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An extended hybrid chemistry framework for complex hydrocarbon fuels

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Abstract

An extended hybrid chemistry approach for complex hydrocarbons is developed to capture high-temperature fuel chemistry beyond the pyrolysis stage. The model may be constructed based on time-resolved measurements of oxidation species beyond the pyrolysis stage. The species’ temporal profiles are reconstructed through an artificial neural network (ANN) regression to directly extract their chemical reaction rate information. The ANN regression is combined with a foundational C 0 -C 2 chemical mechanism to model high-temperature fuel oxidation. This new approach is demonstrated for published experimental data sets of 3 fuels: n-heptane, n-dodecane and n-hexadecane. Further, a perturbed numerical data set for n-dodecane, generated using a detailed mechanism, is used to validate this approach with homogeneous chemistry calculations. The results demonstrate the performance and feasibility of the proposed approach.

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... In recent years, neural networks have shown amazing performance in high-dimensional tasks such as image classification 17 and natural language processing. 18 Therefore, it feels natural to use neural networks as a black box to represent kinetic models 19 . However, the lack of physical interpretability, and thus generalizability, limit the usage of such models. ...
... which is mathematically equivalent to the ground truth reaction mechanism. We further demonstrate the necessity of embedding the physical constraint with the technique shown in eqn (19) with a hypothetical reversible reaction only considering the left half of ...
... The results of CRNN with and without considering eqn (19) are compared in Fig. 7(b). With the physics embedded, the CRNN is able to reconstruct the reaction mechanism accurately while otherwise cannot. ...
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Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification.
... The determination of reaction pathways and the associated rate constants is time-consuming and often requires expert knowledge. The recent revolution of deep learning [1] has attracted a growing interest in automatically building kinetic models from experimental data with deep neural network (DNN) [2,3]. As initial attempts [2,3], Ranade et al. proposed to utilize the temporal evolution profiles of chemical species during the pyrolysis and oxidation processes in homogenous reactors as the targets. ...
... The recent revolution of deep learning [1] has attracted a growing interest in automatically building kinetic models from experimental data with deep neural network (DNN) [2,3]. As initial attempts [2,3], Ranade et al. proposed to utilize the temporal evolution profiles of chemical species during the pyrolysis and oxidation processes in homogenous reactors as the targets. In computations, the evolution of species profiles can be described by a system of ordinary differential equations (ODEs) ...
... DNN models have been utilized to predict the evolution of the species profiles by predicting either S( ) in the differential form as in Eq. (1) [2][3][4] or in the residual form as in Eq. (2) [5][6][7][8][9][10][11]. In the differential approach, the evolution of composition states is computed using ODE solvers, while the residual approach only involves the forward pass of DNN models via algebraic operations. ...
Preprint
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... Recently, we have proposed a similar approach to HyChem where the temporal profiles of measured species can be used directly to determine their chemical reaction rates [17,18]. In Ref. [18], we proposed that soon after the pyrolysis stage, the foundational chemistry can be further simplified to account primarily for C0-C2 species. ...
... Recently, we have proposed a similar approach to HyChem where the temporal profiles of measured species can be used directly to determine their chemical reaction rates [17,18]. In Ref. [18], we proposed that soon after the pyrolysis stage, the foundational chemistry can be further simplified to account primarily for C0-C2 species. The hierarchical development during complex fuel oxidation from complex to simpler hydrocarbons has been a key observation on which the HyChem approach and our approach have been based. ...
... The hybrid chemistry models proposed in [17,18] are inspired by the HyChem approach [10]- [17], with some key distinctions. Both models combine a reaction rate models for the fuel fragments with foundational chemistry for the remaining species. ...
Article
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... Depending on the applications, many different neural network architectures have been developed, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Network (GNN). Some of them have also been employed for datadriven physics modeling [1][2][3][4][5][6][7][8], including turbulent flow modeling [9] and chemical kinetic modeling [10]. Those different neural network architectures introduce specific regularization to the neural network based on the nature of the task such as the scale and rotation invariant of the convolutional kernel in CNN. ...
... For example, the maximum concentration of 2 is higher than 16 by 16 orders of magnitude. We then applied QSSA to ten intermediate species of [3,5,6,10,11,13,14,16,19,20], i.e., assuming that the net production rates of these species are zero: In the ten equations above, [ 3 , 5 , 6 , 10 , 11 , 13 , 14 , 16 , 19 , 20 ] are unknown quantities that need to be solved, and we want to express them as functions of non-QSS species via algebraic equations. It is worth noting that it is not appropriate to apply QSSA on species 18 although it is also a species with rapid concentration changes. ...
Preprint
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... Owoyele and Echekki (2017) built a reaction rate model using a PCA-ANN framework for modeling premixed turbulent flames in a 2D DNS. Ranade et al. (2019aRanade et al. ( , 2019b developed an ANN-based framework to construct reduced chemical mechanisms for complex fuels. In this work, a shallow ANN was used to extract fuel chemistry from "noisy" shock tube measurements, The use of data-driven approaches in physics-based simulations is increasing at a fast pace, with ML playing a central role. ...
... In future, it would be interesting to assess the benefits of using shallow ANNs in denoising such data sets. In a different work (Ranade et al., 2019a(Ranade et al., , 2019b) the authors demonstrated the use 238 of shallow ANNs to build smooth fits amidst highly fluctuating experimental data. The same feature may be implemented in this context to reduce uncertainty in instantaneous turbulent flame measurements. ...
... decomposition of forest fuels or pyrolysis of empty fruit bunch, the fitting problems were solved by genetic algorithms [22,23], Levenberg-Marquardt optimization [24] and model-free methods [25,26]. Recent studies [27,28] have demonstrated the promise of artificial neural networks for estimation of the kinetic parameters and discovering of reaction pathways from the timeresolved species concentration data. In passing it should be mentioned that, this novel approach also find an application in solution to stiff ODEs [29] typical for chemical kinetic problems. ...
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... Measurements of pyrolysis products are commonly used to constrain the kinetic parameters in the lumped pyrolysis model [459]. Instead of hypothesizing a particular pyrolysis model, Ranade et al. [461,462] introduced a two-step regression approach to describe the pyrolysis chemistry from measured data; shallow neural networks with limited expressiveness were employed to determine the reaction rates of measured species by fitting concentration profiles to experimental data. A feedforward neural network was then employed to relate the nonlinear reaction rates to concentrations of measured species during the pyrolysis stage. ...
Article
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... Further, the computational burden associated with the stiff chemical kinetic source term integration in NaviereStokes equations may be largely reduced by either ANN-based HyChem kinetics [122] or direct DL on species profiles [123], resulting in typically two orders of magnitude speedup with respect to stiff chemical kinetics solver. An alternate approach training a CNN on CFD flow field data has been demonstrated recently, albeit on a single fuel [124]. ...
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Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines summarizes recent developments in Artificial Intelligence (AI)/Machine Learning (ML) and data driven optimization and calibration techniques for internal combustion engines. The book covers AI/ML and data driven methods to optimize fuel formulations and engine combustion systems, predict cycle to cycle variations, and optimize after-treatment systems and experimental engine calibration. It contains all the details of the latest optimization techniques along with their application to ICE, making it ideal for automotive engineers, mechanical engineers, OEMs and R&D centers involved in engine design.
... Further, the computational burden associated with the stiff chemical kinetic source term integration in NaviereStokes equations may be largely reduced by either ANN-based HyChem kinetics [122] or direct DL on species profiles [123], resulting in typically two orders of magnitude speedup with respect to stiff chemical kinetics solver. An alternate approach training a CNN on CFD flow field data has been demonstrated recently, albeit on a single fuel [124]. ...
... Further, the computational burden associated with the stiff chemical kinetic source term integration in NaviereStokes equations may be largely reduced by either ANN-based HyChem kinetics [122] or direct DL on species profiles [123], resulting in typically two orders of magnitude speedup with respect to stiff chemical kinetics solver. An alternate approach training a CNN on CFD flow field data has been demonstrated recently, albeit on a single fuel [124]. ...
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... The ANN-based hybrid chemistry showed a good agreement with the detailed chemistry. Subsequently, an extended hybrid chemical framework combining ANN in the pyrolysis process and C 0 -C 2 chemical mechanism for complex hydrocarbon fuels was developed by Ranade et al. [85], and their framework was validated using a perturbed numerical dataset. Alqahtani et al. [86] adopted the PCA dimension reduction approach to identify key species that could track the evolution of the chemical system and their reaction rates from simulations using detailed chemical mechanisms and used an ANN model to regress the reaction rates of those key species. ...
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... The lumped reaction steps are mainly determined via a sequential optimization using species profiles during the hightemperature pyrolysis. One can also use a neural network to represent the pyrolysis steps [7,8] and learn the neural network using species profiles. Compared to the species profiles that require expensive laser diagnostic equipment, ignition delay times are easier to measures in shock tubes and rapid compression machines. ...
Conference Paper
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The HyChem (Hybrid Chemistry) approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. Determining the pyrolysis submodel requires extensive experimentation on speciation measurements. Recent work has been directed to learn HyChem from an existing HyChem model for a similar fuel, which requires less data. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.
... Finally, artificial neural networks (ANN) are used to construct regressions for the unconditional means as functions of the Favre averaged PCs. As a multi-variate, non-linear regression method, ANN has found use in different applications in combustion [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. In the present study, separate networks are constructed for different output variables in terms of the retained PCs unconditional means. ...
Preprint
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... In [25], some chemically reasonable requirements were considered such as the mass conservation and the principle of detailed balance. The deep neural networks (DNNs) were applied to extract the chemical reaction rate information in [31,32], but the weights are difficult to interpret physically. In [13], the authors adapted the sparse identification of nonlinear dynamics (SINDy) method [4,9] to the present problem. ...
Article
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... Neural network models have been employed for combustion kinetic modeling for computation acceleration by approximating a given complex model with computationally cheap neural network models [5][6][7][8][9], and for learning combustion models from experimental data when there is no available model [10,11]. While black-box neural network models have shown success in fitting the experimental data, it is often desirable that the learned model can be interpretable such that the model can also elucidate the reaction pathways and thus provide chemical insights. ...
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... On the contrary, neural networks have shown promise in autonomously learning features of interactions among high-dimensional inputs 21 . Traditional neural networks can approximate unknown reaction pathways 22 , but the weights are difficult to interpret physically, i.e., interpreting the reaction pathways and rate constants from the neural network weights to be correlated to traditional chemical models, limiting the capability of model generalization. The booming of deep learning has greatly benefitted from designing problem-specific structures for neural network models. ...
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Chemical reactions occur in energy, environmental, biological, and many other natural systems, and the inference of the reaction networks is essential to understand and design the chemical processes in engineering and life sciences. Yet, revealing the reaction pathways for complex systems and processes is still challenging because of the lack of knowledge of the involved species and reactions. Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data. The proposed chemical reaction neural network (CRNN), by design, satisfies the fundamental physics laws, including the law of mass action and the Arrhenius law. Consequently, the CRNN is physically interpretable such that the reaction pathways can be interpreted, and the kinetic parameters can be quantified simultaneously from the weights of the neural network. The inference of the chemical pathways is accomplished by training the CRNN with species concentration data via stochastic gradient descent. We demonstrate the successful implementations and the robustness of the approach in elucidating the chemical reaction pathways of several chemical engineering and biochemical systems. The autonomous inference by the CRNN approach precludes the need for expert knowledge in proposing candidate networks and addresses the curse of dimensionality in complex systems. The physical interpretability also makes the CRNN capable of not only fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems.
... Alleviating the computational cost related to the inclusion of complex kinetics mechanisms in the CFD simulations is a key to make high-fidelity simulations of realistic combustion systems possible. Many strategies have been recently implemented to this purpose, involving the use of reduced kinetic mechanisms [2,3] or Neural Networks [4]. ...
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The large number of species included in the detailed kinetic mechanisms represents a seriouschallenge for numerical simulations of reactive flows, as it can lead to large CPU times, even forrelatively simple systems. One possible solution to mitigate the computational cost of detailed numericalsimulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive ReducedChemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemicalspace partitioning for the generation of locally reduced mechanisms, but this task is often challengingbecause of the high-dimensionality, as well as the high non-linearity associated to reacting systems.Moreover, the importance of this step in the overall approach is not negligible, as it has effects on themechanisms’ level of chemical reduction and, consequently, on the accuracy and the computationalspeed-up of the adaptive simulation. In thiswork, two different clustering algorithms for the partitioningof the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteadylaminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach basedon the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is theLocal Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction(i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtainedfor both the tested methods, but LPCA showed superior performances in terms of reduced mechanismsuniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lowersensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also beingoptimal, with respect to SKM, for massive dataset clustering tasks.
... On the contrary, neural networks have shown promise in autonomously learning the features of interactions among high-dimensional inputs. 21 Traditional neural networks can approximate unknown reaction pathways, 22 but the weights are difficult to interpret physically, that is, interpreting the reaction pathways and rate constants from the neural network weights to be correlated to traditional chemical models, limiting the capability of model generalization. The booming of deep learning has greatly benefited from designing problem-specific structures for neural network models. ...
Preprint
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The inference of chemical reaction networks is an important task in understanding the chemical processes in life sciences and environment. Yet, only a few reaction systems are well-understood due to a large number of important reaction pathways involved but still unknown. Revealing unknown reaction pathways is an important task for scientific discovery that takes decades and requires lots of expert knowledge. This work presents a neural network approach for discovering unknown reaction pathways from concentration time series data. The neural network denoted as Chemical Reaction Neural Network (CRNN), is designed to be equivalent to chemical reaction networks by following the fundamental physics laws of the Law of Mass Action and Arrhenius Law. The CRNN is physically interpretable, and its weights correspond to the reaction pathways and rate constants of the chemical reaction network. Then, inferencing the reaction pathways and the rate constants are accomplished by training the equivalent CRNN via stochastic gradient descent. The approach precludes the need for expert knowledge in proposing candidate reactions, such that the inference is autonomous and applicable to new systems for which there is no existing empirical knowledge to propose reaction pathways. The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems. Finally, the approach is applied to several chemical systems in chemical engineering and biochemistry to demonstrate its robustness and generality.
... These unconditional means are "tabulated" vs. the PCs' unconditional means using artificial neural networks (ANN). As a multi-variate, non-linear regression method, ANN has found use in different applications in combustion [31][32][33][34][35][36][37][38][39][40][41][42][43][44]. ...
Preprint
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In this work, we demonstrate a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construction of conditional means and joint scalar PDFs from experimental data based on the parameterization of composition space using principal component analysis (PCA). The resulting principal components (PCs) act as both conditioning variables and transport variables. Their chemical source terms are constructed starting from instantaneous temperature and species measurements using a variant of the pairwise mixing stirred reactor (PMSR) approach. A multi-dimensional kernel density estimation (KDE) approach is used to construct the joint PDFs in PC space. Convolutions of these joint PDFs with conditional means are used to determine the unconditional means for the closure terms: the mean PCs chemical source terms and the density. These means are parameterized in terms of the mean PCs using artificial neural networks (ANN). The framework is demonstrated a posteriori using the data from the Sandia piloted turbulent jet flames D, E and F by performing RANS calculations. The radial profiles of mean and RMS of temperature and measured species mass fractions agree well with the experimental means for these flames.
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Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the burning of solid fuels remains poorly understood, which can be partly attributed to the unknown chemical kinetics of most solid fuels. Most available kinetic models were built upon expert knowledge, which requires chemical insights and years of experience. This work presents a framework for autonomously discovering biomass pyrolysis kinetic models from thermogravimetric analyzer (TGA) experimental data using the recently developed chemical reaction neural networks (CRNN). The approach incorporated the CRNN model into the framework of neural ordinary differential equations to predict the residual mass in TGA data. In addition to the flexibility of neural-network-based models, the learned CRNN model is interpretable, by incorporating the fundamental physics laws, such as the law of mass action and Arrhenius law, into the neural network structure. The learned CRNN model can then be translated into the classical forms of biomass chemical kinetic models, which facilitates the extraction of chemical insights and the integration of the kinetic model into large-scale fire simulations. We demonstrated the effectiveness of the framework in predicting the pyrolysis and oxidation of cellulose. This successful demonstration opens the possibility of rapid and autonomous chemical kinetic modeling of solid fuels, such as wildfire fuels and industrial polymers.
Chapter
Fuel composition plays an important role both in efficiency and effectiveness of engines. Combined with the engine variables, fuel can span a wide range of composition space, which makes it demanding to find an optimal composition. Artificial intelligence (AI) algorithms are attracting significant interest for predicting complex phenomenon. In this chapter, a discussion is presented on exploiting the advantages presented by machine learning algorithms for fuel formulation. The present fuel modeling scenario and a holistic approach necessary for fuel optimization is first presented. A wealth of AI algorithms are available to make use of in fuel formulation. These algorithms are discussed in line with their application to fuel formulation and the literature of the explored space in this area is presented. Additionally, a discussion is presented on how AI also helps in assisting the traditional computational fluid dynamic and chemical kinetic analysis for an elaborate study of fuels. Fuel development is just a step in the entire engine innovation cycle, and a perspective of how the AI fits in to this scenario is presented.
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An experiment-based closure framework for turbulent combustion modeling is further validated using the Sydney piloted turbulent partially premixed flames with inhomogeneous inlets. The flames are characterized by the presence of mixed mode combustion. The framework’s closure is “trained” on multi-scalar measurements to construct thermo-chemical scalar statistics parameterized in terms of principal components (PCs). Three flame conditions are used for this training, while an additional flame is used for validation. The results show that the leading PCs exhibit complex features near the jet inlet where effects of partial premixing and the presence of different burning modes are strong. These features may not be captured through a strict definition for the mixture fraction or measures of reaction progress. Further downstream, the first 2 PCs tend to be reasonably correlated with parameters that are characteristic of nonpremixed flames, including the mixture fraction and the progress variable. Comparisons of the model predictions for unconditional mean and RMS for the measured quantities show a very good qualitative and quantitative agreement with experimental statistics for all 4 flames using the same closure for the PCs governing equations.
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Probability density function (PDF) based turbulent combustion modelling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various machine-learning techniques that represent the thermo-chemical quantities of a PDF table using mathematical functions. These functions can be computationally more expensive than the existing interpolation methods used for thermo-chemical quantities. More importantly, the training time can amount to a considerable portion of the simulation time. In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is designed to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm. SOM clustering divides the PDF table into several parts based on similarities in data. Each cluster of data is trained using an MLP algorithm on simple network architectures to generate local functions for thermo-chemical quantities. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations and the results of the PDF tabulation are compared to the standard linear interpolation method. The comparison yields a very good agreement between the two tabulation techniques and establishes the MLP-SOM approach as a viable method for PDF tabulation.
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Full-text available
Probability density function (PDF) based turbulent combustion modeling is limited by the need to store multi-dimensional PDF tables that can take up large amounts of memory. A significant saving in storage can be achieved by using various machine-learning techniques that represent the thermo-chemical quantities of a PDF table using mathematical functions. These functions can be computationally more expensive than the existing interpolation methods used for thermo-chemical quantities. More importantly, the training time can amount to a considerable portion of the simulation time. In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is designed to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm. SOM clustering divides the PDF table into several parts based on similarities in data. Each cluster of data is trained using an MLP algorithm on simple network architectures to generate ‘local’ functions for thermo-chemical quantities. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations and the results of the PDF tabulation are compared to the standard linear interpolation method. The comparison yields a very good agreement between the two tabulation techniques and establishes the MLP-SOM approach as a viable method for PDF tabulation.
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The development of renewable, alternative jet fuels presents an exigent challenge to the aviation community. In this work, a streamlined methodology for building computationally efficient kinetic models of real fuels from shock tube experiments is developed and applied to a low cetane-number, broad-boiling alternative jet fuel (termed C-4). A multi-wavelength laser absorption spectroscopy technique was used to determine species time-histories during the high-temperature pyrolysis of C-4, and a batch gradient descent optimization routine built a hybrid-chemistry (HyChem) kinetic model from the measured data. The model was evaluated using combustor-relevant, high-pressure ignition delay time measurements with satisfactory agreement. The present model enables predictive simulations of C-4 in practical environments, while the underlying methodology described here can be readily extended to build kinetic models for a broad range of real fuels of interest.
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In this work, we demonstrate a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construction of conditional means and joint scalar PDFs from experimental data based on the parameterization of the composition space using principal component analysis (PCA). The resulting principal components (PCs) act as both conditioning variables and transported variables. Their chemical source terms are constructed starting from instantaneous temperature and species measurements using a variant of the pairwise mixing stirred reactor (PMSR) approach. A multi-dimensional kernel density estimation (KDE) approach is used to construct the joint PDFs in PC space. Convolutions of these joint PDFs with conditional means are used to determine the unconditional means for the closure terms: the mean PCs chemical source terms and the density. These means are parameterized in terms of the mean PCs using artificial neural networks (ANN). The framework is demonstrated a posteriori using the data from the Sandia piloted turbulent jet flames D, E and F by performing RANS calculations. The radial profiles of mean and RMS of temperature and measured species mass fractions agree well with the experimental means for these flames.
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Real fuels may contain thousands of hydrocarbon components. This paper examines how nature simplifies the problem. We will discuss the internal structure of the fuel oxidation process at high temperatures. Over a wide range of conditions, large hydrocarbon molecules undergo thermal decomposition to form a small set of low-molecular weight fragments, and in the case of conventional petroleum-derived fuels, the composition variation in the decomposition products is reduced by the law of large numbers. From a joint consideration of elemental conservation, thermodynamics and chemical kinetics, it will be shown also that the composition of the thermal decomposition products is a weak function of the thermodynamic condition, the fuel-oxidizer composition and fuel composition within the range of temperatures of direct relevance to flames and high temperature ignition. Based on these findings, we explore a hybrid chemistry (HyChem) approach to modeling high-temperature oxidation of real fuels: the kinetics of thermal or oxidative thermal decomposition of the fuel is lumped using kinetic parameters derived from experiments, while the oxidation of the decomposed fragments is described by a detailed reaction model. Sample results will be given that supports this modeling approach.
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Several alternative techniques have been proposed in the literature in order to avoid the CPU-intensive numerical integration of the thermochemical equations in the simulation of combustion processes. The present paper introduces a new approach, which is based on two artificial neural-network (ANN) paradigms, namely the self-organizing map (SOM) and the multilayer perceptron (MLP). The SOM is first employed for the automatic partitioning of the thermochemical space into subdomains. Then, a specialized MLP is trained in order to fit the thermochemical points belonging to a given subdomain. The presented strategy is tested on a partially stirred reactor (PaSR) with a reduced methane-air mechanism, and encouraging results are reported. The relatively modest CPU-time and memory requirements of the method make the SOM-MLP approach a promising technique for the inclusion of large chemical mechanisms in the context of complex applications, such as the multidimensional simulation of combustion.
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Chemical kinetic modeling of high temperature hydrocarbon oxidation in combustion is reviewed. First, reaction mechanisms for specific fuels are discussed, with emphasis on the hierarchical structure of reaction mechanisms for complex fuels. The concept of a comprehensive mechanism is developed, requiring model validation by comparison with data from a wide range of experimental regimes. Fuels of increasing complexity from hydrogen to n-butane are described in detail, and further extensions of the general approach to other fuels are discussed.Kinetic modification to fuel oxidation kinetics is considered, including both inhibition and promotion of combustion. Simplified kinetic models are then described by comparing their features with those of detailed kinetic models. Finally, application of kinetic models to study real combustions systems are presented, beginning with purely kinetic-thermodynamic applications, in which transport effects such as diffusion of heat and mass can be neglected, such as shock tubes, detonations, plug flow reactors, and stirred reactors. Laminar flames and the coupling between diffusive transport and chemical kinetics are then described, together with applications of laminar flame models to practical combustion problems.
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The oxidation chemistry of complex hydrocarbons involves large mechanisms with hundreds or thousands of chemical species and reactions. For practical applications and computational ease, it is desirable to reduce their chemistry. To this end, high-temperature fuel oxidation for large carbon number fuels may be described as comprising two steps, fuel pyrolysis and small species oxidation. Such an approach has recently been adopted as ‘hybrid chemistry’ or HyChem to handle high-temperature chemistry of jet fuels by utilizing time-series measurements of pyrolysis products. In the approach proposed here, a shallow Artificial Neural Network (ANN) is used to fit temporal profiles of fuel fragments to directly extract chemical reaction rate information. This information is then correlated with the species concentrations to build an ANN-based model for the fragments’ chemistry during the pyrolysis stage. Finally, this model is combined with a C0-C4 chemical mechanism to model high-temperature fuel oxidation. This new hybrid chemistry approach is demonstrated using homogeneous chemistry calculations of n-dodecane (n-C12H26) oxidation. The experimental uncertainty is simulated by introducing realistic noise in the data. The comparison shows a good agreement between the proposed ANN hybrid chemistry approach and detailed chemistry results.
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Real distillate fuels usually contain thousands of hydrocarbon components. Over a wide range of combustion conditions, large hydrocarbon molecules undergo thermal decomposition to form a small set of low molecular weight fragments. In the case of conventional petroleum-derived fuels, the composition variation of the decomposition products is washed out due to the principle of large component number in real, multicomponent fuels. From a joint consideration of elemental conservation, thermodynamics and chemical kinetics, it is shown that the composition of the thermal decomposition products is a weak function of the thermodynamic condition, the fuel-oxidizer ratio and the fuel composition within the range of temperatures of relevance to flames and high temperature ignition. Based on these findings, we explore a hybrid chemistry (HyChem) approach to modeling the high-temperature oxidation of real, distillate fuels. In this approach, the kinetics of thermal and oxidative pyrolysis of the fuel is modeled using lumped kinetic parameters derived from experiments, while the oxidation of the pyrolysis fragments is described by a detailed reaction model. Sample model results are provided to support the HyChem approach.
Article
We propose and test an alternative approach to modeling high-temperature combustion chemistry of multicomponent real fuels. The hybrid chemistry (HyChem) approach decouples fuel pyrolysis from the oxidation of fuel pyrolysis products. The pyrolysis (or oxidative pyrolysis) process is modeled by seven lumped reaction steps in which the stoichiometric and reaction rate coefficients are derived from experiments. The oxidation process is described by detailed chemistry of foundational hydrocarbon fuels. We present results obtained for three conventional jet fuels and two rocket fuels as examples. Modeling results demonstrate that HyChem models are capable of predicting a wide range of combustion properties, including ignition delay times, laminar flame speeds, and non-premixed flame extinction strain rates of all five fuels. Sensitivity analysis shows that for conventional, petroleum-derived real fuels, the uncertainties in the experimental measurements of C2H4 and CH4 impact model predictions to an extent, but the largest influence of the model predictability stems from the uncertainties of the foundational fuel chemistry model used (USC Mech II). In addition, we introduce an approach in the realm of the HyChem approach to address the need to predict the negative-temperature coefficient (NTC) behaviors of jet fuels, in which the CH2O speciation history is proposed to be a viable NTC-activity marker for model development. Finally, the paper shows that the HyChem model can be reduced to about 30 species in size to enable turbulent combustion modeling of real fuels with a testable chemistry model.
Article
In this work, a methodology for the tabulation of combustion mechanisms via Artificial Neural Networks (ANNs) is presented. The objective of the methodology is to train the ANN using samples generated via an abstract problem, such that they span the composition space of a family of combustion problems. The abstract problem in this case is an ensemble of laminar flamelets with an artificial pilot in mixture fraction space to emulate ignition, of varying strain rate up to well into the extinction range. The composition space thus covered anticipates the regions visited in a typical simulation of a non-premixed flame. The ANN training consists of two-stage process: clustering of the composition space into subdomains using the Self-Organising Map (SOM) and regression within each subdomain via the multilayer Perceptron (MLP). The approach is then employed to tabulate a mechanism of CH4–air combustion, based on GRI 1.2 and reduced via Rate-Controlled Constrained Equilibrium (RCCE) and Computational Singular Perturbation (CSP). The mechanism is then applied to simulate the Sydney flame L, a turbulent non-premixed flame that features significant levels of local extinction and re-ignition. The flow field is resolved through Large Eddy Simulation (LES), while the transported probability density function (PDF) approach is employed for modelling the turbulence–chemistry interaction and solved numerically via the stochastic fields method. Results demonstrate reasonable agreement with experiments, indicating that the SOM-MLP approach provides a good representation of the composition space, while the great savings in CPU time allow for a simulation to be performed with a comprehensive combustion model, such as the LES-PDF, with modest CPU resources such as a workstation.
Conference Paper
The hybrid chemistry modeling approach, termed HyChem, was used to explore the combustion chemistry of blended petroleum and bio-derived jet fuels. The pyrolysis products of conventional petroleum derived-fuels, such as Jet A, are dominated by ethylene and propene, whereas in many bio-derived fuels, such as alcohol to jet (ATJ) fuels, the fuel comprises highly branched alkanes and produces isobutene as a main pyrolysis product. We report here an investigation of blends of Jet A (designated A2) and an ATJ fuel (designated C1) with the central question of whether the HyChem models for neat fuels can be combined to model the blend combustion behaviors. The pyrolysis and oxidation of several blends of A2 and C1 were investigated. Flow reactor experiments were carried out at 1 atm, 1030 and 1140K, with equivalence ratios of 1.0 and 2.0. Shock tube measurements of blended fuel pyrolysis were performed at 1 atm from 1025 to 1325 K. Good agreement between measurements and model predictions was found showing that formation of the products in the blended fuels were predicted by a simple combination of the HyChem models for the two individual fuels, thus demonstrating that the HyChem models for two jet fuels of very different compositions are “additive.”
Conference Paper
With increasing use of alternative fuels, approaches that can efficiently model their combustion chemistry are essential to facilitate their utilization. The hybrid chemistry (HyChem) method incorporates a basic understanding about the combustion chemistry of multicomponent liquid fuels that overcomes some of the limitations of the surrogate fuel approach. The present work focused on a comparative study of one conventional, petroleum-derived Jet A fuel (designated as A2), with an alternative, bio-derived fuel (designated as C1), using a variety of experimental techniques as well as HyChem modeling. While A2 is composed of a mixture of n-, iso-, and cyclo-alkanes, and aromatics, C1 is composed primarily of two highly branched C12 and C16 alkanes. Upon decomposition, A2 produces primarily ethylene and propene, while C1 produces mostly isobutene. HyChem models were developed for each fuel, using both shock tube and flow reactor speciation data. The developed models were tested against a wide range of experimental data, including shock tube ignition delay time and laminar flame speed. The stringent validations and agreement between the models and experimental measurements highlight the validity as well as potential wider applications of the HyChem concept in studying combustion chemistry of liquid hydrocarbon fuels.
Conference Paper
In this work we introduce an unconventional approach to modeling the high-temperature combustion chemistry of multicomponent real fuels. The hybrid chemistry (HyChem) approach decouples fuel pyrolysis from the oxidation of fuel decomposition intermediates. The thermal decomposition and oxidative thermal decomposition processes are modeled by seven lumped reaction steps in which the stoichiometric and reaction rate coefficients may be derived from experiments. The oxidation process is described by detailed chemistry of foundational hydrocarbon fuels. We present results obtained for three petroleum-derived fuels: JP-8, Jet A and JP-5 as examples. The experimental observations show only a small number of intermediates are formed during thermal decomposition under pyrolysis and oxidative conditions, and support the hypothesis that the stoichiometric coefficients in the lumped reaction steps are not a strong function of temperature, pressure, or fuel-oxidizer composition, as we discussed in a companion paper. Modeling results demonstrate that HyChem models are capable of predicting a wide range of combustion properties, including ignition delay times, laminar flame speeds, and non-premixed flame extinction strain rates of all three fuels.
Article
Species concentration time-histories were measured during oxidation for the large, normal-alkane, diesel-surrogate component n-hexadecane. Measurements were performed behind reflected shock waves in an aerosol shock tube, which allowed for high fuel loading without pre-test heating and possible decomposition and oxidation. Experiments were conducted using near-stoichiometric mixtures of n-hexadecane and 4% oxygen in argon at temperatures of 1165–1352 K and pressures near 2 atm. Concentration time-histories were recorded for five species: C2H4, CH4, OH, CO2, and H2O. Methane was monitored using DFG laser absorption near 3.4 μm; OH was monitored using UV laser absorption at 306.5 nm; C2H4 was monitored using a CO2 gas laser at 10.5 μm; and CO2 and H2O were monitored using tunable DFB diode laser absorption at 2.7 and 2.5 μm, respectively. These time-histories provide critically needed kinetic targets to test and refine large reaction mechanisms. Comparisons were made with the predictions of two diesel-surrogate reaction mechanisms (Westbrook et al. [1]; Ranzi et al. [9]) that include n-hexadecane, and areas of needed improvement in the mechanisms were identified. Comparisons of the intermediate product yields of ethylene for n-hexadecane with those found for other smaller n-alkanes, show that an n-hexadecane mechanism derived from a simple hierarchical extrapolation from a smaller n-alkane mechanism does not properly simulate the experimental measurements.
Article
Concentration time-histories were measured behind reflected shock waves during n-dodecane oxidation for five species: n-dodecane, C2H4, OH, CO2, and H2O. Experiments were conducted at temperatures of 1300–1600K and pressures near 2atm using mixtures of 400ppm n-dodecane and 7400ppm oxygen (ϕ=1) in argon. n-Dodecane and ethylene were monitored using IR gas laser absorption at 3.39 and 10.53μm, respectively; OH was monitored using UV laser absorption at 306.5nm; and CO2 and H2O were monitored using tunable IR diode laser absorption at 2.7 and 2.5μm, respectively. These time-histories provide kinetic targets to test and refine large reaction mechanisms for n-dodecane and demonstrate the potential of this type of data for validation of large reaction mechanisms. Comparisons are made with the predictions of two recently developed large-alkane reaction mechanisms, and the need for improved rate measurements of higher alkene reactions is discussed.
Article
Concentration time-histories were measured behind reflected shock waves during n-heptane oxidation for five species: n-heptane, C2H4, OH, CO2, and H2O. Experiments were conducted at temperatures of 1300–1600 K and a pressure of 2 atm using a mixture of 300 ppm n-heptane and 3300 ppm oxygen (ϕ = 1) in argon. n-Heptane and ethylene were monitored using IR gas laser absorption at 3.39 and 10.53 μm, respectively; OH was monitored using UV laser absorption at 306.5 nm; and CO2 and H2O were monitored using tunable IR diode laser absorption at 2.7 and 2.5 μm, respectively. These time-histories provide kinetic targets to test and refine large reaction mechanisms for n-heptane and demonstrate the potential of this type of data for validation of large reaction mechanisms. Comparisons are made with the predictions of several recently developed reaction mechanisms.
A high-temperature chemical kinetic model of n-alkane (up to n-dodecane), cyclohexane, and methyl-, ethyl-, n-propyl and n-butyl-cyclohexane oxidation at high temperatures, JetSurF version 2.0
  • H Wang
  • E Dames
  • B Sirjean
  • D A Sheen
  • R Tango
  • A Violi
  • Jyw Lai
  • F N Egolfopoulos
  • D F Davidson
  • R K Hanson
  • C T Bowman
  • C K Law
  • W Tsang
  • N P Cernansky
  • D L Miller
  • R P Lindstedt
USC Mech Version II. High-Temperature Combustion Reaction Model of H2/CO/C1-C4 Compounds
  • H Wang
  • X You
  • A V Joshi
  • S G Davis
  • A Laskin
  • F Egolfopoulos
  • C K Law