Biojet fuels and emissions mitigation in aviation: An integrated assessment modeling analysis
Not Available
SEARCH · Search NASA
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.
Not Available
Accurate model hindcast of wave climate in complex estuarine systems is challenging because high-resolution wave model and wind forcing are required. In this study, a modeling approach using the unstructured-grid Simulating WAves Nearshore and wind product from a high-resolution regional Weather Research and Forecasting hindcast was used to simulate the swell and wind-sea climate in the Salish Sea, a large estuary with many interconnected waterways on the Pacific Northwest coast of North America. The model hindcast was validated with observed data at four wave buoys in the Salish Sea. Spatial distribution and seasonal variations of wave climate in the Salish Sea were analyzed. Of the three major basins in the Salish Sea, the Strait of Juan de Fuca has the largest waves and is dominated by swells propagated from the Pacific Ocean. Significant wave heights in the Strait of Georgia have spatial and seasonal distribution patterns similar to those found in the Strait of Juan de Fuca. Waves in Puget Sound are small and primarily dominated by the wind-sea climate. Strong seasonal variations are observed in the Strait of Juan de Fuca and Strait of Georgia, but there is little seasonality of wave climate in Puget Sound. The high-resolution wave hindcast conducted in this study provides a comprehensive and important data set for better understanding the role of wave climate in coastal processes and natural hazards assessment in the Salish Sea.
Not Available
A quick modeling analysis approach for predicting the slag-steel reaction and desulfurization kinetics in argon gas-stirred ladles has been developed in this study. The model consists of two uncoupled components: (i) a computational fluid dynamics (CFD) model for predicting the fluid flow and the characteristics of slag-steel interface, and (ii) a multicomponent reaction kinetics model for calculating the desulfurization evolution. The steel-slag interfacial area and mass transfer coefficients predicted by the CFD simulation are used as the processing data for the reaction model. Since the desulfurization predictions are uncoupled from the CFD simulation, the computational time of this uncoupled predictive approach is decreased by at least 100 times for each case study when compared with the CFD-reaction kinetics fully coupled model. The uncoupled modeling approach was validated by comparing the evolution of steel and slag compositions with the experimentally measured data during ladle metallurgical furnace (LMF) processing at Nucor Steel Tuscaloosa, Inc. Then, the validated approach was applied to investigate the effects of the initial steel and slag compositions, as well as different types of additions during the refining process on the desulfurization efficiency. The results revealed that the sulfur distribution ratio and the desulfurization reaction can be promoted by making Al and CaO additions during the refining process. It was also shown that by increasing the initial Al content in liquid steel, both Al oxidation and desulfurization rates rapidly increase. In addition, it was found that the variation of the initial Si content in steel has no significant influence on the desulfurization rate. Lastly, if the initial CaO content in slag is increased or the initial Al{sub 2}O{sub 3} content is decreased in the fluid-slag compositional range, the desulfurization rate can be improved significantly during the LMF process.
The Policy Analysis Modeling System is a spreadsheet tool developed to provide an estimate of costs and benefits of appliance efficiency standard and labeling programs. PAMS is a self-contained spreadsheet model that provides both a consumer-oriented analysis and a national cost-benefit analysis in the style of the analysis performed for U.S. appliance efficiency standards. The tool allows policy analysts from many countries to produce a first-cut analysis of appliance efficiency program costs and benefits, examine the sensitivities of the analysis with respect to different policy parameters and assumptions, and continually refine the analysis as more data becomes available. The methodology is a bottom-up approach, using technical specifications for particular products in estimating the increased cost to the consumer resulting from implementation of particular energy-saving designs. It is designed to operate for the widest possible variety of countries, and with as little need as possible for detailed input data. For more accurate results, it can be easily customized to use the most reliable country-specific data inputs. In addition to consumer financial impacts, the tool provides national primary energy savings and estimates of carbon emissions mitigation resulting from the program.
Recently, anomalies in the temperature dependences of the carrier density and/or mobility derived from analysis of the magnetoresistivities using the conventional two-band model have been used to unveil intriguing temperature-induced Lifshitz transitions in various materials. For instance, two temperature-driven Lifshitz transitions were inferred to exist in the Dirac nodal-line semimetal ZrSiSe, based on two-band model analysis of the Hall magnetoconductivities where the second band exhibits a change in the carrier type from holes to electrons when the temperature decreases below T=106K and a dip is observed in the mobility vs temperature curve at T=80K. Here, in this study, we revisit the experiments and two-band model analysis on ZrSiSe. We show that the anomalies in the second band may be spurious because the first band dominates the Hall magnetoconductivities at T>80K, making the carrier type and mobility obtained for the second band from the two-band model analysis unreliable. That is, care must be taken in interpreting these anomalies as evidence for temperature-driven Lifshitz transitions. Our skepticism on the existence of such phase transitions in ZrSiSe is further supported by the validation of Kohler's rule for magnetoresistances for T≤180K. In this paper, we showcase potential issues in interpreting anomalies in the temperature dependence of the carrier density and mobility derived from the analysis of magnetoconductivities or magnetoresistivities using the conventional two-band model.
Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.
Abstract Differences, arising from differences in gross primary production (GPP) model structures and driving forces, have fuelled arguments concerning interannual changes of GPP in China since 2000. To better investigate the interannual variability of GPP and its covariance with climate factors in China, this study adopted a multi‐model analysis based on three GPP models (i.e., Terrestrial Ecosystem Carbon flux model [TEC], Breathing Earth System Simulator model [BESS], and MOD17 GPP model). The results show that annual GPP in China increased by 0.021–0.057 Pg C year −1 from 2000 to 2015 attributable to atmospheric‐CO 2 fertilization effects and favourable climate change, that is, increasing precipitation ( P r ) and temperature ( T a ). However, northern China and southern China had a large difference in the amplitude of these GPP changes; annual GPP increased by 0.017–0.039 Pg C year −1 in northern China but only 0.001–0.018 Pg C year −1 in southern China. Northern China and southern China occupy contrasting climate zones and this contrast produced different interannual variability of GPP through different mechanisms. Northern China has a dry climate with GPP changes sensitive to P r . As a result, more P r along with higher T a in northern China produced the strong uptrend of GPP from 2000 to 2015. In contrast, southern China has a wet climate with its GPP sensitive to solar radiation and T a . For the interval of 2000–2015, decreasing radiation plus drought exerted a negative influence on GPP in southern China. This study highlights the diverse mechanisms in which climate change affects GPP in dry and wet climate zones. A robust multi‐model analysis is preferred to reduce uncertainties arising from a single GPP model and its driving data.
Earth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long-term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long-term SOC dynamics under scenarios of alternating drying-wetting cycles using the Microbial-ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R 2 = 0.67) and validation (R 2 = 0.69) against heterotrophic respiration. We then used the model to simulate the long-term SOC dynamics under five scenarios of alternating drying-wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m -2 after the 100-year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions.
With an increased dataset obtained during the Run-2 of the LHC at CERN, the even larger forthcoming Run-3 data and the expected increase of the dataset by more than one order of magnitude for the HL-LHC, the AT-LAS experiment is reaching the limits of the current data production model in terms of disk storage resources. The anticipated availability of an improved fast simulation will enable ATLAS to produce significantly larger Monte Carlo samples with the available CPU, which will then be limited by insufficient disk resources. The ATLAS Analysis Model Study Group for Run-3 was setup at the end of Run-2. Its tasks have been to analyse the efficiency and suitability of the current analysis model and to propose significant improvements. The group has considered options allowing ATLAS to save, for the same sample of data and simulated events, at least 30% disk space overall, and has given recommendations on how significantly larger savings could be realised for the HL-LHC. Furthermore, suggestions were made to harmonise the current stage of analysis across the collaboration. The group has now completed its work: key recommendations will be the new small sized analysis formats DAOD_PHYS and DAOD_PHYSLITE and the increased usage of a tape carousel mode in the centralised production of these formats. This proceeding reviews the recommended ATLAS analysis model for Run-3 and the status of its implementation. It also provides an outlook to the HL-LHC analysis.
PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.
Researchers at Pacific Northwest National Laboratory served as members of a U.S. Nuclear Regulatory Commission review team for the Florida Power & Light Company’s application for two combined construction permits and operating licenses (combined licenses or COLs) for two proposed new reactor units—Turkey Point Units 6 and 7. The review team evaluated the environmental impacts of the proposed action based on the October 29, 2014 revision of the COL application, including the Environmental Report, responses to requests for additional information, and supplemental information. As part of this effort, team members tasked with assessing the environmental effects of proposed construction and operation of Units 6 and 7 at the Turkey Point site reviewed two separate modeling studies that analyzed the interaction between surface water and groundwater that would be altered by the operation of radial collector wells (RCWs) at the site. To further confirm their understanding of the groundwater hydrodynamics and to consider whether certain actions, proposed after the two earlier modeling studies were completed, would alter the earlier conclusions documented by the review team in their draft environmental impact statement (EIS; NRC 2015), a third modeling analysis was performed. The third modeling analysis is discussed in this report.
We present the first systematic dispersive-optical-model analysis of 16,18 O, 40,48 Ca, 58,64 Ni, 112,124 Sn, and 208 Pb. In all systems, roughly half the total nuclear binding energy is associated with the most-bound 10% of the total nucleon density. The extracted neutron skins reveal interplay of asymmetry, Coulomb, and shell effects on the skin thickness. Here, our results indicate that optical-model fits on isotopic pairs are effective for constraining nuclear structural quantities otherwise difficult to observe experimentally.
With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energy-economy models using harmonized policy scenarios reaching economy-wide, net-zero CO 2 emissions across the United States in 2050. Here we employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and net-zero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.
Presentation for HPS Meeting. Comparison of Analyzed Soil Activation Results to FLUKA Analysis Model
plutus is an R package that post-processes outputs from the Global Change Analysis Model (GCAM) to calculate the power sector capital investment costs and stranded asset costs associated with GCAM projections of electricity generation by technology. GCAM is a market equilibrium model used to examine the dynamics of the coupled human-Earth system and the response of this system to global change, including socioeconomics, technology, climate, and policy. GCAM tracks electricity generation by technology and vintage over 32 geopolitical regions throughout the lifetime of each generating technology. plutus extends GCAM functionality by (1) estimating the foregone economic value of prematurely retired power plants as a result of economic-induced retirements compared to scheduled lifetimes; and (2) estimating the new installations and capital investments driven by future changes in economic, energy, agriculture, and land-use systems in GCAM.
The workshop identified how modeling and analysis can be used for energy system design, optimization, and planning to help identify opportunities to enhance the performance and potential of current and future energy systems, with a specific focus on integrated, hybrid energy systems. Comprehensive understanding of these systems requires models at different scales (from market to grid to process system to energy device). As discussed within the context of three detailed case studies, there is a need to develop useful datasets for a variety of purposes and to extend and connect existing models and analysis tools across scales and across domains to address the challenges associated with developing technologies to enable the integrated energy systems of the future.
In this work, a nonlocal dispersive optical model analysis is carried out for neutrons and protons in 208 Pb. Elastic-scattering angular distributions, total and reaction cross sections, single-particle energies, neutron and proton numbers, the charge distribution, and the binding energy are fitted to extract the neutron and proton self-energies both above and below the Fermi energy. From the single-particle propagator derived from these self-energies, we determine the charge and matter distributions in 208 Pb. The predicted spectroscopic factors are consistent with results from the ($\textit{e,e}'\textit{p}$) reaction and inelastic-electron-scattering data to very high-spin states. Sensible results for the high-momentum content of neutrons and protons are obtained, with protons appearing more correlated, in agreement with experiment and ab initio calculations of asymmetric matter. A neutron skin of 0.25 ± 0.05 fm is deduced. An analysis of several nuclei leads to the conclusion that finite-size effects play a nonnegligible role in the formation of the neutron skin in finite nuclei.