Description

Maximize the area of the valve opening for one rotation of a
convex cam with constraints on the curvature and on the radius
of the cam.

This model is from the COPS benchmarking suite.
See http://www-unix.mcs.anl.gov/~more/cops/.

The number of discretization points can be specified using the command
line parameter --n. COPS performance tests have been reported for n =
100, 200, 400, 800

Large Model of Type : NLP

Category : GAMS Model library

Main file : camshape.gms

$title Shape Optimization of a Cam COPS 2.0 #4 (CAMSHAPE,SEQ=232)

$onText
Maximize the area of the valve opening for one rotation of a
convex cam with constraints on the curvature and on the radius
of the cam.

This model is from the COPS benchmarking suite.
See http://www-unix.mcs.anl.gov/~more/cops/.

The number of discretization points can be specified using the command
line parameter --n. COPS performance tests have been reported for n =
100, 200, 400, 800


Dolan, E D, and More, J J, Benchmarking Optimization
Software with COPS. Tech. rep., Mathematics and Computer
Science Division, 2000.

Anitescu, M, and Serban, R, A Sparse Superlinearly
Convergent SQP with Applications to Two-Dimensional Shape
Optimization. Tech. rep., Argonne National Laboratory, 1998.

Keywords: nonlinear programming, engineering, shape optimization, cam design problem
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$if not set n $set n 100

Set i 'discretization points' / i1*i%n% /;

Alias (i,j);

Scalar
   R_v     'design parameter related to the valve shape' / 1 /
   R_max   'maximum allowed radius of the cam'           / 2 /
   R_min   'minimum allowed radius of the cam'           / 1 /
   alpha   'curvature limit parameter'                   / 1.5 /
   d_theta 'angle between discretization points';

d_theta = 2*pi/(5*(%n% + 1));

Set first(i), last(i), middle(i);

first('i1')   = yes;
last('i%n%')  = yes;
middle(i)     = yes;
middle(first) = no;
middle(last)  = no;

Variable
   r(i)     'radius of the cam at discretization points'
   rdiff(i) 'intermediate'
   area     'valve area';

Equation
   obj      'objective'
   convexity(i)
   convex_edge1(i)
   convex_edge3(i)
   convex_edge4(i)
   eqrdiff(i);

obj.. area =e= ((pi*R_v)/%n%) * sum(i, r(i));

convexity(middle(i))..   -r(i-1)*r(i) - r(i)*r(i+1) + 2*r(i-1)*r(i+1)*cos(d_theta) =l= 0;

convex_edge1(first(i)).. -R_min*r(i)  - r(i)*r(i+1) + 2*R_min*r(i+1)*cos(d_theta)  =l= 0;

convex_edge3(last(i))..  -r(i-1)*r(i) - r(i)*R_max  + 2*r(i-1)*R_max*cos(d_theta)  =l= 0;

convex_edge4(last(i))..  -2*R_max*r(i) + 2*sqr(r(i))*cos(d_theta) =l= 0;

eqrdiff(j(i+1))..        rdiff(i) =e= r(i+1) - r(i);

r.lo(i) = R_min;
r.up(i) = R_max;

rdiff.lo(i(j+1)) = -alpha*d_theta;
rdiff.up(i(j+1)) =  alpha*d_theta;

r.lo('i1') = max(-alpha*d_theta + R_min, r.lo('i1'));
r.up('i1') = min( alpha*d_theta + R_min, r.up('i1'));

r.lo('i%n%') =  max(R_max - alpha*d_theta, r.lo('i%n%'));
r.up('i%n%') =  min(R_max + alpha*d_theta, r.up('i%n%'));

r.up('i1') = min(R_min/(2*cos(d_theta) - 1), r.up('i1'));
r.l(i)     = (R_min+R_max)/2;

Model camshape / all /;

$if set workSpace camshape.workSpace = %workSpace%

solve camshape using nlp maximizing area;