A New Method of Chemical Kinetic Model Reduction for CFD Applications (original) (raw)
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A novel linear transformation model for the analysis and optimisation of chemical kinetics
Combustion Theory and Modelling, 2016
In this study a novel model for the analysis and optimisation of numerical and experimental chemical kinetics is developed. Concentration time profiles of non-diffusive chemical kinetic processes and flame speed profiles of fuel oxidiser mixtures can be described by certain characteristic points, so that relations between the coordinates of these points and input parameters of chemical kinetic models become almost linear. This linear transformation model simplifies the analysis of chemical kinetic models, hence creating a robust global sensitivity analysis and allowing a quick optimisation and reduction of these models. Firstly, in this study the model is extensively validated by the optimisation of a syngas combustion model with a large data set of imitated ignition experiments. The optimisation with the linear transformation model is quick and accurate, revealing the potential to decrease numerical costs of the optimisation process by at least one order of magnitude, compared to established methods. Additionally, the optimisation on this data set demonstrates the capability of predicting reaction rate coefficients, more accurately than by currently known confidence intervals. In a first application, methane combustion models are optimised with a small experimental set, consisting of OH(A) and CH(A) concentration profiles from shock tube ignition experiments, species profiles from flow reactor experiments and laminar flame speeds. With the optimised models, especially the predictability for the flame speeds of mixtures of hydrogen, carbon monoxide and methane can be increased compared to established models. With the analysis of the optimised models, new information for the low pressure reaction coefficient of the fall-off reaction H+CH 3 (+M)<=>CH 4 (+M) is determined. In addition, the optimised combustion model is quickly and efficiently reduced to validate a new rapid reduction scheme for chemical kinetic models.
Optimization of Reduced Kinetic Models for Reactive Flow Simulations
Journal of Engineering for Gas Turbines and Power, 2013
A robust optimization scheme, known as rkmGen, for reaction rate parameter estimation has been developed for the generation of reduced kinetics models of practical interest for reactive flow simulations. It employs a stochastic optimization algorithm known as simulated annealing (SA), and is implemented in C++ and coupled with Cantera, a chemical kinetics software package, to automate the reduced kinetic mechanism generation process. Reaction rate parameters in reduced order models can be estimated by optimizing against target data generated from a detailed model or by experiment. Target data may be of several different kinds: ignition delay time, blow-out time, laminar flame speed, species time-history profiles, and species reactivity profiles. The software allows for simultaneous optimization against multiple target data sets over a wide range of temperatures, pressures, and equivalence ratios. In this paper, a detailed description of the optimization strategy used for the reactio...
Chemical Reaction Thermodynamics and Reaction Rate Theory
Chemical reactor modeling requires the formulation of Heat, Mass, and Chemical Species balances and depending on reactor configuration Computational Fluid Dynamic (CFD) to account for mixing effects. Thermochemical properties such as the species enthalpy have to be considered to account for heat of chemical reactions when conducting an overall or finite energy balance. To properly calculate the heat of reaction due to a reversible reaction, knowledge of the forward and reverse rate coefficients is required. Similarly, the species balance requires knowledge of the net rates of chemical reactions within the reactor to account for composition changes and the equilibrium constant is needed.
Analysis and reduction of chemical kinetics for combustion applications
2021
Combustion of fossil fuels has been used for decades for all kinds of purposes, from generating electricity to make air planes fly but they are also the main source of pollution leading to climate change. New sustainable, less polluting fuels must be studied in order to diminish as much as possible the human impact on the planet. Combustion is a very complex process combining fluid dynamics, thermodynamics and chemistry with hundreds of species involved. In order to be able to use all the tools the numerical simulation has to offer with increasing complexity, from canonical cases to 3D Large Eddy Simulations (LES) with two-phase flows, analysing the relevant chemical pathways and reducing the reaction mechanisms describing this chemistry is necessary. Analytically Reduced Chemistry (ARC) is a way of reducing the size and the complexity of chemical mechanisms where only the species and reactions relevant to given conditions are kept while keeping a physically coherent mechanism. ARC ...
Computer Construction of Detailed Chemical Kinetic Models for Gas-Phase Reactors
Industrial & Engineering Chemistry Research, 2001
The combustion, oxidation, and pyrolysis chemistry of even simple light hydrocarbons can be extremely complex, involving hundreds or thousands of kinetically significant species. Even relatively minor species can play an important role in the formation of undesirable emissions and byproducts, and their properties and reactions need to be modeled in some detail in order to make accurate predictions. In many technologically important applications, the reaction chemistry is closely coupled with the mixing and heat flow, dramatically increasing the computational difficulty. The most reasonable way to deal with this complexity is to use a computer not only to solve the simulation numerically, but also to construct the model in the first place. We are developing the methods needed to make this sort of computer-aided kinetic modeling feasible for real systems. The computer is used to calculate most of the molecular properties and rate parameters in the model by a variety of quantum-and group-additivitybased techniques. We summarize our new computer methods for modeling the pressure dependence (falloff and chemical activation) of gas-phase reactions. Our approach to determining the optimal reduced kinetic models for various reaction conditions is discussed. Adaptivechemistry methods that allow one to solve detailed macroscopic reacting flow simulations involving hundreds of species are outlined.
Reduced models in chemical kinetics via nonlinear data-mining
The adoption of detailed mechanisms for chemical kinetics often poses two 1 types of severe challenges: First, the number of degrees of freedom is large; and second, 2 the dynamics is characterized by widely disparate time scales. As a result, reactive flow 3 solvers with detailed chemistry often become intractable even for large clusters of CPUs, 4 especially when dealing with direct numerical simulation (DNS) of turbulent combustion 5
Combustion and Flame, 2007
Large-scale simulations of multidimensional unsteady reacting flows with detailed chemistry and transport can be computationally extremely intensive even on distributed computing architectures. With the development of computationally efficient reduced chemical kinetic models, the smaller number of scalar variables to be integrated can lead to a significant reduction in the computational time required for the simulation with limited loss of accuracy in the results. A general MATLAB-based automated procedure for the development of reduced reaction models is presented. Based on the application of the quasi-steady-state (QSS) approximation for certain chemical species and on the elimination of selected fast elementary reactions, any complex starting reaction mechanism (detailed or skeletal) can be reduced with minimal human intervention. A key feature of the reduction procedure is the decoupling of the QSS species appearing in the QSS algebraic relations, enabling the explicit solution of the QSS species concentrations, which are needed for the evaluation of the elementary reaction rates. In contrast, previous approaches mainly relied on an implicit solution, requiring computationally intensive inner iterations. The automated procedure is first tested with the generation of an implicit 5-step reduced reaction model for CH 4 /air flame propagation. Next, two explicit robust reduced reaction models based on ignition data (18-step) and on flame propagation data (15-step) are systematically developed and extensively validated for ignition delay time, flame propagation, and extinction predictions of C 2 H 4 /air, CH 4 /air, and H 2 /air systems over a wide range of equivalence ratios, initial temperatures, pressures, and strain rates. In order to assess the computational advantages of the explicit reduced reaction models, comparisons of the computational time required to evaluate the chemical source terms as well as for the integration of unsteady nonpremixed flames for each model are also presented.
Kinetic model reduction using genetic algorithms
Large reaction networks pose difficulties in simulation and control when computation time is restricted. We present a novel approach to simplification of reaction networks that formulates the model reduction problem as an optimization problem and solves it using a genetic algorithm (GA). Two formulations of kinetic model reduction and their encodings are considered, one involving the elimination of reactions and the other the elimination of species. The GA approach is applied to reduce an 18-reaction, 10-species network, and the quality of solutions returned is evaluated by comparison with global solutions found using complete enumeration. The two formulations are also solved for a 32-reaction, 18-species network. © 1997 Elsevier Science Ltd