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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamics and chemical mode analysis of plasma thermal-chemical instability

The stability of the weakly ionized plasma and the transition from a stable homogeneous discharge to unstable filaments play an important role in gas laser physics, plasma-assisted combustion, chemical reforming, and material synthesis. Here, theoretical stability analysis and thermal-chemical mode analysis were performed to understand the mechanism of plasma thermal-chemical instability by using a zero-dimensional plasma system with both simplified and detailed chemical kinetics of H 2 /O 2 /N 2 mixtures. The plasma dynamic and kinetic models accounted for multiple physical mechanisms in the chemically-reactive weakly ionized plasma, including ionization, attachment/detachment, recombination, vibrational and electronic energy relaxation, convective and diffusive species/heat removal, Joule heating, and detailed chemical kinetics. An analytical criterion and the explosive mode species/temperature pointers were formulated while the representative active species were identified for different thermal-chemical modes. The results showed that in addition to the classical thermal-ionization mechanism, various chemical modes from chemical heat imbalance and elementary kinetics significantly modified the time dynamics and the stability of the weakly ionized plasma. The present analysis provides insights and guidance to control plasma instability using chemical kinetics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Investigation on the explosion dynamics of large-format lithium-ion pouch cells

Explosion is the most extreme case of thermal runaway of lithium-ion (Li-ion) batteries. In this study, explosion dynamics of large-format Li-ion cells are investigated experimentally and numerically. Overcharge-to-explosion tests are conducted on 40 Ah Li-ion cells with Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 cathode. Based on the explosion physics, shockwave and detonation models are used to characterize the shock effect of the cell explosion and evaluate the explosion equivalent. Von Neumann peak is observed on the pressure curves, and the shockwave velocity is supersonic at this time; the unwrinkled spherical flame phenomenon observed in the experiment indicates that it is detonation. Additionally, a geometric model is established based on the real testing scenario, and the explosion behavior is numerically studied. The characteristics of the explosion dynamics process are interpreted and the propagation mechanism of the shockwave are revealed; the deflagration to detonation transition (DDT) phenomenon in this process is caused by the formation of “hot spots”, and the explosion of the cells eventually turns into stable combustion. Here this study fills the gap in the research on thermal runaway of Li-ion cells, especially in the extreme cases of fire and explosion, and provide useful guidance for battery safety.

42 ENGINEERING↗
DOE OSTI2021

Atomic Layer Deposition with TiO2 for Enhanced Reactivity and Stability of Aromatic Hydrogenation Catalysts

Hydrogenation of aromatic molecules in fossil- and bio-derived fuels is essential for decreasing emissions of harmful combustion products and addressing growing concerns around urban air pollution. In this work, we used atomic layer deposition to significantly enhance the hydrogenation performance of a conventional supported Pd catalyst by applying an ultrathin coating of TiO2 in a scalable powder coating process. The TiO2-coated catalyst showed substantial gains in the conversion of multiple aromatic molecules, including a 5-fold improvement in turnover frequency versus the uncoated catalyst in the hydrogenation of naphthalene. This activity enhancement was maintained upon scaling the coating synthesis process from 3 to 100 g. Based on the results from Xray photoelectron spectroscopy, X-ray absorption spectroscopy, and computational modeling, the activity enhancement was attributed to ensemble effects resulting from partial TiO2 coverage of the Pd surface rather than fundamental changes to the Pd electronic structure. Additional durability testing confirmed that the TiO2 coating improved the thermal and hydrothermal stability of the catalyst as well as tolerance toward sulfur impurities in the reactant stream. Using an economic model of an industrial deep hydrogenation process, we found that an increase in catalyst activity or lifetime of 2× would justify even a relatively high estimate for the cost of TiO2 atomic layer deposition coatings at scale

atomic layer↗
DOE OSTI2022

Multi-stage heat release of multi-component fuels: Insights and implications for advanced engine operation

Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.

42 ENGINEERING↗
DOE OSTI2022

Modular Adaptive Packing for Integrally Cooled Absorbers

Process intensification is one cornerstone in ION Clean Energy’s (ION) efforts to lowering CO2 capture cost. ION has modeled, designed, and fabricated an innovative gas-liquid contactor known as Modular Adaptive Packing (MAP). During two successful SBIR Phase I and II projects entitled: “Rapid Design and Testing of Novel Gas-Liquid Contacting Devices for Post-Combustion CO2 Capture via 3D-printing”, ION developed and proved this new technology at bench scale. MAP, a 3D-printed lattice-structured packing, combines the absorber gas/liquid contactor with an innovative in-situ heat-exchanger. Thanks to the capabilities of 3D-printing, the lattice structure of MAP contains hollow channels through which coolant water can be pumped to remove the heat of reaction from CO2 absorption. ION refers to this novel method of heat exchange as intracooling. After 25.4 cm (10 in) diameter MAP modules were fabricated, ION built and tested a packing characterization rig at its pilot facility in Boulder, Colorado, U.S.A. To provide baseline results for the characterization rig, ION tested Sulzer’s Mellapak™ 250Y (MP250Y) as a standard structured packing. ION’s MAP was then compared directly to the baseline MP250Y packing to evaluate key indicators including pressure drop, liquid hold-up, and effective area. MAP has a higher pressure drop than MP250Y at the same gas velocities in addition to greater liquid holdup. However, ION found that MAP displays a higher wetting coverage of 93% compared to 65% for MP250Y and reduces shearing forces that result in undesirable droplet formation. Using Optimized Gas Treating’s (OGT) rate-based simulation software ProTreat®, a conceptual evaluation of MAP was modeled for a CO2 absorber using 30 wt% MEA solvent over a range of lean loadings at 90% CO2 capture from a coal-fired power plant. ION modeled a 25-meter column absorber for both the standard MP250Y packing and a hybrid column. The hybrid absorber contained 10 m of MP250Y packing at the top and bottom with the middle 5 meters comprised of MAP. Compared to a traditional intercooled absorber, MAP can remove 22% more heat and increase overall MEA carrying capacity by 4% without increasing overall pressure drop.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Mechanistic study of chemical looping reactions between solid carbon fuels and CuO

Copper (Cu) based chemical-looping combustion (CLC) is a promising process that utilizes solid carbon fuel such as coal and biomass. Understanding the reaction kinetics in this process can facilitate the industrial design of CLC units. Here, in this work, molecular dynamics (MD) simulations were performed to investigate the reaction kinetics of n-butane and two simplified solid carbon fuels (lignite and anthracite) with/without copper oxide (CuO) nanoparticle. In addition, experiments were conducted on the thermal characteristics, flammability, and flame speeds of CuO and solid carbon mixtures. For n-butane, oxidation on the CuO surface is the primary reaction since the activation energy of the surface reaction is much lower than that of oxygen (O 2 ) combustion (9.2 vs. 53.3 kcal/mol). On the other hand, for lignite, there is a smaller difference in the activation energy of O 2 combustion and surface reaction (23.04 vs. 6.34 kcal/mol). We hypothesize that some solid carbon fuels can have different reaction kinetics dependent upon temperature. This is proven by the case of a simplified anthracite coal: an Arrhenius plot shows that this solid carbon fuel has two different reaction kinetic regimes and the critical temperature for the change in kinetics is related to the oxygen uncoupling of the CuO nanoparticle. Like the modeling simulations, a change in activation energy is observed in the experimental results, where desorption of molecular oxygen from CuO becomes important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Benzo[a]pyrene (BaP) metabolites predominant in human plasma following escalating oral micro-dosing with [ 14 C]-BaP

Benzo[a]pyrene (BaP) is formed by incomplete combustion of organic materials (petroleum, coal, tobacco, etc.). BaP is designated by the International Agency for Research on Cancer as a group 1 known human carcinogen; a classification supported by numerous studies in preclinical models and epidemiology studies of exposed populations. Risk assessment relies on toxicokinetic and cancer studies in rodents at doses 5–6 orders of magnitude greater than average human uptake. Using a dose–response design at environmentally relevant concentrations, this study follows uptake, metabolism, and elimination of [ 14 C]-BaP in human plasma by employing UPLC - accelerator mass spectrometry (UPLC-AMS). Volunteers were administered 25, 50, 100, and 250 ng (2.7–27 nCi) of [ 14 C]-BaP (with interceding minimum 3-week washout periods) with quantification of parent [ 14 C]-BaP and metabolites in plasma measured over 48 h. [ 14 C]-BaP median T max was 30 min with C max and area under the curve (AUC) approximating dose-dependency. Marked inter-individual variability in plasma pharmacokinetics following a 250 ng dose was seen with 7 volunteers as measured by the C max (8.99 ± 7.08 ng × mL -1 ) and AUC0-48hr (68.6 ± 64.0 fg × hr -1 × mL -1 ). Approximately 3–6% of the [ 14 C] recovered (AUC 0-48 hr ) was parent compound, demonstrating extensive metabolism following oral dosing. Metabolite profiles showed that, even at the earliest time-point (30 min), a substantial percentage of [ 14 C] in plasma was polar BaP metabolites. The best fit modeling approach identified non-compartmental apparent volume of distribution of BaP as significantly increasing as a function of dose (p = 0.004). Bay region tetrols and dihydrodiols predominated, suggesting not only was there extensive first pass metabolism but also potentially bioactivation. AMS enables the study of environmental carcinogens in humans with de minimus risk, allowing for important testing and validation of physiologically based pharmacokinetic models derived from animal data, risk assessment, and the interpretation of data from high-risk occupationally exposed populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2023

High Speed Data Acquisition for Real Time Feedback in a Light Duty Engine Combustion-Mode Switching Application

The paper describes the integration of a high-speed data acquisition and diagnostics controller used in an advanced engine platform. The controller enables ultra-low emissions and new benchmarks of engine efficiency while running a Gasoline Compression Ignition (GCI) cycle on a 2.2L, 4-cylinder engine. The system enables real-time combustion feedback and vibration analysis in engines. The paper focuses on: (1) the development of an interpolative sampling algorithm for transposition of time acquired data to the crank angle domain using a production crank sensor (60-2 tooth wheel); (2) the control unit, high-speed data acquisition, communication rates between the dedicated data acquisition and base controller to ensure cycle-to-cycle feedback; and, (3) validation exercises using cylinder pressure measurements. Here, the study shows how the algorithm resolves cylinder pressure information over an engine cycle, validating its robustness across acquisition rates of 50 and 200 kHz, with crank angle resolutions of 0.5° and 0.1°. Results show that the 50 kHz, 0.5° hardware yield combustion phasing and torque estimates within 0.1° and 1% torque of those attained with the 200 kHz, 0.1° hardware. Finally, study shows the dedicated controller and communication speeds allow for next-cycle fuel injection correction for speeds above 5000 rpm.

42 ENGINEERING↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2023

The Impact of Oxidation-Induced Degradation On Materials Used in Hydrogen-Fired Microturbines

Hydrogen-fueled microturbines are being considered as part of the future green microgrid. However, the use of hydrogen as a fuel presents new challenges for selection and development of suitable high temperature materials for hydrogen combustion. The burning of hydrogen is expected to result in higher operating temperatures and higher than typically observed water vapor contents in exhaust gases versus burning natural gas. In the present work, foil specimens of various Fe- and Ni-based alloys were oxidized in air + 10 % H 2 O and air + 60% H 2 O for up to 5,000 h at 700 °C to simulate the exhaust atmosphere of natural gas and hydrogen-fueled microturbines. Here, the impact of alloy composition and water vapor content on the oxidation/ volatilization induced loss of wall thickness was experimentally evaluated. Enhanced external oxidation and volatilization of Cr 2 O 3 and Ti-doped Cr 2 O 3 scales was observed in air + 60% H 2 O compared to air + 10% H 2 O. No significant impact of the higher water vapor content was observed on Al 2 O 3 scales formed on Fe-based alumina forming alloys. Lifetime modeling was employed to predict the combined effects of water vapor content, gas flow rates, temperature and alloy composition on the oxidation-induced lifetime of the investigated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of Methane Emissions Model to Assess Fuel Recovery Potential at Gas Well Sites Using On Site Compression

The U.S. natural gas production and consumption has increased 85.5% since 2005 primarily due to the unconventional production methods of horizontal drilling and hydraulic fracturing. Natural gas used as a fuel has a lower greenhouse gas (GHG) footprint than coal and petroleum due to lower Carbon Dioxide (CO2) emissions when combusted. However, the “greener” benefit to natural gas may be negated by leaks in production and transmission systems. Methane (CH4), the primary hydrocarbon in natural gas, has an estimated Global Warming Potential (GWP) of 28-36 over 100 years, meaning it can absorb 28-36 more energy than CO2 which has a GWP of 1.0. Natural gas well sites are prone to methane emissions, or leaks and irregular gas releases, vented to atmosphere throughout production and transmission. The U.S. Department of Energy (DOE) and the National Energy Technology Laboratory (NETL) has recently granted West Virginia University (WVU) funding under agreement DE-FOA-0002005, to “Advance technologies to mitigate methane emissions and increase the efficiency of the natural gas transportation infrastructure”. As part of this funding WVU was tasked with identifying and quantifying sources of methane emissions at unconventional well sites, processing this data, and developing a system to recapture these emissions. A 0-D Simulink model was developed, utilizing standardized methodologies, data from previously conducted studies, as well as collected data from well sites in the Marcellus shale play region. The model was developed to predict emission rates from various components at natural gas well sites as well as the potential to utilize these emissions as fuel for the natural gas powered compressor engines on-site. This model was utilized to run high, medium, and low cases for four identified emission sources, engine size, pneumatic controller count, liquid level production which dictates tank emissions, and compressor packing vent emissions. Due to discrepancies in transient tank emission data, a high and low emission factor for tanks was used, resulting in two sets of 81 executed cases, and 162 unique cases of total site emissions and potential for fuel consumption. Each of the cases were run over 86,400 seconds at a 1 Hz, representative of a full 24 hour day of operation. The fuel consumption offset an average of 557% of fuel consumption on an energy density basis across all 81 cases with the high tank emission factor with a maximum offset of 2334%. The fuel consumption offset was an average of 82.9% for all 81 cases with the low tank emission factor with a maximum offset of 337%. This study highlights flaws in the use of publicly available methane number calculations to determine natural gas’s suitability as an engine fuel as well as the lack of public data for transient liquid storage tank emissions.

03 NATURAL GAS↗
DOE OSTI2022

A Review of Computational Capabilities and Requirements in High-Resolution Simulation of Nonferrous Pyrometallurgical Furnaces

We report that tremendous progress has been made over the last several decades in improving the pyrometallurgical processing routes of nonferrous metals, such as aluminum, copper, and lead. Advances in the numerical modeling of pyrometallurgical processes has aided in these improvements by providing a better understanding of the complex transport phenomena occurring in modern furnaces. However, there is a need for a comprehensive discussion of the numerical modeling of a primary and a secondary nonferrous pyrometallurgical furnaces. This review provides such a discussion by surveying recent attempts at capturing the physico-chemical phenomena occurring within these furnaces, including gas-phase combustion, melting/smelting, and multiphase heat- and mass-transfer with the molten phases. This work then identifies a complete set of approaches for simulating these types of furnaces and provides recommendations for applying high-resolution numerical tools towards full-furnace simulation. By identifying gaps in the current state of the art, this review offers suggestions for future developments in commercial codebases, outlining the path to fulfilling the promise of CFD-enabled pyrometallurgy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SAF Combustion & Soot Processes [Slides]

The atomization, mixing, combustion and emissions characteristics of aviation fuels were measured using a novel approach based on a non-continuous injection scheme called the single-hole atomizer (SHA). High-speed microscopy revealed differences between fuels in terms of evaporation and mixing regimes over conditions relevant to modern and next generation aero-engine combustors. Measurements of liquid and vapor penetration, mixing fields, combustion and emissions metrics (ignition delay, lift-off length, PAH formation, soot mass) highlighted the effects of fuels and combustor conditions. The experimental results are being leveraged to adjust and validate chemical and CFD models. Detailed analysis of sampled soot showed subtle differences in soot morphology between fuels. The results revealed the presence of contaminants potentially affecting surface chemistry and the nucleation propensity of water droplets on particles. Chemical mechanisms for NJFCP A-2, C-1 and C-4 showed good performance over a large parameter space. Spray breakup at relight conditions is vastly different from the atomization observed at high pressure. CFD simulations of the SHA target conditions confirmed the good behavior of the C-1 kinetic mechanism. The simulations support the strong relationship between low and high temperature reactions. New altitude chamber facility to enable detailed characterization of the heterogeneous nucleation process of water on aerosol particles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Validation Framework for Post-Combustion Carbon Capture CFD Simulations

First-principles based computational fluid dynamics (CFD) simulations are proposed as a fundamental tool for investigating solvent-based CO2 absorption in packed columns, due to the ability to accurately represent the underlying non-linear multiscale dynamics. In this work, we employ such models to investigate hydrodynamics of columns with structured by assessing the key hydrodynamic metrics, such as pressure drop and liquid holdup. Our models are validated with experimental data from a specifically designed column for this line of work. The test cases of gas and liquid flowrates and operating conditions were selected through a comprehensive sequential design of experiments approach offered by the CCSI2 toolset.

Panagakos, Grigorios↗
DOE OSTI2020

Effects of dilution and pressure on combustion characteristics within externally heated micro-tubes

Here this study investigates the impact of pressure and dilution on combustion behavior of of C 2H6/air/N2 mixtures within an externally heated micro-tube using both experimental and analytical techniques. Experiments are conducted for pressures up to 10 bar at multiple dilution levels and document three flame regimes, i.e. weak flames, FREI and normal flames. Characteristic temperatures for flame stabilization, as well as and ignition and extinction are obtained by applying a novel thin filament pyrometry method (TFP), which uses a thin silicon carbide (SiC) filament that enables temperature measurements for both reacting and non-reacting cases. Results show a decrease of characteristic temperatures with increasing pressure, which can be which can be partially compensated by a decrease of O2 content. Further, regime transitions occur at lower velocity as the pressure increases, whereas weak flame positions are insensitive to dilution at a given pressure. Predictions from the analytical model compare favorably with experimental results. The most prominent finding is that as dilution increases, the transition from weak flame to FREI becomes less abrupt and is completely lost for high dilution levels (i.e. low O2 content). Both experimental and analytical results indicate that the unstable FREI combustion regime does not exist at high dilution levels, i.e. weak flame and normal flame regimes are no longer clearly separated. As FREI are interpreted as a cyclic transition from an ignition zone to a propagating flame, results for highly diluted conditions are interpreted as a combustion regime where classical flame propagation ceases to exist.

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