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Journal ArticleDOI

Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering

13 Oct 2013-Journal of Engineering for Gas Turbines and Power-transactions of The Asme (American Society of Mechanical Engineers)-Vol. 136, Iss: 9, pp 091515
TL;DR: In this paper, two approaches to reduce the demands of internal combustion engine simulations with detailed chemistry are presented, and the results show that significant CPU time reductions, of about one order of magnitude, can be achieved without loss of accuracy in both engine performance and emissions predictions.
Abstract: The need for more efficient and environmentally sustainable internal combustion engines is driving research towards the need to consider more realistic models for both fuel physics and chemistry. As far as compression ignition engines are concerned, phenomenological or lumped fuel models are unreliable to capture spray and combustion strategies outside of their validation domains – typically, high-pressure injection and high-temperature combustion. Furthermore, the development of variable-reactivity combustion strategies also creates the need to model comprehensively different hydrocarbon families even in single fuel surrogates. From the computational point of view, challenges to achieving practical simulation times arise from the dimensions of the reaction mechanism, that can be of hundreds species even if hydrocarbon families are lumped into representative compounds, and thus modeled with nonelementary, skeletal reaction pathways. In this case, it is also impossible to pursue further mechanism reductions to lower dimensions. CPU times for integrating chemical kinetics in internal combustion engine simulations ultimately scale with the number of cells in the grid, and with the cube number of species in the reaction mechanism. In the present work, two approaches to reduce the demands of engine simulations with detailed chemistry are presented. The first one addresses the demands due to the solution of the chemistry ODE system, and features the adoption of SpeedCHEM, a newly developed chemistry package that solves chemical kinetics using sparse analytical Jacobians. The second one aims to reduce the number of chemistry calculations by binning the CFD cells of the engine grid into a subset of clusters, where chemistry is solved and then mapped back to the original domain. In particular, a high-dimensional representation of the chemical state space is adopted for keeping track of the different fuel components, and a newly developed bounding-box-constrained k-means algorithm is used to subdivide the cells into reactively homogeneous clusters. The approaches have been tested on a number of simulations featuring multi-component diesel fuel surrogates, and different engine grids. The results show that significant CPU time reductions, of about one order of magnitude, can be achieved without loss of accuracy in both engine performance and emissions predictions, prompting for their applicability to more refined or full-sized engine grids.

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Citations
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Journal ArticleDOI
TL;DR: In this paper, a set of ultralow-sulfur diesel surrogate fuels were formulated, blended, and characterized in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments.
Abstract: The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments. The surrogate fuels feature increasing levels of compositional accuracy (i.e., increasing exactness in matching hydrocarbon structural characteristics) relative to the single target diesel fuel upon which the surrogate fuels are based. This approach was taken to assist in determining the minimum level of surrogate-fuel compositional accuracy that is required to adequately emulate the performance characteristics of the target fuel under different combustion modes. For each of the four surrogate fuels, an approximately 30 L batch was blended, and a number of the physical and chemical properties were measured. This work documents the surrogate-fuel creation process and the results of the property measurements.

130 citations

Journal ArticleDOI
TL;DR: In this paper, the effect of oxygenated fuel properties on combustion and soot emission was investigated using newly developed chemical mechanisms for various oxygenated fuels such as tri-propylene glycol methyl ether, methyl decanoate, and dimethyl ether.

89 citations

Journal ArticleDOI
TL;DR: It is found that both solver approaches, coupled with efficient function evaluation numerics, were capable of scaling computational time requirements approximately linearly with the number of species, which allows up to three orders of magnitude speed-ups in comparison with the traditional dense solution approach.

50 citations


Cites methods from "Computationally Efficient Simulatio..."

  • ...This approach allows a reduction in 121 CPU times by almost two orders of magnitude in ignition delay calculations 122 using a reaction mechanism with about three thousand species [34], and was 123 capable of reducing the total CPU time of practical internal combustion en124 gine simulations with skeletal reaction mechanisms by almost one order of 125 magnitude in comparison with a traditional, dense-algebra-based reference 126 approach [35, 36]....

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Journal ArticleDOI
TL;DR: Low Temperature Combustion (LTC) strategies are most promising to simultaneously reduce oxides of nitrogen (NOx) and soot emissions from diesel engines along with offering higher thermal efficiency as discussed by the authors.
Abstract: Low Temperature Combustion (LTC) strategies are most promising to simultaneously reduce oxides of nitrogen (NOx) and soot emissions from diesel engines along with offering higher thermal efficiency...

26 citations


Cites background from "Computationally Efficient Simulatio..."

  • ...A summary of the existing surrogate models to represent diesel fuel can be found in the references.(101,195,220) Recently, more precise multi component surrogate models have been proposed to represent biodiesel fuel characteristics....

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Journal ArticleDOI
TL;DR: In this paper, the authors studied the accuracy of computational modeling of the ignition of a pilot injection in the Sandia National Laboratories (SNL) light-duty optical engine facility, using the physical properties of a cetane/iso-cetane Diesel Primary Reference Fuel (DPRF) mixture and the reaction kinetics of a well-validated mechanism for primary reference fuels.
Abstract: In this paper, we studied the accuracy of computational modeling of the ignition of a pilot injection in the Sandia National Laboratories (SNL) light-duty optical engine facility, using the physical properties of a cetane/iso-cetane Diesel Primary Reference Fuel (DPRF) mixture and the reaction kinetics of a well-validated mechanism for primary reference fuels. Local fuel-air equivalence ratio measurements from fuel tracer based planar laser-induced fluorescence (PLIF) experiments were used to compare the mixture formation predictions with KIVA-ERC-based simulations. The effects of variations in injection mass from 1 mg to 4 mg, in-cylinder swirl ratio, and near-TDC temperatures on non-combusting mixture preparation were analyzed, to assess the accuracy of the model in capturing average jet behavior, despite its inability to model the nonnegligible jet-by-jet variations seen in the experiments. Fired simulations were able to capture well the measured ignitability trends at the different injection conditions tested, but showed some deviations in the minimum temperature needed for robust ignition, pointing out the need for further work to focus on achieving fully comprehensive modeling with detailed chemical kinetics of the DPRF58 mixture and a full engine geometry representation.

12 citations

References
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Book ChapterDOI
04 Jan 2001
TL;DR: This paper examines the behavior of the commonly used L k norm and shows that the problem of meaningfulness in high dimensionality is sensitive to the value of k, which means that the Manhattan distance metric is consistently more preferable than the Euclidean distance metric for high dimensional data mining applications.
Abstract: In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a efficiency and/or effectiveness perspective. Recent research results show that in high dimensional space, the concept of proximity, distance or nearest neighbor may not even be qualitatively meaningful. In this paper, we view the dimensionality curse from the point of view of the distance metrics which are used to measure the similarity between objects. We specifically examine the behavior of the commonly used Lk norm and show that the problem of meaningfulness in high dimensionality is sensitive to the value of k. For example, this means that the Manhattan distance metric (L1 norm) is consistently more preferable than the Euclidean distance metric (L2 norm) for high dimensional data mining applications. Using the intuition derived from our analysis, we introduce and examine a natural extension of the Lk norm to fractional distance metrics. We show that the fractional distance metric provides more meaningful results both from the theoretical and empirical perspective. The results show that fractional distance metrics can significantly improve the effectiveness of standard clustering algorithms such as the k-means algorithm.

1,614 citations


"Computationally Efficient Simulatio..." refers methods in this paper

  • ...Distance between points in the high-dimensional domain is accomplished by using the ‘Manhattan’, or ‘taxicab’ formulation, that is acknowledged to provide the best results when clustering high-dimensional datasets [35]....

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Journal ArticleDOI
TL;DR: VODE is a new initial value ODE solver for stiff and nonstiff systems that uses variable-coefficient Adams-Moulton and Backward Differentiation Formula methods in Nordsieck form, treating the Jacobian as full or banded.
Abstract: VODE is a new initial value ODE solver for stiff and nonstiff systems. It uses variable-coefficient Adams-Moulton and Backward Differentiation Formula (BDF) methods in Nordsieck form, as taken from the older solvers EPISODE and EPISODEB, treating the Jacobian as full or banded. Unlike the older codes, VODE has a highly flexible user interface that is nearly identical to that of the ODEPACK solver LSODE.In the process, several algorithmic improvements have been made in VODE, aside from the new user interface. First, a change in stepsize and/or order that is decided upon at the end of one successful step is not implemented until the start of the next step, so that interpolations performed between steps use the more correct data. Second, a new algorithm for setting the initial stepsize has been included, which iterates briefly to estimate the required second derivative vector. Efficiency is often greatly enhanced by an added algorithm for saving and reusing the Jacobian matrix J, as it occurs in the Newton m...

1,601 citations


"Computationally Efficient Simulatio..." refers methods in this paper

  • ...CHEMKIN-II [25] or by the SpeedCHEM [12] chemistry solver....

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  • ...[12] 2) CHEMKIN-II [25]...

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  • ...Multi-step predictor-corrector methods, such as VODE [12] and LSODE [13], are fast and reliable for integrating chemistry problems in combustion as they employ an explicit (predictor) step to produce an estimate of the solution at the next time value, and then iterating implicit step equation (corrector) to convergence....

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Journal ArticleDOI
TL;DR: In this paper, a detailed chemical kinetic mechanism has been developed and used to study the oxidation of iso-octane in a jet-stirred reactor, flow reactors, shock tubes and in a motored engine.

1,279 citations


"Computationally Efficient Simulatio..." refers methods in this paper

  • ...CPU time comparison of the adiabatic constant volume problem ODE functions using the SpeedCHEM package, at different reaction mechanism dimensions [19,20,4,21,22,23]....

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