def print_to_moohst(show=True, save=False):
if show:
shows = True
saves = False
elif save:
shows = False
saves = True
#* Catalogue data
models, data = load_catalogues()
plot_catalogue_data(
models,
data,
show_figure=shows,
save_figure=saves,
)
plot_histograms(
models,
data,
show_figure=shows,
save_figure=saves,
)
#* Validation
plot_validation(show_figure=shows, save_figure=saves)
#* HST initialization
hst = HST(*set_defaults('sizing'))
hst.compute_sizes(displ=500)
hst.compute_speed_limit(models['pump_speed'])
#* Oil setting
hst.oil = 'SAE 15W40'
hst.oil_temp = 100
hst.load_oil()
hst.plot_oil(show_figure=shows, save_figure=saves)
#* Maps preparation
input_speed, pressure_charge, pressure_discharge = set_defaults(
'performance')
max_speed, max_pressure, hst.max_power_input, hst.input_gear_ratio = set_defaults(
'map')
hst.engine = None
#* Maps plotting
hst.plot_eff_map(
max_speed,
max_pressure,
pressure_charge=pressure_charge,
show_figure=shows,
save_figure=saves,
)
hst.plot_power_map(
max_speed,
max_pressure,
pressure_charge=pressure_charge,
show_figure=shows,
save_figure=saves,
)
eff_temp(hst, save_figure=save, show_figure=show)
def __init__(self):
super().__init__(n_var=3,
n_obj=2,
n_constr=1,
xl=np.array([200, 1000, 100]),
xu=np.array([800, 3000, 700]),
elementwise_evaluation=True)
self.hst = HST()
self.hst.oil = 'SAE 15W40'
self.hst.oil_temp = 100
self.hst.load_oil()
self.reg_models, _ = ha.load_catalogues()
def test_motor_speed(self):
self.assertEqual(round(test_performance['motor']['speed'], ndigits=3),
2068.024)
def test_motor_torque(self):
self.assertEqual(round(test_performance['motor']['torque'], ndigits=3),
489.130)
def test_motor_power(self):
self.assertEqual(round(test_performance['motor']['power'], ndigits=3),
105.927)
def test_hst_volumetric(self):
self.assertEqual(
round(test_efficiency['hst']['volumetric'], ndigits=3), 68.934)
def test_hst_mechanical(self):
self.assertEqual(
round(test_efficiency['hst']['mechanical'], ndigits=3), 92.266)
def test_hst_total(self):
self.assertEqual(round(test_efficiency['hst']['total'], ndigits=3),
63.603)
if __name__ == '__main__':
test_hst = HST()
test_hst.compute_sizes(100)
test_hst.load_oil()
test_efficiency, test_performance = test_hst.compute_eff(3000, 350)
unittest.main(verbosity=2)
def plot_hst_comparison(displ_1,
displ_2,
speed,
pressure,
temp,
show_figure=True,
save_figure=False,
format='pdf'):
"""
Prints a bar plot to compare total efficiencies of two HSTs.
Parameters:
---
displ_1, displ_2: float
Displacements of the HSTs to be comapred
speed, pressure, temp, charge: floats
Operational parameters for the comparison
"""
effs_1, effs_2 = [], []
motor_pows_1, motor_pows_2 = [], []
pump_pows_1, pump_pows_2 = [], []
oils = ('SAE 15W40', 'SAE 10W40', 'SAE 10W60', 'SAE 5W40', 'SAE 0W30',
'SAE 30')
hst_1, hst_2 = HST(oil_temp=temp), HST(oil_temp=temp)
hst_1.compute_sizes(displ_1)
hst_2.compute_sizes(displ_2)
for oil in oils:
hst_1.oil, hst_2.oil = oil, oil
hst_1.load_oil()
hst_2.load_oil()
eff_1 = hst_1.compute_eff(speed, pressure)[0]
eff_2 = hst_2.compute_eff(speed, pressure)[0]
effs_1.append(eff_1['hst']['total'])
effs_2.append(eff_2['hst']['total'])
motor_pows_1.append(hst_1.performance['motor']['power'])
motor_pows_2.append(hst_2.performance['motor']['power'])
pump_pows_1.append(hst_1.performance['pump']['power'])
pump_pows_2.append(hst_2.performance['pump']['power'])
fig_eff = go.Figure()
fig_eff.add_trace(
go.Bar(
x=oils,
y=effs_1,
text=[f'{eff:.2f}' for eff in effs_1],
textposition='auto',
name=f'{displ_1} cc/rev',
marker_color='steelblue',
))
fig_eff.add_trace(
go.Bar(x=oils,
y=effs_2,
text=[f'{eff:.2f}' for eff in effs_2],
textposition='auto',
name=f'{displ_2} cc/rev',
marker_color='indianred'))
fig_eff.update_layout(
# title=
# f'Total efficiency of {displ_1} and {displ_2} cc/rev HSTs at {speed} rpm, {pressure} bar, {temp}C oil',
yaxis=dict(
title='Total HST efficiency, %',
range=[50, 90],
),
template='none',
showlegend=True,
legend_orientation='h',
width=800,
height=500,
font=dict(size=14),
)
fig_pow = go.Figure()
fig_pow.add_trace(
go.Bar(
x=oils,
y=pump_pows_1,
text=[f'{pow:.2f}' for pow in pump_pows_1],
textposition='auto',
name=f'{displ_1} cc/rev in',
marker_color='steelblue',
))
fig_pow.add_trace(
go.Bar(
x=oils,
y=motor_pows_1,
text=[f'{pow:.2f}' for pow in motor_pows_1],
textposition='auto',
name=f'{displ_1} cc/rev out',
marker_color='lightblue',
))
fig_pow.add_trace(
go.Bar(
x=oils,
y=pump_pows_2,
text=[f'{pow:.2f}' for pow in pump_pows_2],
textposition='auto',
name=f'{displ_2} cc/rev in',
marker_color='indianred',
))
fig_pow.add_trace(
go.Bar(x=oils,
y=motor_pows_2,
text=[f'{pow:.2f}' for pow in motor_pows_2],
textposition='auto',
name=f'{displ_2} cc/rev out',
marker_color='pink'))
fig_pow.update_layout(
# title=
# f'Power balance of {displ_1} and {displ_2} cc/rev HSTs at {speed} rpm, {pressure} bar, {temp}C oil',
yaxis=dict(title='Power, kW', ),
template='none',
showlegend=True,
legend_orientation='h',
width=900,
height=600,
font=dict(size=14, color='black'),
)
if save_figure:
if not os.path.exists('images'):
os.mkdir('images')
fig_eff.write_image(f'images/hst_eff_comparison.{format}')
fig_pow.write_image(f'images/hst_power_comparison.{format}')
if show_figure:
fig_eff.show()
fig_pow.show()
def plot_validation_plotly(show_figure=False, save_figure=False, format='pdf'):
"""Performs validation of the efficiency model by computing the volumetric efficiency of the benchmark HST, for which the test data is available containing results of measurements of the input/output speeds, and comparing the computed efficiency with the test data.
"""
data = pd.read_csv(
'https://raw.githubusercontent.com/ivanokhotnikov/effmap_demo/master/data/test_data.csv'
)
data.dropna(
subset=['Forward Speed', 'Reverse Speed', 'Volumetric at 1780RPM'],
inplace=True)
speeds = data[['Forward Speed', 'Reverse Speed']].astype(float)
speeds = speeds.stack()
vol_eff = speeds / 1780 * 1e2
piston_max = 1.1653 * 25.4 * 1e-3
piston_min = 1.1650 * 25.4 * 1e-3
bore_max = 1.1677 * 25.4 * 1e-3
bore_min = 1.1671 * 25.4 * 1e-3
rad_clearance_max = (bore_max - piston_min) / 2
rad_clearance_min = (bore_min - piston_max) / 2
benchmark = HST(swash=15, oil='SAE 30', oil_temp=60)
benchmark.compute_sizes(displ=196,
k1=.7155,
k2=.9017,
k3=.47,
k4=.9348,
k5=.9068)
benchmark.load_oil()
eff_min = benchmark.compute_eff(speed_pump=1780,
pressure_discharge=207,
pressure_charge=14,
h3=rad_clearance_max)[0]
eff_max = benchmark.compute_eff(speed_pump=1780,
pressure_discharge=207,
pressure_charge=14,
h3=rad_clearance_min)[0]
fig = ff.create_distplot(
[vol_eff],
[
f"Test data. Mean = {round(vol_eff.mean(),2)}%, SD = {round(vol_eff.std(),2)}%"
],
show_hist=True,
bin_size=.3,
show_rug=False,
)
fig.add_scatter(
x=[eff_max['hst']['volumetric'], eff_max['hst']['volumetric']],
y=[0, .6],
mode='lines',
name=
f"Prediction at min clearance, {round(eff_max['hst']['volumetric'],2)}%",
line=dict(width=1.5, ),
)
fig.add_scatter(
x=[eff_min['hst']['volumetric'], eff_min['hst']['volumetric']],
y=[0, .6],
mode='lines',
name=
f"Prediction at max clearance, {round(eff_min['hst']['volumetric'],2)}%",
line=dict(width=1.5, ),
)
fig.add_scatter(
x=[vol_eff.mean(), vol_eff.mean()],
y=[0, .6],
mode='lines',
name=f"Test mean, {round(vol_eff.mean(),2)}%",
line=dict(width=1.5, dash='dash'),
)
fig.add_scatter(
x=[vol_eff.mean() + vol_eff.std(),
vol_eff.mean() + vol_eff.std()],
y=[0, .6],
mode='lines',
name='Test mean + SD',
line=dict(width=1.5, dash='dash'),
)
fig.add_scatter(
x=[vol_eff.mean() - vol_eff.std(),
vol_eff.mean() - vol_eff.std()],
y=[0, .6],
mode='lines',
name='Test mean - SD',
line=dict(width=1.5, dash='dash'),
)
fig.update_layout(
# title=
# f'Sample of {len(vol_eff)} measurements of the {benchmark.displ} cc/rev HST with {benchmark.oil} at {benchmark.oil_temp}C',
template='none',
width=1000,
height=600,
xaxis=dict(
title='HST volumetric efficiency, %',
showline=True,
linecolor='black',
showgrid=True,
gridcolor='LightGray',
gridwidth=0.25,
linewidth=0.25,
range=[84, 94],
dtick=2,
mirror='all',
),
yaxis=dict(
title='Probability density',
showline=True,
linecolor='black',
showgrid=True,
gridcolor='LightGray',
gridwidth=0.25,
linewidth=0.25,
range=[0, .6],
mirror='all',
),
showlegend=True,
legend_orientation='v',
legend=dict(x=1.05, y=1, xanchor='left', yanchor='top'),
font=dict(size=14, color='black'))
if save_figure:
if not os.path.exists('images'): os.mkdir('images')
fig.write_image(f'images/validation.{format}')
if show_figure: fig.show()
def plot_validation(show_figure=False, save_figure=False, format='pdf'):
data = pd.read_csv(
'https://raw.githubusercontent.com/ivanokhotnikov/effmap_demo/master/data/test_data.csv'
)
data.dropna(
subset=['Forward Speed', 'Reverse Speed', 'Volumetric at 1780RPM'],
inplace=True)
data['Reverse Speed'] = data['Reverse Speed'].astype(np.float64)
data['Volumetric at 1780RPM'] = data['Volumetric at 1780RPM'].map(
lambda x: float(x[:-1]))
data.loc[data['Reverse Speed'] == 568.0, 'Reverse Speed'] = 1568.0
data.loc[data['Volumetric at 1780RPM'] == 60.1,
'Volumetric at 1780RPM'] = round(
(1571 / 1780 + 1568 / 1780) / 2 * 100, ndigits=1)
speeds = data[['Forward Speed', 'Reverse Speed']].astype(float)
speeds = speeds.stack()
vol_eff = speeds / 1780 * 1e2
piston_max = 1.1653 * 25.4 * 1e-3
piston_min = 1.1650 * 25.4 * 1e-3
bore_max = 1.1677 * 25.4 * 1e-3
bore_min = 1.1671 * 25.4 * 1e-3
rad_clearance_max = (bore_max - piston_min) / 2
rad_clearance_min = (bore_min - piston_max) / 2
benchmark = HST(swash=15, oil='SAE 30', oil_temp=60)
benchmark.compute_sizes(displ=196,
k1=.7155,
k2=.9017,
k3=.47,
k4=.9348,
k5=.9068)
benchmark.load_oil()
eff_min = benchmark.compute_eff(speed_pump=1780,
pressure_discharge=207,
pressure_charge=14,
h1=29e-6,
h2=29e-6,
h3=rad_clearance_max)[0]
eff_max = benchmark.compute_eff(speed_pump=1780,
pressure_discharge=207,
pressure_charge=14,
h1=29e-6,
h2=29e-6,
h3=rad_clearance_min)[0]
eff_midrange = benchmark.compute_eff(speed_pump=1780,
pressure_discharge=207,
pressure_charge=14,
h1=29e-6,
h2=29e-6,
h3=np.mean((rad_clearance_min,
rad_clearance_max)))[0]
sns.set_style('ticks', {
'spines.linewidth': .25,
})
sns.set_palette('Set1')
plot = sns.histplot(
data=vol_eff,
kde=True,
element='step',
label='Test data',
color='steelblue',
)
sns.despine()
plot.axes.axvline(
eff_max['hst']['volumetric'],
label=
f"Prediction at min clearance, {round(eff_max['hst']['volumetric'], 2)}%",
linestyle=':',
color='crimson',
linewidth=1,
)
plot.axes.axvline(
eff_min['hst']['volumetric'],
label=
f"Prediction at max clearance, {round(eff_min['hst']['volumetric'], 2)}%",
linestyle=':',
color='seagreen',
linewidth=1,
)
plot.axes.axvline(
eff_midrange['hst']['volumetric'],
label=
f"Prediction at midrange clearance, {round(eff_midrange['hst']['volumetric'], 2)}%",
linestyle=':',
color='royalblue',
linewidth=1,
)
plot.axes.axvline(
vol_eff.mean(),
label=f"Test mean, {round(vol_eff.mean(), 2)}%",
linestyle='--',
color='steelblue',
linewidth=1,
)
plot.axes.axvline(
vol_eff.mean() + vol_eff.std(),
label=f"Test mean + SD, {round(vol_eff.mean() + vol_eff.std(),2)}%",
linestyle='--',
color='darkorange',
linewidth=1,
)
plot.axes.axvline(
vol_eff.mean() - vol_eff.std(),
label=f"Test mean - SD, {round(vol_eff.mean() - vol_eff.std(),2)}%",
color='slateblue',
linestyle='--',
linewidth=1,
)
plt.xlim(86, 92)
plt.ylim(0, 90)
plt.xlabel('HST volumetric efficiency, %')
plt.legend(loc='upper right', fontsize='x-small')
if save_figure:
if not os.path.exists('images'): os.mkdir('images')
plt.savefig(f'images/validation.{format}')
if show_figure: plt.show()
plt.clf()
plt.close('all')