Evaluation of La AlO_3 as top coat material for thermal barrier coatings

Perovskite is a versatile group of oxide materials allowing their properties to be tailored by composition towards specific requirements. La Al O3 was prepared to study and report its properties in the context of its potential in thermal barrier coatings（TBCs） technology. A citric acid method was used for synthesis and the perovskite structure was confirmed using XRD and FT-IR. Viscosity of the solution precursor was checked as well as the particle size by laser particle size analysis. Densification behavior of the material was followed by conventional sintering and by spark plasma sintering. Apparent porosity by the Archimedes method, thermal conductivity and thermal expansion coefficient were studied. Mechanical and fracture properties were measured at elevated temperatures up to 1300 ℃ For samples sintered at 1200-1400 ℃, coefficient of thermal expansion ranged from 5.5×10^-6 to 6.5×10^-6 K^-1 and thermal conductivity ranged between 2.2 and 3.4 W/（m？K）. Elastic modulus and ultimate stress were measured at 1000-1300 ℃, while by micro-indentation, fracture toughness was found to be 3 MPa·m1/2. As the sintering temperature increased from 1200 to 1500 ℃, significant densification from 3.21 to 5.81 g/cm^3 was found, indicating that material annealing should be made at least at 1400 ℃. Under this condition, negligible dimensional change in phase transition temperature of La Al O3 from the rhombohedral（R3 c） to the ideal cubic（Pm3 m） is found. Data reported in this work can be useful for comparing the mechanical and fracture behaviours of different TBCs developed involving La Al O3 as well as input for numerical simulations.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.