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Title: Predicting octane number from microscale flame dynamics

Abstract

The standard method for measuring the octane number of fuels requires large sample volumes (~1L) and access to a Cooperative Fuel Research (CFR) engine. This method reliably quantifies the knock resistance of fuels in spark ignition engines, however the large sample volume requirement prevents testing of new experimental fuels (often produced in quantities of just ~1 mL), and the large equipment size impedes mobile, decentralized testing of remote fuel supplies. When direct measurements of octane number are impractical, other methods are needed. Micro flow reactors have shown promise in measuring ignition characteristics that are sensitive to octane number, and they are compact and operate on small volumes (~1 mL). This study uses simulations to demonstrate that measurements of the unsteady flame dynamics in a micro flow reactor can provide valuable data for accurate octane number predictions. Simulations of the flow reactor are used to obtain ignition characteristics for over 200 ethanol-toluene primary reference fuels (ETPRF) and 21 biofuel blends. A feed forward neural network is trained using the micro flow reactor ignition characteristics, fuel properties, and known research octane number (RON) and motor octane number (MON) for the ETPRF fuels. Here, the neural network is able to predict the RONmore » and MON of the biofuel blends to within 2 octane number on average. Prediction results are compared to other methods available in the literature. Additional neural network models are trained that show improved prediction accuracy as additional fuel training data becomes available.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1568012
Alternate Identifier(s):
OSTI ID: 1532568
Report Number(s):
LLNL-JRNL-763291
Journal ID: ISSN 0010-2180; 952725
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Combustion and Flame
Additional Journal Information:
Journal Volume: 208; Journal Issue: C; Journal ID: ISSN 0010-2180
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 02 PETROLEUM; Micro flow reactor; Flames with repetitive extinction and Ignition (FREI); Fuel testing; Octane number; Neural network

Citation Formats

Druzgalski, Clara L., Lapointe, Simon, Whitesides, Russell, and McNenly, Matthew J. Predicting octane number from microscale flame dynamics. United States: N. p., 2019. Web. doi:10.1016/j.combustflame.2019.06.019.
Druzgalski, Clara L., Lapointe, Simon, Whitesides, Russell, & McNenly, Matthew J. Predicting octane number from microscale flame dynamics. United States. doi:10.1016/j.combustflame.2019.06.019.
Druzgalski, Clara L., Lapointe, Simon, Whitesides, Russell, and McNenly, Matthew J. Tue . "Predicting octane number from microscale flame dynamics". United States. doi:10.1016/j.combustflame.2019.06.019. https://www.osti.gov/servlets/purl/1568012.
@article{osti_1568012,
title = {Predicting octane number from microscale flame dynamics},
author = {Druzgalski, Clara L. and Lapointe, Simon and Whitesides, Russell and McNenly, Matthew J.},
abstractNote = {The standard method for measuring the octane number of fuels requires large sample volumes (~1L) and access to a Cooperative Fuel Research (CFR) engine. This method reliably quantifies the knock resistance of fuels in spark ignition engines, however the large sample volume requirement prevents testing of new experimental fuels (often produced in quantities of just ~1 mL), and the large equipment size impedes mobile, decentralized testing of remote fuel supplies. When direct measurements of octane number are impractical, other methods are needed. Micro flow reactors have shown promise in measuring ignition characteristics that are sensitive to octane number, and they are compact and operate on small volumes (~1 mL). This study uses simulations to demonstrate that measurements of the unsteady flame dynamics in a micro flow reactor can provide valuable data for accurate octane number predictions. Simulations of the flow reactor are used to obtain ignition characteristics for over 200 ethanol-toluene primary reference fuels (ETPRF) and 21 biofuel blends. A feed forward neural network is trained using the micro flow reactor ignition characteristics, fuel properties, and known research octane number (RON) and motor octane number (MON) for the ETPRF fuels. Here, the neural network is able to predict the RON and MON of the biofuel blends to within 2 octane number on average. Prediction results are compared to other methods available in the literature. Additional neural network models are trained that show improved prediction accuracy as additional fuel training data becomes available.},
doi = {10.1016/j.combustflame.2019.06.019},
journal = {Combustion and Flame},
number = C,
volume = 208,
place = {United States},
year = {2019},
month = {7}
}