- Page 1: Dynamic Tailoring and Tuning for Sp
- Page 4 and 5: Acknowledgments This work was suppo
- Page 6 and 7: 3.2 RPT Formulation . . . . . . . .
- Page 9 and 10: List of Figures 1-1 Timeline of Ori
- Page 11 and 12: List of Tables 1.1 Effect of simula
- Page 13 and 14: Nomenclature Abbreviations ACS atti
- Page 15: dk optimization search direction f
- Page 18 and 19: 1.1 Space-Based Interferometry NASA
- Page 20 and 21: unfettered by the Earth’s atmosph
- Page 22 and 23: the SCI, both the size and flexibil
- Page 24 and 25: maybethatitbecomescertain that the
- Page 26 and 27: Table 1.1: Effect of simulation res
- Page 28 and 29: Table 1.2: Effect of simulation res
- Page 30 and 31: has been found that structural desi
- Page 32 and 33: precision telescope structure for m
- Page 34 and 35: attractive, and more conservative a
- Page 36 and 37: to solve the performance tailoring
- Page 40 and 41: optical metric. Disturbance analysi
- Page 42 and 43: of freedom in the model. The stiffn
- Page 44 and 45: epresentation as follows: ⎧ ⎨
- Page 46 and 47: The transfer function from torque t
- Page 48 and 49: FRF Magnitude: T z to OPD [µm/Nm]
- Page 50 and 51: the eigenvalue equation, the deriva
- Page 52 and 53: percent change in the performance d
- Page 54 and 55: π 2 Lρ 2� i=1 0.03 − di ≤ 0
- Page 56 and 57: major iteration an approximation of
- Page 58 and 59: Data: initial iterate, x0, initial
- Page 60 and 61: defined by the user and indicate th
- Page 62 and 63: Simulated annealing results in a ve
- Page 64 and 65: non-convex with respect to the lump
- Page 66 and 67: Norm. Cum. Var. [µm 2 ] PSD [µm 2
- Page 68 and 69: Y−coordinate [m] Y−coordinate [
- Page 70 and 71: problem of minimizing a dynamic cos
- Page 72 and 73: 3.1 Uncertainty Historically, struc
- Page 74 and 75: literature, and four methods compat
- Page 76 and 77: Table 3.1: Uncertainty parameters f
- Page 78 and 79: # of occurrences 70 60 50 40 30 20
- Page 80 and 81: method are shown with dashed lines.
- Page 82 and 83: Y−coordinate [m] 0.05 0.04 0.03 0
- Page 84 and 85: formance. This worst-case performan
- Page 86 and 87: minimized: JMM � �� � n�
- Page 88 and 89:
This formulation differs from that
- Page 90 and 91:
Multiple Model The multiple model R
- Page 92 and 93:
ence in cost is due to the fact tha
- Page 94 and 95:
Performance [µm] 1400 1200 1000 80
- Page 96 and 97:
and truss mass for the designs are
- Page 98 and 99:
almost exactly with the collector l
- Page 100 and 101:
Norm. Cum. Var. [µm 2 ] PSD [µm 2
- Page 102 and 103:
while the nodal points of the RPT d
- Page 104 and 105:
Distinct design regimes appear when
- Page 106 and 107:
problem. The optimizations are run
- Page 108 and 109:
updating for tuning is developed an
- Page 110 and 111:
tailoring parameters are fixed by t
- Page 112 and 113:
generated and SQP is run from each
- Page 114 and 115:
anges from 300µ m to 3650µ, while
- Page 116 and 117:
Norm. Cum. Var. [µm 2 ] PSD [µm 2
- Page 118 and 119:
tuning effort there. Physical Inter
- Page 120 and 121:
Y−coordinate [m] 0.04 0.03 0.02 0
- Page 122 and 123:
2% uncertainty, but the tuned RPT d
- Page 124 and 125:
and a lower bound constraint at zer
- Page 126 and 127:
Data: initial iterate, y0, terminat
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equired by the optimization or stoc
- Page 130 and 131:
Optimized Model Tuning None of the
- Page 132 and 133:
parameters obtained by tuning the m
- Page 134 and 135:
model updating [60, 55, 12] and is
- Page 136 and 137:
actually worse. In contrast, the ha
- Page 138 and 139:
an isoperformance set for both biva
- Page 140 and 141:
Robust tuning with anti-optimizatio
- Page 142 and 143:
∆E 2 0.1 0.05 0 −0.05 −0.1 is
- Page 144 and 145:
it is necessary to generate a new h
- Page 146 and 147:
Since neither tuning configuration
- Page 148 and 149:
configurations until one works. The
- Page 150 and 151:
150
- Page 152 and 153:
and efficiency. The resulting RPTT
- Page 154 and 155:
which are constrained by �g. The
- Page 156 and 157:
The first constraint, Equation 5.6,
- Page 158 and 159:
Table 5.1: Algorithm performance: R
- Page 160 and 161:
timization (Equation 4.2) to the mo
- Page 162 and 163:
Norm. Cum. Var. [µm 2 ] PSD [µm 2
- Page 164 and 165:
Frequency [Hz] Relative Modal Displ
- Page 166 and 167:
are shown in Figure 5-4, the final
- Page 168 and 169:
two. The effects of this weighting,
- Page 170 and 171:
If the hardware meets the requireme
- Page 172 and 173:
Performance [µm] % of Simulations
- Page 174 and 175:
at α =0.1 to further explore the t
- Page 176 and 177:
Performance [µm] % of Simulations
- Page 178 and 179:
Performance [µm] 600 550 500 450 4
- Page 180 and 181:
180
- Page 182 and 183:
Controls Structure) tools [24]. The
- Page 184 and 185:
inant disturbance source and much w
- Page 186 and 187:
25 cm Y Z 120 (a) Configuration 45
- Page 188 and 189:
Table 6.2: TPF SCI model mass break
- Page 190 and 191:
node located on the negative Z surf
- Page 192 and 193:
Table 6.8: TPF SCI instrument eleme
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6.1.4 Attitude Control System The m
- Page 196 and 197:
(a) (b) (c) (d) Figure 6-8: Mode Sh
- Page 198 and 199:
h1. h 1 w1 w 2 h 2 (a) Y (b) Figure
- Page 200 and 201:
the truss material is a source of u
- Page 202 and 203:
x,y,p Build NASTRAN model [MATLAB]
- Page 204 and 205:
are tailored such that the mass is
- Page 206 and 207:
(a) (b) (c) Figure 6-13: SCI TPF RP
- Page 208 and 209:
from Chapter 2 that SA is only a st
- Page 210 and 211:
210
- Page 212 and 213:
trade between design flexibility an
- Page 214 and 215:
eal-time hardware optimizations tha
- Page 216 and 217:
eigenvalue derivatives are used to
- Page 218 and 219:
- Design and conduct experiments to
- Page 220 and 221:
A.1 Steepest Descent The simplest g
- Page 222 and 223:
αk. A one-dimensional line search
- Page 224 and 225:
224
- Page 226 and 227:
[11] P. J. Attar and E. H. Dowell.
- Page 228 and 229:
[36] O. L. de Weck. Multivariable I
- Page 230 and 231:
[61] C. D. Jilla. A Multiobjective,
- Page 232 and 233:
[85] J. W. Melody and G. W. Neat. I
- Page 234:
[109] H. Uchida and J. Onoda. Simul