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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Physics and Dynamics Coupling Across Scales in the Next Generation CESM: Meeting the Challenge of High-Resolution (Final Report)

This project examines physics/dynamics coupling, that is, exchange of meteorological profiles and tendencies between an atmospheric model’s dynamical core and its various physics parameterizations. Most model physics parameterizations seek to represent processes that occur on scales smaller than the smallest scale resolved by the dynamical core. As a consequence a key conceptual aspect of parameterizations is an assumption about the subgrid variability of quantities such as temperature, humidity or vertical wind. Most existing parameterizations of processes such as turbulence, convection, cloud, and gravity wave drag make relatively ad hoc assumptions about this variability and are forced to introduce empirical parameters, i.e., “tuning knobs” to obtain realistic simulations. These knobs make systematic dependences on model grid size difficult to quantify.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework

Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2018

Evaluation of autoconversion and accretion enhancement factors in general circulation model warm-rain parameterizations using ground-based measurements over the Azores

A great challenge in climate modeling is how to parameterize subgrid cloud processes, such as autoconversion and accretion in warm-rain formation. In this study, we use ground-based observations and retrievals over the Azores to investigate the so-called enhancement factors, E auto and E accr , which are often used in climate models to account for the influence of subgrid variance of cloud and precipitation water on the autoconversion and accretion processes. E auto and E accr are computed for different equivalent model grid sizes. The calculated E auto values increase from 1.96 (30 km) to 3.2 (180 km), and the calculated E accr values increase from 1.53 (30 km) to 1.76 (180 km). Comparing the prescribed enhancement factors in Morrison and Gettleman (2008, MG08) to the observed ones, we found that a higher E auto (3.2) at small grids and lower E accr (1.07) are used in MG08, which might explain why most of the general circulation models (GCMs) produce too-frequent precipitation events but with too-light precipitation intensity. The ratios of the rain to cloud water mixing ratio ( q r / q c ) at E accr =1.07 and E accr =2.0 are 0.063 and 0.142, respectively, from observations, further suggesting that the prescribed value of E accr =1.07 used in MG08 is too small to simulate precipitation intensity correctly. Both E auto and E accr increase when the boundary layer becomes less stable, and the values are larger in precipitating clouds ( CLWP>75 gm -2 ) than those in non-precipitating clouds ( CLWP<75 gm -2 ). Therefore, the selection of E auto and E accr values in GCMs should be regime- and resolution-dependent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Determining Reduced Order Models for Optimal Stochastic Reduced Order Models

The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model’s Wind Farm Parameterization

Wind power installations have been increasing in recent years. Because wind turbines can influence local wind speeds, temperatures, and surface fluxes, weather forecasting models should consider their effects. Wind farm parameterizations do currently exist for numerical weather prediction models. They generally consider two turbine impacts: elevated drag in the region of the wind turbine rotor disk and increased turbulent kinetic energy production. The wind farm parameterization available in the Weather Research and Forecasting (WRF) Model calculates this drag and TKE as a function of hub-height wind speed. However, recent work has suggested that integrating momentum over the entire rotor disk [via a rotor-equivalent wind speed (REWS)] is more appropriate, especially for cases with high wind shear. In this study, we implement the REWS in the WRF wind farm parameterization and evaluate its impacts in an idealized environment, with varying amounts of wind speed shear and wind directional veer. Specifically, we evaluate three separate cases: neutral stability with low wind shear, high stability with high wind shear, and high stability with nonlinear wind shear. For most situations, use of the REWS with the wind farm parameterization has marginal impacts on model forecasts. However, for scenarios with highly nonlinear wind shear, the REWS can significantly affect results.

17 WIND ENERGY↗
DOE OSTI2014

Sensitivity of Global Terrestrial Gross Primary Production to Hydrologic States Simulated by the Community Land Model Using Two Runoff Parameterizations

The terrestrial water and carbon cycles interact strongly at various spatio-temporal scales. To elucidate how hydrologic processes may influence carbon cycle processes, differences in terrestrial carbon cycle simulations induced by structural differences in two runoff generation schemes were investigated using the Community Land Model 4 (CLM4). Simulations were performed with runoff generation using the default TOPMODEL-based and the Variable Infiltration Capacity (VIC) model approaches under the same experimental protocol. The comparisons showed that differences in the simulated gross primary production (GPP) are mainly attributed to differences in the simulated leaf area index (LAI) rather than soil moisture availability. More specifically, differences in runoff simulations can influence LAI through changes in soil moisture, soil temperature, and their seasonality that affect the onset of the growing season and the subsequent dynamic feedbacks between terrestrial water, energy, and carbon cycles. As a result of a relative difference of 36% in global mean total runoff between the two models and subsequent changes in soil moisture, soil temperature, and LAI, the simulated global mean GPP differs by 20.4%. However, the relative difference in the global mean net ecosystem exchange between the two models is small (2.1%) due to competing effects on total mean ecosystem respiration and other fluxes, although large regional differences can still be found. Our study highlights the significant interactions among the water, energy, and carbon cycles and the need for reducing uncertainty in the hydrologic parameterization of land surface models to better constrain carbon cycle modeling.

carbon↗
DOE OSTI2023

Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Abstract Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Aircraft Observations of Turbulence in Cloudy and Cloud-Free Boundary Layers Over the Western North Atlantic Ocean From ACTIVATE and Implications for the Earth System Model Evaluation and Development

This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Further, bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Photodegradation accelerates ecosystem N cycling in a simulated California grassland

Abstract Photodegradation accelerates litter decay in arid grasslands where plant growth and litter decay are strongly controlled by precipitation and evapotranspiration. However, the effects of photodegradation on ecosystem C and N dynamics are not well understood. We examined the effects using an ecosystem biogeochemical model DayCent‐ UV with photodegradation explicitly represented and validated. The model was parameterized for a California grassland where photodegradation was documented to release CO 2 from litter. The model was parameterized with an inverse modeling approach using an extensive data set of six years of daily observed carbon and water gas exchange (gross primary production, ecosystem respiration, and evapotranspiration), soil temperature, and soil moisture. DayCent‐ UV correctly simulated the seasonal patterns of the observed gas exchange and closely simulated the inter‐annual variation in the gas exchange and biomass production rates. The simulations suggested that the inter‐annual variation is driven more by actual evapotranspiration than by precipitation because a large portion of precipitation is lost as runoff during wet years. Photodegradation in DayCent‐ UV accelerated C and N cycling, decreasing system C and N by 9.2% and 9.5% and C and N residence times by 9.4% and 18.2%. Accelerated N cycling made a greater fraction of system N available for plants, increasing net N mineralization and plant production for a given amount of system N. Increased net N mineralization was due to decreased immobilization by microbes in the aboveground organic matter. Photodegradation did not alter the control on plant production by evapotranspiration. These results suggest that at the ecosystem level, the central effect of photodegradation is to suppress microbial activity. We conclude that photodegradation accelerates N cycling at the expense of microbes in this grassland, making it more efficient in supporting plant growth for a given amount of N in the system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Calibration of a convective parameterization scheme in the WRF model and its impact on the simulation of East Asian summer monsoon precipitation

Reasonably modeling the magnitude, south-north gradient and seasonal propagation of precipitation associated with the East Asian Summer Monsoon (EASM) is a challenging task in the climate community. In this study we calibrate five key parameters in the Kain-Fritsch convection scheme in the WRF model using an efficient importance-sampling algorithm to improve the EASM simulation. We also examine the impacts of the improved EASM precipitation on other physical process. Our results suggest similar model sensitivity and values of optimized parameters across years with different EASM intensities. By applying the optimal parameters, the simulated precipitation and surface energy features are generally improved. The parameters related to downdraft, entrainment coefficients and CAPE consumption time (CCT) can most sensitively affect the precipitation and atmospheric features. Larger downdraft coefficient or CCT decrease the heavy rainfall frequency, while larger entrainment coefficient delays the convection development but build up more potential for heavy rainfall events, causing a possible northward shift of rainfall distribution. The CCT is the most sensitive parameter over wet region and the downdraft parameter plays more important roles over drier northern region. Long-term simulations confirm that by using the optimized parameters the precipitation distributions are better simulated in both weak and strong EASM years. Due to more reasonable simulated precipitation condensational heating, the monsoon circulations are also improved. Lastly, by using the optimized parameters the biases in the retreating (beginning) of Mei-yu (northern China rainfall) simulated by the standard WRF model are evidently reduced and the seasonal and sub-seasonal variations of the monsoon precipitation are remarkably improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating and Improving Scale–Awareness of a Convective Parameterization Closure Using Cloud–Resolving Model Simulations of Convection

This study aims to evaluate a revised closure of the Zhang-McFarlane (ZM) scheme for deep convection and to further improve its scale awareness. Output from two cloud-resolving model (CRM) simulations of both midlatitude organized (MC3E) and tropical unorganized (TWP-ICE) convection are used for the evaluation. By averaging the CRM output over different subdomain sizes from 64 km to 4 km, convection-associated fields are obtained to represent the fields at different horizontal resolutions of global climate models (GCMs). The convection parameterization closure is then tested using these fields. Results show that the cloud base mass flux M b determined from the closure has a relatively good relationship with CRM-simulated convection at coarse resolutions for both MC3E and TWP-ICE, but their correlation degrades at higher resolutions, especially when approaching the gray zone scale under 10 km. The fluctuation of CAPE consumption rate per unit cloud base mass flux for different convective events is found to be an important factor affecting the correlation. When averaged over the base domain, the cloud base mass flux determined by the closure is found to increase with decreasing subdomain size, implying that the closure is not scale-aware. To improve its scale-awareness, a modified closure is designed to alleviate the overprediction of convection at high GCM resolutions. Furthermore, results show that the modified closure has the scale-aware ability to suppress the overprediction problem at high GCM resolutions for both MC3E and TWP-ICE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluating the Bias of South China Sea Summer Monsoon Precipitation Associated with Fast Physical Processes Using a Climate Model Hindcast Approach

Abstract The present study aims to identify the precipitation bias associated with the interactions among fast physical processes in the Community Atmospheric Model, version 5 (CAM5), during the abrupt onset of the South China Sea (SCS) summer monsoon, a key precursor of the overall East Asia summer monsoon (EASM). The multiyear hindcast approach is utilized to obtain the well-constrained synoptic-scale horizontal circulation each year during the onset period from the years 1998 to 2012. In the pre-onset period, the ocean precipitation over the SCS is insufficiently suppressed in CAM5 hindcasts and thus weaker land–ocean precipitation contrasts. This is associated with the weaker and shallower convection simulated over the surrounding land, producing weaker local circulation within the SCS basin. In the post-onset period, rainfall of the organized convection over the Philippine coastal ocean is underestimated in the hindcasts, with overestimated upper-level heating. These biases are further elaborated as the underrepresentation of the convection diurnal cycle and coastal convection systems, as well as the issue of precipitation sensitivity to environmental moisture during the SCS onset period. The biases identified in hindcasts are consistent with the general bias of the EASM in the climate simulation of CAM5. The current results highlight that the appropriate representation of land–ocean–convection interactions over coastal areas can potentially improve the simulation of seasonal transition over the monsoon regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

EVOLUTIONARY TRACKS OF THE CLIMATE OF EARTH-LIKE PLANETS AROUND DIFFERENT MASS STARS

The climatic evolution of the Earth depends strongly on the evolution of the insolation from the Sun and the amount of the greenhouse gasses, especially CO{sub 2} in the atmosphere. Here, we investigate the evolution of the climate of hypothetical Earths around stars whose masses are different from the solar mass with a luminosity evolution model of the stars, a mantle degassing model coupled with a parameterized convection model of the planetary interiors, and an energy balance climate model of the planetary surface. In the habitable zone (HZ), the climate of the planets is initially warm or hot, depending on the orbital semimajor axes. We found that, in the inner HZ, the climate of the planets becomes hotter with time owing to the increase in the luminosity of the central stars, while, in the outer HZ, it becomes colder and eventually globally ice-covered owing to the decrease in the CO{sub 2} degassing rate of the planets. The orbital condition for maintaining the warm climate similar to the present Earth becomes very limited, and more interestingly, the planet orbiting in the outer HZ becomes globally ice-covered after a certain critical age (∼3 Gyr for the hypothetical Earth with standard parameters), irrespective of the mass of the central star. This is because the critical age depends on the evolution of the planets and planetary factors, rather than on the stellar mass. The habitability of the Earth-like planet is shown to be limited with age even though it is orbiting within the HZ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2024

Divertor-safe nonlinear burn control based on a SOLPS parameterized core-edge model for ITER

Abstract For ITER operations, the range of desirable burning-plasma regimes with high fusion power output will be restricted by various operational constraints. These constraints include the saturation of ITER’s various heating and fueling actuators such as the neutral beam injectors, the ion and electron cyclotron heating systems, the gas puffing system, and the deuterium–tritium pellet injectors. In addition to these actuator constraints, the H-mode power threshold, divertor detachment, and the heat load on the divertor targets may apply limitations to ITER’s operational space. In this work, Plasma Operation Contour (POPCON) plots that map the aforementioned constraints to the temperature-density space are used to investigate which constraints are most limiting towards accessing regimes with high fusion power output. The presented POPCON plots are based on a control-oriented core-edge model that couples the nonlinear density and energy response models for the core-plasma region with SOLPS4.3 parameterizations for conditions in the edge-plasma regions (scrape-off-layer and divertor). Using this control-oriented core-edge model, a nonlinear burn controller, which aims to regulate the plasma temperature and density in the core-plasma region, is constructed in this work. This controller is augmented with an online optimization scheme that governs the control references such that the plasma can be guided towards regimes with high fusion powers while protecting the divertor targets from dangerously high heat loads. A closed-loop simulation study illustrates the capability of this burn control scheme.

Physics↗
DOE OSTI2014

Simulating weather regimes: impact of model resolution and stochastic parameterization

The simulation of quasi-persistent regime structures in an atmospheric model with horizontal resolution typical of the Intergovernmental Panel on Climate Change fifth assessment report simulations, is shown to be unrealistic. A higher resolution configuration of the same model, with horizontal resolution typical of that used in operational numerical weather prediction, is able to simulate these regime structures realistically. The spatial patterns of the simulated regimes are remarkably accurate at high resolution. A model configuration at intermediate resolution shows a marked improvement over the low-resolution configuration, particularly in terms of the temporal characteristics of the regimes, but does not produce a simulation as accurate as the very-high-resolution configuration. It is demonstrated that the simulation of regimes can be significantly improved, even at low resolution, by the introduction of a stochastic physics scheme. At low resolution the stochastic physics scheme drastically improves both the spatial and temporal aspects of the regimes simulation. These results highlight the importance of small-scale processes on large-scale climate variability, and indicate that although simulating variability at small scales is a necessity, it may not be necessary to represent the small-scales accurately, or even explicitly, in order to improve the simulation of large-scale climate. It is argued that these results could have important implications for improving both global climate simulations, and the ability of high-resolution limited-area models, forced by low-resolution global models, to reliably simulate regional climate change signals.

Meteorology & Atmospheric Sciences↗