Optimizing combustion process by adaptive tuning technology based on Integrated Genetic Algorithm and Computational Fluid Dynamics

2012 ◽  
Vol 56 ◽  
pp. 53-62 ◽  
Author(s):  
X. Liu ◽  
R.C. Bansal
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Combustion Process ◽  
Adaptive Tuning

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

Reactive computational fluid dynamics modelling methane–hydrogen admixtures in internal combustion engines part II: Large eddy simulation

2020 ◽  
pp. 146808742091034
Author(s):  
Jann Koch ◽  
Christian Schürch ◽  
Yuri M Wright ◽  
Konstantinos Boulouchos
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Large Eddy Simulation ◽  
Combustion Process ◽  
Flame Speed ◽  
Hydrogen Addition ◽  
Eddy Simulation ◽  
Modelling Framework ◽  
Large Eddy ◽  
Dynamics Modelling

Fuels based on admixtures of methane/natural gas and hydrogen are a promising way to reduce CO2 emissions of spark ignition engines and increase their efficiency. A lot of work was conducted experimentally, whereas only limited numerical work is available in the context of three-dimensional modelling of the full engine cycle. This work addresses this fact by proposing a reactive computational fluid dynamics modelling framework to consider the effects of hydrogen addition on the combustion process. Part I of this two-part study focuses on the modelling and crucial considerations in order to predict the mean cycle based on the G-equation combustion model using the Reynolds-averaged Navier–Stokes equations. There, the effect of increased burning speed was globally captured by increasing the flame speed coefficient A, appearing in the considered flame speed closure. The proposed simplified modelling of the early flame stage proved to be robust for the conducted hydrogen variation from 0 to 50 vol% H2 for stoichiometric and lean operation. Scope of this work, Part II, are cyclic fluctuations and the hydrogen influence thereon using large eddy simulation and the proposed modelling framework. The model is probed towards its capabilities to predict the fluctuation of the combustion process for 0 and 50 vol% H2 and correlations influencing the observed peak pressure of the individual cycle are presented. It is shown that the considered approach is capable to reproduce the cyclic fluctuations of the combustion process under the influence of hydrogen addition as well as lean operation. The importance of the early flame phase with respect to arising fluctuations is highlighted as well as the contribution of the resolved scales in terms of the flame front wrinkling.

Review of Computational Fluid Dynamics Studies on Chemical Looping Combustion

2020 ◽  
Vol 143 (8) ◽  
Author(s):  
Yali Shao ◽  
Ramesh K. Agarwal ◽  
Xudong Wang ◽  
Baosheng Jin
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Oxygen Carrier ◽  
Combustion Process ◽  
Chemical Looping Combustion ◽  
Chemical Looping ◽  
Flow Process ◽  
Gas Leakage ◽  
Fuel Reactor ◽  
Energy Penalty

Abstract Chemical looping combustion (CLC) is an attractive technology to achieve inherent CO2 separation with low energy penalty. In CLC, the conventional one-step combustion process is replaced by two successive reactions in two reactors, a fuel reactor (FR) and an air reactor (AR). In addition to experimental techniques, computational fluid dynamics (CFD) is a powerful tool to simulate the flow and reaction characteristics in a CLC system. This review attempts to analyze and summarize the CFD simulations of CLC process. Various numerical approaches for prediction of CLC flow process are first introduced and compared. The simulations of CLC are presented for different types of reactors and fuels, and some key characteristics including flow regimes, combustion process, and gas-solid distributions are described in detail. The full-loop CLC simulations are then presented to reveal the coupling mechanisms of reactors in the whole system such as the gas leakage, solid circulation, redox reactions of the oxygen carrier, fuel conversion, etc. Examples of partial-loop CLC simulation are finally introduced to give a summary of different ways to simplify a CLC system by using appropriate boundary conditions.

Optimal Arrangement Design of a Tube Bundle in Cross-Flow Using Computational Fluid Dynamics and Multi-Objective Genetic Algorithm

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Ya Ge ◽  
Feng Xin ◽  
Yao Pan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Pressure Drop ◽  
Tube Bundle ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Optimal Arrangement ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

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