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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics

Smart proxy technology leverages the art of artificial intelligence and machine learning in order to build accurate and very fast proxy models for highly complex numerical simulation models. In this project, smart proxy technology is used to replicate the results of a highly complex single phase CFD simulation with reasonable degree of accuracy while reducing the computational cost associated with such CFD simulations. The CFD model under study simulates the combustion of natural gas under various conditions such as varying natural gas composition and flow rate, inlet air flow rate and temperature, and outlet pressure in a High-Pressure Combustion Facility (B6 Combustor) with more than 4 million simulation cells. Only eight CFD simulation runs were used to create a smart proxy model that replicates the detail distribution of Pressure, Temperature, Nitrogen, Oxygen and Carbon-dioxide concentration in the CFD simulation model in seconds with less than 10% error.

03 NATURAL GAS↗
DOE OSTI2020

Cooperative Research and Development Agreement between National Energy Technology Laboratory and Korea Institute of Energy Research [Abstract]

The Korea Institute of Energy Research (KIER) and the National Energy Technology Laboratory (NETL) intend to collaborate under a CRADA to advance the development of oxy-combustion technology, with an emphasis on process modeling, optimization, and techno-economic analysis. This collaboration will result in the development of a full-scale process model of a commercial oxy-fueled circulating fluid bed combustion (oxy-CFBC) plant, informed from pilot-scale data and built using the Institute for the Design of Advanced Energy Systems (IDAES) process systems engineering framework. It will also generate cost comparisons of the optimized oxyCFBC process with an air-CFBC process with and without carbon capture and sequestration.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

An Experimental Kinetics Study of Isopropanol Pyrolysis and Oxidation behind Reflected Shock Waves

Isopropanol has potential as a future bio-derived fuel and is a promising substitute for ethanol in gasoline blends. Even so, little has been done in terms of high-temperature chemical kinetic speciation studies of this molecule. To this end, experiments were conducted in a shock tube using simultaneous CO and H2O laser absorption measurements. Water and CO formation during isopropanol pyrolysis was also examined at temperatures between 1127 and 2162 K at an average pressure of 1.42 atm. Species profiles were collected at temperatures between 1332 and 1728 K and at an average pressure of 1.26 atm for equivalence ratios of 0.5, 1.0, and 2.0 in highly diluted mixtures of 20% helium and 79.5% argon. Species profiles were also compared to four modern C3 alcohol mechanisms, including the impact of recent rate constant measurements. The Li et al. (2019) and Saggese et al. (2021) models both best predict CO and water production under pyrolysis conditions, while the AramcoMech 3.0 and Capriolo and Konnov models better predict the oxidation experimental profiles. Additionally, previous studies have collected ignition delay time (τign) data for isopropanol but are limited to low pressures in highly dilute mixtures. Therefore, real fuel–air experiments were conducted in a heated shock tube with isopropanol for stoichiometric and lean conditions at 10 and 25 atm between 942 and 1428 K. Comparisons to previous experimental results highlight the need for real fuel–air experiments and proper interpretation of shock-tube data. The AramcoMech 3.0 model over predicts τign values, while the Li et al. model severely under predicts τign. The models by Capriolo and Konnov and Saggese et al. show good agreement with experimental τign values. A sensitivity analysis using these two models highlights the underlying chemistry for isopropanol combustion at 25 atm. Additionally, modifying the Li et al. model with a recently measured reaction rate shows improvement in the model’s ability to predict CO and water profiles during dilute oxidation. Finally, a regression analysis was performed to quantify τign results from this study.

09 BIOMASS FUELS↗
DOE OSTI2022

On the effect of mixing-driven vaporization in a homogeneous relaxation modeling framework

The homogeneous relaxation model (HRM) is one of the most widely used models to describe the liquid–gas phase transition in multiphase flows due to the occurrence of cavitation. However, in its original formulation, the HRM does not account for the presence of ambient gas species, which generally limits its applicability to the injector's internal flow where ambient gases are negligible. In this work, a mixing-driven vaporization (MDV) model was developed to extend the capability of the HRM in handling the mixing effect in the regions external to the nozzle, where vapor–liquid equilibrium for multi-species mixtures of fuel and ambient gas is considered. Herein, to assess the model performance, simulations of the Engine Combustion Network's Spray G injector were performed with the HRM and the MDV model under both flash-boiling and evaporating conditions. It was found that the MDV model led to a better match against x-ray measurements of fuel density in the near-nozzle region. In contrast to the HRM, the MDV model was able to reproduce the vaporization process in the mixing zone at the edge of the fuel jet, which aligns with the expected physics. This resulted in substantial differences in the prediction of other flow characteristics such as mixture temperature and pressure. Furthermore, this work demonstrates that evaporation timescales have a considerable effect on the MDV model's predictions, as shown by a parametric study in which a time factor was introduced to mimic the effect of different timescales due to different phase change mechanisms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Benchmarking the Performance of the ReaxFF Reactive Force Field on Hydrogen Combustion Systems

A thorough understanding of the kinetics and dynamics of combusting mixtures is of considerable interest, especially in regimes beyond the reach of current experimental validation. The ReaxFF reactive force field method has provided a way to simulate large-scale systems of hydrogen combustion via a parametrized potential that can simulate bond breaking. This modeling approach has been applied to hydrogen combustion, as well as myriad other reactive chemical systems. In this work, we benchmark the performance of several common parametrizations of this potential against higher-level quantum mechanical (QM) approaches. We demonstrate instances where these parametrizations of the ReaxFF potential fail both quantitatively and qualitatively to describe reactive events relevant for hydrogen combustion systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Time-dependent-bases with local CUR decomposition method for accelerating turbulent combustion simulations

Here, this study presents a novel reduced-order modeling framework, Time-Dependent Bases with Local CUR decomposition (TDB-L-CUR), designed to efficiently and accurately approximate the species transport equations in reacting flow simulations. The method extends the existing TDB-CUR approach for chemically reacting flows (Jung et al. Comput. Methods Appl. Mech. Engrg. 437 (2025) 117758), which leverages matrix decomposition techniques to form a global-in-space, time-dependent low-dimensional manifold. While TDB-CUR performs well in homogeneous systems, it may be less well-suited to spatially heterogeneous systems such as turbulent flames, where higher-rank approximations are typically required. The proposed TDB-L-CUR framework introduces two methodological extensions to the baseline approach. First, it applies unsupervised clustering to partition the physical domain into distinct regions, enabling spatially localized manifold construction, thereby reducing the rank required for the reduced-order representation. Second, it incorporates a computational singular perturbation (CSP)-based scheme for identifying and penalizing fast species, allowing for spatio-temporally adaptive mitigation of chemical stiffness. The proposed framework is validated on a hierarchy of test cases, including a one-dimensional premixed flame, a two-dimensional nonpremixed ignition case with vortex interaction, and a three-dimensional turbulent premixed flame. TDB-L-CUR significantly improves accuracy over TDB-CUR while further reducing computational cost. The fully on-the-fly formulation of TDB-L-CUR (i.e., requiring no offline training or prior knowledge) makes it a robust and scalable tool for reduced-order modeling of reactive flows.

Local manifold↗
DOE OSTI2020

MULTI-DIMENSIONAL MODELING OF THE CFR ENGINE FOR THE INVESTIGATION OF SI NATURAL GAS COMBUSTION AND CONTROLLED END-GAS AUTOIGNITION

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emission limits and the wide range of chemical reactivity in pipeline quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its methane number (analogous to the octane number for liquid SI fuels) can span from 65 - 95. In order to realize diesel-like efficiencies, SI natural gas engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding the combustion-engine interactions pertaining to flame propagation and end-gas autoignition (EGAI). However, EGAI, if controlled, provides an opportunity to increase SI natural gas engine efficiency by increasing combustion rate and the total burned fuel, mitigating the effects of the slow flame speeds of natural gas fuels which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, in order to study EGAI phenomenon, the present work highlights multi-dimensional computational fluid dynamics (CFD) models of the Cooperative Fuel Research (CFR) engine. The CFR engine models are used to investigate fuel-engine interactions that lead to EGAI with natural gas, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). A Three-Pressure Analysis, performed with GT-Power, was used to estimate initial and boundary conditions for the three-dimensional CFD model. CONVERGE CFD v2.4 was used for the three-dimensional CFD modeling where the level set G-Equation model and SAGE detailed chemical kinetics solver were used. An assessment of the different modeling approaches is also provided to evaluate their limitations, advantages and disadvantages, and for which situations they are most applicable. Model validation was performed with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction, and IMEP and shows good agreement in Peak Cylinder Pressure (PCP), PCP crank angle, and the location of the 10%, 50%, and 90% mass fraction burned (CA10, CA50, and CA90, respectively). The models can predict the onset crank angle and pressure rise rate for light, medium, and heavy EGAI under a variety of fuel reactivities and engine operating conditions.

Bestel, Diego↗
DOE OSTI2021

Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

We report surrogate mixtures are routinely used for understanding gasoline fuel combustion in engine simulations. The general trend in surrogate formulation has been to increase the number of fuel components in a mixture to better emulate real fuel properties. Recently, a new surrogate design strategy based on functional group analysis of real gasolines was proposed using a minimal number of species [minimalist functional group (MFG)-approach]. MFG surrogates (having just one or two components) could experimentally capture the ignition delay time (IDT), threshold sooting index, and smoke point of different gasoline fuels with hundreds of components. However, other combustion characteristics were not explored, and kinetic modeling of MFG surrogates was not reported. These aspects are addressed in this paper, where the combustion behavior of MFG surrogates for various gasolines was assessed by simulating IDT, jet-stirred reactor oxidation, and premixed laminar flame speeds using chemical kinetic modeling. MFG simulations were compared with experimental data of the real gasolines as well as with the more complex multicomponent (five to nine species) surrogates. This study reveals that binary MFG surrogate mixtures are capable of accurately simulating the combustion behavior of more complex gasoline fuels with hundreds of components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Investigating the Effects of Chemical Mechanism on Soot Formation Under High-Pressure Fuel Pyrolysis

We performed Computational Fluid Dynamics (CFD) simulations using a Reynolds-Averaged Navier-Stokes (RANS) turbulence model of high-pressure spray pyrolysis with a detailed chemical kinetic mechanism encompassing pyrolysis of n-dodecane and formation of polycyclic aromatic hydrocarbons. We compare the results using the detailed mechanism and those found using several different reduced chemical mechanisms to experiments carried out in an optically accessible, high-pressure, constant-volume combustion chamber. Three different soot models implemented in the CONVERGE CFD software are used: an empirical soot model, a method of moments, and a discrete sectional method. There is a large variation in the prediction of the soot between different combinations of chemical mechanisms and soot model. Furthermore, the amount of soot produced from all models is substantially less than experimental measurements. All of this indicates that there is still substantial work that needs to be done to arrive at simulations that can be relied on to accurately predict soot formation.

02 PETROLEUM↗
DOE OSTI2020

Autoignition behavior of gasoline/ethanol blends at engine-relevant conditions

Ethanol is an attractive oxygenate increasingly used for blending with petroleum-derived gasoline yielding beneficial combustion and emissions behavior for a range of internal combustion engine schemes, including stoichiometric spark-ignition and low temperature combustion (LTC). As such, it is important to fundamentally understand the autoignition behavior of gasoline/ethanol blends. This work utilizes a rapid compression machine (RCM) and a homogeneous charge compression ignition (HCCI) engine to experimentally quantify changes in fuel reactivity, through ignition delay times and preliminary heat release, for blends of 0 to 30% vol./vol. into a full boiling range research gasoline (FACE-F). Diluted/stoichiometric and undiluted/fuel-lean conditions are explored covering a wide range of compressed temperatures and pressures relevant to conventional and advanced, gasoline combustion engines. Detailed chemical kinetic modeling is undertaken using a recently updated gasoline surrogate model in conjunction with a five-component surrogate to model the RCM experiments and provide chemical insight into the perturbative effects of ethanol on the autoignition process. The diluted/stoichiometric RCM measurements reveal that within the low-temperature regime ethanol retards first-stage and main ignition delay times, and suppresses both the rates and extents of low-temperature heat release (LTHR), while within the intermediate-temperature regime ethanol only causes slight changes. Good agreement of ignition delay time and preliminary heat release prediction is found between model and experimental results. Sensitivity and flux analyses further show that ethanol blending effects are dominated by the competition between the H-atom abstraction from ethanol and other fuel components by OH radical at low temperatures and by HO 2 radical at intermediate temperatures. These findings are consistent across both fuel loading conditions explored in this study. In addition, when HCCI engine experiments are mapped onto undiluted/lean RCM measurements under a constant combustion phasing scenario, good correspondence between the two apparatuses is observed for LTHR and start of high-temperature heat release. Finally, the current study highlights the importance of characterizing LTHR in predicting fuel behaviors in high-boost/low-temperature engines, and demonstrates that RCM experiments can provide an alternative, and more-efficient avenue for such characterization.

02 PETROLEUM↗
DOE OSTI2022

Thermal radiation analysis of a pressurized oxy-coal combustor with discrete ordinate model

Concerns over climate change have led to numerous efforts in developing low-carbon energy technologies. Pressurized oxy-combustion (POC) is a promising candidate to reduce carbon emissions in power generation. Pressure strongly impacts thermal radiation, leading to a substantial difference in heat transfer between POC and conventional atmospheric pressure combustion. A good understanding of thermal radiation in POC is needed to aid new combustor development. The present work is a step in this direction, initiating a systematic analysis of thermal radiation and heat transfer in a pilot-scale POC combustor, which has been developed at Washington University in St. Louis (WUSTL). In a POC process, pulverized coal is burned under elevated pressure and O2-CO2 environment. Few researchers have investigated the thermal radiation of pressurized oxy-combustion. This work adopted the discrete ordinate model to simulate the radiation of a 1-D cylinder with predetermined conditions (temperature, pressure, gas compositions) to predict the influence of radiative properties of flue gas on the radiation in the combustor.

Li, Lei↗
DOE OSTI2021

Improved Chemical Kinetics and Algorithms for More Accurate, Faster Simulations

Internal combustion engine design is increasingly driven by computational models used to predict change in performance due to change of design. The design process previously depended on limited intuition and expensive and time-consuming physical testing. Improved model capabilities shorten design cycles and enable the production of cleaner and more efficient engines. This project focuses on advancing the state of the art in internal combustion engine simulations. The overarching goal is to enable predictive models and reduced time to solution for simulations that impact combustion engine design.

02 PETROLEUM↗
DOE OSTI2020

Improved Chemical Kinetics and Algorithms for More Accurate, Faster Simulations

Internal combustion engine design is increasingly driven by computational models used to predict change in performance as a result of design changes, which previously would have been made by limited intuition or expensive and time-consuming physical testing. Improved model capabilities shorten design cycles and enable the production of cleaner and more efficient engines. This project focuses on advancing the state-of-the-art in internal combustion engine simulations. The overarching goal is to enable predictive models and reduced time to solution for simulations that impact combustion engine design.

02 PETROLEUM↗
DOE OSTI2024

Numerical investigation of equivalence ratio effects on Flex-Fuel mixing controlled combustion enabled by prechamber ignition

There is interest in adapting renewable and low-carbon intensity fuels to heavy-duty engines to help displace criteria and greenhouse gas emissions associated with diesel combustion. Low-carbon fuels have inherently low cetane numbers and thus pose a significant challenge when considering direct substitution in diesel combustion systems. Use of an actively fueled prechamber as an ignition source to initiate mixing-controlled combustion (PC-MCC) of low-cetane fuels is becoming an attractive combustion mode to alleviate the identified reactivity deficit. This work focuses on the utilization of an active prechamber to facilitate diesel-like combustion of bioethanol-gasoline fuel blends in a heavy-duty engine. Recent results in this emerging technology suggest that ignition quality is uniquely coupled to prechamber equivalence ratio. In this numerical study, the fundamental implications of prechamber equivalence ratio on the ignition performance of direct injected fuels ranging from E10 to E100 are investigated using CONVERGE. Parametric studies of the prechamber operating strategy were assessed at diesel-like conditions to characterize the performance trends relative to a diesel baseline at the same boundary conditions. Simulation results indicate that PC-MCC is flex-fuel capable and achieves diesel-like ignition qualities and combustion processes for all fuels considered under stoichiometric and rich prechamber conditions. To characterize the equivalence ratio trends observed, a novel inflow boundary modeling technique was utilized to prescribe turbulent jets in place of the prechamber where selective speciation of the jet composition was conducted to isolate the ignition contributions of radical and reacting species in lean, stoichiometric, and rich jets. The inflow boundary modeling suggests that excess fuel and combustion intermediates present in jets produced from stratified and fuel-rich prechamber operation promotes higher jet temperatures and as such are superior ignition sources. Relative to a lean prechamber jet, the peak temperature of a reacting jet from a rich prechamber was up to 600 K hotter at fixed distances from the orifice exit. Furthermore, radicals also demonstrated an influence on the ignition process, but the combustion mode was identified to be thermally dominant.

33 ADVANCED PROPULSION SYSTEMS↗