Design of a 100 Bar Large-Bore Optical Dual-Chamber Combustor for Turbulent Jet Ignition Diagnostics

Problem Statement

Conventional combustion research platforms lack simultaneous optical access to both the prechamber and main combustion chamber, limiting the ability to directly observe and correlate turbulent jet ignition dynamics across coupled volumes. Most existing setups either provide pressure data alone or restrict optical diagnostics to the main chamber, leaving the complex flame development and jet formation processes inside the prechamber experimentally unresolved. Additionally, many facilities are not designed for high-pressure, engine-relevant conditions or lack modularity to investigate varying nozzle geometries and chamber configurations. These limitations hinder the fundamental understanding of turbulent jet ignition mechanisms, lean combustion stability, and ignition scalability for large-bore engines. Therefore, there is a critical need for a high-pressure, optically accessible, and modular dual-chamber combustion platform capable of resolving transient reacting flow phenomena with synchronized diagnostics under controlled and repeatable conditions.

Bill of Materials (BOM)

The following table lists the components used in the prototype, including part numbers, quantities, materials, estimated costs, and potential suppliers.

Motor Review

Basic PID Controller Script

PYTHON
import time

class PIDController:
    """A PID controller for precise control in robotic systems.

    Attributes:
        kp (float): Proportional gain.
        ki (float): Integral gain.
        kd (float): Derivative gain.
        setpoint (float): Desired target value.
        prev_error (float): Previous error for derivative calculation.
        integral (float): Accumulated integral term.
        dt (float): Time step in seconds.
    """

    def __init__(self, kp: float, ki: float, kd: float, setpoint: float = 0.0):
        """Initialize PID controller with gains and setpoint.

        Args:
            kp: Proportional gain for error response.
            ki: Integral gain for accumulated error.
            kd: Derivative gain for error rate of change.
            setpoint: Desired target value (default: 0.0).
        """
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.setpoint = setpoint
        self.prev_error = 0.0
        self.integral = 0.0
        self.dt = 0.01

    def compute(self, current_value: float) -> float:
        """Compute PID output based on current system value.

        Args:
            current_value: Current measured value of the system.

        Returns:
            float: Control signal to adjust the system.
        """
        # Calculate error
        error = self.setpoint - current_value

        # Proportional term
        p_term = self.kp * error

        # Integral term
        self.integral += error * self.dt
        i_term = self.ki * self.integral

        # Derivative term
        derivative = (error - self.prev_error) / self.dt
        d_term = self.kd * derivative

        # Calculate total output
        output = p_term + i_term + d_term

        # Update previous error
        self.prev_error = error

        return output


if __name__ == "__main__":
    

    # Initialize PID controller
    pid = PIDController(kp=1.0, ki=0.1, kd=0.05, setpoint=10.0)

    # Simulate mode (e.g., motor position)
    current_value = 0.0
    for _ in range(100):
        control_signal = pid.compute(current_value)
        # Simulate system response: position updates based on control signal
        current_value += control_signal * 0.1
        print(f"Current Value: {current_value:.2f}, "
              f"Control Signal: {control_signal:.2f}")
        time.sleep(pid.dt)