Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method

Dynamic parametric modeling-based model updating strategy of aeroengine casings

One critical issue for manufacturing 3D MCM packages is the warpage induced during the process. The effects of several important parameters, including processing conditions, package geometry and materials, are specifically studied on the occurrence of warpage and optimized to reduce the warpage in many previous literatures. However, the design parameter values are not always precisely known but can contain a level of uncertainty to some extent due to, for example manufacturing tolerance. In some case, the design parameters variations can not be neglected. In this paper, the effect of structure geometry tolerance and material property tolerance on package warpage robustness was took into account and analyzed. The robust optimization mathematic model of the 3D packages, considering the uncertainty of the design parameters, was built. And the robust optimization method based on the Monte-carlo simulations and Genetic Algorithm was present to solve the optimization problem above. Firstly, the parametric design finite element model of the specific 3D MCM package was built. Secondly, the statistic distribution of the warpage induced by the design parameters variations was obtained based on the Monte-carlo simulations. Finally, the GA was implemented to optimize the mean value and standard deviation of the warpage. The resulting robust optimization design shows that the robust optimization design is insensitive to variations on design variables within a feasible region.

Structural optimisation of composite wind turbine blade structures with variations of internal geometry configuration

Parametric finite element model of lower cervical spine (C3–C7) in sagital plane

Biomechanical assessment of stability in the metastatic spine following percutaneous vertebroplasty: effects of cement distribution patterns and volume

The paper considers the optimal design of an open slot, external rotor torque motor. Combinations of pole and slot numbers are presented for which the winding factor is maximal and torque ripple is minimal. A detailed optimisation of the magnetic circuit design of the six most promising pole-slot configurations has been undertaken using the combination of a genetic algorithm (GA) and a parametric finite element model. These candidate motor versions are characterised by the same total slot area available for the winding and by equal volumes of permanent magnet. An alternative two-segment Halbach arrangement of the rotor permanent magnets is also proposed to further increase the volumetric torque density

Presently, three-dimensional printing technology is developing rapidly. This happens due to the need to create products of complex shape, the production of which by existing standard methods is very difficult and disadvantageous, and sometimes technically impossible. The study aims to investigate the problem of temperature stability of a thin-walled cylindrical structure under unsteady local thermal exposure, simulating the motion of a laser beam spot along one of the ends. To solve the problem, the finite element method was used. Since the geometric areas of calculation were parameterized, then for each set of parameters, its own finite element mesh was used for each type of analysis. A parametric spatial finite element model of cylindrical shell pinched at the bottom end was constructed. The local moving heat flux acts on the opposite side, simulating the movement of a laser beam during the additive formation of a thin-walled element. Numerical solutions of the nonstationary dynamic heat conduction problem were obtained, spatiotemporal temperature dependences were obtained, and solutions of the quasistatic problem of the loss of both local and complete loss of stability of the equilibrium state of the cylinder at various times due to the occurrence of local compressive stresses during intense heating were obtained. The dependences of critical heat flux power corresponding to the loss of stability on the cylinder wall thickness and its height were obtained. The above results can be used as calibration parameters of the process of creating thin-walled structures using additive technologies of Laser Melting class for products that require increased requirements for manufacturing accuracy.

A direct and effective approach is presented for the inverse problem of dynamic structural systems, which is related to structural optimization, system identification, and damage detection. The structural modifications are sought for the characteristic changes assigned from the design goals or modal measurements. A finite element method is used for the system analysis and inverse problem. Mathematical programming techniques are applied for the minimization of the deviation of the finite element model from the desired inverse system, along with an objective function of least structural change. The modal method is based on the perturbation equations of a set of selected degrees of freedom and the energy equation associated with the frequency change. The mode shape change is expressed as the sum of the baseline mode shape and complementary vector, which plays a very important role in the search for the inverse solution. The linear perturbation equation is employed to get an initial approximation, which can be improved through iterations with the nonlinear perturbation equation. The proposed method does not involve the system reduction. Therefore, it is believed to be superior to conventional methods, which suffer from the residual error due to transformation of the eigenproblem.

Smooth plant production is utmost expected by plant owners since any unplanned shutdown is very much undesirable as it may incur loss of company’s profits and other repercussions. Critical operational assets such as rotating machineries and their components, e.g., motor, pump, compressor, and turbines are crucial to be monitored in ensuring continuous operation of the plant. Therefore, vibration being one of the elements in predictive maintenance is much needed. This paper addressesthe monitoring and findings for predictive maintenance on high vibration of a make-up gas compressor in one of the oil & gas and petrochemical plant in Malaysia. The conventional vibration measurement using vibration analyzer was utilized to identify the severity of the machine vibrations through the overall vibration value obtained on the motor and compressor. A specialized vibration measurement technique, namely, Operational Modal Analysis (OMA) was further deployed to verify the presence of resonance on the equipment by obtaining its dynamic characteristics, i.e., natural frequency and mode shape. The findings showed clear indication of resonance occurrence as similar frequency (112 Hz) was identified from both vibration measurement and operational modal analysis.

Metamaterials exhibit enhanced properties such as auxetic deformation and superior energy absorption, making them promising for applications in energy engineering, aerospace, and advanced manufacturing. Among the various types of metamaterials, two-dimensional (2D) auxetic metamaterials are composed of periodic arrays of negative Poisson’s ratio (NPR) cells, whose geometric structure and dimensions dictate their unique mechanical behaviors. However, quantifying the relationship between the geometric disorder (non-uniform dimensional distributions of cellular structures) and the macroscopic mechanical properties in 2D metamaterials remains a significant challenge. To address this, a machine learning (ML) model is developed based on the parameterized finite element method (PFEM) for multi-objective iterative optimization of 2D auxetic metamaterials. In this model we derive the optimal non-uniform dimensional distributions of cellular structures under specific design objectives. Firstly, a parameterized finite element model is established to comprehensively analyze the quantitative relationship between the non-uniform dimensional distributions of cellular structures and the macroscopic responses. Next, the deep neural network (DNN) prediction models are developed to forecast the macroscopic responses based on the non-uniform dimensional distributions of cellular structures. Additionally, an optimization model using the genetic algorithm (GA) is implemented to determine the optimal non-uniform dimensional distributions for given objectives. Finally, quasi-static uniaxial tensile tests on designed samples validate their macroscopic responses. This study provides a solid foundation for more complex designs of 2D auxetic metamaterials, enhancing their NPR characteristic and energy absorption capability.

Finite element analysis is a powerful tool in the biomechanical study to analyse whole body vibration. The objective of this work is to develop a biomechanical model of human trunk-neck-head (above the pelvis level) and analyse its mode shape characteristics using finite element analysis (FEA). A parametric finite element model is developed using solid-works and analysed using ANSYS software. The natural frequencies were obtained for the developed human trunk-neck-head model using free vibration modal analysis, and compared with the results obtained in the past literatures. Mode shapes and deflection due to whole body vibration in all resonance frequency ranges are numerically obtained and interpreted. A total of ten vibration modes were obtained from the finite element model. The principal resonance at 5.11 Hz consisted of a half body mode, in which the head and spinal column move almost rigidly, with axial and shear deformation lower than L5 occurring in the fourth mode at 18.35 Hz. A bending mode of the entire spine was found in the eighth mode at 33 Hz, which was making a minor contribution to the principal resonance. As in the experimental results, a neck rotation was found in both the ninth mode at 35.26 Hz and in the tenth mode 43.24 Hz.

Genetic algorithm in structural damage detection

Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Geometric imperfections play a significant role in the evaluation of the buckling capacity of thin-walled structures. Since the pattern and distribution of such imperfections take a random variation, the determination of a critical imperfect shape that leads to the minimum buckling load becomes a challenge. In this study, an optimization technique based on binary coded genetic algorithms is used together with a nonlinear finite element model to identify the critical imperfection pattern for thin-walled welded structures. The finite element model is based on a consistent sub-parametric shell element that accounts for the effect of both geometric and material nonlinearities. The binary coded genetic algorithm is used as the optimization means to arrive at the imperfection pattern leading to the near globally minimum buckling load. A previously developed mathematical model is employed to describe the imperfection bulges associated with lines of weldment. The method is demonstrated by considering the nonlinear stability analysis of both a circular cylinder subjected to pure axial compressive load and a conical tank under hydrostatic pressure. The combined genetic algorithm – finite element code is used to determine the critical imperfection bulge patterns for the two problems, as well as their associated buckling loads.

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.