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Optimal shape design and transition uncertainty analysis of transonic axisymmetric natural laminar flow nacelle at high Reynolds number

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期121卷 107345-107345页

作者： Tang, Zhili Zhang, Meifang Hu, Xiao Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China

Laminar flow drag reduction is an important way to improve the cost effectiveness of civil aircraft. In this paper, a hybrid game based optimization method is applied to optimize an axisymmetric natural laminar flow nacelle with inflow and geometric constraints at high Reynolds number. The effectiveness of the optimization algorithm and the ability to explore the solution of multi-objective optimization problem from engineering science are confirmed. In order to maintain the stable laminar flow performance and to achieve robust aerodynamic drag reduction in changing flight conditions, the global sensitivity and uncertainty analysis of the designed axisymmetric laminar nacelle with respect to the coupling of geometric parameters and the coupling of Mach number and turbulence intensity are finally performed, and the effects of the input parameters on the laminar flow range and the total drag coefficient are analyzed. The sensitivities of each parameter and its contribution to the laminar flow region were obtained. This lays the foundation for the subsequent development of a robust optimization modeling and the specification of its effective search space for the three-dimensional natural laminar flow nacelle. (C) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Natural laminar flow nacelle Transition prediction Uncertainty analysis Sensitivity analysis hybrid optimization algorithm

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Optimal design of large-scale solar-aided hydrogen production process via machine learning based optimisation framework

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APPLIED ENERGY 2022年 305卷 117751-117751页

作者： Wang, Wanrong Ma, Yingjie Maroufmashat, Azadeh Zhang, Nan Li, Jie Xiao, Xin Univ Manchester Ctr Proc Integrat Dept Chem Engn & Analyt Sci Manchester M13 9PL Lancs England HEC Montreal Dept Decis Sci GERAD Montreal PQ Canada Chinese Acad Sci Inst Proc Engn Beijing 100191 Peoples R China

Hydrogen is an important energy carrier in the transportation sector and an essential industrial feedstock for petroleum refineries, methanol, and ammonia production. Renewable energy sources, especially solar energy have been investigated for large-scale hydrogen production in thermochemical, electrochemical, or photochemical manners due to considerable greenhouse gas emissions from the conventional steam reforming of natural gas and oil-based feedstock. The solar steam methane reforming using molten salt (SSMR-MS) is superior due to its unlimited operation hours and lower total annualized cost (TAC). In this work, we extend the existing optimisation framework for optimal design of SSMR-MS in which machine learning techniques are employed to describe the relationship between solar-related cost and molten salt heat duty and establish relationships of TAC, hydrogen production rate and molten salt heat duty with independent input variables in the whole flowsheet based on 18,619 sample points generated using the Latin hypercube sampling technique. A hybrid global optimisation algorithm is adopted to optimise the developed model and generate the optimal design, which is validated in SAM and Aspen Plus V8.8. The computational results demonstrate that a significant reduction in TAC by 14.9 % similar to 15.1 %, and CO2 emissions by 4.4 % similar to 5.2 % can be achieved compared to the existing SSMR-MS. The lowest Levelized cost of Hydrogen Production is 2.4 $ kg(-1) which is reduced by around 17.2 % compared to the existing process with levelized cost of 2.9 $ kg(-1).

关键词： Solar energy Hydrogen Machine learning hybrid optimization algorithm Surrogate model

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Path Planning of Laser Soldering System Based on Intelligent algorithm

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SENSORS 2022年第21期22卷 8120-8120页

作者： Zhang, Cong Fan, Zige Dai, Yaonan Chen, Hang Wang, Sikai Chen, Xubing Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Prov Engn Technol Res Ctr Green Chem Equipm Sch Mech & Elect Engn Wuhan 430205 Peoples R China

Laser soldering has been gradually applied to the soldering of electronic components due to the rapid development of microelectronics. However, it is inefficient to use a mechanical shaft to move a laser beam. Here, a laser soldering system is constructed using galvanometer scanning, and an intelligent algorithm is also introduced to optimize the soldering path. Firstly, a laser soldering system for scanning of galvanometers is established, and the functions of visual monitoring, motion planning and parameter integration are presented. Secondly, the position of the laser beam and the corresponding soldering spot are determined, and the coordinate information is provided to plan a route by camera calibration and coordinate system transformation. Finally, the problem of path planning in this system is decomposed into the generation of the soldering point full coverage processing frame, and the route optimization of processing platform and laser beam motion. Furthermore, an improved clustering algorithm, based on the characteristics of system structure, and a hybrid optimization algorithm are designed to deal with the generation of the soldering point full coverage processing frame, the route optimization of processing platform and laser beam motion. In addition, the simulations and experiments are verified by test board. These findings shown that the established system and designed optimization algorithm can promote the efficiency of laser soldering.

关键词： laser soldering system improved clustering algorithm hybrid optimization algorithm path planning

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A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route

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OCEAN ENGINEERING 2022年 261卷

作者： Lin, Yan Bian, Xuan-yi Dong, Zong-ran Dalian Univ Technol Sch Naval Architecture & Ocean Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Foreign Languages Sch Software Dalian 116044 Peoples R China

Based on Differential Evolution and Cuckoo Search, a hybrid optimization algorithm, called DDECS, is proposed in this paper for solving the ship pipe route layout optimization problem. The purpose of DDECS algorithm is to quickly and accurately optimize the ship pipe route under various kinds of constraints. Firstly, this paper pro-posed a vector coding method for ship pipe routes by studying the existing coding methods and ship pipe routes' attributes. The proposed vector coding method has a concise structure, strong compatibility and is independent of the gridding space. And then, the Discrete hybrid Differential Evolution and Cuckoo Search (DDECS) algo-rithm was implemented by using the proposed vector coding. The DDECS algorithm has few parameters and high computational efficiency, and it can effectively find the global optimal solution by maintaining the diversity of the population. Finally, three comparative simulations were presented to validate the effectiveness and engi-neering practicality of DDECS algorithm. The results showed that, the proposed DDECS algorithm can get an optimization scheme with fewer elbows and more remaining areas, which is significant for the layout optimi-zation of the ship pipe route.

关键词： Ship pipe Layout optimization Coding technology hybrid optimization algorithm Automation design

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Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks

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DIGITAL SIGNAL PROCESSING 2022年第0期130卷

作者： Roberts, Michaelraj Kingston Ramasamy, Poonkodi Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India

The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery powered, and may be completely drained, resulting in deterioration in network lifetime. Energy efficient protocols aim to maximize network lifetime while minimizing overall energy consumption to avoid interruptions to the wireless sensor nodes deployed for monitoring and recording physical information from the environment. Moreover, hybrid metaheuristic-optimization based clustering and routing protocols are widely used for attaining energy stability and network lifetime. In this study, the golden eagle optimization algorithm (GEOA) and improved grasshopper optimization algorithm (IGHOA) based on the energy efficient cluster -based routing protocol (GEIGOA) are proposed for sustaining energy stability and augmenting network lifetime longevity by overcoming challenges in the cluster head (CH) selection process. In particular, by optimizing the node centrality, node degree, distance to the base station, distance to the neighbors, and residual node energy, GEOA selects an optimal CH from the deployed group of sensor nodes in the network. Furthermore, IGHOA is utilized to determine a reliable and optimal route between the CH and base station (BS) by assessing node degree, residual energy, and distance parameters. Moreover, the proposed GEIGOA was confirmed to be sufficiently capable in improving the probability of preventing the worst nodes from being selected as CHs with different number of sensor nodes, which was predominantly enhanced by 12.64%, 15.82%, 18.96%, and 20.98% compared with the competitive CH selection schemes. In addition, the computational cost incurred by the proposed GEIGOA with different number of sensor nodes was also confirmed to be minimized by 14.98%,

关键词： hybrid optimization algorithm Wireless sensor networks Network lifetime Routing protocols Residual energy

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A metaheuristic algorithm based on simulated annealing for optimal sizing and techno-economic analysis of PV systems with multi-type of battery energy storage

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期53卷

作者： Yi, Huaihai Yang, Xingang China Univ Geosci Beijing Sch Earth Sci & Resources Beijing 100083 Peoples R China China Inst Lab Relat Beijing 100048 Peoples R China

The battery energy storage unit is one of the main components of hybrid photovoltaic (PV)/battery systems to ensure the economy and reliability of the system to satisfy the electrical loads of green building, especially in remote areas. The main objective of this study is the establishment of a method for optimal design of PV arrangements based on multi-type of battery, in which the annual system cost (ASC) and the loss of load supply probability of the hybrid systems are simultaneously minimized. The hybrid method with simulated annealing and a harmony search methods is suggested in the sizing of the hybrid systems based on three types of energy storage which are lithium-ion, absorbed glass mat, and lead-acid. To calculate the future performance of the hybrid scheme, the effect of reliability index variations and the battery unit cost variations on the sizing problem, ASC, and also loss of power probability are calculated. The simulation results show that the battery storage level increases by increasing the ideal unite of PV and storage values to maintain the system reliability level. Also, it is found that an efficient battery type with the optimal size is techno-economically more beneficial, and leads to greater system reliability.

关键词： hybrid solar/energy storage system Multi-type of battery energy storage hybrid optimization algorithm Green building Optimal sizing Techno-economic analysis

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An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2020年第10期35卷 1520-1569页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Sinha, Nikhil K. Indian Inst Technol ISM Dept Elect Engn Dhanbad 826004 Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Bihar India

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.

关键词： cuckoo search algorithm enhanced grey wolf optimization hybrid optimization algorithm inter-area oscillations power system stabilizer STATCOM

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Task Planning of Space Maintenance Robot Using Modified Clustering Method

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IEEE ACCESS 2020年 8卷 45618-45626页

作者： Xiao, Pengfei Ju, Hehua Li, Qidong Xu, Hao Lu, Chenxin Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210000 Peoples R China

This paper investigates the on-orbit maintenance task planning of large-scale space solar power station. The research is of great significance for the normal operation of large-scale space equipment. Space robots are very maneuverable, which makes space maintenance possible. A new task planning algorithm is proposed for the on-orbit maintenance of space robots. Firstly, based on the discretization method, the modified clustering method is used to cluster the maintenance area of large-scale space solar power stations. Secondly, the hierarchical structure principle is used to assign tasks to the space robot, the inner layer plans the shortest path within the cluster set, and the outer layer plans the optimal path between the cluster sets. Finally, based on the optimal path of the outer layer of the maintenance task, a hybrid optimization algorithm based on tabu algorithm and modified ant colony algorithm is proposed. The algorithm can meet the space machine task allocation requirements and analyze the maintenance path under certain energy conditions. The simulation results show that the proposed algorithm can effectively solve the optimal problem of space robot mission planning under resource constraints.

关键词： Task analysis Maintenance engineering Space power stations Planning Power generation Space robot task clustering hybrid optimization algorithm task planning

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A novel convolutional neural network framework based solar irradiance prediction method

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 114卷 105411-000页

作者： Dong, Na Chang, Jian-Fang Wu, Ai-Guo Gao, Zhong-Ke Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

As an important part of solar power system, photovoltaic grid-connected system and solar thermal system, solar irradiance has the inherent characteristics of variability and uncertainty. Existing data analysis methods are difficult to demonstrate better generalization. Hence, resource planners must be adaptable to accommodate these uncertainties while conducting planning. To improve the accuracy of solar energy prediction and efficiently organize the utilization of solar energy, a novel convolutional neural networks framework has been constructed. Firstly, we have established a convolutional neural network framework for solar prediction based on meteorological data from surrounding sites and different sampling times. Secondly, the chaotic GA/PSO1 hybrid algorithm is applied to optimize the hyper parameters of the novel framework, which alleviates the imperfect performance caused by improper hyper parameters. At the meantime, the hybrid algorithm can reduce the manpower and resources of manual parameter adjustment. Excitability of the novel framework has been verified by benchmark tests. In the solar irradiance prediction studies, the annual average Mean Absolute Error of the proposed method is reduced by 0.1463 MJ.m(-2) compared with single CNN2 framework. The annual average Mean Absolute Error of the proposed method is reduced by 49.47%, 47.6%, 20.34%, respectively, compared with ANN(3), K-means-RBF4 and GBRT.(5) The superiority has been fully illustrated through all the simulation test results. Therefore, the proposed method provides a basis for accurate estimation of solar power, which can promote further development of the whole power system.

关键词： Solar irradiance prediction Convolutional neural network hybrid optimization algorithm Hyperparameter Renewable energy

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An Approach Combining an Efficient and Global Evolutionary algorithm with a Gradient-Based Method for Airfoil Design Problems

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SMART SCIENCE 2020年第1期8卷 14-23页

作者： Ariyarit, Atthaphon Kanazaki, Masahiro Bureerat, Sujin Suranaree Univ Technol Inst Engn Sch Mech Engn 111 Maha Witthayalai Rd Nakhon Ratchasima Thailand Tokyo Metropolitan Univ Grad Sch Syst Design Div Aeronaut & Astronaut Tokyo Japan Khon Kaen Univ Fac Engn Dept Mech Engn Sustainable & Infrastruct Res & Dev Ctr Khon Kaen Thailand

This paper presents a hybrid optimization approach using an evolutionary algorithm and a gradient-based method with an efficient weighting function for multi-objective problems (MoPs) to obtain deterministic solutions in real-world design. The proposed method starts with finding the approximate Pareto optimal set using the multi-objective evolutionary algorithm (MOEA). In this study, a non-dominated sorting genetic algorithm (NSGA-II) with multi-modal distribution crossover (MMDX) was applied to GA exploration as the initial data set. Then, the gradient-based method was used to find the deterministic non-dominated solution using the non-dominated solutions obtained using the MOEA. A quasi-Newton method was employed as a gradient-based method for unconstrained problems whereas sequential quadratic programming (SQP) was used for constrained problems. An efficient weighting function was proposed to define the weights for the scalarizing function. Test problems with and without constraints were considered for numerical experiments that compared the proposed method and the uniform weighting function, which was calculated based on the trade-off ratio conventionally used for translating MoPs to a single objective problem. The airfoil design problem was also solved as a real-world problem. This problem has two objectives: to maximize thickness and to minimize the drag force (C-d). Comparing the cover rate metrics (CR), the result of the proposed hybrid method was a uniform non-dominated solution that maintained the diversity of solutions compared with the conventional weighting function. In addition, the airfoil design solution demonstrated that this method can maintain higher solution diversity compared to the NSGA-II.

关键词： Airfoil design problem gradient-based optimization problem evolutionary computation hybrid optimization algorithm multi-objective optimization

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