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
problem of minimizing a dynamic cost, RMS performance, by varying geometric prop- erties of the structure, cross-sectional diameters and mass distribution, is addressed. The design variables are introduced and practical constraints as well as analytical calculation of cost gradients are discussed. Two well-known optimization algorithms, sequential quadratic programming (SQP) and simulated annealing (SA), are applied to the problem. The results are compared for performance and efficiency and it is found that this particular problem can be solved with a combination of heuristic and gradient methods with reasonable com- putational effort. The PT design obtained through a heuristic SQP approach shows dramatic improvement in performance over the nominal design. The nominal and PT designs are examined in detail to understand why the tailored design produces such a large improvement in performance RMS. 70
Chapter 3 Robust Performance Tailoring “There is nothing so wrong with the analysis as believing the answer!... Uncertainties appear everywhere in the model.. When using a mathematical model, careful attention must be given to the uncertainties in the model.” - Richard Feynman [43] Performance tailoring optimization results in a design that is tailored to achieve a high level of performance given the design variables and constraints. However, in the early design stage, the performance assessment is based only on the model predictions and not on real data. Therefore, there is a question regarding how well the model can predict the actual behavior of the system. In the following chapter this issue of prediction accuracy is explored in detail. First, model uncertainty is defined and methods for quantifying it and assessing the effects of uncertainty on the performance predictions are discussed. Then, parametric uncertainty is identified in the SCI development model and its effect on the performance predictions of the PT design is explored. This study motivates the need for robust design techniques and three existing Robust Performance Tailoring (RPT) cost functions are presented. Results of RPT optimization on the SCI development model are presented and ana- lyzed. The chapter concludes with a discussion of the limitations of robust design on high-performance systems. 71
- Page 20 and 21: unfettered by the Earth’s atmosph
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<strong>Chapter</strong> 3<br />
<strong>Robust</strong> <strong>Performance</strong> <strong>Tailoring</strong><br />
“There is nothing so wrong <strong>with</strong> the analysis as believing the answer!... Uncertainties<br />
appear everywhere in the model.. When using a mathematical model, careful attention<br />
must be given to the uncertainties in the model.” - Richard Feynman [43]<br />
<strong>Performance</strong> tailoring optimization results in a design that is tailored to achieve<br />
a high level of performance given the design variables and constraints. However,<br />
in the early design stage, the performance assessment is based only on the model<br />
predictions and not on real data. Therefore, there is a question regarding how well<br />
the model can predict the actual behavior of the system. In the following chapter<br />
this issue of prediction accuracy is explored in detail. First, model uncertainty is<br />
defined and methods for quantifying it and assessing the effects of uncertainty on<br />
the performance predictions are discussed. Then, parametric uncertainty is identified<br />
in the SCI development model and its effect on the performance predictions of the<br />
PT design is explored. This study motivates the need for robust design techniques<br />
and three existing <strong>Robust</strong> <strong>Performance</strong> <strong>Tailoring</strong> (RPT) cost functions are presented.<br />
Results of RPT optimization on the SCI development model are presented and ana-<br />
lyzed. The chapter concludes <strong>with</strong> a discussion of the limitations of robust design on<br />
high-performance systems.<br />
71
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