Genetic Algorithm Optimization Methodology Research Articles

Development and verification of RP-3 aviation kerosene surrogate fuel models using a genetic algorithm

For emulation of the physicochemical characteristics and hydrogen-carbon group composition of RP-3 aviation kerosene, two models were composed of three components including n-dodecane, n-butylcyclohexane and n-butylbenzene by genetic algorithm optimization methodology with physical properties measurements (S1, 27.9/63.0/9.1 mol%; S2, 58.0/30.0/12.0 mol%). The numerical results of properties using the surrogate fuels were compared with experimental data. Both models had good performance in predicting the physical properties by analyzing the trends of density and kinematic viscosity under different temperature and pressure conditions. Based on the proposed surrogates, a detailed RP-3 surrogate chemical reaction mechanism was developed by decoupling methodology which including 1408 species and 8965 reactions. Autoignition properties, oxidation properties and laminar combustion properties of surrogates were compared with RP-3 experimental data. Two models exhibited good matching on ignition behaviors with RP-3 at high and low temperatures. Moreover, the components proportion difference had a great influence on the ignition properties in the negative temperature coefficient region. It also made the predicted value of the laminar combustion velocity of S1 higher than that of S2.

Development of physical-chemical surrogate models and skeletal mechanism for the spray and combustion simulation of RP-3 kerosene fuels

RP-3 kerosene is a widely used fuel for civil and military transportation in China. Two RP-3 kerosene surrogates (UM1 and UM2) were proposed by genetic algorithm optimization methodology, aiming to simulate viscosity, density, surface tension, cetane number, lower heating value, H/C ratio and molecular weight along with sooting tendency under spray and engine relevant conditions. The RP-3 kerosene surrogates were composed of four components including n-dodecane, 2,2,4,6,6-pentamethylheptane, decalin and n-propylbenzene (UM1, 0.3374/0.3042/0.1954/0.163. UM2, 0.1449/0.3706/0.2059/0.0195/0.2591 by mole fraction). The carbon number of n-dodecane and 2,2,4,6,6-pentamethylheptane was both much closer to that of the real RP-3. Based on the proposed surrogates, a skeletal RP-3 surrogate chemical reaction mechanism was developed by decoupling methodology. The skeletal RP-3 surrogate mechanism was reduced only including 89 species and 225 reactions, which was efficient and reliable in CFD simulation. The surrogate models were validated during spray process under vapor and nonevaporating environment. The skeletal mechanism was verified against the foundational experiments such as ignition delay times, species concentrations and laminar flame speed under wide conditions. The applicability of skeletal mechanism was also well verified with our test data from a real compression-ignition engine.

Computational optimization of the combustion system of a heavy duty direct injection diesel engine operating with dimethyl-ether

A genetic algorithm optimization methodology is applied to the design of the combustion system of a heavy-duty diesel engine fueled with dimethyl ether (DME). The optimization includes the key combustion system related hardware, bowl geometry and injection nozzle design, together with the most relevant air management and injection settings. The GA was linked to the KIVA computational fluid dynamics code and an automated grid generation tool to perform a single-objective optimization. The optimization target focused on maximizing efficiency, while keeping NOx emissions, peak pressure and maximum pressure rise rate under the baseline engine levels. This research work not only provides the optimum combustion system definition, but also the cause-effect relation between the inputs and outputs under investigation, identifying the most relevant parameters controlling the performance of a DME fueled engine. Piston bowl geometry is found to primarily influence heat transfer and combustion efficiency due to its impact on the surface area and fuel distribution, respectively. Mixing is most affected by the injection system parameters. Finally, the optimum DME engine configuration provides 6.9% absolute net indicated efficiency improvement over the baseline engine fueled with DME. This study confirms the potential of DME as a promising fuel for the future generation of compression ignition engines and demonstrates the need to co-optimize the fuel and combustion system.

Liquid propellant engine conceptual design by using a fuzzy-multi-objective genetic algorithm (MOGA) optimization method

This paper presents an extension of fuzzy-multi-objective genetic algorithm (MOGA) optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. The primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and the minimum weight. The independent design variables in this model are combustion chamber pressure, exit pressure, oxidizer to fuel mass flow rate. To handle the mentioned problems, a fuzzy-multi-objective genetic algorithm optimization methodology is developed based on Pareto optimal set. Liquid propellant engine, F-1 is modeled to illustrate accuracy and efficiency of proposed methodology.

Parametric study to develop an empirical correlation for undersaturated crude oil viscosity based on the minimum measured input parameters

In this study, the correlations published in the literature for predicting undersaturated oil viscosity data have been evaluated based on field-measured data collected from PVT reports for different Omani fields. It was found that most of these correlations provide good prediction for undersaturated Omani crude oil viscosity with Bergman and Sutton [1] being the best. Then, evaluation analysis was carried out using both calculated bubble point pressure and bubble point oil viscosity data and adopting published correlations for these two parameters. It was found that the calculated bubble point pressure have insignificant effect on the predicted viscosity; therefore it was indicated that the correlations published by Standing [2] and Al-Shammasi [3] can be used to predict bubble point pressure in case of lack of these data. On the other hand, the calculated bubble point oil viscosity was found to have a significant effect on the calculated undersaturated oil viscosity. Therefore, a new correlation for this parameter was developed by applying the genetic algorithm optimization methodology on the collected experimental data. The validation test indicated that the correlation developed in this study for bubble point oil viscosity outperformed all the correlations available in the literature. Hossain et al. [4] correlation proved to have the best prediction for the undersaturated oil viscosity, while the Standing [2] correlation is recommended for predicting the bubble point pressure. On the other hand, the newly developed correlation gave the best performance for predicting the bubble point oil viscosity.

Genetic algorithm optimization methodology for PWM Inverters of Intelligent universal transformer for the advanced distribution automation of future

Genetic algorithm optimization methodology for PWM Inverters of Intelligent universal transformer for the advanced distribution automation of future

Genetic algorithm optimization methodology for PWM Inverters of Intelligent universal transformer for the advanced distribution automation of future

This paper intends to optimize the control fashion of PWM inverters of Intelligent Universal Transformer (IUT) using Genetic Algorithm (GA). IUT is the state of art proposed as an electrical key point of Advanced Distribution Automation (ADA) technology. It comprises the power electronic construction and high frequency transformer. ADA will be raised for tomorrow's distribution system to perform the exchanging platform for both data and information which will be fully monitored and automatically controlled. IUT is introduced as intelligent electronic devices in lieu of traditional transformer with the controllable architecture that enhances the reliability and system performances and leading to the various services and user benefits like desirable output voltage, level and frequency, DC voltage options and sag correction. In case of oil elimination benefit it also yield to size and weight reduction. In this approach current and voltage source controllers based on GA optimization are developed for controlling the firing angle of switching pattern of PWM inverters which are recommended for voltage level and frequency control of IUT. The GA smart optimization fashion with the proposed three levels IUT topology emphasizes on the very smooth control and voltage regulation in both input and output inverters of IUT. GA improves the system characteristics under load and input disturbances with regulating the voltage and harmonic elimination.

Vibrational genetic algorithm enhanced with neural networks in RCS problems

PurposeThe purpose of this paper is to develop a new genetic optimization strategy which provides computationally more efficient and accurate solutions, and to provide practically applicable optimization method in radar cross‐section (RCS) minimization problems.Design/methodology/approachThe problem of RCS minimization for three‐dimensional air vehicle is considered. New computationally efficient optimization tool; neural networks (NNs) coupled multi‐frequency vibrational genetic algorithm (NN‐coupled VGAm) is based on genetic algorithm (GA) search strategy together with NNs. The results include RCS minimization problem of an air vehicle under structural and aero dynamical‐related geometry constraints.FindingsFor the demonstration problem considered, remarkable reduction in the computational time has been accomplished.Research limitations/implicationsThe results reported in this paper suggest an efficient GA optimization methodology for engineering problems.Originality/valueOwing to reduction in computational time, the new method provides a shorter design cycle for engineering problems.

Development and calibration of a reduced chemical kinetic model of n-heptane for HCCI engine combustion

A new reduced chemical kinetic model for the Homogeneous Charge Compression Ignition (HCCI) combustion of n-heptane in an engine has been developed. The new model is based on two previous reduced kinetic models for alkane oxidation, from which some reactions have been eliminated and with enhanced treatment of the oxidization of CO and CH 3O. The kinetic parameters of the key reactions in the new model were adjusted by using a genetic algorithm optimization methodology to improve ignition timings predictions over the range of equivalence ratios from 0.2 to 1.2, temperature from 300 to 3000 K. The final model contains 40 species and 62 reactions and was validated under HCCI engine conditions. The results showed the well-known two-stage ignition characteristics of n-heptane, which involve low and high temperature regimes followed by a branched chain explosion. The optimized reduced model generally agrees well with those of the detailed chemical kinetic model (544 species and 2446 reactions); the computational time of using the former is less 1/1000 that of the latter.

Optimization of a hydrocarbon fuel ignition model for two single component surrogates of diesel fuel

The auto-ignition predictions of the Shell hydrocarbon fuel ignition model have been improved for initial conditions similar to those in diesel engine environments. A series of modifications were made to the model, including a more accurate calculation of the heat release of the fuel, a new mass balance for the products of the termination reactions, and the enthalpies of the Shell model species, R∗, B and Q were revised. In addition the twenty-six kinetic parameters of the revised model were determined using a genetic algorithm optimization methodology guided by auto-ignition results obtained from a detailed chemical kinetic mechanism. The optimization was performed for a broad range of conditions that is representative of the operating conditions in diesel engines at the start-of-injection (SOI). This range includes: equivalence ratios from 0.5–4.0, initial pressures from 40–120 bar, initial temperatures from 650 K to 1175 K and EGR percentages from 0–75%. The model constants were optimized for two single component hydrocarbons species, n-heptane and tetradecane, that are representative of diesel fuel. Finally, the model was implemented into the KIVA-3V CFD code and a series of engine simulations was performed in order to assess its agreement with experimental data for engine applications. The results were found to be promising in both cases of short and long ignition delays.