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.

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.

In a real wind farm,complex airflow conditions result in complexities of wind speed and direction,with possibly significant intermittency and fluctuations.This problem can be alleviated if the wind speed distribution over a wind farm is known in advance.In this paper,a new method is proposed for real-time wind field reconstruction for large areas, based on the idea of a “virtual time”, i.e., a time span needed for an object to travel across a certain distance.The distribution of wind speed and direction can be acquired prior to its occurrence in the wind farm with refined spatial resolutions.A procedure is also developed to stabilize the solution process,and this stabilization leads to an optimal allocation of the wind speed sensors;this allocation is necessary for the efficient use of a limited number of sensors.The reconstruction algorithm has been substantially studied,and a mathematical quantity was correlated to the reconstruction error.This correlation enables us to obtain good reconstruction results by using the Greedy algorithm we proposed in this study.Simulation and experimental results demonstrated the strong feasibility of successful reconstructions by our proposed algorithm.Moreover,the sensor optimization scheme not only reduces the error significantly but also improves the efficiency of sensor applications;this improvement should apply to a wide range of conditions.

In this study, we propose a new sensor structure with driving electrodes and a new excitation mode in electrical capacitance tomography (ECT) to improve: 1) the low sensitivity distribution in the central area caused by low potential distribution in the central area and 2) the nonuniform sensitivity distribution caused by the nonuniform potential distribution. An imaging method based on sensitivity map optimization is derived through electromagnetic field analysis and numerical simulation. In addition, the change rule of the optimal driving voltage is studied when the excitation voltage of the excitation electrodes and the length of the driving electrodes change. The numerical simulation and experimental results show that the imaging method proposed in this article can distinctively alter the potential distribution and sensitivity distribution in the central area by selecting the optimal driving voltage, which brings significant improvement in image reconstruction.

As a fast and non-intrusive measurement and visualization technique, Electrical Capacitance Tomography (ECT) is rapidly expanding its applications in the research on multiphase flow, fluidization, drying, combustion, and so on. However, the marked unevenness of the sensitivity maps sometimes causes unexpected effects in imaging reconstruction, particularly in three-dimension cases. To exploit the positive potential of this phenomenon, the authors proposed an image fusion method using the data from two units of ECT sensors in this study. This method is used in image fusion on the reconstructed images for a planar sensor and a cylindrical sensor. In contrast to the conventional fusion models that use fixed weight factors for two sources of data, our model forges weight functions that are set preference the strength of the sensitivity maps. The new algorithm is implemented by first extracting the characteristic information out of the ECT images using the Latent Low-Rank Representation and then performing a fusion algorithm with linear weight functions in preference to the significance of the sensitivity maps. The simulation results show that the algorithm effectively retains the advantages of the two units of sensors and mutually compensates the weak points of theirs, and significantly improves the reconstruction quality. The fusion image quality by the new method can response the real situation better in different heights. The results imply that the data fusion might to a significant extend amend the weakness of ECT caused by the uneven sensitivity maps.