This thesis focuses on permanent magnet "direct drive" electrical generators for wind turbines with large power output. A variety of such generator topologies is reviewed, tested and optimised in an attempt to increase their potential as commercial concepts for the wind industry. Direct drive electrical generators offer a reliable alternative to gearbox drivetrains. This novel technology reduces energy loses thus allowing more energy to be yield from the wind and decreases the maintenance cost at the same time. A fundamental issue for these generators is their large size which makes them difficult to manufacture, transport and assembly. A number of structural designs have been suggested in the literature in an attempt to minimise this attribute. A set of design tools are set out in an attempt to investigate the structural stiffness of the different permanent magnet direct drive generator topologies against a number of structural stresses that apply to such wind turbine energy converters. Optimisation techniques, both analytical and structural, are also developed for minimising the total mass of a variety of "directly driven" machines with power output of 5MW or greater. Conventional and promising generator designs are modelled and optimised with the use of these optimisation techniques. The topologies under examination are then compared in terms of structural mass, stiffness and cost. As the number of wind turbine manufactures who adopt the direct drive concept increases, it is important to outline the unique characteristics of the different topologies and increase their manufacturing potential. Discussions and conclusions will provide an indication of the design solutions that could help decrease the mass and cost of such machines.

A microstructure-based plasticity-damage model is used to predict the mechanical behavior of commercially available AISI 4140 steel. Monotonic tension, compression and torsion tests were performed to obtain the set of plasticity and damage constants required for model calibration. Then, tension tests on Bridgman notched specimens were undertaken to study the damage-triaxiality dependence. Three different notch radii generated different levels of triaxiality at the notch. The modeled triaxiality-damage correlation was validated with SEM fracture surface analysis. Stress-strain correlations under different strain rate and temperature testing conditions were also studied. Little influence of the strain rate was observed. A preliminary study in high-porosity LENS materials was later performed, with satisfactory stress-strain correlation at two different temperatures on tension tests. Finally, a multistage fatigue model was used to predict life in AISI 4140 steel. The goal was to create a baseline for future application of these mathematical models into LENS manufactured materials in component design

Mechanical structure-property relations have been quantified for AISI 4140 steel under different strain rates and temperatures. The structure-property relations were used to calibrate a microstructure-based internal state variable plasticity-damage model for monotonic tension, compression and torsion plasticity, as well as damage evolution. Strong stress state and temperature dependences were observed for the AISI 4140 steel. Tension tests on three different notched Bridgman specimens were undertaken to study the damage-triaxiality dependence for model validation purposes. Fracture surface analysis was performed using Scanning Electron Microscopy (SEM) to quantify the void nucleation and void sizes in the different specimens. The stress-strain behavior exhibited a fairly large applied stress state (tension, compression dependence, and torsion), a moderate temperature dependence, and a relatively small strain rate dependence.

No description available.

The increasingly competitive market is forcing the industry to develop higher-quality products more quickly and less expensively. Engineering analysis, at the same time, plays an important role in helping designers evaluate the performance of the designed product against design requirements. In the context of automated CAD/FEA integration, the domain-dependent engineers different usage views toward product models cause an information gap between CAD and FEA models, which impedes the interoperability among these engineering tools and the automatic transformation from an idealized design model into a solvable FEA model. Especially in highly coupled variable topology multi-body (HCVTMB) problems, this transformation process is usually very labor-intensive and time-consuming. In this dissertation, a knowledge-based FEA modeling method, which consists of three information models and the transformation processes between these models, is presented. An Analysis Building Block (ABB) model represents the idealized analytical concepts in a FEA modeling process. Solution Method Models (SMMs) represent these analytical concepts in a solution technique-specific format. When FEA is used as the solution technique, an SMM consists of a Ready to Mesh Model (RMM) and a Control Information Model (CIM). An RMM is obtained from an ABB through geometry manipulation so that the quality mesh can be automatically generated using FEA tools. CIMs contain information that controls the FEA modeling and solving activities. A Solution Tool Model (STM) represents an analytical model at the tool-specific level to guide the entire FEA modeling process. Two information transformation processes are presented between these information models. A solution method mapping transforms an ABB into an RMM through a complex cell decomposition process and an attribute association process. A solution tool mapping transforms an SMM into an STM by mimicking an engineers selection of FEA modeling operations. Four HCVTMB industrial FEA modeling cases are presented for demonstration and validation. These involve thermo-mechanical analysis scenarios: a simple chip package, a Plastic Ball Grid Array (PBGA), and an Enhanced Ball Grid Array (EBGA), as well as a thermal analysis scenario: another PBGA. Compared to traditional methods, results indicate that this method provides better knowledge capture and decreases the modeling time from days/hours to hours/minutes.

No description available.