def make_prlc_rand():
md_RLC_rand = (
gr.Model("RLC with component tolerances") >> gr.cp_vec_function(
fun=lambda df: gr.df_make(
Rr=df.R * (1 + df.dR),
Lr=df.L * (1 + df.dL),
Cr=df.C * (1 + df.dC),
),
var=["R", "dR", "L", "dL", "C", "dC"],
out=["Rr", "Lr", "Cr"],
) >> gr.cp_vec_function(
fun=lambda df: gr.df_make(omega0=np.sqrt(1 / df.Lr / df.Cr)),
var=["Lr", "Cr"],
out=["omega0"],
) >> gr.cp_vec_function(
fun=lambda df: gr.df_make(Q=df.omega0 * df.Rr * df.Cr),
name="parallel RLC",
var=["omega0", "Rr", "Cr"],
out=["Q"]) >> gr.cp_bounds(
R=(1e-3, 1e0),
L=(1e-9, 1e-3),
C=(1e-3, 100),
) >> gr.cp_marginals(
dR=dict(dist="uniform",
loc=R_percent_lo,
scale=R_percent_up - R_percent_lo),
dL=dict(dist="uniform",
loc=L_percent_lo,
scale=L_percent_up - L_percent_lo),
dC=dict(dist="uniform",
loc=C_percent_lo,
scale=C_percent_up - C_percent_lo),
) >> gr.cp_copula_independence())
return md_RLC_rand
def make_test():
md = Model() >> \
cp_function(fun=fun, var=3, out=1) >> \
cp_bounds(x0=(-1,+1), x1=(-1,+1), x2=(-1,+1)) >> \
cp_marginals(
x0={"dist": "uniform", "loc": -1, "scale": 2},
x1={"dist": "uniform", "loc": -1, "scale": 2}
) >> \
cp_copula_independence()
return md
def make_poly():
md = gr.Model("Polynomials") >> \
gr.cp_function(fun=lambda x: x, var=1, out=1, name="linear") >> \
gr.cp_function(fun=lambda x: x**2, var=1, out=1, name="quadratic") >> \
gr.cp_function(fun=lambda x: x**3, var=1, out=1, name="cubic") >> \
gr.cp_marginals(
x0={"dist": "uniform", "loc": -1, "scale": 2},
x1={"dist": "uniform", "loc": -1, "scale": 2},
x2={"dist": "uniform", "loc": -1, "scale": 2}
) >> \
gr.cp_copula_independence()
return md
def make_ishigami():
"""Ishigami function
The Ishigami function is commonly used as a test case for estimating Sobol'
indices.
Model definition:
y0 = sin(x1) + a sin(x2)^2 + b x3^4 sin(x1)
x1 ~ U[-pi, +pi]
x2 ~ U[-pi, +pi]
x3 ~ U[-pi, +pi]
Sobol' index data:
V[y0] = a^2/8 + b pi^4/5 + b^2 pi^8/18 + 0.5
T1 = 0.5(1 + b pi^4/5)^2
T2 = a^2/8
T3 = 0
Tt1 = 0.5(1 + b pi^4/5)^2 + 8 b^2 pi^8/225
Tt2 = a^2/8
Tt3 = 8 b^2 pi^8/225
References:
T. Ishigami and T. Homma, “An importance quantification technique in uncertainty analysis for computer models,” In the First International Symposium on Uncertainty Modeling and Analysis, Maryland, USA, Dec. 3–5, 1990. DOI:10.1109/SUMA.1990.151285
"""
md = gr.Model(name = "Ishigami Function") >> \
gr.cp_function(
fun=fun,
var=["a", "b", "x1", "x2", "x3"],
out=1
) >> \
gr.cp_bounds(a=(6.0, 8.0), b=(0, 0.2)) >> \
gr.cp_marginals(
x1={"dist": "uniform", "loc": -np.pi, "scale": 2 * np.pi},
x2={"dist": "uniform", "loc": -np.pi, "scale": 2 * np.pi},
x3={"dist": "uniform", "loc": -np.pi, "scale": 2 * np.pi}
) >> \
gr.cp_copula_independence()
return md
def make_channel_nondim():
r"""Make 1d channel model; dimensionless form
Instantiates a model for particle and fluid temperature rise; particles are suspended in a fluid with bulk velocity along a square cross-section channel. The walls of said channel are transparent, and radiation heats the particles as they travel down the channel.
References:
Banko, A.J. "RADIATION ABSORPTION BY INERTIAL PARTICLES IN A TURBULENT SQUARE DUCT FLOW" (2018) PhD Thesis, Stanford University, Chapter 2
"""
md = (
Model("1d Particle-laden Channel with Radiation; Dimensionless Form")
>> cp_vec_function(
fun=lambda df: df_make(beta=120 * (1 + df.Phi_M * df.chi)),
var=["Phi_M", "chi"],
out=["beta"],
) >> cp_vec_function(
fun=lambda df: df_make(
T_f=(df.Phi_M * df.chi) / (1 + df.Phi_M * df.chi) *
(df.I * df.xst - df.beta**(-1) * df.I *
(1 - exp(-df.beta * df.xst))),
T_p=1 / (1 + df.Phi_M * df.chi) *
(df.Phi_M * df.chi * df.I * df.xst + df.beta**(-1) * df.I *
(1 - exp(-df.beta * df.xst))),
),
var=["xst", "Phi_M", "chi", "I", "beta"],
out=["T_f", "T_p"],
) >> cp_bounds(
## Dimensionless axial location (-)
xst=(0, 5), ) >> cp_marginals(
## Mass loading ratio (-)
Phi_M={
"dist": "uniform",
"loc": 0,
"scale": 1
},
## Particle-fluid heat capacity ratio (-)
chi={
"dist": "uniform",
"loc": 0.1,
"scale": 0.9
},
## Normalized radiative intensity (-)
I={
"dist": "uniform",
"loc": 0.1,
"scale": 0.9
},
) >> cp_copula_independence())
return md
def make_linear_normal():
md = Model("Linear-Normal Reliability Problem") >> \
cp_function(
fun=limit_state,
var=2,
out=["g_linear"],
name="limit state"
) >> \
cp_marginals(
x0={"dist": "norm", "loc": 0, "scale": 1, "sign":+1},
x1={"dist": "norm", "loc": 0, "scale": 1, "sign":+1}
) >> \
cp_copula_independence()
return md
def make_pareto_random(twoDim=True):
""" Create a model of random points for a pareto frontier evaluation
Args:
twoDim (bool): determines whether to create a 2D or 3D model
"""
if twoDim == True:
# Model to make dataset
md_true = (Model() >> cp_vec_function(
fun=lambda df: df_make(
y1=df.x1 * cos(df.x2),
y2=df.x1 * sin(df.x2),
),
var=["x1", "x2"],
out=["y1", "y2"],
) >> cp_marginals(
x1=dict(dist="uniform", loc=0, scale=1),
x2=dict(dist="uniform", loc=0, scale=pi / 2),
) >> cp_copula_independence())
return md_true
else:
# Model to make dataset
md_true = (Model() >> cp_vec_function(
fun=lambda df: df_make(
y1=df.x1 * cos(df.x2),
y2=df.x1 * sin(df.x2),
y3=df.x1 * tan(df.x2),
),
var=["x1", "x2", "x3"],
out=["y1", "y2", "y3"],
) >> cp_marginals(x1=dict(dist="uniform", loc=0, scale=1),
x2=dict(dist="uniform", loc=0, scale=pi / 2),
x3=dict(dist="uniform", loc=0, scale=pi / 4)) >>
cp_copula_independence())
return md_true
def make_cantilever_beam():
"""Cantilever beam
A standard reliability test-case, often used for benchmarking reliability
analysis and design algorithms.
Generally used in the following optimization problem:
min_{w,t} c_area
s.t. P[g_stress <= 0] <= 1.35e-3
P[g_disp <= 0] <= 1.35e-3
1 <= w, t <= 4
Deterministic Variables:
w: Beam width
t: Beam thickness
Random Variables:
H: Horizontal applied force
V: Vertical applied force
E: Elastic modulus
Y: Yield stress
Outputs:
c_area: Cost; beam cross-sectional area
g_stress: Limit state; stress
g_disp: Limit state; tip displacement
References:
Wu, Y.-T., Shin, Y., Sues, R., and Cesare, M., "Safety-factor based approach for probability-based design optimization," American Institute of Aeronautics and Astronautics, Seattle, Washington, April 2001.
Sues, R., Aminpour, M., and Shin, Y., "Reliability-based Multi-Disciplinary Optimiation for Aerospace Systems," American Institute of Aeronautics and Astronautics, Seattle, Washington, April 2001.
"""
md = Model(name = "Cantilever Beam") >> \
cp_vec_function(
fun=function_area,
var=["w", "t"],
out=["c_area"],
name="cross-sectional area",
runtime=1.717e-7
) >> \
cp_vec_function(
fun=function_stress,
var=["w", "t", "H", "V", "E", "Y"],
out=["g_stress"],
name="limit state: stress",
runtime=8.88e-7
) >> \
cp_vec_function(
fun=function_displacement,
var=["w", "t", "H", "V", "E", "Y"],
out=["g_disp"],
name="limit state: displacement",
runtime=3.97e-6
) >> \
cp_bounds(
w=(2, 4),
t=(2, 4)
) >> \
cp_marginals(
H={"dist": "norm", "loc": MU_H, "scale": TAU_H, "sign": +1},
V={"dist": "norm", "loc": MU_V, "scale": TAU_V, "sign": +1},
E={"dist": "norm", "loc": MU_E, "scale": TAU_E, "sign": 0},
Y={"dist": "norm", "loc": MU_Y, "scale": TAU_Y, "sign": -1}
) >> \
cp_copula_independence()
return md
def make_channel():
r"""Make 1d channel model; dimensional form
Instantiates a model for particle and fluid temperature rise; particles are suspended in a fluid with bulk velocity along a square cross-section channel. The walls of said channel are transparent, and radiation heats the particles as they travel down the channel.
Note that this takes the same inputs as the builtin dataset `df_channel`.
References:
Banko, A.J. "RADIATION ABSORPTION BY INERTIAL PARTICLES IN A TURBULENT SQUARE DUCT FLOW" (2018) PhD Thesis, Stanford University, Chapter 2
Examples:
>>> import grama as gr
>>> from grama.data import df_channel
>>> from grama.models import make_channel
>>> md_channel = make_channel()
>>> (
>>> df_channel
>>> >> gr.tf_md(md_channel)
>>> >> gr.ggplot(gr.aes("T_f", "T_norm"))
>>> + gr.geom_abline(slope=1, intercept=0, linetype="dashed")
>>> + gr.geom_point()
>>> + gr.labs(x="1D Model", y="3D DNS")
>>> )
"""
md = (
Model("1d Particle-laden Channel with Radiation; Dimensional Form") >>
cp_vec_function(
fun=lambda df: df_make(
Re=df.U * df.H / df.nu_f,
chi=df.cp_p / df.cp_f,
Pr=df.nu_f / df.alpha_f,
Phi_M=df.rho_p * 0.524 * df.d_p**3 * df.n / df.rho_f,
tau_flow=df.L / df.U,
tau_pt=(df.rho_p * df.cp_p * 0.318 * df.d_p) / df.h_p,
tau_rad=(df.rho_p * df.cp_p * 0.667 * df.d_p * df.T_0) /
(df.Q_abs * 0.78 * df.I_0),
),
var=[
"U", # Fluid bulk velocity
"H", # Channel width
"nu_f", # Fluid kinematic viscosity
"cp_p", # Particle isobaric heat capacity
"cp_f", # Fluid isobaric heat capacity
"alpha_f", # Fluid thermal diffusivity
"rho_p", # Particle density
"rho_f", # Fluid density
"d_p", # Particle diameter
"n", # Particle number density
"h_p", # Particle-to-gas convection coefficient
"T_0", # Initial temperature
"Q_abs", # Particle radiation absorption coefficient
"I_0", # Incident radiation
],
out=[
"Re", # Reynolds number
"Pr", # Prandtl number
"chi", # Particle-fluid heat capacity ratio
"Phi_M", # Mass Loading Ratio
"tau_flow", # Fluid residence time
"tau_pt", # Particle thermal time constant
"tau_rad", # Particle temperature doubling time (approximate)
],
name="Dimensionless Numbers",
) >> cp_vec_function(
fun=lambda df: df_make(
## Let xi = x / L
xst=(df.xi * df.L) / df.H / df.Re / df.Pr,
## Assume an optically-thin scenario; I/I_0 = 1
Is=df.Re * df.Pr * (df.H / df.L) *
(df.tau_flow / df.tau_rad) * 1,
beta=df.Re * df.Pr * (df.H / df.L) *
(df.tau_flow / df.tau_pt) * (1 + df.Phi_M * df.chi),
),
var=[
"xi", "chi", "H", "L", "Phi_M", "tau_flow", "tau_rad", "tau_pt"
],
out=[
"xst", # Flow-normalized channel axial location
"Is", # Normalized heat flux
"beta", # Spatial development coefficient
],
name="Intermediate Dimensionless Numbers",
) >> cp_vec_function(
fun=lambda df: df_make(
T_f=(df.Phi_M * df.chi) / (1 + df.Phi_M * df.chi) *
(df.Is * df.xst - df.Is / df.beta *
(1 - exp(-df.beta * df.xst))),
T_p=1 / (1 + df.Phi_M * df.chi) *
(df.Phi_M * df.chi * df.Is * df.xst + df.Is / df.beta *
(1 - exp(-df.beta * df.xst))),
),
var=["xst", "Phi_M", "chi", "Is", "beta"],
out=["T_f", "T_p"],
) >> cp_bounds(
## Normalized axial location; xi = x/L (-)
xi=(0, 1), ) >> cp_marginals(
## Channel width (m)
H={
"dist": "uniform",
"loc": 0.038,
"scale": 0.004
},
## Channel length (m)
L={
"dist": "uniform",
"loc": 0.152,
"scale": 0.016
},
## Fluid bulk velocity (m/s)
U={
"dist": "uniform",
"loc": 1,
"scale": 2.5
},
## Fluid kinematic viscosity (m^2/s)
nu_f={
"dist": "uniform",
"loc": 1.4e-5,
"scale": 0.1e-5
},
## Particle isobaric heat capacity (J/(kg K))
cp_p={
"dist": "uniform",
"loc": 100,
"scale": 900
},
## Fluid isobaric heat capacity (J/(kg K))
cp_f={
"dist": "uniform",
"loc": 1000,
"scale": 1000
},
## Fluid thermal diffusivity (m^2/s)
alpha_f={
"dist": "uniform",
"loc": 50e-6,
"scale": 50e-6
},
## Particle density (kg / m^3)
rho_p={
"dist": "uniform",
"loc": 1e3,
"scale": 9e3
},
## Fluid density (kg / m^3)
rho_f={
"dist": "uniform",
"loc": 0.5,
"scale": 1.0
},
## Particle diameter (m)
d_p={
"dist": "uniform",
"loc": 1e-6,
"scale": 9e-6
},
## Particle number density (1 / m^3)
n={
"dist": "uniform",
"loc": 9.5e9,
"scale": 1.0e9
},
## Particle-to-gas convection coefficient (W / (m^2 K))
h_p={
"dist": "uniform",
"loc": 1e3,
"scale": 9e3
},
## Initial temperature (K)
T_0={
"dist": "uniform",
"loc": 285,
"scale": 30
},
## Particle radiation absorption coefficient (-)
Q_abs={
"dist": "uniform",
"loc": 0.25,
"scale": 0.50
},
## Incident radiation (W/m^2)
I_0={
"dist": "uniform",
"loc": 9.5e6,
"scale": 1.0e6
},
) >> cp_copula_independence())
return md