Sensitivity to energy technology costs: A multi-model comparison analysis
Not Available
Bosetti, Valentina↗
SEARCH · Search NASA
Results for “Modeled”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A model for droplet heating and evaporation of water-in-oil emulsified fuel
Water-in-oil (W/O) emulsified fuel is a promising alternative fuel by inducing flash boiling of water at high temperature that can improve the atomization of fuel spray. The heating and evaporation process of emulsified fuel droplet is affected by the diffusion and coalescence of small dispersed water droplets in oil during the heating. In this study, a model is developed for the droplet heating and evaporation of W/O emulsified fuel with these key physics considered. The diffusivity of dispersed water droplets in oil is calculated using the Stokes-Einstein equation. The deactivation temperature of surfactant is selected as a criterion for the water coalescence since the water coalescence is due to the deactivation of surfactant. The process of water coalescence is simplified that the dispersed water droplets coalesce into one single water sub-droplet at the center of the oil droplet instantly as the droplet temperature reaches the deactivation temperature of surfactant. The model is validated against experimental data of single droplet under different heating temperatures, surfactant concentrations, and sizes of dispersed water droplets. Based on the proposed model, the effects of fuel properties and heating conditions on the droplet heating and evaporation are analyzed.
Droplet coalescence↗
Interferometric SAR modelling of near surface data to improve geological model in the Surat Basin, Australia
Here we present a study on geophysical inverse modelling for subsurface structural properties of an unconventional hydrocarbon site that was monitored previously by Interferometric Synthetic Aperture Radar (InSAR) technology for surface deformation. A static three-dimensional geomodel along with extracted property maps replicates the depth of each underlying stratigraphic unit and structural feature with the density of each geological layer. We examine the hypothesis that integration of elastic properties of each formation layer with InSAR observations in a stratified elastic medium will lead to a viscoelastic geophysical inverse problem that can be solved to estimate fractional volume change at the reservoir level. Moreover, we examine synthetic scenarios in which the elastic properties of the formations are perturbed before determining the resulting impact on the rate of surface deformation. The results show that although the slope of underlying formations, their density and depth can define the extent and pattern of a deformation signal, their properties have a marginal impact on volumetric change compared to the dense network of shallow depth Coal Seam Gas (CSG) mining wells. Besides, it is also demonstrated that the inversion of InSAR deformation maps can resolve the uncertainties associated with low-resolution seismic interpretation as well as filling the data gaps within seismic acquisitions. A significant contribution of this investigation to the geological basin modelling involves a) introducing a remote and non-invasive technology such as InSAR to improve geophysical mapping of subsurface structures such as faults in areas with sparse or no reflective seismic information, and b) applying a multi-layer viscoelastic geophysical source model for an unconventional hydrocarbon reservoir such as CSG.
58 GEOSCIENCES↗
Modeling the homogenization kinetics of as-cast U-10wt% Mo alloys
Not Available
Xu, Zhijie (ORCID:0000000304594531)↗
Model predictive control of a rotary kiln for fast electric demand response
Not provided.
Engineering↗
A Comparison of Exact and Model Exchange–Correlation Potentials for Molecules
Accurate exchange–correlation (XC) potentials for three-dimensional systems–via solution of the inverse density functional theory (DFT) problem–are now available to test the quality of DFT approximations. Herein, the exact XC potential for seven molecules–dihydrogen at four different bond-lengths, lithium hydride, water, and ortho-benzyne–are computed from full configuration interaction reference densities. These are compared to model XC potentials from nonlocal (B3LYP, HSE06, SCAN0, and M08-HX) and semilocal/local (SCAN, PBE, and PW92) XC functionals. Whereas for most systems, relative errors in the ground-state densities are O(10 –3 –10 –2 ) , the model XC potentials have much higher errors of O(10 –1 –10 0 ) . Among the model XC functionals, SCAN0 offers the best agreement with the exact XC potential, underlining the significance of satisfying exact conditions as well as including nonlocal effects in XC functionals. This work indicates that tests against the exact XC potential will provide a promising new direction for building more accurate XC functionals for DFT.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Te/CdTe and Al/CdTe Interfacial Energy Band Alignment by Atomistic Modeling
A synergistic approach that incorporates first-principles atomistic modeling with numerical device simulations is used to systematically evaluate the role of heterointerfaces within metal-chalcogenide-based photovoltaic technologies. Two interfaces involving either a tellurium back contact or aluminum back electrode combined with a cadmium telluride absorber layer within cadmium-telluride-based thin-film solar cells are investigated on an atomic scale to determine the mechanisms contributing to variations in device performance. Electronic structures and predicted charge transport behavior with respect to cadmium and tellurium termination of the absorber layer are studied along the polar oriented CdTe{111} facets. The computational methodology reveals a noticeable contrast between the Schottky barrier forming Al/CdTe interface versus the Type I Te/CdTe heterojunction. Greater band bending features are exhibited by the cadmium termination as opposed to the tellurium termination for each interface case. Subsequent device modeling suggests that 3.6% higher photovoltaic conversion efficiency is achievable for the cadmium termination relative to the tellurium termination of the Te/CdTe interface. Based strictly on an idealistic representation, both interface models show the importance of atomic-scale interfacial properties for cadmium telluride solar cell device performance with their bulk properties being validated in comparison to published experimental data. Here, the synergistic approach offers a suitable method to analyze solar cell interfaces through a predictive computational framework for the engineering and optimization of metal-chalcogenide-based thin-film photovoltaic technologies.
Green’s function↗
Machine learning model to project the impact of COVID-19 on U.S. motor gasoline demand
Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people's resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.
COVID-19 pandemic↗
Modelling grain-scattered ultrasound in austenitic stainless-steel welds: A hybrid model
The ultrasonic inspection of austenitic stainless steel welds can be challenging due to their coarse grain structure, charaterised by preferentially oriented, elongated grains. The anisotropy of the weld is manifested as both a ‘steering’ of the beam and the back-scatter of energy due to the macroscopic granular structure of the weld. However, the influence of weld properties, such as mean grain size and orientation distribution, on the magnitude of scattered ultrasound is not well understood. A hybrid model has been developed to allow the study of grain-scatter effects in austenitic welds. An efficient 2D Finite Element (FE) method is used to calculate the complete scattering response from a single elliptical austenitic grain of arbitrary length and width as a function of the specific inspection frequency. A grain allocation model of the weld is presented to approximate the characteristic structures observed in austenitic welds and the complete scattering behaviour of each grain calculated. This model is incorporated into a semi-analytical framework for a single-element inspection of a typical weld in immersion. Experimental validation evidence is demonstrated indicating excellent qualitative agreement of SNR as a function of frequency and a minimum SNR difference of 2 dB at a centre frequency of 2.25 MHz. Additionally, an example Monte-Carlo study is presented detailing the variation of SNR as a function of the anisotropy distribution of the weld, and the application of confidence analysis to inform inspection development.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Modeling methane dynamics in three wetlands in Northeastern China by using the CLM-Microbe model
Wetlands account for up to 70% of the natural source of methane (CH 4 ) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH 4 in wetlands. Therefore, simulating microbial mechanisms of CH 4 production and consumptionwould improve the predictability of CH 4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH 4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH 4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH 4 flux across sites. Marsh wetlands had higher CH 4 flux than mountain peatlands. Ebullition dominated the CH 4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH 4 flux, making all wetlands a CH 4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH 4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH 4 dynamics under the changing environment.
54 ENVIRONMENTAL SCIENCES↗
Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison
The economics of climate change involves a vast array of uncertainties, complicating our understanding of climate change. This study explores uncertainty in baseline trajectories using multiple integrated assessment models commonly used in climate policy development. The study examines model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the probability distributions of key output variables, including CO2 concentrations, temperature, damages, and social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting distributions provide a useful input into climate policy discussions
climate policy, integrated assessment models, Unce↗
The curled wake model: equivalence of shed vorticity models
In this work, we explore the spanwise velocities in the wake of a yawed wind turbine. In the curled wake model, these motions are induced by a collection of vortices shed from the rotor plane. The direction of the vorticity generated by yaw is aligned with the main flow (streamwise) direction. The streamwise vorticity induces velocities in the spanwise directions. These are the motions responsible for creating the curled wake mechanism. In this work, we explore a more accurate formulation for this mechanism, using a vortex cylinder. Under certain assumptions, the new and original curled wake models yield the same mathematical formulation. Also, both models predict an elliptic distribution of vortex strength, where the main difference is the location of the vortices.
17 WIND ENERGY↗
Three-dimensional Dendritic Needle Network model with application to Al-Cu directional solidification experiments
Abstract not provided
Tourret, D.↗
Chelation Model Validation: Modeling of a Plutonium-238 Inhalation Incident Treated with DTPA at Los Alamos National Laboratory
Accidental inhalation of plutonium at the workplace is a non-negligible risk, even when rigorous safety standards are in place. The intake and retention of plutonium in the human body may be a source of concern. Thus, if there is a suspicion of a significant intake of plutonium, medical countermeasures such as chelation treatment may be administered to the worker. The present work aimed to interpret the bioassay data of a worker involved in an inhalation incident due to a glovebox breach at Los Alamos National Laboratory’s plutonium facility. The worker was treated with intravenous injections of calcium salts of diethylenetriaminepentaacetic acid (DTPA) in an attempt to reduce the amount of plutonium from the body and therefore reduce the internal radiation dose. It is well known in the internal dosimetry field that the administration of chelation treatment poses additional challenges to the dose assessment. Hence, a recently developed chelation model was used for the modeling of the bioassay data. The objectives of this work are to describe the incident, model the chelation-affected and non-affected bioassay data, estimate the plutonium intake, and assess the internal radiation dose.
238Pu↗
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.
PDEs↗