Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

Abstract

Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography.

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

“…In practice, the sample set can be constructed according to the characteristics of the temperature field, and then the dictionary and dictionary selector with superior adaptability to the signal can be trained. As a concrete example, this paper randomly adopts the model shown in Equation (10) [ 13 , 14 ] or (11) [ 17 ] to construct the temperature field set of six peak-types of temperature fields (average distribution( P 0 ), unipeak ( P 1 ), double-peak ( P 2 ), three-peak ( P 3 ), four-peak ( P 4 ) and five-peak ( P 5 )), where is the background temperature of the temperature field, and peak is the temperature rise area on the background temperature in the temperature field, which is generated by the sum term contained in Equations (10) and (11). The number of “peaks” is the number of summation terms contained in the temperature field.…”

“…Existing reconstruction algorithms typically only consider measurement information. Li Yanqiu et al proposed an objective function that also takes into account acoustic measurement information, spatial constraints of temperature field and dynamic development information, and solved the objective function by combining Tikhonov regularization and optimization [ 17 ].…”

Temperature Distribution Reconstruction Method for Acoustic Tomography Based on Compressed Sensing

“…As a result, the measuring time per frame was reduced from 320 ms to 60 ms. 17 The existing algorithms usually only consider the measurement information. Li et al 18 built an objective function considering both the measurement information and dynamic evolution information of the temperature distribution, extended the function with robust estimation, and solved the function by using a method combining a tunneling algorithm and a local minimization technique.…”

Measurement of indoor air temperature distribution using acoustic travel-time tomography: Optimization of transducers location and sound-ray coverage of the room

“…To reconstruct the air temperature and flow velocity distributions of a fluid in a volume (three-dimensional problem), the technique of Acoustic travel-time TOMography (ATOM) can be applied [1,2,3,4,5]. ATOM utilizes the dependency of the speed of sound on air temperature and flow velocity in the medium.…”