def find_cfl_number(config, state):
# setup result data
results = ordered_bunch()
results.CONFIG = copy.deepcopy(config)
results.ANALYSES = ordered_dict()
# run a bracketing algorithm
sp.optimize.brent(
func=run_su2,
args=(config, state, results),
brack=tuple(SEARCH_BRACKET),
tol=0.001,
maxiter=MAX_ANALYSES -
3, #analyses are used in the first bracket pass and aren't counted by this algorithm
)
## run a search method
#sp.optimize.fminbound(
#func = run_su2,
#x1 = SEARCH_BRACKET[0],
#x2 = SEARCH_BRACKET[2],
#args = ( config, state, results ),
#xtol = 0.001,
#maxfun = MAX_ANALYSES,
#disp = 0,
#)
# get final result
cfl = np.array([r.CFL_NUMBER for r in results.ANALYSES.values()])
its = np.array([r.ITERATIONS for r in results.ANALYSES.values()])
ind = np.lexsort((-cfl, its))
# done!
print 'Final Result'
print ' CFL = %.4f' % cfl[ind[0]]
print ' Iterations = %i' % its[ind[0]]
# save the data
SU2.io.save_data('experiment_results.pkl', results)
return
def find_cfl_number(config,state):
# setup result data
results = ordered_bunch()
results.CONFIG = copy.deepcopy(config)
results.ANALYSES = ordered_dict()
# run a bracketing algorithm
sp.optimize.brent(
func = run_su2,
args = ( config, state, results ),
brack = tuple(SEARCH_BRACKET),
tol = 0.001,
maxiter = MAX_ANALYSES-3, #analyses are used in the first bracket pass and aren't counted by this algorithm
)
## run a search method
#sp.optimize.fminbound(
#func = run_su2,
#x1 = SEARCH_BRACKET[0],
#x2 = SEARCH_BRACKET[2],
#args = ( config, state, results ),
#xtol = 0.001,
#maxfun = MAX_ANALYSES,
#disp = 0,
#)
# get final result
cfl = np.array([ r.CFL_NUMBER for r in results.ANALYSES.values() ])
its = np.array([ r.ITERATIONS for r in results.ANALYSES.values() ])
ind = np.lexsort((-cfl,its))
# done!
print 'Final Result'
print ' CFL = %.4f' % cfl[ind[0]]
print ' Iterations = %i' % its[ind[0]]
# save the data
SU2.io.save_data('experiment_results.pkl',results)
return