4.34 Cooling method for gas turbines. Refer to the Journal of Engineering for Gas Turbines and Power (January 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 4.15 (p. 190). Recall that you fit a first-order model for heat rate (y) as a function of speed (x1), inlet temperature (x2), exhaust temperature (x3), cycle pressure ratio (x4), and air flow rate (x5) to data saved in the GASTURBINE file.
a, Researchers hypothesize that the linear relationship between heat rate (y) and temperature (both inlet and exhaust) depends on air flow rate. Write a model for heat rate that incorporates the researchers’ theories.
b. Use statistical software to fit the interaction model, part a, to the data in the GASTURBINE file. Give the least squares prediction equation.
c. Conduct a test (at α = .05) to determine whether inlet temperature and air flow rate interact to affect heat rate.
d, Conduct a test (at α = .05) to determine whether exhaust temperature and air flow rate interact to affect heat rate.
e. Practically interpret the results of the tests, parts c and d.
(a) The model for heat rate is:
Heart rate = 16,569.8271 - 14.1293*inlet temperature + 20.6848*exhaust temperature
R² | 0.795 | |||||
Adjusted R² | 0.788 | |||||
R | 0.891 | |||||
Std. Error | 733.823 | |||||
n | 67 | |||||
k | 2 | |||||
Dep. Var. | HeatRate | |||||
ANOVA table | ||||||
Source | SS | df | MS | F | p-value | |
Regression | 13,34,33,406.1993 | 2 | 6,67,16,703.0997 | 123.89 | 9.87E-23 | |
Residual | 3,44,63,802.2484 | 64 | 5,38,496.9101 | |||
Total | 16,78,97,208.4478 | 66 | ||||
Regression output | confidence interval | |||||
variables | coefficients | std. error | t (df=64) | p-value | 95% lower | 95% upper |
Intercept | 16,569.8271 | |||||
Inlettemp | -14.1293 | 0.9592 | -14.730 | 3.10E-22 | -16.0456 | -12.2131 |
exhtemp | 20.6848 | 2.9865 | 6.926 | 2.51E-09 | 14.7185 | 26.6510 |
(b) The model for heat rate is:
Heart rate = 13,696.6672 - 11.7055*inlet temperature + 26.0407*exhaust temperature - 0.0045*inlet temperature*exhaust temperature
R² | 0.795 | |||||
Adjusted R² | 0.785 | |||||
R | 0.892 | |||||
Std. Error | 739.111 | |||||
n | 67 | |||||
k | 3 | |||||
Dep. Var. | HeatRate | |||||
ANOVA table | ||||||
Source | SS | df | MS | F | p-value | |
Regression | 13,34,81,260.9118 | 3 | 4,44,93,753.6373 | 81.45 | 1.20E-21 | |
Residual | 3,44,15,947.5360 | 63 | 5,46,284.8815 | |||
Total | 16,78,97,208.4478 | 66 | ||||
Regression output | confidence interval | |||||
variables | coefficients | std. error | t (df=63) | p-value | 95% lower | 95% upper |
Intercept | 13,696.6672 | |||||
Inlettemp | -11.7055 | 8.2462 | -1.419 | .1607 | -28.1843 | 4.7733 |
exhtemp | 26.0407 | 18.3441 | 1.420 | .1607 | -10.6172 | 62.6985 |
Inlettemp*exhtemp | -0.0045 | 0.0152 | -0.296 | .7682 | -0.0348 | 0.0258 |
(c) The hypothesis being tested is:
H0: β3 = 0
H1: β3 ≠ 0
The p-value from the output is 0.0002.
Since the p-value (0.0002) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that inlet temperature and air flow rate interact to affect heat rate.