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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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Equilibrium temperature distribution, thermal emission, and limb function computed for Venus cloud cover model
Described are two basic types of theoretical models - radiative equilibrium and empirical - that are used to represent stellar chromospheres. The construction of radiative-equilibrium model atmospheres that show an outward temperature increase in the surface layers is reported. Also discussed is chromospheric cooling due to spectral lines. Solar empirical models describe the empirical determination of solar-type chromospheric models that, in order to match observations, imply a temperature rise substantially greater than that predicted by radiative equilibrium. Such a temperature rise must be largely due to mechanical heating. An attempt is made to apply a scaled solar chromospheric model to a star with a different surface gravity. The results suggest that the chromospheric optical thickness is sensitive to gravity and that the width of chromospheric line emission increases with stellar luminosity.
The topics covered include the following: general characteristics of sea ice drift; sea ice rheology; ice thickness distribution; sea ice thermodynamic models; equilibrium thermodynamic models; effect of internal brine pockets and snow cover; model simulations of Arctic Sea ice; and sensitivity of sea ice models to climate change.
A variety of liquid droplet evaporation models, including both classical equilibrium and non-equilibrium Langmuir-Knudsen formulations, are evaluated through comparisons with experiments with particular emphasis on computationally efficient procedures for many-droplet gas-liquid flow simulations.
The objective of the NREL Thermochemical Platform Analysis (WBS 2.1.0.302) project is to inform and guide R&D priorities for thermal and catalytic conversion processes by providing process design and techno-economic analysis (TEA). This is achieved through close collaboration with researchers and external experts, along with the use of both commercially available modeling tools and the development or use of collaboration-derived domain-specific tools and resources, such as refinery integration, kinetic and reactor models, phase equilibrium models, and pertinent bio-products market studies. This project is directly aligned with DOE-BETO goals, with the enabling of technology advancements and cost reduction for biomass derived biofuels being one of its primary objectives. TEA-guided research facilitated by this project has helped achieve significant modeled cost reductions for the ex situ catalytic fast pyrolysis (CFP) pathway and the indirect liquefaction (IDL) pathway for the conversion of syngas to high-octane gasoline (HOG). Cost reduction through refinery integration, development of valuable co-products, and other options are being identified for future research to help reduce the modeled MFSP to $2.50/GGE by 2030. Additional priorities anticipated in the future, such as the use of renewable electricity for liquid fuels and products, and emphasis on waste utilization are also being explored in conjunction with research on catalytic utilization of syngas and other gases (including CO2). Industry-relevant parameters are given deliberate attention as part of the work done under this project to help answer questions important for future commercialization and address associated risks.
The mixing and reaction of a supersonic jet of hydrogen in coaxial free and ducted high temperature test gases were investigated. The importance of chemical kinetics on computed results, and the utilization of free-jet theoretical approaches to compute enclosed flow fields were studied. Measured pitot pressure profiles were correlated by use of a parabolic mixing analysis employing an eddy viscosity model. All computations, including free, ducted, reacting, and nonreacting cases, use the same value of the empirical constant in the viscosity model. Equilibrium and finite rate chemistry models were utilized. The finite rate assumption allowed prediction of observed ignition delay, but the equilibrium model gave the best correlations downstream from the ignition location. Ducted calculations were made with finite rate chemistry; correlations were, in general, as good as the free-jet results until problems with the boundary conditions were encountered.
The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective56equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climatecsensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique amongst intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than un-aggregated simulations. Plain Language Summary This study investigates tropical clouds and climate using results from more than 30 different numerical models set up in a simplified framework. The dataset of model simulations is unique in that it includes a wide range of model types configured in a consistent manner. We address some of the biggest open questions in climate science, including how cloud properties change with warming and the role that the tendency of clouds to form clusters plays in determining the average climate and how climate changes. While there are large differences in how the different models simulate average temperature, humidity, and cloudiness, in a majority of models, the amount of high clouds decreases as climate warms. Nearly all models simulate a tendency for clouds to cluster together. There is agreement that when the clouds are clustered, the atmosphere is drier with fewer clouds overall. We don’t find a conclusive result for how cloud clustering changes as the climate warms.
We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.
An investigation of the effects of turbulence models on the prediction of transonic wing flows is performed. The turbulence models used in this study are the equilibrium model of Baldwin and Lomax, and the original and modified models of Johnson and King. Comparisons with experimental data are presented which show clearly that the modified Johnson-King model works much better than the equilibrium model.
Solutions for the equilibrium configuration of a slab with ionizing radiation incident equally from both sides are explored. Radiation effects (photoionization, Ly-alpha photon trapping, and mock gravity) as well as external pressure and self gravity (with and without dark matter) are included. The general formalism is applied to structure growth on small scales at very high z due to mock gravity on dust. Emphasis is placed on the application of slab models at z of less than 5, particularly those that may correspond to Ly-alpha forest, Lyman limit, and damped Ly-alpha systems. The regime with a dominant outward force contributed by trapping of Ly-alpha photons is discussed. General expressions are given for the equilibrium, including dark matter, assuming various relationships between the density of the dark matter halo and the total gas column density.
Kinetic theories relevant to the geomagnetic tail are reviewed. The topics discussed include kinetic instabilities, simulations, and current-sheet particle acceleration. Tearing mode and reconnection theories are emphasized. Kinetic treatment is appropriate for these topics since the tail plasma is collisionless. Fluid calculations are appropriate when stochastic processes dominate and for studies where long wavelengths are important. However, fluid treatments of tearing modes and reconnection require a finite resistivity in the diffusion region. Thus, although 'anomalous resistivity' can be guessed or in some cases calculated, ideally the kinetic treatment is often to be preferred. Particle motion and acceleration in the current sheet can give rise to beam-like distributions in the plasma-sheet boundary layer. Studies of current-sheet particle motion have also been used as the basis for 'kinetic' tail equilibrium models. Furthermore, quite recently current-sheet particle motion is used directly in Coroniti's explosive tail reconnection model. The 'inertial conductivity' from the equilibrium models provides the 'dissipation' necessary for reconnection.
In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used.
Estimating the equilibrium state for pebble bed reactors (PBRs) presents complex challenges as it requires simultaneous consideration of changes in the pebbles’ movement as well as their fuel compositions. Whereas traditional approaches use multigroup diffusion codes for neutronics calculations of PBRs’ equilibrium state, the double-heterogeneity of PBRs complicates neutron cross-section generation. Continuous-energy Monte Carlo (MC) methods are better suited for detailed PBR analysis because of their natural handling of double-heterogeneity, but they demand substantially more computational resources. Here, this study introduces a novel method for efficiently estimating the equilibrium state in small and micro PBRs with reduced computational cost. The method is anticipated to accelerate the processes of core design and performing parametric studies for utilizing advanced fuel and structural materials. The HTR-10 reactor design was used for validating the method’s predictions and evaluating its computational efficiency. When compared to reference calculation values from the literature, criticality (k-effective) was predicted to be approximately within the margin of error of the MC transport calculation, average core power density (in megawatts per cubic meter) was predicted within 2.5% relative error, and maximum thermal flux (10 13 n/cm 2 .s −1 ) was predicted within 1.8% relative error. The calculated inventory of fission products and fuel composition in the equilibrium core were within 15% and 16.6%, respectively, when compared to reported values from the literature. The difference is attributed to variance in the considered values of the core temperature, which was found to significantly affect the depletion analyses.
This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors.
Thanks to fast learning and sustained growth, solar photovoltaics (PV) is today a highly cost-competitive technology, ready to contribute substantially to CO 2 emissions mitigation. However, many scenarios assessing global decarbonization pathways, either based on integrated assessment models or partial-equilibrium models, fail to identify the key role that this technology could play, including far lower future PV capacity than that projected by the PV community. In this perspective, we review the factors that lie behind the historical cost reductions of solar PV and identify innovations in the pipeline that could contribute to maintaining a high learning rate. We also aim at opening a constructive discussion among PV experts, modelers, and policymakers regarding how to improve the representation of this technology in the models and how to ensure that manufacturing and installation of solar PV- can ramp up on time, which will be crucial to remain in a decarbonization path compatible with the Paris Agreement.
The industrial processing of sugars and sugar-containing mixtures is gaining widespread use as a form of renewable manufacturing from biomass. To aid in process modeling for design and optimization, a commonly available thermodynamic model is needed that describes the phase equilibrium of these compounds. This work compiles and compares models for solid–liquid and vapor–liquid phase equilibrium from the available data for the representative sugars glucose, fructose, and sucrose in the representative solvents water, methanol, and ethanol, including data for multisugar, multisolvent systems. Additionally, the nonrandom two-liquid (NRTL) model was chosen for these systems because of its widespread use in industry and the availability of parameters for many solvents and cosolutes. The association-NRTL (aNRTL) model was investigated as an improvement for modeling sugars, which may experience a high degree of association because of their many hydroxy groups. Both models accurately capture the data and are able to predict the behavior of multisugar systems with only solute–solvent interaction parameters. The aNRTL model shows an improvement over the baseline NRTL model that is most significant for sucrose, the component with the highest association strength, and least significant for fructose, which has the lowest association strength.
A multiblock, laminar heating analysis for the shuttle orbiter at three trajectory points ranging from Mach 24.3 to Mach 12.86 on re-entry is described. The analysis is performed using the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) with both a seven species chemical nonequilibrium model and an equilibrium model. A finite-catalytic-wall model appropriate for shuttle tiles at a radiative equilibrium wall temperature is applied. Computed heating levels are generally in good agreement with the flight data though a few rather large discrepancies remain unexplained. The multiblock relaxation strategy partitions the flowfield into manageable blocks requiring a fraction of the computational resources (time and memory) required by a full domain approach. In hot, the computational cost for a solution at even a single trajectory point would be prohibitively expensive at the given resolution without the multiblock approach. Converged blocks are reassembled to enable a fully coupled converged solution over the entire vehicle, starting from a nearly converged initial condition.
With the forecasted increase in the construction and operation of nuclear reactors, there will be a corresponding increase in the quantity of spent nuclear fuel (SNF) that requires long-term storage. In SNF, transuranic isotopes contribute the most to the long-term radiotoxicity of the fuel and pose a proliferation risk. One option that has been explored to address these issues is the removal of the transuranic isotopes from SNF and the conversion of these isotopes into transuranic fuel (TRU fuel). Here, this work sought to determine how effective a micro-modular Pebble-Bed High-Temperature Gas-Cooled Reactor (PB-HTGR); the 10-MW High Temperature Gas-cooled Test Reactor (HTR-10); and a salt-cooled small-modular pebble-bed reactor (PBR), i.e. the generic Fluoride-cooled High-temperature Reactor (gFHR), are at reducing the inventory of transuranic isotopes while still maintaining the intrinsic safety features of the PBR designs, such as negative temperature coefficients of reactivity. Optimized pebble designs utilizing TRU fuel were found for both reactors through the adjustment for the packing fraction of fuel in each pebble. The Axial Zone Equilibrium Modeling (A-ZEM) method was used in this work to help select the optimized pebble design. Once an optimized pebble design was selected and an equilibrium model was produced, the results from the deep burn (DB) HTR-10 and gFHR designs were compared to the results of two models from the literature. While both the DB gFHR and the DB HTR-10 were able to reduce the weapons-usable transuranic inventory, the performance of these reactors did not match that of the small-modular PB-HTGRs in the literature. Therefore, a need was identified for further refinement of the gFHR design using TRU fuel, as the results for this model were more promising than those of the DB HTR-10, which was strongly limited by the high leakage intrinsic to micro-modular PB-HTGRs.