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Optimal design of passive-adaptive pendulum tuned mass damper for the global vibration control of offshore wind turbines

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WIND ENERGY 2021年第6期24卷 573-595页

作者： Bertollucci Colherinhas, Gino Petrini, Francesco de Morais, Marcus Vinicius Girao Bontempi, Franco Univ Brasilia Fac Tecnol Campus Darcy Ribeiro Brasilia DF Brazil Sapienza Univ Roma Sch Engn Via Eudossiana 18 Rome Italy

This paper presents an optimal design procedure for a pendulum tuned mass damper (PTMD) to mitigate the global structural vibrations of offshore wind turbines (OWTs) in the fore-aft and side-side directions. The procedure is tested to the design of a PTMD to be applied to the 5-MW benchmark baseline monopile wind turbine proposed by the National Renewable Energy Lab (NREL). The computation of wind and wave spectra, as well as the evaluation of the hydrodynamic and aerodynamic loads, is conducted by using an in-house built MATLAB (R) routine working together with an ANSYS (R) 3-D finite element (FE) global model for evaluating the resultant peak displacement response at the OWT hub by a power spectral density (PSD) analysis. In order to validate the OWT FEM model, a result comparison is made with the NREL OpenFAST, finding good matches between the two codes. An in-house built genetic algorithm (GA) toolbox, coded in MATLAB (R), is then used to optimally design the parameters of a PTMD with a simplified 2-degrees-of-freedom (2DOF) model. The chosen GA fitness function targets the minimization of the peak response of the primary structure as evaluated by the 2DOF model. The design parameters of the PTMD are the flexural rigidity and damping, the mass ratio and pendulum length. After the 3-D FE model of the OWT without any control device has been validated, and the PTMD has been optimized by the simplified 2DOF model, the performances of the PTMD are examined on a 3-D global FE OWT + PTMD model in ANSYS (R).

关键词： genetic algorithm optimization offshore wind turbine pendulum tuned mass damper power spectral density analysis structural control

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A novel hysteretic restoring force model for shear link dampers: A machine learning approach

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STRUCTURES 2024年 70卷

作者： Elgammal, Ahmed Ali, Yasmin Sichuan Univ Coll Architecture & Environm Dept Civil Engn Chengdu 610065 Peoples R China

This research presents an enhanced restoring force model for shear link dampers along with a machine learningbased approach to predict its hardening parameters. The conducted work comprised three distinct phases. Initially, a numerical investigation utilizing ANSYS Workbench was conducted on 350 shear link dampers, taking into account the nonlinearity of the material as well as geometric imperfections. The study focused on the impact of geometric properties on key hysteretic parameters, and analytical formulas were derived to estimate shear yield strength, cyclic web shear buckling rotation angle, and elastic stiffness. In the second phase, the original restoring force model from earlier studies was enhanced and further simplified. Subsequently, a genetic algorithm implemented in MATLAB determined the optimal hardening parameters of the proposed model for each specimen, aligning the analytical hysteretic curves with those from the numerical study. Results indicated that the proposed model more accurately simulated shear link damper hysteretic response compared to the original model. The third and final phase involved constructing and training an artificial neural network (ANN) using hardening parameters obtained in the second phase as targets, with shear link damper geometric properties as inputs. The ANN demonstrated robust learning and high accuracy. To validate the efficiency of the proposed analytical model, blind testing against experimentally tested specimens was conducted, confirming that the model effectively mimicked the experimental hysteretic response of shear link dampers.

关键词： Cyclic loading Hysteretic response Material nonlinearity Finite element genetic algorithm optimization Artificial neural network

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Enhanced Carbon/Oxygen Ratio Logging Interpretation Methods and Applications in Offshore Oilfields

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PROCESSES 2024年第10期12卷 2301页

作者： Zhou, Wei Lin, Yaoting Gao, Gang Wang, Peng Guangdong Ocean Univ Sch Comp Sci & Engn Yangjiang 529500 Peoples R China Guangdong Ocean Univ Yangjiang Res Inst Yangjiang 529500 Peoples R China Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China

As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil-water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil-water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development.

关键词： carbon/oxygen logging fan chart interpretation ratio chart analysis genetic algorithm optimization residual oil saturation

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XGB-SEGA coupled energy saving method for wastewater treatment plants

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APPLIED WATER SCIENCE 2024年第2期14卷 29页

作者： Wang, Zijian Zhou, Xuwen Wang, Hao Huang, Zehan Ji, Jiantao Peng, Zhaoxu Jiang, Kun Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Harbin Inst Technol Sch Civil & Environm Engn Shenzhen 518055 Peoples R China Cent Plains Environm Protect Co Ltd Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Ecol & Environm Zhengzhou 450001 Peoples R China CSD Water Serv CO Ltd Beijing 100124 Peoples R China

To reduce the amount of energy consumed in wastewater treatment plants, nine methods were used to select the key operation parameters that affected energy consumption according to daily operation records, and an intelligent operation management system based on a genetic algorithm was constructed by mapping the relationships between energy consumption and the key operation parameters. The results showed that the prediction and management of energy consumption could be achieved by incorporating the strengthened elastic genetic algorithm into the extreme gradient boosting model. The main parameters affecting energy consumption were the influent flow rate, effluent total nitrogen, NH4+-N loading rate, etc., and the energy consumption could be reduced by 13-27% (with an average of 22%). The parameters were all selected from the daily operation records of the wastewater treatment plant, and no additional complex data acquisition system was needed to collect specific parameters. This study provided a cost-effective strategy to reduce energy consumption in wastewater treatment plants.

关键词： Energy saving Extreme gradient boosting (XGB) genetic algorithm optimization Carbon neutrality Wastewater treatment

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Comparative multi-objective optimization using neural networks for ejector refrigeration systems with LiBr and LiCl working agents

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CASE STUDIES IN THERMAL ENGINEERING 2024年 60卷

作者： Khanmohammadi, Shoaib Ahmadi, Pouria Jahangiri, Ali Izadi, Ali Tariq, Rasikh Kermanshah Univ Technol Dept Mech Engn Kermanshah Iran Kermanshah Univ Technol Ctr Energy Syst Modeling & Optimizat ESMopti Kermanshah Iran Univ Tehran Coll Engn Sch Mech Engn POB 11155-4563 Tehran Iran Shahid Beheshti Univ Fac Mech & Energy Engn Tehran Iran Inst Future Educ Tecnol Monterrey Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico

Education's evolution in the context of energy systems is essential for addressing sustainable energy challenges and developing a workforce equipped for future innovations, emphasizing both formal curricula and informal lifelong learning through successful energy case studies. As the global energy sector transforms to reduce carbon emissions and reliance on fossil fuels, innovations in renewable technologies like solar thermal are pivotal for promoting energy security and economic stability, supported by an educational foundation that fosters awareness and technical skills for sustainable development. Exposure to successful renewable energy systems, such as solar -powered refrigeration, offers an informal educational experience that enhances understanding and supports global educational goals, initiating with the innovative design and optimization of these systems using artificial intelligence (neural networks). Based on this formulation, the current article developed two sustainable energy systems by comparing the refrigeration cycles with two different operating fluids and various arrangements, and multi -objective optimization with an evolutionary genetic algorithm is performed for the proposed systems. The studied systems are refrigeration cycles using an ejector and without an ejector with two working fluids of lithium bromide and lithium chloride. The present work's main aim is to examine the working fluid and refrigeration system arrangements. Energy and economic modeling were performed for the proposed systems, and then parametric analysis and two -objective optimization were extracted. Parameters such as generator temperature, condenser temperature, absorber temperature, and evaporator temperature, which significantly impact the proposed system's performance, have been selected as decision parameters, and parametric analysis has been extracted for them. In addition to the mentioned parameters, diffusion mixing efficiency, nozzle efficiency, and heat exchang

关键词： genetic algorithm optimization Artificial neural network Ejector refrigeration systems Lifelong learning Educational innovation Higher education Computational thinking

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Performance analysis and optimization of a vehicular LT-PEMFC for maximum power density and efficiency based on NSGA-II algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： Gu, Yongming Ma, Zheshu Guo, Qilin Wang, Yuting Nanjing Forestry Univ Coll Automobile & Traff Engn 159 Longpan Rd Nanjing 210037 Jiangsu Peoples R China

Based on finite temporal thermodynamics (FTT), a model for a low-temperature proton exchange membrane fuel cell (LT-PEMFC) is created. The link between the LT-PEMFC's output power density and efficiency was determined using the aforementioned model. The experimental results confirm the algorithm model's accuracy. Investigations are also conducted into how the design and operating parameters affect the LT-PEMFC's output outcomes. Moreover, the NSGA-II technique is used to maximize the power density and efficiency of the LT-PEMFC for multi-objective optimization. The experiment outcomes demonstrate that the optimized LT-PEMFC model performs better at output. The related powertrain design scheme for the various output performances of Fuel cell vehicles (FCVs) is provided by LT-PEMFC optimization. It is demonstrated by the study of simulation data that the optimized LT-PEMFC achieves reduced hydrogen consumption and higher FCV efficiency.

关键词： LT-PEMFC parameter research genetic algorithm optimization powertrain design simulation analysis

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A sustainable exergy model for energy-water nexus in the hot regions: integrated combined heat, power and water desalination systems

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2021年第3期145卷 709-726页

作者： Ansari, Mehran Beitollahi, Armin Ahmadi, Pouria Rezaie, Behnaz Univ Tehran Sch Mech Engn Coll Engn POB 11155-4563 Tehran Iran Univ Idaho Appl Energy Res Lab AERL Dept Mech Engn Coll Engn 875 Perimeter Dr Moscow ID 83844 USA

Water scarcity is a worldwide concern for Earth citizens. Finding new methods for water concentration is essential for the extension of life. The water issue is more intense in the regions with a warm to the tropical environment. Considering the cooling demand of these regions, which consequently requires excess energy to satisfy the cooling load, having a thermal system to support three concerns of the water, cooling, and power would be the key for the warm/hot weather areas. In the present study, a novel model by integration of gas turbine power cycle with a solar parabolic collector, a steam turbine, heat recovery, steam generator, multi-effect desalination, and absorption chiller is proposed. The suggested model is optimized through developing a comprehensive multi-objective function to maximize the exergy efficiency and minimize the cost. Using the genetic algorithm method, the model is optimized based on six design parameters such as condenser pressure, number of solar parabolic through collector rows, gas turbine and steam turbine inlet temperature, high and low pressure, high- and low-pressure pinch points. The final optimal design point of this cycle enables the overall exergy efficiency of 36.16% and 188.43 $ h(-1) of the total cost rate value;also, this integrated energy system provides the net electrical generation of 5.18 MW and the cooling load rate of 406.18 KW and generates 2.57 kg s(-1) of desalinated water. In this novel cycle solar energy is used for preheating the inflow of the combustion chamber. A dual pressure heat recovery exploits thermal energy of flue gas, which runs both desalination and multi-effect absorption system and circulates in simultaneous water and cooling load generation. Finally, by utilizing the genetic 1 algorithm, the optimal system is developed.

关键词： Integrated combined power plant Gas turbine Heat recovery Absorption chiller genetic algorithm optimization Double-effect desalination

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Evolutionary offloading in an edge environment

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EGYPTIAN INFORMATICS JOURNAL 2021年第3期22卷 257-267页

作者： Zakaryia, Samah A. Ahmed, Safaa A. Hussein, Mohamed K. Fac Comp & Informat Dept Comp Sci Ismailia Egypt

Due to increasing complexity of mobile applications, and limited computation resources of smart mobile devices, the quality of service requirements of mobile application can be enhanced by offloading the computation tasks of the mobile applications to edge servers, such as cloudlets, which exist at the edge of wireless networks. However, improper placement of mobile tasks on the edge servers may increase the waiting time and the transmission time. This, in turn, will increase the response time, and eventually violates the quality of service. This paper proposes an effective offloading strategy in a mobile edge environment using the queuing networks and an evolutionary algorithm, namely the genetic algorithm (GA). The queuing network is used to model the waiting time and the service time of the mobile tasks on the edge servers. The genetic algorithm finds the best allocation of mobile tasks on the edge servers to minimize tasks response time considering the transmission times and the load conditions on edge servers represented by the waiting times and the service times which are calculated using the queuing network. The proposed GA-based offloading algorithm is compared with another evolutionary algorithm, namely particle swarm optimization (PSO). Experimental evaluations show that the GA-based offloading algorithm outperforms both of round robin offloading and the PSO-based offloading algorithms, and effectively improves mobile applications response time. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University.

关键词： Mobile edge computing Computation offloading genetic algorithm optimization Evolutionary optimization Particle swarm optimization

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Electric vehicle battery-ultracapacitor hybrid energy storage system and drivetrain optimization for a real-world urban driving scenario

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第5期43卷 1-15页

作者： Silva, Ludmila C. A. Eckert, Jony J. Lourenco, Maria A. M. Silva, Fabricio L. Correa, Fernanda C. Dedini, Franco G. Univ Campinas UNICAMP Campinas Integrated Syst Lab LabSIn Campinas Brazil Fed Univ Technol Parana UTFPR Ponta Grossa Parana Brazil

A battery has normally a high energy density with low power density, while an ultracapacitor has a high power density but a low energy density. Therefore, this paper has been proposed to associate more than one storage technology generating a hybrid energy storage system (HESS), which has battery and ultracapacitor, whose objective is to improve the electric vehicle (EV) driving range. The HESS parameters have been evaluated in a configuration of EV powered by two in-wheel electric motors, coupled straight into the front wheels, and by a unique EM, connected to a differential transmission to drive the rear wheels. Moreover, this paper considers a real-world drive cycle based on the urban driving behavior of Campinas city, one of the most populous cities in Brazil. Aiming to minimize the HESS size and enhance the EV driving range, an optimization problem was formulated and solved using a genetic algorithm technique, in which the EV drivetrain parameters and HESS components and control are optimized. Finally, the obtained Pareto frontier defines the optimum EV configurations, in which the best-selected configurations were able to perform up to 188 km with a 418 kg HESS (maximum drive range solution), or 82.75 km with a 146.58 kg HESS (minimum HESS solution) and 319 km with a 188.43 kg HESS (best trade-off solution), without presenting performance losses.

关键词： Electric vehicle (EV) Hybrid energy storage system (HESS) genetic algorithm optimization Real-world driving cycle

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Optimum sizing and configuration of electrical system for telecommunication base stations with grid power, Li-ion battery bank, diesel generator and solar PV

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Journal of Energy Storage 2025年 123卷

作者： Silva, C.M.M.R.S. Premadasa, P.N.D. Chandima, D.P. Karunadasa, J.P. Wheeler, Patrick Department of Electrical Engineering University of Moratuwa Moratuwa10400 Sri Lanka Department of Electrical Electronic & Systems Engineering NSBM Green University Homagama Sri Lanka PEMC Research Group Faculty of Engineering University of Nottingham Nottingham United Kingdom

The rising demand for cost effective, sustainable and reliable energy solutions for telecommunication base stations indicates the importance of integration and exploring the feasibility of hybrid energy systems. This study develops a mathematical model and investigates an optimization approach for optimal sizing and deployment of solar photovoltaic (PV), battery bank storage and a diesel generator for grid connected telecommunication base station. The proposed optimum hybrid electrical system is designed to minimize total capital and operational costs while achieving 100% power availability for telecommunication equipment under varying grid power availabilities. A detailed analysis was conducted under different grid power availabilities and base station load profiles heterogeneous to different geographical locations where telecommunication base stations are located. A comprehensive analysis is conducted by genetic algorithm for different system configurations under specified constraints and specifications of electrical systems. The analysis results demonstrate the dynamic and adaptive behaviour in the levelized cost of energy against grid power availability, combinations in energy resources and cost intensity of selected resources while maintaining the levelized cost of energy of the proposed electrical system between 0.047 $/kWh to 0.060 $/kWh which is well below the considered tariff of utility grid of 0.087 $/kWh in the optimization. © 2025

关键词： Adaptive operation genetic algorithm optimization Hybrid energy system Network availability Optimal sizing Telecommunication base station

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