Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.
Abstract: This work presents a numerical model developed to
simulate the dynamics and vibrations of a multistage tractor gearbox.
The effect of time varying mesh stiffness, time varying frictional
torque on the gear teeth, lateral and torsional flexibility of the shafts
and flexibility of the bearings were included in the model. The model
was developed by using the Lagrangian method, and it was applied to
study the effect of three design variables on the vibration and stress
levels on the gears. The first design variable, module, had little effect
on the vibration levels but a higher module resulted to higher bending
stress levels. The second design variable, pressure angle, had little
effect on the vibration levels, but had a strong effect on the stress
levels on the pinion of a high reduction ratio gear pair. A pressure
angle of 25o resulted to lower stress levels for a pinion with 14 teeth
than a pressure angle of 20o. The third design variable, contact ratio,
had a very strong effect on both the vibration levels and bending
stress levels. Increasing the contact ratio to 2.0 reduced both the
vibration levels and bending stress levels significantly. For the gear
train design used in this study, a module of 2.5 and contact ratio of
2.0 for the various meshes was found to yield the best combination
of low vibration levels and low bending stresses. The model can
therefore be used as a tool for obtaining the optimum gear design
parameters for a given multistage spur gear train.