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Optimal seismic retrofit design method for asymmetric soft first-story structures

STRUCTURAL ENGINEERING AND MECHANICS 2022年第6期81卷 677-689页

作者： Dereje, Assefa Jonathan Kim, Jinkoo Sungkyunkwan Univ Dept Global Smart City Suwon South Korea

Generally, the goal of seismic retrofit design of an existing structure using energy dissipation devices is to determine the optimum design parameters of a retrofit device to satisfy a specified limit state with minimum cost. However, the presence of multiple parameters to be optimized and the computational complexity of performing non-linear analysis make it difficult to find the optimal design parameters in the realistic 3D structure. In this study, genetic algorithm-based optimal seismic retrofit methods for determining the required number, yield strength, and location of steel slit dampers are proposed to retrofit an asymmetric soft first-story structure. These methods use a multi-objective and single-objective evolutionary algorithms, each of which varies in computational complexity and incorporates nonlinear time-history analysis to determine seismic performance. Pareto-optimal solutions of the multi-objective optimization are found using a non-dominated sorting genetic algorithm (NSGAII). It is demonstrated that the developed multi-objective optimization methods can determine the optimum number, yield strength, and location of dampers that satisfy the given limit state of a three-dimensional asymmetric soft first-story structure. It is also shown that the single-objective distribution method based on minimizing plan-wise stiffness eccentricity turns out to produce similar number of dampers in optimum locations without time consuming nonlinear dynamic analysis.

关键词： genetic algorithm optimization multi objective optimization seismic retrofit slit dampers soft first-story

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Design optimization of Water Distribution Networks with Dynamic Search Space Reduction GA

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WATER RESOURCES MANAGEMENT 2024年第1期38卷 63-79页

作者： Gangwani, Laxmi Dongre, Shilpa Gupta, Rajesh Abdy Sayyed, Mohd Abbas H. Tanyimboh, Tiku Shri Ramdeobaba Coll Engn & Management Dept Civil Engn Nagpur 440013 India Visvesvaraya Natl Inst Technol VNIT Dept Civil Engn Nagpur 440010 India Indian Inst Technol Bombay IITB Ctr Urban Sci & Engn Mumbai 400076 India Univ Witwatersrand Sch Civil & Environm Engn WITS Private Bag 3 ZA-2050 Johannesburg South Africa

Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit and is obtained using pressure dependent analysis. In this study, the methodology is demonstrated using a genetic algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs.

关键词： Combined flow and pressure deficit penalty genetic algorithm optimization Pressure dependent analysis Water distribution network Dynamic search space reduction (DSSR) Self-adaptive penalty function

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Management and coordination of LTC, SVR, shunt capacitor and energy storage with high PV penetration in power distribution system for voltage regulation and power loss minimization

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年 100卷 178-192页

作者： Aryanezhad, Majid Natl Iranian Oil Co Elect Power Engn Dept Tehran Iran

The optimal coordination of load tap changers (LTCs), step voltage regulators (SVRs), switched shunt capacitors (SCs) and energy storages (ESs) with high penetration of photovoltaic (PV) energy sources for simultaneously minimizing energy loss and improving voltage profile are performed using genetic algorithm (GA). The GA is developed to find out the optimal settings of the LTCs, SVRs and SCs, including the dispatch of energy storages. The influence of the PV penetration level on voltage profile and tap movement is to be analyzed. Five scenarios are involved in this case study. In the first scenario, regulator devices are disabled and voltage profile is monitored and recorded. When the LTC is enabled in the second scenario, the voltage profile is propped up yet still below the voltage limits. The voltage is regulated within the limits, when the SVRs are enabled in the third scenario. In the fourth scenario, switching 'on' the capacitor banks increases the voltage profile. This maintains the distribution system security as the voltage profile gets flattened off. Finally, the integration of the ES warrants voltage profile improvement, particularly when the voltage is low at night. The voltage deviation is substantially lowered as well from a 0.063 pu without the ES to a 0.018 pu with the ES. The proposed method is applied to the IEEE 123 test feeder, using time series analysis over a diurnal 24-h simulation period. Test results clearly show that the close coordination among the VAR control devices and energy storage scales back system energy losses and upholds voltage at customer terminals within statutory limits, under high penetration levels of solar-fueled generation. Comparison between proposed method with evolutionary algorithm (EA) and particle swarm optimization (PSO) are investigated that shows the proposed algorithm has better results than the other approaches.

关键词： Voltage regulation Power losses minimization High PV penetration genetic algorithm optimization

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Experimental characterization and control of a magnetic shape memory alloy actuator using the modified generalized rate-dependent Prandtl-Ishlinskii hysteresis model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2018年第5期232卷 506-518页

作者： Shakiba, Saeid Zakerzadeh, Mohammad Reza Ayati, Moosa Univ Tehran Sch Mech Engn Coll Engn POB 11155-4563 Tehran Iran

In this article, two models are used, namely rate-independent and rate-dependent generalized Prandtl-Ishlinskii, to characterize a magnetic shape memory alloy actuator. The results show that the rate-independent model cannot consider the effect of input excitation frequency, while the rate-dependent model omits this drawback by defining a time-dependent operator. For the first time, the effects of excitation frequency on the hysteretic behavior of magnetic shape memory alloy actuator are investigated. In this study, five excitation voltages with different frequencies in the range of 0.05-0.4Hz are utilized as inputs to the magnetic shape memory alloy actuator and the displacement outputs are measured. Experimental results indicate that, with increasing the excitation frequency, the size of the hysteresis loops changes. Since the generalized rate-dependent Prandtl-Ishlinskii model cannot consider the asymmetric hysteresis loops, in the developed model, a tangent hyperbolic function is applied as an envelope function in order to improve the capability of the model in characterizing the asymmetric behavior of magnetic shape memory alloy actuator. The parameters of both rate-dependent and rate-independent models are identified by genetic algorithm optimization. The results reveal that the rate-independent form is not capable of accurately describing the hysteretic behavior of magnetic shape memory alloy actuator for different input frequencies. Simulation and experimental results also demonstrate the proficiency of the developed model for precise characterization of the saturated rate-dependent hysteresis loops of magnetic shape memory alloy actuator. In addition, the proposed model is utilized for determining a proper range for controller coefficients during controller design.

关键词： Prandtl-Ishlinskii model hysteresis loops rate-dependent model magnetic shape memory alloy actuator control genetic algorithm optimization

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Design considerations of a direct drive brushless machine with concentrated windings

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2008年第1期23卷 1-8页

作者： Wrobel, Rafal Mellor, Phil H. Univ Bristol Dept Elect & Elect Engn Bristol BS8 1UB Avon England

This paper discusses the performance attributes of the open-slot, modular-wound, external-rotor, topology of electrical machine. Combinations of pole and slot numbers are presented for which the winding factor is maximal and torque ripple is minimal. An optimization of the magnetic circuit design of six promising pole-slot configurations is undertaken using a parametric finite element model (FEM) combined with a genetic algorithm (GA). These designs are benchmarked against a conventional 1.5 slots/pole external-rotor brushless dc machine. These candidate electrical machine versions are characterized by having the same external-rotor diameter, total slot area available for the winding and by equal volumes of permanent magnet (PM). Based on the analysis, the most promising motor structure was selected and a prototype wheel-hub motor has been built for application in a small electrical vehicle. Test data from the prototype is used to validate the findings of the initial analyses and practically demonstrate the attributes of the topology.

关键词： brushless permanent magnet motor external rotor genetic algorithm optimization

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optimization of Taylor spatial frame half-pins diameter for bone deformity correction: Application to femur

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2018年第7期232卷 673-681页

作者： Chavoshnejad, Pooria Ayati, Moosa Abbasspour, Aziz Karimpur, Morad George, Daniel Remond, Yves Rouchi, Alireza Heidary Baniassadi, Majid Univ Tehran Sch Mech Engn Fac Engn Tehran *** Iran Univ Tokushima Inst Hlth Biosci Dept Orthoped Grad Sch Tokushima Japan Univ Strasbourg CNRS ICube Lab Strasbourg France Univ Tehran Med Sci Iranian Tissue Bank Tehran Iran Univ Tehran Med Sci Res Ctr Tehran Iran

Using external fixtures for bone deformity correction takes advantages of less soft tissue injury, better bone alignment and enhances strain development for bone formation on cutting section, which cause shorter healing time. Among these fixtures, Taylor spatial frame is widely used and includes two rings and six adjustable struts developing 6 degrees of freedom, making them very flexible for this type of application. The current study describes a method to optimize Taylor spatial frame pin-sizes currently chosen from the surgeon's experiences. A three-dimensional model of femur was created from computed tomography images;segmentation of the medical images was made based on the Hounsfield unit (gray scale) in order to allocate adequate mechanical properties into cortical and trabecular bone sections. Both the cortical and trabecular sections were assumed to be isotropic and homogeneous. The diameter optimization of Taylor spatial frame's half-pins was carried out by coupling genetic algorithm and finite element analysis. The finite element analysis was based on a static mechanical load corresponding to a standing person's body weight. Finite element analysis results were validated with experimentally measured strains obtained from bone compression tests. A cost function, based on the developed bone stresses, was defined close to the Taylor spatial frame's half-pins. The calculated cost function showed a decrease of over 33% from the initial half-pin selection by the surgeon and the genetic algorithm optimization. Consequently, the maximum stresses experienced by the bone in the connected location of the half-pins decreased from 121.4 MPa in the surgeon's selection to 73.07 MPa as a result of the optimization process.

关键词： Taylor spatial frame finite element analysis genetic algorithm optimization

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Structural analysis and optimization of anisogrid composite lattice cylindrical shells

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COMPOSITES PART B-ENGINEERING 2018年 139卷 203-215页

作者： Belardi, V. G. Fanelli, P. Vivio, F. Univ Roma Tor Vergata Dept Enterprise Engn Via Politecn 1 I-00133 Rome Italy Univ Tuscia DEIM I-01100 Viterbo Italy

A structural analysis and an optimization method for anisogrid composite lattice shell structures is proposed, considering cylindrical structures simultaneously subjected to different external loads and multiple stiffness constraints. A discrete approach is used to exactly estimate the critical buckling load of the anisogrid lattice structure, independently of the buckling failure mode. The method makes use of a full FE parametric modeling technique able to manage all the geometrical parameters of the anisogrid composite lattice structure. Then an optimization procedure based on the genetic algorithm NSGA-II has been performed;it allows to analyze different alternatives in terms of geometrical variables, both continuous and discrete, driving the search towards the optimal solution in term of mass and conformity with all structural and stiffness constraints, aiming at the preliminary design of an actual structure. The practical usefulness and applicability of the proposed procedure to industrial cases was demonstrated through numerical examples where the anisogrid lattice structure was subjected to multiple external loads and stiffness constraints simultaneously applied.

关键词： Carbon fiber Buckling Finite element analysis (FEA) Filament winding genetic algorithm optimization

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A New Optimal Under-frequency Load-shedding Method Using Hybrid Culture-Particle Swarm optimization-Co-evolutionary algorithm and Artificial Neural Networks

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第1期43卷 69-82页

作者： Moazzami, Majid Khodabakhshian, Amin Hooshmand, Rahmat-Allah Univ Isfahan Dept Elect Engn Esfahan *** Iran

Optimum under-frequency load shedding during contingency situations is one of the most important issues in power system security analysis;if carried out online fast enough, it will prevent the system from going to a complete blackout. This article presents a new fast load-shedding method in which the amounts of active and reactive power to be shed are optimized with a dynamic priority list by using a hybrid culture-particle swarm optimization-co-evolutionary algorithm and artificial neural network method. The proposed method uses a five-step load-shedding scenario and is able to determine the necessary active and reactive load-shedding amounts in all steps simultaneously on a real-time basis. An artificial neural network database is established by using offline N - K (K = 1, 2, and 3) contingency analysis of the IEEE 118-bus test system. The Levenberg-Marquardt back-propagation training algorithm is used for the artificial neural network, and the training process is optimized by using a genetic algorithm. The artificial neural network database is updated based on new contingency events that occur in the system. The simulation results show that the proposed algorithm will give optimal load shedding for different N - K contingency scenarios in comparison with other available under-frequency load-shedding methods.

关键词： hybrid culture-particle swarm optimization-co-evolutionary algorithm genetic algorithm optimization artificial neural network under-frequency load shedding

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Prediction surface roughness of 3D printed parts using genetic algorithm optimized hybrid learning model

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JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS 2024年第7期37卷 2225-2245页

作者： Akgun, Gazi Ulkir, Osman Marmara Univ Dept Mechatron Engn RTE Maltepe CampusIdealtepe Yolu 15 Istanbul Turkiye Mus Alparslan Univ Dept Elect & Energy Mus Turkiye

The final product of additive manufacturing (AM) or 3D printing critically depends on the surface quality. An experimental study on the 3D printed intake manifold flange using acrylonitrile butadiene styrene (ABS) material was executed by varying the four process parameters. A fused deposition modeling (FDM) based 3D printer was used to fabricate the flanges. The association between the parameters and the surface roughness of printed ABS flanges was investigated. A feed forward neural network (FFNN) model trained on particle swarm optimization (PSO) optimized with a genetic algorithm (GA) was used to estimate the surface roughness. A Box-Behnken design (BBD) with printing parameters at three levels was used, and 25 parts were fabricated. The suggested model demonstrated a coefficient of determination (R2) of 0.9865 on test values, mean of root-mean-square-error (RMSE) of 0.1231 after 500 times training for generalization. And also mean of overfitting factor is 0.7110. This means that the suggested system could generalize. Comparing the results from the suggested model and ANN, the suggested hybrid model outperformed ANN in predicting the surface roughness values with no overfitting. This suggests that GA optimized PSO based FFNN may be a more suitable method for estimating product quality in terms of surface roughness.

关键词： Additive manufacturing surface roughness fused deposition modeling particle swarm optimization learning artificial neural network genetic algorithm optimization

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Design, analysis analysis and optimization of anisogrid composite lattice conical shells

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COMPOSITES PART B-ENGINEERING 2018年 150卷 184-195页

作者： Belardi, Valerio G. Fanelli, Pierluigi Vivio, Francesco Univ Roma Tor Vergata Dept Enterprise Engn Via Politecn 1 I-00133 Rome Italy Univ Tuscia DEIM I-01100 Viterbo Italy

A methodology for structural analysis and optimal design of conical anisogrid composite lattice shell structures subject to different external loads concurrently applied and multiple stiffness constraints is presented. The critical buckling load of the anisogrid lattice conical structure is exactly assessed, independently of the buckling failure mode, by means of a discrete approach. The method makes use of a full FE parametric modeling technique able to manage all the geometrical parameters of the anisogrid composite lattice structure. Additionally, the genetic algorithm NSGA-II is employed to set up an optimization procedure which allows to analyze different sets of geometrical variables, both continuous and discrete, to reach the optimal solution in terms of mass amount and fulfilling of structural and stiffness requirements, aiming at the preliminary design of an actual structure. Numerical case -studies are outlined in order to demonstrate the practical usefulness and versatility of the proposed procedure to industrial cases where the anisogrid lattice conical structure undergoes multiple external loads and various stiffness constraints must be satisfied.

关键词： Carbon fiber Buckling Finite element analysis (FEA) Filament winding genetic algorithm optimization

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