Simplifying ignition delay prediction for homogeneous charge compression ignition engine design and control

Zhou, Apeng; Dong, Ting; Akih-Kumgeh, Benjamin

doi:10.1177/1468087416630882

Cited by 9 publications

(5 citation statements)

References 42 publications

(83 reference statements)

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“…In effect, our results suggest that such measurements need only determine τ SO and peak [OH] accurately and that they may tolerate large uncertainty in [OH] at all other times. Numerous studies show that τ 1 is a key parameter for estimating total IDT at low and intermediate temperatures. − Integrating the inverse of the ignition time over full pressure–temperature trajectories, including the low- T regime, is a common tool for predicting engine performance at various operating conditions . Thus, experimental probing of oxidation markers that we consider here can play a part in selecting fuel candidates for the desired low- T combustion behavior.…”

Section: Results and Discussionmentioning

confidence: 99%

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

et al. 2022

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Using chemical kinetic modeling and statistical analysis, we investigate the possibility of correlating key chemical “markers”typically small moleculesformed during very lean (ϕ ∼ 0.001) oxidation experiments with near-stoichiometric (ϕ ∼ 1) fuel ignition properties. One goal of this work is to evaluate the feasibility of designing a fuel-screening platform, based on small laboratory reactors that operate at low temperatures and use minimal fuel volume. Buras et al. [Combust. Flame 2020, 216, 472–484] have shown that convolutional neural net (CNN) fitting can be used to correlate first-stage ignition delay times (IDTs) with OH/HO2 measurements during very lean oxidation in low-T flow reactors with better than factor-of-2 accuracy. In this work, we test the limits of applying this correlation-based approach to predict the low-temperature heat release (LTHR) and total IDT, including the sensitivity of total IDT to the equivalence ratio, ϕ. We demonstrate that first-stage IDT can be reliably correlated with very lean oxidation measurements using compressed sensing (CS), which is simpler to implement than CNN fitting. LTHR can also be predicted via CS analysis, although the correlation quality is somewhat lower than for first-stage IDT. In contrast, the accuracy of total IDT prediction at ϕ = 1 is significantly lower (within a factor of 4 or worse). These results can be rationalized by the fact that the first-stage IDT and LTHR are primarily determined by low-temperature chemistry, whereas total IDT depends on low-, intermediate-, and high-temperature chemistry. Oxidation reactions are most important at low temperatures, and therefore, measurements of universal molecular markers of oxidation do not capture the full chemical complexity required to accurately predict the total IDT even at a single equivalence ratio. As a result, we find that ϕ-sensitivity of ignition delay cannot be predicted at all using solely correlation with lean low-T chemical speciation measurements.

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Section: Results and Discussionmentioning

confidence: 99%

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

et al. 2022

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“…This strategy was implemented in a real engine, which was tested on a test bench and on-board a vehicle, and showed promising results in terms of combustion stability, pollutant emissions and noise. Finally, Zhou et al [17] proposed mathematical correlations for the ignition delay compared to experiments in a single cylinder engine over a wide range of operating conditions, confirming that knock can be accurately predicted.…”

Section: Introduction Justification and Objectivementioning

confidence: 87%

Sensitivity analysis and validation of a predictive procedure for high and low-temperature ignition delays under engine conditions for n -dodecane using a Rapid Compression-Expansion Machine

Desantes

Bermúdez

López

et al. 2017

Energy Conversion and Management

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“…Still, most of the experimental literature on piston engines is published without uncertainty quantification. Also, simply repeating several times a particular operating condition 3–5 cannot fully represent the uncertainty on the engine output of interest and overlooks significant systematic errors as it shall be demonstrated in this article.…”

Section: Introduction – Need For Engine Uncertainty Quantificationmentioning

confidence: 94%

“…The need for quality uncertainty analysis of experimental work is also a necessary condition for quality numerical simulations, as expressed in a recent research by Zhou et al 5 Computational fluid dynamics (CFD) simulations massively used in research and development require accurate uncertainty to be provided on their input parameters in order to be validated and to perform robust optimisation.…”

Section: Introduction – Need For Engine Uncertainty Quantificationmentioning

confidence: 99%

Uncertainty quantification from raw measurements to post-processed data: A general methodology and its application to a homogeneous-charge compression–ignition engine

Pochet

Jeanmart

Contino

2019

International Journal of Engine Research

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Internal combustion engines have been improved for many decades. Yet, complex phenomena are now resorted to, for which any optimum might be unstable: noise, low-temperature heat release timing, stratification, pollutant sweet spots, and so on. In order to make reliable statements on an improvement, one must specify the uncertainty related to it. Still, uncertainty quantification is generally missing in the piston engine experimental literature. Therefore, we detailed a mathematical methodology to obtain any engine parameter uncertainty and then used it to derive the uncertainty expressions of the physical quantities of the most generic homogeneous-charge compression–ignition research engine (mass-flow-induced mixture with [Formula: see text] fuel). We then applied those expressions on an existing hydrogen homogeneous-charge compression–ignition test bench. This includes the uncertainty propagation chain from sensor specifications, user calibrations, intake control, in-cylinder processes, and post-processing techniques. Directly measured physical quantities have uncertainties of around 1%, depending on the sensor quality (e.g. pressure, volume), but indirectly measured quantities relying on modelled parameters have uncertainties higher than 5% (e.g. wall heat losses, in-cylinder temperature, gross heat release, pressure rise rate). Other findings that such an analysis can bring relate, for example, to the physical quantities driving the uncertainty and to the ones that can be neglected. In the case of the homogeneous-charge compression–ignition engine considered, the effects of blow-by, bottle purity and air moisture content were found negligible; the post-processing for effective compression ratio, effective in-cylinder temperature, and top dead centre offset were found essential; and the pressure and volume uncertainties were found to be the main drivers to a large extent. The obtained numeric values serve the general purpose of alerting the experimenter on uncertainty order of magnitudes. The developed methodology shall be used and adapted by the experimenter willing to study the uncertainty propagation in their setup or willing to assess the adequacy of a sensor performance.

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Simplifying ignition delay prediction for homogeneous charge compression ignition engine design and control

Cited by 9 publications

References 42 publications

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Sensitivity analysis and validation of a predictive procedure for high and low-temperature ignition delays under engine conditions for n -dodecane using a Rapid Compression-Expansion Machine

Uncertainty quantification from raw measurements to post-processed data: A general methodology and its application to a homogeneous-charge compression–ignition engine

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