Design and optimization of rudder bulb based on CFD and genetic algorithm

In this paper, multi-objective optimization (MOO) of Al2O3-water nanofluid flow in microchannel heat sinks (MCHS) with triangular ribs is performed using Computational Fluid Dynamics (CFD) techniques and Non-dominated Sorting Genetic Algorithms (NSGA II). At first, nanofluid flow is solved numerically in various MCHS with triangular ribs using CFD techniques. Finally, the CFD data will be used for Pareto based multi-objective optimization of nanofluid flow in MCHS with triangular ribs using NSGA II algorithm. In the MOO process there are seven geometrical and non-geometrical parameters and the conflicting objective functions are to simultaneously maximize the amount of heat transfer and minimize the pressure drop. Five optimum designs are determined and discussed for both nanofluid and base fluid flows. Details of design variables for each of five optimum points are deeply discussed. It is shown that the achieved Pareto solution includes important design information on nanofluid flow in MCHS with triangular ribs.

An investigation of the thermal behavior of constructal theory-based pore-scale porous media by using a combination of computational fluid dynamics and machine learning

The application of genetic algorithms in behavioural ecology, illustrated with a model of anti-predator vigilance

Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions. A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.

This study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best-fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. ‘High- and low-growth scenarios’ are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.

Reducing Greenhouse Gas Emissions from vessels is one of the greatest challenges the maritime industry is currently facing. International Maritime Organization has set the goal of reducing CO2 emissions from international shipping by at least 40% by 2030, compared to 2008. Emissions regulations are also leading to a progressive reduction of ships life span, together with a decrease in economic value. To cope with these challenges, the preferred strategy suggested by IMO for new vessels -Energy Efficiency Design Index- aims at increasing the energy efficiency over time by stimulating innovation and continuous development of technical elements. In this context, ship builders are indirectly led to develop vessels that will be “changeable” in terms of propulsion systems over time. This paper presents a conceptual framework to maritime vessels for propulsion system changeability, which integrates contributions from literature review with the knowledge of design thinking experts and precious insights of maritime industry professionals. The aim of this framework is support the integration of renewable fuel sources for vessel propulsion systems through an extended value approach, while improving propulsion efficiency over time.

Last-mile delivery is an important part of a logistics activity in the city. Usually, delivery workers are required to manually unload goods at customer locations. These manual tasks induce physiological fatigue in the workers and increase delivery time. This paper discusses a genetic algorithm (GA) approach to the open vehicle routing problem with split delivery (SDOVRP), where manual unloading is addressed. The workers are pre-assigned to vehicle and split delivery is allowed. Its objective is to minimize the total cost of total fixed cost of vehicles and delivery workers and total transportation cost. For safety, the total physical workload imposed on each worker must not exceed the daily limit. Since an optimization approach cannot find the optimal solution within reasonable computation time especially when solving large size problem. A GA with heuristic for pre-determine split delivery is developed to solve the problem. The computational experiment results show that the GA approach is efficient and can obtain near-optimal SDOVRP solutions.

With rapid development of technology, interconnect will occupy much chip area and gradually dominate circuit performance, interconnect optimization in high level design become more and more important. In this paper, we formulate an interconnect reuse based high level synthesis algorithm with GA (Genetic Algorithm) approach. The main contribution is that we put forward a novel coding method and design corresponding genetic operator for avoidance of generation of infeasible solutions. Experiments will show the efficiency of the algorithm.

In this paper, an effective genetic algorithm (GA) approach is proposed for tuning the parameters of membership functions based on input-output pairs. By minimizing a quadratic measure of the error in the least-squares sense, the real-valued chromosomes of a population are evolved to get the best coefficients. Comparison to the well-known back-propagation algorithm for fuzzy logic system shows that both are powerful training algorithms, but much better performance is obtained with the proposed technique. Several numerical design examples are presented to demonstrate the efficiency and effectiveness of this proposed approach.

The demand for high-efficiency air vehicles and the development of distributed electric propulsion systems for aircraft are exposing the need for accurate aerodynamic prediction of multiple integrated propulsion systems. Conventional high-fidelity computational methods are too expensive, and low-cost low-fidelity methods make too many aerodynamic assumptions to be applied to these analyses. Dual-solver hybrid computational fluid dynamics (CFD) methods bridge the gap between these two categories of solvers, but many lack capabilities that are needed for complex vehicles. Recent improvements to the hybrid solver OVERFLOW-CHARM provide efficient and accurate analysis of integrated propulsion systems with hybrid CFD methods, as demonstrated on the wing–propeller system of the AIAA Workshop for Integrated Propeller Prediction. Predictions of propeller thrust are within 1% of conventional CFD data, and wing pressure coefficient distributions agree well with both experimental data and conventional CFD methods. The dual-solver results were obtained at half the computational cost of the full CFD simulation.

Design of two-dimensional FIR digital filters for sampling structure conversion by genetic algorithm approach

Clinching is a convenient and efficient cold forming process that can join two sheets without any additional part. This study establishes an intelligent system for optimizing the clinched joint. Firstly, a mathematical model which introduces the ductile damage constraint to prevent cracking during clinching process is proposed. Meanwhile, an optimization methodology and its corresponding computer program are developed by integrated finite element model (FEM) and genetic algorithm (GA) approach. Secondly, Al6061-T4 alloy sheets with a thickness of 1.4mm are used to verify this optimization system. The optimization program automatically acquires the largest axial strength which is approximately equal to 872N. Finally, sensitivity analysis is implemented, in which the influence of geometrical parameters of clinching tools on final joint strength is analyzed. The sensitivity analysis indicates the main parameters to influence joint strength, which is essential from an industrial point of view.

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

Aerodynamic characteristics of a guided rocket projectile are numerically simulated. Simulations are performed in a range of flight conditions using a commercial CFD software FLUENT, and variations of lift coefficients and drag coefficients with Mach numbers and angles of attack were demonstrated. The numerical results also indicate that the angle of canard deflection has significant influence on the flow field near tailfins.