Interaction Of Input Parameters Research Articles

Optimization of a gas turbine model combustor due to variations in geometrical characteristics of stabilizing air jets

• Interaction of input parameters (diameter, angle, and position) is considered. • ANN is used to extract fitness (trained) function for GA. • NOx, CO, and PF are improved by up to 11.31%, 2.88%, and 21.07%, respectively. • Soot is decreased by up to 24.46%. The main purpose of this paper is to optimize the objective parameters in a gas turbine model combustor. These parameters include N O x and C O pollutants as well as pattern factor. C O is based on a maximum produced value in the model combustor. To optimize the objective parameters, the input characteristics (such as diameter, angle, and position of the stabilizing air jets) are varied. Simulation of the combustion process comprises turbulent flow, combustion, radiative heat transfer, and fuel injection modeling. The models employed for this simulation, based on RANS approach, are realizable k - ε for flow turbulence, steady flamelet model for combustion, discrete ordinates model for radiative heat transfer, and Eulerian-Lagrangian approach for fuel injection. A diesel fuel (C 10 H 22 ) is used in the model combustor and N O x modeling is conducted via post-processing. Artificial neural network is applied to gain an approximated trained function based on results and input geometrical characteristics. Data for training the artificial neural network is generated by means of design of experiments. For finding the optimum point, genetic algorithm is utilized. The obtained neural network function is applied to genetic algorithm so as to extract the optimum points. Since there is more than one objective parameter a set of optimum points is obtained which is called the Pareto front. Using multiple-criteria decision making method (LINMAP model), the final optimum point is achieved. The optimum point shows that the amounts of N O x , C O , and pattern factor are improved by up to 11.31 %, 2.88 %, and 21.07 %, respectively. The value of soot which is not a part of optimization is also improved by up to 24.46 %.

Assessment of induced delamination drilling of natural fiber reinforced composites: a statistical analysis

This work focuses on the study of the drilling performance of bio composites reinforced with date palm fibers (CDPF). It is a new fiber that is characterized in terms of piercing behavior for the first time. The experimental study was carried out with three types of drills, with a diameter of 10 mm and feed and spindle speeds of the order of (40, 80 and 200 mm/min) and (560, 1120 and 2240 rpm), respectively. The drilling performance was assessed in terms of delamination and surface quality, such as circularity and cylindricity of the drilled holes. Using the CMM machine, the value of the delamination factor was determined using Image J software. The results showed that the value of the latter increased with the increase in feed rate and decreased with the increase in spindle speed. The delamination factor has maximum and minimum values of approximately 1.98 and 1.01 respectively. For the first time, RSM and ANOVA were used to assess the influence and interaction of input parameters (cutting conditions) on the output parameters (delamination factor Fd) during drilling of CDPF. The results obtained help manufacturers, to choose the most appropriate machining conditions to achieve better machinability for other newly developed NFRCs in the aeronautical and defense industry where the substitution of the synthetic fibers by the natural fibers becomes inevitable for economic reasons.

ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm

Testing is an indispensable part of the software development life cycle. It is performed to improve the performance, quality and reliability of the software. Various types of testing such as functional testing and structural testing are performed on software to uncover the faults caused by an incorrect code, interaction of input parameters, etc. One of the major factors in deciding the quality of testing is the design of relevant test cases which is crucial for the success of testing. In this paper we concentrate on generating test cases to uncover faults caused by the interaction of input parameters. It is advisable to perform thorough testing but the number of test cases grows exponentially with the increase in the number of input parameters, which makes exhaustive testing of interaction of input parameters imprudent. An alternative to exhaustive testing is combinatorial interaction testing (CIT) which requires that every t-way interaction of input parameters be covered by at least one test case. Here, we present a novel strategy ABC-CAG (Artificial Bee Colony-Covering Array Generator) based on the Artificial Bee Colony (ABC) algorithm to generate covering an array and a mixed covering array for pair-wise testing. The proposed ABC-CAG strategy is implemented in a tool and experiments are conducted on various benchmark problems to evaluate the ecacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms.

Modeling and Optimizing the Hardness of the Melted Zone in Submerged Arc Welding Process Using Taguchi Method

One of the important characteristics of weld quality, which is influenced by welding parameters, is the hardness of the melted zone (HMZ). In this paper, experiments were conducted by Taguchi experimental design and Minitab 14 statistical software, and the interaction of input parameters was not taken into account. After collecting data, the signal to noise ratio (S/N) was calculated to obtain optimal levels for all input parameters. Then, using analysis of variance (ANOVA), the significance level of (P) for each input parameter was determined and validated for the hardness of the melted area. The results show that current intensity, welding speed, arc voltage, nozzle distance from work piece and thickness of magnesium oxide nanoparticles had respectively the highest impact on the hardness of melted zone.

Influência da variação do tamanho de grão austenítico na qualidade superficial no processo de microfresamento do aço AISI H13 endurecido

O foco deste trabalho foi o estudo da influência do tamanho de grão austenítico na qualidade superficial durante o processo de micro fresamento de aços endurecidos. Corpos de prova com durezas de 46 HRC e com diferentes tamanhos de grão foram fresados com micro fresas de topo com 0,5 mm de diâmetro. Nos testes experimentais foram empregadas duas velocidades de corte e dois avanços por dente. Os resultados foram analisados empregando-se uma análise de variância e demonstraram que a velocidade de corte e o avanço não tiveram influência no comportamento da rugosidade considerando os parâmetros Ra e Rz. Dessa forma, pode-se observar que o fator mais importante foi o tamanho de grão que influenciou significativamente os valores de rugosidade Ra e Rz. Além disso, observou-se também que a interação das variáveis de entrada não demonstrou influência significativa sobre as respostas rugosidade Ra e Rz.