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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Li, Lingbo [Univ. of Houston, TX (United States); ↗
DOE OSTI2023

Linking water quality, fouling layer composition, and performance of reverse osmosis membranes

Fouling of polyamide membranes during reverse osmosis (RO) is a major challenge for adopting membrane technologies to treat highly contaminated waters, especially those containing organic foulants (e.g., natural organic matter (NOM), polysaccharides) and dominant cations (e.g., sodium, magnesium, calcium). Here, this work combines bench-scale membrane fouling experiments with detailed characterization of feedwater chemistry and fouling layer composition/morphology to reveal fundamental mechanisms of (in)organic fouling during RO. Divalent cations are shown to promote fouling by hydrophobic NOM containing aromatic and carboxyl groups, while NOM fouling in the presence of a monovalent cation, sodium, occurs by smaller fulvic acids containing larger fractions of carboxyl groups and other oxygen-rich moieties. Calcium-carboxyl bridging occurs in solution and near the membrane surface to induce NOM aggregation on nanometer length scales. In complex waters containing foulant mixtures, co-fouling by calcium-carboxyl bridging and CaCO 3 precipitation influence membrane performance at longer timeframes. However, the flux decline observed for the co-fouling mechanism was less significant than the sum of its parts, suggesting both synergistic and antagonistic fouling mechanisms should be considered in membrane design/operation. These results encourage the design of pretreatment processes to reduce concentrations of multivalent ions and hydrophobic NOM in RO feedwaters, and of membrane materials to limit attachment/deposition of aggregates to/on polyamide surfaces.

42 ENGINEERING↗
DOE OSTI2024

Improving generative model-based unfolding with Schrödinger bridges

Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area; one based on discriminative models and one based on generative models. The main advantage of discriminative models is that they learn a small correction to a starting simulation while generative models scale better to regions of phase space with little data. We propose to use Schrödinger bridges and diffusion models to create , an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of is that its generative model maps one set of events into another without having to go through a known probability density as is the case for normalizing flows and standard diffusion models. We show that achieves excellent performance compared to state of the art methods on a synthetic Z + jets dataset. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Clean Hydrogen Production R&D

Comprehensive, concerted efforts supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Hydrogen and Fuel Cell Technologies Office (HFTO) are advancing research and development to demonstrate clean hydrogen production and industrial decarbonization pathways. These pathways enable an economically competitive and environmentally beneficial future energy system across sectors and can address specific applications that are difficult to decarbonize. NREL's research accelerates development, integration, and scale up of hydrogen and fuel cell technologies to enable widespread deployment across multiple energy sectors. Our work helps industry overcome technical challenges and supports DOE's H2@Scale vision for clean hydrogen across multiple applications and economic sectors. We also bridge technologies with other research areas across the lab and through multiple DOE and national lab research initiatives, consortia, and collaborations including: H2NEW: Hydrogen from Next-generation Electrolyzers of Water Consortium, HydroGEN: Advanced Water Splitting Materials Consortium, and BioH2. Within the plenary panel called: From the Classroom to the Lab to the Board Room, I will represent the Lab in this panel and will be talking about hydrogen technology at NREL, the lab's role in bridging university research with industry commercialization. I will also talk about my personal career path and what it's like to work at NREL, along with work force development and DEIA programs at NREL.

BIL↗
DOE OSTI2022

A three-dimensional pore-scale model for redox flow battery electrode design analysis

A three-dimensional (3-D) pore-scale model has been developed to construct multiscale fibrous electrodes for redox flow batteries (RFB). New designs, such as biporous electrodes modify electrode structures by creating secondary pores on single carbon fiber to reduce internal battery resistance. Existing pore-scale models only resolve electrodes to the single carbon fiber scale and cannot incorporate recent multiscale electrode designs into numerical models. Our new model aims to bridge this gap and provide a tool to rapidly screen new electrode configurations. Two multiscale electrodes, laser-perforated and biporous electrodes, were investigated at varying operation conditions with the proposed 3-D pore-scale models. The laser-perforated electrode exhibits a reduced pressure drop, but follows the same permeability correlation compared to the corresponding pristine electrode. For the biporous electrode, the added specific surface area and faster reaction kinetics from the secondary pores are the most influential factors leading to improved battery efficiency. However, operating the biporous electrode in limiting current density conditions should be avoided due to the decreased mass transfer efficiency and a more significant voltage loss. Finally, we believe that our 3-D pore-scale model can accelerate the flow battery electrode design process and provide new insights into electrode geometry optimizations.

25 ENERGY STORAGE↗
DOE OSTI2025

Microstructurally informed synchrotron x-ray analysis revealing helium defect transitions in ultrafine grained tungsten

The formation of insoluble gaseous defects in materials due to nuclear transmutation or ion implantation involves the diffusion of impurity atoms to form atomic defect clusters that coalesce into bubbles or cavities and ultimately degrade the material properties. Transmission electron microscopy (TEM) is limited in its ability to resolve sub-nanometer gas clusters whereas X-ray diffraction (XRD) provides information pertaining to local atomic changes. Here, in this study, helium (He) implanted ultrafine grained tungsten is explored through a multimodal defect characterization campaign combining TEM-informed Small Angle X-ray Scattering (SAXS) analysis, XRD lattice parameter measurements, and nanoscale He cluster quantification from a region of reciprocal space accessible via Wide Angle X-ray Scattering (WAXS). Moderate elevated temperature implantations are shown to produce high concentrations of sub-nanoscale He clusters and small, homogeneously distributed cavities, which collectively are linked to lattice expansion and further substantiated through complementary atomistic simulations. Increased implantation temperatures encourage the diffusion of these defects to the grain boundaries (GBs), leading to lattice relaxation and the growth of large GB cavities manifesting as bimodal size distributions in the SAXS analysis. Overall, our results demonstrate the utility of multimodal synchrotron X-ray analysis in bridging the gap between microscale He cavity quantification and atomic-scale defect analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phosphoserine Charge State Drives Ion Condensation and Spatial Polyamine Presentation in Multirepeat Silaffin

Diatom silaffins direct silica biomineralization through heavily post-translationally modified repeat domains, yet how these modifications reshape the multirepeat conformational ensemble remains unknown. We report all-atom MD simulations of a 195-residue construct spanning repeats R1–R7 of Sil1p from Cylindrotheca fusiformis, carrying the full complement of native PTMs: phosphoserine (pSer), long-chain polyamines (LCPA), dimethyllysine (MLY), and trimethylhydroxylysine phosphate (TPL). We simulated three variants (Native, P1/singly deprotonated phosphate, and P2/doubly deprotonated phosphate) at two NaCl concentrations in triplicate for 500 ns each. All systems disorder from the AlphaFold 3 starting structure. Phosphate charge state, not ionic strength, is the dominant control of ensemble compaction and ion organization. Doubly deprotonated phosphate organizes an extensive Na + condensation shell (∼100 ions, 20% of box Na + ) and a heterogeneous bridging network that integrates both pSer and TPL phosphate groups. The resulting ensemble is compact with LCPA side chains exhibiting above-median solvent accessibility in 84% of simulation frames in P2 at 300 mM. This is higher than any other condition we simulated and indicates that polyamine groups are preferentially surface-presented in the most compact, ion-organized state. A charge-neutralization control confirms that this compact state is a structured intermediate maintained by the bridging network, not a simple collapsed globule. Here, this repeat-scale spatial organization is not captured by single-repeat peptide studies. Understanding the dynamics, mechanism, and spatial organization of PTM-rich silaffin at the repeat scale is a step closer to hierarchical biomimetic materials beyond simple silica morphologies.

Amines↗
DOE OSTI2026

Extension of Clad Damage Propagation Model for Fission Gas Dispersal and Two-Phase Flow Effects in MOOSE SubChannel Module

This report presents an extension of the Clad Damage Propagation (CDAP) model implemented in the MOOSE SubChannel Module (SCM) to capture post-failure fission-gas dispersal and two-phase flow effects in sodium-cooled fast reactor assemblies. The extended model tracks discharged gas axially and radially, computes channel-averaged flow quality and void fraction using a Lockhart–Martinelli framework, evaluates two-phase frictional pressure-drop multipliers, determines inlet mass-flow degradation under fixed core pressures, and applies an intensified-void-based heat-transfer degradation to affected fuel pins. Radial plume expansion is parameterized using mineral-oil jet experiments mapped to sodium conditions via Reynolds–Weber similarity. Implementation details are documented, along with the new methods and user inputs needed to control plume mapping and two-phase behavior. Demonstration simulations for 19- and 37-pin bundles show that breach size and inlet velocity strongly influence propagation potential: small breaches (≤0.5 mm) produce limited degradation while larger breaches (~1 mm) can drive oscillatory temperature spikes and enhanced failure propagation, especially at higher velocities. These results demonstrate that the extended CDAP model provides a more complete framework for quantifying cladding damage propagation and evaluating propagation potential in transient scenarios. The approach remains computationally efficient, consistent with subchannel-level analysis, yet incorporates sufficient physics to bridge localized post-failure effects with bundle- and assembly-scale degradation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Adhesion-force micro-scale study of desiccating granular material

Experiments on five-, four-, three- and two-wet-hydrophilic-grain clusters were performed to investigate evolution of adhesion of granular media during drying on the micro-scale. The experiments show that the adhesion-force of a cluster initially grows at most to three times the original value before decreasing to zero by the end of evaporation. The adhesion-force is composed of capillary pressure force acting over the liquid/solid contact surface area, and surface tension forces acting over the three-phase contact perimeter length. This is in contrast with most macro-scale phenomenological models, in which the only desaturation process variables affecting strength are suction and saturation. Both the contact surface area and contact perimeter length are reduced to zero upon complete liquid evaporation. The morphology of an evaporating water body evolves through slow flow controlled by evaporation rate, interrupted by various modes of fast air entry, which are non-equilibrium jumps of liquid/gas interfaces (Haines jumps). The instabilities involve large adhesion force discontinuities and substantial water mass reconfiguration with water flow in an extremely short time, which makes the process transient. The reconfigurations can reduce the original multi-grain water clusters to four-, three- and two-grain clusters by way of three different instability modes: of thin-sheet instability, or meniscus snap-through instability, depending on the sign of the Gauss curvature of the liquid surface, or finally, for two-grain bridges only, a liquid wire pinch-off. For larger meso-scale assemblies, however, the global adhesion-force evolution is little affected by the jumps. The air entries are potential sites for drying cracks. The (approximately) calculated capillary pressure for two- and three-grain clusters, in no cases is seen to reach high values, predicted from water retention curves.

Engineering↗
DOE OSTI2022

Pore-Scale Simulation of Spectral Induced Polarization

A new model was developed to simulate spectral induced polarization (SIP) in porous media. The model is based on a pore-network approximation of the soil or rock, in which bonds between pores are represented by equivalent circuits. In the circuit model, electrolytic and surface conduction occur in parallel, with the electrolyte behaving as a single resistor, and the mineral surface behaving as a resistor and capacitor in series. Based on the pore geometry, fluid conductivity, and mineral surface conductivity, an impedance network is generated. SIP spectra are calculated by solving the sinusoidal steady-state electrical problem for a range of frequencies. The new model provides a tool to bridge the gap between (1) our fundamental understanding of polarization processes at the scale of a single pore or grains and (2) existing simulation capabilities for macroscale systems that do not account for pore-scale properties. The model is implemented in Matlab and can solve two-dimensional and three-dimensional (2D and 3D) networks. Examples for regular cubic lattices are presented, but the method and code are flexible with respect to pore geometries and allow for future leveraging of existing open-source capabilities for generation of pore networks and extraction of network geometries from micro imaging of core samples.

58 GEOSCIENCES↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scaling Equitable Finance

Driven by dramatic declines in up-front cost, the U.S. solar photovoltaics (PV) industry has taken off over the past decade, growing from 1 gigawatt of installed capacity in 2009 to 89 gigawatts in 2020—or enough capacity to power roughly 19 million homes. The industry is expected to double in size over just the next 5 years.1 Much of the growth has been driven by large, utility-scale projects that can produce 5 mega- watts or more of power—enough to power at least 1,000 homes. The cost of electricity produced by these projects has decreased by more than 70 percent since 2010. As of Q3 2020, development costs of large, util- ity-scale solar PV power plants were under $1 per watt, down by more than 70 percent from 2010.2 A robust array of investors has come forward to efficiently deliver capital to these kinds of utility-scale projects including large banks, insurance companies, pension funds, and others. But low- and moderate-income communities, including communities of color, are at risk of being left behind in the transition to clean energy. Mission- driven solar project developers and financial institu- tions have been working alongside energy justice advocates to open up solar access for these communi- ties, using strategies ranging from community solar, to solar installations on affordable multifamily housing, to distributed solar and storage programs, and more. Their goals go beyond simply generating more green energy to advancing social equity by: • empowering communities to control their energy future • stabilizing energy prices, saving money, and build- ing wealth for low-income families • creating quality jobs • improving health by reducing pollution • providing energy resilience for vulnerable communities Mission-driven actors are successfully deploying a wide variety of strategies to meet these goals, from helping low-income homeowners get solar—and some- times battery storage, to developing solar projects serv- ing affordable rental housing and community facilities, to building larger “shared solar” projects to which households from across the community can subscribe. However, the financing ecosystem does not work nearly as well for these “mission driven” solar proj- ects as it does for utility-scale projects. For home rooftop solar, even if low-income consumers have a home and suitable roof, they may fail to qualify for federal tax incentives, lack adequate credit to qualify for a loan—or the mission-driven lenders seeking to serve them may not be adequately capitalized to make long-term loans. For mission-driven commercial or community-scale projects, assembling nearly every component of the project capital stack—whether bridging early-stage costs, attracting tax credit equity investors, securing long-term debt, or coming up with sponsor equity and filling gaps—can present challenges. A variety of obstacles contribute to the scarcity of financing for low-income solar, including small project sizes, lack of developer balance sheet capacity, both real and perceived issues with credit risk, elevated technical assistance needs, and greater subsidy requirements to pursue goals such as deep energy affordability, climate resilience, or job creation. Still other obstacles are regulatory: for example, not all states allow community solar projects or Power Purchase Agreements, common strategies used for providing low-income solar—and the potential for regulations to shift over time creates risks that mission-driven projects can ill afford. This report synthesizes information garnered from 47 key informant interviews, four focus group discus- sions involving 60 stakeholders, and a review of the substantial existing literature on low-income solar finance to assess the current landscape of mission- driven solar development in the United States, examine the roles that community-based financial institutions could play, and recommend public invest- ments and policy changes that could help to scale the provision of equitable solar finance. Key recommen- dations for policymakers and funders in the renew- able energy and community development fields that emerge from this process include the following: • Help to capitalize and support community-based lenders to provide flexible, low-cost, and long- term financing to mission-driven solar projects— including providing guarantees or other forms of credit enhancement. • Provide federal support for equitable solar, including a grant-in-lieu-of-credits option for the Investment Tax Credit to improve access to this critical government subsidy. • Develop pools of government and philanthropic support that can complement financing from community-based lenders to complete the capi- tal stack for mission-driven projects, as well as to support education and technical assistance to both consumers and potential project sponsors. • Create a national Renewable Energy Credits pro- gram that includes social equity targets to provide a baseline of support for clean energy generation. • Change utility regulations to remove barriers to low-income solar projects; lower permitting costs; provide greater certainty for developers, consumers and owners; and measure progress toward equity in renewable energy policy implementation.

14 SOLAR ENERGY↗
DOE OSTI2021

A physics-based model for frost buildup under turbulent flow using direct numerical simulations

We present a new model for frost buildup under turbulent (and laminar) flow using direct numerical simulations. The physical model consists of two layers, the air and the frost. The air layer is fully resolved and consists of solving for the velocity, temperature, and vapor mass fraction fields. The frost layer thickness is resolved using conservation of mass and energy. Both phases are dynamically coupled using the immersed boundary method. Three-dimensional simulations are conducted in an open-channel configuration. A number of challenges need to be overcome to make these simulations feasible. First, to enforce far-field conditions of zero gradient and prescribed mean temperature and humidity, a source term is added to the energy and transport equations in the flow solver. Second, the mean frost thickness is subtracted after each time step to ensure a constant mean flow thickness and level of turbulence in the numerical domain. Third, a slow-time acceleration approach, which accelerates the frost buildup by a predetermined factor, is employed to bridge the gap between the fast turbulent and slow frost buildup time scales. Finally, a frost densification scheme is used to overcome the difficulties of vertically varying frost properties. The model is validated by comparing the frost thickness and frost thickness buildup rate over a period of one hour from a cooled flat plate experiment. As a result, both quantities compare favorably with experiments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Evidence of a toroidal magnetic field in the core of 3C 84

The spatial scales of relativistic radio jets, probed by relativistic magneto-hydrodynamic (RMHD) jet launching simulations and by most very long baseline interferometry (VLBI) observations differ by an order of magnitude. Bridging the gap between these RMHD simulations and VLBI observations requires selecting nearby active galactic nuclei (AGN), the parsec-scale region of which can be resolved. The radio source 3C 84 is a nearby bright AGN fulfilling the necessary requirements: it is launching a powerful, relativistic jet powered by a central supermassive black hole, while also being very bright. Using 22 GHz globe-spanning VLBI measurements of 3C 84 we studied its sub-parsec region in both total intensity and linear polarisation to explore the properties of this jet, with a linear resolution of ~0.1 parsec. We tested different simulation set-ups by altering the bulk Lorentz factor Γ of the jet, as well as the magnetic field configuration (toroidal, poloidal, helical). We confirm the persistence of a limb brightened structure, which reaches deep into the sub-parsec region. The corresponding electric vector position angles (EVPAs) follow the bulk jet flow inside but tend to be orthogonal to it near the edges. Our state-of-the-art RMHD simulations show that this geometry is consistent with a spine-sheath model, associated with a mildly relativistic flow and a toroidal magnetic field configuration.

3C 84 (NGC 1275)↗
DOE OSTI2025

Regulatory Sandboxes and Other Processes to Expedite Utility Adoption of Advanced Grid Technologies

Advanced grid technologies are increasingly important to enable electric transmission and distribution systems to meet growing demands. However, traditional regulatory processes typically lag technological advancements. Regulatory sandboxes, which provide a structured environment for testing new technologies and business approaches under modified rules to increase the speed of adoption, aim to bridge the gap between grid needs and opportunities to deliver solutions at scale. This report examines the role of regulatory sandboxes in promoting utility innovation, highlights examples of successful sandbox mechanisms, and provides emerging best practices for designing and implementing regulatory sandboxes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint

In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.

deep Q-learning↗
DOE OSTI2021

Real-time Implementation of a Dual-Active-Bridge Based Multi-Level Photovoltaic Converter

Silicon-carbide based, multi-level converters with dual-active-bridge (DAB) are getting attention for medium voltage, direct grid-connected, utility-scale photovoltaic (PV) applications due to their higher efficiency and power density. To de-risk complex control development for these high-voltage and high-power inverters, hardware-in-the-loop techniques are often required. Due to the high switching frequency and the large number of power electronic switches, off-the-shelf, real-time simulation tools are not yet fully developed for this application, even with FPGA-based simulators. To address this, a new co-simulation approach in FPGA is presented that utilizes Time-Stamped Bridge (TSB) for the higher frequency converters along with detailed fixed conductance based discrete device simulation of the rest of the circuit. A complex DAB-based PV converter with maximum switching frequency of 50 kHz is successfully simulated in real-time in the FPGA and the results are compared with offline simulation results. A controller hardware-in-the-loop (CHIL) platform is developed to demonstrate real-time, closed-loop test results for this DAB-based PV converter.

Dong, Zerui↗
DOE OSTI2024

Democratizing uncertainty quantification

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support.

Benchmarks↗