The aim of this paper is to apply genetic algorithm (GA) to the solution of the environmental economic power
dispatch problem. The environmental economic power dispatch is a multi-objective optimization problem. Fuel
cost is considered as one of the objectives. The other objective is emissions such as SO2 or NOx or a
combination of both. A trade-off relation between fuel cost and emissions can be formed through a pareto
optimal front. Valve point opening and prohibited operating zones add non-smoothness and non-convexities to
the objective functions. Evolutionary algorithms can efficiently solve such non-smooth and non-convex
problems. Solutions need to be diversified and distributed among the whole range of the pareto optimal front.
This allows operators to trade-off between fuel cost and emissions in feasible optimal regions. Applying genetic
algorithm with diversity enhancement proves its effectiveness. Application of the algorithm on three and six
unit systems is demonstrated.
Yongeui Hong, Taewoo Lee, Kyoungdyuk Rho, Hyun-Seung Cha,
A knowledge-based fault diagnosis system uses prior knowledge and context knowledge for prediction to improve fault diagnosis performance. The proposed representative fault diagnosis system consists of three levels. With this structure, fault diagnosis can be flexibly performed even in a complicated environment. The three-level consists of the fault diagnosis level, the learning level, and the information processing level. The Fault diagnosis level is able to express the correlation by using the data obtained from the controller and diagnose the fault by logically inferring it. The learning level links the logical language perceived by humans and the numerical data processed by the computer, and keeps it consistent with the situation. The information processing level acquires the feature value required by the higher level in the candidate region among the numerous data obtained from the controller and sends it to the higher level. The proposed algorithm can effectively diagnosis faults by using additional prior knowledge and situation data.
The objective of research work is to establish the influence of environmental factors such as moisture and temperature on the shear properties of metal and polymer joints. The specimens of metal and polymer joints were prepared with aluminium plate and glass fibre reinforced plastics. Here three types of resins were used for joining the metal and polymer composites viz. epoxy vinyl ester and polyester. The joint specimens were exposed to 80 C temperature and relative humidity of 90% for 25 days in environmental chamber. The single and double shear strength of the joints for ascast and hygrothermal exposed specimens are investigated. The results showed that the strength of hydrothermal specimens decreased about 25%, 18% and 33% for epoxy, vinyl ester and polymer adhesive joints respectively. The result also shows that vinyl ester joints exhibit lower water absorption and property degradation of the joints.
This research carries out the advanced phase in correlation with the previous published design of KF
Implemented Flying Wing. At the primary stage the basic design was considered under omission of non-static
components and turbulent conditions. At this stage the simulations have taken a step ahead with improved flow
conditions and advanced modeling of the design. As per the design aspects the engines, pylons, landing gears
and shape improvements were done with solid modeling. Due to the computational limitations this was divided
in to two phases as cruising conditions with non-static components and further studies to be carried out in Takeoff
and Landing conditions with extended landing gears. Under the stability and control conditions a separate
research is being carried out in achieving the optimum capability. Propfan engine selected for extreme condition
evaluations. The implementations were made without disrupting the base design which was presented in phase
one basic simulation carried out prior to this. The simulation results deemed to be promising for the first stage
as well as the effect of new components. The secondary target areas are to be carried out in further ongoing
research as well.
Al 7075 T6 is one of the highest strength aluminum alloys in 7000 series family which is used in highly stressed structural parts of aircrafts. The high surface roughness lowers the fatigue resistance and also affects the quality of the parts. Hence, this work deals with the application of teaching learning based optimization to minimize the roughness in the CNC end milling process. Here, taguchi L9 orthogonal array is used as experimental design. The depth of cut, feed and speed are used as control factors with three levels each and roughness as the response. The regression model was developed to find the effect of process parameters on response. The regression model was used by Teaching Learning Based Optimization (TLBO) algorithm and optimum process parameters were obtained. The optimal process parameters obtained by TLBO gave 60% reduction in roughness as compared to that given by initial setting of parameters used for machining of this material.