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Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
Not Available
On the pressure effect in energetic deposition of Cu thin films by modulated pulsed power magnetron sputtering: A global plasma model and experiments
The modulated pulsed power magnetron sputtering (MPPMS) discharge processes are numerically modeled and experimentally investigated, in order to explore the effect of the pressure on MPPMS discharges as well as on the microstructure of the deposited thin films. A global plasma model has been developed based on a volume-averaged global description of the ionization region, considering the loss of electrons by cross-B diffusion. The temporal variations of internal plasma parameters at different pressures from 0.1 to 0.7 Pa are obtained by fitting the model to duplicate the experimental discharge data, and Cu thin films are deposited by MPPMS at the corresponding pressures. The surface morphology, grain size and orientation, and microstructure of the deposited thin films are investigated by scanning electron microscopy, transmission electron microscopy, and x-ray diffraction. By increasing the pressure from 0.1 to 0.7 Pa, both the ion bombardment energy and substrate temperature which are estimated by the modeled plasma parameters decrease, corresponding to the observed transition of the deposited thin films from a void free structure with a wide distribution of grain size (zone T) into an underdense structure with a fine fiber texture (zone 1) in the extended structure zone diagram (SZD). The microstructure and texture transition of Cu thin films are well-explained by the extended SZD, suggesting that the primary plasma processes are properly incorporated in the model. The results contribute to the understanding of the characteristics of MPPMS discharges, as well as its correlation with the microstructure and texture of deposited Cu thin films.
Development of a MOOSE-based crystal plasticity model with irradiation defect evolution for irradiation creep in 316
Irradiation creep and irradiation swelling are two of the lifetime limiting factors for structural materials in nuclear reactors. These mechanical effects are driven by irradiation defect evolution and the interaction of those defects with dislocations in the microstructure. We present here a coupled cluster dynamics – crystal plasticity approach to model irradiation swelling and creep behavior in 316 SS. The time-dependent evolution of irradiation defects is calculated with a cluster dynamics approach and passed to the crystal plasticity model to compute the dislocation evolution. We show the impact of the irradiation defect evolution on the stress state in the material, which drives inelastic deformation through dislocation motion. The inelastic deformation in the 316 SS is dependent on the dose rate, where the inelastic deformation driven by the early-stage irradiation defect evolution determines the mechanical behavior of the 316 SS.
Developing a generalized ML-based NLTE spectral model for HED applications
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Modeling of Inverter Based AC Microgrids for Control Development.
Abstract not provided.
Developing physically-based multi-scale computational model to understand materials variability in structural applications.
Abstract not provided.
Applying Model Based Systems Engineering to Reduce Nuclear Weapons Development Cycle Time.
Abstract not provided.
Applying Model Based Systems Engineering to Reduce Weapon System Development Cycle Time.
Abstract not provided.
Development of Computational Tools for Modeling Thermal and Radiation Effects on Grain Boundary Segregation and Precipitation in Fe-Cr-Ni-based Alloys
This work aims at developing computational tools for modeling thermal and radiation effects on solute segregation at grain boundaries (GBs) and precipitation. This report described two major efforts. One is the development of computational tools on integrated modeling of thermal equilibrium segregation (TES) and radiation-induced segregation (RIS), from which synergistic effects of thermal and radiation, pre-existing GB segregation have been taken into consideration. This integrated modeling was used in describing the Cr and Ni segregation in the Fe-Cr-Ni alloys. The other effort is thermodynamic modeling on the Fe-Cr-Ni-Mo system which includes the major alloying elements in the investigated alloys in the Advanced Radiation Resistant Materials (ARRM) program. Through thermodynamic calculation, we provide baseline thermodynamic stability of the hardening phase Ni2(Cr,Mo) in selected Ni-based super alloys, and contribute knowledge on mechanistic understanding on the formation of Ni2(Cr,Mo) in the irradiated materials. The major outcomes from this work are listed in the following: 1) Under the simultaneous thermal and irradiation conditions, radiation-induced segregation played a dominant role in the GB segregation. The pre-existing GB segregation only affects the subsequent radiation-induced segregation in the short time. For the same element, the segregation tendency of Cr and Ni due to TES is opposite to it from RIS. The opposite tendency can lead to the formation of W-shape profile. These findings are consistent with literature observation of the transitory W-shape profile. 2) While TES only affects the distance of one or two atomic layers from GBs, the RIS can affect a broader distance from GB. Therefore, the W-shape due to pre-existing GB segregation is much narrower than that due to composition gradient formed during the transient state. Considering the measurement resolution of Auger or STEM analysis, the segregation tendency due to RIS should play a dominant role in the measured values. However, The GB segregation due to pre-existing GB segregation may affect the chemical potential of element at GB, and subsequently the corrosion resistance. 3) Based on the newly developed thermodynamic database of Fe-Cr-Ni-Mo, we predicted the Ni2(Cr,Mo) as a thermodynamically stable phase in all investigated low Fe-content Ni-based alloys. The calculated phase amount decreases with the increasing Fe content, being consistent with that observed in the irradiated materials. 4) The formation of the Ni2(Cr,Mo) phase in irradiated materials is due to irradiation enhanced diffusion. The calculated equilibrium Ni2(Cr,Mo) amount is more than that observed in the irradiated materials, suggesting that the amount of Ni2(Cr,Mo) is likely to increase more with further irradiation.
A Copula-Based Conditional Probabilistic Forecast Model for Wind Power Ramps
Efficient management of wind ramping characteristics can significantly reduce wind integration costs for balancing authorities. By considering the stochastic dependence of wind power ramp (WPR) features, this paper develops a conditional probabilistic wind power ramp forecast (cp-WPRF) model based on Copula theory. The WPRs dataset is constructed by extracting ramps from a large dataset of historical wind power. Each WPR feature (e.g., rate, magnitude, duration, and start-time) is separately forecasted by considering the coupling effects among different ramp features. To accurately model the marginal distributions with a copula, a Gaussian mixture model (GMM) is adopted to characterize the WPR uncertainty and features. The Canonical Maximum Likelihood (CML) method is used to estimate parameters of the multivariable copula. The optimal copula model is chosen based on the Bayesian information criterion (BIC) from each copula family. Finally, the best conditions based cp-WPRF model is determined by predictive interval (PI) based evaluation metrics. Numerical simulations on publicly available wind power data show that the developed copula-based cp-WPRF model can predict WPRs with a high level of reliability and sharpness.
Improving Energy Efficiency by Developing Components for Distributed Cooling and Heating Based on Thermal Comfort Modeling (General Motors LLC Final Project Report)
On November 3, 2009, General Motors (GM) accepted U.S. Department of Energy (DOE) Cooperative Agreement award number DE-EE0000014 from the National Energy Technology Laboratory (NETL). GM was selected to execute a three-year cost shared research and development project on Solid State Energy Conversion for Vehicular Heating, Ventilation & Air Conditioning (HVAC) and for Waste Heat Recovery. The primary objective of this project was to develop a distributed automotive HVAC system that provides thermal comfort equivalent to current state-of-the-art centralized systems while using significantly less energy. To satisfy this objective, the team identified the following goals: • Reduce by at least 30% of the “billions of gallons” the fuel used in cooling mode to maintain occupant comfort through the localized use of thermoelectric (TE) technology while maintaining occupant comfort and safety. • Develop TE HVAC components with a coefficient of performance (COP) greater than 1.3 for cooling and greater than 2.3 for heating, then integrate and test these components as part of a reliable distributed HVAC system in a conventional vehicle (an eAssist Buick Lacrosse) and an extended range electric vehicle (a Chevrolet Volt). • Update the UCB Thermal Comfort model to predict the response of vehicle occupants to localized heating and cooling based on human subject testing, and develop associated computer-aided engineering (CAE) tools to support the integration of distributed HVAC components into future vehicle designs. A secondary objective of the project was to improve the efficiency of thermoelectric generators for converting engine waste heat into electricity.
Analyses of Model-Based Predictive Control for a S-CO 2 Brayton Cycle Power Converter
The supercritical carbon dioxide (S-CO 2 ) Brayton cycle is a promising alternative to steam cycles as an energy converter for advanced nuclear reactors as well as fossil-fired and concentrated solar power plants. For advanced reactors, such as Sodium-cooled Fast Reactors (SFRs), the S-CO 2 cycle offers the benefits of high cycle efficiency, very compact turbomachinery, and elimination of sodium-water reactions, all of which may enable improved economics. The efficiency gains calculated for the S-CO 2 cycle are particularly significant if advantage can be taken of the sharp changes in the carbon dioxide thermo-physical properties near the critical point. On the other hand, the same property variations give rise to challenges in the cycle design, analysis, and control. The onset of two-phase flow (where the minimum cycle temperature and pressure drop drop below the critical value) can degrade compressor performance and potentially cause damage to the compressor blades. In previous work, the feasibility of the Model-based Predictive Control (MPC) approach was assessed for improving regulation of the precooler outlet temperature. A linear reduced-order model for the precooler was generated and for this simplified standalone system operating with an MPC controller the response of outlet temperature was evaluated through simulations. These evaluations included the presence of temperature measurement time delay representing a degraded sensor. It was demonstrated that the MPC controller maintained stable precooler temperature for those delay cases where a proportional-integral controller became unstable. In the present work the simulations have been extended to evaluate the MPC controller operating under more realistic conditions, namely in the split-flow recompression cycle non-linear plant. This compares with the previous standalone reduced-order linear system described above. In the present work the MPC-based control scheme was implemented in a GPASS 1-D systems code representation of the split-flow recompression cycle. The performance of the controller was again investigated for degraded sensor conditions and compared to that of a proportional-integral controller. The results were qualitatively similar to the previous results with the MPC controller and again providing superior stability characteristics. This work provides a sound starting point for the development of model-based control systems. In particular, the MPC control approach applied to Multiple Input Multiple Output systems can be used to synchronize the operation of the different system components to improve control system dynamic performance.
Enabling CO 2 Isothermal Compression Using Liquid Piston and Integrated Gas Cooler
This report outlines the development of a near-isothermal liquid piston compressor. Initially, a proof-of-concept experiment was conducted to validate the feasibility and potential of near-isothermal compression. A first-of-its-kind near-isothermal liquid piston compressor was then successfully built and tested. Additionally, a Matlab-based transient model was developed to capture performance trends accurately. These efforts laid the groundwork for developing a second-generation prototype with enhanced efficiency and design. Subsequent field tests at the Helix Innovation Center in Ohio demonstrated a cooling capacity of 2 kW with a COP of 1.64 at an ambient temperature of 25°C. An improved model was also created to suggest different heat exchanger geometries for better compression efficiency.
SU-E-T-595: Output Factor Calculation for a Uniform Scanning Proton Therapy System
Purpose: To develop an analytical model that predicts dose output factors for patient treatment fields in a uniform scanning proton therapy system. Methods: A model to predict output factors for patient specific treatment fields was produced based on the methods developed by Kooy et al. (2003). The Kooy model predicts the output factor based on the ratio of the entrance dose under calibration conditions to that for a given range and modulation corrected for the inverse square effect. Field specific output factors were plotted as a function of a single parameter r, where r = (Range-Modulation)/Modulation. The model targeted user range 1 sub-span 3 through user range 2 sup-span 2 of the IBA uniform scanning proton therapy system. The data set included points measured using the 10 cm and 18 cm snout sizes to eliminate stem effects on the monitor unit chambers. The data was fit using equation 15 from Kooy et al. (2003), and the resulting model was tested against measurements that were not included in the original data set. Results: For the range and sub span investigated, 120 data points were tested against the model prediction. The model predicted the output factor within 2% for 96% of the points tested and within 2.5% for 99% of the points tested. All points were within 3% of the predicted values. Conclusion: Monitor units for patient treatment fields with proton ranges that fall within the tested interval can be predicted using a model based on the methods developed at MGH. With further evaluation, it will be possible to model all user ranges and sub-spans of the IBA system. Further testing is also needed to predict output factors using a 25 cm snout which introduces variable scanning pattern sizes and stem effects on the monitor unit chambers.
Multi-scale modeling and simulation of a novel membrane reactor (MR)/adsorptive reactor (AR) process
Not provided.
Lead Slowing-Down Spectrometry for Spent Fuel Assay: FY11 Status Report
Executive Summary Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R&D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration to study the feasibility of Lead Slowing Down Spectroscopy (LSDS). This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertainty considerably lower than the approximately 10% typical of today’s confirmatory assay methods. This document is a progress report for FY2011 collaboration activities. Progress made by the collaboration in FY2011 continues to indicate the promise of LSDS techniques applied to used fuel. PNNL developed an empirical model based on calibration of the LSDS to responses generated from well-characterized used fuel. The empirical model demonstrated the potential for the direct and independent assay of the sum of the masses of 239Pu and 241Pu to within approximately 3% over a wide used fuel parameter space. Similar results were obtained using a perturbation approach developed by LANL. Benchmark measurements have been successfully conducted at LANL and at RPI using their respective LSDS instruments. The ISU and UNLV collaborative effort is focused on the fabrication and testing of prototype fission chambers lined with ultra-depleted 238U and 232Th, and uranium deposition on a stainless steel disc using spiked U3O8 from room temperature ionic liquid was successful, with improving thickness obtained. In FY2012, the collaboration plans a broad array of activities. PNNL will focus on optimizing its empirical model and minimizing its reliance on calibration data, as well continuing efforts on developing an analytical model. Additional measurements are planned at LANL and RPI. LANL measurements will include a Pu sample, which is expected to provide more counts at longer slowing-down times to help identify discrepancies between experimental data and MCNPX simulations. RPI measurements will include the assay of an entire fresh fuel assembly for the study of self-shielding effects as well as the ability to detect diversion by detecting a missing fuel pin in the fuel assembly. The development of threshold neutron sensors will continue, and UNLV will calibrate existing ultra-depleted uranium deposits at ISU.
Uncertainty characterization in a coupled human-natural system: Modeling agricultural adaptation in the Great Lakes Region
The Great Lakes Region's water quality and ecological health are threatened by the export of nutrients from agricultural lands, which causes eutrophication, hypoxia, and destructive algal blooms. The intensification of hydrologic cycles brought about by climate change is expected to exacerbate nutrient loading in the region, and, at the same time, agricultural adaptation to changing conditions is also expected to affect loading through shifting amounts and timing of fertilization. Quantifying these future effects and their interactions necessitates modeling both the human and natural processes as a coupled system, by pairing land use and agricultural management with hydrologic modeling. At the same time, compounding uncertainties arising from the complex interactions in both systems significantly limit our predictive understanding of the region's impacts. This study utilizes the Soil and Water Assessment Tool (SWAT), developed for simulating the impact of various farmer decisions on watershed functions in Western Lake Erie watersheds, and an under-development agent-based model (ABM) for agricultural management decisions. The aim of this study is to use global sensitivity analysis on the coupled ABM and SWAT models to quantify how uncertainty in both models interactively affects nutrient loading. To do so, we will conduct Sobol sensitivity analysis experiments at different levels of coupling assumptions to quantify how various uncertain factors (e.g., soil moisture and crop choice) and their interactions affect our estimates of nutrient loading. The results of this analysis will allow us to quantify how complex interactions and dependencies between both systems amplify the effect of uncertainties. Insights gained from this study will have broader implications for modeling the adaptive co-evolution of human and natural systems under climate change and can inform effective management of nutrient loading in the Great Lakes Region.