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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 OSTI2025

Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Results from Fuel Matrix Degradation Model Parameterization Experiments and Model Development Activities

This work is being performed as part of the DOE NE Spent Fuel and Waste Science and Technology Campaign, Argillite and Crystalline Rock R&D work packages: SF-20AN01030101 and SF-20AN01030201 respectively. This document is submitted in fulfillment of milestone M4SF-20AN010301013 for Argillite R&D and the milestone M4SF-20AN010302013 for Crystalline R&D

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Interparameter tradeoff quantification for isotropic-elastic full-waveform inversion with various model parameterizations

Simultaneous determination of multiple physical parameters using full-waveform inversion (FWI) suffers from interparameter tradeoff diffculty. Analyzing the interparameter tradeoffs in different model parameterizations of isotropic-elastic FWI, and thus determining the appropriate model parameterization, are critical for efficient inversion and obtaining reliable inverted models. Five different model parameterizations are considered and compared including velocity-density, modulus-density, impedance-density and two velocity-impedance parameterizations. The scattering radiation patterns are first used for interparameter tradeoff analysis. Furthermore, a new framework is developed to evaluate the interparameter tradeoff based upon multiparameter Hessian-vector products: multiparameter point spread functions (MPSFs) and interparameter contamination sensitivity kernels (ICSKs), which provide quantitative, second-order measurements of the interparameter contaminations. In the numerical experiments, the interparameter tradeoffs in various model parameterizations are evaluated using the MPSFs and ICSKs. Inversion experiments are then carried out with simple Gaussian-anomaly models and complex Marmousi model. Overall, the parameterization of P-wave velocity, S-wave velocity and density and the parameterization of P-wave velocity, S-wave velocity and S-wave impedance perform best for reconstructing all of the physical parameters. Isotropic-elastic FWI of the Hussar low frequency dataset with various model parameterizations verifies the conclusions.

58 GEOSCIENCES↗
DOE OSTI2019

From Escherichia coli mutant 13 C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13 C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13 C-Metabolic Flux Analysis ( 13 C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13 C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Aggregated DER_A Model Parameterization via Online Moving Horizon Estimation

Here, this paper introduces a methodology for parameterizing the DER_A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER_A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodology’s effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER_A model closely mirrors the real system’s voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration.

42 ENGINEERING↗
NASA NTRS2023

Mshpy23: A User-Friendly, Parameterized Model of Magnetosheath Conditions

Lunar Environment heliospheric X-ray Imager (LEXI) and Solar wind – Magnetosphere - Ionosphere Link Explorer (SMILE) will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under varioussolar wind conditions after their respective launches in 2024 and 2025. Magnetosheath conditions, namely, plasma density, velocity, and temperature, are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions. We developed a user-friendly model of magnetosheath that parameterizes number density, velocity, temperature, and magnetic field by utilizing the global Magnetohydrodynamics (MHD) model as well as the pre-existing gas-dynamic and analytic models. Using this parameterized magnetosheath model, scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models. First, we created an MHD based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities (1, 5, 10, 15, 20, 25, and 30cm−3) and 2 interplanetary magnetic field BZ components (± 4nT), and then parameterizing the results in new magnetosheath conditions. We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77, especially for plasma density and plasma velocity. Second, we compiled a suite of magnetosheath models incorporating previous magnetosheath models (gas-dynamic, analytic), and did two case studies to test the performance. The MHD based model was comparable to or better than the previous models while providing self consistency among the magnetosheath parameters. Third, we constructed a tool to calculate a soft X-ray image from any given vantage point, which can support the planning and data analysis of the aforementioned LEXI and SMILE missions. A release of the code has been uploaded to a Github repository.

Magnetosheath↗
DOE OSTI2020

Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm

Existing generator parameterization methods, typically developed for large turbine generator units, are difficult to apply to small kW-level diesel generators in microgrid applications. Here, this article presents a model parameterization method that estimates a complete set of kW-level diesel generator parameters simultaneously using only load-step-change tests with limited measurement points. This method provides a more cost-efficient and robust approach to achieve high-fidelity modeling of diesel generators for microgrid dynamic simulation. A two-stage hybrid box-constrained Levenberg-Marquardt (H-BCLM) algorithm is developed to search the optimal parameter set given the parameter bounds. A heuristic algorithm, namely Generalized Opposition-based Learning Genetic Algorithm (GOL-GA), is applied to identify proper initial estimates at the first stage, followed by a modified Levenberg-Marquardt algorithm designed to fine tune the solution based on the first-stage result. The proposed method is validated against dynamic simulation of a diesel generator model and field measurements from a 16kW diesel generator unit.

42 ENGINEERING↗
DOE OSTI2019

Battery Model Parameterization Using Manufacturer Datasheet and Field Measurement for Real-Time HIL Applications

Here, this paper presents a novel battery model parameterization method using actual field measurement and manufacturer datasheet for real-time hardware-in-the-loop (HIL) applications. It is critical that real-time HIL models can accurately reproduce field test results so that tests can be conducted on HIL testbeds instead of in the field. In the past, numerical heuristic optimization algorithms were often used to derive parameters for battery models. However, the deterministic algorithms often reach a locally optimal solution and stochastic heuristic searching strategies suffer from low searching efficiency. Therefore, in this paper, we propose a global-local searching enhanced genetic algorithm (GL-SEGA). By applying the generalized opposition-based learning mechanism, GL-SEGA can efficiently explore the global solution space. By using the trust-region-reflective method to perform the local search, the GL-SEGA can improve the accuracy and convergence in its local exploitations. Field measurements and manufactory datasheets are used to test and validate the accuracy and robustness of the GL-SEGA algorithm.

25 ENERGY STORAGE↗
DOE OSTI2026

Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification

Data generated in studies of cellular regulatory systems are often qualitative. For example, measurements of signaling readouts in the presence and absence of mutations may reveal a rank ordering of responses across conditions but not the precise extents of mutation-induced differences. Qualitative data are often ignored by mathematical modelers or are considered in an ad hoc manner, as in the study of Kocieniewski and Lipniacki (2013) [Phys Biol 10: 035006], which was focused on the roles of MEK isoforms in ERK activation. In this earlier study, model parameter values were tuned manually to obtain consistency with a combination of qualitative and quantitative data. This approach is not reproducible, nor does it provide insights into parametric or prediction uncertainties. Here, starting from the same data and the same ordinary differential equation (ODE) model structure, we generate formalized statements of qualitative observations, making these observations more reusable, and we improve the model parameterization procedure by applying a systematic and automated approach enabled by the software package PyBioNetFit. We also demonstrate uncertainty quantification (UQ), which was absent in the original study. Our results show that PyBioNetFit enables qualitative data to be leveraged, together with quantitative data, in parameterization of systems biology models and facilitates UQ. These capabilities are important for reliable estimation of model parameters and model analyses in studies of cellular regulatory systems and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

Multiscale Modeling of Grain-Boundary Fracture: Cohesive Zone Models Parameterized From Atomistic Simulations

A multiscale modeling strategy is developed to study grain boundary fracture in polycrystalline aluminum. Atomistic simulation is used to model fundamental nanoscale deformation and fracture mechanisms and to develop a constitutive relationship for separation along a grain boundary interface. The nanoscale constitutive relationship is then parameterized within a cohesive zone model to represent variations in grain boundary properties. These variations arise from the presence of vacancies, intersticies, and other defects in addition to deviations in grain boundary angle from the baseline configuration considered in the molecular dynamics simulation. The parameterized cohesive zone models are then used to model grain boundaries within finite element analyses of aluminum polycrystals.

Glaessgen, Edward H.↗
DOE OSTI2022

Seismic Tomographic Modeling of the Crust and Upper Mantle beneath Israel and the Middle East: Improved Resolution through Optimized Model Parameterization

Accurate regional seismic travel-time (RSTT) predictions rely on regional phases (e.g., Pg, Lg, Pn, Sn ) to account for 3D effects in the crust and upper mantle that are not captured by 1D models traditionally used for real-time location. The RSTT prediction model accounts for regional-scale crust and upper mantle structure globally by incorporating regional seismic phases into its travel-time calculations. Previous versions of the RSTT model have used a constant grid cell size of 1°. To improve the tomographic accuracy of recovering velocity structure at regional scales, we, in this study, perform data-driven grid refinement on the RSTT model down to a 0.125° grid (~14 km) in pursuit of two main goals: (1) to test the limits of RSTT capability and accuracy of determined velocity structure through variable grid refinement and (2) to image smaller structures in Israel and the Middle East and illuminate upper mantle dynamics operating in this complex tectonic area. We investigate the effects of model parameterization as grid cell size decreases and the trade-offs between recovered velocity structures. Our final dataset includes 4751 events and 499 stations that recorded 79,344 Pn and 7489 Pg . The variable grid refinement method allows recovery of finer-scale velocity structures and reduces travel-time residuals in areas with the highest data coverage. At smaller grid cell sizes, longer paths need to be upweighted to stabilize the inversion. Results illuminate tectonic features undefined in coarser grid-size models; in particular, we observe mantle perturbations related to the subduction zone around the Cyprian arc and crustal anomalies near the Dead Sea fault and throughout the Anatolian plate.

58 GEOSCIENCES↗
DOE OSTI2023

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

land data assimilation↗
DOE OSTI2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗
DOE OSTI2018

Evaluating soil moisture feedback on convective triggering: Roles of convective and land–model parameterizations

Feedbacks between soil moisture and convective precipitation are not well represented in current Earth system models, and can affect projections of drought and heavy-precipitation extremes. To explore the atmospheric and land-surface influences on the initiation (triggering) of daytime deep convection, single-column model experiments were performed using the NCAR Community Earth System Model (CESM1.2), over the U.S. Southern Great Plains. The results indicate that the positive and negative feedback mechanisms found in earlier studies (using simplified boundary-layer models) are robust to large-scale forcing and interactions between boundary-layer turbulence, cloud dynamics, and radiation. However, systematic responses of convective triggering to soil moisture emerged only after switching from a convective available potential energy-based to a convective inhibition energy-based convective parameterization. This suggests that the choice of convective mass-flux closure largely determines the sensitivity of parameterized convective clouds to land-surface state. Errors in land-model parameterizations of evapotranspiration also affect the probability of deep convection, with vegetation (transpiration) playing an important role in linking soil moisture to surface energy partitioning and clouds. Parameterizations that permit the triggering feedback mechanism better predict the statistics of daytime shallow and deep convection, with respect to observations from the Atmospheric Radiation Measurement site in the Southern Great Plains. Here, the results illustrate how errors in the representation of evapotranspiration in land-surface models can propagate through the chain of coupled land-atmosphere processes to affect convective clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Preston-Tonks-Wallace (PTW) Model Parameterization of Al-6061 With Sesame EoS

In this report, we make a recommendation for the PTW model parameters for an aluminum alloy, Al 6061-T6 based on the quasi-static and split-Hopkin pressure bar experimental data, provided by the experimentalists in MST-8, LANL. Al 6061 is an aluminum alloy with magnesium and silicon, and the temper designation T6 means it has gone through heat treatment followed by quenching, cooling, and aging. (Note that T4 is naturally aged and T6, artificially.) We adopted the shear modulus and melt curve models that are necessary to parameterize the PTW model, from Sesame EoS. In the lack of those information on this particular aluminum alloy, we used what is available for pure aluminum.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Cirrus cloud model parameterizations: Incorporating realistic ice particle generation

Recent cirrus cloud modeling studies have involved the application of a time-dependent, two dimensional Eulerian model, with generalized cloud microphysical parameterizations drawn from experimental findings. For computing the ice versus vapor phase changes, the ice mass content is linked to the maintenance of a relative humidity with respect to ice (RHI) of 105 percent; ice growth occurs both with regard to the introduction of new particles and the growth of existing particles. In a simplified cloud model designed to investigate the basic role of various physical processes in the growth and maintenance of cirrus clouds, these parametric relations are justifiable. In comparison, the one dimensional cloud microphysical model recently applied to evaluating the nucleation and growth of ice crystals in cirrus clouds explicitly treated populations of haze and cloud droplets, and ice crystals. Although these two modeling approaches are clearly incompatible, the goal of the present numerical study is to develop a parametric treatment of new ice particle generation, on the basis of detailed microphysical model findings, for incorporation into improved cirrus growth models. For example, the relation between temperature and the relative humidity required to generate ice crystals from ammonium sulfate haze droplets, whose probability of freezing through the homogeneous nucleation mode are a combined function of time and droplet molality, volume, and temperature. As an example of this approach, the results of cloud microphysical simulations are presented showing the rather narrow domain in the temperature/humidity field where new ice crystals can be generated. The microphysical simulations point out the need for detailed CCN studies at cirrus altitudes and haze droplet measurements within cirrus clouds, but also suggest that a relatively simple treatment of ice particle generation, which includes cloud chemistry, can be incorporated into cirrus cloud growth.

Sassen, Kenneth↗
NASA NTRS1985

Tracer transport by planetary waves - A comparison of explicit and parameterized models

A two-dimensional stratospheric tracer transport parameterization is developed using the theory of small-amplitude generalized Lagrangian means and tested by two experiments. The first tests the parameterization for mean climatological winter conditions without wave transience; the second tests it in the presence of transience and critical layers, when nonacceleration conditions are strongly violated. Tracer transport by Eulerian velocity, the residual velocity, and the nondivergent mean meridional Lagrangian velocity are compared.

Hess, P. G.↗