A. G. Kyne

Reaction mechanism reduction and optimisation for modelling aviation fuel oxidation using standard and hybrid genetic algorithms

Abstract This study describes the development of a new binary encoded genetic algorithm for the combinatorial problem of determining a subset of species and their associated reactions that best represent the full starting point reaction mechanism in modelling aviation fuel oxidation. The genetic algorithm has a dual objective in finding a reduced mechanism that best represents aviation fuel oxidation in both a laminar premixed flame and perfectly stirred reactor systems. The number of species in the subset chosen is kept fixed and is specified at the start of the procedure. The genetic algorithm chooses ever improving mechanisms based on an objective function which measures how well the new reduced mechanisms predict a set of species’ profiles simulated by the full mechanism. In order to verify the validity of our approach, a full enumeration was performed on a reduced problem and it was found that the genetic algorithm was able to find the optimum solution to this reduced problem after a few generations. The reduction involved going from 338 reactions involving 67 species to 215 reactions involving 50 species. This corresponded to a 90% CPU time saving in each function evaluation. A second step was to take the reduced reaction mechanism and to use a second real encoded genetic algorithm for the parameter optimisation problem of determining the optimal reaction rate parameters that best model an experimental set of premixed flame and jet stirred reactor species’ profiles. A significant improvement could be seen in the species profiles obtained using the mechanism with the GA optimised rates over those obtained from the original reduced mechanism. Further, in order to increase the efficiency of the second reaction rate coefficient optimisation step, a new hybrid method was developed which incorporates a direct optimisation method (Rosenbrock method) into the GA. A significant improvement in both accuracy and efficiency was apparent in using this new hybrid approach.

Incorporation of physical bounds on rate parameters for reaction mechanism optimization using genetic algorithms

In this study a genetic algorithm (GA) approach for determining new reaction rate parameters ( A , g , and E a in the non-Arrhenius expressions) for the combustion of a hydrogen/air mixture in a perfectly stirred reactor (PSR) is assessed. A new floating-point coded GA and fitness function have been developed that dramatically increase both the rate of convergence and the predictive accuracy of the algorithm, thus promising the extension of the method to more detailed reaction schemes. Output profiles of species for 20 sets of PSR conditions, obtained from an original set of rate constants, are reproduced following a GA optimization inversion process. The new sets of rate constants following each iteration are constrained to lie between predefined boundaries that represent the uncertainty associated with the experimental findings listed in the National Institute of Standards and Technology (NIST) database. Comparisons with previous optimization work have demonstrated that those mechanisms generated using the NIST constraints can be applied to combustion scenarios outside those used in the mechanisms construction. In addition, the flexibility of the GA has been demonstrated by its success in generating reaction rate coefficients that reproduce a set of randomly perturbed species profiles.

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

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/O 2 /N 2 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.

The use of ignition delay time in genetic algorithms optimisation of chemical kinetics reaction mechanisms

This study presents the use of a genetic algorithm to optimise new chemical kinetic reaction mechanisms using ignition delay time measurements. It is well recognised that many important combustion phenomena are kinetically controlled. Therefore it is important to determine accurately the reaction rate parameters associated with a given reaction mechanism. The genetic algorithm employed, uses perfectly stirred reactor, laminar premixed flame and ignition delay time data in the inversion process in order to produce efficient reaction mechanisms valid for a wide range of combustion processes and various operating conditions.

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

This study uses a multi-objective genetic algorithm to determine new reaction rate parameters (As, βs and E a 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 flame 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 optimized methane-air reaction mechanism is presented which offers a remarkable improvement over a previously validated starting mechanism in modeling the flame structure in a stoichiometric methane-air premixed flame (http:// wwwpersonal.leeds.ac.uk/∼fuensm/project/mech.html). In addition, the mechanism out-performs the predictions of more detailed schemes and is still capable of modeling combustion phenomena that were not part of the optimization process. Therefore, the results of this study demonstrate that the genetic algorithm inversion process promises the ability to assess combustion behavior for fuels where the reaction rate coefficients are not known with any confidence and, subsequently, accurately predict emission characteristics, stable species concentrations and flame characterization. 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

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 Doute 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.Crown copyright 2000. Published with the permission of the DERA on behalf of the controller of HMSO

An Informed Operator Based Genetic Algorithm for Tuning the Reaction Rate Parameters of Chemical Kinetics Mechanisms

A reduced model technique based on a reduced number of numerical simulations at a subset of operating conditions for a perfectly stirred reactor is developed in order to increase the rate of convergence of a genetic algorithm (GA) used for determining new reaction rate parameters of chemical kinetics mechanisms. The genetic algorithm employed uses perfectly stirred reactor, laminar premixed flame and ignition delay time data in the inversion process in order to produce efficient reaction mechanisms that are valid for a wide range of combustion processes and various operating conditions.

Validation of a Flamelet Approach to Modelling 3-D Turbulent Combustion Within an Airspray Combustor

Over the past two decades Computational Fluid Dynamics (CFD) has become increasingly popular with the gas turbine industry as a design tool. By applying CFD techniques during the early stages of designing a product, engineers can establish the key parameters and dimensions of a system before any experimental trial and error tests are made, thus reducing the product cycle time and costs. This study compares CFD predictions with a comprehensive set of experimental measurements made at QinetiQ on the combustion of aviation fuel within a modem airspray combustor. The performances of two separate models describing the chemical interactions are compared. First, an equilibrium model was employed and linked to the 3D commercial solver, FLUENT 5.5, through a mixture fraction/PDF lookup table approach. Similarly a flamelet model was implemented using a recently developed detailed chemical reaction mechanism describing aviation fuel combustion which has previously received rigorous testing with regard to its predictive performance over a wide range of combustion conditions (Patterson et al., 2001). Both cases predicted heat transfer through a new non-adiabatic PDF lookup table generator developed within the department. This allowed the implementation of a discrete phase model that treats the fuel entering the combustor as a fine liquid spray before evaporating and arriving in the gaseous phase. Two turbulence models (k-e and Reynolds Stress models) were also used and the results of each compared.Copyright

The Optimisation of Reaction Rate Parameters for Chemical Kinetic Modelling Using Genetic Algorithms

It is well recognised that many important combustion phenomena are kinetically controlled. Whether it be the burning velocity of a premixed flame, the formation of pollutants in an exhaust stack or the conversion of NO to NO2 in a gas turbine combustor, it is important that a detailed chemical kinetic approach be undertaken in order to fully understand the chemical processes taking place. This study uses a genetic algorithm to determine new reaction rate parameters (A’s, β’s and Ea ’s in the Arrhenius expressions) for the combustion of both a hydrogen/air and methane/air mixture in a perfectly stirred reactor. In both cases, output species profiles obtained from an original set of rate constants are reproduced by a new different set obtained using a genetic algorithm inversion process. The new set of rate constants lie between predefined boundaries (±25% of the original values) which in future work can be extended to represent the uncertainty associated with experimental findings. In addition, this powerful technique may be used in developing reaction mechanisms whose newly optimised rate constants reproduce all the experimental data available, enabling a greater confidence in their predictive capabilities. The results of this study therefore 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.Copyright

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

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.Copyright