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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Slides to be presented at NEAMS-Studsvik workshop to establish collaboration. Slides detail current NEAMS efforts to develop a physics-based model of pulverization of high-burnup UO2 fuel for the BISON code.
The National Risk Assessment Partnership (NRAP) has developed a suite of tools to assess and manage risk at CO 2 sequestration sites. The NRAP tool suite includes the Aquifer Impact Model (AIM), which evaluates the potential for groundwater impacts from leaks of CO 2 and brine through abandoned wellbores. There are two aquifer reduced-order models (ROMs) included with the AIM tool, a confined alluvium aquifer, and an unconfined carbonate aquifer. The models accept aquifer parameters as a range of variable inputs so they may have broad applicability. The generic aquifer models may be used at the early stages of site selection, when site-specific data is not available. Guidelines have been developed for determining when the generic ROMs might be applicable to a new site. This work considers the application of the AIM to predicting the impact of CO 2 or brine leakage were it to occur at the Illinois Basin Decatur Project (IBDP). Results of the model sensitivity analysis can help guide characterization efforts; the hydraulic parameters and leakage source term magnitude are more sensitive than clay fraction or cation exchange capacity. Sand permeability was the only hydraulic parameter measured at the IBDP site. More information on the other hydraulic parameters could reduce uncertainty in risk estimates. Some non-adjustable parameters are significantly different for the ROM than for the observations at the IBDP site. The generic ROMs could be made more useful to a wider range of sites if the initial conditions and no-impact threshold values were adjustable parameters.
The use of digital equipment has not been widespread in U.S. nuclear facilities. A major contributing factor to this situation arises from regulatory concerns about common cause failures (CCFs) stemming from the use of identical software in redundant systems, questions as to how to quantify the reliability of software-based products, validity of tools that are used for software verification and validation (V&V), and the effectiveness of software QA procedures. Compounding the concerns are sporadic incidents of digital equipment malfunctions in nuclear power plants, including lockups resulting from software memory leaks, feedwater control malfunctions due to excessive traffic on Ethernet networks, software logic errors, and other problems leading to plant trips. Additionally, the limited amount of demonstrated research and a lack of industry consensus on quantitative software reliability methodologies has hindered the widespread adoption of digital I&C in the nuclear industry. In a recent Regulatory Issue Summary (RIS), the U.S. Nuclear Regulatory Commission (NRC) identified concerns about the impact of greater use of instrumentation and control (I&C) equipment with an embedded digital device (EDD) [1]. Specifically, the NRC staff states that increased use of such devices “may increase a facility’s vulnerability to a CCF” (i.e., common-cause failure). The prevailing NRC guidance regarding software CCF (SCCF) identifies two design attributes that are acceptable for eliminating CCF concerns: (1) diversity or (2) testability (specifically, 100% testability). Either solution can result in high costs and remaining licensing uncertainty (i.e., how much diversity is enough? how to ensure test coverage of every possible sequence of device states?). Consequently, many utilities and reactor designers have limited or avoided more extensive use of digital technology to minimize licensing, scheduling, and financial risk. Without development of cost effective qualification methods to satisfy regulatory requirements and address the potential for CCF vulnerability associated with EDDs, the nuclear power industry may not be able to realize the benefits of digital technology achieved by other industries. In order to address these challenges, the U.S. Department of Energy (DOE) is sponsoring this research to assess the prospect of CCF in digital I&C equipment. The purpose of the current research is to develop an effective approach employing science-based methods to resolve concerns about CCF vulnerability that serve to inhibit deployment of advanced instrumentation (e.g., sensors, actuators, microcontrollers) with EDDs in nuclear power applications. The research objectives address the challenge of establishing high levels of safety and reliability assurance needed for the qualification of EDDs (e.g., microprocessors, programmable logic devices) that are subject to software design faults, complex failure modes, and CCF vulnerability. Specific objectives are: (1) assess the regulatory context for treatment of CCF vulnerability a graded approach to their qualification, (3) develop and extend model-based testing methods to enable effective demonstration of whether devices are subject to CCF, which may arise from vulnerabilities introduced at any stage of the design lifecycle, (4) establish a cost-effective testing framework that incorporates automation and test scenario prioritization, and (5) demonstrate the qualification approach through selection and testing of candidate digital device(s). This report documents an assessment of the results from experimental testing of a representative instrument with an embedded digital device. The experiment serves as a proof-of-concept demonstration of a model-based testing (MBT) framework. The objective of MBT is to ensure that an EDD undergoes suitably comprehensive testing to establish that it is not subject to SCCF. The approach by which MBT accomplishes this objective is through development and application of an effective test suite that provides the appropriate coverage to detect the full range of postulated faults that may arise at the requirements, design, and code (implementation) level of the EDD software. The findings of the experiment involve the application of the MBT framework by Ohio State University (OSU) through testing of the selected demonstration instrument (i.e., the prototype smart pressure sensor developed by Virginia Commonwealth University, VCU). In addition, the experiment findings also involve application of a more conventional black-box testing approach that serves as a baseline against which the MBT methodology can be compared. The primary outcome documented in this report is the comparative analysis of the capabilities demonstrated for each method. The results of the analysis demonstrate clear benefits that arise from the MBT methodology. The comparison indicates greater effectiveness from the more comprehensive evidence available through MBT and enhanced efficiency achieved through automation within the MBT framework. The structure of the report addresses the approach and application of each test method as well as the comparative analysis of the experimental results. Chapter 2 gives a summary overview of the demonstration approach. Chapter 3 covers the baseline testing and Chapter 4 documents the model-based testing. Chapter 5 describes the findings of the comparative analysis and presents the conclusions that can be drawn about the effectiveness and efficiency of the MBT methodology.
Sockeye is a MOOSE-based computer code designed to simulate the thermal-hydraulic two-phase flows of heat pipes in a nuclear micro-reactor. For this application, a significantly modified version of the fully non-equilibrium, compressible, 7-equation, two-phase flow model of RELAP-7 was developed. This summary report documents the significant points of this model development.
The pharmacokinetics of nicotine, the pharmacologically active alkaloid in tobacco responsible for addiction, are well characterized in humans. We developed a physiologically based pharmacokinetic/pharmacodynamic model of nicotine pharmacokinetics, brain dosimetry and brain nicotinic acetylcholine receptor (nAChRs) occupancy. A Bayesian framework was applied to optimize model parameters against multiple human data sets. The resulting model was consistent with both calibration and test data sets, but in general underestimated variability. A pharmacodynamic model relating nicotine levels to increases in heart rate as a proxy for the pharmacological effects of nicotine accurately described the nicotine related changes in heart rate and the development and decay of tolerance to nicotine. The PBPK model was utilized to quantitatively capture the combined impact of variation in physiological and metabolic parameters, nicotine availability and smoking compensation on the change in number of cigarettes smoked and toxicant exposure in a population of 10,000 people presented with a reduced toxicant (50%), reduced nicotine (50%) cigarette Across the population, toxicant exposure is reduced in some but not all smokers. Reductions are not in proportion to reductions in toxicant yields, largely due to partial compensation in response to reduced nicotine yields. This framework can be used as a key element of a dosimetry-driven risk assessment strategy for cigarette smoke constituents.
Transposition models have been widely used in the solar energy industry to simulate solar radiation on inclined PV panels. Following numerous studies comparing the performance of transposition models, this paper aims to understand the quantitative uncertainty in the state-of-the-art transposition models and the sources leading to the uncertainty. Our results show significant differences between two highly used isotropic transposition models with one substantially underestimating the diffuse plane-of-array (POA) irradiances when diffuse radiation is perfectly isotropic. In the empirical transposition models, the selection of empirical coefficients and land surface albedo can both result in uncertainty in the output. This study can be used as a guide for future development of physics-based transposition models.
It has been shown that shared automated electric vehicles (SAEVs) hold great promise for improving transportation access in urban centers while drastically reducing transportation-related energy consumption and air pollution. Using taxi-trip data from New York City, we develop an agent-based model to predict the battery range and charging infrastructure requirements of a fleet of SAEVs operating on Manhattan Island. We also develop a model to estimate the cost and environmental impact of providing service and perform extensive sensitivity analysis to test the robustness of our predictions. We estimate that costs will be lowest with a battery range of 50-90 mi, with either 66 chargers per square mile, rated at 11 kW or 44 chargers per square mile, rated at 22 kW. We estimate that the cost of service provided by such an SAEV fleet will be $0.29-$0.61 per revenue mile, an order of magnitude lower than the cost of service of present-day Manhattan taxis and $0.05-$0.08/mi lower than that of an automated fleet composed of any currently available hybrid or internal combustion engine vehicle (ICEV). We estimate that such an SAEV fleet drawing power from the current NYC power grid would reduce GHG emissions by 73% and energy consumption by 58% compared to an automated fleet of ICEVs.
The effect of hydration on the molecular structure of amorphous poly(D,L-lactic acid) (PDLLA) with 50:50 L-to-D ratio has been studied by combining experiments with molecular simulations. X-ray diffraction measurements revealed significant changes upon hydration in the structure functions of the copolymer. Large changes in the structure functions at 10 days of incubation coincided with the large increase in the water uptake from {approx} 1 to {approx} 40% and the formation of voids in the film. Computer modeling based on the recently developed TIGER2/TIGER3 mixed sampling scheme was used to interpret these changes by efficiently equilibrating both dry and hydrated models of PDLLA. Realistic models of bulk amorphous PDLLA structure were generated as demonstrated by close agreement between the calculated and the experimental structure functions. These molecular simulations were used to identify the interactions between water and the polymer at the atomic level including the change of positional order between atoms in the polymer due to hydration. Changes in the partial O-O structure functions, about 95% of which were due to water-polymer interactions, were apparent in the radial distribution functions. These changes, and somewhat smaller changes in the C-C and C-O partial structure functions, clearly demonstrated the ability of the model to capture the hydrogen-bonding interactions between water and the polymer, with the probability of water forming hydrogen bonds with the carbonyl oxygen of the ester group being about 4 times higher than with its ether oxygen.
Water hyacinth (Eichhornia crassipes) is a fast-growing, invasive aquatic species and represents a potential biomass resource for bioenergy production in the Togo and Kenya regions on the African continent. Literature shows multiple conversion pathways exist to produce energy from water hyacinth while supporting economic growth, job creation, and environmental sustainability. ECO2LIBRIUM (ECO2) is investigating the feasibility of developing feedstock logistics modelling capabilities to facilitate water hyacinth based bioenergy production. Developing a water hyacinth feedstock pipeline capable of supplying biomass at consistent quantity and quality requires sophisticated feedstock logistics modelling to minimize costs while maximizing ecosystem services. However, developing this type of modelling capability requires specific spatial data and analysis resources. Idaho National Laboratory (INL) maintains research capabilities in the areas of bioenergy, feedstock logistics, spatial analysis, and remote sensing. INL researchers have compiled spatial data and analysis resources in this report to back development of a water hyacinth biomass feedstock logistics model.
An experimental 30 kW boost composite converter is described in this paper. The composite converter architecture, which consists of a buck module, a boost module, and a dual active bridge module that operates as a DC transformer (DCX), leads to substantial reductions in losses at partial power points, and to significant improvements in weighted efficiency in applications that require wide variations in power and conversion ratio. A comprehensive loss model is developed, accounting for semiconductor conduction and switching losses, capacitor losses, as well as dc and ac losses in magnetic components. Based on the developed loss model, the module and system designs are optimized to maximize efficiency at a 50% power point. Experimental results for the 30 kW prototype demonstrate 98.5%peak efficiency, very high efficiency over wide ranges of power and voltage conversion ratios, as well as excellent agreements between model predictions and measured efficiency curves.
Particle bonding is crucial to coating quality in cold spray, but it has been a challenge to accurately quantify bonding even in single particle impacts. This paper uses FIB-SEM to explicitly map the particle-substrate interface for Cu-on-Cu single microparticle impacts in a full 3D rendering that spans a wide range of impact velocities. This approach permits a detailed quantification of the total bonding area and all of its associated components. In addition to revealing why prior 2D characterization efforts have missed important details about impact bonding, these data quantitatively reveal the evolution of bonding from its onset at the “critical velocity” V cr (where bonding is generally poor, ∼6 %) to its peak at around 1.3‧V cr (where almost 90 % of the particle bonds). Further increase in the velocity to 1.5‧V cr and beyond finds the onset of hydrodynamic penetration and a decrease in bonding. These data then support the development of a simple analytical model based on oxide rarefication and extrusion of bare metal through gaps in the oxide layer as driving the development of bonding. As a result, the model reproduces the experiments and provides guidance on optimization of bonding as a function of material and process parameters.
Building energy estimation for the building sector under various scenarios are needed for building energy regulation and policy making. This often starts with representative baselines (either empirical baseline or modeled baseline). Commercial Buildings Energy Consumption Survey (CBECS) data is a widely used empirical baseline for U.S. commercial buildings, but none of the existing baseline model are developed to represent the CBECS data. This paper aims to develop new baseline models for the U.S. medium office buildings, which can produce modeled baselines consistent with the CBECS data. Here, we introduced the methodology to create baseline models and the criteria to evaluate the performance of baseline models. The methodology consists of three phases: (1) identification of model inputs, (2) model calibration, and (3) model validation with uncertainty analysis. The evaluation index is the coefficient of variation of the root-mean-square deviation (CV(RMSD)) of site energy use intensities (EUIs) between the modeled baseline and empirical baseline. Then 30 new baseline models for two vintages (pre- and post-1980) and 15 climate zones were created. The evaluation shows that the CV(RMSD) is lower than 0.05 for the modeled baselines produced by the new baseline models. As a comparison, the CV(RMSD) is higher than 0.1 for the existing modeled baselines generated by DOE Commercial Reference Building Models. Further analysis shows that the new baseline models are able to capture the uncertainties of the representative features of existing buildings.
II-VI quantum dots, such as CdSe and CdTe, are attractive as downconversion materials for solid-state lighting, because of their narrow linewidth, tunable emission. However, for these materials to have acceptable quantum yields (QYs) requires that they be coated with a II-VI shell material whose valence band offset serves to confine the hole to the core. Confinement prevents the hole from accessing surface traps that lead to nonradiative decay of the exciton. Examples of such hole-confined core/shell QDs include CdTe/CdSe and CdSe/CdS. Unfortunately, the shell can also cause problems due to lattice mismatch, which ranges from 4-6% for systems of interest. This lattice mismatch can create significant interface energies at the heterojunction and places the core under radial compression and the shell under tangential tension. At elevated temperatures (~240°C) interfacial diffusion can relax these stresses, as can surface reconstruction, which can expose the core, creating hole traps. But such high temperatures favor the hexagonal Wurtzite structure, which has lower QY than the cubic zinc blende structure, which can be synthesized at lower temperatures, ~140°C. In the absence of alloying the core/shell structure can become metastable, or even unstable, if the shell is too thick. This can cause result in an irregular shell or even island growth. But if the shell is too thin thermallyactivated transport of the hole to surface traps can occur. In our LDRD we have developed a fundamental atomistic modeling capability, based on Stillinger-Weber and Bond-Order potentials we developed for the entire II-VI class. These pseudo-potentials have enabled us to conduct large-scale atomistic simulations that have led to the computation of phase diagrams of II-VI QDs. These phase diagrams demonstrate that at elevated temperatures the zinc blende phase of CdTe with CdSe grown on it epitaxially becomes thermodynamically unstable due to alloying. This is accompanied by a loss of hole confinement and a severe drop in the QY and emission lifetime, which is confirmed experimentally for the zinc blende core/shell QDs prepared at low temperatures. These QDs have QYs as high as 95%, which makes them very attractive for lighting. Finally, to address strain relaxation in these materials we developed a model for misfit dislocation formation that we have validated through atomistic simulations.
Purpose: Quantitative accuracy of Iodine Hounsfield Unit (HU) in conventional single-kVp scanning is susceptible to beam-hardening effect. Dual-energy CT has unique capabilities of quantification using monochromatic CT images, but this scanning mode requires the availability of the state-of-the-art CT scanner and, therefore, is limited in routine clinical practice. Purpose of this work was to develop a beam-hardening-correction (BHC) for single-kVp CT that can linearize Iodine projections at any nominal energy, apply this approach to study Iodine response with respect to keV, and compare with dual-energy based monochromatic images obtained from material-decomposition using 80kVp and 140kVp. Methods: Tissue characterization phantoms (Gammex Inc.), containing solid-Iodine inserts of different concentrations, were scanned using GE multi-slice CT scanner at 80, 100, 120, and 140 kVp. A model-based BHC algorithm was developed where Iodine was estimated using re-projection of image volume and corrected through an iterative process. In the correction, the re-projected Iodine was linearized using a polynomial mapping between monochromatic path-lengths at various nominal energies (40 to 140 keV) and physically modeled polychromatic path-lengths. The beam-hardening-corrected 80kVp and 140kVp images (linearized approximately at effective energy of the beam) were used for dual-energy material-decomposition in Water-Iodine basis-pair followed by generation of monochromatic images. Characterization of Iodine HU and noise in the images obtained from singlekVp with BHC at various nominal keV, and corresponding dual-energy monochromatic images, was carried out. Results: Iodine HU vs. keV response from single-kVp with BHC and dual-energy monochromatic images were found to be very similar, indicating that single-kVp data may be used to create material specific monochromatic equivalent using modelbased projection linearization. Conclusion: This approach may enable quantification of Iodine contrast enhancement and potential reduction in injected contrast without using dual-energy scanning. However, in general, dual-energy scanning has unique value in material characterization and quantification, and its value cannot be discounted. GE Healthcare Employee.
Impedance spectra of a PEM fuel cell with three Fe-N-C cathodes have been measured under the H 2 /N 2 testing regime. The spectra have been fitted using a recently developed physics-based impedance model, which takes into account finite proton ($σ_p$) and electron ($σ_e$) conductivity of the oxygen-free cathode catalyst layer. Fitting allowed to extract numerical data for $σ_p$, $σ_e$, the double layer capacitance, and the inductance of cables used for measuring impedance spectra. The values of σp and σe are close to what previously found for standard Pt/C electrodes, which is found for the first time using PGM-free catalysts. The method enables simultaneous measurement of reference proton and electron conductivity of PEMFC cathode.
Phenomenological CALPHAD (CALculation of PHAse Diagrams) models, widely used for multicomponent materials, often contain a considerable number of parameters and require fitting using data from a relatively small number of experimental measurements or theoretical calculations. Sometimes these parameters are introduced for the purpose of improving model fits but without clear physical justification, which leads to overparametrized models with poor generalization performance. Automated approaches for optimal model selection based on the available data therefore become critical. Here, in this work, a least absolute shrinkage and selection operator (LASSO)-based approach is developed for model selection by leveraging the linearity of the CALPHAD model with respect to its parameters to convert the model selection and fitting to a LASSO minimization problem. We demonstrate its utility for thermodynamic modeling of thermochemical hydrogen (TCH) production materials using lanthanum strontium manganite (LSM) as an example. Various TCH-relevant properties, including oxygen stoichiometry as a function of oxygen partial pressure, enthalpy of reduction, and entropy of reduction, are successfully predicted with reasonable accuracy using a minimal set of model parameters. Importantly, the model selection and fitting involve minimal human decision; it can therefore be applied to high-throughput DFT defect calculations and yield efficient workflows for TCH material modeling and optimization.
The Solar Resource Calibration, Measurement, and Dissemination project supports the U.S. Department of Energy's Solar Energy Technologies Office (SETO) initiative to make solar energy cost-competitive with other forms of electricity by improving the tools and methods to measure and model solar radiation and therefore reduce uncertainty in predicting solar output and improve the bankability of solar projects. This project has three tasks that conduct research on advancing solar resource measurements and modeling: Task 1: Applied solar Radiation Measurements. Task 2: National Solar Radiation Data Base (NSRDB). Task 3: Knowledge Sharing. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) activities ensure traceable solar measurements throughout the United States that are essential for project feasibility, due diligence, financing, and plant operations. The SRRL houses NREL's Baseline Measurement System (BMS), which provides a platform for improving the accuracy of solar measurements through research to improve the calibration and characterization of instruments and enable the development of new measurement technology and standards. The platform contains a variety of instruments measuring solar radiation and other meteorological parameters. Following best practices, BMS instruments are calibrated to ensure traceability to the World Radiometric Reference in compliance with ISO/IEC 17025 accreditation requirements. The BMS data sets are quality-controlled and made publicly available through the Measurement and Instrumentation Data Center website. The NSRDB is the most accessed public database of solar irradiance and associated weather parameters for use in energy modeling. It supports SETO's goals of reducing barriers to high penetrations of solar technologies by providing easy access to high-quality, foundational data essential for innovative product development and downstream modeling through the NSRDB website. Data are currently available for the contiguous United States and beyond for 20 years covering 1998-2017. The Physical Solar Model (PSM) that underpins the current NSRDB data sets is a unique physics-based model that has been developed at NREL and has opened the door to the use of next-generation satellite data sets for solar resource assessment and forecasting. Through the American Society for Testing Materials (ASTM) International and other international standards organizations, NREL continues to provide leadership in the development of standards that are relevant to the measurement and modeling of solar radiation for solar energy applications. Standards developed cover the calibration method of pyrheliometers (direct normal irradiance measurements), pyranometers (global horizontal irradiance measurements), spectroradiometers, ultraviolet radiometers, correct field deployment of these instruments, spectral distribution reference tables for photovoltaic modeling, and transfer of indoor calibrations. Further, NREL leads the International Energy Agency (IEA) Photovoltaic Power Systems Programme Task 16: Subtask 1, which focuses on the evaluation of current and emerging resource assessment methodologies. NREL and the IEA updated the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, which is widely used by the solar industry.
In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.