Optimum pressure vessel design based on fracture mechanics and reliability criteria

Optimization design methods for spacecraft structural systems and subsystem

Numerical optimization design of advanced transonic wing configurations

A computationally efficient and versatile technique for use in the design of advanced transonic wing configurations has been developed. A reliable and fast transonic wing flow-field analysis program, TWING, has been coupled with a modified quasi-Newton method, unconstrained optimization algorithm, QNMDIF, to create a new design tool. Fully three-dimensional wing designs utilizing both specified wing pressure distributions and drag-to-lift ration minimization as design objectives are demonstrated. Because of the high computational efficiency of each of the components of the design code, in particular the vectorization of TWING and the high speed of the Cray X-MP vector computer, the computer time required for a typical wing design is reduced by approximately an order of magnitude over previous methods. In the results presented here, this computed wave drag has been used as the quantity to be optimized (minimized) with great success, yielding wing designs with nearly shock-free (zero wave drag) pressure distributions and very reasonable wing section shapes

Website optimization, design, and restructuring.

The website has become a staple in the business environment, to provide information and services, and connect business-to-business and business-to-customers. Many of these sites require re-engineering in order to facilitate the needed complexities and frequent changes demanded. For such efforts, it is essential for web management to be adequately quantified with relevant metrics and measures. This thesis investigates useful metrics through a case study approach and presents usability and maintainability as the two primary categories of metrics, which provide useful information for web engineering analysis and development

Computer Axial Flow Fan Optimization Design

In this study, the method of fuzzy multi-criteria decision-making is utilized for carrying out the assessment of axial-flow fan designs on the basis of competitive selection. Furthermore, the flow-field analysis by FLUENT has been conducted for comparison with the testing results by wind-tunnel measurements and experiments. With the modifications of geometric parameters, the internal three-dimensional flow fields of axial fans have been analyzed via the analysis by numerical simulation and a performance testing was conducted. The performance curves obtained have been compared with numerical results. And by taking the design optimization of axial-flow fans as a case, the validity of this design approach has been explained and verified. The research results revealed that this evaluation method not only incorporates both the practicability and the objectivity, it can also assist decision-makers in carrying out a strategic decision in complex and uncertain environments. Through the results of flow-field analysis, designers can effectively master the trend of flow-field distribution for axial-flow fans.     Keywords: method, fan design, numerical simulation, fan experiment

Comparative Analysis of the FTTH Optimization Design using Genetic Algorithm and Ant Colony Algorithm

Fiber to the Home (FTTH) as the promised Technology for the Broadband and Optical Era now applied widely all around the world, especially in the Modern Country. Indonesia as Developing Country having the highest potential demand for Triple Play Services, The Wide Scatter of Demand become the most barrier of Investment, High cost of Investment. Deployment Infrastructure optic (FTTH) at Telkom design by man and manual process base on experience and capability designer. This manual division process is time consuming and non-optimized which very often leads to a high design cost. Ant Colony Algorithm (ACA) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. ACA is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, ACA and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules. The GA and its many versions have been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear, mixed integer optimization problems that are typical of complex engineering systems. The drawback of the GA is its expensive computational cost. This paper attempts to examine the claim that ACA has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency (less function evaluations) by implementing statistical analysis and formal hypothesis testing. The performance comparison of the GA and ACA is implemented using a set of FTTH design.” This paper proposes a comparative analysis Genetic and Ant Colony Algorithm method to optimize routes of distributions from Optical Distribution Cabinet (odc) to optical distribution points (odp) satisfying given constraints. Each sub-area is served by one optical distribution cabinet (odc) containing the optical splitters and maximum capable handle 12 to 24 of distribution cable with full capacity of 144 or 288 cores ODP. In this paper, we focus on how Genetic Algorithm and Ant Colony Algorithm Optimization method can be employed to solve this problem effectively. The outcome from this study optimization path route placement of odp compared to the implemented project, optimization using GA or ACA process give the same result 1,91% for 3 and 5 odp, but for more than 9 odp give different result, ACA give result shorter distribution cable length (lowest distribution attenuation) rather than GA with positive increasing: 3% for 9 odp, 4% for 24 odp, 14% for 74 odp and 23% for 144 odp, Differential between optimization using ACA and GA nearly exponential. Key word : FTTH, Genetic Algoritm, Optimizing, TSP, Ant Colony Algorith

GPflowOpt: A Bayesian Optimization Library using TensorFlow

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that is easy to extend with custom acquisition functions and models. The framework is thoroughly tested and well documented, and provides scalability. The current released version of GPflowOpt includes some standard single-objective acquisition functions, the state-of-the-art max-value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy use of custom modeling strategies implemented in GPflow

The SUMO toolbox: a tool for automatic regression modeling and active learning

Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as a drop-in replacement for the original simulator, in order to reduce evaluation times. In this context, neural networks, kernel methods, and other modeling techniques have become indispensable. Surrogate models have proven to be very useful for tasks such as optimization, design space exploration, visualization, prototyping and sensitivity analysis. We present a fully automated machine learning tool for generating accurate surrogate models, using active learning techniques to minimize the number of simulations and to maximize efficiency