Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
(a) (b) (c) Figure 6-13: SCI TPF RPTT design (a) top view (XY) (b) front view (XZ) (c) isometric view. Performance: RMS OPD [nm] 14 12 10 8 6 4 2 Nominal WC tuned PT RPT RPTT Figure 6-14: Nominal, worst-case and tuned performances for all TPF SCI designs. 206
Table 6.14: Performance predictions for all TPF SCI designs. RMS OPD14 [nm] σ0 σWC σt PT 2.90 12.70 9.01 RPT 3.79 8.38 4.95 RPTT 5.99 13.15 4.28 Each design is tuned with the parameters in Table 6.11 at the worst-case un- certainty vertices using Equation 4.2 and SA. This step serves as a simulation of hardware tuning, and the resulting parameter values are listed in Table 6.15. The tuned RMS performances are listed in Table 6.15 and shown graphically in Figure 6-14 with triangles and dotted error bars. As expected, the best tuned performance (4.28 nm) is achieved by the RPTT design. The PT design does not appear to be very tunable as the worst-case performance only improves from 12.8 nm to 9 nm. The RPT design exhibits more favorable tuning properties and can be adjusted to 4.95 nm from the worst-case performance of 8.4 nm. While the overall trend observed is similar to that in the development model (Figure 5-1), it is interesting to note that in the TPF SCI model the RPT design is more tunable than the PT design. In this problem, robustness to uncertainty and tunability are not coupled as they are in the development problem. As a result, the RPTT system is similar in design to the RPT system. However, it still true that explicitly tailoring for tuning improves the performance of the tuned system. Table 6.15: Tuning parameters for tuned worst-case TPF SCI realizations. rPM fiso PT 0.07 7.53 RPT 0.213 6.96 RPTT 0.246 6.41 Although the results in Figure 6-14 follow the general trends expected for the PT, RPT and RPTT systems, the design improvement effected by RPTT is not as dramatic as in the development model. One explanation is that the optimization is limited by the lack of gradient information and computational inefficiencies. Recall 207
- Page 155 and 156: where the notation yij indicates th
- Page 157 and 158: (Table 4.1), and the uncertainty pa
- Page 159 and 160: Table 5.2: Performance and design p
- Page 161 and 162: it in the worst-case uncertainty re
- Page 163 and 164: The data in Figure 5-2 indicate tha
- Page 165 and 166: configuration. The tuned configurat
- Page 167 and 168: same requirement. The effect become
- Page 169 and 170: indicating that this requirement is
- Page 171 and 172: E 2 [Pa] 7.8 7.6 7.4 7.2 7 6.8 6.6
- Page 173 and 174: than the RPT design, 155.45µm to 5
- Page 175 and 176: have a very small nominal performan
- Page 177 and 178: of these simulations fail to meet r
- Page 179 and 180: and that it is the only design meth
- Page 181 and 182: Chapter 6 Focus Application: Struct
- Page 183 and 184: optical path differences between th
- Page 185 and 186: Table 6.1: RWA disturbance model pa
- Page 187 and 188: FRF Magnitude 10 1 10 0 10 −1 10
- Page 189 and 190: Y Z Z X Y (a) w (c) w Y h Z Figure
- Page 191 and 192: Table 6.6: Primary mirror propertie
- Page 193 and 194: OP1 STAR Z OP2 OP3 Coll 1 Coll 2 Bu
- Page 195 and 196: PSD OPD14 [m 2 /Hz] CumulativeOPD14
- Page 197 and 198: 6.2 Design Parameters In order to a
- Page 199 and 200: 6.2.2 Tuning The tuning parameters
- Page 201 and 202: complex and the normal modes analys
- Page 203 and 204: does not change with the design par
- Page 205: (a) (b) (c) Figure 6-11: SCI TPF PT
- Page 209 and 210: performance trends similar to those
- Page 211 and 212: Chapter 7 Conclusions and Recommend
- Page 213 and 214: a statistical robustness measure su
- Page 215 and 216: and the worst-case performance is a
- Page 217 and 218: - Consider uncertainty analysis too
- Page 219 and 220: Appendix A Gradient-Based Optimizat
- Page 221 and 222: such that the gradient direction is
- Page 223 and 224: the descent direction. In some case
- Page 225 and 226: Bibliography [1] Jpl planet quest w
- Page 227 and 228: [24] Nightsky Systems Carl Blaurock
- Page 229 and 230: [48] S. C. O. Grocott, J. P. How, a
- Page 231 and 232: [74] M. Lieber. Development of ball
- Page 233 and 234: AIAA/ASME/ASCE/AHS/ASC Structures,
(a) (b)<br />
(c)<br />
Figure 6-13: SCI TPF RPTT design (a) top view (XY) (b) front view (XZ) (c)<br />
isometric view.<br />
<strong>Performance</strong>: RMS OPD [nm]<br />
14<br />
12<br />
10<br />
8<br />
6<br />
4<br />
2<br />
Nominal<br />
WC tuned<br />
PT RPT RPTT<br />
Figure 6-14: Nominal, worst-case and tuned performances for all TPF SCI designs.<br />
206
Change language