Digital Twin for a Frequency Mixer Used as a Phase Sensor.

Area of Science:

• Metrology
• Optical Physics
• Data Science

Background:

• Accurate frequency transfer is crucial for metrology.
• Optical fibers are susceptible to phase variations caused by environmental factors like temperature gradients.
• Traditional frequency transfer methods require improvement in real-time accuracy and uncertainty estimation.

Purpose of the Study:

• To develop a digital twin (DT) that replicates the use of a frequency mixer for enhanced frequency transfer.
• To integrate real-time sensor data and machine learning for improved phase detection.
• To provide real-time phase value measurements and their associated uncertainty.

Main Methods:

• A frequency mixer was utilized to detect phase variations in light signals transmitted through optical fiber.
• Real-time temperature data from sensors along the fiber and on the mixer were collected.
• The digital twin was trained using Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks.
• Uncertainty budgets were estimated using JCGM 100:2008 and JCGM 101:2008 guidelines.

Main Results:

• The developed digital twin successfully replicated the frequency mixer's function as a phasemeter.
• The system provided real-time phase difference measurements between optical signals.
• The digital twin incorporated uncertainty analysis, offering a phase value with its uncertainty.
• The integration of machine learning algorithms improved the accuracy of phase variation detection.

Conclusions:

• The combination of digital twin technology and frequency mixers offers a novel approach to real-time phase detection.
• This method enhances the accuracy and provides uncertainty quantification for frequency transfer over optical fibers.
• The developed system has the potential to improve the realization and dissemination of frequency standards.