An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

Zhu, Lei; Gao, Zhan; Cheng, Xiaogang; Ren, Fei; Huang, Zhen

doi:10.1007/s11708-021-0731-6

Cited by 3 publications

(3 citation statements)

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“…In this section, the results of the 'winner' surrogate are compared with the results achieved by the most distant surrogate comprised in the subset of acceptable solutions in the same pool (objective function F ≥ 0.95 max(F)), which will be called bound surrogate. The comparison is performed in order to discuss the 'unicity' of the surrogate composition that is assessed by means of the parameter called compatibility, which is defined as in Equation (15), where P is the number of components in the initial palette, x i,OPT and x i,B represent the volume fractions of the i-th compound in the optimum surrogate and in the bound surrogate, respectively. Figure 8 reports the results of both the overall and the class-by-class compatibility.…”

Section: Composition Unicitymentioning

confidence: 99%

“…It is underlined that the optimum surrogate and the bound surrogate for A, B, D gasolines are fully compatible in ethanol a-priori since the presence of ethanol in those surrogates was constrained at zero by the user, being the real A, B, D gasolines lacking in oxygenates (Table A1). compatibility = 100 − ∑ P i=1 (x i,OPT − x i,B )•100 (15) by-class compatibility. It is underlined that the optimum surrogate and the bound surr gate for A, B, D gasolines are fully compatible in ethanol a-priori since the presence ethanol in those surrogates was constrained at zero by the user, being the real A, B, gasolines lacking in oxygenates (Table A1).…”

Section: Composition Unicitymentioning

confidence: 99%

“…Errors around 15% were detected in reproducing distillation curve, soot tendency and molecular weight likely due to the complexity of the multi-property optimization associated to the 2-component approximation. In [15] Zhu et al, applied a Bayesian multi kernel learning model to define surrogates capable to predict the soot tendency of fuels based on an in-house developed extensive experimental database. The work was focused on jet, diesel and bio-diesel fuels.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

et al. 2021

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Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A Bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the ‘unicity’ of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the ‘unicity’ of the surrogate composition has revealed that (i) the unicity is strongly correlated with the accuracy and that (ii) both ‘unicity’ and accuracy of the prediction are very sensitive to the high presence of aromatics.

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Section: Composition Unicitymentioning

confidence: 99%

Section: Composition Unicitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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