Th. 3e cycle--Mét.--Grenoble, 1971. N°: 3.

Following the trends to diminish the fossil fuel energy production new technologies known for their renewable sources have become a signficant option for helping combat climate change and handle the current oil prices. These new technologies base their power production on already established physical principles that convert mechanical power to electrical power. Generators are the fundamental piece of machinery for electricity production. Among the various types of generators that exist, permanent magnet synchronous generators (PMSGs) are commonly used for renewable electricity production. At present, the most used magnets for PMSGs are alloys of neodymium, iron, and boron which form a tetragonal crystalline structure known as Neodymium magnets (NdFeB). These types of magnets contain rare-earth materials, which makes them highly non-sustainable materials. Research to find new magnet compositions to substitute rare earth magnets or to reduce the weight and increase the efficiency of PMSGs is currently being studied. One option is to use Alnico magnets. This thesis project explores this option. With the help of a finite element analysis (FEA) software (COMSOL Multiphysics), three types of Alnico grades 5, 8 and 9 were implemented in the rotor of a spoke type generator to study the load limits of the rotor magnets, and together with this observe the demagnetization and impact that it has on the power production of the generator, in two different scenarios: 1) When the generator is connected to a nominal load under normal conditions and 2) when the generator is connected to a nominal load after a short circuit (SC). The simulations provided an insight into the load limitations that the generator has by each type of Alnico studied. Alnico 9 showed to be the best candidate magnet from the three magnets implemented with less demagnetization and higher electrical power output, followed by Alnico 8, which presented a good electrical power output at the nominal load scenario. Regardless of the higher demagnetization of Alnico 5, it proved to be a better candidate than Alnico 8 at the SC scenario.

The purpose of this research project was to study the αᵧ phase in Alnico 6. Other phases were studies for comparison. Also the effect of an applied magnetic field on the formation of the αᵧ phase was investigated. Three single crystals of Alnico 6 were heat treated for one hour—one at 1250°C, one at 1000°C, and one at 800°C—and water-quenched. The microstructure of each sample was observed with the optical microscope, and X-ray diffraction patterns were taken to determine the crystal structure of the phases present. Several single crystal samples of Alnico 6 were heat treated at 1000°C and furnace-cooled. Some of the samples were heat treated and furnace-cooled in the presence of an applied magnetic field of about 5000Oe, and the others were heat treated and furnace-cooled without an applied magnetic field. Each set of samples were electrothinned, and the microstructure of the αᵧ and α₁ ₊ α₂ phases was studied using the electron microscope. Electron diffraction patterns and electron micrographs were taken from αᵧ regions and α₁ ₊ α₂ regions of each sample. By a combination of X-ray and electron diffraction, it was determined that the crystal structure of the α, α₁, α₂ and αᵧ phases is body-centered cubic, with a lattice parameter of 2.87 Å. Electron diffraction showed that the crystal structure of the combined α₁ ₊ α₂ phases is ordered body-centered cubic, and that the lattices of these two phases are coherent. Other investigators have found that only the α₁ phase is ordered in Alnico 5 and Alnico 8 and thus is believed be the case in Alnico 6 also. Electron diffraction showed the αᵧ phase to be ordered, with the degree of ordering less than that in the α₁ phase. Some difference was noted between the αᵧ phase formed with a magnetic field and that formed without a magnetic field. The αᵧ formed with a magnetic field showed twinning and some small lattice rotations, while stat formed without a field did not. The reason for this is not known.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.