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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Sensitivity to Glacial Forcing in the CCSM4

Findings are introduced from the Community Climate System Model, version 4 (CCSM4), simulation of the Last Glacial Maximum (LGM) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) at the standard 1° resolution, the same resolution as the majority of the CCSM4 CMIP5 long-term simulations for the historical and future projection scenarios. The forcings and boundary conditions for this simulation follow the protocols of the Paleoclimate Modeling Intercomparison Project, version 3 (PMIP3). Two additional CCSM4 CO 2 sensitivity simulations, in which the concentrations are abruptly changed at the start of the simulation to the low 185 ppm LGM concentrations (LGMCO2) and to a quadrupling of the preindustrial concentration (4×CO 2 ), are also analyzed. For the full LGM simulation, the estimated equilibrium cooling of the global mean annual surface temperature is 5.5°C with an estimated radiative forcing of -6.2 W m -2 . The radiative forcing includes the effects of the reduced LGM greenhouse gases, ice sheets, continental distribution with sea level lowered by approximately 120 m from the present, and orbital parameters, but not changes to atmospheric aerosols or vegetation biogeography. The LGM simulation has an equilibrium climate sensitivity (ECS) of 3.1(±0.3)°C, comparable to the CCSM4 4×CO 2 result. The LGMCO 2 simulation shows a greater ECS of 4.2°C. Other responses found at the LGM in CCSM4 include a global precipitation rate decrease at a rate of ~2% °C -1 , similar to climate change simulations in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4); a strengthening of the Atlantic meridional overturning circulation (AMOC) with a shoaling of North Atlantic Deep Water and a filling of the deep basin up to sill depth with Antarctic Bottom Water; and an enhanced seasonal cycle accompanied by reduced ENSO variability in the eastern Pacific Ocean’s SSTs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Internal Variability and Disequilibrium Confound Estimates of Climate Sensitivity from Observations

An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2-radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.

Marvel, Kate↗
NASA NTRSDate not supplied

Assessment of Numerical and Modeling Errors of RANS based Transition Models for Low-Reynolds Numbers 2-D Flows

In this paper we report the outcome of selected workshops organized as part of the NATO Applied Vehicle Technology (AVT)-313 activity Incompressible Laminar-to-Turbulent Flow Transition Study that focused on assessing the numerical and modeling accuracy of the γ−Reθ and γ transition models coupled to the k−ω Shear-Stress Transport (SST) two-equation eddy-viscosity model. Three different test cases involving nominally 2D flow configurations were selected: flow over a flat plate with two different levels of turbulence intensity at the inlet; flow around the Eppler 387 foil at a Reynolds number of 3×10^5 and angles of attack of 1 deg. and 7 deg. flow around the NACA 0015 foil at a Reynolds number of 1.8×10^5 and angles of attack of 5 deg. and10 deg. The flat plate flow conditions correspond to natural and by-pass transition, whereas the other two test cases include laminar separation bubbles that lead to separation-induced transition. For each test case, the selected quantities of interest include both integral and local flow quantities. Geometrically similar grids with a wide range of grid refinement ratios were generated for each of the test cases to allow the estimation of numerical uncertainties for all quantities of interest selected for this study. Several RANS flow solvers were used, employing common grids with the same boundary conditions and mathematical models. Therefore, it is possible to analyze the consistency of the results, i.e., to check if the intervals defined by the different numerical solutions with their respective uncertainties overlap with each other. Modeling errors can also be addressed for the selected flow quantities that have experimental data available. However, the experimental information available in these cases is not sufficient to guarantee that experiments and simulations are performed with the same settings. Nonetheless, the available experimental data is sufficient to guarantee that modeling errors are significantly reduced with the use of the transition models when compared to simulations performed using only the k−ω SST model.

CFD Modeling↗
NASA NTRSDate not supplied

Study of Martian Polar Caps with GISS ROCKE-3D GCM

Martian polar caps consist of both H 2 O and CO 2 ice. While H 2 O ice is mainly passive on modern Mars, it may have not been the case in recent Martian history, when its obliquity was higher, or when it was changing rapidly. The distribution of ice species in the snowpack affects its physical and thermodynamic properties. In the upper layers, it determines its albedo and thermal emissivity. Thus understanding the mutual effect between these ices and their interaction with the atmosphere is crucial for understanding the evolution of Martian polar regions. In this study, we employ a newly-developed Exotic Ices snow model coupled to the NASA Goddard Institute for Space Studies (GISS) ROCKE-3D planetary General Circulation Model (GCM) [1] to study the behavior of Martian polar caps. ROCKE-3D is a planetary GCM developed at NASA GISS as an extension of its Earth climate model, modelE [2]. It has been extensively used to simulate climate of various planets, including Mars (e.g. [3,4]). The Exotic Ices snow model was specially developed for planetary applications which involve more than one condensable in the atmosphere, in which case snow can contain multiple species of ice (CO2 and H 2 O in the Mars case). For each species of ice, the model uses their proper physical properties and phase diagram, but otherwise it treats all species of ice on an equal footing. The combined effects on albedo, thermal inertia and mutual insulation are treated accordingly. The snowpack interacts with the atmospheric dust cycle, and can accumulate a prognostic amount of dust, though the effect of dust on snow properties is not currently treated explicitly, and is prescribed. In this study, we first validate our model against the modern Martial climate, for which we use mission results from Mars Climate Sounder (atmospheric temperature and dust optical depth), SPICAM on Mars Express (atmospheric water), and Viking 2 (surface pressure). We investigate the effect of snow radiative properties on CO 2 and water cycles and the ability of our model to accurately reproduce those with minimal model tuning. We then perform simulations for several obliquities from a recent Martian past, and investigate the behavior of the Martian polar caps in such conditions.

Mars↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2019

Modelling electron transfer in photosystem I: limits and perspectives

Uncovering the parameters underlying the electron transfer (ET) in photosynthetic reaction centres is of importance for understanding the molecular mechanisms underpinning their functionality. The reductive nature of most cofactors involved in photosynthetic ET makes the direct estimation of their properties difficult. Photosystem I (PSI) operates in a highly reducing regime, making the assessment of cofactor properties even more difficult. Kinetic modelling coupled to a non-adiabatic description of ET is a useful approach in overcoming this hindrance. Here we review the theory and modelling approaches that have been used in assessing parameters associated with ET reactions in PSI, with particular attention to ET reactions involving the phylloquinones and the iron–sulphur clusters. In most modelling studies, the goal is to estimate the driving force of ET, which is usually associated with the cofactor midpoint potentials. The driving force is sensitive to many factors, which define the ET rate, i.e. the reorganisation energy, the coupling with nuclear modes and the electronic matrix elements, which are explored and discussed here. The importance of an inclusive modelling of both forward and reverse ET processes is discussed and highlighted. It is shown that although estimates are indeed sensitive to the exact parameter sets employed in the modelling, a general consensus is still attained, pointing to a scenario where $ΔG^0_{A_{tA→Fx}}/ΔG^0_{A_{tB→Fx}}$ is weakly endergonic/exergonic, respectively. It is emphasised that to further refine those estimates, it will require a joint effort between computational modelling and more wide-ranging experimental studies.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

The ENSO Effects on Tropical Clouds and Top-of-Atmosphere Cloud Radiative Effects in CMIP5 Models

The El Nino-Southern Oscillation (ENSO) effects on tropical clouds and top-of-atmosphere (TOA) cloud radiative effects (CREs) in Coupled Model Intercomparison Project Phase5 (CMIP5) models are evaluated using satellite-based observations and International Satellite Cloud Climatology Project satellite simulator output. Climatologically, most CMIP5 models produce considerably less total cloud amount with higher cloud top and notably larger reflectivity than observations in tropical Indo-Pacific (60 degrees East - 200 degrees East; 10 degrees South - 10 degrees North). During ENSO, most CMIP5 models considerably underestimate TOA CRE and cloud changes over western tropical Pacific. Over central tropical Pacific, while the multi-model mean resembles observations in TOA CRE and cloud amount anomalies, it notably overestimates cloud top pressure (CTP) decreases; there are also substantial inter-model variations. The relative effects of changes in cloud properties, temperature and humidity on TOA CRE anomalies during ENSO in the CMIP5 models are assessed using cloud radiative kernels. The CMIP5 models agree with observations in that their TOA shortwave CRE anomalies are primarily contributed by total cloud amount changes, and their TOA longwave CRE anomalies are mostly contributed by changes in both total cloud amount and CTP. The model biases in TOA CRE anomalies particularly the strong underestimations over western tropical Pacific are, however, mainly explained by model biases in CTP and cloud optical thickness (tau) changes. Despite the distinct model cloud biases particularly in tau regime, the TOA CRE anomalies from cloud amount changes are comparable between the CMIP5 models and observations, because of the strong compensations between model underestimation of TOA CRE anomalies from thin clouds and overestimation from medium and thick clouds.

Su, Wenying↗
DOE OSTI2021

Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Inferring Geothermal Reservoir Processes at the Raft River Geothermal Field, Idaho, USA, Through Modeling InSAR-Measured Surface Deformation

Ground surface deformations detected with Interferometric Synthetic Aperture Radar (InSAR) provide valuable information for inferring subsurface reservoir processes that are difficult to observe directly. This study aims at building a reservoir model that honors the available geological, hydrological and geo-mechanical data and also produces ground surface deformation consistent with InSAR measurements at the Raft River Geothermal Field. In the thermo-hydro-mechanical (THM) coupled model developed, the reservoir deforms as a result of the rock’s poroelastic response to changes in hydrologic pressure and thermal expansion/contraction. The results indicate that the observed deformation is the result of pressure decrease in the deep production reservoir and pressure increase in a shallower reservoir that accommodates the injected fluid (likely in the Salt Lake Formation). The combination of the uplift around injection wells with subsidence around the production wells, affected by the Bridge Fault as a flow barrier, creates a complex pattern of surface deformation in which the center of subtle subsidence significantly deviates from the location of the production wells. A parametric study suggests that (1) the Bridge Fault Zone is likely a flow barrier, (2) the surface deformation appears to be insensitive to the presence of the Narrows Structure, and (3) additional flow barriers likely exist to shape the flow system. This case study demonstrates the utility of a high-fidelity forward model that honors available known information and THM coupled processes in understanding geothermal reservoir characteristics.

58 GEOSCIENCES↗
NASA NTRS2013

Stationarity of the Tropical Pacific Teleconnection to North America in CMIP5 PMIP3 Model Simulations

The temporal stationarity of the teleconnection between the tropical Pacific Ocean and North America (NA) is analyzed in atmosphere-only, and coupled last-millennium, historical, and control runs from the Coupled Model Intercomparison Project Phase 5 data archive. The teleconnection, defined as the correlation between December-January-February (DJF) tropical Pacific sea surface temperatures (SSTs) and DJF 200 mb geopotential height, is found to be nonstationary on multidecadal timescales. There are significant changes in the spatial features of the teleconnection over NA in continuous 56-year segments of the last millennium and control simulations. Analysis of atmosphere-only simulations forced with observed SSTs indicates that atmospheric noise cannot account for the temporal variability of the teleconnection, which instead is likely explained by the strength of, and multidecadal changes in, tropical Pacific Ocean variability. These results have implications for teleconnection-based analyses of model fidelity in simulating precipitation, as well as any reconstruction and forecasting efforts that assume stationarity of the observed teleconnection.

histories↗
DOE OSTI2019

Stochastic multiscale flux basis for Stokes-Darcy flows

Three algorithms are developed for uncertainty quantification in modeling coupled Stokes and Darcy flows. The porous media may consist of multiple regions with different properties. The permeability is modeled as a non-stationary stochastic variable, with its log represented as a sum of local Karhunen-Loève (KL) expansions. The problem is approximated by stochastic collocation on either tensor-product or sparse grids, coupled with a multiscale mortar mixed finite element method for the spatial discretization. A non-overlapping domain decomposition algorithm reduces the global problem to a coarse scale mortar interface problem, which is solved by an iterative solver, for each stochastic realization. In the traditional domain decomposition implementation, each subdomain solves a local Dirichlet or Neumann problem in every interface iteration. To reduce this cost, two additional algorithms based on deterministic or stochastic multiscale flux basis are introduced. The basis consists of the local flux (or velocity trace) responses from each mortar degree of freedom. It is computed for each subdomain independently before the interface iteration begins. The use of the multiscale flux basis avoids the need for subdomain solves on each iteration. The deterministic basis is computed at each stochastic collocation and used only at this realization. The stochastic basis is formed by further looping over all local realizations of a subdomain's KL region before the stochastic collocation begins. It is used over multiple realizations. Numerical tests are presented to illustrate the performance of the three algorithms, with the stochastic multiscale flux basis showing significant savings in computational cost compared to the other two algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deficient precipitation sensitivity to Sahel land surface forcings among CMIP5 models

Abstract The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Biases in southern hemisphere climate trends induced by coarsely specifying the temporal resolution of stratospheric ozone

Abstract Global climate models that do not include interactive middle atmosphere chemistry, such as most of those contributing to the Coupled Model Intercomparison Project Phase 5, typically specify stratospheric ozone using monthly mean, zonal mean values and linearly interpolate to the time resolution of the model. We show that this method leads to significant biases in the simulated climate of the southern hemisphere (SH) over the late twentieth century. Previous studies have attributed similar biases in simulated SH climate change to the effect of the spatial smoothing of the specified ozone, i.e., to using zonal mean concentrations. We here show that the bias in climate trends due to undersampling of the rapid temporal changes in ozone during the seasonal evolution of the Antarctic ozone hole is considerable and reaches all the way into the troposphere. Our results suggest that the bias can be substantially reduced by specifying daily ozone concentrations.

Neely, III, R. R.↗
DOE OSTI2015

How well do CMIP5 climate simulations replicate historical trends and patterns of droughts?

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variability in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. As a result, pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Vetting new models of climate responses to geoengineering: The Seventh Meeting of the Geoengineering Model Intercomparison Project; Newry, Maine, 26 July 2017

On July 27, 2017, a group of approximately 30 scientists met in Newry, Maine to discuss the latest science and future directions in the Geoengineering Model Intercomparison Project (http://climate.envsci.rutgers.edu/GeoMIP/) (GeoMIP). This international effort, which is an official part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), is the largest source of information about the robust climate model response to solar geoengineering, a collection of proposed technologies designed to quickly, temporarily offset the effects of global warming.

Kravitz, Benjamin S.↗
DOE OSTI2019

Strong Influence of Irrigation on Water Budget and Land Surface Temperature in Indian Subcontinental River Basins

Irrigation modulates the terrestrial water budget, especially in intensively irrigated regions such as Indian sub-continent river basins, where 35% of agricultural land has been irrigated during 2010. Here, we investigate the impact of irrigation on water budget components and land surface temperature (LST), as well as seasonal and annual trends of irrigation water demand in response to climate variability over the Indian sub-continental region. We use the Variable Infiltration Capacity (VIC) model coupled with an irrigation scheme (VIC-IRR) to represent the irrigation activities. The VIC-IRR model was calibrated and evaluated against in-situ and remote sensing observations including streamflow from India-Water Resource Information System (WRIS), Terrestrial Water Storage Change (TWSC) from Gravity Recovery and Climate Experiment (GRACE), LST from Advanced Along-Track Scanning Radiometer (AATSR), and observed gravimetric soil moisture data from India Meteorology Department (IMD). We conducted two simulations (i.e., with and without irrigation) over 18 Indian sub-continental basins at 0.25° spatial resolution from 1951 to 2012. The comparison between the two simulations indicates that as a result of irrigation, evapotranspiration (ET) increases in all the basins. We find that mean annual ET of Indus and Ganges basins increases by 47% and 12%, respectively due to irrigation. After irrigation, basin averaged total runoff (TR) was increased in the most irrigated basins (i.e., Sabarmati, Ganges, and Indus) by 4.62%, 11.87% and 7.06% in the monsoon, pre-monsoon, and post-monsoon seasons, respectively. The land surface temperature was cooled down in range of 0.2-0.65 °C in the Indian sub-continental basins especially in Ganges and Indus basins. Compare to other basins; irrigation water demand was high in Indus and Ganges basins during the post-monsoon season. Using the nonparametric Mann–Kendall trend test we find that in the pre-monsoon and monsoon seasons over the study period, irrigation demand decreased by 8.25 mm/year in 13 basins. Whereas for the post-monsoon season, irrigation water demand increased in 12 out of the 18 basins. Changes in irrigation demands are largely driven by changes in soil moisture due to decline in the summer monsoon precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Integrating geophysical monitoring data into multiphase fluid flow reservoir simulation

Simulation of multiphase flow systems are of critical importance in managing hydrological systems. Flow simulations are affected by a number of factors including structure and flow properties including porosity and permeability as well as the anisotropy and heterogeneity of these properties. In many cases traditional hydrological and reservoir data are highly affected by these parameters, but are not directly sensitive to them. As such modellers often adjust these parameters in an ad hoc manner until solutions numerically converge. Simulation models are generally based on structural data from reflection seismics whose physical flow properties are then populated using geostatistical extrapolation techniques utilizing a sparse number of borehole logs and core analysis. In multiphase systems including enhanced oil recovery and carbon capture and sequestration uncertainties regarding phase-dependent physical properties confounds this challenge further. Geophysical methods provide a means by which to gain an improved understanding of phase distributions in the subsurface. In this paper we will look at applications from active carbon capture and sequestration and enhanced oil recovery applications, as well as synthetic examples. Geophysical data including electromagnetic and gravity are inverted using structural constraints from the reservoir model. Inversions are then mapped into flow properties using calibrated relations such as Archie’s Equation. The coupled models can then be used to both verify and improve on the reservoir flow model which improves it’s predictive power and utility as a management tool.

02 PETROLEUM↗
DOE OSTI2018

Development of a Validation Approach for an Integrated Waste Glass Melter Model

Integrated models are being developed to represent the physics occurring within the high-level and low-activity waste melters that will be used to vitrify legacy tank waste at the Hanford site. These models couple the melt pool, cold cap, and plenum region within a single computational domain. Validation of the models is essential to ensure the reliability of the numerical predictions of the operational melters. Experimental data from laboratory- and pilot-scale tests are thus being used to inform and validate various aspects of the melter model. This study presents a tiered approach to model validation consisting of a series of progressively more complex test cases designed to model the physics occurring in the full-scale system. A hierarchical methodology has been developed to segregate and simplify the physical phenomena affecting the multiphase flow and heat transfer within a waste glass melter. Finally, four hierarchical levels are defined in a validation pyramid and built up in levels of increasing complexity from unit problems to subsystem cases, to pilot-scale systems, and then to the full-scale system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗