Crack identification in plates-type structures using natural frequencies coupled with successful history-based adaptive differential evolution algorithm

In this study, a new approach, for identification and characterization of straight cracks in plates-like structures, is presented. The finite element method using a commercial software (Abaqus)is coupled with successful history-based adaptive differential evolution algorithm (SHADE) which, ensures the minimization of the objective function based on the mean relative error, that is defined as the difference between the measured (experimental) frequencies of a plate with an unknown crack identity and numerical frequencies of a cracked plate given by the approach Shade/FEM. This method will be applied on a steel thin plate to find the identity of the crack given by length, orientation and centre coordinates. Two strategies are applied to validate the effectiveness of the proposed approach. The first one, is based on the inverse problem using natural frequencies of a plate withknown crack identity obtained by a modal simulation on Abaqus. In the second, the experimental frequencies of a cracked plate were used. With just a population size of 25 and 150 iterations, the results show a good accuracy of the proposed approach with a relative error of the objective function less than 0.8%

Coupling of inverse method and cuckoo search algorithm for multiobjective optimization design of an axial flow pump

This work describes the application of a multiobjective cuckoo search method for turbomachinery design optimization of an axial pump. Maximization of the total efficiency and minimization of the required net positive suction head of the pump are the two objective functions considered for the optimization problem. The optimization process is carried out on a range of imposed volumetric flow rates, with taking into account at each discretized radius between the hub and tip of the rotor: the profile camber, rotor wall thickness, angular deviation, and the solidity, regarded as geometrical constraints and nominal flow rate as mechanical constraint. Two strategies are proposed in order to solve the problem. In the first one, three forms of mono-objective model with two variables, total efficiency and net positive suction head, are considered. In the second one, a multiobjective model with nondominated sorting scheme is adopted. A comparative evaluation of results obtained from the proposed approach with those of a reference machine and genetic algorithm allowed us to validate the present work

Efficiency of bio- and socio-inspired optimization algorithms for axial turbomachinery design

Turbomachinery design is a complex problem which requires a lot of experience. The procedure may be speed up by the development of new numerical tools and optimization techniques. The latter rely on the parameterization of the geometry, a model to assess the performance of a given geometry and the definition of an objective functions and constraints to compare solutions. In order to improve the reference machine performance, two formulations including the off-design have been developed. The first one is the maximization of the total nominal efficiency. The second one consists to maximize the operation area under the efficiency curve. In this paper five optimization methods have been assessed for axial pump design: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO) and Sequential Linear Programming (SLP). Four non-intrusive methods and the latter intrusive. Given an identical design point and set of constraints, each method proposed an optimized geometry. Their computing time, the optimized geometry and its performances (flow rate, head (H), efficiency (η), net pressure suction head (NPSH) and power) are compared. Although all methods would converge to similar results and geometry, it is not the case when increasing the range and number of constraints. The discrepancy in geometries and the variety of results are presented and discussed. The computational fluid dynamics (CFD) is used to validate the reference and optimized machines performances in two main formulations. The most adapted approach is compared with some existing approaches in literature

Crack identification in plates-type structures using natural frequencies coupled with successful history-based adaptive differential evolution algorithm

In this study, a new approach, for identification and characterization of straight cracks in plates-like structures, is presented. The finite element method using a commercial software (Abaqus)is coupled with successful history-based adaptive differential evolution algorithm (SHADE) which, ensures the minimization of the objective function based on the mean relative error, that is defined as the difference between the measured (experimental) frequencies of a plate with an unknown crack identity and numerical frequencies of a cracked plate given by the approach Shade/FEM. This method will be applied on a steel thin plate to find the identity of the crack given by length, orientation and centre coordinates. Two strategies are applied to validate the effectiveness of the proposed approach. The first one, is based on the inverse problem using natural frequencies of a plate withknown crack identity obtained by a modal simulation on Abaqus. In the second, the experimental frequencies of a cracked plate were used. With just a population size of 25 and 150 iterations, the results show a good accuracy of the proposed approach with a relative error of the objective function less than 0.8%

Energy-based USV maritime monitoring using multi-objective evolutionary algorithms

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Towards an Accurate Aerodynamic Performance Analysis Methodology of Cross-Flow Fans

Cross-flow fans (CFFs) have become increasingly popular in recent years. This is due to their use in several domains such as air conditioning and aircraft propulsion. They also show their utility in the ventilation system of hybrid electric cars. Their high efficiency and performance significantly rely on the design parameters. Up to now, there is no general approach that predicts the CFFs’ performance. This work describes a new methodology that helps deduce the performance of CFFs in turbomachinery, using both analytical modeling and experimental data. Two different loss models are detailed and compared to determine the performance–pressure curves of this type of fan. The efficiency evaluation is achieved by realizing a multidisciplinary study, computational fluid dynamics (CFD) simulations, and an optimization algorithm combined to explore the internal flow field and obtain additional information about the eccentric vortex, to finally obtain the ultimate formulation of the Eck/Laing CFF efficiency, which is validated by the experimental results with good agreement. This approach can be an efficient tool to speed up the cross-flow fans’ design cycle and to predict their global performance

Towards an Accurate Aerodynamic Performance Analysis Methodology of Cross-Flow Fans

Cross-flow fans (CFFs) have become increasingly popular in recent years. This is due to their use in several domains such as air conditioning and aircraft propulsion. They also show their utility in the ventilation system of hybrid electric cars. Their high efficiency and performance significantly rely on the design parameters. Up to now, there is no general approach that predicts the CFFs’ performance. This work describes a new methodology that helps deduce the performance of CFFs in turbomachinery, using both analytical modeling and experimental data. Two different loss models are detailed and compared to determine the performance–pressure curves of this type of fan. The efficiency evaluation is achieved by realizing a multidisciplinary study, computational fluid dynamics (CFD) simulations, and an optimization algorithm combined to explore the internal flow field and obtain additional information about the eccentric vortex, to finally obtain the ultimate formulation of the Eck/Laing CFF efficiency, which is validated by the experimental results with good agreement. This approach can be an efficient tool to speed up the cross-flow fans’ design cycle and to predict their global performance