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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Urban-scale Energy Modeling: Scaling Beyond Tax Assessor Data

In an attempt to attain building-specific characteristics for urban-scale building energy models, county-specific tax assessors’ data is often an initial data source. This data source can contain valuable information such as year built, area, height, HVAC type, and roof/wall descriptions.We will show examples of 2,000 fields from Hamilton County in Tennessee with examples of many fields which are not relevant to urban-scale building energy modeling, are incorrect compared to other data sources, and highlight some lessons learned working with such a data source.There are currently 3,142 counties in the United States, each with their own data format, field definitions, and data access policy. As urban-scale involves city-scale analysis potentially covering multiple counties and matures toward state- or nation-scale analysis, county-by-county approaches are not scalable. While there are efforts to unify these datasets, there is an increasing proliferation of data and algorithms that can cover wider areas and provide more accurate inputs for urban-scale models. This paper summarizes computer vision of imagery, cartographic layers, building type assessment, and model generation used to achieve scalable detection and analysis of buildings.

New, Joshua↗
DOE OSTI2019

From core-scale experiment to reservoir-scale modeling: A scale-up approach to investigate reaction-induced permeability evolution of CO2 storage reservoir and caprock at a U.S. CO2 storage site

Mineral dissolution and secondary mineral precipitation can cause porosity and permeability changes of CO 2 storage reservoirs and caprocks after injection of CO 2 . In this paper, a 3-step approach (core-scale experiment →core-scale modeling →reservoir-scale modeling) is developed to simulate reservoir-scale porosity and permeability evolution of CO 2 storage formation and caprock at a model CO 2 storage site. The model site is based on characteristics of a real site in Mississippi, USA. Important chemical and permeability modeling parameters in the reservoir-scale model are validated by core-scale experimental and reactive transport modeling results. The reservoir-scale model predicts a maximum 3.2% permeability increase of the CO 2 storage formation and a maximum 1.1% permeability increase of the caprock after 1000 years of exposure to CO 2 -rich brine, while the core-scale model predicts 7% permeability decrease for a small CO 2 storage formation core and 296% permeability increase for a small caprock core after 180-day exposure to CO 2 -rich brine. The discrepancy between permeability results of reservoir-scale model and core-scale model is attributed to strong pH buffering effect of CO 2 storage formation with large mass of H+-consuming minerals. Therefore, using core-scale experiments/models only is not sufficient to elucidate reservoir-scale permeability evolution. Variations of key model parameters have a small effect on permeability evolution of both CO 2 storage formation and caprock, except for variations of K eq (SiO 2 (am)) and the exponent n in permeability-porosity correlation. SiO 2 (am) is a key mineral that governs permeability evolution of CO 2 storage formation and caprock, given the characteristics of the model CO 2 storage site.

58 GEOSCIENCES↗
DOE OSTI2020

Machine learning coupled multi-scale modeling for redox flow batteries

The reaction distribution in macro or device-scale has been studied for redox flow batteries. The reaction distribution on electrode pore-scale structure however is not well understood, lacking especially on how the reaction distribution on the pore-scale may impact the overall performance of a flow battery. This study introduces for the first time a framework of a multi-scale model that provides understanding of the relationship between the pore-scale electrode structure reaction and the device-scale electrochemical reaction uniformity within the flow battery. A reduced order model is constructed based on 128 pore-scale simulations, which provide a quantitative relationship between the battery operation conditions (inlet velocity, current density, inlet concentration) and the surface reaction uniformity for the pore-scale sample. The multi-scale framework upscales this pore-scale surface reaction uniformity to device-scale combined uniformity. Based on the multi-scale model, a time-varying optimization of the inlet velocity is established, leading to significant reduction on pump power consumption with targeted surface reaction uniformity. The multi-scale model establishes the critical link between the micro-structure of a flow battery component and its performance at the macro-scale, therefore providing rationale for further operational or material optimization.

flow batteries, machine learning, multi-scale mode↗
DOE OSTI2014

Carbon Capture Simulation Initiative: A Case Study in Multi-Scale Modeling and New Challenges

Abstract: Advanced multi-scale modeling and simulation has the potential to dramatically reduce development time, resulting in considerable cost savings. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry and universities that is developing and deploying a suite of multi-scale modeling and simulation tools including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamic (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

42 ENGINEERING↗
DOE OSTI2023

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2’s flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Roell, Garrett W.↗
DOE OSTI2015

Simulations of ecosystem hydrological processes using a unified multi-scale model

This paper presents a unified multi-scale model (UMSM) that we developed to simulate hydrological processes in an ecosystem containing both surface water and groundwater. The UMSM approach modifies the Navier–Stokes equation by adding a Darcy force term to formulate a single set of equations to describe fluid momentum and uses a generalized equation to describe fluid mass balance. The advantage of the approach is that the single set of the equations can describe hydrological processes in both surface water and groundwater where different models are traditionally required to simulate fluid flow. This feature of the UMSM significantly facilitates modelling of hydrological processes in ecosystems, especially at locations where soil/sediment may be frequently inundated and drained in response to precipitation, regional hydrological and climate changes. In this paper, the UMSM was benchmarked using WASH123D, a model commonly used for simulating coupled surface water and groundwater flow. Disney Wilderness Preserve (DWP) site at the Kissimmee, Florida, where active field monitoring and measurements are ongoing to understand hydrological and biogeochemical processes, was then used as an example to illustrate the UMSM modelling approach. The simulations results demonstrated that the DWP site is subject to the frequent changes in soil saturation, the geometry and volume of surface water bodies, and groundwater and surface water exchange. All the hydrological phenomena in surface water and groundwater components including inundation and draining, river bank flow, groundwater table change, soil saturation, hydrological interactions between groundwater and surface water, and the migration of surface water and groundwater interfaces can be simultaneously simulated using the UMSM. Overall, the UMSM offers a cross-scale approach that is particularly suitable to simulate coupled surface and ground water flow in ecosystems with strong surface water and groundwater interactions.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

From Atoms to Wheels: The Role of Multi-Scale Modeling in the Future of Transportation Electrification

Traditionally, prototype hardware is built for validation testing to ensure battery systems design changes meet vehicle-level requirements, which is expensive both in cost and time. Virtual engineering (VE) of battery systems for electric vehicle (EV) propulsion offers a reduced-cost alternative to the traditional development process and uses multi-scale modeling to virtually probe the impact of design changes in a particular part on the overall performance of the system. This allows for rapid iteration over multiple design spaces, without committing to build hardware. This perspective article discusses current trends in VE for EV applications and proposes improvements to accelerate EV adoption.

Garrick, Taylor R. (ORCID:0000000322518129)↗
DOE OSTI2012

Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142

Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When photosystem II flux is high, terminal oxidases of respiratory electron transport are predicted to be an important mechanism for removing excess electrons. When photosystem I flux is high cyclic electron transport becomes important. Model predictions of growth rates were in good quantitative agreement with measured growth rates, and predictions of reaction usage were ualitatively consistent with protein and mRNA expression data, when these latter datasets were used to constrain the model.

51142↗
DOE OSTI2014

A Unified Multi-Scale Model for Pore-Scale Flow Simulations in Soils

Pore-scale simulations have received increasing interest in subsurface sciences to provide mechanistic insights into the macroscopic phenomena of water flow and reactive transport processes. The application of the pore scale simulations to soils and sediments is, however, challenged because of the characterization limitation that often only allows partial resolution of pore structure and geometry. A significant proportion of the pore space in soils and sediments is below the spatial resolution, forming a mixed media of pore and porous domains. Here we reported a unified multi-scale model (UMSM) that can be used to simulate water flow and transport in mixed media of pore and porous domains under both saturated and unsaturated conditions. The approach modifies the classic Navier-Stokes equation by adding a Darcy term to describe fluid momentum and uses a generalized mass balance equation for saturated and unsaturated conditions. By properly defining physical parameters, the UMSM can be applied in both pore and porous domains. This paper describes the set of equations for the UMSM, a series of validation cases under saturated or unsaturated conditions, and a real soil case for the application of the approach.

computational fluid dynamics↗
DOE OSTI2021

Leveraging observed soil heterotrophic respiration fluxes as a novel constraint on global-scale models

Microbially-explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (RH), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7,821 daily site-scale RH measurements, to evaluate the spatio-temporal performance of one first-order decay model (CASA-CNP) and two microbially-explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different climate datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the meteorological input data used to drive them. Spatial RH variability was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise, but also needs to be improved for realistic spatio-temporal variability of RH. Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.

Jian, Jinshi↗
DOE OSTI2011

Atomic-scale modeling of particle size effects for the oxygen reduction reaction on Pt.

We estimate the activity of the oxygen reduction reaction on platinum nanoparticles of sizes of practical importance. The proposed model explicitly accounts for surface irregularities and their effect on the activity of neighboring sites. The model reproduces the experimentally observed trends in both the specific and mass activities for particle sizes in the range between 2 and 30 nm. The mass activity is calculated to be maximized for particles of a diameter between 2 and 4 nm. Our study demonstrates how an atomic-scale description of the surface microstructure is a key component in understanding particle size effects on the activity of catalytic nanoparticles.

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

Experimental and kinetic analysis for particle scale modeling of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier during reduction with H 2 in chemical looping combustion applications [Experimental and kinetic analysis for particle scale modeling of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier during reduction with CO in chemical looping combustion applications]

A kinetic analysis of the H 2 reduction of a CuO-Fe 2 O 3 -Al 2 O 3 oxygen carrier in gas phase fueled Chemical Looping Combustion of synthesis gas was utilized to derive particle scale representation. An experimentally driven study was carried out to provide an array of operational data sets for kinetic modelling approaches. The impact of key operational variables on the kinetics of the novel oxygen carrier were examined, with emphasis on the application of reliable phenomena driven particle scale models to describe the reduction behavior. Due to the novel nature of the material, a series of experimental studies were carried out to provide a fundamental understanding of how the material changed as oxygen was depleted from the structure due to reduction with H 2 . This include quantification of the complex mixed metal oxide phase and changes due to lattice oxygen depletion. It was found that H 2 reduction occurs in a multistep process where CuFeAlO 4 → Cu 0+ + FeAl 2 O 4 → Cu 0+ + Fe 0+ + Al 2 O 3 as oxygen is depleted from the structure. This multistep process was successfully emulated through the use of a two interface Grainy pellet model in which reaction (kinetic) control was the main rate limiting step. This is validated through the examination of other potential rate limiting resistances. In conclusion, the model emulates changes in key operation variables with good accuracy.

42 ENGINEERING↗