The zero connection

Some fine points or possible different solutions could not be studied through this process within the time constraints of a single semester. For example, the assumption of having cylindrical fuel tanks in the spacecraft was made from the beginning, but this general shape was not the optimal shape for fitting the most fuel inside of this particular spacecraft. In this case, a toroidal shape would have increased the amount of fuel carried. Another issue concerned the solar panels: the available outside area of the spacecraft appeared to be slightly insufficient to fit the total required solar panel area as body mounted cells.

One of the most promising attributes of MATE-CON is its flexibility, already mentioned. The original interviews for the MATE portion of the process are quite difficult and require iteration to be certain that the preferences have been accurately captured. In this particular case, we were unable to perform a validation interview until after the ICE process was well underway.

Unfortunately, the validation interview showed us that the user preferences had not been captured accurately. However, this gave us a very realistic situation in which to test MATE­

CON’s flexibility: what happens to this process if the preferences change?

9.1 Changing User Preferences

MATE-CON is a process that is very adaptable to change, and in fact welcomes it. Feedback from the user is actually sought for to be sure that the designs are satisfying the user’s true preferences. In the validation interview with Dr. Kevin Ray, some values from the first interview had in fact changed. Whether these changes were brought about by misconceptions in the first interview or actual changes in the user preferences does not matter. But being able to detect and incorporate these changes matters significantly. Hence the validation interview was a necessary and useful step in the MATE-CON process.

After reviewing the data from the first interview, Dr. Kevin Ray realized that he had not put enough emphasis on the spacecraft lifetime. The ability to capture many atmospheric cycles (such as day/night, monthly, yearly, and solar cycles) is actually quite important for a successful mission. This retrospection changed the weight factor of the data lifespan attribute from 0.1 (lowest) to 0.3 (second highest). In a similar manner, Kevin realized that there was very little importance on latency for a science mission. He reduced this weight factor from 0.15 (3^rd highest) to 0.1 (lowest). Lastly, Kevin altered the shape of the data lifespan utility curve, which resulted in a somewhat linear relationship between utility and data lifespan. The utility of a 2­

year mission was decreased from 0.35 to 0.3, and the utility of a 4-year mission was increased from 0.35 to 0.44. (Please see charts in Figure 9–1 for a clearer representation of these changes.)

New Weights

Lifespan Utility Function

0.5 1

0.4 0.8 0.3 0.6

0.4

0.2 0.2

0.1 0

0 2 4 6 8 10

0 _{Latency Latitude} Equator Time Lifespan Altitude Data Lifespan (years)

Figure 9–1: New weights and Lifespan utility function

These updated values from Kevin were estimates based on viewing the results from the MIST interview tool. Given more time, Kevin would be asked to run through the MIST software again to confirm that these are indeed his true preferences.

These drastic changes in user preferences, which might create huge setbacks for some design sessions, exploit the power of MATE-CON. These changes were immediately implemented into the utility software, and new utility functions were created within the day. The ICE design

sessions followed suit and began searching for architectures based on the new utility information.

9.2 Final Results

Two of the final possible designs that converged are shown and briefly discussed below:

Table 9-1: MATE baslines and ICE designs acheived

Old New

Utility(0to1) OLD NEW

The sections in Table 9-1 are alternating expected baseline and design achieved. In the first case, the “Original Base” shows the inputs from the architectural space from the MATE part of the class; the line below it, “ICE Result,” shows the design achieved in the ICE sessions. As can be seen, the first result exceeds the utility of the original expectations. The second result does not.

This is due mainly to a lack of time; the semester finished before the class did. As is noted elsewhere in the paper (sections 5.4.4, 6), the first iteration on the customer’s preferences was completed rather late in the process. The resulting change in utility was incorporated into the

design sessions almost instantaneously, with a re-run of the architectural study taking on the order of 45 minutes, and the design change taking on the order of several hours.

The class feels that these results validate the MATE-CON process in several ways: first and foremost, the class was able to quickly converge on a design very close to the baseline design desired. Secondly, because the class knew the user’s preferences so well, it was able to leave the original tradespace by adding more fuel than originally calculated to achieve a higher utility. In mathematical terms, it was as if the class knew not only the user’s preferences, but also the

‘potential field’ of preference: we knew what direction to drive the design to gain the most utility increase.

Due to the lack of time, the class was not able to achieve such a thorough understanding of the revised preferences. It is clear now that the way to manipulate the mission for higher utility is to increase the lifetime. Instead of this, the class developed different ideas independently for increasing the utility of the mission, such as overloading the fuel and using it to dynamically change the orbit of the spacecraft in response to the density and solar cycle launched into.