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Total Absorption Study of Beta Decays Relevant for Nuclear Applications and Nuclear Structure
An overview is given of our activities related to the study of the beta decay of neutron rich nuclei relevant for nuclear applications. Recent results of the study of the beta decay of {sup 87,88}Br using a new segmented total absorption spectrometer are presented. The measurements were performed at the IGISOL facility using trap-assisted total absorption spectroscopy.
Advancing Inverse Sensitivity/Uncertainty Methods for Nuclear Fuel Cycle Applications
The inverse sensitivity/uncertainty quantification (IS/UQ) method has recently been implemented in the Inverse Sensitivity/UnceRtainty Estimator (INSURE) module of the AMPX cross section processing system [M.E. Dunn and N.M. Greene, “AMPX-2000: A Cross-Section Processing System for Generating Nuclear Data for Criticality Safety Applications,” Trans. Am. Nucl. Soc. 86, 118–119 (2002)]. The IS/UQ method aims to quantify and prioritize the cross section measurements along with uncertainties needed to yield a given nuclear application(s) target response uncertainty, and doing this at a minimum cost. Since in some cases the extant uncertainties of the differential cross section data are already near the limits of the present-day state-of-the-art measurements, requiring significantly smaller uncertainties may be unrealistic. Therefore, we have incorporated integral benchmark experiments (IBEs) data into the IS/UQ method using the generalized linear least-squares method, and have implemented it in the INSURE module. We show how the IS/UQ method could be applied to systematic and statistical uncertainties in a self-consistent way and how it could be used to optimize uncertainties of IBEs and differential cross section data simultaneously. We itemize contributions to the cost of differential data measurements needed to define a realistic cost function.
Covariance Applications in Criticality Safety, Light Water Reactor Analysis, and Spent Fuel Characterization
A new covariance data library based on ENDF/B-VII.1 was recently processed for the SCALE nuclear analysis code system. The multigroup covariance data are discussed here, along with testing and application results for critical benchmark experiments. The cross section covariance library, along with covariances for fission product yields and decay data, is used to compute uncertainties in the decay heat produced by a burned reactor fuel assembly.
Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking
In general, the modeling errors of dynamic system model are a set of random variables. The traditional performance index of modeling such as means square error (MSE) and root means square error (RMSE) can not fully express the connotation of modeling errors with stochastic characteristics both in the dimension of time domain and space domain. Therefore, the probability density function (PDF) is introduced to completely describe the modeling errors in both time scales and space scales. Based on it, a novel wavelet neural network (WNN) modeling method is proposed by minimizing the two-dimensional (2D) PDF shaping of modeling errors. First, the modeling error PDF by the tradional WNN is estimated using data-driven kernel density estimation (KDE) technique. Then, the quadratic sum of 2D deviation between the modeling error PDF and the target PDF is utilized as performance index to optimize the WNN model parameters by gradient descent method. Since the WNN has strong nonlinear approximation and adaptive capability, and all the parameters are well optimized by the proposed method, the developed WNN model can make the modeling error PDF track the target PDF, eventually. Simulation example and application in a blast furnace ironmaking process show that the proposed method has a higher modeling precision and better generalization ability compared with the conventional WNN modeling based on MSE criteria. Furthermore, the proposed method has more desirable estimation for modeling error PDF that approximates to a Gaussian distribution whose shape is high and narrow.
Plutonium measurements with a fast-neutron multiplicity counter for nuclear safeguards applications
Measurements were performed at the Joint Research Centre in Ispra, Italy to field test a fast-neutron multiplicity counter developed at the University of Michigan. The measurements allowed the assessment of the system's photon discrimination abilities, efficiency when measuring neutron multiplicity, ability to characterize 240 Pu eff mass, and performance relative to a currently deployed neutron coincidence counter. We describe how this work is motivated by the need to replace and improve upon 3 He neutron detection systems for nuclear safeguards applications.
Validation and uncertainty quantification of detector response functions for a $1"×2"$ NaI collimated detector intended for inverse radioisotope source mapping applications
Detector response functions (DRFs) are often used for inverse analysis. We compute the DRF of a sodium iodide (NaI) nuclear material holdup field detector using the code named g03 developed by the Center for Engineering Applications of Radioisotopes (CEAR) at NC State University. Three measurement campaigns were performed in order to validate the DRF’s constructed by g03: on-axis detection of calibration sources, off-axis measurements of a highly enriched uranium (HEU) disk, and on-axis measurements of the HEU disk with steel plates inserted between the source and the detector to provide attenuation. Furthermore, this work quantifies the uncertainty of the Monte Carlo simulations used in and with g03, as well as the uncertainties associated with each semi-empirical model employed in the full DRF representation. Overall, for the calibration source measurements, the response computed by the DRF for the prediction of the full-energy peak region of responses was good, i.e. within two standard deviations of the experimental response. In contrast, the DRF tended to overestimate the Compton continuum by about 45–65% due to inadequate tuning of the electron range multiplier fit variable that empirically represents physics associated with electron transport that is not modeled explicitly in g03. For the HEU disk measurements, computed DRF responses tended to significantly underestimate (more than 20%) the secondary full-energy peaks (any peak of lower energy than the highest-energy peak computed) due to scattering in the detector collimator and aluminum can, which is not included in the g03 model. We ran a sufficiently large number of histories to ensure for all of the Monte Carlo simulations that the statistical uncertainties were lower than their experimental counterpart’s Poisson uncertainties. The uncertainties associated with least-squares fits to the experimental data tended to have parameter relative standard deviations lower than the peak channel relative standard deviation in most cases and good reduced chi-square values. The highest sources of uncertainty were identified as the energy calibration polynomial factor (due to limited source availability and NaI resolution) and the Ba-133 peak fit (only a very weak source was available), which were 20% and 10%, respectively.
Experimental study of the application of rotating fluidized beds to particle separation
Rotating fluidized beds provide a unique opportunity to exploit fluidization under higher particle forces. The centripetal force in a rotating fluid bed is typically on the order of 10 times the force of gravity. Since the force keeping the particles in the unit is larger, the drag force can also be larger, allowing for higher gas and slip velocities. This operating regime provides intensified gas-solids contact through higher mass transfer, heat transfer, gas throughput, and bubble suppression. One application for using a rotating fluidized bed is in Chemical Looping Combustion (CLC). When solid fuels are used, oxygen carrier and ash are mixed in the process. In order to maintain high carbon capture efficiencies and recyclability of the oxygen carrier, the ash needs to be separated from the oxygen carrier. This separation can be done aerodynamically since the oxygen carrier is larger and heavier than the ash. It is theorized that rotating fluidized beds could improve both the gas-solid and solid-solid separation process efficiency and throughput as compared to conventional fluidized beds. A 43 cm diameter, 2.5 cm long vortex chamber has been designed and constructed to investigate the application to particle separation. A series of experiments have been performed to investigate the separation of different binary mixtures of solids. These experiments demonstrate the use of a rotating fluidized bed for high-G intensified particle separation that can be combined with high-G intensified gas-solids contact and gas-solids separation.¼
Geospatial characterization of low-temperature heating and cooling demand in residential, commercial, manufacturing, agricultural, and data center sectors for potential geothermal applications in the United States
Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. Here, this study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.
A Real-Time Vehicle-Specific Eco-Routing Model for On-Board Navigation Applications Capturing Transient Vehicle Behavior
The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.
Mediated Fuel Cells: Soluble Redox Mediators and their Applications to Electrochemical Reduction of O2 and Oxidation of H2, Alcohols, Biomass, and Complex Fuels
Mediated fuel cells are electrochemical devices that produce power in a manner similar to that of conventional proton exchange membrane fuel cells (PEMFCs). They differ from PEMFCs in their use of redox mediators dissolved in liquid electrolyte to conduct oxidation of the fuel or reduction of the oxidant, typically O2, in bulk solution. The mediators transport electrons (and often protons) between the electrode and the catalysts or chemical reagents in solution. This strategy can help overcome many of the challenges associated with conventional fuel cells, including managing complex multi-phase reactions (as in O2 reduction) or the use of challenging or heterogeneous fuels, such as hydrocarbons, polyols and biomass. Mediators are also commonly used in enzymatic fuel cells, where direct electron transfer from the electrode to the enzymatic active site can be slow. This review provides a comprehensive survey of historical and recent mediated fuel cell efforts, including applications using chemical and enzymatic catalysts. Our research on in this field has been exploring a number of different topics and has been supported different funding sources: the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences (molecular mediators for catalytic oxygen reduction); the Great Lakes Bioenergy Research Center, DOE Office of Science DE-SC0018409 (research on biomass-based fuels; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Catalysis Program, DE-FG02-05ER15690 (copper-catalyzed oxidation reactions); and the NIH NIGMS, R01 GM100143 and R35 GM134929 (use of molecular mediators for organic chemical synthesis).
General H2O Outgassing Model Applicable to Silica-Filled Silicones
Moisture-induced agining and degradation are continuing concerns in long-term storage and use of silicone materials. The silica fillers frequently used to provide mechanical reinforcement in silicone formulations have a high affinity for H2O and act as a source of moisture outgassing later when placed in dry or vacuum applications.
Phase Identification of the Layered Perovskite Ce x Sr 2– x MnO 4 and Application for Solar Thermochemical Water Splitting
Ruddlesden–Popper (layered perovskite) phases are attracting significant interest because of their unique potential for many applications requiring mixed ionic and electronic conductivity. Here we report a new, previously undiscovered layered perovskite of composition, Ce x Sr 2– x MnO 4 ( x = 0.1, 0.2, and 0.3). Furthermore, we demonstrate that this new system is suitable for solar thermochemical hydrogen production (STCH). Synchrotron radiation X-ray diffraction and transmission electron microscopy are performed to characterize this new system. Density functional theory calculations of phase stability and oxygen vacancy formation energy (1.76, 2.24, and 2.66 eV/O atom, respectively with increasing Ce content) reinforce the potential of this phase for STCH application. Experimental hydrogen production results show that this materials system produces 2–3 times more hydrogen than the benchmark STCH oxide ceria at a reduction temperature of 1400 °C and an oxidation temperature of 1000 °C.
Opportunities and Challenges of High-Energy Lithium Metal Batteries for Electric Vehicle Applications
Lithium metal batteries have great advantages over state-of-the-art lithium ion batteries in terms of energy density and cost, which present huge opportunities for long-range and low-cost electric vehicles in the future. However, the automotive industry has critical requirements on specific characteristics of battery cells, including but not limited to cycle life, working temperature range, fast discharge–charge capability, volume change, safety, and so on. In this Focus Review, we discuss both the cell- and system-level requirements and challenges of high-energy-density lithium metal batteries for future electrical vehicle applications and highlight some recent key progress in these aspects. Furthermore, our main objective is to identify key strategies on how to solve these challenges and inspire more research and development effort to accelerate the practical applications of lithium metal batteries.
Metal Organic Frameworks for Xenon Storage Applications
The demand for cheap and convenient xenon storage continues to rise due to its wide spectrum of applications. It is expected that solid-state adsorbents can provide significant advantages over the current isolated stainless-steel tank-based storage technologies. In this context, we investigated metal organic frameworks for use as adsorbents for xenon. Initially, three representative MOFs were synthesized and characterized in terms of Xe storage. The results were used to validate a computational modeling approach, which was later extended to a larger set of materials. The collected results allowed us to rationalize the key parameters (pore volume, surface area, void fraction etc.), which are important for good performance and selection of the best materials for xenon storage
Dye-Sensitized Core/Active Shell Upconversion Nanoparticles for Optogenetics and Bioimaging Applications
Near Infrared (NIR) dye-sensitized upconversion nanoparticles (UCNPs) have recently been proposed in order to broaden the absorption range and to boost upconversion efficiency. However, implementing this strategy has been limited only to bare core UCNP structures that are faintly luminescent. Herein, we report on an approach to achieve significantly enhanced upconversion luminescence in dye-sensitized core-active shell UCNPs with a broadened absorption range via the doping of ytterbium ions in the UCNP shell in order to bridge the energy transfer from the dye to the UCNP core. As a result, we have been able to synergize the two most practical upconversion booster effectors (dye-sensitizing and core/shell enhancement). The absolute quantum yield of our dye-sensitized core/active shell UCNPs at 800 nm was determined to be ~6% at 2 W/cm2, about 33 times larger than the highest value reported to date for existing 800 nm excitable UCNPs. Moreover, for the first time, by using dye-sensitized core/active shell UCNP embedded poly(methyl methacrylate) polymer implantable systems, we successfully shifted the optogenetic neuron excitation window to a wavelength that is compatible with deep tissue penetrable near the infrared wavelength at 800 nm. Finally, amphiphilic triblock copolymer, Pluronic F127 coatings permit the transfer of hydrophobic UCNPs into water, resulting in water-soluble nanoparticles with well-preserved optical property in aqueous solution. We believe that this research offers a new solution to enhance upconversion efficiency for photonic and biophotonic purposes and opens up new opportunities to use UCNPs as a NIR relay for optogenetic applications.
Automated Symbolic Upscaling: 1. Model Generation for Extended Applicability Regimes
Abstract In porous media theory, upscaling techniques are fundamental to deriving rigorous Darcy‐scale models for flow and reactive transport in subsurface systems. Due to limitations in classical techniques, a number of ad hoc approaches have been proposed to address physical regimes in which reactive time scales are similar to, or faster than, diffusive time scales. In Part 1 of this two part series, we present a strategy for expanding the applicability of classical homogenization theory by generalizing the assumed closure form. We detail the implementation of this strategy on two reactive mass transport problems with moderately reactive physics. The strategy produces nontrivial homogenized models with emergent terms and effective parameters that couple reactive, diffusive, and advective transport. The differences in equation forms between the macroscopic and pore‐scale descriptions advise caution to further studies where the forms of macroscopic equations are assumed, as opposed to rigorously derived. Numerical validation is provided for each problem to show that the error estimates of homogenization theory are satisfied, and to justify the implemented strategy. In Part 2, the presented strategy is automated using symbolic computing to expedite its implementation.