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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Exploring ice sheet model sensitivity to ocean thermal forcing and basal sliding using the Community Ice Sheet Model (CISM)

Abstract. Multi-meter sea level rise (SLR) is thought to be possible within the next few centuries, with most of the uncertainty originating from the Antarctic land ice contribution. One source of uncertainty relates to the ice sheet model initialization. Since ice sheets have a long response time (compared to other Earth system components such as the atmosphere), ice sheet model initialization methods can have significant impacts on how the ice sheet responds to future forcings. To assess this, we generated 25 different ice sheet spin-ups, using the Community Ice Sheet Model (CISM) at a 4 km resolution. During each spin-up, we varied two key parameters known to impact the sensitivity of the ice sheet to future forcing: one related to the sensitivity of the ice shelf melt rate to ocean thermal forcing (TF) and the other related to the basal friction. The spin-ups all nudge toward observed thickness and enforce a no-advance calving criterion, such that all final spin-up states resemble observations but differ in their melt and friction parameter settings. Each spin-up was then forced with future ocean thermal forcings from 13 different CMIP6 models under the Shared Socioeconomic Pathway (SSP)5-8.5 emissions scenario and modern climatological surface mass balance data. Our results show that the effects of the ice sheet and ocean parameter settings used during the spin-up are capable of impacting multi-century future SLR predictions by as much as 2 m. By the end of this century, the effects of these choices are more modest, but still significant, with differences of up to 0.2 m of SLR. We have identified a combined ocean and ice parameter space that leads to widespread mass loss within 500 years (low friction and high melt rate sensitivity). To explore temperature thresholds, we also ran a synthetically forced CISM ensemble that is focused on the Amundsen region only. Given certain ocean and ice parameter choices, Amundsen mass loss can be triggered with thermal forcing anomalies between 1.5 and 2 ∘C relative to the spin-up. Our results emphasize the critical importance of considering ice sheet and ocean parameter choices during spin-up for SLR predictions and suggest the importance of including glacial isostatic adjustment in ice sheet simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evolution of the Antarctic Ice Sheet from 2000–2300 and beyond: model sensitivity and uncertainty analysis using MPAS-Albany Land Ice

We present a description of the Antarctic Ice Sheet model configuration submitted to the ISMIP6-Antarctica-2300 experiment using the MPAS-Albany Land Ice model, along with three new sets of simulations: (1) a set of extended simulations to 2500 for three forced experiments and to 2775 for the control experiment; (2) a sensitivity analysis of our model configuration to parameters controlling basal sliding and sub-shelf melt, and to model structural choices including the choice of the energy and stress balances; and (3) a 72-member ensemble run on graphics processing units (GPUs) and analysis of variance to determine the primary sources of uncertainty in our ice-sheet model projections. Our extended simulations predict rapid retreat beginning after 2300 for SSP1-2.6 forcing and after 2500 for present-day (control) forcing, primarily in the Amundsen Sea Embayment. We find that varying the sub-shelf melt parameter between the 5th to 95th percentile values for a mean-Antarctic calibration target results in an up to ∼ ± 40 % change in sea-level contribution relative to our baseline simulations that used the median value. Using a linear basal sliding law reduces sea-level contribution by 51 %–73 % relative to our baseline nonlinear sliding law with an exponent of 1/5. When using basal sliding law exponents of 1/3 and 1/10, the overall difference from our baseline simulations at 2300 is on the order of 10 %. The Amundsen Sea Embayment region displays a strongly non-linear dependence of mass loss on the sliding law exponent, with no discernible relationship between the sliding law exponent and the mass loss by 2300, while the sectors feeding the Ross and Filchner-Ronne ice shelves exhibit more mass loss with a more-plastic sliding law. Our model fidelity sensitivity experiments reveal a 9 %–31 % increase in sea-level contribution when using a depth-integrated stress balance approximation relative to our three-dimensional solver, while using a fixed-in-time temperature field increases sea-level contribution by 14 %–88 % relative to two thermomechanically coupled configurations. Our 72-member ensemble and analysis of variance show that the uncertainty in long-term projections is dominated by the choice of Earth system model forcing and the presence or absence of hydrofracture forcing, rather than uncertainty in sliding and sub-shelf melt parameters.

58 GEOSCIENCES↗
DOE OSTI2023

The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics

Abstract. Anthropogenic emissions of aerosols and precursor compounds are known to significantly affect the energy balance of the Earth–atmosphere system, alter the formation of clouds and precipitation, and have a substantial impact on human health and the environment. Global models are an essential tool for examining the impacts of these emissions. In this study, we examine the sensitivity of model results to the assumed height of SO2 injection, seasonality of SO2 and black carbon (BC) particulate emissions, and the assumed fraction of SO2 emissions that is injected into the atmosphere as particulate phase sulfate (SO4) in 11 climate and chemistry models, including both chemical transport models and the atmospheric component of Earth system models. We find large variation in atmospheric lifetime across models for SO2, SO4, and BC, with a particularly large relative variation for SO2, which indicates that fundamental aspects of atmospheric sulfur chemistry remain uncertain. Of the perturbations examined in this study, the assumed height of SO2 injection had the largest overall impacts, particularly on global mean net radiative flux (maximum difference of −0.35 W m−2), SO2 lifetime over Northern Hemisphere land (maximum difference of 0.8 d), surface SO2 concentration (up to 59 % decrease), and surface sulfate concentration (up to 23 % increase). Emitting SO2 at height consistently increased SO2 and SO4 column burdens and shortwave cooling, with varying magnitudes, but had inconsistent effects across models on the sign of the change in implied cloud forcing. The assumed SO4 emission fraction also had a significant impact on net radiative flux and surface sulfate concentration. Because these properties are not standardized across models this is a source of inter-model diversity typically neglected in model intercomparisons. These results imply a need to ensure that anthropogenic emission injection height and SO4 emission fraction are accurately and consistently represented in global models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Corrigendum to “The Emissions Model Intercomparison Project (Emissions-MIP): quantifying model sensitivity to emission characteristics” published in Atmos. Chem. Phys., 23, 14779–14799, 2023

Anthropogenic emissions of aerosols and precursor compounds are known to significantly affect the energy balance of the Earth–atmosphere system, alter the formation of clouds and precipitation, and have a substantial impact on human health and the environment. Global models are an essential tool for examining the impacts of these emissions. In this study, we examine the sensitivity of model results to the assumed height of SO 2 injection, seasonality of SO 2 and black carbon (BC) particulate emissions, and the assumed fraction of SO 2 emissions that is injected into the atmosphere as particulate phase sulfate (SO 4 ) in 11 climate and chemistry models, including both chemical transport models and the atmospheric component of Earth system models. We find large variation in atmospheric lifetime across models for SO 2 , SO 4 , and BC, with a particularly large relative variation for SO 2 , which indicates that fundamental aspects of atmospheric sulfur chemistry remain uncertain. Of the perturbations examined in this study, the assumed height of SO 2 injection had the largest overall impacts, particularly on global mean net radiative flux (maximum difference of -0.35?W?m-2), SO2 lifetime over Northern Hemisphere land (maximum difference of 0.8?d), surface SO 2 concentration (up to 59?% decrease), and surface sulfate concentration (up to 23?% increase). Emitting SO 2 at height consistently increased SO 2 and SO 4 column burdens and shortwave cooling, with varying magnitudes, but had inconsistent effects across models on the sign of the change in implied cloud forcing. The assumed SO 4 emission fraction also had a significant impact on net radiative flux and surface sulfate concentration. Because these properties are not standardized across models this is a source of inter-model diversity typically neglected in model intercomparisons. These results imply a need to ensure that anthropogenic emission injection height and SO 4 emission fraction are accurately and consistently represented in global models.

Ahsan, Hamza [Pacific Northwest National Laborator↗
DOE OSTI2022

Microstructure-Sensitive modeling of surface roughness and notch effects on extreme value fatigue response

Fatigue life knockdown due to surface roughness induces local stress concentrations. Physical fatigue experiments are crucial to qualify materials for use but are limited in their capability to examine the mechanisms underlying the fatigue crack formation driving force due to these stress concentrations. Microstructure-sensitive computational models can aid in this regard and are employed in this work to examine the effects of surface roughness in Al 7075-T6. Digital microstructure models are subjected to crystal plasticity finite element method (CPFEM) simulations to examine the combined effects of microstructure and grain scale asperities on the driving force for fatigue crack formation. Following elastic–plastic shakedown, mesoscale volume-averaged fatigue indicator parameters (FIPs) are computed within fatigue damage process zones of grains. An increase in the intensity of realistic surface roughness profiles corresponds accordingly to larger FIPs but it is difficult to precisely interpret the mechanisms and effects of this intensification. We thus parametrically investigate surface asperities that couple with microstructure and lack of constraint on slip at the free surface to describe microstructure-sensitive surface roughness knockdown effects on fatigue resistance. The depth of a single valley (i.e., notch) due to surface roughness is found to be more detrimental to fatigue resistance than the radius (i.e., notch width, not to be confused with notch acuity). Finally, we also find that different crystallographic textures, grain morphologies, and number of sampled grains have a limited effect on the depth of influence of these notches.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Determinants of Offshore Wind's Role in a Future U.S. Energy System: A Preliminary Modeling Sensitivity Analysis [Slides]

Offshore wind is an emerging industry in the with rapidly changing technology advancements and an accelerating global deployment. Policy demands and interest in the United States has grown, but the long-term future for offshore wind remains uncertain. This study is designed to evaluate offshore wind's potential role in the future U.S. energy system. The analysis uses long-term power system models to assess a wide-range of power system possibilities. Specific objectives of the analysis are to: (1) identify the determinants of offshore wind deployment under different scenarios and (2) understand the impacts of substantial levels of offshore wind deployment on the power system. We also highlight future research needs for more robust understanding the opportunities and challenges to U.S. offshore wind development.

17 WIND ENERGY↗
DOE OSTI2026

Model Sensitivities of Biomass-Burning Aerosol Chemical Aging, Sulfate Formation, and Cloud Droplet Activation in the Southeastern Atlantic Using CESM and E3SM

Biomass-burning smoke drives large uncertainty in climate projections of the Earth's radiative balance. This is due to the chemical and physical evolution of smoke and its impact on clouds and radiation. Here we focus on the southeastern Atlantic region and its inflow of African biomass-burning smoke during August 2017. We evaluate smoke properties and processes in two coupled earth-system models, the Energy Exascale Earth System Model (E3SM) and Community Earth System Model (CESM). These are compared against in situ aircraft observations from two field campaigns, ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) and CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017). Observations reveal an increase and subsequent decrease in smoke mean diameter, and a steady decrease in the mass ratio of organic aerosol (OA) to black carbon aerosol (BC) (OA:BC) over 4–12 days of aging, neither captured by the base models. Implementation of a photolytic loss scheme for secondary organic aerosol (SOA)—as a proxy for other heterogeneous volatilization chemistry—and a ∼1-day conversion for primary OA to SOA significantly improves the representation of this loss. In the boundary layer, both models show dimethyl sulfide driving a large increase in the sulfate aerosol mass fraction from the free troposphere, which is consistent with observations. Finally, models tend to underpredict cloud droplet number concentration partially due to weak modeled turbulent updraft strength, and model performance improves when the parameterized turbulent updraft strength is increased substantially. These results are expected to provide insights into future model development to reduce climate model uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Responses in the Subpolar North Atlantic in Two Climate Model Sensitivity Experiments with Increased Stratospheric Aerosols

Abstract The subpolar North Atlantic (SPNA) shows contrasting responses in two sensitivity experiments with increased stratospheric aerosols, offering insight into the physical processes that may impact the Atlantic meridional overturning circulation (AMOC) in a warmer climate. In one, the upper ocean becomes warm and salty, but in the other it becomes cold and fresh. The changes are accompanied by diverging AMOC responses. The first experiment strengthens the AMOC, opposing the weakening trend in the reference simulation. The second experiment shows a much smaller impact. Both simulations use the Community Earth System Model with the Whole Atmosphere Community Climate Model component (CESM-WACCM) but differ in model versions and stratospheric aerosol specifications. Despite both experiments using similar approaches to increase stratospheric aerosols to counteract the rising global temperature, the contrasting SPNA and AMOC responses indicate a considerable dependency on model physics, climate states, and model responses to forcings. This study focuses on examining the physical processes involved with the impact of stratospheric aerosols on the SPNA salinity changes and their potential connections with the AMOC and the Arctic. We find that in both cases, increased stratospheric aerosols act to enhance the SPNA upper-ocean salinity by reducing freshwater export from the Arctic, which is closely tied to the Arctic sea ice changes. The impact on AMOC is primarily through the thermal component of the surface buoyancy fluxes, with negligible contributions from the freshwater component. These experiments shed light on the physical processes that dictate the important connections between the SPNA, the Arctic, the AMOC, and their subsequent feedbacks on the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microstructure-sensitive modeling of high temperature creep in grade-91 alloy

Predicting the effects of microstructure on high-temperature creep responses of steel components is critical to minimize the risks of failure and maximize economic viability in the energy sector. In this research, a recently developed advanced mechanistic constitutive model is employed to study the effect of microstructure on the creep responses and to rationalize experimentally reported variability in the performances of grade-91 alloy. Hundreds of experimental creep tests from the literature are used to assess the predictability of the model. The model proposed is shown to accurately predict the steady-state creep rates and also the less commonly considered, yet important, primary-to-secondary transients for a wide range of creep conditions. Further, the model is capable of extrapolating the creep response of grade 91 alloy even outside of the calibration regime. Using this model, the roles of initial microstructure described in terms of grain size, dislocation density, and precipitate content, on the creep responses are investigated. The effect of initial microstructure on the steady-state creep rates and creep-strain is found to vary with stress and temperature. Grain size plays a significant role in the low-stress creep, where the diffusional creep is dominant. On the other hand, the effects of dislocation densities and precipitate content are significant in the dislocation-plasticity-dominated high-stress and temperature regimes. Furthermore, by comparing model predictions against a large experimental creep database, it is found that variability in the creep responses can be rationalized on the basis of differences in the initial microstructure. Overall, this work provides a robust pathway for microstructure-aware engineering design, verification, and validation of metallic components for energy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeled sensitivity of multi-MA accelerator performance to electrode contaminant inventory

Significant particle-in-cell code development has enabled simulations of power flow in multi-MA accelerators to include the desorption of surface contaminants, their ionization into surface plasmas, and the impact of these plasmas on efficiency. The simulations base desorption on an Arrhenius equation, whose most significant unknown is the surface contaminant inventory. The sensitivity of power-flow simulations to this inventory is studied here using Sandia National Laboratories' Z accelerator with a 7-nH MagLIF load [Phys. Plasmas 17, 056303 (2010)]. Simulations are conducted in 3D cylindrical coordinates for the current-adder, or “convolute,” region of Z and in 2D for the final feed only. Simulated contaminant inventories are varied from 1 to 32 monolayers (MLs) in 2D, and 2 to 4 ML in 3D. The results reveal sensitivities to the local ratio of E/B⁠. The high B-field, low E-field region near the short-circuit load is insensitive to the contaminant inventory, where assumed values of 4–32 ML change the load current by ≤ 2%, and agree with experiment to within 2% at peak current. A 1-ML value is the outlier, increasing the load current by 5%, but still within measurement uncertainty. In contrast, the relatively higher E-field, lower B-field convolute region has slower contaminant desorption and higher-magnitude E-field penetration of the surface plasmas. The current loss in the convolute region does increase with contaminant inventory. The loss assuming 4 ML is 12% larger than for 2 ML, with 4 ML being the better match to experiment.

Arrhenius equation↗
DOE OSTI2023

Quantifying model prediction sensitivity to model-form uncertainty

Computational and mathematical models are essential to understanding complex systems and phenomena. However, when developing such models, limited knowledge and/or resources necessitates the use of simplifying assumptions. It is therefore crucial to quantify the impact of such simplifying assumptions on the reliability and accuracy of resulting model predictions. This work develops a first-of-its-kind approach to quantify the impact of physics modeling assumptions on predictions. Here, we leverage the emerging field of model-form uncertainty (MFU) representations, which are parameterized modifications to modeling assumptions, in combination with grouped Sobol’ indices to quantitatively measure an assumption’s importance. Specifically, we compute the grouped Sobol’ index for the MFU representation’s parameters as a single importance measure of the assumption for which the MFU representation characterizes uncertainty. To ensure this approach is robust to the subjective choice of how to parameterize a MFU representation, we establish bounds for the difference between sensitivity results for two different MFU representations based on differences in model prediction statistics. The capabilities associated with this approach are demonstrated on three exemplar problems: an upscaled subsurface contaminant transport problem, ablation modeling for hypersonic flight, and nuclear waste repository modeling. We found that our grouped approach is able to assess the impact of modeling assumptions on predictions and offers computational advantages over classical Sobol’ index computation while providing more interpretable results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling

Sensitivity analysis in terrestrial ecosystem modeling is important for understanding controlling processes, guiding model development, and targeting new observations to reduce parameter and prediction uncertainty. Complex and computationally expensive terrestrial ecosystem models (TEM) limit the number of ensemble simulations, requiring sophisticated and efficient methods to analyze sensitivities of multiple model responses to different types of parameter uncertainties. In this study, we propose a distance-based global sensitivity analysis (DGSA) method. DGSA first classifies model response samples into a small set of discrete classes and then calculates the distance between parameter frequency distributions in different classes to measure the parameter sensitivity. The principle is that, if the parameter distribution is the same in each class, then the model response is insensitive to the parameter, while a large difference in the distributions indicates the parameter is influential to the response. Built on this idea, DGSA can be applied to analyze sensitivity of a single and a group of responses to different kinds of parameter uncertainties including continuous, discrete and even stochastic. Besides the main-effect sensitivity from a single parameter, DGSA can also quantify the sensitivity from parameter interactions. Additionally, DGSA is computationally efficient which can use a small number of model evaluations to obtain an accurate and statistically significant result. We applied DGSA to two TEMs, one having eight parameters and three kinds of model responses, and the other having 47 parameters and a long-period response. We demonstrated that DGSA can be used for sensitivity problems with multiple responses and high-dimensional parameters efficiently.

Lu, Dan↗
DOE OSTI2021

Dynamical System Scaling Application to Zircaloy Cladding Thermal Response During Reactivity-Initiated Accident Experiment

New fuel design and development currently requires 20 to 25 years to be qualified for use by the nuclear power industry. The thermal-hydraulics community has taken advantage of scaling theory to design reduced scale experiments that correctly preserve dominant key phenomena while quantifying distorted phenomena. These techniques can be leveraged in the design and analysis of fuel performance experiments to help reduce the timeline associated with fuel design and development. This study uses the Dynamical System Scaling (DSS) method to analyze cladding temperature data from the recent SETH-C experiment in the TREAT facility and accompanying BISON simulations to assess dynamic distortions occurring throughout the fast power excursion transient. The DSS analysis revealed that on the cool down from peak cladding temperature that the fuel radial power profile is the most sensitive modeling parameter with a heterogenous radial peaking factor corresponding to the lowest distortion compared to a uniform energy deposition. For the heat up to peak cladding temperature the heterogeneous radial power profile corresponded to the shortest process action. Finally, for the heat up to peak cladding temperature, the gap conductance model sensitivity was quantified using process action and shows that the default Light Water Reactor gap conductance model corresponded to the longest process action.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Critical Parameter Identification of Fuel-Cell Models Using Sensitivity Analysis

Numerical modeling has been a vital tool in proton-exchange-membrane fuel-cell (PEMFC) analysis; however, the predictive capabilities of these models depend on the input physical parameters, several of which are either not experimentally measured or have large scatter in measured values. This article presents an uncertainty propagation-based sensitivity analysis to identify the model parameters that impact the model predictions most. A comprehensive 2-D membrane electrode assembly (MEA) model is used to perform local sensitivity analysis at multiple operating conditions, which encompass the range of environments and operating conditions a cell can encounter. While at lower humidities, cathode kinetics and membrane-ohmic-loss related parameters are crucial, gas transport and porous-media saturation behavior are more important at humidified conditions. Several of these findings are different from previous studies presented in literature. Identifying the crucial parameters helps focus future material and cell optimization studies as well as experimental studies to quantify these parameters with higher accuracy.

25 ENERGY STORAGE↗
DOE OSTI2022

Differential methods for assessing sensitivity in biological models

Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to determine which method is best suited for a particular model. In this paper, we explain a variety of differential sensitivity methods and assess their value in some typical biological models. First, we explain the mathematical basis for three numerical methods: adjoint sensitivity analysis, complex perturbation sensitivity analysis, and forward mode sensitivity analysis. We then carry out four instructive case studies. (a) The CARRGO model for tumor-immune interaction highlights the additional information that differential sensitivity analysis provides beyond traditional naive sensitivity methods, (b) the deterministic SIR model demonstrates the value of using second-order sensitivity in refining model predictions, (c) the stochastic SIR model shows how differential sensitivity can be attacked in stochastic modeling, and (d) a discrete birth-death-migration model illustrates how the complex perturbation method of differential sensitivity can be generalized to a broader range of biological models. Finally, we compare the speed, accuracy, and ease of use of these methods. We find that forward mode automatic differentiation has the quickest computational time, while the complex perturbation method is the simplest to implement and the most generalizable.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The FastEddy® Resident-GPU Accelerated Large-Eddy Simulation Framework: Moist Dynamics Extension, Validation and Sensitivities of Modeling Non-Precipitating Shallow Cumulus Clouds

Herein we describe the moist dynamics formulation implemented within the graphics processing unit-resident large-eddy simulation FastEddy® model, which includes a simple saturation adjustment scheme for condensation and evaporation processes. Two LES model intercomparison exercises for non-precipitating shallow cumulus clouds are simulated in order to validate this model extension, including a static forcing and a time-dependent forcing case. Overall, we find our dynamical, thermodynamical and microphysical quantities, along with turbulence variability and fluxes, to be commensurate with the corresponding model intercomparison results. In addition, sensitivities to specific model settings are investigated. Among these settings, it is shown that boundary layer and cloud layer structure and characteristics are sensitive to use of higher-order advection schemes impacting the vertical distribution of cloud content and associated turbulence statistics. Increasing the timescale of the saturation scheme leads to enhanced liquid water presence and decreases vertical velocity variance within the cloud deck. In some cases, these sensitivities agree with the model-to-model variability reported in the intercomparison exercises, highlighting the important role of specific model implementation choices in the context of shallow cumulus convection simulations. These analyses and findings also provide the basis for future extensions and applications of FastEddy® for modeling moist convection and precipitation scenarios.

54 ENVIRONMENTAL SCIENCES↗