No abstract

A wing-in-ground-effect (WIGE) crafts can be deployed to fly by utilizing the ground effect, which is a natural phenomenon known to improve the efficiency of airplanes during take-off and landing approaches. In contrast, WIGE craft is not commercially viable for public transport mainly due to the difficulties in controlling its longitudinal stability. As an attempt to support the development of WIGE crafts, this paper presents a case study in aerodynamics based on certain published reports, specifically to reveal the available research data that are considered of interest and can be used as a lesson for further study and analysis. The wind tunnel procedure and testing, as well as numerical investigation of a WIGE craft, are applied and the results are then analyzed. The discussions are oriented in the perspectives of aerodynamics. Based on the tests and calculation, parameters concerning the ground effect as the WIGE crafts approaching the ground surfaces may be identified and hence their values can be determined. Thus, the static longitudinal stability may then be established and optimized for control of the WIGE craft.

This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industrial chemical processing and spray coating. The aim is to model the coupled tank system using mixed UKF and DE method to estimate the parameters of the tank. First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. The obtained results demonstrate good performances.