Optimization of wind farm layout

U radu je predstavljen metod određivanja optimalnih položaja vetrogeneratora u okviru farme, postavljene na terenu proizvoljne orografije. Optimalni položaji pojedinačnih vetrogeneratora su određeni tako da se postigne njihova maksimalna efikasnost. Metod je zasnovan na genetskom algoritmu kao optimizacionoj tehnici. Aerodinamički proračun vetrogeneratora je izveden na nestacionarnom potencijalnom strujnom polju. Lopatice vetrogeneratora su modelirane kao vrtložne površine, a vrtložni trag je modeliran upotrebom 'freewake' metode. Optimizacioni model je razvijen za dve funkcije cilja. Obe funkcije koriste ukupnu energiju dobijenu iz farme kao jednu od ključnih promenljivih. Druga funkcija cilja uključuje i ukupno ulaganje u svaku pojedinačnu turbinu, tako da optimizacioni proces uključuje i ukupan broj vetrogeneratora kao promenljivu. Metod je testiran na nekoliko proizvoljnih konfiguracija terena, pri čemu je posebna pažnja posvećena izboru parametara genetskog algoritma, kako bi se postigle povoljne performanse optimizacionog procesa.This paper presents a method for determination of optimum positions of single wind turbines within the wind farms installed on arbitrary configured terrains, in order to achieve their maximum production effectiveness. This method is based on use of the genetic algorithm as optimization technique. The wind turbine aerodynamic calculation is unsteady, based on the blade modeled as a vortex lattice and a free-wake type airflow behind the blade. Optimization method is developed for two different fitness functions. Both functions use the total energy obtained from the farm as one of the key variables. The second also involves the total investments in a single wind turbine, so the optimization process can also include the total number of turbines as an additional variable. The method has been tested on several different terrain configurations, with special attention paid to the overall algorithm performance improvements by selecting certain genetic algorithm parameters

Unstructured mesh algorithms for aerodynamic calculations

The use of unstructured mesh techniques for solving complex aerodynamic flows is discussed. The principle advantages of unstructured mesh strategies, as they relate to complex geometries, adaptive meshing capabilities, and parallel processing are emphasized. The various aspects required for the efficient and accurate solution of aerodynamic flows are addressed. These include mesh generation, mesh adaptivity, solution algorithms, convergence acceleration, and turbulence modeling. Computations of viscous turbulent two-dimensional flows and inviscid three-dimensional flows about complex configurations are demonstrated. Remaining obstacles and directions for future research are also outlined

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Optimal and intelligent decision making in sustainable development of electronic products

Increasing global population and consumption are causing declining natural and social systems. Multi-lifecycle engineering and sustainable development address these issues by integrating strategies for economic successes, environmental quality, and social equity. Based on multi-lifecycle engineering and sustainable development concepts, this doctoral dissertation aims to provide decision making approaches to growing a strong industrial economy while maintaining a clean, healthy environment. The research develops a methodology to complete both the disassembly leveling and bin assignment decisions in demanufacturing through balancing the disassembly efforts, value returns, and environmental impacts. The proposed method is successfully implemented into a demanufacturing module of a Multi-LifeCycle Assessment and Analysis tool. The methodology is illustrated by a computer product example. Since products during the use stage may experience very different conditions, their external and internal status can vary significantly. These products, when coming to a demanufacturing facility, are often associated with incomplete/imprecise information, which complicates demanufacturing process decision making. In order to deal with uncertain information, this research proposes Fuzzy Reasoning Petri nets to model and reason knowledge-based systems and successfully applies them to demanufacturing process decision making to obtain the maximal End-of-Life (BOL) value from discarded products. Besides the BOL management of products by means of product/material recovery to decrease environmental impacts, the concepts of design for environment and sustainable development are investigated. Based on Sustainability Target Method, a sensitivity analysis decision-making method is proposed. It provides a company with suggestions to improve its product\u27s sustainability in the most cost-effective manner

Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization

An approach to support the computational aerodynamic design process is presented and demonstrated through the application of a novel multi-objective variant of the Tabu Search optimization algorithm for continuous problems to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and ultimately of the whole engine. The integrated system developed for this purpose is described. This combines the optimizer with an existing geometry parameterization scheme and a well- established CFD package. The system’s performance is illustrated through case studies – one two-dimensional, one three-dimensional – in which flow characteristics important to the overall performance of turbomachinery blades are optimized. By showing the designer the trade-off surfaces between the competing objectives, this approach provides considerable insight into the design space under consideration and presents the designer with a range of different Pareto-optimal designs for further consideration. Special emphasis is given to the dimensionality in objective function space of the optimization problem, which seeks designs that perform well for a range of flow performance metrics. The resulting compressor blades achieve their high performance by exploiting complicated physical mechanisms successfully identified through the design process. The system can readily be run on parallel computers, substantially reducing wall-clock run times – a significant benefit when tackling computationally demanding design problems. Overall optimal performance is offered by compromise designs on the Pareto trade-off surface revealed through a true multi-objective design optimization test case. Bearing in mind the continuing rapid advances in computing power and the benefits discussed, this approach brings the adoption of such techniques in real-world engineering design practice a ste

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research

Parallel processing for nonlinear dynamics simulations of structures including rotating bladed-disk assemblies

The principal objective of this research is to develop, test, and implement coarse-grained, parallel-processing strategies for nonlinear dynamic simulations of practical structural problems. There are contributions to four main areas: finite element modeling and analysis of rotational dynamics, numerical algorithms for parallel nonlinear solutions, automatic partitioning techniques to effect load-balancing among processors, and an integrated parallel analysis system

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced

Multiobjective Design Optimization Of Gas Turbine Blade With Emphasis On Internal Cooling

In the design of mechanical components, numerical simulations and experimental methods are commonly used for design creation (or modification) and design optimization. However, a major challenge of using simulation and experimental methods is that they are timeconsuming and often cost-prohibitive for the designer. In addition, the simultaneous interactions between aerodynamic, thermodynamic and mechanical integrity objectives for a particular component or set of components are difficult to accurately characterize, even with the existing simulation tools and experimental methods. The current research and practice of using numerical simulations and experimental methods do little to address the simultaneous “satisficing” of multiple and often conflicting design objectives that influence the performance and geometry of a component. This is particularly the case for gas turbine systems that involve a large number of complex components with complicated geometries. Numerous experimental and numerical studies have demonstrated success in generating effective designs for mechanical components; however, their focus has been primarily on optimizing a single design objective based on a limited set of design variables and associated values. In this research, a multiobjective design optimization framework to solve a set of userspecified design objective functions for mechanical components is proposed. The framework integrates a numerical simulation and a nature-inspired optimization procedure that iteratively perturbs a set of design variables eventually converging to a set of tradeoff design solutions. In this research, a gas turbine engine system is used as the test application for the proposed framework. More specifically, the optimization of the gas turbine blade internal cooling channel configuration is performed. This test application is quite relevant as gas turbine engines serve a iv critical role in the design of the next-generation power generation facilities around the world. Furthermore, turbine blades require better cooling techniques to increase their cooling effectiveness to cope with the increase in engine operating temperatures extending the useful life of the blades. The performance of the proposed framework is evaluated via a computational study, where a set of common, real-world design objectives and a set of design variables that directly influence the set of objectives are considered. Specifically, three objectives are considered in this study: (1) cooling channel heat transfer coefficient, which measures the rate of heat transfer and the goal is to maximize this value; (2) cooling channel air pressure drop, where the goal is to minimize this value; and (3) cooling channel geometry, specifically the cooling channel cavity area, where the goal is to maximize this value. These objectives, which are conflicting, directly influence the cooling effectiveness of a gas turbine blade and the material usage in its design. The computational results show the proposed optimization framework is able to generate, evaluate and identify thousands of competitive tradeoff designs in a fraction of the time that it would take designers using the traditional simulation tools and experimental methods commonly used for mechanical component design generation. This is a significant step beyond the current research and applications of design optimization to gas turbine blades, specifically, and to mechanical components, in general