Littérature scientifique sur le sujet « Turbina a ga »
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Articles de revues sur le sujet "Turbina a ga"
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Nikolić, Gojko. « Originalnost izuma Fausta Vrančića ». Studia lexicographica 12, no 23 (2019) : 9–31. http://dx.doi.org/10.33604/sl.12.23.1.
Texte intégralRésumé :
U radu je istražena originalnost triju izuma Fausta Vrančića sa stajališta današnjega poimanja izuma. Prvi izum koji je u radu istražen jest padobran, za koji je utvrđeno da Faust nije njegov izumitelj, već da ga je iznimno dobro tehnički unaprijedio, koncepcijski vrlo blisko suvremenim rješenjima. S obzirom na to da se uz padobran posvuda veže njegovo ime, mnoge će iznenaditi činjenica da Vrančić nije njegov izumitelj. Drugi izum koji je analiziran, a danas se nedovoljno ističe, odnosi se na mlin na plimu i oseku. Ideja nije nova, koristila se davno u povijesti, posebno u Irskoj i Engleskoj. Inovacija u Vrančićevu mlinu jest uporaba dvosmjernoga strujanja mora, pri čemu konstrukcija okretnoga pogonskoga vodeničnoga kola omogućuje njegovo okretanje uvijek u istom smjeru, neovisno o smjeru strujanja mora. Treća inovacija, koja se odnosi na Vrančićevo rješenje primjene statora i rotora kod vjetrenjače koje omogućuje korištenje vjetra iz bilo kojega smjera, danas se koristi kod turbina za razne fluide (vodu, paru, plinove).
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Mrzljak, Vedran, Nikola Anđelić, Ivan Lorencin et Sandi Sandi Baressi Šegota. « The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction ». Pomorstvo 35, no 1 (30 juin 2021) : 69–86. http://dx.doi.org/10.31217/p.35.1.8.
Texte intégralRésumé :
This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms – SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.
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Yan, Tao, Xian Min Lin et You Ping Zhong. « A Load Allocation Optimization among Turbine-Generators Based on GA-ACO ». Advanced Materials Research 614-615 (décembre 2012) : 1049–54. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1049.
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With regard to the slow-convergence disadvantage in the latter generations of GA, ACO is combined with GA in this paper to solve the optimal load allocation problem among thermal power plant units. By comparison with GA method, results show that GA-ACO has faster convergence than the GA method.
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Liu, Zheng, Xin Liu, Kan Wang, Zhongwei Liang, José A. F. O. Correia et Abílio De Jesus. « GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades ». Energies 12, no 6 (15 mars 2019) : 1026. http://dx.doi.org/10.3390/en12061026.
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This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.
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Alawaad, Nasir Ahmed. « Steam turbine controllers design based on soft-computing techniques ». IAES International Journal of Robotics and Automation (IJRA) 9, no 4 (1 décembre 2020) : 281. http://dx.doi.org/10.11591/ijra.v9i4.pp281-291.
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Steam turbine is viewed as a standout among hotspots for control age in the most recent decades, its elements examination end up being dynamically more basic. For this investigation, the model chose is of turbine speed control framework. The purpose behind this is that model is regularly experienced in refineries in a type of steam turbine that utilization hydraulic governor to control the speed of the turbine. To suit plan prerequisites, a mathematical model for the turbine was determined in light of transfer function and state space definition. There are two sorts of controllers for steam turbines which are traditional and modern controllers. Internal mode control with proportional integral derivative (IMC-PID) and linear quadratic controller (LQR) are classical type. Fuzzy logic controller (FLC) and intelligent optimization techniques like, ant colony algorithm (ACOA) and genetic algorithm (GA) are modern type. The proposed work centers on classical verses modern controllers. Results got demonstrate that embracing such a controller (GA) improves the design requirements and transient stability. The system control was actualized in simulation utilizing MATLAB/Simulink.
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Sun, Wei, et Le Shen. « Comprehensive Evaluation of Wind Turbine Type Selection Based on GA-SVR Model ». Advanced Materials Research 468-471 (février 2012) : 579–82. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.579.
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Aiming at the current situation of wind turbine type selection in China, this paper has built a more scientific and systematic index system for comprehensive evaluation of wind turbine type selection, and also applied the Support Vector Regression machine evaluation model with parameters optimized by Genetic Algorithm. Through automatic global optimization for parameters, this model has reached an extremely high accuracy required for evaluation of type selection. Empirical analysis shows that the application of this model has a realistic popularized significance for improving the method of the wind turbine type selection and enhancing its efficiency.
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Nugroho, Herminarto, Nabilla Ananda Yusva et Ortega Incon Marama Pandiangan. « Penerapan Metode Particle Swarm Optimization dan Genetic Algorithm pada Optimisasi Sudut Kelengkungan Turbin Air Banki Untuk Mendapatkan Efisiensi Daya Optimal ». ENERGI & ; KELISTRIKAN 14, no 1 (27 juin 2022) : 82–89. http://dx.doi.org/10.33322/energi.v14i1.1636.
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Indonesia memiliki banyak sumber daya seperti panas bumi, surya, angin, air, dan lain sebagainya. Diantara banyaknya sumber daya, air merupakan sumber daya yang paling dominan bagi Indonesia. Karena jumlah ketersediaan air di Indonesia mencapai 694 milyar meter kubik pertahunnya. Sehingga, pembangkit listrik tenaga air banyak digunakan sebagai pemasok energi. Pada jurnal ini membahas efisiensi pembangkit listrik tenaga air menggunakan model turbin air Banki yang ditulis oleh Mockmore dan Merryfield pada tahun 1949. Permasalahan yang akan dibahas adalah bagaimana memaksimalkan daya keluaran turbin dengan menggunakan metode optimasi Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Perbedaan yang didapat dengan menggunakan kedua metode ini adalah nilai maksimum daya keluaran, efisiensi dan jumlah iterasi maksimum yang dilakukan. Berdasarkan perbedaan tersebut maka dapat dikatakan bahwa metode PSO lebih baik daripada GA dalam memaksimalkan daya keluaran dan efisiensi dari turbin
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Jiao, Bin, et Zhi Wei Gao. « The Fault Diagnosis of Wind Turbine Gearbox Based on QGA—LSSVM ». Applied Mechanics and Materials 543-547 (mars 2014) : 950–55. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.950.
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A quantum genetic algorithm (QGA) with good global optimization ability and fast convergence speed is proposed to solve the parameter selection problems of least squares support vector machine (LSSVM) on wind turbine gearbox fault diagnosis model. The method can convert the LSSVM model parameter selection into optimization. It overcomes the problem that GA is easy to fall into local optimum in the optimization process and it also improves the optimization performance. A series experiments are carried out on the data sets of UCI database. Compared with GA—LSSVM and CV—LSSVM, the classification accuracy is improved. Finally QGA—LSSVM model is applied to the wind turbine gearbox diagnosis and a good result is achieved.
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He, Qing, et Jian Ding Zhang. « Application of Improved Genetic Algorithm in Maintenance Decision for Turbine-Generator Unit ». Applied Mechanics and Materials 44-47 (décembre 2010) : 2940–44. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2940.
Texte intégralRésumé :
The complicated function relations are more prone to appear in the maintenance scheduling of steam-turbine generator unit. Many constrained conditions are often attendant with these function relations. In these situations, the traditional method often can not obtain the exact value. The genetic algorithm (GA), a kind of the heuristic algorithms, does not need the function own good analytic properties. In addition, as the operating unit of GA is the group, so it applies to the parallel computing process. In GA executive process, the offspring continually inherit the genes from the parents, so it is more prone to be involved in the local convergence. An improved genetic algorithm is proposed and used in the model of maintenance decision of turbine-generator unit under. The goal of the model is to seek to the rational maintenance scheduling of the generator unit, so as to minimize the sum of the maintenance expense, the loss of the profit on the generated energy, and the loss of the penalty. It is proved by the example that IGA is highly efficient.
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Wang, Li Ying, Wei Guo Zhao et Chuan Hong Zhang. « Application of ANN Trained with GA in Energy Characteristics of Hydraulic Turbine ». Advanced Materials Research 108-111 (mai 2010) : 692–95. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.692.
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The learning algorithm of artificial neural network (ANN) trained with genetic algorithm (GA) are introduced, based on the operation data of hydropower station, the network model of energy characteristics is established based on GA-ANN, the relationship curve between head H and output N is gained under some efficiency. The results show that the algorithm is better than BP neural network and avoid the limitations of BP neural network, the results can be used in the optimal operation of hydropower, and it has a practical significance. The results show the new model has a great importance in hydraulic unit study. It could be generalized into other all efficiency prediction, and it offers a new way in water conservancy and at the meantime a new method for the study of ANN and GA.
Thèses sur le sujet "Turbina a ga"
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Gobbato, Paolo. « Studio delle instabilità termoacustiche in un combustore di turbina a gas ». Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3427348.
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Combustion instabilities are a major technical problem in most of industrial applications since they cause a performance deterioration of the combustion process. Under unstable operation the large amplitude oscillations of the flow induce many dangerous effects such as large mechanical vibrations, noise, augmented heat transfer rates at the combustor walls and increased pollutant emissions. In the gas turbines, an unstable heat release inside the combustion chamber can damage the hottest components of the combustor and reduce the life of the turbine blades.
This study presents an investigation of the thermoacoustic behaviour of a single can gas turbine combustor. The combustor, originally conceived for operation with liquid and gaseous fossil fuels, was modified by the manufacturer to burn pure hydrogen or hydrogen/natural gas mixtures. Combustor design development was supported by experimental activities performed on a full-scale full-pressure test rig.
A detailed procedure is proposed in this work to study the thermoacoustic instabilities in the combustor. Both hydrogen and natural gas operation are simulated by means of CFD RANS simulations carried out on a finite volume commercial code. The three-dimensional CFD analyses are performed on a coarse grid and take advantage of simplified numerical models to reduce the computation time. Due to this approach, the CFD analyses can simulate the time dependent thermoacoustic reactive flow field for a period of time large enough to capture unstable oscillation regimes, if present. Experimental measurements are used to impose the model boundary conditions and to validate the numerical results.
The pressure signals recorded during the simulated period show a constant low-amplitude oscillation (a limit cycle) which does not affect the combustor performance. This behaviour agrees with the experimental data acquired during the combustion tests.
The final part of this study compares the computed frequency spectra with the measured ones. The good agreement between the numerical results and the experimental values validate the potential of the low computational cost CFD approach to describe the thermoacoustic behaviour of the considered combustor.L'instabilità di combustione peggiora le prestazioni di un combustore a flusso continuo e pertanto deve essere considerata un fenomeno indesiderato. Fluttuazioni della pressione e del rilascio termico possono infatti causare vibrazioni meccaniche, rumore, formazione di punti caldi sulle pareti della camera di combustione e incremento delle emissioni inquinanti. La combustione instabile è particolarmente dannosa nei combustori per turbina a gas nei quali ampie oscillazioni di portata e di rilascio termico possono danneggiare irreparabilmente le parti fisse e rotanti della turbina. Nel lavoro che si presenta viene studiato il comportamento termoacustico di un combustore di turbina a gas. Il combustore esaminato è del tipo tubolare, con singolo bruciatore a fiamma diffusiva ed è stato modificato dal costruttore per essere alimentato non solo a gas naturale ma anche a idrogeno. Il processo di sviluppo è stato supportato da prove di combustione su scala reale eseguite su un banco prova in grado di riprodurre le condizioni di pieno carico. L’analisi termoacustica viene condotta seguendo una procedura di indagine basata sulla simulazione numerica del fenomeno mediante un codice numerico commerciale con modelli di turbolenza di tipo RANS. Nelle analisi numeriche i modelli numerici e le griglie di calcolo sono scelti in modo da minimizzare tempi e risorse di calcolo. In questo modo è possibile simulare un intervallo temporale sufficientemente ampio da consentire al sistema di evolvere liberamente fino alle condizioni di regime per poter così valutare l’eventuale presenza di instabilità termoacustiche. Le misure raccolte durante le prove sperimentali sono impiegate nei calcoli sia per l’imposizione delle condizioni al contorno sia per la valutazione dei risultati. I segnali di pressione registrati durante le simulazioni mostrano la permanenza di oscillazioni di pressione nel combustore caratterizzate da un’ampiezza piuttosto ridotta. Queste oscillazioni sono dunque ampiamente tollerabili dal sistema (la combustione è ovunque completa e non vi sono fenomeni di estinzione di fiamma e di surriscaldamento delle pareti del combustore), in accordo con quanto osservato durante le prove sperimentali. Gli spettri calcolati al termine delle simulazioni sono comparati con gli spettri acquisiti durante le prove di combustione. Dal confronto emerge una sostanziale corrispondenza tra i modi di vibrare calcolati e quelli misurati al banco prova.
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Odofin, Sarah. « Robust fault diagnosis by GA optimisation with applications to wind turbine systems and induction motors ». Thesis, Northumbria University, 2016. http://nrl.northumbria.ac.uk/36111/.
Texte intégralRésumé :
This investigation focuses and analyses the theoretical and practical performance of a dynamic system, which affords condition monitoring and robust fault diagnosis. The importance of robustness in fault diagnosis is becoming significant for controlled dynamic systems in order to improve operating reliability, critical-safety and reducing the cost often caused by interruption shut down and component repairing. There is a strong motivation to develop an effective real-time monitoring and fault diagnosis strategy so as to ensure a timely response by supervisory personnel to false alarms and damage control due to faults/malfunctions. Environmental disturbances/noises are unavoidable in practical engineering systems, the effects of which usually reduce the diagnostic ability of conventional fault diagnosis algorithms, and even cause false alarms. As a result, robust fault diagnosis is vital for practical application in control systems, which aims to maximize the fault detectability and minimize the effects of environment disturbances/noises. In this study, a genetic algorithm (GA) optimization model-based fault diagnosis algorithm is investigated for applications in wind turbine energy systems and induction motors through concerns for typical types of developing (incipient) and sudden (abrupt) faults. A robust fault detection approach is utilized by seeking an optimal observer gain when GA optimisation problems become solvable so that the residual is sensitive to the faults, but robust against environmental disturbances/noises. Also, robust fault estimation techniques are proposed by integrating augmented observer and GA optimisation techniques so that the estimation error dynamics have a good robustness against environmental disturbances/noises. The two case studies investigated in this project are: a 5MW wind turbine model where robust fault detection and robust fault estimation are discussed with details; and a 2kW induction motor experimental setup is investigated, where robust fault detection and robust fault estimation are both examined, and modelling errors are effectively attenuated by using the proposed algorithms. The simulations and experimental results have demonstrated the effectiveness of the proposed fault diagnosis methods.
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BONO, ANDREA. « Criticità nelle esigenze e nelle offerte energetiche : il ruolo rilevante della progettazione e della gestione ottimizzata delle macchine a fluido e dei sistemi per la conversione di energia. Aspetti applicativi nella piccola fornitura di energia e nella propulsione navale ». Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1046981.
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The present work deals with environmental sustainability and specific engineering solutions able to cope with such a global issue. Attention is focused on renewable energy and innovative fuels as effective strategies in contributing valuable techniques in order to face the need of mitigating environmental problems concerning climate change and global warming.
The research study is targeted on optimized design and management of fluid machinery, and extensively on optimized energy conversion systems, conceptualized in accordance with current standards and regulations, governing the reference sector. The analysis investigates small energy supply from renewables (wind power) and innovative marine propulsion (alternative fuels and unconventional propulsion systems). Regulations and technical design are constantly focused for the study.
The work proposes case solutions for energy design and management actions dealing with the theme of environmental sustainability: engineering analyses (design, technical-economical evaluation, performance results) for hybrid wind powered plants empowering SWRO (Sea Water Reverse Osmosis) desalination processes; engineering analyses (design, technical evaluation, performance results) for wind turbine rotors operating in sites characterized by a small wind resource; engineering analyses (design, technical evaluation, performance results) for marine ship propulsion empowered by LNG as an alternative sustainable fuel and by gas turbines as prime movers coupled to combined cycles as an innovative propulsion system (COGES configuration).
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CAPPELLETTI, ALESSANDRO. « On the study of hydrogen fueling in premixed gas turbine combustor chamber ». Doctoral thesis, 2013. http://hdl.handle.net/2158/794611.
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ANDREINI, ANTONIO. « SVILUPPO DI MODELLI NUMERICI PER L’ANALISI DELLA COMBUSTIONE TURBOLENTA PREMISCELATA NELLE TURBINE A GAS ». Doctoral thesis, 2004. http://hdl.handle.net/2158/771852.
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L’oggetto del presente lavoro di tesi, `e la descrizione delle principali fasi di sviluppo, messa a punto e validazione di una libreria di modelli numerici per l’analisi CFD1 reattiva dei combustori per turbine a gas (TAG). Gran parte del lavoro costituisce l’argomento di una collaborazione di ricerca tra
l’Università degli Studi di Firenze e la società GE-Nuovo Pignone. L’esigenza da parte della società di dotarsi di strumenti di simulazione all’avanguardia per quanto riguarda l’ottimizzazione fluidodinamica dei propri prodotti, e l’interesse da parte dell’ateneo nei confronti della possibilit`a di applicare sul campo il proprio know-how scientifico nell’ambito delle turbine a gas, ha dato
il via ad una proficua collaborazione.
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Siano, P., et Geev Mokryani. « Evaluating the Benefits of Optimal Allocation of Wind Turbines for Distribution Network Operators ». 2015. http://hdl.handle.net/10454/9223.
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NoThis paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.
Chapitres de livres sur le sujet "Turbina a ga"
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Zhang, Jianxin, et Zhange Zhang. « GA Based Optimal Design for Megawatt-Class Wind Turbine Gear Train ». Dans Lecture Notes in Computer Science, 323–32. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21963-9_30.
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Srivastava, Tushar, et M. M. Tripathi. « Predictive Analysis of Wind Turbine Output Power Using Support Vector Machine(SVM) Based on Genetic Algorithm(GA) ». Dans Lecture Notes in Electrical Engineering, 117–33. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6840-4_10.
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Siano, P., G. Rigatos et A. Piccolo. « Active Distribution Networks and Smart Grids : Optimal Allocation of Wind Turbines by Using Hybrid GA and Multi-Period OPF ». Dans Atlantis Computational Intelligence Systems, 579–99. Paris : Atlantis Press, 2012. http://dx.doi.org/10.2991/978-94-91216-77-0_27.
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Hamdan, Mohammad, et Mohammad Hassan Abderrazzaq. « Optimization of Small Wind Turbines Using Genetic Algorithms ». Dans Renewable and Alternative Energy, 1484–99. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1671-2.ch052.
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This paper presents a detailed optimization analysis of tower height and rotor diameter for a wide range of small wind turbines using Genetic Algorithm (GA). In comparison with classical, calculus-based optimization techniques, the GA approach is known by its reasonable flexibilities and capability to solve complex optimization problems. Here, the values of rotor diameter and tower height are considered the main parts of the Wind Energy Conversion System (WECS), which are necessary to maximize the output power. To give the current study a practical sense, a set of manufacturer's data was used for small wind turbines with different design alternatives. The specific cost and geometry of tower and rotor are selected to be the constraints in this optimization process. The results are presented for two classes of small wind turbines, namely 1.5kW and 10kW turbines. The results are analyzed for different roughness classes and for two height-wind speed relationships given by power and logarithmic laws. Finally, the results and their practical implementation are discussed.
Actes de conférences sur le sujet "Turbina a ga"
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Chen, Xiaomin, et Ramesh Agarwal. « Optimization of Flatback Airfoils for Wind Turbine Blades ». Dans ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90373.
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In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In this paper, we employ a genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA is significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT is used for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. It is shown that the combined GA/ANN optimization technique is capable of accurately and efficiently finding globally optimal flatback airfoils.
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Chen, Xiaomin, et Ramesh Agarwal. « Optimization of Flatback Airfoils for Wind Turbine Blades Using a Multi-Objective Genetic Algorithm ». Dans ASME 2012 6th International Conference on Energy Sustainability collocated with the ASME 2012 10th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/es2012-91004.
Texte intégralRésumé :
In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In a previous paper, ASME ES2010-90373, we employed a single objective genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA was significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT was employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. In this paper, we employ a multi-objective GA to optimize the flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. It is shown that the multi-objective GA optimization can generate superior flatback airfoils compared to those obtained by using single objective GA algorithm.
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Kim, Youjin, Ali Al-Abadi et Antonio Delgado. « Strategic Blade Shape Optimization for Aerodynamic Performance Improvement of Wind Turbines ». Dans ASME Turbo Expo 2016 : Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56836.
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This study introduces strategic methods for improving the aerodynamic performance of wind turbines. It was completed by combining different optimization methods for each part of the wind turbine rotor. The chord length and pitch angle are optimized by a torque-matched method (TMASO), whereas the airfoil shape is optimized by the genetic algorithm (GA). The TMASO is implemented to produce an improved design of a reference turbine (NREL UAE Phase V). The GA is operated to generate a novel airfoil design that is evaluated by automatic interfacing for the highest gliding ratio (GR). The adopted method produces an optimized wind turbine with an 11% increase of power coefficient (Cp) with 30% less of the corresponding tip speed ratio (TSR). Furthermore, the optimized wind turbine shows reduced tip loss effect.
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Chi, Zhongran, Jing Ren et Hongde Jiang. « Cooling Structure Optimization for a Rib-Roughed Channel in a Turbine Rotor Blade ». Dans ASME Turbo Expo 2013 : Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94527.
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Cooling design for the air-cooled turbine blades is a critical issue in modern gas turbine engineering. Advances in CFD technology is providing new prospects for turbine cooling design, as the optimum cooling structures of the blades could be designed through the optimization search coupled with the Conjugate Heat Transfer (CHT) analysis. In this paper, the optimization study for the rib arrangement of a rib-roughed channel in a rotor blade is discussed. The optimization study introduced is realized utilizing a parametric analysis platform, which consists of the parametric design and mesh generation tool and the commercial CHT solver ANSYS CFX. For the optimization study, firstly a group of Design of Experiments (DoE) analysis of a rib-roughed rectangular channel is performed in order to find the optimum rib arrangement and to explore the objective of the optimization search. Then, the optimization search of the optimum rib arrangement is performed for a rib-roughed channel within a rotor blade based on the multi-island Genetic Algorithms (GA) of iSIGHT. During optimization search, a constant pressure drop is assumed within the cooling system, and the CHT simulations are approached for the interior only in order to make the search computationally faster. According to the DoE analysis, minimizing the averaged wall temperature on blade surface is chosen as the optimization objective for the design of rib arrangement. The results of the GA search shows that the optimal rib arrangement with best cooling performance can be decided, and the optimal mass flow rate for the cooling channel is found simultaneously. The optimum schemes of the rib arrangement found by the DoE analysis and GA search are quite identical, which further validates the feasibility of design optimization for the blade cooling structure with the GA and CHT simulations.
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Sampath, Suresh, Stephen Ogaji et Riti Singh. « Improving Power Plant Availability Through Advanced Engine Diagnostic Techniques ». Dans 2002 International Joint Power Generation Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/ijpgc2002-26080.
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Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas turbine and combined cycle models able to undertake appropriate diagnostics and life cycle costing. These are complex areas as they include the simulation of such issues as performance and assessment of degraded gas turbines, life usage and risk analysis. This paper describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Networks (ANN) and Genetic Algorithm (GA) based technique have opened new opportunities in the field of engine fault diagnostics. It also discusses the potential of advanced engine diagnostics using such features as ANN and GA in contributing to the management of availability for industrial gas turbines.
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Dash, A. K., D. K. Agarwalla, H. C. Das, M. K. Pradhan et S. K. Bhuyan. « Application of Genetic Algorithm for Fault Detection in Cracked Composite Structure ». Dans ASME 2014 Gas Turbine India Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gtindia2014-8269.
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Machines and beam like structures used in various industries require continuous monitoring for the fault identification for ensuring uninterrupted service. Different non-destructive techniques (NDT) are generally used for this purpose, but they are costly and time consuming. Vibration based methods can be useful to detect cracks in structures using various artificial intelligence (AI) techniques. The modal parameters from the dynamic response of the structure are used for the purpose. In the current analysis, the vibration characteristics of a glass fiber reinforced composite cracked cantilever beam having different crack locations and depths have been studied. Numerical and finite element methods have been used to extract the diagnostic indices (natural frequencies, mode shapes) from cracked and intact beam structure. An intelligent Genetic Algorithm (GA) based controller has been designed to automate the fault identification and location process. Single point crossover and in some cases mutation procedure have been followed to find out the optimal solution from the search space. The controller has been trained in offline mode using the simulation and experimental results (initial data pool) under various healthy and faulty conditions of the structure. The outcome from the developed controller shows that the system could not only detect the cracks but also predict their locations and severities. Good agreement between the simulation, experimental and GA controller results confirms the effectiveness of the proposed controller.
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Safari, A., H. G. Lemu et M. Assadi. « A Novel Combination of Adaptive Tools for Turbomachinery Airfoil Shape Optimization Using a Real-Coded Genetic Algorithm ». Dans ASME Turbo Expo 2013 : Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gt2013-94093.
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An automated shape optimization methodology for a typical heavy-duty gas turbine (GT) compressor rotor blade section is presented in this paper. The approach combines a Non-Uniform Rational B-Spline (NURBS) driven parametric geometry description, a two-dimensional flow analysis, and a Genetic Algorithm (GA)-based optimization route. The objective is minimizing the total pressure losses for design condition as well as maximizing the airfoils operating range which is an assessment of the off-design behavior. To achieve the goal, design optimization process is carried out by coupling an established MATLAB code for the Differential Evolution (DE)-based optimum parameterized curve fitting of the measured point cloud of the airfoils’ shape, a blade-to-blade flow analysis in COMSOL Multiphysics, and a developed real-coded GA in MATLAB script. Using the combination of these adaptive tools and methods, the first results are considerably promising in terms of computation time, ability to extend the methodology for three-dimensional and multidisciplinary approach, and last but not least airfoil shape performance enhancement from efficiency and pressure rise point of view.
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Qian, Dianwei, Jianqiang Yi, Xiangjie Liu et Xin Li. « GA-based fuzzy sliding mode governor for hydro-turbine ». Dans 2010 International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2010. http://dx.doi.org/10.1109/icicip.2010.5565249.
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Garavello, A., M. Russo, Claudio Comis da Ronco, R. Ponza et E. Benini. « Aerodynamic Shape Optimization of Air-Intakes of a Helicopter Turboshaft ». Dans ASME 2012 Gas Turbine India Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gtindia2012-9506.
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The research project HEAVYcOPTer, a sub task of the European R&D program Clean-Sky GRC2 [1], is devoted to the efficient design and the shape optimization of the Agusta Westland AW101 helicopter turboshaft engine intake and exhaust system, to be carried out by means of advanced multi-objective optimization algorithms coupled with CFD Navier-Stokes solvers. The present paper describes the outcomes of HEAVYcOPTer in relation to the air intakes shape optimisation activities. This paper describes the technical details of such program. The optimisation method chosen for the redesign of the engine installation involves the application of the state of the art genetic algorithm GDEA, developed at the University of Padova and successfully applied in several fluid-dynamics applications, especially in the field of turbomachinery. For the present application, the set of geometrical designs constituting the genetic algorithm population are generated by means of morphing the original CFD model surface mesh: shapes are applied to baseline surface nodes with a displacement intensity driven by the GA chosen scaling factors. Then, CFD models of new designs are automatically generated and analyzed by the flow solver, returning to the GA the evaluation of the selected objective functions required in order to evolve the population in the next step of the evolutionary process. AW101 intakes have been optimised following a multi-objective/multi-point approach, minimizing inlet total pressure loss in both hovering and forward flight conditions simultaneously; optimised solutions were also constrained so as to not exceed the total pressure distortion level at the engine aerodynamic interface plane, so as to ensure inlet/engine compatibility with respect to the compressor surge limit. This approach ensured the improvement of the engine/airframe integration efficiency for the overall rotorcraft flight envelop, reducing fuel burn and increasing the helicopter propulsive efficiency.
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Paul, Diplina, et Abhisek Banerjee. « Genetic Algorithm Based Optimization Technique for Savonius-Style Wind Turbine ». Dans ASME 2021 Gas Turbine India Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gtindia2021-76041.
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Abstract In this article, authors have studied genetic algorithm-based optimization technique to optimize rotor profile for elliptic shaped Savonius-style wind turbine with an aim to maximize the coefficient of performance. Genetic algorithm has been used to optimize design variables having distinct values and discontinuous and nondifferentiable objective functions. Optimization procedure using genetic algorithm uses the following steps: initialization, assessment, assortment, crossover and lastly alteration. Once the genetic algorithm is initialized, then the evaluation process trails, where each parametric value is evaluated based on the fitness function stated as objective function. Then the GA operators i.e assortment, cross over and alteration are applied. At the end of GA operation procedure, a new set of values of design parameter is generated. This procedure is endlessly iterated until the convergence criteria is met. Then the optimized and non-optimized profiles are studied using numerical simulation. Initially a two-dimensional numerical model is developed and validated against experimental results. The two-dimensional analysis is conducted using k-ω shear stress transport model. Unsteady Reynold’s Averaged Navier Stoke’s equations have been solved to simulate the flow field of a Savonius-style rotor. This analysis has been executed using finite volume approach in Fluent 17.2 version. Grid independence study is performed to curtail the effect of grid size on the flow field portrayals. The optimization technique implemented on the Savonius-style wind turbine, generated design parameters that were able to yield a coefficient of performance value of 0.398. The coefficient of torque and coefficient of performance values are studied for both optimized and non-optimized profile as a function of tip speed ratio. Numerical simulation predicted a maximum gain of 41% for coefficient of performance at TSR = 1.0 over for optimized profile over the non-optimized profile.