Discrete optimization of isolator locations for vibration isolation systems: An analytical and experimental investigation

An analytical and experimental study is conducted to investigate the effect of isolator locations on the effectiveness of vibration isolation systems. The study uses isolators with fixed properties and evaluates potential improvements to the isolation system that can be achieved by optimizing isolator locations. Because the available locations for the isolators are discrete in this application, a Genetic Algorithm (GA) is used as the optimization method. The system is modeled in MATLAB{trademark} and coupled with the GA available in the DAKOTA optimization toolkit under development at Sandia National Laboratories. Design constraints dictated by hardware and experimental limitations are implemented through penalty function techniques. A series of GA runs reveal difficulties in the search on this heavily constrained, multimodal, discrete problem. However, the GA runs provide a variety of optimized designs with predicted performance from 30 to 70 times better than a baseline configuration. An alternate approach is also tested on this problem: it uses continuous optimization, followed by rounding of the solution to neighboring discrete configurations. Results show that this approach leads to either infeasible or poor designs. Finally, a number of optimized designs obtained from the GA searches are tested in the laboratory and compared to the baseline design. These experimental results show a 7 to 46 times improvement in vibration isolation from the baseline configuration

Optimization strategies for complex engineering applications

LDRD research activities have focused on increasing the robustness and efficiency of optimization studies for computationally complex engineering problems. Engineering applications can be characterized by extreme computational expense, lack of gradient information, discrete parameters, non-converging simulations, and nonsmooth, multimodal, and discontinuous response variations. Guided by these challenges, the LDRD research activities have developed application-specific techniques, fundamental optimization algorithms, multilevel hybrid and sequential approximate optimization strategies, parallel processing approaches, and automatic differentiation and adjoint augmentation methods. This report surveys these activities and summarizes the key findings and recommendations

Application of reuseable interface technology for thermal parameter estimation

A Reuseable Interface Technology is presented for application to thermal parameter estimation problems. It is applied to the estimation of thermal conductivity of compacted Al{sub 2}O{sub 3} powder without binder. As temperature increases, the thermal conductivity of Al{sub 2}O{sub 3} powder without binder decreases

Integration of finite element analysis and numerical optimization techniques for RAM transport package design

Type B radioactive material transport packages must meet strict Nuclear Regulatory Commission (NRC) regulations specified in 10 CFR 71. Type B containers include impact limiters, radiation or thermal shielding layers, and one or more containment vessels. In the past, each component was typically designed separately based on its driving constraint and the expertise of the designer. The components were subsequently assembled and the design modified iteratively until all of the design criteria were met. This approach neglects the fact that components may serve secondary purposes as well as primary ones. For example, an impact limiter`s primary purpose is to act as an energy absorber and protect the contents of the package, but can also act as a heat dissipater or insulator. Designing the component to maximize its performance with respect to both objectives can be accomplished using numerical optimization techniques

Application of optimization to the inverse problem of finding the worst-case heating configuration in a fire

Thermal optimization procedures have been applied to determine the worst-case heating boundary conditions that a safety device can be credibly subjected to. There are many interesting aspects of this work in the areas of thermal transport, optimization, discrete modeling, and computing. The forward problem involves transient simulations with a nonlinear 3-D finite element model solving a coupled conduction/radiation problem. Coupling to the optimizer requires that boundary conditions in the thermal model be parameterized in terms of the optimization variables. The optimization is carried out over a diverse multi-dimensional parameter space where the forward evaluations are computationally expensive and of unknown duration a priori. The optimization problem is complicated by numerical artifacts resulting from discrete approximation and finite computer precision, as well as theoretical difficulties associated with navigating to a global minimum on a nonconvex objective function having a fold and several local minima. In this paper we report on the solution of the optimization problem, discuss implications of some of the features of this problem on selection of a suitable and efficient optimization algorithm, and share lessons learned, fixes implemented, and research issues identified along the way