Virtualized Viscosity Sensor for Onboard Energy Management

Abstract

Essential for decision-making, measurement is a cornerstone of various fields including energy management. While direct methods exist for some quantities like length, most physico-chemical properties require indirect assessment based on observable effects. Historically, pressure was measured by the water column height, and temperature by mercury expansion. Recent advancements in artificial intelligence (AI) offer a transformative approach by combining vast datasets with traditional measurements. This holds immense potential for applications facing extreme conditions and involving complex fluids where measurement is extremely challenging (over 1500 K and 5 MPa). In this study, an AI model is evaluated to replace online rheometers (293–1173 K, 0.15–3.5 MPa). A machine learning model utilizes a neural network with up to 8000 neurons, eight hidden layers, and over 448 million parameters. Trained, tested, and validated on three experimental databases with over 600 test conditions, the New Generation Predicted Viscosity Sensor (NGPV sensor) achieves exceptional accuracy (less than 4.8 × 10−7 Pa·s). This virtualized sensor proves highly relevant for hypersonic airbreathing applications involving fuel degradation and energy conversion. It maintains excellent predictability (accuracy below 6 × 10−6 Pa·s) even at flow rates 10 times higher than calibration, surpassing traditional rheometers limited by calibration needs and a lower viscosity measurement threshold (10−4 Pa·s).