【OpenFOAMとOpenMoelica連携（２）】OpenFOAMとOpenMoelicaの連携：buoyantPimpleFoamソルバ

計算実行
グラフ化
まとめ
参考書

計算実行

いつも通り計算実行します。

buoyantPimpleFoam

1	buoyantPimpleFoam

途中で以下のような記述が出ます。

ExecutionTime = 0.36 s  ClockTime = 3 s

===== codeTempFieldValue (coded) start =====
 === probe Temperature ===
1789 Tprobe = 328.723
 === probe Temperature end ===
===== Execute Python updateMOS_and_Run.py =====
Lock file created: "constant/updateMOS.lock"
[INFO] Read from outletWeightAreaTempFile: Tout=311.973, Tprobe=328.723, Time=250.25
[INFO] Wrote updated HVACSystem002.mos
[INFO] Running: ./runMos.sh HVACSystem002.mos in /mnt/d/work/openfoam/20251017_OpenCAE2025/heatedRoom
{"", ""}
LOG_SUCCESS       | info    | The initialization finished successfully without homotopy method.
LOG_SUCCESS       | info    | The simulation finished successfully.
0
250.25
293.26641552174266
1.1490000000000002e-5
0.0024516407236118407
328.72299999999996
328.0
true
true
true
true
true
true
true

ExecutionTime = 0.36 s ClockTime = 3 s

===== codeTempFieldValue (coded) start =====

=== probe Temperature ===

1789 Tprobe = 328.723

=== probe Temperature end ===

===== Execute Python updateMOS_and_Run.py =====

Lock file created: "constant/updateMOS.lock"

[INFO] Read from outletWeightAreaTempFile: Tout=311.973, Tprobe=328.723, Time=250.25

[INFO] Wrote updated HVACSystem002.mos

[INFO] Running: ./runMos.sh HVACSystem002.mos in /mnt/d/work/openfoam/20251017_OpenCAE2025/heatedRoom

{"", ""}

LOG_SUCCESS | info | The initialization finished successfully without homotopy method.

LOG_SUCCESS | info | The simulation finished successfully.

250.25

293.26641552174266

1.1490000000000002e-5

0.0024516407236118407

328.72299999999996

328.0

true

毎回OpenModelicaの計算を挟んでいるので、少し計算の遅さを感じます。

グラフ化

output.datがOpenModelicaの結果なのでそれをグラフ化して、思っている挙動を示しているかを確認します。

import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np

# ====== ファイルパス設定 ======
file_path = Path("output.dat")

# ====== データ読み込み ======
df = pd.read_csv(file_path, sep=r"[\t,]+", engine="python")

# ====== 図と軸の設定 ======
fig, axes = plt.subplots(1, 4, figsize=(20, 4))  # 横4つのグラフ

# X軸の範囲と刻み幅
x_min, x_max = df["Time"].min(), df["Time"].max()
x_ticks = np.arange(x_min, x_max + 10, 10)  # 10秒刻み

# --- ① Target and Measured T ---
axes[0].plot(df["Time"], df["TSensor"], label="TSensor")
axes[0].plot(df["Time"], df["ramp.y"], label="Target")
axes[0].set_xlabel("time [s]")
axes[0].set_ylabel("T [K]")
axes[0].set_title("Target and Measured T")
axes[0].set_xticks(x_ticks)
axes[0].grid(True)
axes[0].legend()

# --- ② Mass Flux ---
axes[1].plot(df["Time"], df["mout"], label="mout")
axes[1].set_xlabel("time [s]")
axes[1].set_ylabel("mdot [kg/s]")
axes[1].set_title("Mass Flux")
axes[1].set_xticks(x_ticks)
axes[1].grid(True)
axes[1].legend()

# --- ③ Inlet Temperature ---
axes[2].plot(df["Time"], df["Tout"], label="Tinlet")
axes[2].set_xlabel("time [s]")
axes[2].set_ylabel("T [K]")
axes[2].set_title("Tout (OpenFOAM Inlet Temperature)")
axes[2].set_xticks(x_ticks)
axes[2].grid(True)
axes[2].legend()

# --- ④ Heat Flow ---
axes[3].plot(df["Time"], df["heatFlowSensor.Q_flow"], label="Q_flow")
axes[3].set_xlabel("time [s]")
axes[3].set_ylabel("Q [W]")
axes[3].set_title("Heat Flow")
axes[3].set_xticks(x_ticks)
axes[3].grid(True)
axes[3].legend()

plt.tight_layout()
plt.show()

import pandas as pd

import matplotlib.pyplot as plt

from pathlib import Path

import numpy as np

# ====== ファイルパス設定 ======

file_path = Path("output.dat")

# ====== データ読み込み ======

df = pd.read_csv(file_path, sep=r"[\t,]+", engine="python")

# ====== 図と軸の設定 ======

fig, axes = plt.subplots(1, 4, figsize=(20, 4)) # 横4つのグラフ

# X軸の範囲と刻み幅

x_min, x_max = df["Time"].min(), df["Time"].max()

x_ticks = np.arange(x_min, x_max + 10, 10) # 10秒刻み

# --- ① Target and Measured T ---

axes[0].plot(df["Time"], df["TSensor"], label="TSensor")

axes[0].plot(df["Time"], df["ramp.y"], label="Target")

axes[0].set_xlabel("time [s]")

axes[0].set_ylabel("T [K]")

axes[0].set_title("Target and Measured T")

axes[0].set_xticks(x_ticks)

axes[0].grid(True)

axes[0].legend()

# --- ② Mass Flux ---

axes[1].plot(df["Time"], df["mout"], label="mout")

axes[1].set_xlabel("time [s]")

axes[1].set_ylabel("mdot [kg/s]")

axes[1].set_title("Mass Flux")

axes[1].set_xticks(x_ticks)

axes[1].grid(True)

axes[1].legend()

# --- ③ Inlet Temperature ---

axes[2].plot(df["Time"], df["Tout"], label="Tinlet")

axes[2].set_xlabel("time [s]")

axes[2].set_ylabel("T [K]")

axes[2].set_title("Tout (OpenFOAM Inlet Temperature)")

axes[2].set_xticks(x_ticks)

axes[2].grid(True)

axes[2].legend()

# --- ④ Heat Flow ---

axes[3].plot(df["Time"], df["heatFlowSensor.Q_flow"], label="Q_flow")

axes[3].set_xlabel("time [s]")

axes[3].set_ylabel("Q [W]")

axes[3].set_title("Heat Flow")

axes[3].set_xticks(x_ticks)

axes[3].grid(True)

axes[3].legend()

plt.tight_layout()

plt.show()

以下のようになっていたらOK。

まとめ

OpenFOAMとOpenModelicaを、FMU4FOAMを使わずに連携させる仕組みを作りました。
OpenFOAMで取得した温度や流量をPython経由でOpenModelicaへ渡し、PID制御結果を再びOpenFOAMに反映する流れです。
毎ステップでMOSファイルを自動生成して計算を回すことで、モデル間のやりとりを完全に自動化できました。

次回は、FMU4FOAMが対応していないマルチリージョン環境でも動作の確認をしたいと思います。