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DOE OSTI2025

IM3 Data Center Driven Grid Stress Dataset for the U.S. Western Interconnection

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from an open-source grid operations modeling framework - GO), for the Integrated Multisector, Multiscale Modeling (IM3) project, under varying levels of data center demand growth between 2025 and 2035 in the U.S. Western Interconnection. The scenarios and sensitivity experiments are combinations of different data center demand growth rates and energy, weather, population and economic pathways. Data center demand growth projections were sourced from the Electric Power Research Institute (EPRI). The data center demand growth projection names are: Low (3.71% annual data center demand growth) Moderate (5% annual data center demand growth) High (10% annual data center demand growth) Higher (15% annual data center demand growth) Energy, weather, population and economic pathways are informed by two Shared Socioeconomic Pathways (SSP3 and SSP5) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) following the hotter general circulation model (GCM) forcing group from a set of perturbed thermodynamics simulations. The resulting pathway names are: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The main scenarios and sensitivity experiments are detailed below. Reference scenario: The projected grid stress and reliability results for the U.S. Western Interconnection from a previous study. This scenario does not consider data center demand growth explicitly. Data center scenario: Building on the reference scenario, this scenario considers various data center growth rates and how they impact the U.S. Western Interconnection. Data center loads are modeled as flat 8760-hr profiles. This scenario does not consider new generation and transmission capacities specifically designed to meet the new data center demands. The related folder is named "flat". Delayed generator retirements sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different levels of natural gas and nuclear generator retirement delays. The resulting scenario names are: (1) postponing 100% nuclear retirements; (2) postponing 100% nuclear and 25% natural gas retirements; (3) postponing only 50% natural gas retirements; (4) postponing 100% nuclear and 50% natural gas retirements; (5) postponing 100% nuclear and 75% natural gas retirements; and (6) postponing 100% nuclear and 100% natural gas retirements. The related folder names are: no_gen_retire_0_gas, no_gen_retire_25_gas, no_gen_retire_50_gas, no_gen_retire_50_gas_only, no_gen_retire_75_gas, and no_gen_retire_100_gas. Demand response through curtailment sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different participation and compensation levels of data center demand response. The resulting scenario names are: (1) 5% demand available for curtailment with 750 $/MWh compensation; (2) 5% demand available for curtailment with 500 $/MWh compensation; (3) 5% demand available for curtailment with 250 $/MWh compensation; (4) 15% demand available for curtailment with 750 $/MWh compensation; (5) 15% demand available for curtailment with 500 $/MWh compensation; and (6) 15% demand available for curtailment with 250 $/MWh compensation. The related folder names are: dr_cost_250_drup_0_drdown_5, dr_cost_250_drup_0_drdown_15, dr_cost_500_drup_0_drdown_5, dr_cost_500_drup_0_drdown_15, dr_cost_750_drup_0_drdown_5, and dr_cost_750_drup_0_drdown_15. Combination of delayed generator retirements and demand response through curtailment sensitivity experiment: The impact of combining postponing 100% nuclear and 25% natural gas retirements with 5% demand available for curtailment with 750 $/MWh compensation is simulated. The related folder is named "dr_cost_750_drup_0_drdown_5_nuc_100_gas_25". Please refer to the README file for a detailed description of the dataset including individual files and references.

Artificial Intelligence↗

NASA NTRS2005

Coupled fvGCM-GCE Modeling System, 3D Cloud-Resolving Model and Cloud Library

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional singlecolumn models in simulating various types of clouds and cloud systems from Merent geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloudscale model (termed a super-parameterization or multiscale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameteridon NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. A seed fund is available at NASA Goddard to build a MMF based on the 2D Goddard cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM). A prototype MMF in being developed and production nms will be conducted at the beginning of 2005. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes, (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), (3) A cloud library generated by Goddard MMF, and 3D GCE model, and (4) A brief discussion on the GCE model on developing a global cloud simulator.

Tao, Wei-Kuo↗

NASA NTRS2014

On the Evaluation of Global Sea-Salt Aerosol Models at Coastal/Orographic Sites

Sea-salt aerosol global models are typically evaluated against concentration observations at coastal stations that are unaffected by local surf conditions and thus considered representative of open ocean conditions. Despite recent improvements in sea-salt source functions, studies still show significant model errors in specific regions. Using a multiscale model, we investigated the effect of high model resolution (0.1deg x 0.1deg vs. 1deg x 1.4deg) upon sea-salt patterns in four stations from the University of Miami Network: Baring Head, Chatam Island, and Invercargill in New Zealand, and Marion Island in the sub-antarctic Indian Ocean. Normalized biases improved from +63.7% to +3.3% and correlation increased from 0.52 to 0.84. The representation of sea/land interfaces, mesoscale circulations, and precipitation with the higher resolution model played a major role in the simulation of annual concentration trends. Our results recommend caution when comparing or constraining global models using surface concentration observations from coastal stations.

sea-salt aerosol↗

DOE OSTI2021

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

Electroconvection is a multiphysics problem involving coupling of the flow field with the electric field as well as the cation and anion concentration fields. Here, we use electroconvection as a benchmark problem to put forward a new data assimilation framework, the DeepM&Mnet, for simulating multiphysics and multiscale problems at speeds much faster than standard numerical methods using pre-trained neural networks. We first pre-train DeepONets that can predict independently each field, given general inputs from the rest of the fields of the coupled system. DeepONets can approximate nonlinear operators and are composed of two sub-networks, a branch net for the input fields and a trunk net for the locations of the output field. DeepONets, which are extremely fast, are used as building blocks in the DeepM&Mnet and form constraints for the multiphysics solution along with some sparse available measurements of any of the fields. We demonstrate the new methodology and document the accuracy of each individual DeepONet, and subsequently we present two different DeepM&Mnet architectures that infer accurately and efficiently 2D electroconvection fields for unseen electric potentials. Furthermore, the DeepM&Mnet framework is general and can be applied for building any complex multiphysics and multiscale models based on very few measurements using pre-trained DeepONets in a “plug-and-play” mode.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Frontiers in the Simulation of Dislocations

Dislocations play a vital role in the mechanical behavior of crystalline materials during deformation. To capture dislocation phenomena across all relevant scales, a multiscale modeling framework of plasticity has emerged, with the goal of reaching a quantitative understanding of microstructure–property relations, for instance, to predict the strength and toughness of metals and alloys for engineering applications. This review describes the state of the art of the major dislocation modeling techniques, and then discusses how recent progress can be leveraged to advance the frontiers in simulations of dislocations. Furthermore, the frontiers of dislocation modeling include opportunities to establish quantitative connections between the scales, validate models against experiments, and use data science methods (e.g., machine learning) to gain an understanding of and enhance the current predictive capabilities.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Unravelling the radiation-induced redox chemistry of plutonium ions in aqueous solution

Plutonium plays a critical role in nuclear fuel cycle technologies, but our understanding of its fundamental radiation-induced redox chemistry is limited. Changes in oxidation states affect the speciation and transport of plutonium ions in solution. For example, solvent extraction techniques used to separate and recover plutonium from used nuclear fuel rely on the selective formation, maintenance, and complexation of specific plutonium oxidation states. However, radiolytically generated radicals, ions, and molecules can drive the oxidation state distribution of plutonium ions far from equilibrium, ultimately changing the physical and chemical properties of the bulk system. These radiation-induced processes are inevitable due to the ionizing radiation fields generated by the radioactive decay of plutonium and its daughter nuclides. Therefore, mechanistically understanding how plutonium's various oxidation states respond to ionizing radiation is essential for predicting its behavior in solution. Here, we present significant advances in our understanding of radiation-induced plutonium redox chemistry by using time-resolved (electron pulse) and dose accumulation (alpha and gamma) irradiation techniques, along with quantitative multiscale modeling methods.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2026

Documentation, assessment, and improvements of fission gas modeling capabilities in BISON

Fission gas behavior critically affects nuclear fuel performance, influencing thermal conductivity, microstructure, swelling, and fuel-cladding interactions. The Simple Integrated Fission Gas Release and Swelling (Sifgrs) model, implemented within the BISON fuel performance code, provides advanced capabilities for modeling these phenomena. This manuscript reviews and assesses Sifgrs’ modular structure and submodels describing intragranular, intergranular, and release mechanisms. The modular implementation enables rapid improvements as our understanding advances and new fuel forms emerge. After an overview of capabilities, we present a comprehensive validation suite, comparing predictions to experimental data from tests under normal and transient conditions, including reproduction of the Vitanza curve. Results demonstrate Sifgrs’ accuracy, robustness, and applicability. Current limitations and ongoing efforts are discussed, including mechanistic, multiscale modeling, uncertainty quantification, and validation within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, supporting the safe and efficient use of nuclear fuels.

BISON↗

DOE OSTI2020

Understanding and Strategies for Controlled Interfacial Phenomena in Lithium-Ion Batteries and Beyond

Electrolyte chemistry and properties and electrode structure and chemical properties particularly at the interfaces are crucial for the development of advanced battery components. This project focused on elucidating the roles of the electrolyte and electrode on the formation and evolution of the SEI layer and cell electrochemical performance on silicon and on Li metal anodes. For Si nano/microstructures we evaluated lithiation, volume changes, reactivity, and chemo-mechanical transformations as functions of nanoparticle size, shape, presence of coatings, electrolyte composition, electron leakage to the electrolyte, and cycling. For Li metal anodes we provided detailed analyses of the electrode and electrolyte effects on interfacial reactivity and as a function of microstructural evolution and underlying stochasticity as dendrites nucleate and grow. We thoroughly investigated and simulated how the chemistry of the various components of the electrolyte and the electrode architecture may affect the electrochemical reactions as well as cell degradation. A multiscale modeling approach was utilized, where atomistic simulations informed about the microscopic behavior of the system, and intermediate time and length scales were investigated with mesoscopic models. Results were also tested against selected experiments from our collaborators. The combined theoretical-experimental strategy saves costs by utilizing predictions from computational analyses to guide experimentation.

25 ENERGY STORAGE↗

DOE OSTI2022

Magnesium- and intermetallic alloys-based hydrides for energy storage: modelling, synthesis and properties

Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group ‘Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage’. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.

Energy storage↗

DOE OSTI2024

Towards predictive control of reversible nanoparticle assembly with solid-binding proteins

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, in this work, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Radiation-Induced Plutonium Redox Chemistry

Plutonium plays a key role in global actinide research and nuclear fuel cycle technologies, and yet, our fundamental understanding of its inherent radiation-induced chemical behavior is limited. These radiation-induced processes cannot simply be switched off, as they are as fundamentally inherent to plutonium as the impact of relativistic effects on its f-electrons. In less chemically complex actinide systems, such as aqueous solutions of neptunium and americium, , radiolysis products play a significant role in the redox cycling of their oxidation states. However, plutonium's multiple, coexisting, and chemically active oxidation states, which comprise of bare ions and dioxo cations, provide additional redox pathways that complicate radiation-induced processes. Oxidation state control is critical for the manipulation of plutonium, especially in used nuclear fuel reprocessing technologies, where oxidation specific states are successfully extracted, and others rejected. Consequently, mechanistically understanding the behavior of plutonium’s multiple oxidation states in the presence of intense ionizing radiation fields is essential for predicting the behavior of this element under multiple conditions that support the development and innovation of nuclear fuel cycle technologies. Here, we present recent advances in our understanding of plutonium radiation chemistry, including the first-ever multiscale model for predicting gamma radiation-induced plutonium redox chemistry, and new chemical kinetics for the reaction of plutonium and its complexes of tributyl phosphate (TBP), N,N-di-(2-ethylhexyl)butyramide (DEHBA), and N,N-di-(2-ethylhexyl)isobutyramide (DEHiBA) with transients radiolysis products, a measured using electron pulse radiolysis.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Fractal Model for Drag Reduction on Multiscale Nonwetting Rough Surfaces

Rough surfaces in contact with a flow of fluid exhibit alternating no-slip and free shear boundary conditions at the solid–liquid and air–liquid interfaces, respectively, thereby potentially offering drag reduction benefits. The balance between the dynamic pressure in the flow and the restoring capillary pressure in the interasperity spaces determines the stability of the Cassie state of wettability and is a function of the relative extent of no-slip and free shear regions per unit surface area. In the present study, using a fractal representation of rough surface topography, an analytical model is developed to quantify the stability of the Cassie state of wettability as well as drag reduction and the friction factor for laminar flow in a rectangular channel between nonwetting multiscale rough surfaces. A systematic study is conducted to quantify the effects of fractal parameters of the surfaces and the flow Reynolds number on drag reduction and the friction factor. The studies are used to develop friction factor curves extending the classical Moody diagram to hydrophobic and superhydrophobic surfaces. On the basis of the studies, regime maps are derived for estimating the extent of drag reduction offered by hydrophobic and superhydrophobic surfaces, revealing that superhydrophobic surfaces do not always offer the best drag reduction performance. The application of the fractal model to practical topographies of nonwetting surfaces of copper, aluminum, and zinc oxide fabricated via electrodeposition and etching is also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRSDate not supplied

Software Architecture and Hierarchy of the Nasa Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) serves as a state-of-the-art, “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling for hierarchical materials and structures subjected to thermomechanical loads on high performance computing systems. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Finally, application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described.

multiscale modeling↗

DOE OSTI2023

Understanding of Low‐Porosity Sulfur Electrode for High‐Energy Lithium–Sulfur Batteries

Abstract The lithium–sulfur (Li–S) battery is a promising technology for large‐scale energy storage and vehicle electrification due to its high theoretical energy density and low cost. Reducing the sulfur cathode porosity has been identified recently as a viable strategy for improving the cell practical energy density and minimizing pore‐filling electrolytes to extend cell life at lean electrolyte conditions. Direct use of a low‐porosity cathode for Li–S battery results in poor electrode wetting, nonuniform electrode reactions, and thus early cell failure. To understand and mitigate the barriers associated with the use of low‐porosity electrodes, multiscale modeling is performed to predict electrode wetting, electrolyte diffusion, and their impacts on sulfur reactions in Li–S cells by explicitly considering the electrode wettability impacts and electrode morphologies. The study elucidates the critical impact of low tortuosity and large channel pore design for promoting electrode wetting and species diffusion. It is suggested that the secondary particle size should be comparable with the electrode thickness to effectively promote electrolyte wettability and sulfur reactivity. This study provides new insights into the low‐porosity electrode material and designs and is expected to accelerate the development of practical high‐energy Li–S batteries.

25 ENERGY STORAGE↗

DOE OSTI2024

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗

DOE OSTI2020

An Improved Fracture Mechanics-Informed Multiscale Thermomechanical Damage Model for Ceramic Matrix Composites

This paper extends recent work done by the authors in modeling length scale-dependent damage behavior of ceramic matrix composites (CMCs) to include effects of local anisotropy introduced by matrix cracking. This model captures scale-dependent damage initiation and propagation behavior of the brittle matrix by employing internal state variable (ISV) theory within a multiscale modeling framework to obtain damaged matrix stress/strain constitutive relationships at each length scale. The damage ISV captures the effects of matrix cracking and growth by using fracture mechanics and the self-consistent scheme to determine the reduced stiffness of the cracked matrix. Matrix cracks, which activate when stress intensity factors near manufacturing induced cavities exceed the fracture toughness of the material, are assumed to be transversely isotropic in the plane of the crack, and matrix anisotropy occurs when the damaged stiffness tensor is rotated from the crack plane to the global axes. The crack progression and temporal evolution of the damage ISV are governed by fracture mechanics and crack growth kinetics. The model effectively captures first matrix cracking, which is the first significant deviation from linear elasticity. The nonlinear predictive capabilities of the material model are demonstrated for monolithic silicon carbide (SiC) and a 2D woven five-harness satin (5HS) carbon fiber SiC matrix (C/SiC) CMC.

Skinner, Travis↗

DOE OSTI2023

Effects of surface vacancy-adatom pair formation on PFC tungsten surface morphological response

Abstract We report a simulation study of the effect of He-irradiation-induced surface vacancy-adatom pair formation on the surface morphological evolution of plasma-facing component (PFC) tungsten and examine a number of factors that impact such evolution. Our analysis is based on self-consistent dynamical simulations according to an atomistically-informed, continuum-scale surface evolution model that has been developed following a hierarchical multiscale modeling strategy and can access the spatiotemporal scales of relevance to fuzz formation. The model accounts for the flux of surface adatoms generated as a result of the surface vacancy-adatom pair formation effect upon He implantation, which contributes to the anisotropic growth of surface nanostructural features due to the different rates of adatom diffusion along and across step edges of islands on the tungsten surface. We have carried out atomic-scale computations of optimal diffusion pathways along and across island step edges on the W(110) surface and calculated Ehrlich–Schwoebel (ES) barriers in adatom diffusion along and across such step edges. This aspect of surface adatom diffusion contributes to anisotropic surface atomic fluxes, terrace and step diffusive currents, and has been incorporated into our PFC surface evolution model, which predicts the formation of preferentially aligned nanoridge stripe patterns on the PFC surface. We establish that these anisotropic diffusive currents accelerate nanotendril growth on the PFC surface and the onset of surface nanostructure pattern formation. We also explore systematically the dependence of the PFC surface morphological response on the surface temperature and He ion incident flux, characterize in detail the resulting surface topographies and growth kinetics, and compare the predicted surface morphologies with experimental observations. Our simulation predictions for the emerging surface nanostructure patterns under certain plasma exposure conditions are consistent with experimental findings in the literature.

36 MATERIALS SCIENCE↗

DOE OSTI2026

MATEY: multiscale adaptive transformer models for spatiotemporal physical systems

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.

adaptive tokenization↗