From the flow diagram below we see that four different systems are invoked to do an Ideal Adiabatic simulation. The main program sea (stirling engine analysis) first defines the system to be simulated in terms of the set of global variables set up by the define set of functions, as described previously. It then invokes function adiabatic which in turn invokes function set adiab to solve the set of differential equations (function dadiab) over a number of cycles until convergence is attained. Function adiab then fills in the solution matrix for a complete cycle (function fillmatrix) and displays various performance results (power, efficiency). Function adiabatic then invokes function plotadiab to display various relevant plots. The differential equation set is solved by using the Classical Fourth Order Runge-Kutta method which is which is described separately in a Technical Note on ordinary differential equations.
The dynamics
of the solution algorithm lies in the function set adiab,
which initializes the variables, invokes the Runge-Kutta function
over a number of cycles, checks for cyclic convergence, then fills in
the solution matrix. Notice that the function m-file volume.m
includes the
sinusoidal volume variations (function sinevol),
the Ross Yoke-drive volume variations (function yokevol),
and (new
- not in the above flow diagram)
the rockerV drive volume variations function rockvol).
The nine functions of the set adiabatic
are included in the
following seven m-files (refer to the diagram above): [adiabatic.m,
adiab.m,
dadiab.m,
rk4.m,
volume.m,
filmatrix.m
and plotadiab.m].
As before, these can be directly copied from this website and used in
a system which has MATLAB installed. It is intended that the user
will modify and augment this system as required for specific engine
designs.
On
to the case study - D-90
Ross Yoke-drive Engine______________________________________________________________________________________
Stirling Cycle Machine Analysis by Israel
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