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Numerical and experimental investigation of erosive wear in Francis runner blade optimized for sediment laden hydropower projects in Nepal

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年 51卷

作者： Gautam, Saroj Acharya, Nirmal Lama, Ram Chitrakar, Sailesh Neopane, Hari Prasad Zhu, Baoshan Kathmandu Univ KU Dept Mech Engn Turbine Testing Lab Kathmandu Nepal Norwegian Univ Sci & Technol NTNU Waterpower Lab Trondheim Norway Tsinghua Univ THU Dept Energy & Power Engn Beijing Peoples R China

The design of the Francis turbine aims to maximize its performance in terms of efficiency. This design approach has overlooked effects on the turbine due to sediment erosion for power plants operating under high sediment load. In this paper, a multi-objective design procedure to develop a Francis turbine runner for maximizing efficiency, as well as minimizing sediment erosion is discussed. Parameterized runner designs obtained from the design algorithm are implemented in the Design of Experiment (DOE) to get sample runners. These sample runners are investigated using a commercial CFD tool and validating with an experiment in a model turbine test rig to obtain efficiency and sediment erosion data. Subsequently, a multi-objective genetic algorithm (MOGA) is implemented to generate the optimized runner blades. Two different optimized runners are selected after the optimization. The optimized runner generated from this technique is further investigated experimentally in the laboratory scale sediment erosion test rig. From both numerical and experimental results, it is found that the optimized runner blade has improved erosion handling capability. Implementation of the procedure discussed in this article can be a crucial step for designing the turbines which are vulnerable to sediment erosion.

关键词： Francis Turbine Design of Experiment multi-objective genetic algorithm Sediment Erosion Efficiency

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Experimental investigation on the optimal control of vortex shedding of a circular cylinder with rotating rods at moderate Reynolds numbers

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JOURNAL OF FLUIDS AND STRUCTURES 2024年 124卷

作者： Silva, Paulo H. N. Assi, Gustavo R. S. Univ Sao Paulo Dept Naval Architecture & Ocean Engn Escola Politecn Sao Paulo Brazil

This paper presents an experimental study of fluid-structure interaction conducted at Reynolds numbers around 104 with scale models in a water channel. A circular cylinder was equipped with eight control rods positioned around its perimeter to interact with the external flow. Each rod could be driven independently to rotate about its axis. The controlled rotation of the rods interfered with the vortex generation mechanism mitigating the formation of a coherent wake. The results showed that it is possible to simultaneously reduce the mean drag and fluctuating lift forces when the rods rotate at a uniform speed or with each rod set at a different rate. A multi-objective genetic algorithm was employed to find the optimum rotation speeds. The optimum rotation produced a more significant reduction in both objectives and also consumed less energy. The contribution of each rod depends significantly on its angular position around the body and the flow conditions resulting from the upstream control rods. The present work clarifies the physical principles of the phenomenon and paves the way for the development of technological applications.

关键词： Bluff body Flow control Vortex shedding Control rods multi-objective genetic algorithm

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BI-CRITERIA SCHEDULING ON A BATCHING MACHINE WITH NON-IDENTICAL JOB SIZES

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IFAC Proceedings Volumes 2006年第3期39卷 727-732页

作者： Ali Husseinzadeh Kashan Behrooz Karimi Fariborz Jolai Department of Industrial Engineering Amirkabir University of Technology P.O.Box: 15875-4413 Tehran Iran Department of Industrial Engineering Faculty of Engineering University of Tehran P.O.Box: 11365-4563 Tehran Iran

This paper addresses scheduling jobs with non-identical sizes on a single batch-processing machine. Two different multi-objective genetic algorithms (MOGA) have been proposed. The first one is a sequence based MOGA (SMOGA) that generates random sequences of jobs and applies the batch first fit heuristic to group the jobs. The second one is a batch based hybrid MOGA (BHMOGA) that generates random batches of jobs and ensures feasibility through using knowledge of the problem. Computational results show that non-dominated solutions obtained by BHMOGA are superior in closeness to the true Pareto-optimal solutions and to keep diversity in the obtained Pareto-set, as the problem size increases.

关键词： Scheduling Batch processing machine multi-objective genetic algorithm Makespan Maximum tardiness

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Proportional integral controller gain tuning using real time digital simulation models and multi-objective optimization based co-simulation

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IFAC-PapersOnLine 2015年第30期48卷 473-478页

作者： Kumaraguru Prabakar Fangxing Li The University of Tennessee Knoxville

Proportional integral (PI) controller is a widely used controller strategy. It has two gain constants: proportional(k p ) and integral(k i ) gain constant. The dynamics of the PI controller depends on the values of the gain constant used. If gains of these control terms are not selected properly, instability will occur. Gain tuning methodologies are available to tune these controllers to avoid instability. But, these gain tuning strategies and algorithms utilize simplified mathematical model of the plant in order to obtain gain values. This approach might not provide optimal gain values for models used in real time digital simulator. In the proposed approach, the algorithm utilizes the step response of the system to obtain optimal gain constants. The plant is modeled using the real time digital simulator (RTDS). A multi-objective optimization formulation is used to find the optimal gain constants. Results are presented here to support the effectiveness of proposed multi-objective gain tuning strategy.

关键词： Proportional integral control Gain tuning multi-objective optimization multi-objective genetic algorithm

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Determination of Optimal Location and Capacity of Detention Facilities

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Procedia Engineering 2014年 70卷 1037-1045页

作者： S. Lim V.H. Ho S.Y. Lee D.G. Yoo J.H. Kim Sch. Of Civil. Environmental & Architecture Engineering Coll. of Engineering Korea Univ Anamdpng Seongbuk-gu Seoul 136-713 Korea

The importance of the effective distribution of runoff in sewer network throughout a detention facility has recently been recognized. This study aims to determine the location and the capacity of detention facilities in urban sewer network. The optimal location and capacity of the detention facilities can be determined by numerous iterations of an optimization model. In this paper, an interfacing Visual Basic (VB) programming tool with a SWMM (Storm Water Management Model) DLL for hydraulic and hydrological computation in sewer network completes the optimization routine. A multi-objective genetic algorithm (MOGA) is adopted to minimize both the total overflow and the installing cost. The developed model is applied to S city in South Korea. The goal in prevention performance of urban disaster is used as rainfall. The model will help engineers find effective alternatives to achieve the goal of the urban disaster prevention performance.

关键词： optimal location and capacity detention facility goal in prevention performance of urban disaster multi-objective genetic algorithm

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A new approach to portfolio selection based on forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Corberan-Vallet, Ana Vercher, Enriqueta Segura, Jose, V Bermudez, Jose D. Univ Valencia Dept Stat & Operat Res Dr Moliner 50 Burjassot 46100 Spain Univ Miguel Hernandez Elche Ctr Operat Res Av Ferrocarril S-N Elche 03202 Spain

In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio's value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, which is solved using a multi-objective genetic algorithm. To show the performance of this procedure, we use a data set of asset prices from the New York Stock Market.

关键词： Portfolio optimization Finance Time series analysis Forecasting multi-objective genetic algorithm

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Optimized Design of Neural Networks for a River Water Level Prediction System

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SENSORS 2021年第19期21卷 6504-6504页

作者： Lineros, Miriam Lopez Luna, Antonio Madueno Ferreira, Pedro M. Ruano, Antonio E. Univ Seville Design Engn Dept Seville 41013 Spain Univ Seville Aerosp Engn & Fluid Mech Dept Seville 41013 Spain Univ Lisbon Dept Informat LASIGE Fac Ciencias P-1749016 Lisbon Portugal Univ Algarve Fac Sci & Technol P-8005139 Faro Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

In this paper, a multi-objective genetic algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrion river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 x 10(-3), which compares favorably with results obtained by alternative design.

关键词： multi-objective genetic algorithm artificial neural networks river stage data

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Optimization of rapeseed production using exergy analysis methodology

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 43卷

作者： Esmaeilpour-Troujeni, Mahdi Rohani, Abbas Khojastehpour, Mehdi Ferdowsi Univ Mashhad Dept Biosyst Engn Mashhad Razavi Khorasan Iran

The exergy analysis helps to determine the energy losses of different inputs through identifying the actual amount of energy consumed in a process. In this regard, a new approach was applied in this study to predict and optimize the rainfed production systems (RPS) and irrigated production systems (IPS) of rapeseed in the north of Iran, by using exergy analysis methodology. Two indices of cumulative degree of perfection (CDP) and renewability index (RI) were used to evaluate the sustainability of systems. The Support Vector Machine (SVM) and multi-objective genetic algorithm (MOGA) were also employed to predict and optimize the rapeseed production systems, respectively. In the study of conventional system, CDP and RI were determined to be 2.19 and 0.72, on average, respectively, in which, the values of both indices were higher in the RPS than IPS. Optimization results indicated that by reducing the consumption of irrigation water, electricity and biocides as well as increasing the use of chemical fertilizers, farmyard manure and diesel fuel, the yield increased by 24.55%. Sustainability assessment in optimal scenario revealed that CDP and RI increased to 2.75 and 0.81, respectively. As a result, under optimal conditions, the production process becomes more environmentally friendly.

关键词： Exergy multi-objective genetic algorithm Optimization Rapeseed Sustainability

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A new accessibility based team orienteering approach for urban tourism routes optimization (A Real Life Case)

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105620-105620页

作者： Heris, Fateme Sadat Moosavi Ghannadpour, Seyed Farid Bagheri, Morteza Zandieh, Fatemeh Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran Iran Univ Sci & Technol Sch Railway Engn Tehran *** Iran

One of the most crucial issues in the tourism industry that can help attract more tourists is to design optimal travel routes for tourists. The Team Orienteering Problem with Time Windows (TOPTW) is one of the approaches that can solve Tourist Trip Design Problem (TTDP). In this study, TOPTW has been applied to design a multiobjective model to determine the optimal travel routes, considering the accessibility. The present study aims 1) to maximize the profit from visiting the Points of Interest (POIs) to increase the satisfaction level of the travel agency, and 2) to maximize the number of visited POIs to improve the satisfaction level of the tourists. Two concepts of "indirect coverage" and "neighborhood radius" are applied to increase the accessibility of the tourist attractions and POIs. Thus, around each POI, a small radius and a large radius are considered, and if the points are in the radius coverage of the points located in the main routes, they can receive indirect coverage from those points. Also the time-window overlap issue for POIs is vital to determine the possibility of indirect coverage and makes the conceptual model of the research closer to reality. Also, several indicators are proposed for calculating accessibility. A multi-objective genetic algorithm (MOGA) is formulated to solve the studied problem, and the results obtained by the proposed algorithm are compared with those of CPLEX software (for small-scale) and NSGA-II algorithm (for large-scale). The findings indicate that the genetic algorithm outperforms the NSGA-II algorithm. A sensitivity analysis is also performed to evaluate the effects of the proposed model parameters on the optimization process. Finally, the model applicability in practice is examined based on a case study in Tehran, the capital of Iran.

关键词： Tourism Team orienteering problem with time windows Profit Accessibility Indirect coverage multi-objective genetic algorithm

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Fuzzy multi-SVR Learning Model for Reliability-Based Design Optimization of Turbine Blades

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MATERIALS 2019年第15期12卷 2341-2341页

作者： Zhang, Chun-Yi Wang, Ze Fei, Cheng-Wei Yuan, Zhe-Shan Wei, Jing-Shan Tang, Wen-Zhong Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin 150080 Heilongjiang Peoples R China Fudan Univ Dept Aeronaut & Astronaut Shanghai 200433 Peoples R China Beihang Unviers Sch Comp Sci & Engn Beijing 100191 Peoples R China

The effectiveness of a model is the key factor of influencing the reliability-based design optimization (RBDO) of multi-failure turbine blades in the power system. A machine learning-based RBDO approach, called fuzzy multi-SVR learning method, was proposed by absorbing the strengths of fuzzy theory, support vector machine of regression (SVR), and multi-response surface method. The model of fuzzy multi-SVR learning method was established by adopting artificial bee colony algorithm to optimize the parameters of SVR models and considering the fuzziness of constraints based on fuzzy theory, in respect of the basic thought of multi-response surface method. The RBDO model and procedure with fuzzy multi-SVR learning method were then resolved and designed by multi-objective genetic algorithm. Lastly, the fuzzy RBDO of a turbine blade with multi-failure modes was performed regarding the design parameters of rotor speed, temperature, and aerodynamic pressure, and the design objectives of blade stress, strain, and deformation, and the fuzzy constraints of reliability degree and boundary conditions, as well. It is revealed (1) the stress and deformation of turbine blade are reduced by 92.38 MPa and 0.09838 mm, respectively. (2) The comprehensive reliability degree of the blade was improved by 3.45% from 95.4% to 98.85%. (3) It is verified that the fuzzy multi-SVR learning method is workable for the fuzzy RBDO of complex structures just like a multi-failure blade with high modeling precision, as well as high optimization, efficiency, and accuracy. The efforts of this study open a new research way, i.e., machine learning-based RBDO, for the RBDO of multi-failure structures, which expands the application of machine learning methods, and enriches the mechanical reliability design method and theory as well.

关键词： reliability-based design optimization turbine blades multi-objective genetic algorithm fuzzy support vector machine of regression uncertainty

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