Temperature Field Reconstruction Research Articles

Synergistic Reconstruction of Temperature Field and Vortex Distribution Based On Nonlinear Acoustic Tomography and Particle Swarm Optimization

Synergistic Reconstruction of Temperature Field and Vortex Distribution Based On Nonlinear Acoustic Tomography and Particle Swarm Optimization

Temperature Field Reconstruction of 3d Components Through a Neural Network-Based Method

Temperature Field Reconstruction of 3d Components Through a Neural Network-Based Method

Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems

Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may decrease the availability and reliability of the system and sharply increase the monitoring cost. To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly. First, we define the TFR-HSS task mathematically, and numerically model the task, and hence transform the task as an image-to-image regression problem. Then this work develops the deep reversible regression model which can better learn the physical information, especially over the boundary. Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and jointly learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness of the proposed method.

Machine learning applied to the retrieval of three-dimensional scalar fields of laminar flames from hyperspectral measurements

Accurate measurement of three-dimensional temperature and species mole fraction fields for combustion systems provides comprehensively detailed information for optimizing combustion process and improving combustion efficiency. The state-of-art three-dimensional combustion diagnostic techniques for temperature and species mole fraction reconstructions, either laser-based or radiation imaging-based, require solving problems of huge matrices with iterative processes based on the multiple projection measurements of flame emission or absorption. These techniques are typically computationally intensive, with limited spatial resolution and can be hardly applied to retrieve three-dimensional temperature and multiple species mole fractions simultaneously. In the present study, we extended the machine learning methodology we previously proposed (Ren et al. 2021) for the reconstruction of two-dimensional temperature and mixture species mole fraction fields to three-dimensional for a group of non-axisymmetric flames with different equivalence ratios. The developed method demonstrates its excellent capability to retrieve three-dimensional temperature with CO2, H2O, and CO mole fractions simultaneously for these targeted flames. The accuracy of the machine learning reconstructions was found to be excellent, while computational effort was reduced by at least five orders of magnitude, as opposed to conventional gradient-based optimization methods.

Research on tomography algorithm of furnace flame temperature field based on acoustic method in thermal power station

An improved Tikhonov regularization algorithm MCTR based on Markov radial basis function is proposed to obtain the feedback of closed-loop control for pulverized coal injection process in thermal power plant, so as to solve the ill posed problem in temperature field reconstruction in furnace. The algorithm constructs a new regularization matrix to replace the standard Tikhonov regularization element matrix, so as to achieve the correction effect of the new algorithm. MCTR algorithm determines the boundary singular value of small singular value defined in this paper by determining the proportion between the standard deviation component corresponding to small singular value and the sum of standard deviation components corresponding to all singular values after singular value decomposition of coefficient matrix. After determining the boundary, a new regularization matrix is constructed according to the eigenvector corresponding to small singular value. Compared with the MTR algorithm using the identity matrix as the regularization matrix, MCTR algorithm only selectively modifies the parameters corresponding to small singular values after determining the regularization parameters, which improves the stability of the parameter solution and the reconstruction accuracy of the temperature field, and is helpful to the automatic control of thermal power plants and the efficiency of coal combustion.

A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography

Acoustic tomography (AT), as a noninvasive temperature measurement method, can achieve temperature field measurement in harsh environments. In order to achieve the measurement of the temperature distribution in the furnace and improve the accuracy of AT reconstruction, a temperature field reconstruction algorithm based on the radial basis function (RBF) interpolation method optimized by the evaluation function (EF-RBFI for short) is proposed. Based on a small amount of temperature data obtained by the least square method (LSM), the RBF is used for interpolation. And, the functional relationship between the parameter of RBF and the root-mean-square (RMS) error of the reconstruction results is established in this paper, which serves as the objective function for the effect evaluation, so as to determine the optimal parameter of RBF. The detailed temperature description of the entire measured temperature field is finally established. Through the reconstruction of three different types of temperature fields provided by Dongfang Boiler Works, the results and error analysis show that the EF-RBFI algorithm can describe the temperature distribution information of the measured combustion area globally and is able to reconstruct the temperature field with high precision.

Reconstruction of Temperature Field in Coal-fired Boiler Based on Limited Flame Image Information

ABSTRACT The internal combustion system of coal-fired boiler is a complex nonlinear system with large delay and strong coupling. The most basic requirement for the boiler combustion system is that the combustion flame inside the boiler is stable and uniform, and the flame temperature is the most important combustion characteristic of the combustion flame, so the method of using flame temperature as an intermediate variable to control the boiler system has been proposed by many scholars. Compared with the traditional control method that takes the main steam pressure and other parameters as the control variable, this method greatly reduces the delay of the system, and at the same time improves the stability and economy of the system. In this paper, a temperature field reconstruction method based on the improved two-color temperature measurement method is proposed. In this method, a mathematical model between the gray value of flame image and the parameters to be solved in the improved two-color temperature measurement method is established by using BP neural network. Then, the temperature field in furnace is reconstructed according to the gray value of flame image. This method solves the problem that traditional furnace temperature field reconstruction algorithm can not obtain accurate radiation energy data, which makes the measurement and calculation of this method simplified, and the accuracy is improved. The simulation results show that the maximum temperature error of this method is 4% and the average error is around 0.68%, which can meet the requirements of industry.

A data assimilation approach to last millennium temperature field reconstruction using a limited high-sensitivity proxy network

A data assimilation approach to last millennium temperature field reconstruction using a limited high-sensitivity proxy network

Reconstruction of three-dimensional temperature fields in micro-fixed bed reactor based on infrared thermography and thermal probe method

Aiming at determining the characteristics of small volume and complex heat and mass transfer of micro-reactors, an online measurement system was designed and built. The temperature of the reactor surface was measured by infrared thermography. For reconstruction of the three-dimensional temperature field of the micro-fixed bed reactor, mathematical and physical models were established by finite difference of governing equations and boundary conditions, and the objective function was established based on the principle of least squares. Then, using the conjugate gradient algorithm, the three-dimensional temperature field was rebuilt in the fixed bed reactor. It was verified that the micro-reactor filled with resin particles had a porosity of 0.432 under an initial temperature of 20 °C and adiabatic conditions; distilled water of a constant temperature of 50 °C was introduced into the reactor at a flow rate of 3.2 ml/min, and the internal temperature field was deduced from the surface temperature of the reactor. The experiment also used micro-thermocouples to measure the internal temperature at multiple points to compare with the inverse temperature field. The temperature change trend was consistent, and the maximum error was 4.85% in a no reaction condition and 7.1% in catalytic reactions, which verified the accuracy of the inverse method.

A data assimilation approach to last millennium temperature field reconstruction using a limited high-sensitivity proxy network

AbstractWe use theNorthern Hemisphere Tree-RingNetwork Development (NTREND) tree-ring database to examine the effects of using a small, highly-sensitive proxy network for paleotemperature data assimilation over the last millennium. We first evaluate our methods using pseudo-proxy experiments. These indicate that spatial assimilations using this network are skillful in the extratropical Northern Hemisphere and improve on previous NTREND reconstructions based on Point-by-Point regression. We also find our method is sensitive to climate model biases when the number of sites becomes small. Based on these experiments, we then assimilate the real NTREND network. To quantify model prior uncertainty, we produce 10 separate reconstructions, each assimilating a different climate model. These reconstructions are most dissimilar prior to 1100 CE, when the network becomes sparse, but show greater consistency as the network grows. Temporal variability is also underestimated before 1100 CE. Our assimilation method produces spatial uncertainty estimates and these identify treeline North America and eastern Siberia as regions that would most benefit from development of new millennial-length temperature-sensitive tree-ring records. We compare our multi-model mean reconstruction to five existing paleo-temperature products to examine the range of reconstructed responses to radiative forcing. We find substantial differences in the spatial patterns and magnitudes of reconstructed responses to volcanic eruptions and in the transition between the Medieval epoch and Little Ice Age. These extant uncertainties call for the development of a paleoclimate reconstruction intercomparison framework for systematically examining the consequences of proxy network composition and reconstruction methodology and for continued expansion of tree-ring proxy networks.

Temperature distribution in the cross section of wavy and falling thin liquid films

Wave instabilities of falling liquid films are crucial in many applications to enhance heat and mass transfer. Despite the importance of this issue, the interplay between the heat transfer and the wavy dynamics of falling films is still not completely understood. To get more insight, a planar laser-induced fluorescence technique has been developed for imaging the temperature distribution in the cross section of thin liquid films (approximately 0.5–1 mm thick), which are falling down an inclined heated surface. This study reports on the implementation of this imaging technique. It also discusses its advantages and limitations for the investigation of the heat transfer in the falling liquid films. Two-dimensional flow conditions and regular waves are considered for the reconstruction of a complete temperature field in the waves. Measurements provide new understanding of the wave ability to generate mixing within the film. Temperature maps reveal preferential regions where mixing occurs first, before eventually spreading to the rest of the film if the wave amplitude and the travel distance are large enough. The increase in the heat transfer coefficient is directly related to the internal mixing observed in the temperature images.

Characterization of temperature distributions in a swirled oxy-fuel coal combustor using tomographic absorption spectroscopy with fluctuation modelling

Oxy-fuel combustion promises efficient and inexpensive carbon dioxide sequestration and is therefore subject to active experimental research. However, the transfer of mature, non-intrusive diagnostic methods for temperature measurement like Coherent Anti-Stokes Raman Spectroscopy (CARS) from air-fed to oxy-fuel systems is challenging due to the deficiency in diatomic species suitable for thermometry. Although not limited to oxy-fuel atmospheres, we demonstrate the application of linear hyperspectral absorption tomography on water vapor in a swirled oxy-fuel coal combustor as a complementary diagnostic method, supplementing reference and validation data sets. Due to the burner design an axisymmetric reconstruction of the time-averaged temperature field is conducted. To compensate the temperature bias expected when evaluating time-averaged spectroscopic data we incorporate turbulent temperature fluctuations into our spectroscopic model, providing a fluctuation measure in addition to mean temperatures. The results quantitatively agree with vibrational O2-CARS measurements and qualitatively recreate spatial structures known from particle image velocimetry (PIV) flow fields.

Reconstruction of complex temperature field based on improved Tikhonov regularization

In order to improve the reconstruction accuracy of complex temperature field by acoustic CT, an improved Tikhonov regularization temperature field reconstruction algorithm is proposed. In this method, the filtering factor of standard Tikhonov regularization method is changed, so that no correction is made for larger singular values, while the correction for smaller singular values will increase with the decrease of singular values. The reconstruction results of typical temperature fields based on simulation data and experiment data verify the the effectiveness of the proposed algorithm on reducing reconstruction errors of temperature fields.

A temperature field reconstruction method for spacecraft leading edge structure with optimized sensor array

Aimed at monitoring the aerodynamic heating on the spacecraft leading edge structure, a temperature field reconstruction method for curved surface using a distributed sensor array is proposed in this paper. Limited by the sensor number and system weight, a sparse sensor array is usually used to measure the temperatures at the discrete positions. Considering the majority of heat energy transfers on the leading edge surface rather than the hollow inside or thermal insulation material, the temperature field on the curved surface of leading edge structure can be reconstructed through inverse distance weighting (IDW) method with improved accuracy by introducing arc-length distances rather than straight-line distances. Furthermore, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to optimize the distribution of the sensor array. Taking a three-dimensional spacecraft leading edge structure as an example, a finite element model is used to validate the proposed method. The influences of search radius and random initial population inputs on the optimized results are investigated. The results show that the use of search radius plays an important role in optimizing the arrangement of sensor array.

Flame 3D temperature field reconstruction based on damped LSQR-LMBC

Flame 3D temperature field reconstruction based on damped LSQR-LMBC

A Reconstruction Method of Boiler Furnace Temperature Distribution Based on Acoustic Measurement

The temperature distribution in the furnace of power plant boilers is an important parameter to indicate the pulverized coal combustion state. The real-time and precise monitoring of the temperature field in the furnace is essential to ensuring the safe operation of the power plant and improving production efficiency. Acoustic thermometry is a typical noncontact temperature measurement and one of its cores is to derive the temperature distribution of the original temperature field by reconstruction algorithms. The existing temperature field reconstruction algorithms do not perform satisfactorily, and there are some problems such as incomplete reconstruction results, low reconstruction precision, and poor anti-interference ability. In order to further improve the reconstruction performance, an acoustic thermometry reconstruction algorithm based on logarithmic-quadratic (LQ) radial basis function (RBF) and singular value decomposition (LQ-SVD) is proposed in this article. This algorithm first uses the linear combination of the LQ RBFs to fit the reciprocal distribution of the acoustic velocity, and then uses the SVD method to solve the inversion model. The simulation results show that, compared with the commonly used algorithms, the proposed algorithm can obtain complete reconstruction results with significantly improved reconstruction precision, stronger robustness, and better anti-interference ability. In addition, the proposed algorithm also has good performance in the actual experiment, which verifies the feasibility and effectiveness of the algorithm in the engineering application.

Towards broad‐scale temperature reconstructions for Eastern North America using blue light intensity from tree rings

AbstractSummer temperatures across eastern North America (hereafter East) will soon reach a level consistently above any observation experienced during the instrumental period. Increasing temperatures will have negative impacts on natural (e.g., water, plant and animal communities) and human (e.g., health, infrastructure, economies) systems upon which the large and growing centres of human population across the region depend. Within the network of Northern Hemisphere tree‐ring temperature proxy records, one of the most obvious geographic holes is the East, where few temperature‐sensitive proxies exist. Here we present the first steps towards building a network of temperature‐sensitive proxy records across the East using blue light intensity (BI) methods applied to the tree rings of multiple temperature sensitive tree species situated from North Carolina to Maine, USA. Our overall objective is to report on the most viable species for BI analysis across different regions of the East (e.g., Southeast US, Midwest US, Northeast US/Canadian Maritimes) by exploring temporal (e.g., since ca. 1900) and spatial relationships between instrumental temperatures and BI metrics. We found BI to be a strong predictor of March–October mean air temperature (R2 = 0.61) across the Northeast US/eastern Canada, and Sep‐Oct maximum air temperature (R2 = 0.42) across the Southeast US. Of all species tested, Tsuga canadensis and Picea rubens contained the strongest BI temperature signal. Adding more BI sites from these and potentially other species, as well as inclusion of other temperature proxies (e.g., ring widths) will allow for the development of a skilful broad‐scale and long‐term temperature field reconstruction across the East.

Deep learning methods for super-resolution reconstruction of temperature fields in a supersonic combustor

A general super-resolution (SR) reconstruction strategy is proposed to address the super-resolution reconstruction of temperature fields from low-resolution coarse temperature field data using convolutional neural networks. Two deep learning (DL) models were applied to augment the spatial resolution of temperature fields. One is the classical super-resolution convolutional neural network, and the other is the novel multiple path super-resolution convolutional neural network (MPSRC). Three paths with and without a pooling layer are designed in the MPSRC to fully capture spatial distribution features of temperature. Numerical simulations of combustion in a strut scramjet combustor at various Mach numbers are carried out to establish a dataset for network training and testing. The corresponding high-resolution temperature fields were successfully reconstructed with remarkable accuracy. The reconstruction performances of those models were comprehensively investigated and compared with the bicubic interpolation method. The results demonstrated that both DL methods can greatly improve the super-resolution reconstruction accuracy and the MPSRC can provide a better reconstruction result with a lower mean square error and a higher peak signal-to-noise ratio.

Interannual Variability of the Wind-Wave Regime Parameters in the Black Sea

Purpose. The investigation is aimed at increasing accuracy of the temperature field reconstruction in the Black Sea upper layer. For this purpose, satellite observations of the sea surface temperature and the three-dimensional fields of temperature (in the 50–500 m layer) and salinity (in the 2.5–500 m layer) pseudo-measurements, previously calculated by the altimetry and the Argo floats data, were jointly assimilated in the Marine Hydrophysical Institute model. Methods and Results. Assimilation of the sea surface temperature satellite observations is the most effective instrument in case the discrepancies between the sea surface and the model temperatures are extrapolated over the upper mixed layer depth up to its lower boundary. Having been analyzed, the temperature profiles resulted from the forecast calculation for 2012 and from the Argo float measurements made it possible to obtain a simple criterion (bound to the model grid) for determining the upper mixed layer depth, namely the horizon on which the temperature gradient was less or equal to ≤ 0.017 °C/m. Within the upper mixed layer depth, the nudging procedure of satellite temperature measurements with the selected relaxation factor and the measurement errors taken into account was used in the heat transfer equation. The temperature and salinity pseudo-measurements were assimilated in the model by the previously proposed adaptive statistics method. To test the results of the sea surface temperature assimilation, the Black Sea hydrophysical fields were reanalyzed for 2012. The winter-spring period (January – April, December) is characterized by the high upper mixed layer depths, well reproducible by the Pacanowski – Philander parameterization, and also by the low values (as compared to the measured ones) of the basin-averaged monthly mean square deviations of the simulated temperature fields. The increased mean square deviations in July – September are explained by absence of the upper mixed layer in the temperature profiles measured by the Argo floats that is not reproduced by the Pacanowski – Philander parameterization. Conclusions. The algorithm for assimilating the sea surface temperature together with the profiles of the temperature and salinity pseudo-measurements reconstructed from the altimetry data was realized. Application of the upper mixed layer depths estimated by the temperature vertical profiles made it possible to correct effectively the model temperature by the satellite-derived sea surface temperature, especially for a winter-spring period. It permitted to reconstruct the temperature fields in the sea upper layer for 2012 with acceptable accuracy.

Deep learning algorithms for temperature field reconstruction of nonlinear tomographic absorption spectroscopy

Nonlinear tomography can be combined with line-of-sight measurement techniques and take the full advantage of the nonlinear relationship between the target fields to be reconstructed and the measured projections. For example, it has been integrated with absorption spectroscopy for simultaneous imaging of temperature and species concentration, and the technique is referred to as nonlinear tomographic absorption spectroscopy (NTAS) which is especially suitable to applications where the optical access is extremely limited. However, the major drawback of nonlinear tomography is its high computational costs for the inversion process, which involves the solution of a large-scale nonlinear equation system. The situation becomes more severe when thousands of tomographic frames need to be processed. This limitation can be potentially overcome by applying a deep learning algorithm, which can build efficient mapping between the projections and the target fields for rapid reconstruction. Nevertheless, only preliminary study of convolutional neural networks was reported so far, and there are also other well-established methods which have not been investigated yet. In this work, we cite NTAS as an example and demonstrate the reconstruction of temperature field using deep brief network (DBN) and recurrent neural network (RNN) for the first time. This work also aims to provide systematic comparative studies between these representative algorithms. As expected, all the deep learning algorithms are very efficient in solving NTAS problems, and a typical reconstruction time is on the order of milliseconds. In addition, the results suggest that, in NTAS problem, RNN is superior to the other methods in terms of both accuracy and noise immunity, and it is more favorable for practical applications.