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NASA NTRS1995

Windowed and Wavelet Analysis of Marine Stratocumulus Cloud Inhomogeneity

To improve radiative transfer calculations for inhomogeneous clouds, a consistent means of modeling inhomogeneity is needed. One current method of modeling cloud inhomogeneity is through the use of fractal parameters. This method is based on the supposition that cloud inhomogeneity over a large range of scales is related. An analysis technique named wavelet analysis provides a means of studying the multiscale nature of cloud inhomogeneity. In this paper, the authors discuss the analysis and modeling of cloud inhomogeneity through the use of wavelet analysis. Wavelet analysis as well as other windowed analysis techniques are used to study liquid water path (LWP) measurements obtained during the marine stratocumulus phase of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment. Statistics obtained using analysis windows, which are translated to span the LWP dataset, are used to study the local (small scale) properties of the cloud field as well as their time dependence. The LWP data are transformed onto an orthogonal wavelet basis that represents the data as a number of times series. Each of these time series lies within a frequency band and has a mean frequency that is half the frequency of the previous band. Wavelet analysis combined with translated analysis windows reveals that the local standard deviation of each frequency band is correlated with the local standard deviation of the other frequency bands. The ratio between the standard deviation of adjacent frequency bands is 0.9 and remains constant with respect to time. This ratio defined as the variance coupling parameter is applicable to all of the frequency bands studied and appears to be related to the slope of the data's power spectrum. Similar analyses are performed on two cloud inhomogeneity models, which use fractal-based concepts to introduce inhomogeneity into a uniform cloud field. The bounded cascade model does this by iteratively redistributing LWP at each scale using the value of the local mean. This model is reformulated into a wavelet multiresolution framework, thereby presenting a number of variants of the bounded cascade model. One variant introduced in this paper is the 'variance coupled model,' which redistributes LWP using the local standard deviation and the variance coupling parameter. While the bounded cascade model provides an elegant two- parameter model for generating cloud inhomogeneity, the multiresolution framework provides more flexibility at the expense of model complexity. Comparisons are made with the results from the LWP data analysis to demonstrate both the strengths and weaknesses of these models.

Gollmer, Steven M.↗

DOE OSTI2022

HI-STORM Overpack and MPC-32 Thermal-Hydraulic Model with MOOSE Framework

Nuclear power is a significant source of electricity in the United States, but the average age of nuclear power plants is around 40 years old. The safe management of the spent nuclear fuel (SNF) is a key aspect of the back-end of the nuclear fuel cycle. Spent fuel dry storage systems are becoming a popular and effective solution in this area, given the absence of a final disposal system. The spent fuel cask system (dry cask method) provides a feasible solution to maintain spent fuel for 60 years before final disposal. Dry cask storage has many characteristics that make it attractive. It fulfills the safety requirements of the Nuclear Regulatory Commission (NRC) while providing modularity and flexibility to contractors. The HI-STORM overpack and MPC-32 canister are the main parts of the HI-STORM 100 dry cask storage system. These components remove heat from the system using natural circulation, requiring no human intervention. This is the characteristic that provides passive heat removal and low maintenance features in dry cask storage systems. To develop a thermal model for a dry cask storage system, the physics behind the system should be defined clearly. There are two natural circulation loops in the system; circulation of helium cools down the nuclear assemblies in the MPC, while circulation of air cools down the walls of the MPC. This work aims to develop a thermal model of the MPC-32 canister and HI-STORM overpack using the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE is an open-source framework developed by Idaho National Laboratory (INL) for multiscale, multiphysics simulations. In this study, we will investigate and demonstrate the thermal-hydraulics modeling capabilities of the MOOSE framework, including natural circulation, heat transfer, and porous flow.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Development of a MOOSE thermal model of the MPC-32 canister and HI-STORM overpack

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Molecular Rotations, Multiscale Order, Hyperuniformity, and Signatures of Metastability during the Compression/Decompression Cycles of Amorphous Ices

We model, via large-scale molecular dynamics simulations, the isothermal compression of low-density amorphous ice (LDA) to generate high-density amorphous ice (HDA) and the corresponding decompression extending to negative pressures to recover the low-density amorphous phase (LDA HDA ). Both LDA and HDA are nearly hyperuniform and are characterized by a dynamical HBN, showing that amorphous ices are nonstatic materials and implying that nearly hyperuniformity can be accommodated in dynamical networks. In correspondence with both the LDA-to-HDA and the HDA-to-LDA HDA phase transitions, the (partial) activation of rotational degrees of freedom activates a cascade effect that induces a drastic change in the connectivity and a pervasive reorganization of the HBN topology which, ultimately, break the samples’ hyperuniform character. Key to this effect is the rapid rate at which changes occur, and not their magnitude. The inspection of structural properties from the short- to the long-range shows that signatures of metastability are present at all length-scales, hence providing further solid evidence in support of the liquid–liquid critical point scenario. LDA and LDA HDA differ in terms of HBN and structural properties, implying that they are distinct low-density glasses. Our work unveils the role of molecular rotations in the phase transitions between amorphous ices and shows how the unfreezing of rotational degrees of freedom generates a cascade effect that propagates over multiple length-scales. Our findings greatly improve our basic understanding of water and amorphous ices and can potentially impact the field of molecular network-forming materials at large.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1996

Kinetic Space Weather: Toward a Global Hybrid Model of the Polar Ionosphere-Lower Magnetosphere Plasma Transport

During the indicated period of performance, we had a number of publications concerned with kinetic polar ionosphere-lower magnetosphere plasma transport. For the IUGG 1991-4 Quadrennial Report, we reviewed aspects of U.S. accomplishments concerned with polar plasma transport, among other issues. In another review, we examined the computer simulations of multiple-scale processes in space plasmas, including polar plasma outflow and transport. We also examined specifically multiscale processes in ionospheric outflows. We developed a Generalized Semi-Kinetic(GSK) model for the topside-lower magnetosphere which explored the synergistic action of wave heating and electric potentials in the formation of auroral Ion conics, in particular the "pressure cooker" mechanism. We extended the GSK model all the way down to 120 km and applied this code to illustrate the response of the ionosphere- magnetosphere to soft-electron precipitation and convection-driven frictional ion heating, respectively. Later, the convection-driven heating work was extended to a paper for the Journal of Geophysical Research. In addition to the above full published papers, we also presented the first developments of the coupled fluid-semikinetic model for polar plasma transport during this period. The results from a steady-state treatment were presented, with the second presentation being concerned with the effects of photo-electrons on the polar wind, and the first garnering an outstanding student paper award from the American Geophysical Union. We presented the first results from a time-dependent version of this coupled fluid-semikinetic model.

Horwitz, James L.↗

DOE OSTI2026

Multi-resolution enhancement for full-spectrum neural representations

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-basedrepresentations increasingly intractable. Implicit neural representations (INRs) offer a promising solutionby encoding signals through coordinate-based neural networks, serving as surrogates of data, withcomputational and storage requirements scaling with network complexity rather than data dimensionality.However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency informationand fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, atheoretically guided hierarchical INR framework that distributes modelling across resolution scales andenables improved representation capacity through a novel enhancement network to recover subtle details.This multiscale architecture allows smaller networks to retain the full spatial-frequency content of thesignal as well as preserve training efficiency and lower storage cost. Evaluated on distinct raw experimentalmeasurements across scales and complexities, WIEN-INR represents a practical step towards a broaderadoption of neural representations in scientific workflows, delivering compact, robust and high-fidelityrepresentations.

Ni, Yuan [SLAC National Accelerator Laboratory (SL↗

NASA NTRS2013

Genesis of Hurricane Sandy (2012) Simulated with a Global Mesoscale Model

In this study, we investigate the formation predictability of Hurricane Sandy (2012) with a global mesoscale model. We first present five track and intensity forecasts of Sandy initialized at 00Z 22-26 October 2012, realistically producing its movement with a northwestward turn prior to its landfall. We then show that three experiments initialized at 00Z 16-18 October captured the genesis of Sandy with a lead time of up to 6 days and simulated reasonable evolution of Sandy's track and intensity in the next 2 day period of 18Z 21-23 October. Results suggest that the extended lead time of formation prediction is achieved by realistic simulations of multiscale processes, including (1) the interaction between an easterly wave and a low-level westerly wind belt (WWB) and (2) the appearance of the upper-level trough at 200 hPa to Sandy's northwest. The low-level WWB and upper-level trough are likely associated with a Madden-Julian Oscillation.

multiscale processes↗

DOE OSTI2025

Multiscale Thermal-hydraulic analysis of the MARVEL microreactor using a coupled SAM and SCM simulation. (PPTX)

This is .pptx document presenting a summary of the paper of the same name that has been submitted and accepted to NURETH-21: MARVEL is a natural-convection-cooled sodium-potassium microreactor that is anticipated to generate 85 kilowatts of thermal energy. It will operate within Idaho National Laboratory Transient Reactor Test Facility and is being developed by the DOE Microreactor Program. MARVEL will be used to test microreactor applications, evaluate systems for remote monitoring, and develop autonomous control technologies. A thermal-hydraulic computational model of this facility is a valuable tool to study important transients and calculate the safety limits of the micro-reactor design. For this purpose, the authors have chosen to use a multiscale coupled simulation: SCM for modeling the reactor core and SAM for the reactor's primary cooling system. SCM is MOOSE physics module for subchannel analysis, which was designed to model single-phase flows through liquid-metal cooled, wire-wrapped fuel pin sub-assemblies, ordered in a triangular lattice. The SCM code was modified to be able to model MARVEL?s unique geometry. SAM is a systems analysis module based on the MOOSE framework. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for various advanced reactor types. The coupling between the two SCM and SAM for MARVEL modeling is done implementing a domain over-lapping approach. The resulting coupled simulation can model transients such as reactor startup/shutdown and provide an intermediate fidelity picture of the temperature field and other variables, in the core. Results for the steady-state simulations are presented in the article as well as flow blockage transient.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2025

An analytical model of “Electron-Only” magnetic reconnection rates

Abstract “Electron-only” reconnection, which is both uncoupled from the surrounding ions and much faster than standard reconnection, is arguably ubiquitous in turbulence. One critical step to understanding the rate in this novel regime is to model the outflow speed that limits the transport of the magnetic flux, which is super ion Alfvénic but significantly lower than the electron Alfvén speed based on the asymptotic reconnecting field. Here we develop a simple model to determine this limiting speed by taking into account the multiscale nature of reconnection, the Hall-mediated electron outflow speed, and the pressure buildup within the small system. The predicted scalings of rates and various key quantities compare well with fully kinetic simulations and can be useful for interpreting the observations of NASA’s Magnetospheric-Multiscale (MMS) mission and other ongoing missions.

Physics↗

DOE OSTI2023

Data assimilation in operator algebras

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2023

Comparison of Dust Optical Depth From Multi-Sensor Products and MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) Dust Reanalysis Over North Africa, the Middle East, and Europe

Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.

Michail Mytilinaios↗

DOE OSTI2025

Openpronghorn

OpenPronghorn is a simulation tool specifically tailored for modeling thermal-hydraulic phenomena in advanced nuclear reactors. It is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source platform that facilitates the development of high-performance scientific computing applications. OpenPronghorn solves the Navier-Stokes equations, which describe the conservation of mass, momentum, and energy in fluid flows, using the finite volume numerical method. The code supports a wide range of fluid flow conditions that are applicable to nuclear reactors, including incompressible and weakly compressible flows, as well as single-phase and multiphase flows. It is capable of modeling diverse flow regimes, including laminar and turbulent flows, using various turbulence models such as the standard k-epsilon models, the v2f model, and the mixing length model. For multiphase flows, OpenPronghorn employs a mixture a Eulerian modeling approach with mixture, drift-flux, and full Eulerian models, and includes open-sourced interfacial transfer correlations for drag, exchange, and heat transfer coming from the scientific literature. OpenPronghorn's modular design allows it to handle multiscale simulations, ranging from detailed Reynolds-Averaged Navier Stokes (RANS) simulations to coarse-mesh and lumped parameter models. This flexibility enables users to perform high-fidelity simulations of specific reactor components as well as system-level analyses of entire reactor circuits. The code can be coupled with other MOOSE-based tools using the MultiApp system, allowing for the transfer of coupling quantities such as mass flow rates, heat fluxes, and boundary conditions between different simulation scales. One of the main features of OpenPronghorn is the it includes built-in validation cases from the open-source scientific literature and supports the implementation of user-defined models and correlations through MOOSE's FunctorMaterial system. OpenPronghorn is designed to be computationally efficient, leveraging the SIMPLE projection method for large-scale problems, and can be run on high-performance computing systems to handle the extensive computational demands of detailed reactor simulations. Overall, OpenPronghorn is a versatile and robust tool that provides critical insights into the thermal-hydraulic behavior of advanced nuclear reactors, supporting the design, safety, and optimization of next-generation nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

DOE OSTI2021

Robust Molecular Predictive Methods for Novel Polymer Discovery and Applications

Polymeric materials are ubiquitous in modern society and they play an instrumental role in almost all industries, undoubtedly including the energy and environment sectors. Increased demand of energy and awareness to sustainability both necessitates the development of novel polymers with enhanced properties. Unfortunately, their structural and behavioral complexity render such discovery challenging and impeded. To address this problem, scientists are developing various computational modeling techniques and leveraging their power to depict the relationship between structural characteristics of polymers and their properties (such as rheological behaviors), and use such prediction to guide the design and syntheses of novel polymeric materials with enhanced performances. Unfortunately, predicting the relationships between polymer structure and composition with rheological properties via atomistic modeling is still a major challenge because of the extended time and length scales involved. Studying dynamic shear viscosity and linear viscoelasticity using molecular models requires capabilities that have been elusive, including representation of large molecular weight chains with an effective internal scale capable of describing entanglement, shear-rates that are in the s-1 scale with accurate quantitative stresses, and chemically-realistic combinations of both homogeneous and heterogeneous systems. Motivated by these unmet challenges, the overall technical objective of this DOE-STTR Phase II project is to develop robust molecular predictive methods for advanced polymer discovery and applications and especially for designing and demonstrating the “smart” polymer-based waterflooding enhanced oil recovery (EOR) process. In particular, we apply state-of-the-art molecular modeling methods developed by our academic partner, Materials Stimulation Center (MSC) at California Institute of Technology (Caltech), to facilitate and accelerate the experimental discovery processes. During the Phase I of this project, we had focused on development and demonstration of the molecular modeling methods to describe rheological properties of non-Newtonian polymer fluids, and to improve our fundamental understandings of shear-thickening mechanism and kinetics. In Phase II, we further apply the theoretical models to guide our experimental programs to improve our design of smart rheology modifier (SRM) polymers and their optimization for EOR. Specifically, we have three objectives in the Phase II study: (1) to further improve out computational modeling methods, coupling with the advanced machine learning algorithms; (2) to develop cost-effective and efficient SRM-flooding process suitable for EOR applications under typical reservoir conditions; and (3) to further explore the application of our molecular predictive models for innovative material discovery in other industrial applications. The recent development of our multiscale predictive framework allows the successful prediction of rheological properties from the chemical structure for polymers of experimentally relevant molecular weights, and provides an in-silico machine learning engine for screening novel compositions and structures with optimized non-Newtonian response, required for both shear-thinning and shear-thickening applications. Our framework provides: (1) procedures and tools for systematic coarsening from atomistic models and reverse mapping of coarse-grain models to atomistic, (2) unique ab initio methods to characterize the atomistic origin of colloidal and interfacial interactions and phenomena, (3) systematic structure and composition builders based on practical descriptors that drive rheological changes in polymer melts and diluted polymer mixtures, (4) a rheological properties engine capable of predicting viscosity in the zero-shear limit and under realistic dynamic conditions (for shear-rates commensurate with experiments) for large heterogeneous systems, (5) coarse-grain force fields with improved non-bond descriptions based on accurate quantum mechanics, (6) an in-silico screening machine learning engine that feeds from the systematic model builders to cover the descriptors search space, computes the rheological properties from converged trajectories spanning sub-milliseconds and ranks them for each structure/composition using an automated viscosity-vs-shear rate fitness function that can be tuned for shear-thickening, shear-thinning and other rheological responses.

02 PETROLEUM↗

NASA NTRS2021

Particle Acceleration by Dense Impulsive Structures Moving in Ambient Magnetospheric Plasma. 3-D Hybrid Kinetic Modeling and MMS Observations

High resolution observations of dense plasma impulsive structures moving through an ambient background magnetospheric flows were captured by the Magnetospheric Multiscale mission. The observations show particle heating and acceleration, shock-like wave formation, and whistler wave excitation inside the interface between the dense impulsive plasma structures and the ambient plasma. A multiscale hybrid kinetic simulation provides an explanation of the observed wave-particle interactions with the assumption that the dense plasma structures may be represented by plasma clouds which are formed at the magnetopause layer due to reconnection processes. Plain Language Summary: Dense, impulsive plasma structures moving through a background plasma were captured by the NASA Magnetospheric Multiscale mission. The observations show that the dense structures can generate strong perturbations in the electromagnetic field and shock-like waves. Interactions between these electromagnetic waves and the particles results in particle acceleration. 3-D hybrid kinetic modeling (particle description for ions and fluid description for electrons) was used to investigate the plasma physics of the observed structures. It was assumed that the plasma clouds were formed by magnetic field reconnection inside the magnetopause, which is the interface between the solar wind particles and the cold low-density magnetospheric plasma. The work helps us understand the plasma environment at the interface between the Earth and solar wind, near planetary moons, within astrophysical explosions, and possibly at the interface between the solar wind and local interstellar medium.

A. S. Lipatov↗

DOE OSTI2023

The Role of Topography in Controlling Evapotranspiration Age

Abstract Evapotranspiration (ET) age is a key metric of water sustainability but a major unknown partly due to the extreme difficulty in modeling it. Groundwater is found to be important in ET age variations in small‐scale studies, yet our understanding is insufficient because groundwater systems are nested across scales. Here, we conducted GPU‐accelerated particle tracking with integrated hydrologic modeling to quantify the variations in ET age at a regional scale of ∼0.4 M km 2 . Simulation results reveal topography‐driven flow paths shaping the spatial and temporal patterns of ET age variations. On ridges, where root zone decoupling with deep subsurface storage, ET age is generally young, with seasonal variations dominated by meteorological conditions. In the valley bottom, ET age is generally old, with significant subseasonal variations caused by the convergence of subsurface flow paths. On hillslopes with water table depths ranging from 1 to 10 m, ET age shows strong seasonal variations caused by the connections with lateral groundwater regulated by ET demand. Our modeling approach provides insights into the basic linkages between ET age and topography at large scale. Our work highlights the perspective of multiscale studies of ET age, suggesting new field experiments to test these process connections and to determine if such linkages warrant inclusion in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Anisotropic Coarse-Grained Model for Conjugated Polymers: Investigations into Solution Morphologies

The optoelectronic properties of conjugated polymers are dictated by the interplay of multiscale structural features, including intrachain dihedrals, interchain pi-pi stacking, and the complex mesoscale morphology. While much is known about the structures of polymers with isotropically interacting monomers, little is known about polymers with strongly anisotropic backbone monomers, a class of materials to which conjugated polymers belong. Fundamental understanding is further complicated when the semiflexible and molecularly heterogeneous nature of conjugated polymers is taken into account. We present an anisotropic coarse-grained (CG) model for conjugated polymers that incorporates key molecular features (monomer anisotropy, intermonomer dihedrals, and side chains). Finally, the model is employed to characterize the single-chain conformational properties of conjugated polymers, revealing a rich temperature-dependent conformational landscape. These studies provide a critical link between CG molecular descriptors and conformational ordering in conjugated polymers.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review

The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as density functional theory (DFT) and molecular dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the phase-field method (PFM) and calculation of phase diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist, and Bayesian methodologies. Finally, challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Autonomous MultiScale Library

AMSLib provides infrastructure to tightly couple multi-scale physics simulation code with ML surrogate model inference. It provides a wholistic runtime execution paradigm to supports uncertainty quantification, surrogate model inference, persistent data storing throughout the execution of a simulation.

Bhatia, Harsh↗