Marios Giouvanakis

This paper introduces a non-invasive ultrasonic sensing system for two-phase refrigerant flow characterization in heat pump circuits used in building energy systems, validated through machine learning (ML)-based regression of the acquired signals. Heat pumps play a crucial role in energy-efficient buildings. However, the absence of a low-cost, non-invasive instrument capable of measuring mass flow rate, mixture density, and vapor quality without disrupting the thermodynamics of a refrigerant circuit remains a gap for smart HVAC systems. A carbon dioxide (CO₂) refrigerant circuit was designed to calibrate such a sensing system under representative operating heat pump conditions. Ultrasonic measurements were conducted using piezoelectric transducers clamped onto the refrigerant pipeline. A calibration framework was structured with ground-truth flowmeter labels, establishing a thermodynamic envelope across 10–20 bar and down to −25 °C, and achieving R² = 0.901 for flow rate, 0.997 for density, and 0.971 for quality, with an overall R² = 0.956. The proposed measurement system is a plug-and-play kit enabling more efficient next-generation heat pumps, supporting building energy management and performance monitoring. The labeled dataset and calibration methodology provide a basis for training and validating ML regression models for real-time flow property inference in operational HVAC systems.

In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the formation of salient audio embeddings in latent states of a deep temporal convolutional neural network. By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different operational states of industrial machines. Moreover, we enable the exploitation of spatial audio information in the learning process, by formulating a novel front-end processing strategy for circular microphone arrays. Experimental results on the MIMII dataset demonstrate the effectiveness of the proposed method, reaching a state-of-the-art mean AUC score of 91.0%. Anomaly detection performance is significantly improved by incorporating multi-channel audio data in the feature extraction process, as well as training the convolutional neural network on the spatially invariant front-end. Finally, the proposed semi-supervised approach allows the concise modeling of normal machine conditions and accurately detects system anomalies, compared to existing anomaly detection methods.

The proposed compound sound sources for low-frequency noise control applications are composed of dipole sources. Their spatial radiation, which is critical in the modal field of small, closed spaces, is intended to be controlled with independent driving signals of each dipole. The need for small and efficient low-frequency elementary monopole sources led to the proposed vented sub-woofer loudspeaker design with low force factor (low-Bl) drivers. The investigated sources are set up in quadrupole configurations and measured in terms of polar near field response patterns to verify the theoretical predictions. The measurement results consist of the validation of the proposed compound sound sources on the implementation of active noise control problems in the low-frequency range. Also, their small size and modular construction make them interesting for use in other applications.

A novel method of active noise control using adaptive radiation sound sources is investigated. A finite element model of a modal enclosed sound field is excited harmonically, representing a noise field in the low-frequency range. The control sources are comprised of elementary dipole sources for which the driving signals are adjusted by an optimization method. Two set-up cases of the proposed compound sources are investigated. The coupling of the control sources with the modal sound field is discussed. The simulated performance of the proposed method is compared with that of a system with distributed simple sources and the results show the effectiveness of the sources with adaptive radiation for active noise control in small enclosures.