Join Christopher Laughman (Mitsubishi Electric Research Laboratories) and Avinash Subramanian (JuliaHub) for a webinar exploring how advanced simulation tools can transform HVAC system modeling.
Accurately estimating refrigerant mass in vapor-compression systems such as air conditioners and heat pumps, is essential for both performance and environmental assessment. Traditional methods are invasive, costly, or insufficiently precise.
In this webinar, Laughman and Subramanian will share how MERL used Dyad’s ModelingToolkit-based workflows together with machine learning techniques to develop a non-invasive state estimation approach. By leveraging pressure and temperature data, their method can predict refrigerant mass and other hard-to-measure system variables with high accuracy, achieving errors of less than 2%.
What you’ll learn:
The limitations of traditional diagnostic methods in HVAC systems
How Dyad enables high-fidelity modeling of vapor-compression cycles
Combining physics-based models and machine learning for improved state estimation
A MERL case study demonstrating <2% error in refrigerant mass prediction
The potential of simulation-driven approaches to improve diagnostics, efficiency, and sustainability in HVAC
Click here to sign-up for the webinar.
