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chore: Update examples to use modern API #4887

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e6834ef
Update examples to use modern API. Mostly untested and not currently …
Gobot1234 Feb 4, 2026
c48f1fb
chore: adding changelog file 4887.maintenance.md [dependabot-skip]
pyansys-ci-bot Feb 4, 2026
af1147a
respond to some feedback
Gobot1234 Feb 18, 2026
d3d147a
respond to some feedback
Gobot1234 Feb 19, 2026
1ffeecb
fix couple more imports etc
Gobot1234 Feb 19, 2026
79f0eb6
variable rename
Gobot1234 Feb 19, 2026
80092df
update allowed_values to all
Gobot1234 Mar 16, 2026
4a54bea
Update modeling_cavitation.py
Gobot1234 Apr 14, 2026
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198 changes: 101 additions & 97 deletions examples/00-fluent/DOE_ML.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,20 +62,28 @@

# flake8: noqa: E402

import os
import itertools
import json
from pathlib import Path
from typing import TYPE_CHECKING, cast

from ansys.units import Quantity
from ansys.units.quantity import ArrayLike
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
# import plotly.express as px
# import plotly.graph_objects as go
# import seaborn as sns
# import tensorflow as tf
# from tensorflow import keras
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@Gobot1234 Gobot1234 Feb 4, 2026

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obvious


import ansys.fluent.core as pyfluent
from ansys.fluent.core import examples
from ansys.fluent.core.generated.solver.settings_builtin import SurfaceIntegrals
from ansys.fluent.core.generated.solver.settings_builtin_261 import iterate, read_case
from ansys.fluent.core.solver import Initialization, VelocityInlet, RunCalculation
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@Gobot1234 Gobot1234 Feb 4, 2026

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Imports need merging

from ansys.units.common import m, s

###########################################################################
# Specifying save path
Expand All @@ -84,7 +92,7 @@
import_filename = examples.download_file(
"elbow.cas.h5",
"pyfluent/examples/DOE-ML-Mixing-Elbow",
save_path=os.getcwd(),
save_path=Path.cwd(),
)

#######################
Expand All @@ -95,69 +103,67 @@
# Launch Fluent session with solver mode and print Fluent version
# ===============================================================

solver_session = pyfluent.launch_fluent(
precision="double",
solver = pyfluent.Solver.from_install(
precision=pyfluent.Precision.SINGLE,
processor_count=4,
)
print(solver_session.get_fluent_version())
print(solver.get_fluent_version())


#############################################################################
# Read case
# =========

solver_session.settings.file.read_case(file_name=import_filename)
solver.upload(import_filename)
read_case(solver, file_name=import_filename)

##############################################################################################
# Design of Experiments
# =====================

# Specify inlet velocities for the cold and hot streams.
# Each pair of (coldVel, hotVel) will be used to run one Fluent case.
coldVelArr = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7])
hotVelArr = np.array([0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
# Each pair of (cold_value, hot_value) will be used to run one Fluent case.
cold_velocities = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7] * m / s

hot_velocities = [0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0] * m / s


# Allocate a results array. Entry (i, j) will hold the Mass‑Weighted
# Average Temperature at the outlet for coldVelArr[i] and hotVelArr[j].
resArr = np.zeros((coldVelArr.size, hotVelArr.size))

for idx1, coldVel in np.ndenumerate(coldVelArr):
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ndenumerate is the same as enumerate cause it's a vector

for idx2, hotVel in np.ndenumerate(hotVelArr):
cold_inlet = solver_session.settings.setup.boundary_conditions.velocity_inlet[
"cold-inlet"
]
cold_inlet.momentum.velocity.value = coldVel

hot_inlet = solver_session.settings.setup.boundary_conditions.velocity_inlet[
"hot-inlet"
]
hot_inlet.momentum.velocity.value = hotVel

solver_session.settings.solution.initialization.initialization_type = "standard"
solver_session.settings.solution.initialization.standard_initialize()
solver_session.settings.solution.run_calculation.iterate(iter_count=200)

res_tui = solver_session.scheme.exec(
(
"(ti-menu-load-string "
'"/report/surface-integrals/mass-weighted-avg outlet () '
'temperature no")',
)
)
resArr[idx1][idx2] = eval(res_tui.split(" ")[-1])
# Average Temperature at the outlet for cold_velocities[i] and hot_velocities[j].
results = np.zeros((len(cold_velocities), len(hot_velocities)))

for (idx1, cold_value), (idx2, hot_value) in itertools.product(
enumerate(cold_velocities), enumerate(hot_velocities)
):
cold_inlet = VelocityInlet.get(solver, name="cold-inlet")
cold_inlet.momentum.velocity = cold_value

hot_inlet = VelocityInlet.get(solver, name="hot-inlet")
hot_inlet.momentum.velocity = hot_value

initialize = Initialization(solver)
initialize.initialization_type = "standard"
initialize.standard_initialize()

iterate(solver,iter_count=200)

temperatures = SurfaceIntegrals(solver).get_mass_weighted_avg(
surface_names=["outlet"], report_of="temperature"
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Can this use variable catalog?

)

results[idx1][idx2] = temperatures["outlet"]

##############################################################################################
# Close the session
# =================

solver_session.exit()
solver.exit()

####################################
# Plot Response Surface using Plotly
# ==================================

fig = go.Figure(data=[go.Surface(z=resArr.T, x=coldVelArr, y=hotVelArr)])
fig = go.Figure(data=[go.Surface(z=results.T, x=cold_velocities, y=hot_velocities)])

fig.update_layout(
title={
Expand All @@ -170,14 +176,14 @@
)

fig.update_layout(
scene=dict(
xaxis_title="Cold Inlet Vel (m/s)",
yaxis_title="Hot Inlet Vel (m/s)",
zaxis_title="Outlet Temperature (K)",
),
scene={
"xaxis_title": "Cold Inlet Vel (m/s)",
"yaxis_title": "Hot Inlet Vel (m/s)",
"zaxis_title": "Outlet Temperature (K)",
},
width=600,
height=600,
margin=dict(l=80, r=80, b=80, t=80),
margin={"l": 80, "r": 80, "b": 80, "t": 80},
)
fig.show()

Expand All @@ -189,19 +195,16 @@
############################################
# Create Pandas Dataframe for ML Model Input
# ==========================================
coldVelList = []
hotVelList = []
ResultList = []
df = pd.DataFrame({"cold_velocities": [], "hot_velocities": [], "result": []})

for idx1, coldVel in np.ndenumerate(coldVelArr):
for idx2, hotVel in np.ndenumerate(hotVelArr):
coldVelList.append(coldVel)
hotVelList.append(hotVel)
ResultList.append(resArr[idx1][idx2])

tempDict = {"coldVel": coldVelList, "hotVel": hotVelList, "Result": ResultList}
for (idx1, cold_vel_val), (idx2, hot_vel_val) in itertools.product(
enumerate(cold_velocities), enumerate(hot_velocities)
):
df["cold_velocities"].append(cold_vel_val)
df["hot_velocities"].append(hot_vel_val)
df["result"].append(results[idx1][idx2])

df = pd.DataFrame.from_dict(tempDict)

from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
Expand All @@ -227,7 +230,7 @@

x_ct = ColumnTransformer(
[
("transformer1", transformer1, ["coldVel", "hotVel"]),
("transformer1", transformer1, ["cold_velocities", "hot_velocities"]),
],
remainder="drop",
)
Expand All @@ -237,8 +240,8 @@
X_train = x_ct.fit_transform(train_set)
X_test = x_ct.fit_transform(test_set)

y_train = train_set["Result"]
y_test = test_set["Result"]
y_train = train_set["result"]
y_test = test_set["result"]
y_train = np.ravel(y_train.T)
y_test = np.ravel(y_test.T)

Expand All @@ -258,18 +261,24 @@
# from sklearn.linear_model import LinearRegression
from xgboost import XGBRegressor

if TYPE_CHECKING:
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from xgboost import XGBRegressor

np.set_printoptions(precision=2)
out_file = Path.cwd() / "PyFluent_Output.csv"


def display_scores(scores):
def display_scores(scores: np.ndarray):
"""Display scores."""
print("\nCross-Validation Scores:", scores)
print("Mean:%0.2f" % (scores.mean()))
print("Std. Dev.:%0.2f" % (scores.std()))
print(f"Mean:{scores.mean():0.2f}")
print(f"Std. Dev.:{scores.std():0.2f}")


def fit_and_predict(model):
"""Fit abd predict."""
def fit_and_predict(model: LinearRegression | XGBRegressor | RandomForestRegressor):
"""Fit and predict."""
cv = RepeatedKFold(n_splits=5, n_repeats=3, random_state=42)
cv_scores = cross_val_score(
model, X_train, y_train, scoring="neg_mean_squared_error", cv=cv
Expand All @@ -282,33 +291,26 @@ def fit_and_predict(model):
test_predictions = model.predict(X_test)
print(train_predictions.shape[0])
print("\n\nCoefficient Of Determination")
print("Train Data R2 Score: %0.3f" % (r2_score(train_predictions, y_train)))
print("Test Data R2 Score: %0.3f" % (r2_score(test_predictions, y_test)))
print(f"Train Data R2 Score: {r2_score(train_predictions, y_train):0.3f}")
print(f"Test Data R2 Score: {r2_score(test_predictions, y_test):0.3f}")
print(
"\n\nPredictions - Ground Truth (Kelvin): ", (test_predictions - y_test), "\n"
)

com_train_set = train_set
com_test_set = test_set

train_list = []
for i in range(train_predictions.shape[0]):
train_list.append("Train")
train_list = ["train"] * cast(int, train_predictions.shape[0])
test_list = ["test"] * cast(int, test_predictions.shape[0])

test_list = []
for i in range(test_predictions.shape[0]):
test_list.append("Test")

com_train_set["Result"] = train_predictions.tolist()
com_train_set["Set"] = train_list
com_test_set["Result"] = test_predictions.tolist()
com_test_set["Set"] = test_list
com_train_set["result"] = train_predictions.tolist()
com_train_set["set"] = train_list
com_test_set["result"] = test_predictions.tolist()
com_test_set["set"] = test_list
Comment on lines -294 to +311
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@Gobot1234 Gobot1234 Feb 4, 2026

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Honestly this code kinda confused me so much IDK if it's right


df_combined = pd.concat([com_train_set, com_test_set])

df_combined.to_csv(
os.path.join(os.getcwd(), "PyFluent_Output.csv"), header=True, index=False
)
df_combined.to_csv(out_file, header=True, index=False)

fig = plt.figure(figsize=(12, 5))

Expand All @@ -334,10 +336,10 @@ def fit_and_predict(model):
# * Call fit_and_predict

# model = LinearRegression()
# model = RandomForestRegressor(random_state=42)
model = XGBRegressor(
n_estimators=100, max_depth=10, eta=0.3, subsample=0.8, random_state=42
)
# model = RandomForestRegressor(random_state=42)

fit_and_predict(model)

Expand All @@ -359,13 +361,15 @@ def fit_and_predict(model):
# 3D Visualization of Model Predictions on Train & Test Set
# =========================================================

df = pd.read_csv("PyFluent_Output.csv")
df = pd.read_csv(out_file)

fig = px.scatter_3d(df, x="coldVel", y="hotVel", z="Result", color="Set")
fig.update_traces(marker=dict(size=4))
fig.update_layout(legend=dict(yanchor="top", y=1, xanchor="left", x=0.0))
fig = px.scatter_3d(
df, x="cold_velocities", y="hot_velocities", z="result", color="set"
)
fig.update_traces(marker={"size": 4})
fig.update_layout(legend={"yanchor": "top", "y": 1, "xanchor": "left", "x": 0.0})

fig.add_traces(go.Surface(z=resArr.T, x=coldVelArr, y=hotVelArr))
fig.add_traces(go.Surface(z=results.T, x=cold_velocities, y=hot_velocities))

fig.update_layout(
title={
Expand All @@ -378,14 +382,14 @@ def fit_and_predict(model):
)

fig.update_layout(
scene=dict(
xaxis_title="Cold Inlet Vel (m/s)",
yaxis_title="Hot Inlet Vel (m/s)",
zaxis_title="Outlet Temperature (K)",
),
scene={
"xaxis_title": "Cold Inlet Vel (m/s)",
"yaxis_title": "Hot Inlet Vel (m/s)",
"zaxis_title": "Outlet Temperature (K)",
},
width=500,
height=500,
margin=dict(l=80, r=80, b=80, t=80),
margin={"l": 80, "r": 80, "b": 80, "t": 80},
)

fig.show()
Expand Down Expand Up @@ -451,8 +455,8 @@ def fit_and_predict(model):
test_predictions = np.ravel(test_predictions.T)
print(test_predictions.shape)

print("\n\nTrain R2: %0.3f" % (r2_score(train_predictions, y_train)))
print("Test R2: %0.3f" % (r2_score(test_predictions, y_test)))
print(f"\n\nTrain R2: {r2_score(train_predictions, y_train):0.3f}")
print(f"Test R2: {r2_score(test_predictions, y_test):0.3f}")
print("Predictions - Ground Truth (Kelvin): ", (test_predictions - y_test))

fig = plt.figure(figsize=(12, 5))
Expand Down
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