Ultrasonic Thermometry Algorithm Based on Inverse Quadratic Function

Li, Zhao; Zhou, Xin; Dong, Chenlong; Wu, Yuhui; Wang, Hailin

doi:10.1109/tuffc.2020.3036116

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Following the acquisition of the TOF and the discretization of the temperature field for each path, an algebraic equation representing the relationship between temperature and TOF in the discrete grid is established, transforming the temperature field reconstruction problem into a problem of solving a system of equations. , The propagation velocity of sound waves in a gas medium is a function of the gas temperature. Assuming that gas composition and content are known, the relationship between the propagation velocity of sound waves and the temperature is as follows: − v = γ R T m = Z T where v is the speed of acoustic wave propagation in m/s ; γ is the ratio of constant pressure specific heat capacity to constant volume specific heat capacity of the gas medium and is related to gas composition; R is the gas constant, which is 8.314 J / (mol·K); T is the thermodynamic temperature in K ; m is the molecular weight of the gas in kg/mol; and Z is derived as follows: Z = γ R m where Z is a constant Z of 20.03 for the given gas. Kong et al used a constant Z of 19.98 for the flue gas.…”

Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

“… 61 , 62 The propagation velocity of sound waves in a gas medium is a function of the gas temperature. Assuming that gas composition and content are known, the relationship between the propagation velocity of sound waves and the temperature is as follows: 63 − 68 where v is the speed of acoustic wave propagation in m/s ; γ is the ratio of constant pressure specific heat capacity to constant volume specific heat capacity of the gas medium and is related to gas composition; R is the gas constant, which is 8.314 J / (mol·K); T is the thermodynamic temperature in K ; m is the molecular weight of the gas in kg/mol; and Z is derived as follows: 68 where Z is a constant Z of 20.03 for the given gas. Kong et al 69 used a constant Z of 19.98 for the flue gas.…”

Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

“…Inverse quadratic functions can be used to fit sparse data with high accuracy, and SVD can be used to solve the matrix. Zhao et al 79 combined IQ with SVD to solve the problem of matrix inversion in the reconstruction of the temperature field. They used a Gaussian distribution to model combustion noise during boiler operation and found that reconstruction results are strongly influenced by combustion noise.…”

Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

See 2 more Smart Citations

Early Detection of Coal Spontaneous Combustion by Complex Acoustic Waves in a Concealed Fire Source

et al. 2023

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Prevention and control of coal spontaneous combustion are key to coal mining and storage. Existing technologies for the detection of coal spontaneous combustion have limitations, but coal spontaneous combustion creates some serious disasters in areas of the world where coal mining and/or storage exists. New technologies to detect coal spontaneous combustion are urgently needed to reduce the loss of life and resources. The article reviews the main techniques employed to detect coal spontaneous combustion and their advantages and disadvantages; it also reviews the good application prospect of acoustic temperature measurement technology on coal spontaneous combustion and introduces the basic principle of acoustic coal temperature measurement. The evolution of combustion sound and the propagation and attenuation of acoustic waves in quasi-porous media are discussed to form the basis for the development of acoustic thermometry technologies that can be used to accurately identify acoustic signals and temperature fields in loose coal. The concept of "single-source" coal temperature measurement to "dual-source" coal temperature measurement achieved by using combustion sound and an additional sound source device in the automatic combustion of loose coal in the mined area is discussed. The deep learning methods and correlation analyses are available to map the relationships between combustion sound, coal temperature, and sound velocity, and acquire coal temperature from dual source composite acoustic signals. The study lays the foundation for the development of acoustic thermometry technologies that have applications in different stages of combustion and applied to the early warning, prevention, and control of spontaneous combustion in coal, and it contributes to improving the environmental safety and efficiency of coal mining and storage.

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Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

Section: Fire Detection Using Acoustic Thermometrymentioning

confidence: 99%

See 1 more Smart Citation

Early Detection of Coal Spontaneous Combustion by Complex Acoustic Waves in a Concealed Fire Source

et al. 2023

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“…According to the literature, there are two common methods for SOS measurement. One is to measure the time required for an ultrasonic pulse to travel a given distance [19][20][21] and the other is to measure the resonant frequency (RF) of an acoustic resonator. The SOS is derived from its relationship with the RF and the cavity length of the resonator [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A speakerless acoustic thermometer

Xiong¹,

Yan²,

Cai³

et al. 2023

Meas. Sci. Technol.

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Existing acoustic thermometers are often implemented using speaker-microphone systems, which are power-consuming and inconvenient to operate. Here we demonstrate a simple speakerless acoustic thermometer, which is an acoustic Fabry-Perot resonator (AFPR) consisting of a tubular acoustic waveguide and a microphone whose diaphragm acts as a reflective surface of the AFPR. Theoretical analysis shows that the resonant frequency (fm) at a given mode order (m) for the AFPR is a linear function of m with the slope (Δf/Δm) depending on the ambient temperature. Therefore, when the linear relationship between fm and m for the AFPR is measured, the ambient temperature can be determined from its slope. The values of fm at different m can be easily obtained by using the AFPR to detect ambient white noise rather than the sound signal from a loudspeaker. The thermometric performance of the prepared AFPR was investigated in a range of temperatures from -17 °C to 60 °C. The measured temperatures show the mean absolute error below 0.9 °C relative to those simultaneously obtained with a commercial electronic thermometer. As experimentally demonstrated in this work, the AFPR can detect extremely weak white noise in the anechoic room and thus enables to accurate measure the ambient temperature there, attributable to its ultrahigh pressure sensitivity at each resonant frequency. The advantages of simple structure, low power consumption, convenient operation, and high detection accuracy offer the AFPR outstanding applicability for on-site temperature measurements in various environments.

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“…The first author to employ ultrasound for temperature monitoring was Gilbert and coauthors in 1985 during an experiment on the cryogenic treatment of hepatocellular carcinoma (Gilbert et al, 1985). Since then, several specific methods of ultrasound thermometry have been proposed (Afaneh et al, 2011b;Bailey et al, 2003;Bharat et al, 2005;Casper et al, 2013;de Oliveira et al, 2010;Konofagou et al, 2002;Lewis et al, 2015;Li et al, 2022;Song et al, 2013;Zhao et al, 2021). Recently, an excellent review covering different ultrasound thermometry methodologies was written by Sinan Li and coauthors (Li et al, 2022), which included enough detail about several modalities that have been proposed in the literature over the past decade.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic measurements of temperature in materials

Andrade

Melo

Vieira

2022

RSD

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Manufacturing processes and product development for industrial and medical applications are submitted to rigorous quality control. Quality assurance may provide accuracy of the physical quantities concerned to guarantee that defects do not arise when the product is developed. Quantities, such as temperature distribution play a significant role and must be monitored to prevent material damage. However, the number of techniques to sense temperature distribution inside a bulk material in a non-invasive and non-ionizing way is rare. Ultrasonic temperature estimation can overcome this issue, providing an alternative technique that matches such requirements to measure heating within materials. Here, we investigate ultrasonic velocity as a function of temperature in metals, polymers gel to mimic soft tissues (phantom), and biological tissues. Through a customized computational algorithm, we successfully estimated the heat source temperature at the surface, and throughout the length of the material.

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Ultrasonic Thermometry Algorithm Based on Inverse Quadratic Function

Cited by 13 publications

References 25 publications

Early Detection of Coal Spontaneous Combustion by Complex Acoustic Waves in a Concealed Fire Source

Early Detection of Coal Spontaneous Combustion by Complex Acoustic Waves in a Concealed Fire Source

A speakerless acoustic thermometer

Ultrasonic measurements of temperature in materials

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