Designing a Low Activation Pressure Drip Irrigation Emitter With Constraints for Mass Manufacturing

This work discusses the modeling and optimization of a drip irrigation emitter for reducing activation pressure. Our model formulation focuses on analytically characterizing fluidstructure interactions in an existing 8 liters per hour (lph) pressure-compensating online emitter. A preliminary experimental validation of the resulting model was performed for three different emitter architectures. This model was used as a basis for a genetic algorithm-based optimization algorithm that focused on minimizing activation pressure. The design variables considered in our formulation include, geometric features of the emitter architecture, and practical constraints from manufacturing. We applied our optimization approach to four emitters (with flow rates of 4, 6, 7 and 8.2 lph) and were able to lower activation pressure by more than half in each case. The optimization results for all four emitters were experimentally validated in lab-studies. We performed a more exhaustive validation study for the 8.2 lph emitter with an emitter manufacturer. Results from these experiments (which followed ISO standards) showed that the optimized 8.2 lph emitter had a 75% lower activation pressure when compared to the original emitter design.Jain Irrigation System Ltd

Emergence of robustness against noise: A structural phase transition in evolved models of gene regulatory networks

We investigate the evolution of Boolean networks subject to a selective pressure which favors robustness against noise, as a model of evolved genetic regulatory systems. By mapping the evolutionary process into a statistical ensemble and minimizing its associated free energy, we find the structural properties which emerge as the selective pressure is increased and identify a phase transition from a random topology to a "segregated core" structure, where a smaller and more densely connected subset of the nodes is responsible for most of the regulation in the network. This segregated structure is very similar qualitatively to what is found in gene regulatory networks, where only a much smaller subset of genes --- those responsible for transcription factors --- is responsible for global regulation. We obtain the full phase diagram of the evolutionary process as a function of selective pressure and the average number of inputs per node. We compare the theoretical predictions with Monte Carlo simulations of evolved networks and with empirical data for Saccharomyces cerevisiae and Escherichia coli.Comment: 12 pages, 10 figure

AMoEBA: the adaptive modeling by evolving blocks algorithm

This dissertation presents AMoEBA, the Adaptive Modeling by Evolving Blocks Algorithm. AMoEBA is an evolutionary technique for automatic decomposition of data fields and solver/descriptor placement. By automatically decomposing a numerical data set, the algorithm is able to solve a variety of problems that are difficult to solve with other techniques. Two key features of the algorithm are its ability to work with discrete data types and its unique geometric representation of the domain. AMoEBA uses genetic programming generated parse trees to define data segregation schemes. These trees also place solver/descriptors in the decomposed regions. Since the segregation trees define the boundaries between the regions, discrete representations of the data set are possible. AMoEBA is versatile and can be applied to many different types of geometries as well as different types of problems. In this thesis, three problems will be used to demonstrate the capabilities of this algorithm. For the first problem, AMoEBA used approximated algebraic expressions to match known profiles representing a steady-state conduction heat transfer problem and the fully-developed laminar flow through a pipe. To further illustrate the versatility of the algorithm, an inverse engineering problem was also solved. For this problem, AMoEBA placed different materials in the segregated regions defined by the trees and compared this to known temperature profiles. The final demonstration illustrates the application of AMoEBA to computational fluid dynamics. In this implementation, AMoEBA segregated an elbow section of pipe and placed numerical solvers in the regions. The resulting solver networks were solved and compared to a known solution. Both the time and accuracy of the networks were compared to determine if a faster solution method can be found with a reasonably accurate solution. Although AMoEBA is adapted for each application, the core algorithm of AMoEBA is unaltered in each application. This illustrates the flexibility of the algorithm

RANS-based Aerodynamic Shape Optimization Investigations of the Common Research Model Wing

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140409/1/6.2014-0567.pd