Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.

Analysis and performance of a UPC implementation of a parallel longest common subsequence algorithm

An important problem in computational biology is finding the longest common subsequence (LCS) of two nucleotide sequences. This paper examines the correctness and performance of a recently proposed parallel LCS algorithm that uses successor tables and pruning rules to construct a list of sets from which an LCS can be easily reconstructed. Counterexamples are given for two pruning rules that were given with the original algorithm. Because of these errors, performance measurements originally reported cannot be validated. The work presented here shows that speedup can be reliably achieved by an implementation in Unified Parallel C that runs on an Infiniband cluster. This performance is partly facilitated by exploiting the software cache of the MuPC runtime system. In addition, this implementation achieved speedup without bulk memory copy operations and the associated programming complexity of message passing.

Parallel algorithm for solving integer linear programs

Linear programs, or LPs, are often used in optimization problems, such as improving manufacturing efficiency of maximizing the yield from limited resources. The most common method for solving LPs is the Simplex Method, which will yield a solution, if one exists, but over the real numbers. From a purely numerical standpoint, it will be an optimal solution, but quite often we desire an optimal integer solution. A linear program in which the variables are also constrained to be integers is called an integer linear program or ILP. It is the focus of this report to present a parallel algorithm for solving ILPs. We discuss a serial algorithm using a breadth-first branch-and-bound search to check the feasible solution space, and then extend it into a parallel algorithm using a client-server model. In the parallel mode, the search may not be truly breadth-first, depending on the solution time for each node in the solution tree. Our search takes advantage of pruning, often resulting in super-linear improvements in solution time. Finally, we present results from sample ILPs, describe a few modifications to enhance the algorithm and improve solution time, and offer suggestions for future work.

Characterization of thermal and mechanical properties of polypropylene-based composites for fuel cell bipolar plates and development of educational tools in hydrogen and fuel cell technologies

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi-walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbon's Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International's FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through-plane and in-plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in-plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through-plane and in-plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single-filler formulations. For thermal conductivity, Nielsen's model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen's model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

Development of a high performance parallel computing platform and its use in the study of nanostructures : clusters, sheets and tubes .

Small clusters of gallium oxide, technologically important high temperature ceramic, together with interaction of nucleic acid bases with graphene and small-diameter carbon nanotube are focus of first principles calculations in this work. A high performance parallel computing platform is also developed to perform these calculations at Michigan Tech. First principles calculations are based on density functional theory employing either local density or gradient-corrected approximation together with plane wave and gaussian basis sets. The bulk Ga2O3 is known to be a very good candidate for fabricating electronic devices that operate at high temperatures. To explore the properties of Ga2O3 at nonoscale, we have performed a systematic theoretical study on the small polyatomic gallium oxide clusters. The calculated results find that all lowest energy isomers of GamOn clusters are dominated by the Ga-O bonds over the metal-metal or the oxygen-oxygen bonds. Analysis of atomic charges suggest the clusters to be highly ionic similar to the case of bulk Ga2O3. In the study of sequential oxidation of these slusters starting from Ga2O, it is found that the most stable isomers display up to four different backbones of constituent atoms. Furthermore, the predicted configuration of the ground state of Ga2O is recently confirmed by the experimental result of Neumark's group. Guided by the results of calculations the study of gallium oxide clusters, performance related challenge of computational simulations, of producing high performance computers/platforms, has been addressed. Several engineering aspects were thoroughly studied during the design, development and implementation of the high performance parallel computing platform, rama, at Michigan Tech. In an attempt to stay true to the principles of Beowulf revolutioni, the rama cluster was extensively customized to make it easy to understand, and use - for administrators as well as end-users. Following the results of benchmark calculations and to keep up with the complexity of systems under study, rama has been expanded to a total of sixty four processors. Interest in the non-covalent intereaction of DNA with carbon nanotubes has steadily increased during past several years. This hybrid system, at the junction of the biological regime and the nanomaterials world, possesses features which make it very attractive for a wide range of applicatioins. Using the in-house computational power available, we have studied details of the interaction between nucleic acid bases with graphene sheet as well as high-curvature small-diameter carbon nanotube. The calculated trend in the binding energies strongly suggests that the polarizability of the base molecules determines the interaction strength of the nucleic acid bases with graphene. When comparing the results obtained here for physisorption on the small diameter nanotube considered with those from the study on graphene, it is observed that the interaction strength of nucleic acid bases is smaller for the tube. Thus, these results show that the effect of introducing curvature is to reduce the binding energy. The binding energies for the two extreme cases of negligible curvature (i.e. flat graphene sheet) and of very high curvature (i.e. small diameter nanotube) may be considered as upper and lower bounds. This finding represents an important step towards a better understanding of experimentally observed sequence-dependent interaction of DNA with Carbon nanotubes.

PARTITIONING THE BLOCKS OF A STEINER TRIPLE SYSTEM INTO PARTIAL PARALLEL CLASSES

Does there exist a Steiner Triple System on v points, whose blocks can be partitioned into partial parallel classes of size m, where m ≤ [v⁄3], m | b and b is the number of blocks of the STS(v)? We give the answer for 9 ≤ v ≤ 43. We also show that whenever 2|b, v ≡ 3 (mod 6) we can find an STS(v) whose blocks can be partitioned into partial parallel classes of size 2, and whenever 4|b , v ≡ 3 (mod 6), there exists an STS(v) whose blocks can be partitioned into partial parallel classes of size 4.