INTRODUCTION TO COMPOSITE MATERIALS Chapter Objectives Introduction Classi?cation Recycling Fiber-Reinforced Composites Mechanics Terminology Summary Key Terms Exercise Set References MACROMECHANICAL ANALYSIS OF A LAMINA Chapter Objectives Introduction Review of De?nitions Hooke's Law for Different Types of Materials Hooke's Law for a Two-Dimensional Unidirectional Lamina Hooke's Law for a Two-Dimensional Angle Lamina Engineering Constants of an Angle Lamina Invariant Form of Stiffness and Compliance Matrices for an Angle Lamina Strength Failure Theories of an Angle Lamina Hygrothermal Stresses and Strains in a Lamina Summary Key Terms Exercise Set References APPENDIX A: MATRIX ALGEBRA Key Terms APPENDIX B: TRANSFORMATION OF STRESSES AND STRAINS Transformation of Stress Transformation of Strains Key Terms MICROMECHANICAL ANALYSIS OF A LAMINA Chapter Objectives Introduction Volume and Mass Fractions, Density, and Void Content Evaluation of the Four Elastic Moduli Ultimate Strengths of a Unidirectional Lamina Coefficients of Thermal Expansion Coefficients of Moisture Expansion Summary Key Terms Exercise Set References MACROMECHANICAL ANALYSIS OF LAMINATES Chapter Objectives Introduction Laminate Code Stress-Strain Relations for a Laminate In-Plane and Flexural Modulus of a Laminate Hygrothermal Effects in a Laminate Summary Key Terms Exercise Set References FAILURE, ANALYSIS, AND DESIGN OF LAMINATES Chapter Objectives Introduction Special Cases of Laminates Failure Criterion for a Laminate Design of a Laminated Composite Other Mechanical Design Issues Summary Key Terms Exercise Set References BENDING OF BEAMS Chapter Objectives Introduction Symmetric Beams Nonsymmetric Beams Summary Key Terms Exercise Set References INDEX

/pdf/mechanics-of-composite-materials-2nk382057e.pdf

Mechanics of composite materials

Review of performance optimization techniques applied to wind turbines

https://pure.tue.nl/ws/files/67843762/1_s2.0_S0306261917303744_main.pdf

Effect of pitch angle on power performance and aerodynamics of a vertical axis wind turbine

A review on optimization of composite structures Part I: Laminated composites

Wind energy is one of the most promising renewable energy resources for power generation, and rapid growth has been seen in its acceptance since 2000. The most acceptable classification for wind turbines is by its axis of orientation: Horizontal Axis Wind Turbines (HAWT) and Vertical Axis Wind Turbines (VAWT). HAWTs are used in many countries for medium-to-large scale power projects, and most commercial installations around the globe are solely based on these turbines. On the other hand, HAWTs are not recognized as a viable option to harness the energy of the wind in urban areas, where the wind is less intense, much more chaotic and turbulent. VAWTs are suggested as a better choice for cities and isolated semi-urban areas. Several attributes have been suggested for the large-scale deployment of VAWTs, e.g., good performance under the weak and unstable wind, no noise and safety concerns, and aesthetically sound for integration in urban areas. Significant research has been published on wind turbine technology and resources assessment methodologies, and this review paper is a modest attempt to highlight some of the major developments of VAWTs, with a focus on the integration with urban infrastructure. Several recommendations have been drawn based on the state-of-the-art information on the subject for future studies and acceptance of wind turbines in the urban areas. It was concluded that further research is critical in making VAWTs a viable, dependable, and affordable power generation technology for many low and decentralized power applications.

A critical review of vertical axis wind turbines for urban applications

Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA’s) shine as popular evolutionary algorithms (EA’s) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.

/pdf/a-constraint-handling-technique-for-genetic-algorithms-using-3ieib3unb2.pdf

A constraint-handling technique for genetic algorithms using a violation factor

This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf) and via an optimal partitioning Kopt (KGAo) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

https://www.mdpi.com/1999-4893/10/4/123/pdf

A selection process for genetic algorithm using clustering analysis

Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of manufacturers has been to minimize the cost of energy of wind turbines in order to compete with fossil‐fuel sources. Therefore, it has been argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi‐objective optimization algorithms, the designers are able to identify a trade‐off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. In this chapter, we present the fundamental principles of multi‐objective optimization in wind turbine design and solve a classic multi‐objective wind turbine optimization problem using a genetic algorithm.

/pdf/wind-turbine-design-multi-objective-optimization-4rrijdwmc4.pdf

Wind turbine design : multi‐objective optimization

The current diminution of fossil-fuel reserves, stricter environmental guidelines and the world’s ever-growing energy needs have directed to the deployment of alternative renewable energy sources. Among the many renewable energies, wind energy is one of the most promising and the fastest growing installed alternative-energy production technology. In order to meet the production goals in the next few decades, both significant increases in wind turbine installations and operability are required, while maintaining a profitable and competitive energy cost. As the size of the wind turbine rotor increases, the structural performance and durability requirements tend to become more challenging. In this sense, solving the wind turbine design problem is an optimization problem where an optimal solution is to be found under a set of design constraints and a specific target. Seen the world evolution towards the renewable energies and the beginning of an implementation of a local wind industry in Quebec, it becomes imperative to follow the international trends in this industry. Therefore, it is necessary to supply the designers a suitable decision tool for the study and design of optimal wind turbine blades. The developed design tool is an open source code named winDesign which is capable to perform structural analysis and design of composite blades for wind turbines under various configurations in order to accelerate the preliminary design phase. The proposed tool is capable to perform a Pareto optimization where optimal decisions need to be taken in the presence of trade-offs between two conflicting objectives: the annual energy production and the weight of the blade. For a given external blade shape, winDesign is able to determine an optimal composite layup, chord and twist distributions which either minimizes blade mass or maximizes the annual energy production while simultaneously satisfying design constraints. The newly proposed graphical tool incorporates two novel VCH and KGA techniques and is validated with numerical simulation on both mono-objective and multi-objective optimization problems.

Adam Chehouri

Papers

Review of performance optimization techniques applied to wind turbines

A constraint-handling technique for genetic algorithms using a violation factor

A selection process for genetic algorithm using clustering analysis

Wind turbine design : multi‐objective optimization

Optimisation of wind turbine blade structures using a genetic algorithm