def fco_swu(series):
"""FcoSwu metric returns the separative work units required for each
year in a simulation. This is written for simulations that
use the Bright-lite Fuel Fab (i.e., the U235 and U238 are given separately
in the FCO simulations, and the filtering is archetype-specific).
"""
tools.raise_no_pyne('SWU Required could not be computed', HAVE_PYNE)
mass = pd.merge(series[0].reset_index(), series[1].reset_index(),
on=['ResourceId'], how='inner').set_index(['ObjId', 'TimeCreated', 'NucId'])
swu = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc == 922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
swu0 = enr.swu(x_feed, x_prod, x_tails, product=prods[obj]) / 1e6
swu.append(swu0)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
swu = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'SWU': swu}, columns=['Year', 'SWU'])
swu = swu.groupby('Year').sum()
rtn = swu.reset_index()
return rtn
def fco_swu(series):
"""FcoSwu metric returns the separative work units required for each
year in a 200-yr simulation. This is written for FCO databases that
use the Bright-lite (i.e., the U235 and U238 are given separately
in the FCO simulations).
"""
tools.raise_no_pyne('SWU Required could not be computed', HAVE_PYNE)
mass = pd.merge(series[0].reset_index(), series[1].reset_index(),
on=['ResourceId'], how='inner').set_index(['ObjId', 'TimeCreated', 'NucId'])
swu = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc == 922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
swu0 = enr.swu(x_feed, x_prod, x_tails, product=prods[obj]) / 1e6
swu.append(swu0)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
swu = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'SWU': swu}, columns=['Year', 'SWU'])
swu = swu.groupby('Year').sum()
rtn = swu.reset_index()
return rtn
def fco_u_mined(mats, trans):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a simulation. This is written for simulations that use the
Bright-lite Fuel Fab (i.e., the U235 and U238 are given separately in the
FCO simulations, and the filtering is archetype-specific).
"""
tools.raise_no_pyne('U_Mined could not be computed', HAVE_PYNE)
mass = pd.merge(mats, trans, on=['ResourceId'], how='inner')
mass = mass.set_index(['ObjId', 'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc == 922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(x_feed, x_prod, x_tails, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={
'Year': m.TimeCreated.apply(lambda x: x // 12),
'UMined': u
},
columns=['Year', 'UMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def fco_u_mined(mats, trans):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a simulation. This is written for simulations that use the
Bright-lite Fuel Fab (i.e., the U235 and U238 are given separately in the
FCO simulations, and the filtering is archetype-specific).
"""
tools.raise_no_pyne('U_Mined could not be computed', HAVE_PYNE)
mass = pd.merge(mats, trans, on=['ResourceId'], how='inner')
mass = mass.set_index(['ObjId', 'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc==922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(x_feed, x_prod, x_tails, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'UMined': u}, columns=['Year', 'UMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def decay_heat(series):
"""Decay heat metric returns the instantaneous decay heat of a nuclide
in a material (Q value * activity) indexed by the SimId, QualId,
ResourceId, ObjId, TimeCreated, and NucId.
"""
tools.raise_no_pyne('DecayHeat could not be computed', HAVE_PYNE)
act = series[0]
dh = []
for (simid, qual, res, obj, time, nuc), a in act.iteritems():
val = (data.MeV_per_MJ * a * data.q_val(nuc))
dh.append(val)
dh = pd.Series(dh, index=act.index)
dh.name = 'DecayHeat'
rtn = dh.reset_index()
return rtn
def decay_heat(series):
"""Decay heat metric returns the instantaneous decay heat of a nuclide
in a material (Q value * activity) indexed by the SimId, QualId,
ResourceId, ObjId, TimeCreated, and NucId.
"""
tools.raise_no_pyne('DecayHeat could not be computed', HAVE_PYNE)
act = series[0]
dh = []
for (simid, qual, res, obj, time, nuc), a in act.iteritems():
val = (data.MeV_per_MJ * a * data.q_val(nuc))
dh.append(val)
dh = pd.Series(dh, index=act.index)
dh.name = 'DecayHeat'
rtn = dh.reset_index()
return rtn
def activity(series):
"""Activity metric returns the instantaneous activity of a nuclide
in a material (material mass * decay constant / atomic mass)
indexed by the SimId, QualId, ResourceId, ObjId, TimeCreated, and NucId.
"""
tools.raise_no_pyne('Activity could not be computed', HAVE_PYNE)
mass = series[0]
act = []
for (simid, qual, res, obj, time, nuc), m in mass.iteritems():
val = (1000 * data.N_A * m * data.decay_const(nuc) \
/ data.atomic_mass(nuc))
act.append(val)
act = pd.Series(act, index=mass.index)
act.name = 'Activity'
rtn = act.reset_index()
return rtn
def activity(series):
"""Activity metric returns the instantaneous activity of a nuclide
in a material (material mass * decay constant / atomic mass)
indexed by the SimId, QualId, ResourceId, ObjId, TimeCreated, and NucId.
"""
tools.raise_no_pyne('Activity could not be computed', HAVE_PYNE)
mass = series[0]
act = []
for (simid, qual, res, obj, time, nuc), m in mass.iteritems():
val = (1000 * data.N_A * m * data.decay_const(nuc) \
/ data.atomic_mass(nuc))
act.append(val)
act = pd.Series(act, index=mass.index)
act.name = 'Activity'
rtn = act.reset_index()
return rtn
