![]() ![]() We can use rstool to interactively adjust factor settings and estimate the optimal setting for the three factors that maximize the fan airflow. Clearly, pitch has the strongest influence on airflow. The plots confirm what the coefficients already reveal: Effects of the blade pitch angle, distance, and blade-tip clearance are, respectively, nonlinear, linear, and negligible. Figure 4 shows a response surface plot of our airflow data generated from the quadratic model. The Statistics and Machine Learning Toolbox includes the Response Surface Tool (rstool), which lets you interactively generate response surface plots from your data. We can look at the relationship between multiple input variables and one output variable by generating a response surface plot. The small coefficients for the blade-tip clearance terms indicate that this factor has little effect on airflow. The negative coefficient for distance reflects the fact that airflow increases when the fan is closer to the radiator. The large linear pitch coefficient indicates that a greater pitch angle generally results in higher airflow, while the large negative quadratic term indicates that the relationship between pitch angle and airflow is not strictly linear. Figure 3 shows how much each factor contributes to the total airflow. ![]() ft 3 per minute, inches, and degrees), making interpretation more difficult. Without normalization, the coefficients of the factors will have to be adjusted for their unit values (e.g. We normalize the factors over the range to aid in identification of important factors and interactions. ![]()
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