94
L. Prabhu et al.
Fig. 1 Supersonic swirling separator
properties to the dew point. After the condensation, condensate is to be separated
via the drain pipe. Hussaini [1] proposed a theoretical method for an identification
of the normal shockwave location using the adiabatic flow equations. Niknam et al.
[2] predicted the normal shockwave location using the artificial neural networks and
coined an iterative process for identification of normal shockwave location.
Gangadhar Venkata Ramana and Prabhu [3] identified the optimal nozzle parameters such as area ratio (AR) and operating pressure ratio (OPR) to develop the normal
shock at the known location in a 3S device using a genetic algorithm optimization
scheme. Shooshtari and Shahsavand [4] studied the effect of diverging angles on
shock location, pressure recovery coefficient (PRC), and the condensation conditions. Numerical simulation was carried out to study the phase change and fluid
properties of the undesired components of natural gas in a supersonic separator [5].
Jinnah [6] identified the turbulence parameters due to the presence of shockwave in
the nozzle divergent. Shooshtari and Shahsavand [7] designed an optimal 3S device
for efficient separation of condensed liquid and increased PRC. Bian et al. [8] identified the pressure and temperature distribution of a flow in a separator device. Cao
and Yang [9] studied the influence of PRC on a dew point depression.
In this work, the optimal parameters required to develop the shock at a specified
location in a CD nozzle of a 3S device are obtained using a meta-heuristic algorithm in conjunction with the surrogate model. The source used is air. Initially, a
non-conventional optimization scheme firefly algorithm is employed to obtain the
optimal parameters AR and OPR of CD nozzle with PRC as the constraint to produce
the shock at a specified location. To reduce the computational cost, mathematical
modelling is replaced by a neural network-based surrogate model. Finally, to validate the optimal parameters obtained from an optimization scheme, the nozzle is
modelled and simulated in ANSYS Fluent.
L. Prabhu et al.
Fig. 1 Supersonic swirling separator
properties to the dew point. After the condensation, condensate is to be separated
via the drain pipe. Hussaini [1] proposed a theoretical method for an identification
of the normal shockwave location using the adiabatic flow equations. Niknam et al.
[2] predicted the normal shockwave location using the artificial neural networks and
coined an iterative process for identification of normal shockwave location.
Gangadhar Venkata Ramana and Prabhu [3] identified the optimal nozzle parameters such as area ratio (AR) and operating pressure ratio (OPR) to develop the normal
shock at the known location in a 3S device using a genetic algorithm optimization
scheme. Shooshtari and Shahsavand [4] studied the effect of diverging angles on
shock location, pressure recovery coefficient (PRC), and the condensation conditions. Numerical simulation was carried out to study the phase change and fluid
properties of the undesired components of natural gas in a supersonic separator [5].
Jinnah [6] identified the turbulence parameters due to the presence of shockwave in
the nozzle divergent. Shooshtari and Shahsavand [7] designed an optimal 3S device
for efficient separation of condensed liquid and increased PRC. Bian et al. [8] identified the pressure and temperature distribution of a flow in a separator device. Cao
and Yang [9] studied the influence of PRC on a dew point depression.
In this work, the optimal parameters required to develop the shock at a specified
location in a CD nozzle of a 3S device are obtained using a meta-heuristic algorithm in conjunction with the surrogate model. The source used is air. Initially, a
non-conventional optimization scheme firefly algorithm is employed to obtain the
optimal parameters AR and OPR of CD nozzle with PRC as the constraint to produce
the shock at a specified location. To reduce the computational cost, mathematical
modelling is replaced by a neural network-based surrogate model. Finally, to validate the optimal parameters obtained from an optimization scheme, the nozzle is
modelled and simulated in ANSYS Fluent.