Search NASA⌕ Search

SEARCH · Search NASA

Results for “Coupled process modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Simulating eroded soil organic carbon with the SWAT-C model

The soil erosion and associated lateral movement of eroded carbon (C) have been identified as a possible mechanism explaining the elusive terrestrial C sink of ca. 1.7-2.6 PgC yr(-1). Here we evaluated the SWAT-C model for simulating long-term soil erosion and associated eroded C yields. Our method couples the CENTURY carbon cycling processes with a Modified Universal Soil Loss Equation (MUSLE) to estimate C losses associated with soil erosion. The results show that SWAT-C is able to simulate well long-term average eroded C yields, as well as correctly estimate the relative magnitude of eroded C yields by crop rotations. We also evaluated three methods of calculating C enrichment ratio in mobilized sediments, and found that errors associated with enrichment ratio estimation represent a significant uncertainty in SWAT-C simulations. Furthermore, we discussed limitations and future development directions for SWAT-C to advance C cycling modeling and assessment.

Agriculture↗
DOE OSTI2024

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC-Juniata-hydroDL2) that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale hydrologic flood simulations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Integrating Kinetic Effects into Global Models for Reconnection

Magnetic reconnection is the most striking example of how the coupling between global and kinetic scales can lead to fast energy release. Explosive solar activity, such as coronal mass ejections and flares for example, is widely believed to be due to the release of magnetic energy stored on global scales by magnetic reconnection operating on kinetic scales. Understanding how processes couple across spatial scales is one of the most difficult challenges in all of physics, and is undoubtedly the main obstacle to developing predictive models for the Sun's activity. Consequently, the NASA Living With a Star Program selected a Focused Science Team to attack the problem of cross-scale coupling in reconnection. In this talk I will present some of the results of the Team and review our latest theories and methods for modeling the global-local coupling in solar reconnection.

Antiochos, S. K.↗
NASA NTRS1996

Coupled thermal/chemical/mechanical modeling of energetic materials in ALE3D

We must improve our ability to model the response of energetic materials to thermal stimuli and the processes involved in the energetic response. We have developed and used a time step option to efficiently and accurately compute the hours that the energetic material can take to react. Since on these longer film scales, materials can be expected to have significant motion, it is even more important to provide high-order advection for all components, including the chemical species. We show an example cook-off problem to illustrate these capabilities.

WEAPONS↗
DOE OSTI2024

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models–Advances, Applications, and Challenges

Earth System Models (ESMs) simulate the exchange of mass and energy between the land surface and the atmosphere, with a key focus on modeling natural greenhouse gas feedbacks. Methane is the second most important greenhouse gas after carbon dioxide. There are growing concerns over the rapidly increasing methane concentration in the atmosphere, underscoring the need for accurate global modeling of its emissions using ESMs. Of the multitude of sources of methane globally, wetlands are the largest natural emitters for methane, leading to significant efforts targeting their representation in ESMs with a special focus on their methane emissions. In this review, we first provide a historical overview of including wetland–methane components in ESMs and how methane modeling approaches have evolved over time. Second, we discuss recent modeling advancements that show promise for improvements in methane emissions predictions, namely the coupling of surface and atmospheric modules of ESMs, the representation of microtopography and transport mechanisms, the resolution of microbial processes at different spatial–temporal scales, and the improved mapping of wetland area extent across the different wetland types. Third, we shed light on the different challenges hindering accurate estimations of wetland–methane emissions, as shown by the consistent discrepancy between bottom–up and top–down models' predictions. Finally, we emphasize that more detailed representation of biogeochemistry and dynamic hydrology while resolving the within–wetland vegetation heterogeneity should improve model predictions, especially when coupled with expanding ground–based measurement networks and high–resolution remote sensing mapping of methane–relevant variables, such as water elevation, water table depth, and methane concentration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

PigmentHunter: A point-and-click application for automated chlorophyll-protein simulations

Chlorophyll proteins (CPs) are the workhorses of biological photosynthesis, working together to absorb solar energy, transfer it to chemically active reaction centers, and control the charge-separation process that drives its storage as chemical energy. Yet predicting CP optical and electronic properties remains a serious challenge, driven by the computational difficulty of treating large, electronically coupled molecular pigments embedded in a dynamically structured protein environment. To address this challenge, we introduce here an analysis tool called PigmentHunter, which automates the process of preparing CP structures for molecular dynamics (MD), running short MD simulations on the nanoHUB.org science gateway, and then using electrostatic and steric analysis routines to predict optical absorption, fluorescence, and circular dichroism spectra within a Frenkel exciton model. Inter-pigment couplings are evaluated using point-dipole or transition-charge coupling models, while site energies can be estimated using both electrostatic and ring-deformation approaches. The package is built in a Jupyter Notebook environment, with a point-and-click interface that can be used either to manually prepare individual structures or to batch-process many structures at once. Here, we illustrate PigmentHunter’s capabilities with example simulations on spectral line shapes in the light harvesting 2 complex, site energies in the Fenna–Matthews–Olson protein, and ring deformation in photosystems I and II.

14 SOLAR ENERGY↗
DOE OSTI2017

H/D exchange mass spectrometry and statistical coupling analysis reveal a role for allostery in a ferredoxin-dependent bifurcating transhydrogenase catalytic cycle

Recent investigations into ferredoxin-dependent transhydrogenases, a class of enzymes responsible for electron transport, have highlighted the biological importance of flavin-based electron bifurcation (FBEB). FBEB generates biomolecules with very low reduction potential by coupling the oxidation of an electron donor with intermediate potential to the reduction of high and low potential molecules. Bifurcating systems can generate biomolecules with very low reduction potentials, such as reduced ferredoxin (Fd), from species such as NADPH. Metabolic systems that use bifurcation are more efficient and confer a competitive advantage for the organisms that harbor them. Structural models are now available for two NADH-dependent ferredoxin-NADP + oxidoreductase (Nfn) complexes. These models, together with spectroscopic studies, have provided considerable insight into the catalytic process of FBEB. However, much about the mechanism and regulation of these multi-subunit proteins remains unclear. Using hydrogen/deuterium exchange mass spectrometry (HDX-MS) and statistical coupling analysis (SCA), we identified in this work specific pathways of communication within the model FBEB system, Nfn from Pyrococus furiosus , under conditions at each step of the catalytic cycle. HDX-MS revealed evidence for allosteric coupling across protein subunits upon nucleotide and ferredoxin binding. SCA uncovered a network of co-evolving residues that can provide connectivity across the complex. Together, the HDX-MS and SCA data show that protein allostery occurs across the ensemble of iron-sulfur cofactors and ligand binding sites using specific pathways that connect domains allowing them to function as dynamically coordinated units.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2021

Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land-surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snow pack evolution now support a range of drought indicators that better reflect coupled water, energy and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Drought↗
DOE OSTI2018

Coupled Kinetics of Ferrihydrite Transformation and As(V) Sequestration under the Effect of Humic Acids: A Mechanistic and Quantitative Study

In natural environments, kinetics of As(V) sequestration/release is usually coupled with the dynamic iron mineral transformation, which is further influenced by the presence of the natural organic matter (NOM). However, both quantitative and mechanistic understanding of this process is still lacking. In this study, we investigated the effect of humic acids (HA), a major component of NOM, on the coupled kinetics of ferrihydrite transformation into hematite/goethite and sequestration of As(V) in iron minerals. Time-resolved As(V) and HA interactions with iron minerals were studied in stirred-flow kinetic experiments, using aberration-corrected scanning transmission electron microscopy (STEM), and X-ray adsorption spectroscopy. Based on the experimental results, we developed a mechanistic kinetics model for As(V) reactions with iron minerals with varying iron mineral compositions and solution chemistry. Our results showed that HA slowed ferrihydrite transformation and the rates of As(V) speciation changes within iron minerals, while HA did not incorporate into iron minerals. Our kinetics model is able to account for the variations of iron mineral compositions, solution chemistry, and As(V) speciation, which has significant environmental implications on predicting the As(V) behavior in the environment.

ferrihydrite, humic acid↗
DOE OSTI2022

Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers

Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To illustrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Scalable computations for nonstationary Gaussian processes

Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial datasets. However, the large number of observations in modern datasets makes fitting such models computationally intractable with conventional dense linear algebra. In addition, derivative-free or even first-order optimization methods can be very slow to converge when estimating many spatially varying parameters. In this paper, we present a computational framework which couples an algebraic block diagonal plus low-rank covariance matrix approximation with stochastic trace estimation to facilitate the efficient use of second-order solvers for maximum likelihood estimation of Gaussian process models with many parameters. We demonstrate the effectiveness of these methods by simultaneously fitting 192 parameters in the popular nonstationary model of Paciorek and Schervish using 107,600 sea surface temperature anomaly measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A Multilayer Reservoir Thermal Stratification Module for Earth System Models

Thermal stratification in reservoirs is a critical process that regulates downstream riverine energy and biogeochemical cycling. Current stratification models either simplify reservoir geometry or neglecting the effects of reservoir operation. Here we present a new multi-layer reservoir stratification model that can be coupled to earth system models at regional or global scale. With a multi-layer vertical discretization, we introduce a newly developed storage-area-depth dataset to improve parameterization of advection processes in and out of the reservoir. The new model better represents vertical temperature gradient and subsequently temperature of water released to downstream. The stratification model is applied to 1400 reservoirs over the contiguous US and validated against observed surface, profile, and outflow temperature data over 130 reservoirs subjected to various levels of regulation. The Nash-Sutcliffe values are higher than 0.5 for about 77% of the validated reservoirs using surface temperature while the average values of root mean square error and bias are 3.6oC and -1.1oC, respectively. Using the new reservoir storage-area-depth dataset improves the simulation of surface temperature at over 69% of the validated reservoirs compared to using simplified reservoir geometry. The reservoir stratification model contributes to improving predictive understanding of anthropogenic impact on terrestrial hydrological, ecological and biogeochemical cycles.

Yigzaw, Wondmagegn↗
DOE OSTI2021

Towards multiscale modeling of ocean surface turbulent mixing using coupled MPAS-Ocean v6.3 and PALM v5.0

A multiscale modeling approach for studying the ocean surface turbulent mixing is explored by coupling an ocean general circulation model (GCM) MPAS-Ocean with the Parallelized Large Eddy Simulation Model (PALM). The coupling approach is similar to the superparameterization approach that has been used to represent the effects of deep convection in atmospheric GCMs. However, the focus of this multiscale modeling approach is on the small-scale turbulent mixing and their interactions with the larger-scale processes in the ocean, so that a more flexible coupling strategy is used. To reduce the computational cost, a customized version of PALM is ported on the general-purpose graphics processing unit (GPU) with OpenACC, achieving 10–16 times overall speedup as compared to running on a single CPU. Even with the GPU-acceleration technique, a superparameterization-like approach to represent the ocean surface turbulent mixing in GCMs using embedded high fidelity and three-dimensional large eddy simulations (LESs) over the global ocean is still computationally intensive and infeasible for long simulations. However, running PALM regionally on selected MPAS-Ocean grid cells is shown to be a promising approach moving forward. The flexible coupling between MPAS-Ocean and PALM allows further exploration of the interactions between the ocean surface turbulent mixing and larger-scale processes, as well as future development and improvement of ocean surface turbulent mixing parameterizations for GCMs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

Identification of a linear model of rotor-fuselage dynamics from nonlinear simulation data

Linear regression techniques are used to obtain 9- and 12-degree-of-freedom linear rotorcraft models from the input-output data generated by a nonlinear, blade-element rotorcraft simulation in hover. The resulting models are used to evaluate the coupling of the fuselage modes with the rotor flapping and lead-lag modes at various frequencies. New techniques are proposed and evaluated to improve the identification process, including a method of verifying the assumed model structure by using data sets generated at different input frequencies.

Duval, R. W.↗
DOE OSTI2021

Modeling and simulation of vascular tumors embedded in evolving capillary networks

Here in this work, we present a coupled 3D–1D model of solid tumor growth within a dynamically changing vascular network to facilitate realistic simulations of angiogenesis. Additionally, the model includes erosion of the extracellular matrix, interstitial flow, and coupled flow in blood vessels and tissue. We employ continuum mixture theory with stochastic Cahn–Hilliard type phase-field models of tumor growth. The interstitial flow is governed by a mesoscale version of Darcy’s law. The flow in the blood vessels is controlled by Poiseuille flow, and Starling’s law is applied to model the mass transfer in and out of blood vessels. The evolution of the network of blood vessels is orchestrated by the concentration of the tumor angiogenesis factors (TAFs); blood vessels grow towards the increasing TAFs concentrations. This process is not deterministic, allowing random growth of blood vessels and, therefore, due to the coupling of nutrients in tissue and vessels, makes the growth of tumors stochastic. We demonstrate the performance of the model by applying it to a variety of scenarios. Numerical experiments illustrate the flexibility of the model and its ability to generate satellite tumors. Simulations of the effects of angiogenesis on tumor growth are presented as well as sample-independent features of cancer.

3D–1D coupled blood flow models↗
DOE OSTI2022

An all optical approach for comprehensive in-operando analysis of radiative and nonradiative recombination processes in GaAs double heterostructures

We demonstrate an all optical approach that can surprisingly offer the possibility of yielding much more information than one would expect, pertinent to the carrier recombination dynamics via both radiative and nonradiative processes when only one dominant deep defect level is present in a semiconductor material. By applying a band-defect state coupling model that explicitly treats the inter-band radiative recombination and Shockley–Read–Hall (SRH) recombination via the deep defect states on an equal footing for any defect center occupation fraction, and analyzing photoluminescence (PL) as a function of excitation density over a wide range of the excitation density (e.g., 5–6 orders in magnitude), in conjunction with Raman measurements of the LO-phonon plasmon (LOPP) coupled mode, nearly all of the key parameters relevant to the recombination processes can be obtained. They include internal quantum efficiency (IQE), minority and majority carrier density, inter-band radiative recombination rate (W r ), minority carrier nonradiative recombination rate (W nr ), defect center occupation fraction (f), defect center density (N t ), and minority and majority carrier capture cross-sections (σ t and σ tM ). While some of this information is thought to be obtainable optically, such as IQE and the W r /W nr ratio, most of the other parameters are generally considered to be attainable only through electrical techniques, such as current-voltage (I-V) characteristics and deep level transient spectroscopy (DLTS). Following a procedure developed herein, this approach has been successfully applied to three GaAs double-heterostructures that exhibit two distinctly different nonradiative recombination characteristics. The method greatly enhances the usefulness of the simple PL technique to an unprecedented level, facilitating comprehensive material and device characterization without the need for any device processing.

14 SOLAR ENERGY↗