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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.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Operational and geological controls of coupled poroelastic stressing and pore-pressure accumulation along faults: Induced earthquakes in Pohang, South Korea

Coupled poroelastic stressing and pore-pressure accumulation along pre-existing faults in deep basement contribute to recent occurrence of seismic events at subsurface energy exploration sites. Our coupled fluid-flow and geomechanical model describes the physical processes inducing seismicity corresponding to the sequential stimulation operations in Pohang, South Korea. Simulation results show that prolonged accumulation of poroelastic energy and pore pressure along a fault can nucleate seismic events larger than M w 3 even after terminating well operations. In particular the possibility of large seismic events can be increased by multiple-well operations with alternate injection and extraction that can enhance the degree of pore-pressure diffusion and subsequent stress transfer through a rigid and low-permeability rock to the fault. This study demonstrates that the proper mechanistic model and optimal well operations need to be accounted for to mitigate unexpected seismic hazards in the presence of the site-specific uncertainty such as hidden/undetected faults and stress regime.

58 GEOSCIENCES↗
DOE OSTI2025

Hydrology controls thermokarst and alters carbon cycling and methane emissions in peatlands near the southern limit of permafrost

Permafrost peatlands store vast amounts of frozen carbon across northern landscapes. When ground ice melts, surface subsidence produces thermokarst landforms that expand wetlands at the edges of permafrost plateaus. Thermokarst represents an accelerating climate feedback, but uncertainties remain about how ground ice, hydrology, and vegetation interact to shape landscape change and carbon fluxes. We extended the process-based model ecosys to simulate thermokarst dynamics in laterally coupled 2D transects at a well-characterized boreal peatland site in Canada’s Northwest Territories. After benchmarking against site observations, we varied ground ice content and hydrologic boundary conditions across ranges typical near the southern permafrost limit. Simulations revealed distinct degradation regimes governed by the elevation difference between the frost table and the external water table. Rates of lateral retreat, the thaw-driven encroachment of wetlands into adjacent plateaus, ranged from 0 to >2 m yr −1 under identical weather forcing, consistent with observations and highlighting the strong role of WT and ground ice. Simulated vegetation dynamics indicate that black spruce mortality cannot be explained by anoxia alone, pointing to additional stressors such as root damage, pathogens, or physical destabilization. Despite large hydrologic shifts, net ecosystem CO 2 exchange remained a slight sink after collapse, while methane (CH 4 ) emissions rose by one to two orders of magnitude. As a result, lateral retreat substantially increases the greenhouse warming potential of permafrost peatlands (1.7 million km 2 in area), with simulated emissions of 0.1–10 Mt CO 2 -eq decade −1 depending on hydrology and retreat rates. These results underscore the need to account for both ground ice and hydrologic dynamics when assessing thermokarst-driven climate feedbacks.

carbon cycling↗
DOE OSTI2020

Algae/Wood Blends Hydrothermal Liquefaction and Upgrading: 2019 State of Technology

The fiscal year (FY) 2019 State of Technology (SOT) Assessment for a microalgae and woody biomass blend feedstock hydrothermal liquefaction (HTL) and biocrude upgrading system has been completed and reported here. This study is a preliminary economic analysis for this system. Inputs from the 2019 NREL open pond algae cultivation model provided microalgae seasonal flowrates and dewatered algae feedstock price. These inputs have been coupled with modeled experimental data from PNNL for algae/wood blends processed via HTL to assess the cost impacts. The TEA results demonstrated a 28% reduction in the conversion cost only from $1.22/gge in 2018 to $0.88/gge in 2019.

09 BIOMASS FUELS↗
DOE OSTI2023

Crack-Free Joining of High-Strength AA7055 Sheets by Friction Based Self-Piercing Riveting with the Aid of Numerical Design

Unique friction-based self-piercing riveting (F-SPR) was employed to join high-strength, low-ductility aluminum alloy 7055 for lightweight vehicle applications. This study aimed to maximize the joint strength of the AA7055 F-SPR joint while avoiding cracking issues due to low ductility at room temperature. A fully coupled Eulerian–Lagrangian (CEL) model was employed to predict the process temperature during F-SPR, and the temperature field was then mapped onto a 2D axisymmetric equivalent model for accelerated numerical analysis. The geometry, dimensions, and material strength of the rivet, as well as the depth of the die cavity and plunging depth, were investigated to enhance joint formation. Also, a static finite-element analysis model was developed to predict and analyze the stress distribution in the rivet under different mechanical testing loading conditions. Overall, the numerical model showed good agreement with the experiment results, such as joint formation and mechanical joint strength. With the aid of virtual fabrication through numerical modeling, the joint design iterations and process development time of F-SPR were greatly reduced regarding the goal of lightweight, high-strength aluminum joining.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CMIP7 data request: impacts and adaptation priorities and opportunities

The Coupled Model Intercomparison Project Phase 7 (CMIP7) undertook an extensive process to gather community input and refine data requests related to impacts and adaptation applications of Earth System Model (ESM) outputs. The Impacts and Adaptation (I&A) Data Request Team worked with CMIP7 leadership to distribute an open solicitation across many communities that use climate model outputs requesting inputs for new and existing variables, the most applicable temporal characteristics, and groupings of variables that together allow for specific application opportunities. This input was then collated and translated into CMIP7 standard templates for inclusion in the broader data request, leading to 13 I&A data request opportunities, 60 variable groups and 539 unique variables sought by vulnerability, impacts, adaptation, and climate services user communities. Here, we describe these opportunities and variable groups, as well as new insights into how ESM groups can prioritize outputs that set off a chain of further analyses, ultimately informing decisions impacting society and natural systems. These include an emphasis on high-resolution outputs to allow further modeling of climate impacts at regional and local scales, improved representation of extreme weather events, enhanced accuracy of downscaling and bias-adjustment techniques, and support for more detailed assessments for decision-making in adaptation and mitigation strategies. There is also broad interest in more extensive provisioning of two-dimensional variables at the Earth's surface, prioritizing experiments that enhance our understanding of both the recent past and future scenarios, and providing outputs that allow further downscaling and bias adjustment. We emphasize that variable groups are the fundamental level at which to engage with the I&A data request, matching the scale of input and the way output provision enables specific I&A applications. Given resource constraints, we applaud CMIP7 efforts to foster strong engagement and communication between ESM groups and the I&A team to build consensus around prudent compromises in priority variables, temporal resolutions, simulation experiments, time subsets, and ensemble members.

Ruane, Alex C. [NASA Goddard Inst. for Space Studi↗
DOE OSTI2021

Multi-Scale Land-Atmosphere Interactions: Modeling Convective Processes from Plants to Planet

Research accomplishments include: 1)Two case studies of the effects of heterogeneous soil moisture and surface energy budgets on organization and propagation of convective precipitation during MC3E. 2) Development and evaluation of a new approach for simulating the effect of heterogeneous soil moisture at ARM-SGP using an innovative modeling approach. 3) Investigation of the effects of spatial coupling scale using multidecade global simulations in CESM with the multiscale modeling framework. 4) Exploration of changes to future precipitation intensity resulting from two different climate change scenarios using the multiscale model. 5) Provision of the new cloud-scale coupled multiscale Earth System Model to the larger community through the CESM process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. Here this work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization.

42 ENGINEERING↗
DOE OSTI2023

Modeling Land-Atmosphere Coupling at Cloud-Resolving Scale Within the Multiple Atmosphere Multiple Land (MAML) Framework in SP-E3SM

Representing subgrid variabilities of land surface processes and their upscaled effects is crucial for global climate modeling. Here, we implement a Multiple Atmosphere Multiple Land (MAML) framework in the superparamaterized version of E3SM (SP32 E3SM) to explicitly simulate the subgrid variabilities of land states and fluxes at cloud-resolving scale and their interactions with atmosphere. Comparing to the standard SP-E3SM in which all the atmospheric columns of the cloud resolving model embedded within the global atmospheric model grid interact with the same land surface (i.e., Multiple Atmosphere Single Land (MASL)), the impact of MAML on the strength of land-atmosphere coupling is limited, partly because the current implementation mainly facilitates one-way coupling between the cloud-resolving model and the land surface model. Despite such limitation, MAML increases the surface latent heat flux at the expense of sensible heat flux, and increases precipitation in India, Amazon, and Central Africa, reducing the model dry bias compared to the standard SP-E3SM. By employing a normalized gross moist stability (NGMS) diagnostic framework, we find that the increase in precipitation minus evaporation (PE) is primarily driven by the change in large-scale moisture convergence, particularly by the increase of water vapor in the lower atmosphere, while the local effect of total surface energy flux plays a minor role in the P-E change. More specifically, MAML changes the surface energy partitioning (evaporative fraction), increases the atmosphere water vapor, and further increases P-E by decreasing the NGMS. Finally, future development in the MAML framework is discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observed Land Surface Influence on Atmospheric Heat and Moisture Profiles During Interstorms

Land-atmospheric (L-A) feedbacks have historically been studied using models whose structure and parameterizations influence outcomes and insights. The representation of L-A feedbacks based on observations alone remains an ongoing challenge for understanding boundary layer development and precipitation. To address this gap, we use ground-based passive remote sensing and in-situ observations to present an analysis of the atmosphere during 103 interstorm soil moisture drydown events spanning nine warm seasons (2016–2024) in the U.S. Southern Great Plains region. By separating events based on local L-A coupling signals and characterizing the profiles of atmospheric heat and moisture to surface energy flux behavior, we investigate the physical mechanisms linking land surface processes to boundary layer development. We find that during interstorm drydowns, the atmospheric column follows a consistent pattern: moisture increases within the boundary layer, peaks near its top, and declines rapidly above, while warming occurs through the depth. Drydowns that shift toward evaporation produce stronger and deeper thermodynamic responses than cases dominated by sensible heating, which are weaker and shallower. Additionally, moisture is accumulated faster within the boundary layer during shorter drydowns, with longer drydowns representing slower, moisture-limited growth. Drydowns with wetter initial soil moisture will sustain stronger moistening within and above the boundary layer, accelerating buoyancy growth and convective potential toward the next storm. These results provide observational evidence linking surface flux evolution to boundary layer thermodynamics and offer a process-level benchmark for evaluating coupled L-A representations in models and demonstrating the influence of soil moisture on short-term weather forecasting skill.

Zhang, M. S. [Massachusetts Inst. of Technology (M↗
DOE OSTI2020

Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) and TempestRemap offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

58 GEOSCIENCES↗
DOE OSTI2021

Drying model of a high salt content cementitious waste form: Effect of capillary forces and salt solution

Highlights: • Drying model for a high salt content cementitious waste form is developed. • Water vapor diffusion and capillary liquid flow are distinguished. • Capillary and salt solution effects are considered in vapor-liquid equilibrium. A water transport model coupling capillary liquid flow with vapor diffusion is developed to describe the drying process for a cementitious waste form with high salinity porewater. Vapor-liquid equilibrium is formulated as the driving force for vapor diffusion and the model accounts for pore capillary and high salinity effects on water thermodynamic activity. Pore filling and porewater surface tension as a function of pore size distribution and water saturation have been quantified for the material. Geochemical speciation modeling is used to simulate porewater activity as a function of composition over the range of saturation. The theoretical relationship between relative humidity and water saturation generally agrees with experimental measurement, and the developed model is capable of predicting drying rates under various external relative humidity conditions. The model was developed to be incorporated into reactive transport models considering the effects of drying such as salt redistribution and efflorescence.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Material analysis of metal hydrides for bulk hydrogen storage

The United States Department of Energy (DOE) Hydrogen Storage Engineering Center of Excellence (HSECoE), 2009–2014, was established to evaluate the viability of material-based hydrogen storage for light duty vehicles. The HSECoE concluded that there were no metal hydrides that simultaneously met the DOE targets for gravimetric and volumetric capacity, and rate of onboard refueling. However, it was noted by the DOE that potential applications existed for stationary hydrogen storage systems. An application of particular interest is using a metal-hydride to store a hydrogen supply for fuel cell backup power to a 5 MWe data center. A generalized numerical model that coupled mass and energy conservation, chemical kinetics and thermodynamics was developed to simulate the discharge process for a given metal-hydride. The model was applied to an interstitial and a complex metal-hydride. In conclusion, it was found that the interstitial metal-hydride, which is discharged by a pressure swing is preferrable in a practical sense to the complex metal-hydride, which requires a temperature swing discharge process.

08 HYDROGEN↗
DOE OSTI2022

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.

Energy & Fuels↗
DOE OSTI2020

DECOVALEX-2019 (Task C Final Report)

The DECOVALEX Project is an on-going international research collaboration, established in 1992, to advance the understanding and modeling of coupled Thermal (T), Hydrological (H), Mechanical (M) and Chemical (C) processes in geological in geological systems. This document is the final report of Task C which was proposed and coordinated by the Japan Atomic Energy Agency (JAEA), presenting the technical definitions of the problems studied, approaches applied, achievements made and outstanding issues for future research. DECOVALEX-2019 Task C aims to develop modelling and prediction methods using numerical simulation based on the water-filling experiment to examine the post-driftclosure environment recovery processes. Three research teams, JAEA, Sandia National Laboratories in the United States (SNL), and the Technical University of Liberec in the Czech Republic (TUL) were challenged to simulate the observed environmental changes. The Task consisted of following three main steps. The simulation targets were the water pressure drawdown/recovery and the variation of water chemistry during the tunnel excavation and closure, as observed in the monitoring boreholes and the CTD.

58 GEOSCIENCES↗
DOE OSTI2021

Photoporomechanics: An Experimental Method to Visualize the Effective Stress Field in Fluid-Filled Granular Media

Effective stress governs the mechanical behavior of porous media. In this study, we use photoelasticimetry to visualize the evolving effective stress field in fluid-filled granular media in processes that couple fluid flow and mechanical deformation. We refer to this experimental method as photoporomechanics. We develop a fabrication process to produce millimeter-scale residual-stress-free photoelastic spheres with high geometric accuracy. We use color to quantify the forces acting on the particles over a wide range of forces, while using light intensity for a small range of forces. We then provide an application of photoporomechanics to illustrate the evolution of effective stress during one-dimensional consolidation: a process by which the stresses caused by a sudden load are gradually transmitted through a fluid-filled granular pack as the fluid drains and excess pore pressures dissipate. We report our technique provides a powerful experimental model system to study the grain-scale underpinning of coupled solid-fluid processes in granular media.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Search for the nonresonant production of Higgs boson pairs via gluon fusion and vector-boson fusion in the $b\bar{b}$τ + τ – final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for the nonresonant production of Higgs boson pairs in the HH → $b\overline{b}$$\tau^{+}\tau^{-}$ channel is performed using 140 fb -1 of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. The analysis strategy is optimized to probe anomalous values of the Higgs boson self-coupling modifier $\kappa_{\tau}$ and of the quartic HHVV (v = W,Z) coupling modifier $\kappa$ 2⁢V . No significant excess above the expected background from Standard Model processes is observed. An observed (expected) upper limit μ HH < 5.9⁢(3.3) is set at 95% confidence-level on the Higgs boson pair production cross section normalized to its Standard Model prediction. The coupling modifiers are constrained to an observed (expected) 95% confidence interval of -3.1 <$\kappa_{\tau}$ < 9.0 (-2.5 < $\kappa_{\tau}$ < 9.3) and -0.5 <$\kappa$ 2⁢V < 2.7 (-0.2 <$\kappa$ 2⁢V < 2.4), assuming all other Higgs boson couplings are fixed to the Standard Model prediction. The results are also interpreted in the context of effective field theories via constraints on anomalous Higgs boson couplings and Higgs boson pair production cross sections assuming different kinematic benchmark scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Theoretical Modeling of Electrochemical Proton-Coupled Electron Transfer

Proton-coupled electron transfer (PCET) plays an essential role in a wide range of electrocatalytic processes. Here, a vast array of theoretical and computational methods have been developed to study electrochemical PCET. These methods can be used to calculate redox potentials and pKa values for molecular electrocatalysts, proton-coupled redox potentials and bond dissociation free energies for PCET at metal and semiconductor interfaces, and reorganization energies associated with electrochemical PCET. Periodic density functional theory can also be used to compute PCET activation energies and perform molecular dynamics simulations of electrochemical interfaces. Various approaches for maintaining a constant electrode potential in electronic structure calculations and modeling complex interactions in the electric double layer (EDL) have been developed. Theoretical formulations for both homogeneous and heterogeneous electrochemical PCET spanning the adiabatic, nonadiabatic, and solvent-controlled regimes have been developed and provide analytical expressions for the rate constants and current densities as functions of applied potential. The quantum mechanical treatment of the proton and inclusion of excited vibronic states have been shown to be critical for describing experimental data, such as Tafel slopes and potential dependent kinetic isotope effects. The calculated rate constants can be used as input to microkinetic models and voltammogram simulations to elucidate complex electrocatalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗