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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of a high temperature pyrolysis mechanism for cyclopentanone, a potential biofuel derived from biomass

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. The Department of Energy has identified CPO as a potential biofuel candidate because of its unique chemical properties. Understanding CPO decomposition is crucial to building a high-temperature combustion model. In this study, we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time histories of carbon monoxide (CO), ethylene (C2H4), and CPO absorbances over the temperature range of 1156-1416 K and 8.53-10.06 atm were measured. A corresponding kinetic model was generated using the Reaction Mechanism Generator (RMG) with the parameters of dominant pathways from either previous studies or quantum calculation within current work. The final model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the C2H4/CO ratio was used as an essential factor to validate the model and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed that H radicals play major roles in both primary reactions and product pyrolysis. The insights from this work can be used to generate a better CPO combustion model and help evaluate CPO as a biofuel. Such understanding can also be helpful when proposing pyrolysis or combustion models for other biofuels.

cyclopentanone, reaction mechanism, pyrolysis, sho↗
DOE OSTI2021

A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling

Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant’s NO x emission rates is performed, directly comparing each modeling method’s performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO x emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. Finally, these efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method’s performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.

42 ENGINEERING↗
DOE OSTI2020

Mesh-Sequenced Realizations for Evaluation of Subgrid-Scale Models for Turbulent Combustion

This paper develops a new approach for analysis of subgrid closures for turbulent combustion as modeled using direct quadrature (finite-rate chemistry) techniques. The approach, termed multiresolution analysis through mesh-sequenced realizations (MRA-MSR), conducts simultaneous, constrained large-eddy simulations on a set of hierarchically coarsened meshes. Furthermore, the availability of underlying fine-mesh (subgrid) data corresponding to coarse-mesh locations allows a clearer assessment of the effects of unresolved fluctuations on apparent reactivity. A key to MRA-MSR is the correlation of eddy structures at coarser mesh levels, which is facilitated by the transfer of filtered fine-mesh velocity information. A seven-mesh MRA-MSR hierarchy using three resolution levels is applied to one of the Sydney bluff-body stabilized methane–hydrogen flames. Analysis of the simultaneously evolved data at different resolution levels reveals several interesting trends. First, at high Damköhler numbers, there is clear evidence of attenuation of apparent reactivity due to the effects of unresolved fluctuations. Secondly, single-point, single-time filtered density functions of a normalized subgrid Damköhler number show a characteristic beta probability density function (PDF) form and display evidence of scale similarity. Interrogation of the MRA-MSR database also shows that the recently-proposed least-squares minimization (LSM) turbulence–chemistry interaction model can account for the observed diminishment in reactivity at high Damköhler numbers but cannot reduce scatter significantly. A new form of the LSM model, which makes use of the normalized subgrid Damköhler number beta PDF distribution, performs slightly better than the original model, illustrating the potential of MRA-MSR both in assessing existing closure concepts and in developing new ones.

42 ENGINEERING↗
DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗
DOE OSTI2020

Chapter 14: Machine Learning of Combustion LES Models from Reacting Direct Numerical Simulation

In this chapter we demonstrate how supervised deep learning techniques can be used to construct models for the filtered progress variable source term necessary for large eddy simulation (LES). The source data for the model is a direct numerical simulation (DNS) of a reacting flow in a low swirl burner configuration. Filtered quantities taken from the DNS data are used to train a deep neural network (DNN)-based model. An efficient data sampling strategy was devised to ensure that a uniform representation of all the states observed in the filtered DNS data are equally present in the training dataset. A-priori testing of the DNN-based model highlights the representative power of DNN to accurately reproduce the filtered reaction progress variable source term over a range of scales and various flame regimes as seen in an industrial burner.

combustion LES models↗
DOE OSTI2021

Multidimensional Numerical Modeling of Combustion Dynamics in a Non-Premixed Rotating Detonation Engine With Adaptive Mesh Refinement

In the present work, a novel computational fluid dynamics (CFD) methodology was developed to simulate full-scale non-premixed rotating detonation engines (RDEs). A unique feature of the modeling approach was the incorporation of adaptive mesh refinement (AMR) to achieve a good trade-off between model accuracy and computational expense. Here, unsteady Reynolds-averaged Navier–Stokes (RANS) simulations were performed for an Air Force Research Laboratory (AFRL) non-premixed RDE configuration with hydrogen as fuel and air as the oxidizer. The finite-rate chemistry model, along with a ten-species detailed kinetic mechanism, was employed to describe the H 2 -Air combustion chemistry. Three distinct operating conditions were simulated, corresponding to the same global equivalence ratio of unity but different fuel/air mass flowrates. For all conditions, the capability of the model to capture essential detonation wave dynamics was assessed. An exhaustive verification and validation study was performed against experimental data in terms of a number of waves, wave frequency, wave height, reactant fill height, oblique shock angle, axial pressure distribution in the channel, and fuel/air plenum pressure. The CFD model was demonstrated to accurately predict the sensitivity of these wave characteristics to the operating conditions, both qualitatively and quantitatively. A comprehensive heat release analysis was also conducted to quantify detonative versus deflagrative burning for the three simulated cases. The present CFD model offers a potential capability to perform rapid design space exploration and/or performance optimization studies for realistic full-scale RDE configurations.

42 ENGINEERING↗
DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗
DOE OSTI2020

Revealing the critical role of radical-involved pathways in high temperature cyclopentanone pyrolysis

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. Understanding CPO decomposition is crucial for building a high-temperature combustion model. Here we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time- histories of carbon monoxide (CO), ethylene (C 2 H 4 ), and CPO absorbances over the temperature range of 1156-1416 K and pressure range of 8.53-10.06 atm were measured during current experiments. A corresponding detailed kinetic model was generated using the Reaction Mechanism Generator (RMG) with dominant unimolecular/radical-involved decomposition pathways from either previous studies or quantum calculations within the current work. The obtained model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the absorbance ratio between C 2 H 4 and CO was used as an important factor to validate models and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed H radicals play a major role in the decomposition, and the whole decomposition process could be divided into three stages based on the H radical concentration. Finally, the insights from present work can be used to generate a better CPO combustion model and help evaluate CPO as an advanced biofuel.

09 BIOMASS FUELS↗
DOE OSTI2024

PeleMP: The Multiphysics Solver for the Combustion Pele Adaptive Mesh Refinement Code Suite

Combustion encompasses multiscale, multiphase reacting flow physics spanning a wide range of scales from the molecular scales, where chemical reactions occur, to the device scales, where the turbulent flow is affected by the geometry of the combustor. This scale disparity and the limited measurement capabilities from experiments make modeling combustion a significant challenge. Recent advancements in high-performance computing (HPC), particularly with the Department of Energy's Exascale Computing Project (ECP), have enabled high-fidelity simulations of practical applications to be performed. The major physics submodels, including chemical reactions, turbulence, sprays, soot, and thermal radiation, exhibit distinctive computational characteristics that need to be examined separately to ensure efficient utilization of computational resources. This paper presents the multiphysics solver for the Pele code suite, called PeleMP, which consists of models for spray, soot, and thermal radiation. Here, the mathematical and algorithmic aspects of the model implementations are described in detail as well as the verification process. The computational performance of these models is benchmarked on multiple supercomputers, including Frontier, an exascale machine. Results are presented from production simulations of a turbulent sooting ethylene flame and a bluff-body swirl stabilized spray flame with sustainable aviation fuels to demonstrate the capability of the Pele codes for modeling practical combustion problems with multiphysics. This work is an important step toward the exascale computing era for high-fidelity combustion simulations providing physical insights and data for predictive modeling of real-world devices.

42 ENGINEERING↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Grid resolution requirement of chemical explosive mode analysis for large eddy simulations of premixed turbulent combustion

Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article Abstract The grid resolution requirement for trustworthy Chemical Explosive Mode Analysis (CEMA) in Large Eddy Simulation (LES) of premixed turbulent combustion is proposed. Explicit filtering, to emulate the effect of the LES filter, is applied to one-dimensional laminar flame and three-dimensional planar turbulent flames across a wide range of Karlovitz numbers (5 - 239). The identification of the flame front by CEMA is found relatively insensitive to the cell size (Δ), while the combustion mode identification shows more significant sensitivity. Specifically, increasing Δ falsely enhances the auto-ignition and local extinction modes and suppresses the diffusion-assisted mode. Limited dependence of the CEMA performance on the turbulent combustion regime (Karlovitz number) is observed. A simple grid size criterion for reliable CEMA mode identification in LES is proposed as Δ ≲ δ L /2; The criterion can be relaxed to Δ ≲ δ L in the laminar flame limit. Furthermore, theoretical analysis is conducted on an idealised chemistry-diffusion system. The effects of the filtering process and turbulence on the local combustion mode are demonstrated, which is consistent with the numerical observations. Further, by incorporating turbulent combustion models in CEMA, potential improvement in identifying local combustion modes can be expected.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An indirect approach to optimize the reaction rates of thermal NO formation for diesel engines

With stringent emission regulations, it has become more important for modern diesel engine manufacturers to accurately predict engine-out nitrogen oxide (NO x ) emissions across a wide range of operating conditions. Thermal NO is the major source of engine-out NO x in modern diesel engines. For thermal NO formation, several earlier studies have recommended the forward and reverse reaction rate coefficients of the rate-limiting reaction (O + N 2 ⇌ NO + N). However, due to deficiencies in sub-models and inadequacies of reduced chemical mechanisms to represent diesel combustion, these recommended values more often than not need to be adjusted in reduced order combustion models to accurately predict engine-out NO x . Hence, in this work a systematic and computationally efficient approach has been proposed to streamline the process of determining the optimum reaction rate coefficients. Here, to develop the optimization approach, four different production diesel engines with different operating conditions in terms of speed, load, and exhaust-gas recirculation have been considered. Numerical simulations have been performed using a detailed zero-dimensional velocity-composition-frequency transported probability density function (0D-VCF-tPDF) model that uses hundreds of notional particles to capture in-cylinder stratification. Four different combinations of hydrocarbon and NO x chemical mechanisms were used to represent chemistry. It was found that for the rate-limiting reaction, the pre-exponent factors (A f1 , A r1 ) and activation energies (E A,f1 , E A,r1 ) of the forward and reverse reaction rates follow a linear band in A f1 - E A,f1 and A r1 - E A,r1 space where predicted engine-out NO x match the measured values closely. By encompassing such bands from different engines and considering constraints on activation energies, a reduced search domain of pre-exponent factors and activation energies was constructed that is expected to be applicable to any diesel engine. Eventually, computationally efficient three-line and one-line search approaches were proposed to determine the optimum values of the pre-exponent factors and activation energies that led to a minimum error between measured and predicted engine-out NO x . Finally, these three-line and one-line NO x optimization approaches were applied to a fifth production diesel engine for which the 0D-VCF-tPDF model showed a very good predictive performance in terms of predicting peak pressure, 50% burn rate, and engine-out NO x when compared to measured and 3D-CFD values.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Real Fuel Modeling for Gasoline Compression Ignition Engine

Increasing regulatory demand for efficiency has led to development of novel combustion modes such as HCCI, GCI and RCCI for gasoline light duty engines. In order to realize HCCI as a compression ignition combustion mode system, in-cylinder compression temperatures must be elevated to reach the autoignition point of the premixed fuel/air mixture. This should be co-optimized with appropriate fuel formulations that can autoignite at such temperatures. CFD combustion modeling is used to model the auto ignition of gasoline fuel under compression ignition conditions. Using the fully detailed fuel mechanism consisting of thousands of components in the CFD simulations is computationally expensive. To overcome this challenge, the real fuel is represented by few major components of create a surrogate fuel mechanism. In this study, 9 variations of gasoline fuel sets were chosen as candidates to run in HCCI combustion mode. A study detailing the development of the gasoline real fuel model was performed and various surrogates for gasoline fuel were investigated. The gasoline real fuel model will be used in subsequent CFD modelling activities for the development of an advanced mixed mode combustion system as part of the Department of Energy funded project DE-EE0008478.

Gasoline Compression Ignition, real fuel modeling,↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2023

Model predictive control of mixing controlled compression ignition operation for low reactivity fuels

Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. Here, in order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.

42 ENGINEERING↗
DOE OSTI2026

Flame Chemistry Workshop: a perspective on challenges and strategic actions in combustion experiments and chemical kinetics modeling

Continued progress in the development of predictive models for combustion chemistry—including ignition and flame behavior, species evolution, and combustion system performance—relies on overcoming enduring and emerging challenges in experimental measurements, theoretical formulations, and chemical kinetics mechanism construction. As combustion science continues to coincide with advances in sustainable fuels development, plasma technologies, and automated modeling capabilities, the need for coordinated, community-driven strategies is essential. The Flame Chemistry Workshop (FCWS), held biennially before the International Symposium on Combustion, serves as a dedicated platform to identify, consolidate, and address these challenges in a structured and collaborative manner. This perspective arises from discussions at the 7th FCWS in Milan, Italy (2024), and presents a collective view of the critical barriers currently limiting progress. Across the five technical domains discussed during the 7th FCWS – sustainable fuels combustion, advanced diagnostics for combustion measurements, experiments and modeling in plasma combustion, artificial intelligence and automated methods for theory and mechanisms generation, and chemical kinetic models—a series of persistent and emerging scientific challenges were identified, highlighting the need for deeper integration between three areas: theory, experiments, and modeling. In conclusion, the present article concisely describes present challenges that were identified in each of the technical domains in an effort to streamline and coordinate solutions to accelerate progress in combustion science.

Chemical kinetics↗
DOE OSTI2023

Commercialization of a Comprehensive Spark-Ignition Model for Automotive Engine Applications in CONVERGE CFD

The work completed under this CRADA commercializes an advanced hybrid Lagrangian-Eulerian spark-ignition (LESI) model for multi-dimensional engine simulations. In this report, LESI is coupled with combustion models (WSR, TFM, G-equation) to model spark channel elongation and kernel and flame development in a constant volume combustion vessel, and a four-valve direct-injection spark-ignition (4VDISI) engine in two operating conditions: stoichiometric E30 with no dilution, and stoichiometric PACE-20 with dilution using CONVERGE CFD solver.

42 ENGINEERING↗
DOE OSTI2024

Skeletal reaction models for methane combustion

A local-sensitivity-analysis technique is employed to generate new skeletal reaction models for methane combustion from the foundational fuel chemistry model (FFCM-1). Here, the sensitivities of the thermo-chemical variables with respect to the reaction rates are computed via the forced-optimally time dependent (f-OTD) methodology. In this methodology, the large sensitivity matrix containing all local sensitivities is modeled as a product of two low-rank time-dependent matrices. The evolution equations of these matrices are derived from the governing equations of the system. The modeled sensitivities are computed for the auto-ignition of methane at atmospheric and high pressures with different sets of initial temperatures, and equivalence ratios. These sensitivities are then analyzed to rank the most important (sensitive) species. A series of skeletal models with different number of species and levels of accuracy in reproducing the FFCM-1 results are suggested. The performances of the generated models are compared against FFCM-1 in predicting the ignition delay, the laminar flame speed, and the flame extinction. The results of this comparative assessment suggest the skeletal models with 24 and more species generate the FFCM-1 results with an excellent accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗