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The Experimental Aspect of Simulation
Published in William Delaney, Erminia Vaccari, Dynamic Models and Discrete Event Simulation, 2020
William Delaney, Erminia Vaccari
The experimenter can use appropriate fractional factorial designs to explore the effects of variables when a large number of factors are of possible interest and then perform a more detailed analysis with full factorial. As a matter of fact, in simulations that include many factors, usually only a few are important. Thus it is necessary to conduct efficient screening experiments to determine the subset of important factors.
Six Sigma for Sustainability
Published in Adedeji B. Badiru, Tina Agustiady, Sustainability, 2021
Adedeji B. Badiru, Tina Agustiady
A fractional factorial design locates the relationship between influencing factors in a process and any resulting processes while minimizing the number of experiments. Fractional factorial DOEs reduce the number of experiments while still ensuring that the information lost is as minimal as possible. These types of DOEs are used to minimize time spent, money spent, and eliminate factors that seem unimportant.
Continuous Improvement Toolkit
Published in Tina Kanti Agustiady, Elizabeth A. Cudney, Building a Sustainable Lean Culture, 2023
Tina Kanti Agustiady, Elizabeth A. Cudney
A fractional factorial design locates the relationship between influencing factors in a process and any resulting processes while minimizing the number of experiments. Fractional factorial DOEs reduce the number of experiments while still ensuring the information lost is as minimal as possible. These types of DOEs are used to minimize time spent, money spent, and eliminating factors that seem unimportant.
Application of fractional factorial design for evaluating the separation performance of the screening machine
Published in International Journal of Coal Preparation and Utilization, 2022
Bharath Kumar Shanmugam, Harsha Vardhan, M. Govinda Raj, Marutiram Kaza, Rameshwar Sah, Harish Hanumanthappa
The Fractional factorial design is highly utilized to reduce the number of experimental trials at the beginning stage of the experiments. The fractional factorial design is an important tool utilized for research and development applications. The fractional factorial design is an efficient tool for evaluating the effect of two or more operational variables on the response variable. The 2k fractional factorial design is applied to new equipment or new processes where many operational variables are more likely to be studied with low resource utilization. Further, the fractional factorial design provides a significant operational variable by running a fraction of the full factorial design. For the full factorial design, as the number of the factor was increased, the experimental trial runs were more, thereby increasing the time and resources required. So, the fractional factorial design was developed to evaluate the screening machine’s performance using minimal resource utilization.
Minimising drag coefficient of a hatchback car utilising fractional factorial design algorithm
Published in European Journal of Computational Mechanics, 2018
Mehrdad Vahdati, Sajjad Beigmoradi, Alireza Batooei
One the most advantages of fractional factorial design is investigating the effect of main parameters and their interactions simultaneously. According to ANOVA, spoiler angle is one of the parameters that has significant impact on aerodynamic drag. According to Figures 8 and 9, setting spoiler angle in low level provides minimum drag force. For spoiler length parameter, even though Figure 8 shows choosing this parameter in low level delivers low drag, spoiler angle and length interaction in Figure 9 discard this point. According to Figure 9 considering spoiler length in high level reduces drag coefficient. In this situation, interaction effect has superiority to main effect plot. According to Figure 9, for diffuser angle similar to spoiler length, variation of drag in diffuser–spoiler angle interaction plot is significant and setting diffuser angle in high level causes for smaller drag coefficient. Comparing Figures 8 and 9, there is a conflict for plot trend in main and interaction figures but priority of interaction effect to main effect caused for choosing diffuser angle at top level. Regarding Figure 8, fifth door and boat tail angle don’t have considerable influence on drag coefficient but in view of their interactions with spoiler angle (Figure 9), choosing fifth door height and boat tail angle at top and low level respectively generates lower drag coefficient.
Comparison of 2- and 3-compartment electrodialytic remediation cells for oil polluted soil from northwest Russia
Published in Environmental Technology, 2021
Fatemeh Shouli Pour, Pernille E. Jensen, Kristine B. Pedersen, Tore Lejon
Variables may be continuous (e.g. liquid/solid ratio, electrical current, time) or discrete (e.g. stirring/no stirring, light/no light) and the extreme settings of these variables define the experimental domain. A two-level factorial design with k-factors thus consists of 2k experiments, with each variable being varied between a high and a low setting. If the number of variables is high or if experiments are time-consuming, a fractional factorial design may be chosen. A fractional factorial design is constructed as a fraction of a 2k complete factorial design, resulting in a 2k-x design. Experiments should be as widely distributed as possible, in order to cover a maximum variation over the experimental domain [15].