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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 OSTI2022

Factors Affecting Override Behavior during Demand Flexibility from High-Resolution Smart Thermostat Data

In this study, we delineate key weather and demographic predictors of override behavior in residential buildings during connected thermostats demand response events. Anticipating and reducing overrides is critical to demand flexibility (DF) program success. We use high- dimensional fixed effects linear regression techniques on a large ecobee dataset for about 5,000 enrolled households in the United States. We identify critical weather (indoor and outdoor temperature), building (housing type), and occupant (previous DF overrides and previous DF event exposure) factors influencing the override patterns of customers during thermostat demand response. We also differentiate DF events by season to understand the different indoor and outdoor conditions that might influence seasonal override rates. We found significant differences in override rates between building types, with single-family semi-detached homes generally having the highest overrides. Having a history of overrides was additionally a critical factor in predicting occupant response to future DF events. Understanding these override differences is necessary for rural electric cooperatives and emerging DF programs without access to large DR historical data. Overall, we provide critical information on technical and local demographic characteristics that may be correlated to building type to influence strategies to reduce overrides and improve the adoption of DF technologies.

connected thermostats (CTs)↗
DOE OSTI2025

Technical Background and Validation Report on the Residential Water Inhalation Risk Calculator Presented in the Risk Assessment Information System

Indoor air quality (IAQ) is critical for human health. Poor IAQ is linked to respiratory issues, cardiovascular diseases, and cancer. Indoor pollutants are emitted by typical household items such as cleaning products, personal care items, building materials, and tap water - an understudied volatile organic compound (VOC) source. This document presents the Residential Water Inhalation Risk Calculator (RWIRC), which estimates daily VOC exposure concentrations from various household water uses, such as showering and dishwashing, to assess exposure risks for the most vulnerable occupant. Integrated into the Risk Assessment Information System (RAIS) and sponsored by the US Department of Energy (DOE), the calculator divides a house into three compartments: shower, bathroom, and other spaces, accounting for daily water usage patterns and calculating VOC concentrations. Exposure data generated using the calculator can assist health assessors in estimating excess lifetime cancer risk (ELCR) and hazard index (HI) from VOC inhalation. Unlike traditional exposure models that utilize Andelman’s constant, the RWIRC continuously assesses variability in VOC concentrations and environmental conditions using differential equations to track VOC concentrations and air exchange between compartments. The calculator also provides unique volatilization fractions for each chemical and appliance, enhancing accuracy of the exposure concentration estimation. The RWIRC is accessible online and allows users to customize parameters (i.e., number of bathrooms, water temperature, and exhaust fan conditions) and input VOC characteristics (i.e., tap water and ambient air concentrations). This document provides a step-by-step guide on implementing the calculator. It also provides comparisons with the ATSDR-SHOWER calculator, using eight VOCs with varying physicochemical properties to reveal differences in algorithms and output concentrations. Simulations also assess how bathroom door positions and exhaust fan usage affect VOC exposure. The calculator results can enhance EPA risk screening levels for inhalation exposure to VOCs from tap water, offering a sophisticated tool for assessing inhalation risks and improving public health protection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Abstract for CRADA between the National Energy Technology Laboratory and General Motors (AGMT-0876)

Lightweight aluminum shape castings have widespread applications for structural components in the automotive, aerospace, and general engineering industries because of their excellent castability, corrosion resistance, and particularly high strength-to-weight ratio in the heat-treated condition. In the automotive industry, cast aluminum alloys have been increasingly used to replace cast iron and steel in vehicle applications such as engine blocks, cylinder heads, intake manifolds, brackets, housings, chassis, transmission parts, and suspension systems to reduce mass. As many of the aluminum shape castings used in automobiles involve cyclic loading, fatigue properties of the castings are critical to their success. The increasing use of lightweight aluminum cast components in critical structures thus requires improved integrity, with more reliable and quantifiable performance. The objective of this project is to demonstrate and further develop GM’s patent-pending, novel casting process, “Pressure-Assistant Precision Sand Casting (PAPSC) for producing flawless cast aluminum cylinder heads with minimum manufacturing and energy costs”. The approach is to leverage NETL’s experience in producing high-quality metal castings through various microstructure control and defect elimination technologies as well as comprehensive quantification of microstructure characteristics and mechanical properties. A baseline (production) alloy together with the melt treatment will be initially evaluated with the new PAPSC process to develop and validate the process window for producing flawless cast aluminum heads.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Film Cooling Performance Predictions For Air And Supercritical CO 2

The current study explores the possibility of cooling the vanes and blades of a direct-fired sCO 2 turbine using film cooling geometries. The operating conditions of a direct-fired sCO 2 cycle and thermophysical properties of the fluid at those conditions can alter the flow field characteristics of the coolant jet and its mixing with the mainstream. Very little information is present in the literature regarding the performance of film cooling geometries employing a supercritical fluid. The objective of this study is to estimate the resulting film cooling effectiveness while also capturing the effects of the crossflow-to-mainstream velocity ratio on the coolant jet. A computational fluid dynamic model is used to study the coolant jet exiting a cylindrical hole located on a flat plate, with the coolant fed by an internal channel. Steady-state RANS equations were solved along with the SST kω model to provide the turbulence closure. The operating conditions for the direct-fired sCO 2 turbine are obtained using an in-house Cooled Turbine Model. Numerical predictions revealed that the crossflow effects and jet lift-off were more pronounced in the case of sCO 2 when compared to air. Spatial distribution of flow field and cooling effectiveness will be presented at different operating conditions. Recommendations for future studies will be given wherever applicable.

42 ENGINEERING↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Film Cooling Performance Prediction for Air and Supercritical CO 2

The current study explores the possibility of cooling the vanes and blades of a direct-fired sCO 2 turbine using film cooling. The operating conditions of a direct-fired sCO 2 cycle and thermophysical properties of the fluid at those conditions can alter the flow field characteristics of the coolant jet and its mixing with the mainstream. Very little information is present in the literature regarding the performance of film cooling geometries employing supercritical CO 2 . The objective of this study is to estimate the resulting film cooling effectiveness while also capturing the effects of the crossflow-to-mainstream velocity ratio on the coolant jet. Herein, a computational fluid dynamic model is used to study the coolant jet exiting a cylindrical hole located on a flat plate, with the coolant fed by an internal channel. Steady-state Reynolds-averaged Navier–Stokes equations were solved along with the (shear-stress transport) SST k–ω model to provide the turbulence closure. The operating conditions for the direct-fired sCO 2 turbine are obtained using an in-house Cooled Turbine Model. Numerical predictions revealed that the crossflow effects and jet lift-off were more pronounced in the case of sCO 2 when compared to air. Spatial distribution of flow field and cooling effectiveness are presented at different operating conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2024

Generating Emissions Inventory for Carbon Capture and Storage Analysis for Carbon-Intensive Industrial Sectors

Decarbonizing the industrial sector is critical to achieve carbon dioxide (CO2) emissions reductions goals of the Biden Administration. Currently available decarbonization options include electrification, fuel switching to zero carbon fuels like green hydrogen (H2) and carbon capture and storage (CCS). Application of post-combustion carbon capture (PCCC) technology in the power sector, as well as research at the U.S. Department of Energy's Fossil Energy and Carbon Management (FECM) Office has shown that its application in the industrial sector could have co-benefits in the form of emissions reductions of non-CO2 regulated pollutants. For example, solvent based PCCC systems require pre-conditioning of flue gas to remove sulfur and particulate matter (PM) upstream of the CO2 absorber. However, there is a lack of understanding about the type of non-CO2 pollutants which can be captured and the amount of reduction possible. PCCC application differs across industrial sectors as it depends on the availability of decarbonization options, characteristics of industrial processes and the amount and composition of pollutant flows. Certain facilities can also have multiple effluent flows with or without a CO2 stream. As such, understanding industrial processes and their effluent flows in detail is required to quantify the co-benefits opportunities presented by PCCC. Considering this requirement, the goal of this analysis is to develop a high-resolution inventory of effluent flows from facilities of 8 industrial sectors in the U.S. These industrial sectors - ethanol, ammonia, cement, steel, natural gas processing, hydrogen, petroleum refining and wood and pulp products - have carbon-intensive effluent flows, and thus are prime candidates for PCCC applications. In this study, we map the composition of pollutant flow from flue stacks across the identified facilities. Using data available in three Environmental Protection Agency (EPA) databases - the Green House Gas Reporting Program (GHGRP), the National Emissions Inventory (NEI) and the Toxic Release Inventory (TRI), we create a combined inventory which lists the type, amount, and concentration of pollutant flows. Using total weight of the pollutant flow back calculated from observed data for CO2 concentrations in flue gas for individual sectors, we calculate the concentration of each pollutant in the flue gas stream. Thus, the resultant emissions inventory includes the following details for each facility in the sector: facility-level and if possible, process-level pollutant flows, concentrations of pollutants in the flue gas, and geographical coordinates of the facilities. A detailed statistical analysis and summary allows us to search for erroneous data and remove them from the final inventory. The generation of the inventory is achieved using a python-based framework which can recreate this inventory for other industrial sectors as well as using newer releases of emission inventories from EPA. The statistical analysis performed on the inventory is also calibrated and automated to identify outliers efficiently.

air pollutants↗
DOE OSTI2023

A Guide to Engaging Underserved Communities in Commercial Energy Efficiency Field Validations

Underserved communities in the United States often experience the negative impacts of climate change and environmental degradation but enjoy few of the benefits of technological and environmental advances. The White House has addressed this inequity through the Justice40 initiative, which requires 40% of the benefits of select federal investments to be directed to underserved communities (The White House, 2022). Clean energy and energy efficiency are two highlighted investment categories, so the U.S. Department of Energy will guide implementation of the Justice40 initiative by, among other things, decreasing energy burdens, increasing parity in clean energy technology access and adoption, and increasing energy resiliency. A strategy for reaching these goals is to evaluate and validate new energy efficiency technologies in commercial buildings in underserved communities, where buildings may be older, smaller, and have deferred maintenance due to historical underinvestment. This paper develops guidance for researchers pursuing field validations with underserved communities. Historical redlining and past negative experiences with government and large institutions may make residents wary of participating in these field validations. Researchers, therefore, may need to spend more time building relationships and matching technologies to buildings. In this paper, we analyzed technical reports to identify common field validation building characteristics and conducted semi-structured expert conversations to identify key stages and major themes of engaging underserved communities. Results indicate there may be flexibility in site selection and there are steps researchers can take to support collaboration with communities. Results also suggest benefits to both the community and energy efficiency research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Addressing the split incentive challenge for rooftop solar PV and battery energy storage in multifamily rental buildings (CRADA 638 Final Report)

This project advances the understanding of how roof solar PV systems and battery energy storage systems (BESS) can be effectively deployed in multifamily residential buildings, a sector that has historically faced barriers due to misaligned incentives between landlords and tenants. By leveraging high-resolution building stock data and simulation tools, the research demonstrates how energy consumption patterns vary across building types, climates, and occupant characteristics, and how these variations influence the optimal sizing and operation of distributed energy resources. A key contribution is the development of a publicly accessible, web-based tool named RESIDE (Residential Energy Systems & Infrastructure Data Evaluation) that allows users to explore building energy use and evaluate solar and battery configurations without requiring specialized expertise. This significantly lowers the barrier to entry for stakeholders such as property owners, utilities, and policymakers. From a technical perspective, the project shows that integrating rooftop solar PV with battery energy storage can substantially reduce electricity costs and peak demand through strategies such as energy arbitrage and peak shaving. The modeling framework incorporates real-world constraints, including time-of-use electricity pricing and battery degradation, providing realistic and actionable insights. Economically, the results indicate that properly sized systems can deliver meaningful cost savings, improving the feasibility of energy investments in multifamily housing. More broadly, the project benefits the public by supporting the transition to affordable and reliable energy, particularly in rental multifamily housing where adoption has traditionally lagged.

14 SOLAR ENERGY↗
DOE OSTI2023

Low Weight, High Strength Coal-Based Building Materials for Infrastructure Products (Rev.A)

Semplastics has developed, tested, and proven the viability of a new class of composite infrastructure components that use coal as the primary component. Phase I resulted in the production of brick and block components, called X-BRIX and X-BLOX, with dimensions comparable to commercially available bricks and concrete blocks, but with superior mechanical strength, lower weight, greater hardness, improved toughness, greater abrasion resistance, and greater chemical resistance than concrete. Multiple samples of full-size (16” × 8” × 8”) X-BLOX and full-size (8” × 4” × 2.5”) X-BRIX were made to demonstrate the technology, and to support the development of mortar or joining techniques. Phase I moved the coal-based composite materials technology from a current Technology Readiness Level (TRL) of 3 to TRL 5. Semplastics’ coal-based composite materials exhibit a number of high-performance characteristics, including high strength (five times the flexure strength of the best commercial brick, and more than twice the compressive strength of construction-grade concrete block), lower density (by 37 to 41%), improved mechanical durability and abrasion resistance, and resistance to chemicals, acids, salts, and chemical wastewater. These properties offer significant improvements over conventional bricks, concrete blocks, and pavers. The coal particles are completely encapsulated and bonded using a specially formulated polymer-derived ceramic (PDC) that is cured to form an “aggregate” of coal and PDC resin. This aggregate can be further processed and lightly pressed (at 10 to 50 psi) to produce a brick. After pyrolyzing (similar to the processing used to make standard clay bricks), the finished coal-based brick is significantly harder and more abrasion-resistant than regular bricks. We have produced prototype X-BRIX and X-BLOX that contain at least 55% coal by weight (71% carbon by weight, due to the controlled carbon content of the PDC) and showed their superior mechanical properties through in-house and contracted testing.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

High-Fidelity Analysis of EV Integration on Real Utility Feeders in Colorado

Residential electric vehicle (EV) charging has the potential to alter long-held assumptions on load characteristics impacting distribution grid planning, operations, and design standards. This study identifies analysis and control methods to increase the affordability of residential EV charging both for Xcel Energy and their customers. The project also provides solutions for more reliable grid interconnection that can support a reliable utility business model prepared for increasing EV charging load in the coming years. For this project, we referenced Level 2 alternating current (AC) onboard charging profiles for various vehicle models and high-fidelity charging data collected at the experimental setup established at the EV Research Infrastructure Laboratory at the National Renewable Energy Laboratory (NREL). Next, we developed EV adoption models for 2030 and 2040 for the Boulder and Aurora regions in Colorado. Moreover, we evaluated different smart charging control algorithms and compared their performance. We developed time-of-use (TOU)-based and grid-aware active EV charging control methods and integrated them within the study region to understand field impacts. Diving deeper, we selected 10 feeders in Boulder and Aurora for high-fidelity grid modeling down to the house level. We executed detailed grid analysis comparing the smart charge management (SCM) algorithms we developed. Finally, we created a novel tool, Electric Vehicle Infrastructure--Distribution System Integration Tool (EVI-DiST), to integrate all the approaches in a single software environment to provide easy integration, fast simulation, and detailed evaluation capability for utility engineers and other stakeholders.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Incentivizing Cold-Climate Efficiency in Juneau (Final Report)

This is the final technical report for the DOE EERE BTO project Incentivizing Cold-Climate Efficiency in Juneau. The project implemented a community energy campaign to deploy heat pumps and efficiency for residents of Juneau, in Southeast Alaska. The project helped Juneau make progress towards its renewable energy goal of reaching 80% renewable energy for space heating by 2045. The Incentivizing Cold-Climate Efficiency in Juneau (ICE-Juneau) project began in 2020 and concluded three years later in 2023. During that time, a group of implementation partners and a research advisory team instituted a beneficial electrification campaign to promote energy savings and carbon reduction in residences of Juneau, Alaska. The campaign, Thermalize Juneau, focused on the installation of single head ductless mini-split heat pumps along with other efficiency upgrades. Thermalize Juneau was the first campaign of its kind in Alaska. After several months of planning, registration opened to the public in early 2021, and over the course of six months 164 participants enrolled. Campaign staff provided education to homeowners on heat pumps and efficiency, and each participant received a one-on-one heat pump assessment using a custom Microsoft Excel-based calculator that estimated energy savings for their residence based on building characteristics and past utility bills. Participants could also obtain a free energy audit from one of the two local energy auditors to further inform their decision. A heat pump installer, electrician, and builder were selected via a competitive RFP process. Participants who felt ready to install a heat pump or other efficiency upgrades received a site visit and custom quote from each of these contractors free of charge, and if they still felt energy upgrades were right for them, could move forward with an individual contract. Participants received a $400 heat pump installation rebate, offered by the installer if 40 heat pump installations occurred through the campaign. Overall, the campaign facilitated 75 heat pump installations (including participants that went with another contractor or heat pump model) and 30 efficiency upgrades (including participants that went with another builder or did DIY upgrades). The campaign created 3 new jobs as the heat pump installer hired an administrative assistant and two apprentices over the course of the campaign. It also helped Juneau work toward achieving its renewable energy goal of 80% renewable energy for space heating by 2045 by upgrading houses from fuel oil to heat pumps powered by the hydropower electric grid. Researchers conducted four surveys to inform Thermalize Juneau and future energy campaigns. The first surveyed existing and prospective heat pump owners in Juneau to identify barriers the campaign could address and inform recruitment efforts. Entry and exit surveys provided information on participant demographics, goals, outcomes, and suggestions for improving future campaigns. And a final survey of community members who had not participated in Thermalize Juneau gave insight on ways future campaigns could include a greater diversity of participants so they could realize similar benefits. Researchers also used pre-campaign energy modeling to predict energy savings, which was then compared to the savings estimated through aggregation of heat pump assessments and energy audits, and later to actual energy savings of 10 participants who installed a heat pump and were able to provide complete energy use data sets. In addition to energy savings, and to assist the electric utility in future planning efforts, researchers analyzed the overall change in electric use across participants with energy data and heat pump installations. They also completed a life cycle cost analysis, showing positive net present values for those displacing a fuel oil appliance, and a more mixed case for those switching from electric baseboard. This project proved the feasibility of energy campaigns, with a goal of beneficial electrification, in cold, remote locations. The Thermalize Juneau team compiled a Guidebook to Thermalize Campaigns, available online. In addition to documenting what occurred in Alaska’s first thermalize campaign, it provides tips and resources for other communities wishing to implement a similar program.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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 OSTI2020

Origin and Suppression of Beam Damage-Induced Oxygen-K Edge Artifact from γ-Al 2 O 3 using Cryo-EELS

Gamma-alumina (γ-Al 2 O 3 ), like other low-Z oxides, is readily damaged when exposed to an electron beam. This typically results in the formation of a characteristic pre-edge peak in the oxygen-K edge of electron energy-loss spectra (EELS) acquired during or after the damage process. This artifact can mask the presence of intrinsic O-K edge fine structure that would reveal chemical properties of the material; therefore, its suppression is key. In this work, we systematically investigate the conditions that give rise to the damage-induced O-K pre-edge peak and show that it can be effectively suppressed by performing EELS experiments at cryogenic (cryo) temperatures. Prolonged exposure of γ-Al 2 O 3 to a focused electron beam results in a hole bored through the sample; this was used as a reproducible beam damage condition. O-K edge EELS spectra were collected from a single-crystal γ-Al 2 O 3 sample both during and after focused 2 electron beam hole drilling, and at room and cryo temperatures, using a monochromated scanning transmission electron microscope (STEM). Furthermore, the characteristic 531 eV pre-edge peak visible in the room temperature EELS spectra was completely suppressed in the cryo-EELS spectra, even in the presence of a visible drilled hole. We then correlated these experimental observations with multiple-scattering EELS simulations to determine the likely atomistic origin of the damage-induced O-K pre-edge peak. The findings indicate that the pre-edge peak is caused primarily by the presence of surface O dimer (O-O) bonds formed during beam damage, and that operating at cryo temperature suppresses the formation of surface O-O bonds, thus preventing formation of the O-K pre-edge peak. Additionally, Al-L 2,3 edge EELS spectra revealed Al loss primarily from tetrahedral sites during hole drilling.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Simulation of a rapid compression machine for evaluation of ignition chemistry and soot formation using gasoline/ethanol blends

Due to the projected decline of demand for gasoline in light duty engines and the advent of ethanol as a green fuel, the use of gasoline/ethanol blend fuels in heavy duty applications are being investigated as they are projected to have lower cost and lower lifecycle green house gas (GHG) emissions. In heavy duty engines, the primary mode of combustion is mixing controlled combustion where wide range of mixture conditions (equivalence ratio) exist. Soot emissions of these fuels in richer conditions are not well understood. The goal of this research is to evaluate some commercially available soot modeling codes for the particulate matter emissions from gasoline/ethanol fuel blends, especially at fuel rich conditions. A Rapid Compression Machine (RCM) is modeled in a three-dimensional numerical simulation using CONVERGE computational software using a reduced chemical kinetic mechanism with SAGE chemistry solver and a RANS k-ϵturbulence model with a sector model including the creviced piston. The creviced piston is used in the experimental setup to reduce boundary layer effects and to maintain a homogeneous core in the reaction cylinder. Computational fluid dynamics simulations are conducted for different gasoline-ethanol fuel blends from E10 (10% ethanol v/v) to E100. The fuel blend is modeled as a surrogate mixture of toluene, iso-octane, n-heptane for gasoline content, and ethanol. The computational results were validated against experimental results using pressure measurements and laser extinction diagnostics. Different soot models are investigated to evaluate their capability of predicting the sooting tendencies of fuel blends, especially in richer conditions experienced during mixing-controlled combustion. The experimental combustion characteristics such as the ignition delay of different blends of fuel are reasonably well predicted. The Particulate Size Mimic (PSM) model accurately predicts the soot generation characteristics of the different fuels, but the Hiroyasu-NSC model falls short in this regard. For accurate prediction of soot with the PSM model, the thermodynamic conditions during combustion must be accurately modeled. While the current computational modeling tools can produce accurate results for the prediction of particulate matter emissions, there is much work to be done in improving our understanding of the underlying fundamental processes.

Energy & Fuels↗