Application of evolutionary algorithms to optimize cooling channels

The design and development is a complex, repetitive, and more often difficult task, as design tasks comprising of restraining and conflicting relationships among design variables with more than one design objectives. Conventional methods for solving more than one objective optimization problems is to build one composite function by scalarizing the multiple objective functions into a single objective function with one solution. But, the disadvantages of conventional methods inspired scientists and engineers to look for different methods that result in more than one design solutions, also known as Pareto optimal solutions instead of one single solution. Furthermore, these methods not only involved in the optimization of more than one objectives concurrently but also optimize the objectives which are conflicting in nature, where optimizing one or more objective affects the outcome of other objectives negatively. This study demonstrates a nature-based and bio-inspired evolutionary simulation method that addresses the disadvantages of current methods in the application of design optimization. As an example, in this research, we chose to optimize the periodic segment of the cooling passage of an industrial gas turbine blade comprising of ribs (also known as turbulators) to enhance the cooling effectiveness. The outlined design optimization method provides a set of tradeoff designs to pick from depending on designer requirements

Evolutionary numerical simulation approach for design optimization of gas turbine blade cooling channels

Gas turbine blade cooling system design is a multidisciplinary, iterative and often tedious task involving complex relationships among multiple design objectives. Typical blade design requires a broad range of expertise in the materials, structural, heat transfer, and cost optimization disciplines. The multiple objectives involved are often conflicting and must be solved simultaneously with equal importance. The traditional approaches researchers scalarize the multiple objectives into a single objective using a weight vector, thus transforming the original multiple objective problem into a single objective problem. This research addresses the shortcomings of existing traditional approaches of the optimization of blade cooling configuration design

Design Optimization Of Gas Turbine Blade Internal Cooling Channels

Gas turbine blade cooling system design is a multidisciplinary, iterative, manual and often tedious task involving complex relationships among multiple objectives and a set of design variables. Typical blade design requires a broad range of expertise in the materials, structural, heat transfer, and cost optimization disciplines. The multiple design objectives involved are often conflicting and must be solved simultaneously with equal importance. The traditional approaches researchers use scalarize the multiple objectives into a single objective using a weight vector, thus transforming the original multiple objective design problem into a single objective problem. However, a known drawback of such an approach is that the weights are subjectively selected. This research describes an approach that addresses the shortcomings of existing traditional approaches of the optimization of blade cooling configuration design. Specifically, two design performance objectives and a set of design decision variables that influence the performance objectives are studied. This proposed approach automatically generates the most appropriate blade cooling channel design specifications that simultaneously optimize the two design objectives

Three-Objective Optimization For The Design Of Mechanical Component Using Evolutionary Numerical Simulation Approach

The mechanical component design is an iterative, manual and often tedious task, as many real-world engineering design problems involving constraints and complex relationships among several design variables and multiple design objectives. Traditional approaches for solving multiobjective engineering design optimization problems typically scalarize the multiple objectives into a single objective, thus transforming the original multiple objective design problem into a single objective problem with a single solution. However, the known drawbacks of these traditional approaches have motivated researchers and practitioners to seek alternative techniques that yield a set of compromised engineering design solutions called Pareto optimal solutions rather than a single solution. Furthermore, these techniques involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective degrades the performance of one or more of the other objectives. This research describes a proposed bio-inspired numerical simulation approach that addresses the shortcomings of existing approaches for mechanical design optimization. In this study, we selected to optimize simplified form of cooling channel of a gas turbine blade with turbulators (ribs). The proposed design approach identifies a set of tradeoff design solutions to select from based on design engineer preferences

Evolutionary numerical simulation approach for design optimization of gas turbine blade cooling channels

Gas turbine blade cooling system design is a multidisciplinary, iterative and often tedious task involving complex relationships among multiple design objectives. Typical blade design requires a broad range of expertise in the materials, structural, heat transfer, and cost optimization disciplines. The multiple objectives involved are often conflicting and must be solved simultaneously with equal importance. The traditional approaches researchers scalarize the multiple objectives into a single objective using a weight vector, thus transforming the original multiple objective problem into a single objective problem. This research addresses the shortcomings of existing traditional approaches of the optimization of blade cooling configuration design