June 1, 2010

Design of Experiments Reduces Time to Market

Dresser Waukesha and Stat-Ease

Dresser Waukesha is a familiar name associated with large, gas-fueled stationary engines for power generation and gas compression applications around the world. Each new project location presents a unique set of site variables that must be taken into consideration—such as fuel quality, air/fuel ratio, temperature, humidity, altitude, load, and exhaust after-treatment—when providing the customer a meaningful performance guarantee. To fully characterize every engine option with such a wide range of fuel types and quality and in widely varying environmental conditions across the global is an impossible chore (Figure 6).

6. Sweet sixteen. In this typical cogeneration plant consisting of five 16-cyclinder gas engines, heat is recovered in an exhaust heat-recovery unit and from the jacket water and lube oil. Source: U.S. Department of Energy

The Brute Force Approach

In the past, developing reliable predictions of engine performance was a painstaking, expensive, and time-consuming manual process. The biggest challenge was using regression analysis to develop equations based on experimental data that described the relationship between the factors (independent performance variables) and the responses (dependent performance data). Engineers ran large numbers of performance computer models where one factor was varied over a range of values while the other variables were held constant, and then recorded the results. Then the next factor was varied, and so on.

When the tests were completed, engineers used statistical analysis, previous data, experience, and no small amount of intuition to estimate actual performance under actual field conditions. This approach required manually fitting the different equation forms to the data set, but the process took weeks and occupied considerable time on the part of engineers who were needed for other tasks.

Predictably, the accuracy of this approach was limited by its inability to account for interdependencies between each of the factors that aren’t considered when using this brute force approach to predicting engine performance in unpredictable circumstances.