SHIELD permeation rig Data Acquisition System

This is a tool to be used with the SHIELD hydrogen permeation rig, providing a way to both record data from the rig and have a live UI displaying plots of the pressure values in the gauges connected to the rig and the temperature of the connected thermocouple.

Installation

The shield DAS package can be downloaded with pip

pip install SHIELD-DAS

However, in order to interact with the Labjack, additional drivers are required from the manufacturers site.

Example data recording script

This is an example of a script that can be used to activate the DAS.

from shield_das import (
    DataRecorder,
    WGM701_Gauge,
    CVM211_Gauge,
    Baratron626D_Gauge
)

# Define gauges
gauge_1 = WGM701_Gauge(
    gauge_location="downstream",
    ain_channel=10,
)
gauge_2 = CVM211_Gauge(
    gauge_location="upstream",
    ain_channel=8,
)
gauge_3 = Baratron626D_Gauge(
    name="Baratron626D_1KT",
    gauge_location="upstream",
    full_scale_torr=1000,
    ain_channel=6,
)
gauge_4 = Baratron626D_Gauge(
    name="Baratron626D_1T",
    gauge_location="downstream",
    full_scale_torr=1,
    ain_channel=4,
)

# Create recorder
my_recorder = DataRecorder(
    gauges=[gauge_1, gauge_2, gauge_3, gauge_4],
    thermocouples=[thermocouple_1],
    run_type="test_mode",
    recording_interval=0.5,
    backup_interval=5,
    furnace_setpoint=500,
)

# Start recording
my_recorder.run()

Example data visualisation script

from shield_das import DataPlotter

data_500C_run1 = "results/08.12/run_2_11h45/"
data_500C_run2 = "results/08.18/run_2_09h47/"
data_500C_run3 = "results/08.19/run_2_09h21/"
data_500C_run4 = "results/08.25/run_1_09h07/"

my_plotter = DataPlotter(
    dataset_paths=[data_500C_run1, data_500C_run2, data_500C_run3, data_500C_run4],
    dataset_names=["500C_run1", "500C_run2", "500C_run3", "500C_run4"],
)
my_plotter.start()

Standalone Analysis Functions

SHIELD_DAS provides analysis functions that can be used independently without running the full plotter application. This is useful when you want to:

• Convert raw voltage data to pressure/temperature values
• Perform custom analysis on experimental data
• Use the conversion functions in your own scripts
• Create your own plots with the converted data

Quick Examples

Convert voltage to pressure:

from shield_das import voltage_to_pressure
import numpy as np

voltage = np.array([5.0, 7.5, 10.0])  # 0-10V gauge readings
pressure = voltage_to_pressure(voltage, full_scale_torr=1000)
print(pressure)  # [500.0, 750.0, 1000.0] torr

Convert thermocouple voltage to temperature:

from shield_das import voltage_to_temperature
import numpy as np

tc_voltage_mv = np.array([10.0, 20.0, 30.0])  # millivolts
local_temp_c = np.array([25.0, 25.0, 25.0])   # cold junction temp

temperature = voltage_to_temperature(local_temp_c, tc_voltage_mv)
print(temperature)  # [270.7, 508.3, 744.9] °C

Calculate flux and permeability:

from shield_das import calculate_flux_from_sample, calculate_error_on_pressure_reading
import numpy as np

# Your experimental data
time = np.linspace(0, 1000, 5000)
pressure = 0.1 + 0.001 * time  # Downstream pressure rise

# Calculate hydrogen flux
flux = calculate_flux_from_sample(time, pressure)
print(f"Flux: {flux:.6e} torr/s")

# Calculate measurement uncertainties
pressure_error = calculate_error_on_pressure_reading(pressure)

Available Functions

Data Conversion:

• voltage_to_pressure(voltage, full_scale_torr) - Convert gauge voltage to pressure
• voltage_to_temperature(local_temp_c, voltage_mv) - Convert thermocouple voltage
• calculate_error_on_pressure_reading(pressure) - Calculate measurement uncertainties

Analysis:

• calculate_flux_from_sample(time, pressure) - Calculate hydrogen flux
• calculate_permeability_from_flux(...) - Calculate permeability using Takaishi-Sensui method
• fit_permeability_data(temps, perms) - Fit Arrhenius equation
• average_pressure_after_increase(time, pressure) - Detect stable pressure after transient
• evaluate_permeability_values(datasets) - Extract permeability from multiple datasets

For complete documentation and examples, see examples_standalone_analysis.py