Coupling chemical lumping to data-driven optimization for the kinetic modeling of dimethoxymethane (DMM) combustion

The kinetic mechanisms describing the combustion of longer-chain fuels often have limited applicability due to the high number of species involved in their oxidation and decomposition paths. This work proposes a combined methodology for developing compact but accurate kinetic mechanisms of these fuels and applies it to dimethoxymethane (DMM), or oxymethylene ether 1 (OME1). An automatic chemical lumping procedure, performed by grouping structural isomers into pseudospecies, was proposed and applied to a detailed kinetic model of DMM pyrolysis and oxidation, built from state-of-the-art kinetic sub-models. Such a methodology proved particularly efficient in delivering a compact kinetic mechanism, requiring only 11 species instead of 35 to describe DMM sub-chemistry. The obtained lumped kinetic model was then improved through a data-driven optimization procedure, targeting data artificially generated by the reference detailed mechanism. The optimization was performed on the physically-constrained parameters of the modified-Arrhenius rate constants of the controlling reaction steps, identified via local sensitivity analyses. The dissimilarities between the predictions of the detailed and lumped models were minimized using a Curve Matching objective function for a comprehensive and quantitative characterization. Above all, the optimized mechanism was found to behave comparably to the starting detailed one, throughout most of the operating space and target properties (ignition delay times in shock tubes, laminar flame speeds, and speciations in stirred and flow reactors). The successful application of the proposed methodology to the DMM chemistry paves the way for its extensive use in the kinetic modeling of longer OMEs as well as heavier fuels, for which the computational advantages are expected to be even higher

Artificial intelligence and chemical kinetics enabled property-oriented fuel design for internal combustion engine

Fuel Genome Project aims at addressing the forward problem of fuel property prediction and the inverse problems of molecule design, retrosynthesis and reaction condition prediction. This work primarily addresses the forward problem by integrating feature engineering theory, artificial intelligence (AI) technologies, gas-phase chemical kinetics. Group contribution method (GCM) is utilized to establish the GCM-UOB (University of Birmingham) 1.0 system with 22 molecular descriptors and the surrogate formulation is to minimize the difference of functional group fragments between target fuel and surrogate. The improved QSPR (quantitative structure–activity relationship)-UOB 2.0 system with 32 molecular features couples with machine learning (ML) algorithms to establish the regression models for fuel ignition quality prediction. QSPR-UOB 3.0 scheme expands to 42 molecular descriptors to improve the molecular resolution of aromatics and specific fuel types. The obtained structural features combining with ML algorithms enable to predict 15 physicochemical properties with high fidelity and efficiency. In addition to the technical route of ML-QSPR models, another route of deep learning-convolution neural network (DL-CNN) is proposed for property prediction and yield sooting index (YSI) is taken as a case study. The predicted accuracy of DL-CNN is inferior to the ML-QSPR model at its current status, but its benefit of automated feature extraction and rapid advance in classification problem make it a promising solution for regression problem. A high-throughput fuel screening is performed to identify the molecules with desired properties for both spark ignition (SI) and compression ignition (CI) engines which contains the Tier 1 physicochemical properties screening (based on the ML-QSPR models) and Tier 2 chemical kinetic screening (based on the detailed chemical mechanisms). Polyoxymethylene dimethyl ether 3 (PODE3) and diethoxymethane (DEM) are promising carbon-neutral fuels for CI engines and they are recommended by the virtual screening results. Their ignition delay time, laminar flame speed and dominant reactions of PODE3 and DEM are examined by chemical kinetics and a new DEM mechanism including both low and high-temperature reactions is constructed. Concluding remarks and research prospects are summarized in the final section

Numerical study of premixed PODE3-4/CH4 flames at engine-relevant conditions

Polyoxymethylene dimethyl ether (PODEn, n ≥ 1) is a promising alternative fuel to diesel with higher reactivity and low soot formation tendency. In this study, PODE3-4 is used as a pilot ignition fuel for methane (CH4) and the combustion characteristics of PODE3-4/CH4 mixtures are investigated numerically using an updated PODE3-4 mechanism. The ignition delay time (IDT) and laminar burning velocity (LBV) of PODE3-4/CH4 blends were calculated at high temperature and high pressure relevant to engine conditions. It is discovered that addition of a small amount of PODE3-4 has a dramatic promotive effect on IDT and LBV of CH4, whereas such a promoting effect decays at higher PODE3-4 addition. Kinetic analysis was performed to gain more insight into the reaction process of PODE3-4/CH4 mixtures at different conditions. In general, the promoting effect originates from the high reactivity of PODE3-4 at low temperatures and it is further confirmed in simulations using a perfectly stirred reactor (PSR) model. The addition of PODE3-4 significantly extends the extinction limit of CH4 from a residence time of ~0.5 ms to that of ~0.08 ms, indicating that the flame stability is enhanced as well by PODE3-4 addition. It is also found that NO formation is reduced in lean or rich flames; moreover, NO formation is inhibited by too short a residence time

Progress Towards Automatic Chemical Kinetic Model Development

In an emerging energy landscape that increasingly discourages the use of traditional fossil fuels, there remain applications for which the continued use of high energy density liquid fuels is required, such as aviation and other uses where space and weight are critical design factors, or long term energy storage where cost and long term availability are required. To achieve this while transitioning to green sources of energy requires the design of next-generation combustion engines that can burn alternative fuels such as bio-derived or synthetic fuels; this process will be heavily dependent on design tools such as computational fluid dynamics packages, underpinned by accurate chemical kinetic models for the fuels in question. These kinetic models often contain thermodynamic information about hundreds of unique chemical species and thousands of chemical reactions forming an interconnected network between species governing their rates of production and destruction. Historically, generation of such high-fidelity kinetic models has required decades of research---too long for the engines that will require advanced fuels. Development of a kinetic model that is predictive of certain quantities of interest (ignition delay times, flame speeds, etc) can broadly be broken into four distinct stages: 1) initial ``crude'' model generation, 2) experimental design, 3) experiments and ab-initio theory calculations, and 4) kinetic model optimization. Advances in data-enabled science and ever-increasing computing power have offered pathways towards eventually automating this process. This work aims to introduce a collection of tools and building blocks that will assist in the overall aim of automatic kinetic model development, and in doing so fill important gaps in the current capabilities available in the literature. In particular, the work here touches on aspects of all four of the stages in the model development process described above. With regard to 1), while there are tools available in the literature for automatic generation of kinetic models for an increasingly large library of fuels, these models remain subject to the constraints imposed by current chemical kinetic model structures and combustion codes. Here, automatic screening procedures are introduced that investigate the impact on kinetic model prediction errors due to two distinct issues related to pressure-dependent chemistry: the lack of a new class of chemical reaction type in current chemical kinetic models, and effects due to how species-specific energy transfer parameters are represented in pressure-dependent stabilization reactions within kinetic models. With regard to 2) and 3), a Bayesian optimal experimental design algorithm is paired with computer-controllable perfectly-stirred reactor experiments with unique capability to both explore a combinatorically complex experiment parameter space (including flowing up to ten unique gas mixtures simultaneously) and measure dozens of chemical species using rapid, on-line diagnostics. This setup allows for key reaction pathways to be carefully "sensitized'' with the addition of trace quantities of key chemical species, a capability that has not been used elsewhere in literature. Generally speaking, other experimental design algorithms in literature have not explored experimental design spaces that are radically different from those used by experienced researchers in their manual experimental design processes, and the complexity of the mixtures explored by most traditional combustion experiments is limited to two or three different chemical species at most. The sensitization of key reaction pathways unlocks the ability to perform truly transformational parameter inferences with minimal amounts of experimental data. With regard to joining step 3) to 4) in the above process, semi-automated post-processing codes allow for rapid optimizations to be performed for a prior kinetic model on the basis of experiments chosen by our experimental design algorithm. Critically, a combination of the experimental design algorithm developed here and the jet-stirred reactor experiments described was tested on the kinetic model for N₂O decomposition, which has uncertainties for key reaction rates that have persisted for decades (indeed, researchers suggest kinetic rate constants for N₂O+O=N₂+O₂ that differ by at least four orders of magnitude!). Optimizations using the Multi-Scale Informatics (MSI) tool developed by our research group were run on the basis of experimental data obtained in the aforementioned experiments, and used to gain insights about the rate constant for a key reaction in N₂O decomposition chemistry, N₂O+O=N₂+O₂ , serving as a proof-of-concept for key portions of what will form the backbone of an automatic kinetic model development pipeline

EXPERIMENTAL AND MODELING STUDY OF IGNITION-RESISTANT FUELS

This research investigates relative ignition behavior of some oxygenated fuels and their blends with gasoline surrogates. It seeks to identify fuels with higher resistance to ignition and validate tentative kinetic models intended to predict their combustion chemistry. It also develops a method for simplified ignition delay time correlation that can allow for a more rapid estimation of the ignition behavior of a given fuel at known thermodynamic conditions. The work is motivated by the fact that in spark-ignition (SI) engines, increasing energy conversion efficiency through increasing the engine compression ratio is limited by the phenomenon of undesired autoignition known as engine knock. This is controlled by the chemical kinetics of the fuels which can be modified toward higher resistance using fuels of higher ignition resistance. In this study, the ignition behavior of the representative fuels is studied using both shock tube experiments and simulations of the kinetics of homogeneous chemical reactors. Specifically, we study: 1) propanol isomers, which are alcohols with three carbon atoms and promising alternative fuels for gasoline fuels; 2) MTBE and ETBE, which are effective ignition-resistant fuel components; 3) blends of a gasoline with ETBE or iso-propanol, to establish the kinetic interactions. The resulting experimental data are used to validate current chemical kinetics models of the individual fuels. To further facilitate the use of fuel blends suggested by this study, combined chemical kinetic models are developed of iso-octane as a gasoline surrogate and each of ignition resistant fuels identified. In order to reduce the computational cost of using the validated detailed models of the fuels studied, reduced kinetic models are developed. These reduced versions are of two kinds. The first uses the model reduction method known as Alternate Species Elimination (ASE) to derive smaller versions of the detailed models. The second reduction approach focuses on the prediction of the chemical time scale associated with ignition. Here a generalized ignition format is developed and detailed model simulations are used to obtain the constraining data. This makes it possible to predict ignition time scales based on knowledge of temperature, pressure, and composition of the combustible mixture. The work advances understanding of biofuels combustion by characterizing ignition properties of promising fuel additives and the effects of fuel blend on ignition. The resulting experimental data sets are useful for validating existing and future kinetic models. The combined models will allow for better insight into the combustion chemistry of ignition-resistant fuels formed from blending iso-octane with iso-propanol or ETBE

Flamelet/progress variable modelling and flame structure analysis of partially premixed flames

This dissertation addresses the analysis of partially premixed flame configurations and the detection and characterization of their local flame regimes. First, the identification of flame regimes in experimental data is intensively discussed. Current methods for combustion regime characterization, such as the flame index, rely on 3D gradient information that is not accessible with available experimental techniques. Here, a method is proposed for reaction zone detection and characterization, which can be applied to instantaneous 1D Raman/Rayleigh line measurements of major species and temperature as well as to the results of laminar and turbulent flame simulations, without the need for 3D gradient information. Several derived flame markers, namely the mixture fraction, the heat release rate and the chemical explosive mode, are combined to detect and characterize premixed versus non-premixed reaction zones. The methodology is developed and evaluated using fully resolved simulation data from laminar flames. The fully resolved 1D simulation data are spatially filtered to account for the difference in spatial resolution between the experiment and the simulation, and experimental uncertainty is superimposed onto the filtered numerical results to produce Raman/Rayleigh equivalent data. Then, starting from just the temperature and major species, a constrained homogeneous batch reactor calculation gives an approximation of the full thermochemical state at each sample location. Finally, the chemical explosive mode and the heat release rate are calculated from this approximated state and compared to those calculated directly from the simulation data. After successful validation, the approach is applied to Raman/Rayleigh line measurements from laminar counterflow flames, a mildly turbulent lifted flame and turbulent benchmark cases. The results confirm that the reaction zones can be reliably detected and characterized using experimental data. In contrast to other approaches, the presented methodology circumvents uncertainties arising from the use of limited gradient information and offers an alternative to known reaction zone identification methods. Second, this work focuses on the flame structure of partially premixed dimethyl ether (DME) flames. DME flames form significant intermediate hydrocarbons in the reaction zone and are classified as the next more complex fuel candidate in research after methane. To simulate DME combustion processes, accurate predictions by computational combustion models are required. To evaluate such models and to identify appropriate flame regimes, numerical simulations are necessary. Therefore, fully resolved simulations of laminar dimethyl ether flames, defined by different levels of premixing, are performed. Further, the qualitative two-dimensional structures of the partially premixed DME flames are discussed and analyses are carried out at selected slices and compared to each other as well as to experimental data. Further, the flamelet/progress variable (FPV) approach is investigated to predict the partially premixed flame structures of the DME flames. In the context of the FPV approach, a rigorous analysis of the underlying manifold is carried out based on the newly developed regime identification approach and an a priori analysis. The most promising flamelet look-up table is chosen for the fully coupled tabulated chemistry simulations and the results are further compared to the fully resolved simulation data

Experimental and Chemical Kinetic Modelling Study on the Combustion of Alternative Fuels in Fundamental Systems and Practical Engines

In this work, experimental data of ignition delay times of n-butanol, gasoline, toluene reference fuel (TRF), a gasoline/n-butanol blend and a TRF/n-butanol blend were obtained using the Leeds University Rapid Compression Machine (RCM) while autoignition (knock) onsets and knock intensities of gasoline, TRF, gasoline/n-butanol and TRF/n-butanol blends were measured using the Leeds University Optical Engine (LUPOE). The work showed that within the RCM, the 3-component TRF surrogate captures the trend of gasoline data well across the temperature range. However, based on results obtained in the engine, it appears that the chosen TRF may not be an excellent representation of gasoline under engine conditions as the knock boundary of TRF as well as the measured knock onsets are significantly lower than those of gasoline. The ignition delay times measured in the RCM for the blend, lay between those of gasoline and n-butanol under stoichiometric conditions across the temperature range studied and at lower temperatures, n-butanol acts as an octane enhancer over and above what might be expected from a simple linear blending law. In the engine, the measured knock onsets for the blend were higher than those of gasoline at the more retarded spark timing of 6 CA bTDC but the effect disappears at higher spark advances. Future studies exploring the blending effect of n-butanol across a range of blending ratios is required since it is difficult to conclude on the overall effect of n-butanol blending on gasoline based on the single blend that has been considered in this study. The chemical kinetic modelling of the fuels investigated has also been evaluated by comparing results from simulations employing the relevant reaction mechanisms with the experimental data sourced from either the open literature or measured in-house. Local as well as global uncertainty/sensitivity methods accounting for the impact of uncertainties in the input parameters, were also employed within the framework of ignition delay time modelling in an RCM and species concentration prediction in a JSR, for analysis of the chemical kinetic modelling of DME, n-butanol, TRF and TRF/n-butanol oxidation in order to advance the understanding of the key reactions rates that are crucial for the accurate prediction of the combustion of alternative fuels in internal combustion engines. The results showed that uncertainties in predicting key target quantities for the various fuels studied are currently large but driven by few reactions. Further studies of the key reaction channels identified in this work at the P-T conditions of relevance to combustion applications could help to improve current mechanisms. Moreover, the chemical kinetic modelling of the autoignition and species concentration of TRF, TRF/n-butanol and n-butanol fuels was carried out using the adopted TRF/n-butanol mechanism as input in the engine simulations of a recently developed commercial engine software known as LOGEengine. Similar to the results obtained in the RCM modelling work, the knock onsets predicted for TRF and TRF/n-butanol blend under engine conditions were consistently higher than the measured data. Overall, the work demonstrated that accurate representation of the low temperature chemistry in current chemical kinetic models of alternative fuels is very crucial for the accurate description of the chemical processes and autoignition of the end gas in the engine

Low temperature ignition properties of n-butanol: key uncertainties and constraints

A recent kinetic mechanism (Sarathy et al., 2012) describing the low temperature oxidation of n-butanol was investigated using both local and global sensitivity/uncertainty analysis methods with ignition delays as predictive targets over temperature ranges of 678-898 K and equivalence ratios ranging from 0.5-2.0 at 15 bar. The study incorporates the effects of uncertainties in forward rate constants on the predicted outputs, providing information on the robustness of the mechanism over a range of operating conditions. A global sampling technique was employed for the determination of predictive error bars, and a high dimensional model representation (HDMR) method was further utilised for the calculation of global sensitivity indices following the application of a linear screening method. Predicted ignition delay distributions spanning up to an order of magnitude indicate the need for better quantification of the most dominant reaction rate parameters. The calculated first-order sensitivities from the HDMR study show the main fuel hydrogen abstraction pathways via OH as the major contributors to the predicted uncertainties. Sensitivities indicate that no individual rate constant dominates uncertainties under any of the conditions studied, but that strong constraints on the branching ratio for H abstraction by OH at the α and γ sites are provided by the measurements

Impacts of Fuel Chemical Structure and Composition on Fundamental Ignition Behavior and Autoignition Chemistry in a Motored Engine.

The autoignition characteristics of individual hydrocarbon species studied in motored engine can provide a better understanding of the autoignition process and complex fuels for homogeneous spark and compression ignition engines, whether the interest is understanding and preventing knock or controlling autoignition. In both instances, there is a critical need to comprehend how fuel molecular structure either retards or promotes autoignition reactivity. This understanding ultimately contributes to the development of kinetic mechanisms, which are needed for simulation of reacting flows and autoignition processes. For this reason, the dissertation discusses autoignition data on i) three pentane isomers (n-pentane, neo-pentane and iso-pentane), ii) ethyl-cycloahexane and its two isomers (1,3-dimethyl-cyclohexane and 1,2-dimethyl-cyclohexane), and iii) diisobutylene in primary reference fuels. looking for their chemical structural impacts on the ignition process. Particularly for exploring the low and intermediate temperature regions, the motored variable compression ratio engine, developed from a Cooperative Fuel Research (CFR) Octane Rating engine, provided a good platform. Analyses of the stable intermediates in the CFR engine exhaust at various end of compression pressures and temperatures can help to identify reaction pathways through which different compounds prefer to autoignite. The approach of those studies is to conduct a systematic investigation of the autoignition, which can provide useful input for qualitative and semi-quantitative validation of kinetic mechanisms for oxidation of target chemical compounds. Finally, the dissertation is further extended to an experimental validation of jet aviation fuel surrogates, potentially emulating a series of physical and chemical ignition processes in diesel engines, with an emphasis on the needs for detailed auto-ignition characteristics of various individual hydrocarbon species.PhDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133374/1/kangdo_1.pd