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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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39 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling cell venting and gas-phase reactions in 18650 lithium ion batteries during thermal runaway

A numerical model is developed to study cell venting, internal pressure, and gas-phase dynamics behavior of 18650 Li-ion cells undergoing thermal runaway. A k-e Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer's equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. Then, a series of computational fluid dynamics (CFD) simulations are conducted on a single 18650 cell at various states-of-charge (100%, 50%, 25%) to study detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is found that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.

25 ENERGY STORAGE↗
DOE OSTI2022

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Thermodynamic control on the decomposition of organic matter across different electron acceptors

The increasing availability of high-resolution characterization of natural organic matter (OM) data has shifted the paradigm of lumped descriptions of OM components and potential microbial activities. Our recent development of a substrate-explicit thermodynamic model uniquely enables incorporating complex OM pools to formulate biogeochemical reaction models based on their elemental compositions. While this previous work facilitates prediction of aerobic respiration of complex OM, it is equally imperative to consider the role of non-oxygenic electron acceptors in regulating OM turnover and the fate of carbon. In this study, we significantly expand our previous model by flexibly incorporating both detailed OM chemistry and electron acceptors other than oxygen. Here, our modeling analysis has revealed substantial variations in the energy status of OM molecules across different soils, which drive the co-occurrence of different electron-accepting processes. We demonstrated the effectiveness of the proposed model using a consistency check with experimental data. Through systematic evaluation of the impact of diverse chemical inputs (both electron donors and acceptors) on OM decomposition, the new model also revealed how key microbial growth parameters such as carbon use efficiency (CUE) and reaction rates vary across different electron-accepting processes. Our model provides a unified framework integrating thermodynamic and kinetic constraints on microbial metabolic activities. It complements traditional kinetic models, which are often designed solely to capture mass fluxes. We conclude that thermodynamic modeling emerges as a powerful tool for describing the mechanisms underlying the interplay between microbial growth and OM chemistry and cycling across different electron acceptors, enhancing our ability to project complex ecosystem behaviors in dynamic environments.

59 BASIC BIOLOGICAL SCIENCES↗