OPTIMIZATION OF A SURROGATE REDUCED AVIATION FUEL-AIR REACTION MECHANISM USING A GENETIC ALGORITHM

Clean Air ◽  
2007 ◽  
Vol 8 (1) ◽  
pp. 1-24
Author(s):  
M. Pourkashanian ◽  
N. S. Mera ◽  
Lionel Elliott ◽  
C. W. Wilson ◽  
Derek B. Ingham ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reaction Mechanism ◽  
Aviation Fuel

A Novel Approach to Mechanism Reduction Optimization for an Aviation Fuel/Air Reaction Mechanism Using a Genetic Algorithm

2004 ◽  
Vol 128 (2) ◽  
pp. 255-263 ◽  
Author(s):  
L. Elliott ◽  
D. B. Ingham ◽  
A. G. Kyne ◽  
N. S. Mera ◽  
M. Pourkashanian ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reaction Mechanism ◽  
Reaction Rate ◽  
Flame Structure ◽  
Chemical Kinetic ◽  
Reaction Scheme ◽  
Operating Conditions ◽  
Aviation Fuel ◽  
Reduced Mechanism ◽  
Genetic Algorithm Approach

This study presents a novel multiobjective genetic-algorithm approach to produce a new reduced chemical kinetic reaction mechanism to simulate aviation fuel combustion under various operating conditions. The mechanism is used to predict the flame structure of an aviation fuel/O2∕N2 flame in both spatially homogeneous and one-dimensional premixed combustion. Complex hydrocarbon fuels, such as aviation fuel, involve large numbers of reaction steps with many species. As all the reaction rate data are not well known, there is a high degree of uncertainty in the results obtained using these large detailed reaction mechanisms. In this study a genetic algorithm approach is employed for determining new reaction rate parameters for a reduced reaction mechanism for the combustion of aviation fuel-air mixtures. The genetic algorithm employed incorporates both perfectly stirred reactor and laminar premixed flame data in the inversion process, thus producing an efficient reaction mechanism. This study provides an optimized reduced aviation fuel-air reaction scheme whose performance in predicting experimental major species profiles and ignition delay times is not only an improvement on the starting reduced mechanism but also on the full mechanism.

A Novel Approach to Mechanism Reduction Optimisation for Aviation Fuel/Air Reaction Mechanism Using a Genetic Algorithm

2004 ◽  
Author(s):  
L. Elliott ◽  
D. B. Ingham ◽  
A. G. Kyne ◽  
N. S. Mera ◽  
M. Pourkashanian ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reaction Mechanism ◽  
Reaction Rate ◽  
Flame Structure ◽  
Chemical Kinetic ◽  
Reaction Scheme ◽  
Operating Conditions ◽  
Aviation Fuel ◽  
Reduced Mechanism ◽  
Novel Approach

This study presents the use of a genetic algorithm to produce a new reduced chemical kinetic reaction mechanism to simulate aviation fuel combustion under various operating conditions. The mechanism is used to predict the flame structure of a aviation fuel/O2/N2 flame in both spatially homogeneous and one-dimensional premixed combustion. Complex hydrocarbon fuels, such as aviation fuel, involve large numbers of reaction steps with many species. As all the reaction rate data is not well known, there is a high degree of uncertainty in the results obtained using these large detailed reaction mechanisms. In this study a genetic algorithm approach is employed for determining new reaction rate parameters for a reduced reaction mechanism for the combustion of aviation fuel/air mixtures. The genetic algorithm employed incorporates both perfectly stirred reactor and laminar premixed flame data in the inversion process, thus producing an efficient reaction mechanism. This study provides an optimised reduced aviation fuel/air reaction scheme whose performance in predicting experimental major species profiles and ignition delay times is not only an improvement on the starting reduced mechanism but also on the full mechanism.

Reconciling multiphase reactivity of oleic acid with ozone using a kinetic flux model

2020 ◽  
Author(s):  
Coraline Mattei ◽  
Manabu Shiraiwa ◽  
Ulrich Pöschl ◽  
Thomas Berkemeier
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Monte Carlo ◽  
Oleic Acid ◽  
Reaction Mechanism ◽  
Reactive Oxygen ◽  
Chem Phys ◽  
Data Sets ◽  
Layer Model ◽  
Bulk Chemistry

<p>The ozonolysis of oleic acid on aerosol particles has been extensively studied in the past and is often used as a benchmark reaction for the study of organic particle oxidation. However, to date, no single kinetic model has reconciled the vastly differing reactive uptake coefficients reported in the literature that were obtained at different oxidant concentrations, particle sizes and with various commonly used laboratory setups (single-particle trap, aerosol flow tube, and environmental chamber). We combine the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB, Shiraiwa et al. 2012) with the Monte Carlo Genetic Algorithm (MCGA, Berkemeier et al. 2017) to simultaneously describe nine experimental data sets with a single set of kinetic parameters. The KM-SUB model treats chemistry and mass transport of reactants and products in the gas and particle phases explicitly, based on molecular-level chemical and physical properties. The MCGA algorithm is a global optimization routine that aids in unbiased determination of these model parameters and can be used to assess parameter uncertainty. This methodology enables us to derive information from laboratory experiments using a “big data approach” by accounting for a large amount of data at the same time.</p><p>We show that a simple reaction mechanism including the surface and bulk ozonolysis of oleic acid only allows for the reconciliation of some of the data sets. An accurate description of the entire reaction system can only be accomplished if secondary chemistry is considered and present an extended reaction mechanism including reactive oxygen intermediates. The presence of reactive oxygen species on surfaces of particulate matter might play an important role in understanding aerosol surface phenomena, organic aerosol evolution, and their health effects.</p><p> </p><p>References</p><p>Berkemeier, T. et al.: Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets, Atmos. Chem. Phys., 17, 8021-8029, 2017.</p><p>Shiraiwa, M., Pfrang, C., and Pöschl, U.: Kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB): the influence of interfacial transport and bulk diffusion on the oxidation of oleic acid by ozone, Atmos. Chem. Phys., 10, 3673-3691, 2010.</p>

A Combined Neural Network and Genetic Algorithm Optimization of Operational Parameter in Nitric Acid Process

2013 ◽  
Vol 441 ◽  
pp. 772-775 ◽  
Author(s):  
Yu Hua Zhu ◽  
Dian Zheng Zhuang ◽  
Ping Li ◽  
Wei Yan Tong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Steady State ◽  
Reaction Mechanism ◽  
Nitric Acid ◽  
Neural Network Model ◽  
Algorithm Optimization ◽  
Operational Parameter ◽  
Steady State Model ◽  
Acid Process

It will be face some problems about the complicated reaction mechanism, environment uncertainty, serious nonlinear in nitric acid process .a method of creating steady-state model of nitric acid process using neural network. and used genetic algorithm to optimize parameter on neural network model. The result can provide reference for analyzing and optimizing the parameters of nitric acid process.

Development of a High Temperature Oxidation Mechanism for Bio-Aviation Fuels

2008 ◽  
Author(s):  
E. Catalanotti ◽  
K. J. Hughes ◽  
M. Pourkashanian ◽  
I. Uryga-Bugajska ◽  
A. Williams
Keyword(s):  
High Temperature ◽  
Reaction Mechanism ◽  
Methyl Esters ◽  
Oxidation Mechanism ◽  
Carbon Chain ◽  
Chemical Kinetic ◽  
Aviation Fuel ◽  
Carbon Chain Length ◽  
Temperature Oxidation ◽  
Kinetic Mechanisms

Almost all current civil and military aviation around the world use a kerosene-type fuel. However one of the alternatives is to use a mixture of petrochemicals and biofuel, especially methyl esters derived from vegetable oil (Fatty Acid Methyl Esters, FAMEs) that given their properties appear to be one of the most suitable for Aviation fuels. Studies were conducted to develop a fundamental and detailed reaction mechanism for the combustion of bio-aviation fuel through a combination of the existing kerosene based reaction mechanism developed previously by the authors (Aviation Fuel Reaction Mechanism v1.1), along with published chemical kinetic mechanisms for methylbutanoate (MB). Methylbutanoate is the simplest FAME that exhibits similar patterns of reactivity to FAME’s of longer carbon chain length typical of those derived from vegetable oils, furthermore it has been the subject of several studies to provide chemical kinetic mechanisms to predict its oxidation behavior. Evaluations of the combined reaction mechanism have been performed using CHEMKIN™ and similar software simulating high temperature/pressure conditions. A comparison between the oxidation processes of the Kerosene and Bio-Aviation fuel was carried out, along with sensitivity analysis to provide insight into some of the differences observed. A similar behaviour was observed for blends of 20%MB/80%Kerosene in the combustion conditions studied, indicating that combustion in current aircraft engines would not be adversely affected by using such a blend.

A Novel Approach to the Optimisation of Reaction Rate Parameters for Methane Combustion Using Multi-Objective Genetic Algorithms

2003 ◽  
Author(s):  
L. Elliott ◽  
D. B. Ingham ◽  
A. G. Kyne ◽  
N. S. Mera ◽  
M. Pourkashanian ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reaction Mechanism ◽  
Reaction Rate ◽  
Flame Structure ◽  
Simulated Data ◽  
Methane Combustion ◽  
Rate Coefficients ◽  
Inversion Process ◽  
Multi Objective ◽  
Novel Approach

This study uses a multi-objective genetic algorithm to determine new reaction rate parameters (A’s, β’s and Ea’s in the non-Arrhenius expressions) for the combustion of a methane/air mixture. The multi-objective structure of the genetic algorithm employed allows for the incorporation of both perfectly stirred reactor and laminar premixed flames data in the inversion process, thus enabling a greater confidence in the predictive capabilities of the reaction mechanisms obtained. Various inversion procedures based on reduced sets of data are investigated and tested on methane/air combustion in order to generate efficient inversion schemes for future investigations concerning complex hydrocarbon fuels. The inversion algorithms developed are first tested on numerically simulated data. In addition, the increased flexibility offered by this novel multi-objective GA has now, for the first time, allowed experimental data to be incorporated into our reaction mechanism development. A GA optimised methane air reaction mechanism is presented which offers a remarkable improvement over a previously validated starting mechanism in modelling the flame structure in a stoichiometric methane-air premixed flame (http://www.leeds.ac.uk/ERRI/research/res.html). In addition, the mechanism outperforms the predictions of more detailed schemes and is still capable of modelling combustion phenomena that were not part of the optimisation process. Therefore, the results of this study demonstrate that the genetic algorithm inversion process promises the ability to assess combustion behaviour for fuels where the reaction rate coefficients are not known with any confidence and, subsequently, accurately predict emission characteristics, stable species concentrations and flame characterisation. Such predictive capabilities will be of paramount importance within the gas turbine industry.

Prediction of Premixed Laminar Flame Structure and Burning Velocity of Aviation Fuel-Air Mixtures

2001 ◽  
Author(s):  
A. G. Kyne ◽  
P. M. Patterson ◽  
M. Pourkashanian ◽  
C. W. Wilson ◽  
A. Williams
Keyword(s):  
Reaction Mechanism ◽  
Laminar Flame ◽  
Burning Velocity ◽  
Flame Structure ◽  
Kinetic Mechanism ◽  
Chemical Kinetic ◽  
Aviation Fuel ◽  
Measured Temperature ◽  
Premixed Laminar ◽  
Increasing Temperature

The structure of a rich burner stabilised kerosene/O2/N2 flame is predicted using a detailed chemical kinetic mechanism where the kerosene is represented by a mixture of n-decane and toluene. The chemical reaction mechanism, consisting of 440 reactions between 84 species, is capable of predicting the experimentally determined flame structure of Douté et al. (1995) with good success using the measured temperature profile as input. Sensitivity and reaction rate analyses are carried out to identify the most significant reactions and based on this the reaction mechanism was reduced to one with only 165 reactions without any loss of accuracy. Burning velocities of kerosene-air mixtures were also determined over an extensive range of equivalence ratios at atmospheric pressure. The initial temperature of the mixture was also varied and burning velocities were found to increase with increasing temperature. Burning velocities calculated using both the detailed and reduced mechanisms were essentially identical.

Optimisation of the Arrhenius Parameters in a Semi-Detailed Mechanism for Jet Fuel Thermal Degradation Using a Genetic Algorithm

2004 ◽  
Author(s):  
A. S. Wade ◽  
D. B. Ingham ◽  
A. G. Kyne ◽  
N. S. Mera ◽  
M. Pourkashanian ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Thermal Degradation ◽  
Rate Constants ◽  
High Speed ◽  
Jet Fuel ◽  
Aviation Fuel ◽  
Inversion Process ◽  
Detailed Reaction Mechanism ◽  
Survival Of The Fittest ◽  
Optimisation Procedure

This paper presents a novel way to determine new reaction rate parameters (A’s and Ea’s in the Arrhenius expression) in a semi-detailed reaction mechanism for the thermal degradation of aviation fuel and surface fouling. The technique employed is a specialised optimisation procedure, namely a genetic algorithm (GA), which utilises an abstraction of the Darwinian principle of survival of the fittest in order to “breed” good solutions over a predefined number of “generations”. Deposition rates for a given fuel which have been measured experimentally over a range of conditions are reproduced by solving the conservation equations of mass, momentum, energy and species using a CFD code and the optimised set of rate constants obtained using a genetic algorithm inversion process. The new set of rate constants lie within predefined boundaries based upon previous values found in the literature for the mechanism being used. In addition, this powerful technique promises the ability to develop reaction mechanisms whose newly optimised rate constants reproduce closely all the experimental data available, enabling a greater confidence in their predictive capabilities. The process is also shown to be an effective tool to facilitate the elucidation of shortcomings in current global chemistry models of fuel degradation. Therefore, the results of this study demonstrate that the genetic algorithm inversion process may be used to develop and calibrate more detailed models for the thermal stability behaviour of aviation fuels than have been seen previously and, subsequently, accurately predict the locations and rates of deposit build-up in fuel handling systems. Furthermore, it has been demonstrated that, modern high speed computers allow for evaluation of increasingly complex and expensive GA objective functions.

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