicity, and 4) development of process based models for using induced seismicity to assess the critical stress-state in Earth’s crust. The majority of the work involved laboratory experiments and analysis of laboratory and numerical results. We created shear fractures under true triaxial stresses designed to mimic the stress state of a geothermal reservoir and we measured fracture permeability as a function of stress state and fluid pressure under these conditions. Our work shows that fracture permeability can be altered by transient changes in stress and fluid flow. We find that fluid pressure has an unexpectedly larger impact on permeability than does applied stress and we interpret that result in terms of fracture contact area and the law of effective stress. We employ elastic waves to image fractures in the lab and develop the theory to apply our work to geothermal reservoir scale. Our work documents changes in elastic wave speed prior to earthquake-like failure in the lab and we show how the frequency magnitude distribution of microseismic lab earthquakes can be used to assess the critical stress state around faults prior to failure.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Code Benchmark of the HTTF Pressurized Conduction Cooldown Test Using SAM

The High Temperature Test Facility (HTTF) at Oregon State University is an integral system test facility to simulate postulated reactor transients of prismatic high-temperature gas-cooled reactors(HTGRs). A series of test campaigns was launched, providing abundant test data that could be used to benchmark reactor system analysis codes like the System Analysis Module (SAM). In this study, a SAM model of the facility is developed based on the two-dimensional (2D) ring model approach. All components including the ceramic matrix, graphite heaters, helium coolant channels, core barrel, upcomer, pressure vessel, and reactor cavity cooling system are modeled as concentric cylindrical rings. The model is used to simulate one of the benchmark problems-Pressurized Conduction Cooldown (PCC)-within the scope of the Organisation for Economic Co-operation and Development Nuclear Energy Agency International HTTF Benchmark. The simulations consist of two parts. In the first part, operating and boundary conditions as well as thermophysical properties of materials are specified for the benchmark problem. In this work, results from the first part will be used in code-to-code comparison. In the second part, the SAM model is used to simulate Test PG-27, which is the first PCC test carried out in the HTTF, with only two of the ten heater banks activated. The results in the second part are used for code-to-data comparison. Because the helium coolant flow rate is not measured in this facility, it is estimated using the input power and inlet/outlet coolant temperatures. Additionally, radial heat flow in the ceramic blocks is complicated by hundreds of cylindrical coolant channels and heater rods embedded in them. As such, it is necessary to deduce an effective thermal conductivity for the ceramic to analyze the core thermal behavior. SAM predictions of the helium coolant and ceramic temperatures are compared with test data measured in three equivalent sectors. Overall, the SAM results agree reasonably well with test data within the variation of data among the three sectors, which demonstrates SAM's capability in capturing transient effects in HTGR using the simplified 2D ring model.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Prognostic model and failure mechanisms of steam generators in Sodium-Cooled fast reactors

This paper presents a prognostic model for sodium-cooled fast reactor (SFR) steam generators (SGs). Here, the purpose of the model is to estimate the remaining useful life of SFR SGs and thus to support the decision-making of autonomous control. SFR SGs are of great interest for plant integrity due to their harsh operation environment. They operate at higher temperatures and higher coolant-to-steam pressure differences than those of current light water reactors (LWR). The severity of the SFR SG failure consequences, which include water-sodium contact, is another reason. Understanding its failure mechanisms is important for the development of its prognostic model. Based on our literature review and physics-based analysis, we concluded that creep would be a dominant degradation mode of SFR SGs due to SFR’s high-temperature and high-pressure environment. Thus, creep is the focus of the prognostic model. Various other failure modes were also investigated in this study. The mechanical fatigue due to flow-induced vibration is usually observed in early developmental phases and would not be an important issue during normal operation. The thermal fatigue due to thermal stripping is occasionally observed in other components in SFRs but does not affect SG integrity. Pure water stress corrosive crack, fretting, and so on are commonly observed in LWR SGs but are significantly less important in SFR SGs because of the high temperatures, high pressure differences, and chemical properties of liquid sodium. Based on these investigations, a prognostic model focusing on creep failures was developed. It estimates the failure probability profile by sampling the Larson-Miller parameter (LMP) and temperature and associated uncertainties through Monte Carlo methods. Two case studies were presented. The first one demonstrated the model’s capability to calculate the failure probability for a new specimen within a given time. If the tube is working under temperatures of 500C ± 3 and pressures of 15.2 MPa, which leads to LMP of 20,100 ± 50, the failure probabilities within 50, 70, and 90 years are approximately 0.1 %, 3.2 %, and 17.5 %. The second demonstrated how adjusting the workload can help to protect the integrity of the component. For an old tube reaching 99 % of its lifespan, continuing to run at temperatures of 500C ± 3 and LMP of 20,100 ± 50 leads to a failure probability of about 29.0 % within a year. If the temperature is reduced by 5C, the failure probability can be reduced to 2.5 %, and, if the pressure is also reduced such that the LMP is increased by 100, the failure probability can further be reduced to 0.12 %.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2011

Mutagenic effect of accelerated heavy ions on bacterial cells

The heavy ion accelerators of the Joint Institute for Nuclear Research were used to study the regularities and mechanisms of formation of different types of mutations in prokaryote cells. The induction of direct (lac{sup −}, ton B{sup −}, col B) mutations for Esherichia coli cells and reverse his{sup −} → His{sup +} mutations of Salmonella typhimurium, Bacillus subtilis cells under the action of radiation in a wide range of linear energy transfer (LET) was studied. The regularities of formation of gene and structural (tonB trp-) mutations for Esherichia coli bacteria under the action of accelerated heavy ions were studied. It was demonstrated that the rate of gene mutations as a function of the dose under the action of Γ rays and accelerated heavy ions is described by linear-quadratic functions. For structural mutations, linear “dose-effect” dependences are typical. The quadratic character of mutagenesis dose curves is determined by the “interaction” of two independent “hitting” events in the course of SOS repair of genetic structures. The conclusion made was that gene mutations under the action of accelerated heavy ions are induced by δ electron regions of charged particle tracks. The methods of SOS chromotest, SOS lux test, and λ prophage induction were used to study the regularities of SOS response of cells under the action of radiations in a wide LET range. The following proposition was substantiated: the molecular basis for formation of gene mutations are cluster single-strand DNA breaks, and that for structural mutations, double-strand DNA breaks. It was found out that the LET dependence of the relative biological efficiency of accelerated ions is described by curves with a local maximum. It was demonstrated that the biological efficiency of ionizing radiations with different physical characteristics on cells with different genotype, estimated by the lethal action, induction of gene and deletion mutations, precision excision of transposons, is determined by the specific features of energy transfer of the radiations that affect the character of induced DNA damage, and the efficiency inducible and constitutive cell repair systems. The growth of relative biological efficiency of heavy charged particles is determined by the growth of the damage yield of the DNA participating in the formation of radiation-induced effects, and higher efficiency of inducible repair systems. It was established that the LET value (L{sub max}) for which the maximum (according to the applied irradiation criteria) coefficients of relative biological efficiency are observed varies depending on the character of the registered radiation induced effect. It was demonstrated that for gene mutations and induction of precision excision of mobile elements the values of L{sub max} are realized in a LET range of ≈20 keV/μm. For lethal effects of irradiation and induction of deletion mutations the value of L{sub max} is ≈ 100 and 50 keV/μm, respectively. The differences in the L{sub max} for the studied radiation gene effectis are determined by the different type of DNA damage participating in the mutation process. A molecular model of the formation of gene mutations in Escherichia coli cells under the action of ionizing radiation was proposed. Basic DNA radiation damage and main repair ways were considered in the framework of this model. The basis is the idea of the decisive role of mutagenic, error-prone, branch of SOS repair in fixing premutation DNA damage into point mutations. It was demonstrated that the central mechanism in this process is the formation of an inducible multi-enzymatic complex including the DNA polymerase V (Umu C), RecA-protease, SSB proteins, subunits of DNA polymerase III, performing erroneous DNA synthesis on the damaged matrix. A mathematical model of induction of gene mutations under ultraviolet cell irradiation was developed based on the molecular model.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗

DOE OSTI2014

High strain rate metalworking with vaporizing foil actuator: Control of flyer velocity by varying input energy and foil thickness

Electrically driven rapid vaporization of thin metallic foils can generate a high pressure which can be used to launch flyers at high velocities. Recently, vaporizing foil actuators have been applied toward a variety of impulse-based metal working operations. In order to exercise control over this useful tool, it is imperative that an understanding of the effect of characteristics of the foil actuator on its ability for mechanical impulse generation is developed. Here, foil actuators made out of 0.0508 mm, 0.0762 mm, and 0.127 mm thick AA1145 were used for launching AA2024-T3 sheets of thickness 0.508 mm toward a photonic Doppler velocimeter probe. Launch velocities ranging between 300 m/s and 1100 m/s were observed. In situ measurement of velocity, current, and voltage assisted in understanding the effect of burst current density and deposited electrical energy on average pressure and velocity with foil actuators of various thicknesses. For the pulse generator, geometry, and flyer used here, the 0.0762 mm thick foil was found to be optimal for launching flyers to high velocities over short distances. Experimenting with annealed foil actuators resulted in no change in the temporal evolution of flyer velocity as compared to foil actuators of full hard temper. A physics-based analytical model was developed and found to have reasonable agreement with experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Investigation of the Role of the Role of Nuclear Physics in Heavy Element Nucleosynthesis, through the Study of Key Reactions, and the Improvement of Theoretical Reaction Rates (Final Report)

Neutrino-driven winds in core-collapse supernovae have been identified as a possible site for the production of elements heavier than iron. Traditionally, these neutrino-driven winds have been proposed as the site of the main r-process. Recent simulations fail to reproduce the conditions required for the main r-process. while they remain a promising site for producing the lightest elements beyond iron, e.g., Sr, Y, and Zr through the νp process. The efficiency of the νp process depends on the hydrodynamical conditions, the electron fraction (which is related to the neutrino properties), and the nuclear reactions on many short-lived nuclei with limited (if at all) experimental information. The reaction rates on these nuclei are based on theoretical predictions using the Hauser-Feshbach model. Recent sensitivity studies have highlighted the importance of neutron-induced reactions on these nuclei along the νp process path. This work aimed to experimentally constrain reaction rates that are known to play a key role in the neutrino-p process nucleosynthesis. A secondary subsequently-added objective was to start the implementation of techniques that improve the description of nuclear properties in the Hauser-Feshbach model by extending the microscopic nuclear level density description offered via the shell model to high excitation energies without using experiment-based renormalizations. The main objective of this work was the experimental constraint of the 56 Ni(n,p) 56 Co reaction rate via a measurement of the inverse reaction 56 Co(p,n) 56 Ni at the National Superconducting Cyclotron Laboratory (NSCL) and later the Facility for Rare Isotope Beams (FRIB). This reaction is considered the key one for determining the yields possible by the neutrino-p process. A technique for this type of measurement in inverse kinematics at low energies did not exist before this work. The work also had two secondary objectives. First, to contribute to efforts to measure the same reaction in direct kinematics using a radioactive target at Los Alamos National Laboratory (LANL), and second, to advance work to implement shell-model-deduced microscopic level densities in Hauser-Feshbach calculations. The project has resulted in the development of the first technique to perform (p,n) cross-section measurements in relevant-for-astrophysics low energies in inverse kinematics using a magnetic spectrometer or separator, and neutron detectors for neutron tagging. It has also resulted in the precise measurement of the cross-section of the 40 Ar(p,n) 40 K reaction in a proof-of-principle experiment realized by using a beam-line quadrupole of the ReA3 accelerator of NSCL/FRIB. As part of this project the technique was successfully adapted to make use of the superior acceptance of the Separator for Capture Reactions (SECAR) at FRIB. In this project, the required experimental setup simulations and beam optics were developed and tested with the measurement of the 58 Fe(p,n) 58 Cu reaction cross-section. Additionally, this project contributed with simulation work to the development of a technique to measure (n,p) reactions with radioactive targets at LANL, and which resulted in the measurement of the key 56 Ni(n,p) 56 Co reaction cross-section at neutron energies above ≈1 MeV. Last, the project initiated work in the development of shell model based level densities using the moments method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗

DOE OSTI2016

Device Scale Modeling of Solvent Absorption using MFIX-TFM

Recent climate change is largely attributed to greenhouse gases (e.g., carbon dioxide, methane) and fossil fuels account for a large majority of global CO 2 emissions. That said, fossil fuels will continue to play a significant role in the generation of power for the foreseeable future. The extent to which CO 2 is emitted needs to be reduced, however, carbon capture and sequestration are also necessary actions to tackle climate change. Different approaches exist for CO 2 capture including both post-combustion and pre-combustion technologies, oxy-fuel combustion and/or chemical looping combustion. The focus of this effort is on post-combustion solvent-absorption technology. To apply CO 2 technologies at commercial scale, the availability and maturity and the potential for scalability of that technology need to be considered. Solvent absorption is a proven technology but not at the scale needed by typical power plant. The scale up and down and design of laboratory and commercial packed bed reactors depends heavily on the specific knowledge of two-phase pressure drop, liquid holdup, the wetting efficiency and mass transfer efficiency as a function of operating conditions. Simple scaling rules often fail to provide proper design. Conventional reactor design modeling approaches will generally characterize complex non-ideal flow and mixing patterns using simplified and/or mechanistic flow assumptions. While there are varying levels of complexity used within these approaches, none of these models resolve the local velocity fields. Consequently, they are unable to account for important design factors such as flow maldistribution and channeling from a fundamental perspective. Ideally design would be aided by development of predictive models based on truer representation of the physical and chemical processes that occur at different scales. Computational fluid dynamic (CFD) models are based on multidimensional flow equations with first principle foundations. CFD models can include a more accurate physical description of flow processes and be modified to include more complex behavior. Wetting performance and spatial liquid distribution inside the absorber are recognized as weak areas of knowledge requiring further investigation. CFD tools offer a possible method to investigating such topics and gaining a better understanding of their influence on reactor performance. This report focuses first on describing a hydrodynamic model for countercurrent gas-liquid flow through a packed column and then on the chemistry, heat and mass transfer specific to CO 2 absorption using monoethanolamine (MEA). The indicated model is implemented in MFIX, a CFD open source software package. The user defined functions needed to build this model are described in detail along with the keywords for the corresponding input file. A test case is outlined along with a few results. The example serves to briefly illustrate the developed CFD tool and its potential capability to investigate solvent absorption.

09 BIOMASS FUELS↗

DOE OSTI2024

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

58 GEOSCIENCES↗

DOE OSTI2013

A general approach to develop reduced order models for simulation of solid oxide fuel cell stacks

A reduced order modeling approach based on response surface techniques was developed for solid oxide fuel cell stacks. This approach creates a numerical model that can quickly compute desired performance variables of interest for a stack based on its input parameter set. The approach carefully samples the multidimensional design space based on the input parameter ranges, evaluates a detailed stack model at each of the sampled points, and performs regression for selected performance variables of interest to determine the responsive surfaces. After error analysis to ensure that sufficient accuracy is established for the response surfaces, they are then implemented in a calculator module for system-level studies. The benefit of this modeling approach is that it is sufficiently fast for integration with system modeling software and simulation of fuel cell-based power systems while still providing high fidelity information about the internal distributions of key variables. This paper describes the sampling, regression, sensitivity, error, and principal component analyses to identify the applicable methods for simulating a planar fuel cell stack.

Pan, Wenxiao↗

DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO 2 – Reservoir oil minimum miscibility pressure prediction

Carbon dioxide (CO 2 ) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO 2 -EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO 2 injection process. Traditional empirical methods to test the CO 2 -oil MMP are time consuming and expensive; derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. Here in this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO 2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature; (2) average critical temperature; (3) molecular weight of pentane plus (C 5+ ) fraction of crude oil, and; (4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗