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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 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 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 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 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↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2026

SCALE HTR-PROTEUS Benchmark Model

This dataset contains input and result files of computational simulations of HTR-PROTEUS benchmark with the latest version of SCALE code system. The simulations cover criticality control rod worth calculations as well as sensitivity analysis and uncertainty quantification. Users wanting to reproduce results from this dataset are required to obtain a license to the SCALE code system for which details on the distribution can be found here: https://www.ornl.gov/scale/releases

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Modeling of inertial deposition in scaled models of rat and human nasal airways: Towards in vitro regional dosimetry in small animals

Rodents are routinely used in inhalation toxicology tests as human surrogates. However, in vitro dosimetry tests in rodent casts are still scarce due to small rodent airways and in vitro tests to quantify sub-regional dosimetry are still impractical. We hypothesized that for inertial particles whose deposition is dominated by airflow convection (Reynolds number) and particle inertia (Stokes number), the deposition should be similar among airway replicas of different scales if their Reynolds and Stokes numbers are kept the same. In this study, we aimed to (1) numerically test the hypothesis in three airway geometries: a USP induction port, a human nose model, and a Sprague-Dawley rat nose model, and (2) find the range of applicability of this hypothesis. Five variants of the USP and human nose models and three variants of the rat nose model were tested. Inhalation rates and particle sizes were scaled to match the Reynolds number and Stokes numbers. A low-Reynolds-number k–ω model was used to resolve the airflow and a Lagrangian tracking algorithm was used to simulate the particle transport and deposition. Statistical analysis of predicted doses was conducted using ANOVA. For normal inhalation rates and particle dia- meters ranging from 0.5 to 24 mm, the deposition differences between the life-size and scaled models are insignificant for all airway geometries considered (i.e., human nose, USP, and rat nose). Furthermore, the deposition patterns and exit particle profiles also look similar among scaled models. However, deposition rates and patterns start to deviate if inhalation rates are too low, or particle sizes are too large. For the rat nose, the threshold velocity was found to be 0.71 m/s and the threshold Froude number to be 50. Results of this study provide a theoretical foundation for sub-regional in vitro dosimetry tests in small animals and for interpretation of data from inter-species or intra-species with varying body sizes.

54 ENVIRONMENTAL SCIENCES↗