Alberini et al. have developed a new technology based on a passive acoustic emission (AE) sensing system that uses only a single sensor, with the goal of providing live and in-situ measurement of rheology. For this study, three different types of fluids were selected to represent common rheological behaviours: Newtonian behaviour, non-Newtonian behaviour with power law, and non-Newtonian behaviour with Herschel-Bulkley relationship. By analyzing the transient energy released during the interaction between the probe and the fluid, distinct acoustic fingerprints were identified in the frequency domain. These acoustic fingerprints were found to be characteristic of the different fluids and their rheology, and were validated in triplicate. Furthermore, the results showed that the intensity of the acoustic emissions increased with higher flow rates (30 to 50 L/min). To test the correlation between flow rate and acoustic response, a neural network regression test was conducted, which demonstrated a direct correlation between AE peaks and flow rate. The neural network used was nonlinear autoregressive network with exogenous inputs (NARX), and the test involved a stepwise regression with 70% training and 30% network validation. The study also introduced the Rheology-AE quotient, which maps fluid constituents against the acoustic signal. Results showed that this was a reliable means of deriving live rheology from a fluid's frequency domain. Finally, the results obtained from this study were validated using an offline rotational rheometer.

Hefft, D.I., Alberini, F. (2023). Using transient energy release measurements for the in-line characterization of non-Newtonian fluids and fluid state in pipe flow. CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 101(12), 7202-7218 [10.1002/cjce.24944].

Using transient energy release measurements for the in-line characterization of non-Newtonian fluids and fluid state in pipe flow

Alberini, F
2023

Abstract

Alberini et al. have developed a new technology based on a passive acoustic emission (AE) sensing system that uses only a single sensor, with the goal of providing live and in-situ measurement of rheology. For this study, three different types of fluids were selected to represent common rheological behaviours: Newtonian behaviour, non-Newtonian behaviour with power law, and non-Newtonian behaviour with Herschel-Bulkley relationship. By analyzing the transient energy released during the interaction between the probe and the fluid, distinct acoustic fingerprints were identified in the frequency domain. These acoustic fingerprints were found to be characteristic of the different fluids and their rheology, and were validated in triplicate. Furthermore, the results showed that the intensity of the acoustic emissions increased with higher flow rates (30 to 50 L/min). To test the correlation between flow rate and acoustic response, a neural network regression test was conducted, which demonstrated a direct correlation between AE peaks and flow rate. The neural network used was nonlinear autoregressive network with exogenous inputs (NARX), and the test involved a stepwise regression with 70% training and 30% network validation. The study also introduced the Rheology-AE quotient, which maps fluid constituents against the acoustic signal. Results showed that this was a reliable means of deriving live rheology from a fluid's frequency domain. Finally, the results obtained from this study were validated using an offline rotational rheometer.
2023
Hefft, D.I., Alberini, F. (2023). Using transient energy release measurements for the in-line characterization of non-Newtonian fluids and fluid state in pipe flow. CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 101(12), 7202-7218 [10.1002/cjce.24944].
Hefft, DI; Alberini, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/939713
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