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The plenoptic imaging technique provides a promising approach to the non-invasive three-dimensional measurement, especially for the high-temperature combustion diagnosis. We establish a light-field convolution imaging model for diffusion flame in this work, considering the radiation transfer process inside the diffusion flame and the light transfer process inside the focused plenoptic camera together. The radiation transfer process is described by the radiation transfer equation and solved by the generalized source multi-flux method. Wave optics theory is adopted to describe the light transfer process, combining Fresnel diffraction and the phase conversion of the lens. The flame light-field image is obtained by the light-field convolution imaging model and adopted as the measurement signal to reconstruct three-dimensional temperature field. The inverse problem of temperature reconstruction is solved by the least square QR decomposition method. The simulative temperature reconstruction work is conducted, including the inverse analysis, the uncertainty analysis, and the measurement noise influence. All the results show that the proposed measurement method is available to reconstruct three-dimensional temperature with satisfactory accuracy and acceptable uncertainty. Both symmetric and asymmetric distributed temperature fields are investigated, and the reconstructed results prove the validity and universality of the measurement method.

Standard plenoptic camera can be used to capture multi-dimensional radiation information of high temperature luminous flame to reconstruct the temperature distribution. In this study, a novel method for reconstructing three-dimensional temperature field is proposed. This method is based on the optical tomography combined with standard plenoptic camera. The flame projection information from different planes is contained in one radiation image. In this model, we introduced the effective concept of the nearest neighbor method in the frequency domain to strip the interference of redundant information in the projection and to realize three-dimensional deconvolution. The flame emission intensity received by the pixels on the charge-coupled device sensor can be obtained according to the optical tomographic model. The temperature distributions of the axisymmetric and non-axisymmetric flames can be reconstructed by solving the mathematical model with the nearest neighbor method. The numerical results show that three-dimensional temperature fields of high temperature luminous flames can be retrieved, proving the validity of the proposed method.

The boundary emissivity, thermal conductivity, and boundary time-varying heat flux in the participating media are retrieved in this work. In the forward model, the coupled radiation-conduction heat transfer in the participating medium is resolved by the finite volume method combined with discrete ordinates method. The inverse model utilizes the temperature signals at the appropriate position of the medium as the known information and uses the unscented Kalman filter (UKF) as an optimization tool to reconstruct the boundary emissivity, thermal conductivity, and boundary time-varying heat flux. It is found that when the emissivity, thermal conductivity, and boundary time-varying heat flux are reconstructed simultaneously, only the temperature information of two locations is required. The influence of the measurement noise, sampling interval, absorption coefficient, process noise covariance, measurement noise covariance on the accuracy and stability of the retrieval results is investigated in detail. All the reconstruction results indicate that the UKF technique is effective and robust for estimating the photothermal parameters and boundary condition of the radiation-conduction heat transfer problems.

The manipulation of microscale fluid has been widely used in biology, medicine, and chemistry. However, the traditional control systems are relatively large, complex, and costly. Optical driving micro- and nanofluid is a new trend of microfluidics, which combines the advantages of both optics and microfluidics in the micro-nano scale. In the present work, we investigated a method to drive microfluid by taking advantage of the localized surface plasmon resonance effect of gold nanoparticles, which can convert optical energy to fluid motion. First, numerical simulation was carried out to calculate the electromagnetic, temperature, and flow field around laser-irradiated gold nanoparticles. Then, the simplified heat source condition was verified. The nanoparticle array was regarded as heat source to induce convection flow. The influence of the spacing and number of nanoparticles in array was investigated. On this basis, the structural parameters of nanoparticle array that can be used to regulate the velocity of microfluidic were obtained.

The photothermal characteristics of semi-transparent medium are the basis for describing the transmission process of photothermal radiation. Therefore, the related measurement researches have important application value for the non-destructive testing of high-tech module. In the present work, an infrared thermal wave radar imaging (TWRI) technique combined with sequential quadratic programming (SQP) algorithm was proposed for simultaneous reconstruction of the photothermal property distributions in 2D semi-transparent materials containing defects. The Fourier transform, Hilbert transform, and the Chirp lock-in correlation algorithm were applied and the results of the amplitude and phase information of the temperature signals were compared, so as to identify the position of internal defects, which reduced the number of parameters needed to reconstruct. Then, the SQP algorithm was introduced to simultaneously reconstruct the absorption coefficient and thermal conductivity coefficient distributions in the medium. The TWRI-SQP technique absorbs the advantages of fast identification of defects by TWRI technique and the accurate reconstruction of the photothermal properties of the semi-transparent materials by SQP algorithm. The number of internal defects, geometric dimensioning, shape, and photothermal properties can be reconstructed accurately. The results show that this method is practical and robust to detect the photothermal properties of 2D semi-transparent materials with defects.

A novel intelligent algorithm, krill herd (KH), is firstly introduced to solve the inverse identification of temperature-dependent thermal properties of materials. To promote the searching ability and accelerate the convergence velocity, three improved KH (IKH) algorithms are proposed and developed for solving the optimization tasks. The temperature-dependent thermal conductivity and specific heat of a building material are estimated by using the KH algorithms, and the IKHs achieve better performance than the original KHs. Moreover, the functional forms of thermal conductivity of insulating and refractory materials are also reconstructed. The IKH algorithm is proved to be more accurate than other algorithms. Finally, a two-dimensional nonhomogeneous heat conduction model is investigated and the thermal conductivities of materials at specified temperatures are reconstructed, in which no prior information is needed for the expressions of the thermal conductivity to be identified. All the retrieval results show that IKH algorithm is robust and effective for solving the inverse heat conduction problems.

The non-destructive testing (NDT) of ceramic materials using mid-infrared ultrashort-pulse laser is investigated in this study. The discrete ordinate method is applied to solve the transient radiative transfer equation in 2D semitransparent medium and the emerging radiative intensity on boundary serves as input for the inverse analysis. The sequential quadratic programming algorithm is employed as the inverse technique to optimize objective function, in which the gradient of objective function with respect to reconstruction parameters is calculated using the adjoint model. Two reticulated porous ceramics including partially stabilized zirconia and oxide-bonded silicon carbide are tested. The retrieval results show that the main characteristics of defects such as optical properties, geometric shapes and positions can be accurately reconstructed by the present model. The proposed technique is effective and robust in NDT of ceramics even with measurement errors.