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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Factors Influencing Building Demand Flexibility

The U.S. Department of Energy’s National Roadmap for Grid-interactive Efficient Buildings (GEB) acknowledged that building demand flexibility (DF) is both an important strategy to decarbonizing the buildings sector and an important resource for meeting the changing needs of the electrical grid such as improving grid reliability. However, understanding the complexity and uncertainties in real building field performance of DF strategies is a large gap hindering stakeholders on both grid and buildings side to make investments on deploying such strategies. The research work in this report intended to advance understanding of the variability and influential factors in building demand flexibility. Adding such knowledge based on lab testing results and measured performance data from real buildings is an important contribution. The report uses standardized metrics and methods to quantify DF performance from field-measured DF datasets of two significant building groups of big-box retail and medium office buildings to present the challenge of building DF variability in multiple dimensions. The report presents findings related to how several key factors influence building demand flexibility from implementing a common, cost-effective DF control strategy (i.e., adjusting zone temperatures). The findings are supported by full-scale lab testing, field data analysis and simulation research. The authors also provided application-oriented recommendations to stakeholders such as building aggregators, utility program design professionals, sophisticated building portfolio owners, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

DE-SC0004913: The role of microstructural neighborhoods in forming and evolving slip localization in polycrystalline metals during cyclic plasticity (Final Report)

During this grant we processed and analyzed data from High Energy X-ray Diffraction (HEXD). We conducted a study of the evolution of an aggregate of commercially pure copper crystals subjected to cyclic loading conditions consistent with low cycle fatigue (LCF); each fatigue cycle contains macroscopically elastic-plastic deformation. The uniaxial test specimen initially loads elastically and yields in tension, then unloads elastically and re– yields in compression. The macroscopic cyclic stress-strain curve forms characteristic hysteresis loops that change in size (typically increase) with each cycle reflecting the plasticity-induced material evolution that is taking place within each crystal. It has been hypothesized that at some point, the to-and-fro plastic slip within one crystal or a group of crystals initiates a fatigue crack. Using HE x-ray diffraction we studied the cyclic plasticity within every deforming crystal by measuring how the lattice misorientation changes as the sample is loaded around the hysteresis loop.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

This paper presents a data-driven distribution system load modeling methodology targeting quasi-static time-series (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes on a circuit in such a way that the diversity factor of the feeder can be defined and tuned, and 3) the load aggregation method can be used to populate the profiles for different nodes at various load aggregation levels. Furthermore, as the foundation of the modeling methodology, variability and diversity libraries have been established based on high-resolution load data collected on customer transformers from real utility feeders. The proposed modeling methodology has been used to build load data sets for both the IEEE 123-bus feeder model and a realistic utility feeder model. The QSTS simulation results on the two evaluation feeders have demonstrated that the load data sets established from the proposed modeling methodology can effectively capture the load impact on feeder operations. For the realistic utility feeder, the effectiveness of the proposed methodology has also been validated by comparing the voltage characteristics of the feeder with modeled loads and the voltage characteristics of realistic voltage data from the same feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Ocpp 2.0.1. Interim Kpi Calculator

The project is split into four pieces. The first is a raw OCPP log parser. The second is a file splitter. The third is a message parser. The final piece is the Interim KPI calculator. The OCPP log parser was created from two different formats of raw OCPP 2.0.1 data. Its intended purpose is to extract device IDs and OCPP event messages from nontabular text logs. The parser looks for specific substrings in the logs to identify which of the two "standards" it should select from. The KPI generator does not perform any of its calculations in parallel. Instead, we opt for a naive batching approach. The splitter takes the file generated from the parser and creates many smaller files for each of the device IDs in the dataset. This allows the pandas queries in the log formatter to be iterate over a significantly smaller slice of data, increasing performance significantly. The message parser step takes messages from each of the files (containing distinct device IDs) and breaks the message out into pieces. The final result is a file with different columns specifying different attributes of the JSON message. The file is an aggregation of all different devices. This is the most complex portion of the code. The KPI calculator takes the parsed messages, as a single file, and calculates the KPI from that data. An excel file is produced with four sheets. These contain the metrics for Session Success, Charge Start Success, Charge End Success, and Charge Start Time. It includes the metrics for the different equations in the Interim KPI Implementation Guide as well as a weighted sum of the different equations for each KPI (excluding Charge End Success and Charge Start Time).

Quinn, Casey↗
DOE OSTI2014

What is the effect of LiDAR-derived DEM resolution on large-scale watershed model results?

This paper examines the effect of raster cell size on hydrographic feature extraction and hydrological modeling using LiDAR derived DEMs. LiDAR datasets for three experimental watersheds were converted to DEMs at various cell sizes. Watershed boundaries and stream networks were delineated from each DEM and were compared to reference data. Hydrological simulations were conducted and the outputs were compared. Smaller cell size DEMs consistently resulted in less difference between DEM-delineated features and reference data. However, minor differences been found between streamflow simulations resulted for a lumped watershed model run at daily simulations aggregated at an annual average. These findings indicate that while higher resolution DEM grids may result in more accurate representation of terrain characteristics, such variations do not necessarily improve watershed scale simulation modeling. Hence the additional expense of generating high resolution DEM's for the purpose of watershed modeling at daily or longer time steps may not be warranted.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US

The outbreak of novel coronavirus disease (COVID-19) has resulted in changes in productivity and daily life patterns, and as a result electricity consumption (EC) has also shifted. In this paper, we construct estimates of EC changes at the metropolitan level across the continental U.S., including total EC and residential EC during the initial two months of the pandemic. The total and residential data on the state level were broken down into the county level, and then metropolitan level EC estimates were aggregated from the counties included in each metropolitan statistical area (MSA). This work shows that the reduction in total EC is related to the shares of certain industries in an MSA, whereas regardless of the incidence level or economic structure, the residential sector shows a trend of increasing EC across the continental U.S. Since the MSAs account for 86% of the total population and 87% of the total EC of the continental U.S., the analytical result in this paper can provide important guidelines for future social-economic crises.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi Time-scale Imputation aided State Estimation in Distribution System

With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for situational awareness. In order to reconcile the heterogeneous multi-scale time-series data, we present a multi-task Gaussian process framework. This framework exploits the spatio-temporal correlation across the time-series data to impute data at any desired timescale while providing confidence bounds on the imputations. The value of the imputed data for distribution system operation is illustrated via a matrix completion based state estimation strategy. Results on the IEEE 37 bus distribution system reveals the superior performance of the proposed approach relative to linear interpolation approaches.

Dahale, Shweta↗
DOE OSTI2023

Is Nematicity in Cuprates Real?

In La 2-x Sr x CuO 4 (LSCO), a prototype high-temperature superconductor (HTS) cuprate, a nonzero transverse voltage is observed in zero magnetic fields. This is important since it points to the breaking of the rotational symmetry in the electron fluid, the so-called electronic nematicity, presumably intrinsic to LSCO (and other cuprates). An alternative explanation is that it arises from extrinsic factors such as the film’s inhomogeneity or some experimental artifacts. We confront this hypothesis with published and new experimental data, focusing on the most direct and sensitive probe—the angle-resolved measurements of transverse resistivity (ARTR). The aggregate experimental evidence overwhelmingly refutes the extrinsic scenarios and points to an exciting new effect—intrinsic electronic nematicity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS)

Global anthropogenic emission inventories remain vital for understanding the sources of atmospheric pollution and the associated impacts on the environment, human health, and society. Rapid changes in today's society require that these inventories provide contemporary estimates of multiple atmospheric pollutants with both source sector and fuel type information to understand and effectively mitigate future impacts. To fill this need, we have updated the open-source Community Emissions Data System (CEDS) (Hoesly et al., 2019) to develop a new global emission inventory, CEDSGBD-MAPS. This inventory includes emissions of seven key atmospheric pollutants (NOx; CO; SO2; NH3; non-methane volatile organic compounds, NMVOCs; black carbon, BC; organic carbon, OC) over the time period from 1970–2017 and reports annual country-total emissions as a function of 11 anthropogenic sectors (agriculture; energy generation; industrial processes; on-road and non-road transportation; separate residential, commercial, and other sectors (RCO); waste; solvent use; and international shipping) and four fuel categories (total coal, solid biofuel, the sum of liquid-fuel and natural-gas combustion, and remaining process-level emissions). The CEDSGBD-MAPS inventory additionally includes monthly global gridded (0.5°?×?0.5°) emission fluxes for each compound, sector, and fuel type to facilitate their use in earth system models. CEDSGBD-MAPS utilizes updated activity data, updates to the core CEDS default scaling procedure, and modifications to the final procedures for emissions gridding and aggregation. Relative to the previous CEDS inventory (Hoesly et al., 2018), these updates extend the emission estimates from 2014 to 2017 and improve the overall agreement between CEDS and two widely used global bottom-up emission inventories. The CEDSGBD-MAPS inventory provides the most contemporary global emission estimates to date for these key atmospheric pollutants and is the first to provide global estimates for these species as a function of multiple fuel types and source sectors. Dominant sources of global NOx and SO2 emissions in 2017 include the combustion of oil, gas, and coal in the energy and industry sectors as well as on-road transportation and international shipping for NOx. Dominant sources of global CO emissions in 2017 include on-road transportation and residential biofuel combustion. Dominant global sources of carbonaceous aerosol in 2017 include residential biofuel combustion, on-road transportation (BC only), and emissions from the waste sector. Global emissions of NOx, SO2, CO, BC, and OC all peak in 2012 or earlier, with more recent emission reductions driven by large changes in emissions from China, North America, and Europe. In contrast, global emissions of NH3 and NMVOCs continuously increase between 1970 and 2017, with agriculture as a major source of global NH3 emissions and solvent use, energy, residential, and the on-road transport sectors as major sources of global NMVOCs. Due to similar development methods and underlying datasets, the CEDSGBD-MAPS emissions are expected to have consistent sources of uncertainty as other bottom-up inventories. The CEDSGBD-MAPS source code is publicly available online through GitHub: https://github.com/emcduffie/CEDS/tree/CEDS_GBD-MAPS (last access: 1 December 2020). The CEDSGBD-MAPS emission inventory dataset (both annual country-total and monthly global gridded files) is publicly available under https://doi.org/10.5281/zenodo.3754964 (McDuffie et al., 2020c).

Mcduffie, Erin↗
DOE OSTI2025

High Resolution Siting Suitability of Various Power Plant Technologies

Energy sector planning models determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. These animations demonstrate the aggregate siting suitability of various power plant technology configurations, considering technology-specific factors that can prohibit development. The data presented is from the GRIDCERF (Geospatial Raster Input Data for Capacity Expansion Regional Feasibility) data package. GRIDCERF is a harmonized, open-source geospatial product that can be used to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. The animations presented here demonstrate a curated selection of the full suite of technology configurations available. GRIDCERF provides the necessary inputs for models that simulate power plant siting for regional capacity expansion planning such as the Capacity Expansion Regional Feasibility (CERF) model.

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2018

Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power

Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV genertaion while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated to optimally dispatch a population of on/off controllable loads to achieve this privacy-preserving DR service.

Dong, Jin↗
DOE OSTI2017

Temperature-dependent self-assembly and rheological behavior of a thermoreversible pmma-P n BA-PMMA triblock copolymer gel

Self-assembly and mechanical properties of triblock copolymers in a mid-block selective solvent are of interest in many applications. Herein, we report physical assembly of an ABA triblock copolymer, [PMMA–PnBA–PMMA] in two different mid-block selective solvents, n-butanol and 2-ethyl-1-hexanol. Gel formation resulting from end-block associations and the corresponding changes in mechanical properties have been investigated over a temperature range of -80 °C to 60 °C, from near the solvent melting points to above the gelation temperature. Shear-rheometry, thermal analysis, and small-angle neutron scattering data reveal formation and transition of structure in these systems from a liquid state to a gel state to a percolated cluster network with decrease in temperature. The aggregated PMMA end-blocks display a glass transition temperature. Our results provide new understanding into the structural changes of a self-assembled triblock copolymer gel over a large length scale and wide temperature range.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hydrothermally robust Ti/SiO 2 epoxidation catalysts via surface modification with oligomeric PMHS

We investigate grafting of poly(methylhydrosiloxane) (PMHS) onto Ti/SiO 2 catalysts for improving their robustness to water, using olefin epoxidation catalysis with organic hydroperoxide as oxidant as a relevant probe reaction. Our approach involves triggering catalyst deactivation with added water in the reactants as a means to simulate catalyst deactivation, thereby causing grafted Ti-site aggregation. When using harsh aqueous Soxhlet extraction conditions as a trigger for this aggregation, significant differences are observed via diffuse-reflectance UV-Vis spectrscopy on samples before and after treatment, for Ti/SiO 2 catalysts containing grafted PMHS. Not only was there a lack of appearance of red-shifted bands in the UV-Vis spectrum following Soxhlet extraction, the batch catalytic activity for catalysts before and after PMHS grafting remained virtually unchanged. This was in stark contrast to an unmodified Ti/SiO 2 catalyst, which showed a more than 60% decrease in catalytic activity following Soxhlet extraction as a result of aggregation, as ascertained by red shifts in its UV-Vis spectrum after Soxhlet extraction. Similar red shifts were also observed for a hydrophobically modified TMS Ti/SiO 2 catalyst. However, when comparing these catalysts under more gentle olefin epoxidation catalysis conditions with added water in a flow reactor, it was more difficult to differentiate the TMS Ti/SiO2 and grafted PMHS catalysts. Both catalysts exhibited similar activity and selectivity after a prolonged time on stream, though the rate of initial deactivation in the latter was less pronounced. Altogether, our data demonstrate that grafted PMHS catalysts are positively differentiated from conventional catalysts based on either TMS surface modification or no surface modification, under conditions involving aggregation induced via extreme hydrothermal environmentst. Thus, we expect that PMHS grafting of Lewis acid catalysts can offer an alternative approach for endowing hydrolytic stability to such catalysts, as demonstrated here for Ti/SiO 2 epoxidation catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Photoactive Blend Morphology Engineering through Systematically Tuning Aggregation in All-Polymer Solar Cells

Polymer aggregation plays a critical role in the miscibility of materials and the performance of all-polymer solar cells (APSCs). However, many aspects of how polymer texturing and aggregation affect photoactive blend film microstructure and photovoltaic performance are poorly understood. Here the effects of aggregation in donor–acceptor blends are studied, in which the number-average molecular weights (Mns) of both an amorphous donor polymer, poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b]dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl)] (PBDTT-FTTE) and a semicrystalline acceptor polymer, poly{[N,N'-bis(2-octyldodecyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)} (P(NDI2OD-T2)) are systematically varied. The photovoltaic performance is correlated with active layer microstructural and optoelectronic data acquired by in-depth transmission electron microscopy, grazing incidence wide-angle X-ray scattering, thermal analysis, and optical spectroscopic measurements. Coarse-grained modeling provides insight into the effects of polymer aggregation on the blend morphology. Notably, the computed average distance between the donor and the acceptor polymers correlates well with solar cell photovoltaic metrics such as short-circuit current density (Jsc) and represents a useful index for understanding/predicting active layer blend material intermixing trends. Importantly, these results demonstrate that for polymers with different texturing tendencies (amorphous/semicrystalline), the key for optimal APSC performance, photovoltaic blend morphology can be controlled via both donor and acceptor polymer aggregation.

bio-inspired↗
DOE OSTI2018

Impact of acidic pH on plant cell wall polysaccharide structure and dynamics: insights into the mechanism of acid growth in plants from solid-state NMR

Acidification of plant primary cell walls causes cell wall expansion and plant growth. To understand how acidic pH affects the molecular structure and dynamics of wall polysaccharides, we have now characterized and compared Arabidopsis thaliana primary cell walls in neutral (pH 6.8) and acidic (pH 4.0) conditions using solid-state NMR spectroscopy. Quantitative 13C solid-state NMR spectra indicate that the pH 4.0 cell wall has neutral galacturonic acid residues in homogalacturonan (HG) and rhamnogalacturonan (RG). 13C INEPT spectra, which selectively detect highly dynamic polymers, indicate that some of the HG and RG chains in the interfibrillar region have become more dynamic in the acidic wall compared to the neutral cell wall, whereas other chains have become more rigid. Consistent with this increased dynamic heterogeneity, C–H dipolar couplings and 2D 13C–13C correlation spectra indicate that some of the HG backbones are partially aggregated in the acidic cell wall. Moreover, 2D correlation spectra measured with long mixing times indicate that the acidic cell wall has weaker cellulose–pectin interactions, and water-polysaccharide 1H spin diffusion data show that cellulose microfibrils are better hydrated at low pH. Taken together, these results indicate a cascade of chemical and conformational changes of wall polysaccharides due to cell wall acidification. These changes start with neutralization of the pectic polysaccharides, which disrupts calcium crosslinking of HG, causes partial aggregation of the interfibrillar HG, weakens cellulose–pectin interactions, and increases the hydration of both cellulose microfibrils and matrix polysaccharides. These molecular-level structural and dynamical changes are expected to facilitate polysaccharide slippage, which underlies cell wall loosening and expansion, and may occur both independent of and as a consequence of protein-mediated wall loosening.

biofuels (including algae and biomass), bio-inspir↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗