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At least 163 records · Page 9

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DOE OSTI2024

A data-driven multiscale model for reactive wetting simulations

Here, we describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions

Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.

08 HYDROGEN↗

DOE OSTI2021

Unraveling the Ion Adsorption Kinetics in Microporous Carbon Electrodes: A Multiscale Quantum-Continuum Simulation and Experimental Approach

With the understanding that sorption in porous carbon electrodes is crucial to many environmental and energy technologies, such as capacitive deionization (CDI), supercapacitor energy storage, and activated carbon filters. In each of these examples, a practical model that can describe ion electrosorption kinetics is highly desirable for accelerating material design. Here, we proposed a multiscale model to study the ion electrosorption kinetics in porous carbon electrodes by combining quantum mechanical simulations with continuum approaches. Our model integrates the Butler-Volmer (BV) equation for sorption kinetics and a continuously stirred tank reactor (CSTR) formulation with atomistic calculations of ion hydration and ion-pore interactions based on density functional theory (DFT). We validated our model experimentally by using ion mixtures in a flow-through electrode CDI device and developed an in-line UV absorption system to provide unprecedented resolution of individual ions in the separation process. We showed that the multiscale model captures unexpected experimental phenomena that cannot be explained by the traditional ion electrosorption theory. The proposed multiscale framework provides a viable approach for modeling separation processes in systems where pore sizes and ion hydration effects strongly influence the sorption kinetics, which can be leveraged to explore possible strategies for improving carbon-based and, more broadly, pore-based technologies

36 MATERIALS SCIENCE↗

DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Multiscale and multiphysics FEA simulation and materials optimization for laser ultrasound transducers

In this study, the relationship between the nanocomposite design and the laser ultrasound transducer (LUT) characteristics was investigated through simulations in multiple scale levels for material behavior, device response, and acoustic wave propagation in media. First, the effects of the nanoparticle size and concentration on the effective properties of composites were quantitatively investigated with the finite element analysis (FEA) method. Second, the effective properties of the nanocomposite were assigned to the layer, which is modeled as a homogeneous material, in the FEA for the LUT simulating the energy conversion from the incident laser to the acoustic wave. Finally, the ultrasound propagation in the water was calculated by a theoretical wave propagation model. The FEA-based prediction was compared with the experimental data in the literature and a theoretical analysis for LUT based on Thermal-Acoustic coupling. As a result, the ultrasound waves on the transducer surface and at a distance in the water could be predicted. Based on the hierarchically integrated prediction procedure, the optimal conditions of the photoacoustic nanocomposites were investigated through the parametric study with the particle size and concentration as variables. The results guide the material designs optimized for different device characteristics, such as high pressure and broad bandwidth.

36 MATERIALS SCIENCE↗

DOE OSTI2023

On the use of a multigrid-reduction-in-time algorithm for multiscale convergence of turbulence simulations

Simulations of turbulent flow present challenges in terms of accuracy and affordability on modern highly-parallel computer architectures. A multigrid-reduction-in-time algorithm is used to provide a framework for separately evolving different scales of turbulence and for parallelizing the temporal domain, thereby increasing the concurrency. It is hypothesized that the space–time locality of the small scales of turbulence can be used to circumvent difficulties in applying temporal multigrid to flows dominated by inertial physics. For algorithms that fall well short of spectral accuracy (fourth-order is used in this work) attention must be paid to the accuracy of features on scales transferred between multigrid levels. Numerical experiments were performed using implicit large-eddy simulation. Results from applying the approach to an infinite-Reynolds number Taylor–Green flow and a double-shear flow at a Reynolds number of 11650 provide strong evidence that the approach has merit. The multigrid-reduction-in-time framework can be used to parallelize the temporal domain of a high-Reynolds-number turbulent flow and permit independent convergence of different scales. Establishing this foundation allows for future research in reducing the wall-clock time to solve turbulent flows while retaining the same accuracy as sequential solvers. In conclusion, current performance results from parallelizing the temporal domain are not competitive with those from sequential-in-time methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Performance Evaluation of the Meteorology and Air Quality Conditions From Multiscale WRF-CMAQ Simulations for the Long Island Sound Tropospheric Ozone Study (LISTOS)

The Long Island Sound (LIS) Tropospheric Ozone Study was a multi-agency collaborative field campaign conducted during the summer of 2018 to improve the understanding of ozone chemistry and transport from New York City to areas downstream, especially the LIS and adjacent Connecticut coastline. Measurements made during this campaign were leveraged to test and evaluate the coupled WRF-CMAQ model at 12 km, 4 and 1.33 km horizontal grid spacing. Special attention was placed on the model's representation of sea breeze circulations, low level jets, and boundary layer evolution. The evaluation suggests using higher resolutions resulted in improved surface meteorology statistics throughout the whole summer, with temperature biases seeing the biggest statistical improvements when using 1.33-km grid spacing, going from –0.12 to 0.08 K. Additionally, 4-km grid spacing provided the biggest advantage when simulating ozone over the region of interest, with biases being reduced from 2.40 to 0.57 to 0.37 ppbV with increased resolution. Case studies of two high ozone concentration events (July 10 and August 6) revealed that sound breezes and low-level jets had a critical role in transporting pollutant-rich, shallow marine air masses from the LIS inland over the Connecticut coast. Modifications were made to the representation of sea surface temperatures, which subsequently improved the simulation of surface ozone predictions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Dissecting the Roles of Stability and Dynamic Regimes in Tropical Cloud Feedbacks Simulated from an Upgraded Multiscale Modeling Framework (MMF)

In this study, we analyze the tropical cloud feedback from a 2 K sea surface temperature perturbation experiment performed with a multiscale modeling framework (MMF). We sort the monthly-mean cloud and radiative properties according to circulation and stability regimes. It is found that the dynamic component of cloud feedback for a given dynamic regime dominates the thermodynamic component in terms of the absolute magnitude.

Kuan-Man Xu↗

NASA NTRS2002

Efficient Low Dissipative High Order Schemes for Multiscale MHD Flows

Accurate numerical simulations of complex multiscale compressible viscous flows, especially high speed turbulence combustion and acoustics, demand high order schemes with adaptive numerical dissipation controls. Standard high resolution shock-capturing methods are too dissipative to capture the small scales and/or long-time wave propagations without extreme grid refinements and small time steps. An integrated approach for the control of numerical dissipation in high order schemes for the compressible Euler and Navier-Stokes equations has been developed and verified by the authors and collaborators. These schemes are suitable for the problems in question. Basically, the scheme consists of sixth-order or higher non-dissipative spatial difference operators as the base scheme. To control the amount of numerical dissipation, multiresolution wavelets are used as sensors to adaptively limit the amount and to aid the selection and/or blending of the appropriate types of numerical dissipation to be used. Magnetohydrodynamics (MHD) waves play a key role in drag reduction in highly maneuverable high speed combat aircraft, in space weather forecasting, and in the understanding of the dynamics of the evolution of our solar system and the main sequence stars. Although there exist a few well-studied second and third-order high-resolution shock-capturing schemes for the MHD in the literature, these schemes are too diffusive and not practical for turbulence/combustion MHD flows. On the other hand, extension of higher than third-order high-resolution schemes to the MHD system of equations is not straightforward. Unlike the hydrodynamic equations, the inviscid MHD system is non-strictly hyperbolic with non-convex fluxes. The wave structures and shock types are different from their hydrodynamic counterparts. Many of the non-traditional hydrodynamic shocks are not fully understood. Consequently, reliable and highly accurate numerical schemes for multiscale MHD equations pose a great challenge to algorithm development. In addition, controlling the numerical error of the divergence free condition of the magnetic fields for high order methods has been a stumbling block. Lower order methods are not practical for the astrophysical problems in question. We propose to extend our hydrodynamics schemes to the MHD equations with several desired properties over commonly used MHD schemes.

Sjoegreen, Bjoern↗

DOE OSTI2024

Leveraging MARVEL and SPHERE to Demonstrate NEAMS Thermal Hydraulics Codes

The Single Primary Heat Extraction and Removal Emulator (SPHERE) and Microreactor Applications Research Validation and Evaluation (MARVEL) were utilized to demonstrate thermal hydraulics codes of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. A recent long-duration test of a sodium heat pipe conducted at the SPHERE facility was utilized for the validation of the heat pipe code Sockeye. The first 10 hours of operation featured a startup of the heat pipe from room temperature; this portion was modeled with Sockeye and compared to the experimental data. Measurements included several thermocouple measurements across the length of the heat pipe surface. Two of Sockeye’s heat pipe models were applied to experiment, as well as several models of the cooling at the condenser end of the heat pipe. Sockeye results gave a decent match to the experimental data, with notable discrepancy in the condenser section, where experimental data indicated a significant inactive length of the heat pipe. The MARVEL microreactor was modeled through a coupled simulation of System Analysis Module (SAM) and SubChannel Module (SCM). This multiscale coupled simulation was performed as an exercise to demonstrate the compatibility and applicability of these codes for thermal-hydraulic analysis of microreactors. The coupling between the SCM and SAM codes for multiscale modeling of MARVEL, was achieved through a domain overlapping approach. In this example a transient SAM simulation provided boundary conditions to SCM, and SCM provided pressure drop to SAM.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2026

KRAS4a and KRAS4b show distinct lipid-dependent regulation of RAS-RAF membrane dynamics

KRAS4a and KRAS4b are important regulators of signaling, and their interactions with the plasma membrane are dynamic and influenced by lipid composition. KRAS 4a and 4b have nearly identical globular domains but differ in their membrane-associated hyper variable region (HVR). The functional distinctions between these isoforms remain unclear, particularly with regards to their dependence on specific lipids and the membrane environment. Previous work showed that the membrane orientation of KRAS4b affects its ability to bind to RAF kinase RBDCRD and that the KRAS–RBDCRD complex adopts different poses on the membrane as well as influences the size and composition of the lipid environment. To model differences between KRAS 4a and 4b protein–lipid interactions, we extended the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) to incorporate continuum simulations in the grand canonical ensemble, enabling sampling across macroscopic, coarse-grained, and all-atom resolutions. Using this framework, we systematically altered PIP2 concentrations, KRAS 4a versus 4b, and RAF RBDCRD complexation to assess impacts on membrane–protein interactions and dynamics. Our results reveal that reducing PIP2 shifts and broadens the membrane orientational preference of both KRAS 4b and 4a, with stronger effects on 4b HVR localization versus 4a. We demonstrate that with depletion of the strong negatively charged PIP2 lipid, the less charged phosphatidylserine replaces PIP2. Our findings highlight similarities and distinctions in the dynamics and lipid dependency of KRAS isoforms and suggest that ordering of the local lipid composition by HVRs is a shared property and key modulator of RAS-mediated signaling at the plasma membrane.

Biological and medical sciences↗

DOE OSTI2020

ddcMD converter

This Python package is developed for converting GROMACS files to ddcMD inputs. The code makes it easier for ddcMD users to convert GROMACS MD simulations into ddcMD. It is used in the MuMMI framework for multiscale MD simulations.

Glosli, JamesN↗

DOE OSTI2021

Operator learning for predicting multiscale bubble growth dynamics

We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2001

Building Blocks for Reliable Complex Nonlinear Numerical Simulations

This chapter describes some of the building blocks to ensure a higher level of confidence in the predictability and reliability (PAR) of numerical simulation of multiscale complex nonlinear problems. The focus is on relating PAR of numerical simulations with complex nonlinear phenomena of numerics. To isolate sources of numerical uncertainties, the possible discrepancy between the chosen partial differential equation (PDE) model and the real physics and/or experimental data is set aside. The discussion is restricted to how well numerical schemes can mimic the solution behavior of the underlying PDE model for finite time steps and grid spacings. The situation is complicated by the fact that the available theory for the understanding of nonlinear behavior of numerics is not at a stage to fully analyze the nonlinear Euler and Navier-Stokes equations. The discussion is based on the knowledge gained for nonlinear model problems with known analytical solutions to identify and explain the possible sources and remedies of numerical uncertainties in practical computations. Examples relevant to turbulent flow computations are included.

Yee, H. C.↗

NASA NTRS2002

Building Blocks for Reliable Complex Nonlinear Numerical Simulations

This talk describes some of the building blocks to ensure a higher level of confidence in the predictability and reliability (PAR) of numerical simulation of multiscale complex nonlinear problems. The focus is on relating PAR of numerical simulations with complex nonlinear phenomena of numerics. To isolate sources of numerical uncertainties, the possible discrepancy between the chosen partial differential equation (PDE) model and the real physics and/or experimental data is set aside. The discussion is restricted to how well numerical schemes can mimic the solution behavior of the underlying PDE model for finite time steps and grid spacings. The situation is complicated by the fact that the available theory for the understanding of nonlinear behavior of numerics is not at a stage to fully analyze the nonlinear Euler and Navier-Stokes equations. The discussion is based on the knowledge gained for nonlinear model problems with known analytical solutions to identify and explain the possible sources and remedies of numerical uncertainties in practical computations. Examples relevant to turbulent flow computations are included.

Yee, H. C.↗

NASA NTRS2004

Reliability of Complex Nonlinear Numerical Simulations

This work describes some of the procedure to ensure a higher level of confidence in the predictability and reliability (PAR) of numerical simulation of multiscale complex nonlinear problems. The focus is on relating PAR of numerical simulations with complex nonlinear phenomena of numerics. To isolate sources of numerical uncertainties, the possible discrepancy between the chosen partial differential equation (PDE) model and the real physics and/or experimental data is set aside. The discussion is restricted to how well numerical schemes can mimic the solution behavior of the underlying PDE model for finite time steps and grid spacings. The situation is complicated by the fact that the available theory for the understanding of nonlinear behavior of numerics is not at a stage to fully analyze the nonlinear Euler and Navier-Stokes equations. The discussion is based on the knowledge gained for nonlinear model problems with known analytical solutions to identify and explain the possible sources and remedies of numerical uncertainties in practical computations. Examples relevant to turbulent flow computations are included.

Yee, H. C.↗

NASA NTRS2005

Building Blocks for Reliable Complex Nonlinear Numerical Simulations

Yee, H. C.↗

DOE OSTI2024

Physics-Reinforced Machine Learning Algorithms for Multiscale Closure Model Discovery

The central objective of this project was to address the challenge of modeling and simulating complex multiscale turbulence phenomena by leveraging physics-guided machine learning (PGML) and hybrid modeling approaches. By integrating physics-based methods with data-driven models, the research focused on achieving robust and scalable solutions for geophysical turbulence, enhancing numerical weather prediction and climate research tools. The project resulted in significant advancements in computational modeling paradigms, predictive tools for reduced-order modeling, and innovative algorithms for fluid dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗