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Development of hybrid performance‑based optimization algorithm for structures equipped with vibration damper devices

Ayyash Al‑jbury Najad Amer, Hejazi Farzad

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e123, 2023

EN

Nowadays, various types of vibration damping systems are being implemented in different buildings to diminish seismic effects on structures. However, engineers are faced with the challenging task of developing an optimum design for structures utilizing a proper type of damping device based on new techniques such as the performance-based design method. Therefore, this research was aimed at developing a multi-objective optimization algorithm by hybridizing the particle swarm optimization (PSO) and gravitational search algorithm (GSA) to obtain an optimum design for structures equipped with vibration damper devices based on the performance-based design method. Then, the developed hybrid algorithm (PSOGSA) would be capable of optimizing the damping system simultaneously with the optimized details of the structural sections, including the steel rebars, by satisfying all the design criteria. For this purpose, a special process for the design of structures equipped with vibration damper devices according to the performance-based design method was developed by considering of a wide range of vibration damping systems. The proposed PSOGSA optimization framework was then implemented to design a 12-storey reinforced concrete structure equipped with different types of dampers to minimize the structural weight while satisfying all the prescribed performance-based design acceptance criteria. The results indicated that the proposed optimization method was able to successfully optimize the details of the structural members as well as the type and properties of the damper, which significantly improved the structural response in terms of the formation of plastic hinges and the structural movements.

2

High performance of multilevel inverter reduced switches for a photovoltaic system

Mohammed Laith A., Hussein Taha A., Sadoon Ahmed T.

Przegląd Elektrotechniczny

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2022

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R. 98, nr 8

14--18

EN

In this paper, optimum switching angles are chosen from slime moiled algorithm (SMA), Artificial Bee Colony (ABC), Genetic algorithms (GA), Whale optimization algorithm (WOA), and Gray wolf algorithm (GWO). These angles are selected according to the lowest total harmonic distortion of output load voltage from reduced switches multilevel inverter. These algorithms are working together in a hybrid seduced to solve the nonlinear equation of switching angles determination. A 25-level inverter fed by isolated unequal PV panel as DC sources with reduced switches and sources is chosen for this study. Theoretical analysis and Simulation are accomplished using Matlab/Simulink for 25 level reduced switches multilevel inverter. The simulated results validated the practical outcomes.

PL

W niniejszym artykule optymalne kąty przełączania zostały wybrane spośród algorytmu śluzowatego (SMA), sztucznej kolonii pszczół (ABC), algorytmów genetycznych (GA), algorytmu optymalizacji wielorybów (WOA) i algorytmu szarego wilka (GWO). Kąty te są dobierane zgodnie z najniższymi całkowitymi zniekształceniami harmonicznymi napięcia obciążenia wyjściowego ze zredukowanych przełączników wielopoziomowych falowników. Algorytmy te współpracują ze sobą w hybrydzie, której celem jest rozwiązanie nieliniowego równania wyznaczania kątów przełączania. Do tego badania wybrano 25-poziomowy falownik zasilany przez izolowany nierówny panel fotowoltaiczny jako źródła prądu stałego o zredukowanych przełącznikach i źródłach. Analiza teoretyczna i symulacja są realizowane przy użyciu Matlab/Simulink dla 25 przełączników o zredukowanych poziomach wielopoziomowego falownika. Symulowane wyniki potwierdziły praktyczne wyniki.

3

Inverse and direct optimization shape of airfoil using hybrid algorithm Big Bang-Big Crunch and Particle Swarm Optimization

Masoumi Heidar, Jalili Farhad

Journal of Theoretical and Applied Mechanics

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2019

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Vol. 57 nr 3

697--711

EN

In this paper, Big Bang-Big Crunch and Particle Swarm Optimization algorithms are combined and used for the first time to optimize airfoil geometry as a aerodynamic cross section. The optimization process is carried out both in reverse and direct directions. In the reverse approach, the object function is the difference between pressure coefficients of the optimized and target airfoils, which must be minimized. In the direct approach, three objective functions are introduced, the first of which is the drag to lift (D/L) ratio. It is minimized considering four different initial geometries, ultimately, all four geometries converge to the same final geometry. In other cases, maximizing lift the coefficient with the fixed drag coefficient constraint and minimizing the drag coefficient while the lift coefficient is fixed are defined as purposes. The results show that by changing the design parameters of the initial airfoil geometry, the proposed hybrid optimization algorithm as a powerful method satisfies the needs with proper accuracy and finally reaches the desired geometry.