Evatt Hawkes University of New South Wales, Australia ([email protected]) Fourth Workshop of the Engine Combustion Network, Kyoto, Japan, 6 September.

ECN

Evatt HawkesUniversity of New South Wales, Australia

([email protected])

Fourth Workshop of the Engine Combustion Network,

Kyoto, Japan, 6 September 2015

ECN 4 – topic 4Chemistry effects on ignition

ECN Background: ECN3 recap

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• All models over-predicting ID in spray flames.• Closest available shock-tube data suggested the

problem could be the chemistry mechanisms which appear to over-predict ID at low T.

Spray flame ignition delay results from ECN3 Shock tube ignition delay results from ECN3

ECN

Prior to ECN4:• Try to improve our predictions of ignition delay, focussing on spray A• Try to pin down whether the problems are with the chemistry or

elsewhere (TCI and other sub-models, numerics)• Working with chemical kinetics experts improve and validate chemical

kinetic sub-models having manageable size

At ECN4:• Comparisons of different models (kinetic and otherwise) in prediction

of experimental ignition delay• Shock tube data as well as spray A• Quantitative comparisons of species relevant to ignition• Understand the reasons behind any differences ignition behaviour,

and any interesting features of these ignitions

Session Objectives

ECN Talk outline

• Motivation and objectives• Discussion of chemistry models

– Comparison to new shock tube data– Features of the modelled ignitions

• Comparisons of model and experiment for ID and LOL for Spray-A parametrics

• Spatial comparison at early times• Conclusions & recommendations

ECN Chemistry mechanisms

Mechanisms available at ECN3 (manageable sized ones)

• Luo: 106 species (topic 5 baseline), reduction of detailed LLNL mechanism (Sarathy et al) by UConn and ANLLuo, Z., Som, S., Sarathy, S.M., Plomer, M., Pitz, W.J., Longman, D.E., and Lu, T., Development and validation of an n-dodecane skeletal mechanism for spray combustion applications, Combustion Theory and Modelling, 18(2), 2014: 187-203.

• Pei, 88 species, further reduction of Luo mechanism by UConn for UNSW; mechanism gives almost identical results to Luo and can be considered the same.Pei, Y., Hawkes, E.R., Kook, S., Goldin, G.M, Lu, T., Modelling n-dodecane spray and combustion with the transported probability density function method, Combust. Flame 162(5), 2015: 2006-2019.

• Narayanaswamy: 255 species, started with LLNL, updated kinetics for small hydrocarbons, lumping+reduction and revised some rates to improve high T performanceNarayanaswamy, K., Pepiot, P., Pitsch, H., A chemical mechanism for low to high temperature oxidation of n-dodecane as a component of transportation fuel surrogates, Combust. Flame 161, 2014: 866–884.

ECN Chemistry mechanismsNew mechanisms since ECN3

• Wang: 100 species, ERC Wisconsin, based on earlier Ra and Reitz mechanism; note that rates were optimised to shock-tube data for n-decaneWang, H., Ra, Y., Jia M., Reitz, R.D., Development of a reduced n-dodecane-PAH mechanism, and its application for n-dodecane soot predictions, Fuel 136 (2014) 25–36.

• Yao: 54 species, reduction and re-optimisation of LLNL by Uconn and ANL; note that spray A was included in the optimisation targets.Yao, T., Pei, Y., Zhong, B.-J., Som, S., Lu, T., A hybrid mechanism for n-dodecane combustion with optimized low-temperature chemistry, 9th U. S. National Combustion Meeting, May 17-20, 2015, Cincinnati, Ohio.

• Polimi: 96 species; reduced mechanism based on comprehensive Polimi detailed mechanismA. Frassoldati, G. D’Errico, T. Lucchini, A. Stagni, A. Cuoci, T. Faravelli, A. Onorati, E. Ranzi, Reduced kinetic mechanisms of diesel fuel surrogate for engine CFD simulations, Combustion and Flame, in press. http://dx.doi.org/10.1016/j.combustflame.2015.07.039.

• Cai: 57 species from Aachen – reduction and re-optimisation of Narayanaswamy mechanismCai. L., Kroger, L., Pitsch, H., Reduced and Optimized mechanism for n-Dodecane Oxidation, 15th International Conference on Numerical Combustion.

ECN Shock-tube data

• Prior to ECN4, only data for n-C12 was for φ=1 and 20 bar, closest available conditions were n-C10 at 50 bar

• New shock tube data obtained from Stanford (Davidson, Hanson) and Rensselaer Poly. (Oehlschlaeger)

• Data reasonably consistent, though no one-to-one comparisons possible.

• Rensselaer n-C12 & 60 bar and Aachen n-C10 & 50 bar for φ=1 and 2 to be used for comparison to models

φ=1 φ=2

ECN Performance of mechanisms

• Luo & Pei (UConn/LLNL) based on big LLNL over-predict almost everywhere and show way too much NTC.

• Narayanaswamy (Aachen) improved high T results considerably compared with Luo (and LLNL not shown), but still low T under-prediction.

• New Yao (UConn/LLNL) mechanism under-predicts & too much NTC. Tuned to spray cases, not shock-tube cases.

• New Cai (Aachen) under-predicts.• New Polimi under-predicts.• New Wang (ERC) excellent agreement. Tuned to match the n-decane data at 50bar.

φ=1 φ=2

ECN Behaviours in mixing space

• For spray A conditions, model predicts two-stage ignition for φ up to about 2, single-stage low-T ignition for richer conditions.

• For higher T, lean mixtures undergo single-stage, high T ignition, while moderately rich mixtures undergo two-stage ignition and very rich mixtures undergo single-stage low-T limit ignition.

• Spray A conditions are very hard because high-T, low-T and NTC all involved!

900K 1100K

Most reactive

• Ignition delays from homogeneous reactor calculations with different mixture-fractions and initial T according to adiabatic mixing.

ECN Behaviours in mixing space

• Simpler to order the mechanisms when most reactive mixture-fraction taken into account.

• Generally: Yao < Cai < Polimi < Wang < Narayanaswamy < Luo/Pei

• Mixture-fraction of ignition mostly consistent between Luo, Narayanaswamy, Yao; Polimi, Cai and Wang ignite richer.

ECN Quenching behaviour

• Flamelet calculations with different scalar dissipation rates, spray A conditions.

• Ordering of ignition delays at zero dissipation rate is not the same as the quenching limit.

ECN Spray experiments and models• Experiments:

– no change since ECN3; used ECN3 data as presented in Section 2.1 Combustion Indicators

– ignition delay (ID) based on broadband chemiluminescence, lift-off length (LOL) based on OH*

– averaged data from Sandia, CMT, IFPEN (acknowledgements please see ECN3)

• Models:– ANL: Argonne National Laboratory (ANL): Yuanjiang Pei, Muhsin Ameen, Sibendu Som– CMT-Motores Térmicos. Universitat Politècnica de València: Eduardo Pérez, Alberto

Viera, J Peraza– ETH-Zurich: Sushant S. Pandurangi, Yuri M. Wright, Konstantinos Boulouchos– PoliMi: Politecnico di Milano: Gianluca d’Errico, Tommaso Lucchini– TUE: Technische Universiteit Eindhoven: L.M.T. Somers– UNSW: University of New South Wales, Australia: Aqib Chishty, Michele Bolla, Evatt

Hawkes; input from Y Pei (now ANL)– USyd: University of Sydney: Fatemeh Salehi, Matt Cleary

ECN Model contributions

Group TCI model(s)* Chemistry model(s)

ANL Large-eddy simulation with well-mixed (LES) Luo et al.Yao et al.

CMT Unsteady flamelet progress-variable (UFPV) Narayanaswamy et al.

ETH Conditional moment closure (CMC) Luo et al.

Polimi Multiple representative interactive flamelets (MRIF)

Yao et al., Luo et al., Wang et al., Narayanaswamy et al., Polimi

TUE Flamelet-generated manifold (FGM)- Manifold from homogeneous reactors or flamelet

Luo et al.

UNSW Transported probability density function (TPDF) & well mixed model (WM)

All but Narayanaswamy

USyd Multiple mapping conditioning LES (MMC-LES)

Luo et al.

*All models are RANS-based except ANL and USyd

ECN Legends

InstitutionTCI

modelKinetic

mechanism

ECN Ignition delay – T variation

• Trends OK but considerable scatter

• We are still talking about factors of two differences between model and between

model and experiment

• This is presumably not acceptable?

ECN Ignition delay – T variation

• Compared with the situation at ECN2 & ECN3 there are some more accurate results

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ECN3 ECN4

ECN Ignition delay (log scale)

• Relative errors are similar across the T range, actually slightly higher for higher T.

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ECN Ignition delay – O2 variation

• Again trends OK but lots of scatter

• O2 trends are quite similar to T trends and will not be further discussed in detail

ECN Luo Mechanism

• For ID, scatter is greatly reduced when mechanism is held fixed. Not much sensitivity to the TCI model and numerical approach.

• Similar to ECN2 and ECN3, models over-predict ID with Luo chemistry. (The reason for having this session.)

• LOL still has a lot of sensitivity even when kinetics are fixed. LOL is sensitive to TCI and/or numerical approach.

ECN Luo Mechanism

• At ECN4, improved convergence of different modelling results at low T if chemistry held fixed

• Also true at higher T if ANL’s LES result not taken into account

ECN3 ECN4

ECN Wang, Polimi Mechanisms

• Similar to the case for Luo mechanism, when the chemistry is constrained the scatter is lower.

• Wang mechanism over-predicts ID but slightly better than Luo. Note that Wang mechanism gave the best results for the shock-tube data.

• Polimi mechanism is great for UNSW well-mixed model, slightly worse for Polimi’s MRIF. Polimi mechanism generally better than Luo.

ECN Yao mechanism

• Very good results for this mechanism with 4 different models.

• This mechanism was tuned based on spray flame results.

ECN Comparison to homogeneous reactors

• HRs generally show the same trends as the spray flame model.• HRs are mostly shorter than the spray flame model. This is expected because of the finite

time to form flammable mixture, strain effects, etc. • However, sometimes the differences are not.• Strain-rate effects (not shown, but looked at) did not seem to explain why some mechanisms

seem to show more differences. E.g. Wang mechanism has the highest quenching dissipation rate but shows large differences between HR and spray flame models.

• UNSW well-mixed results compared with corresponding most-reactive homogeneous reactors (HRs)

ECN TCI Effect

• With the model constrained to one group (with two models done in the same code), ID is all about the chemistry sub-model.

• Similar to ECN2 and ECN3, well-mixed model (which ignores turbulent fluctuations) gives slightly longer ID that TPDF (which models full statistics of fluctuations). For mechanism that under-predict ID, this causes TPDF results to be worse than WM. The effect is pretty small though.

• Similar to ECN2 and ECN3, LOL is much more affected by the inclusion of TCI. LOL is less sensitive to chemistry than ID, with fixed TCI model.

ECN RANS versus LES

• Similar to the comparison of TPDF and WM, comparison of RANS and LES shows ID and LOL are shorter for LES.

• Inclusion of fluctuations shortens ID and LOL. RANS versus LES seems to have a bigger effect than TPDF versus WM. Unclear why. Might just be 3 different codes. Could also be statistical convergence?

ECN Error analysis

• New mechanisms developed since ECN3 significantly improve ID. ID is strongly affected by chemistry.

• Improvement in LOL is also there but less prominent. LOL is affected by chemistry, but less strongly.

ID LOL

ECN2,3ECN4

ECN Why is it so?

Pei, Hawkes, et al. Combust. Flame, submitted

Analysis of Y_OH transport equationfrom TPDF method – steady flame period

Centreline profiles of mixture-fraction and Y_OH at ignition time

• Ignition occurs in a region of low gradients behind the head of the jet

• i.e. region of low macro-mixing between reacting fluid and cold ambient

• Turbulent diffusion is a significant influence

ECN Yao mechanism

• Good ID results and 4 models

=> Opportunity to drill down on spatial data

ECN Spatial structure @ 0.4 ms

FormaldehydeIFPENPLIF

ANL Polimi UNSWWM

UNSWTPDF

ECN Spatial structure @ 0.4 ms

OHIFPENPLIF

ANL Polimi UNSWWM

UNSWTPDF

ECN Summary & conclusions

• ECN2/3– All groups under-predicted ignition delay, sometimes

considerably

• Between ECN3 and ECN4– Mechanism performance at low T, high P conditions

seemed to be problematic based on available shock tube data for n-decane.

– Problem identified not to be in chemistry reduction but to also be present in large detailed mechanisms; not just n-dodecane but also other alkanes.

– A number of reduced chemistry mechanisms addressing this proliferated, some of which adjusted/optimised the low temperature rates.

ECN Summary & conclusions

• ECN4 findings– New shock tube data requested and obtained. Mostly consistent with earlier n-

C10 data.

– Still a lot of scatter in ID results – factors of two– However, better convergence of results between different groups when chemistry

held fixed (compared with earlier ECNs).

– ID seems to be mainly about chemistry and not much affected by TCI/numerical approach (consistent with ECN3 and some but not all ECN2 results). TCI effect results in slightly shorter ID.

– All the new mechanisms improved results for the spray flames. Yao et al. mechanism particularly good.

– BUT new mechanisms are now mostly under-predicting the shock-tube data.– Underlying problems in detailed chemistry not fixed – are we getting the right

answer for the wrong reason?– Still insufficient canonical data in spray-A relevant conditions.

– ECN4 topic 4 “achieved objectives”, but did it do so for the right reasons?

ECN Recommendations• Observations

– Spray A – no fundamental data for kinetics, no validated models in conditions– Detailed kinetics failing– Current approach to fix = tuning

• Recommendations– Abandon Luo mechanism as baseline. Require that all groups submit the new

(TBD) baseline. Additional mechanisms optional.– Seek greater engagement with chemical specialists – experiments and modelling– Fuels and conditions roadmap… chemists need time to generate new fundamental

data and validated kinetic models – can we specify a roadmap in advance?– Further drill down looking at space- and time-resolved data required to understand

structural differences between models despite similar ID

Institute for Combustion Technology | Prof. Dr.-Ing. H. Pitsch

[1] Z. Luo et al., Combustion Theory and Modelling 18 (2014) 187-203[2] H. Wang et al., Fuel 136 (2014) 25-36[3] K. Narayanaswamy et al., Combust. Flame 161 (2014) 866-884

n-Dodecane Mechanism Reduction and Optimization

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Comparison of ignition delay between reduced models[4,5]

Optimized reduced mechanism with 55

species

Compact size, optimized model performance, non-

stiff

• Automatic model reduction + optimization

• Example: Short n-Dodecane mechanism based on Narayanaswamy et al.[3]

• Strong reduction → 55 species → Potential inaccuracies

• Optimization for various targets→ Excellent agreement with

experiments• Potential further reduction

• Exclusion of certain targets• Quasi steady state

assumption

• Mechanism accurate, but• Accuracy through optimization• Root cause of inaccuracies not

fixed

Short and Accurate Mechanism for N-Dodecane

Institute for Combustion Technology | Prof. Dr.-Ing. H. Pitsch[1] S. Vasu et al., Proc. Combust. Inst. 32 (2009) 173-180[2] K. Narayanaswamy et al., Combust. Flame 161 (2014) 866-884[3] J. Bugler et al., J. Phys. Chem. A 119 (2015) 7510-7527

n-Dodecane Mechanism Development

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New detailed mechanism based on optimized reaction rate rules

Improved prediction accuracy Shorter ignition delay at low T Development of short version ongoing

Comparison of ignition delay between experiment[2] and model[3]

• Errors NOT specific for n-dodecane models

• Systematic errors in chemical mechanisms in literature[2]

• Thermodynamic data• Rate rules• Error compensation

• Mechanism improvement in three steps

1. Revision of rate rules and thermo data by Curran[3]

2. Optimization of rate rules for series of n-alkanes• Uncertainty quantification with

Bayesian framework• Calibrated for C7-C11 normal

alkanes Prediction improvement for C7-C11

3. New n-dodecane mechanism based on optimized rate rules

ECN Performance of mechanisms

• Blind test…• Greatly improved results for stoichiometric mixtures• Similar results to small Aachen mechanism for rich

mixtures, but at least there is better confidence here the reasons are right

φ=1 φ=2

ECN Acknowledgements

• Thanks to all contributors of experimental and modelling data

• Special thanks to those who provided the new shock-tube data at quite short notice– Matt Oehlschlaeger of Rensselaer Polytechnic Institute– David Davidson and Ron Hanson of Stanford University

• Thanks to Yuanjiang Pei (ANL), Prithwish Kundu (NCSU), and Chao Yu (UConn) for assistance with analysis of the chemical mechanisms

• Evatt Hawkes acknowledges funding from the Australian Research Council, computing time from the National Computational Infrastructure (Australia), Intersect, and the UNSW Faculty of Engineering cluster.

ECN

Questions and discussion