scholarly journals Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation

2019 ◽  
Vol 9 (13) ◽  
pp. 2679 ◽  
Author(s):  
Berchiolli ◽  
Guarda ◽  
Walsh ◽  
Pesyridis
Keyword(s):  
Genetic Algorithm ◽  
Transient Response ◽  
Shape Optimisation ◽  
Preliminary Design ◽  
Axial Turbine ◽  
Cluster Network ◽  
Multi Objective Genetic Algorithm ◽  
Axial Turbines ◽  
Lower Moment ◽  
Multidisciplinary Design Optimisation

In a previous paper [1], a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.

Conceptual design of UAV using Kriging based multi-objective genetic algorithm

2008 ◽  
Vol 112 (1137) ◽  
pp. 653-662 ◽  
Author(s):  
S. Rajagopal ◽  
R. Ganguli
Keyword(s):  
Genetic Algorithm ◽  
Conceptual Design ◽  
Empirical Relation ◽  
Aircraft Design ◽  
Function Evaluation ◽  
Maximum Speed ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Multidisciplinary Design Optimisation

Abstract This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.

scholarly journals Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

2020 ◽  
Vol 13 (1) ◽  
pp. 290
Author(s):  
Seyed Hashem Mousavi-Avval ◽  
Shahin Rafiee ◽  
Ali Mohammadi
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Fuzzy Inference ◽  
Cost Ratio ◽  
Inference System ◽  
Benefit Cost Ratio ◽  
Multi Objective Genetic Algorithm ◽  
Neuro Fuzzy ◽  
Environmental Emissions ◽  
Benefit Cost

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

Sign in / Sign up

Export Citation Format

Share Document