Temperature Field Reconstruction Research Articles

Direct reconstruction of tissue temperature fields during thermal therapy by dynamic response matching and steady-state correction

In the field of oncology thermotherapy technology, online and real-time reconstruction of biological tissue temperature field is a fundamental work with important practical needs. In this paper, we proposed a new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction. Based on the bioheat transfer equation, the step response model of tissue temperature field during thermal therapy is established, and the dynamic response eigenvectors of biological tissue temperature and the steady-state gain of temperature response are determined offline. According to the optimal matching principle between the temperature dynamic response of the observation point and the internal space point of tissue, the optimal matching matrix of the dynamic response features between the observation point temperature and the internal temperature field of tissue is determined with the use of the dynamic response eigenvectors. Further, through the weighted synthesis of observation point temperature response by using the optimal matching matrix of dynamic response features, the tissue transient temperature field pre-estimation model is established. Finally, by correcting the tissue temperature field pre-estimation model with steady-state gain correction factor, the direct reconstruction scheme of temperature field based on dynamic response feature matching and steady-state gain correction is established. In this work, the non-destructive reconstruction of the internal temperature field of biological tissues during laser-induced thermal therapy is achieved based on the above direct reconstruction scheme. Numerical simulation experiments are performed to investigated the effects of pre-estimation time domain, the number of measurement points, laser irradiation form and measurement error on the temperature field reconstruction results.

Temperature and Acoustic Field Reconstruction in Fiber Optic DAS System Based on Variational Mode Decomposition

Temperature and Acoustic Field Reconstruction in Fiber Optic DAS System Based on Variational Mode Decomposition

Temperature field reconstruction based on femtosecond laser prepared of multiwavelength fibre bragg grating array

Temperature field measurements are of great significance in industrial production, scientific research, and other fields. Contact temperature measurements usually achieve high spatial resolution by increasing the layout density of the sensors. However, in practical applications, achieving high-density sensor placement is often difficult. Therefore, when the number of sensors is limited, it is necessary to perform function fitting or interpolation of the temperature of the sampling points to reconstruct the temperature field distribution. This study proposed a temperature field reconstruction method based on femtosecond laser prepared multiwavelength fibre Bragg grating (FBG) array. A multiwavelength FBG array was prepared using the femtosecond laser phase mask method combined with stress stretching, and applied for measuring the temperature field distribution in a tubular high-temperature furnace. Cubic spline interpolation and backpropagation (BP) neural networks were used to construct two-dimensional temperature field models for the temperature field distribution data measured by the FBGs, and the prediction accuracies of the two models were compared. The test results show that the root mean square error of the temperature field distribution constructed using the BP neural network is 0.7333 °C, which is approximately 23.18% of the predicted results of the cubic spline interpolation model, indicating that it is a high-precision temperature field reconstruction method.

A real-time solution method for three-dimensional steady temperature field of transformer windings based on mechanism-embedded cascade network

To enhance the computation efficiency and accuracy of three-dimensional steady temperature field of transformer windings, we propose a new non-invasive Reduced Order Model (ROM) based on a mechanism-embedded cascade network. Initially, a snapshot matrix is formed from the Full Order Model (FOM) and then combined with Proper Orthogonal Decomposition (POD) to extract key modal features that characterize the temperature field. Subsequently, a cascade network architecture, integrating Multilayer Perceptron (MLP) and Radial Basis Function Neural Network (RBFNN), is devised to swiftly map working condition parameters to modal coefficients. Additionally, the cascade network is embedded with condition sensitivity and modal contribution mechanisms to further enhance prediction accuracy. Finally, by linearly weighting the modes with predicted modal coefficients, a rapid reconstruction of the steady temperature field in transformer windings is achieved. Validation against Fluent software simulations and experimental measurements demonstrate a close agreement, with computational errors of less than 4K and an impressive single solution time of only 0.0087 s, which is 48760 times faster compared to Fluent software.

Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

Joint identification method for thermal–mechanical coupling loads

The crucial role in evaluating structural fatigue life, implementing health monitoring, and optimizing lightweight and reliable design is played by load identification technology. With the development of the aviation industry towards high-speed aircraft, the challenges under high temperature and high load conditions have been intensified. Unfortunately, in environments characterized by the coexistence of force and heat, difficulties are faced by existing load identification methods in achieving key decoupling and accurate identification of force and temperature fields. To address this gap, the Joint Identification Method (JIM), a new method that integrates force and thermal strain patterns, is introduced in this article. This method has been developed through in-depth research on “joint physical fields” and “joint basis functions.” Multiple physical fields for load identification are systematically decomposed by our method, each field is theoretically deduced, and they are seamlessly reassembled using the same structural spatial field. Through the method, mechanical strain and thermal strain are separated in high-precision temperature field reconstruction, and a basis function method for identifying the full field load distribution under a few measurement points is obtained. To verify the accuracy of our proposed Joint Identification Method (JIM), five experiments were designed, and the results consistently demonstrated the superiority of our method in accuracy (with an error always below 5%), efficiency (avoiding a large number of redundant calculations in traditional Duhamel methods), and reliability in static and dynamic load static heat identification. In the end, accurate, efficient, and reliable identification of the structural strain field, structural load field, and temperature field were achieved by our method.

Flame soot absorption coefficient and temperature reconstruction through line-of-sight attenuation and light field imaging

The accuracy and efficiency of 3-D flame temperature field reconstruction using the light field imaging technique heavily depend on soot radiation characteristics. In this study, we employ the line-of-sight attenuation method to reconstruct the soot absorption coefficient distribution in a pure absorbing flame. Utilizing these soot absorption coefficients, the radiative transfer equation is effectively transformed within the framework of the light field imaging technique into a linear inverse problem and also outlines the flame boundary. This proposed strategy reduces the unwanted detection rays significantly, thus eliminating the extensive computational processing. Consequently, the proposed approach substantially enhances the accuracy and efficiency of flame temperature reconstruction. Numerical simulations were carried out on a bimodal asymmetric flame to validate the noise tolerance capabilities, investigate the effects of varying voxel numbers on flame division and carry out a comparative study. Experimental studies were also conducted to reconstruct flame temperature and soot absorption coefficient distributions under different combustion operating conditions. Thermocouple measurements were performed and compared with the reconstructed temperatures. Outcomes achieved from both numerical simulations and experimental studies demonstrate the feasibility, accuracy and robustness of the proposed method.

Extreme springs in Switzerland since 1763 in climate and phenological indices

Abstract. Historical sources report manifold on hazardous past climate and weather events that had considerable impacts on society. Studying changes in the occurrence or mechanisms behind such events is, however, hampered by a lack of spatially and temporally complete weather data. In particular, the spring season has received less attention in comparison to summer and winter but is nevertheless relevant, since weather conditions in spring can delay vegetation and create substantial damage due to late-frost events. For Switzerland, we created a daily high-resolution (1 × 1 km2) reconstruction of temperature and precipitation fields from 1763 to 1960 that forms, together with present-day meteorological fields, a 258-year-long gridded data set. With this data set, we study changes in long-term climate and historical weather events based on climate and phenological indices focusing on the spring season. Climate and phenological indices show few changes in the mean during the first 200 years compared to the most recent period from 1991 to 2020, where climate change signals clearly emerged in many indices. We evaluate the climate and phenological indices for three cases of extreme spring weather conditions: an unusually warm spring, two late-frost events, and three cold springs. Warm springs are much more frequent in the 21st century, but a very warm and early spring also occurred in 1862. Spring temperatures, however, do not agree on how anomalously warm the spring was when comparing the Swiss temperature reconstruction with reanalyses that extend back to 1868. The three springs of 1785, 1837, and 1853 were particularly cold, with historical sources reporting, for example, prolonged lake freezing and abundant snowfall. Whereas the springs of 1837 and 1853 were characterized by cold and wet conditions, in the spring of 1785 wet days were below average, and frost days reached an all-time maximum, in particular in the Swiss Plateau, indicating inversion conditions. Such conditions are in line with a high occurrence of northeasterly and high-pressure weather types and historical sources describing Bise conditions, a regional wind in the Alpine area related to inversions. Studying such historical events is valuable, since similar atmospheric conditions can lead to cold springs affecting vegetation growth and agricultural production.

Research on temperature field reconstruction technology for single-dome combustion chambers based on flame image

In order to compensate for the limitations of traditional temperature measurement methods, glass plates were installed on both sides of the single-dome combustion chamber test piece, and two CCD cameras observed the combustion flame in the furnace from different positions. The obtained flame radiation images were combined into a single image through the video capture card, and then the images were input into the reconstruction algorithm software to calculate the three-dimensional temperature field in the combustion chamber in real-time. The software could dynamically display the temperature distribution at three different positions in the chamber. This marked the first time that the temperature field in the combustion chamber was obtained using this new temperature measurement technology, which preliminarily verifies the feasibility of reconstructing the temperature field in the single-dome combustion chamber through flame image temperature measurement technology.

Reconstruction of a three-dimensional temperature field in flames based on ES-ResNet18.

Currently, the method of establishing the correspondence between the flame light field image and the temperature field by deep learning is widely used. Based on convolutional neural networks (CNNs), the reconstruction accuracy has been improved by increasing the depth of the network. However, as the depth of the network increases, it will lead to gradient explosion and network degradation. To further improve the reconstruction accuracy of the flame temperature field, this paper proposes an ES-ResNet18 model, in which SoftPool is used instead of MaxPool to preserve feature information more completely and efficient channel attention (ECA) is introduced in the residual block to reassign more weights to feature maps of critical channels. The reconstruction results of our method were compared with the CNN model and the original ResNet18 network. The results show that the average relative error and the maximum relative error of the temperature field reconstructed by the ES-ResNet18 model are 0.0203% and 0.1805%, respectively, which are reduced by one order of magnitude compared to the CNN model. Compared to the original ResNet18 network, they have decreased by 17.1% and 43.1%, respectively. Adding Gaussian noise to the flame light field images, when the standard deviation exceeds 0.03, the increase in reconstruction error of the ES-ResNet18 model is lower than that of ResNet18, demonstrating stronger anti-noise performance.

Fast reconstruction of boiler numerical physical field based on proper orthogonal decomposition and conditional deep convolutional generative adversarial networks

ABSTRACT Obtaining real-time physical field data inside the boiler furnace is crucial for combustion diagnostics and optimization. In this work, we propose a fast reconstruction technique for the physical fields in a tangentially-fired coal boiler. The method combines the Proper Orthogonal Decomposition (POD) and Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) based on the boiler physical field obtained by computational fluid dynamics (CFD). Firstly, CFD simulations are performed using data from the boiler Distributed Control System (DCS). After verifying the CFD results, we expanded the simulation to 105 operational condition cases sample sets. Then, the physical fields of the sample set undergo POD to determine the appropriate number of POD bases, thereby ensuring the accuracy of the reconstruction. Finally, the cDCGAN method is employed to establish the correlation between the POD bases and the operational conditions of the boiler. Results show that the POD-cDCGAN method achieves rapid and accurate reconstruction of temperature and velocity fields, with mean relative error (MRE) below 1.5% and root relative squared error (RRSE) below 5.7%. The reconstruction time is reduced to 10.57s, significantly faster than direct CFD methods. The POD-cDCGAN method offers an efficient approach for online operational diagnosis and digital twin construction of boiler equipment.

A non-axisymmetric temperature field reconstruction method based on the compressive sensing background oriented Schlieren method

Measurement of the combustion temperature field is an extremely important issue in industrial production. Temperature is one of the key parameters in combustion studies. With the temperature field distribution of the combustion field obtained, heat transfer, heat convection, and heat radiation can be calculated directly and efficiently. Traditional background oriented Schlieren (BOS) is an effective method for non-axisymmetric temperature field measurements, but it requires simultaneous Schlieren imaging at multiple angles for tomographic reconstruction, which will greatly limit its application. In this paper, the compressive sensing algorithm is introduced into the temperature field reconstruction, which establishes the system of equations between the deflection angle and the refractive index gradient. Then, the reconstruction of the non-axisymmetric temperature field is realized by solving the underdetermined system of equations by the method of solving the sparse solution through the compressive sensing. First, light offsets across the non-axisymmetric temperature field are measured by the under-angled BOS system and image processing method. Second, the spatial refractive index field is reconstructed by the compressive sensing BOS method proposed in this paper. Finally, the spatial temperature field is obtained. The experimental results show that by comparing the iRadon reconstruction algorithm and the compressive sensing reconstruction algorithm, the temperature field reconstructed by the compressive sensing under the condition of the under-angled sampling of projection data had a higher accuracy than that reconstructed by the tomographic reconstruction algorithm under the same condition. The average error of the temperature field was reduced from 34.6 to 29.7 K under the same measurement conditions; however, the accuracy is better maintained by using the compressive sensing algorithm under the condition of undersampling projection.

Real-Time Reconstruction of HIFU Focal Temperature Field Based on Deep Learning.

Objective and Impact Statement: High-intensity focused ultrasound (HIFU) therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects, while avoiding surrounding damage to surrounding normal tissues. Introduction: Accurate and real-time acquisition of the focal region temperature field during HIFU treatment marked enhances therapeutic efficacy, holding paramount scientific and practical value in clinical cancer therapy. Methods: In this paper, we initially designed and assembled an integrated HIFU system incorporating diagnostic, therapeutic, and temperature measurement functionalities to collect ultrasound echo signals and temperature variations during HIFU therapy. Furthermore, we introduced a novel multimodal teacher-student model approach, which utilizes the shared self-expressive coefficients and the deep canonical correlation analysis layer to aggregate each modality data, then through knowledge distillation strategies, transfers the knowledge from the teacher model to the student model. Results: By investigating the relationship between the phantoms, invitro, and invivo ultrasound echo signals and temperatures, we successfully achieved real-time reconstruction of the HIFU focal 2D temperature field region with a maximum temperature error of less than 2.5 °C. Conclusion: Our method effectively monitored the distribution of the HIFU temperature field in real time, providing scientifically precise predictive schemes for HIFU therapy, laying a theoretical foundation for subsequent personalized treatment dose planning, and providing efficient guidance for noninvasive, nonionizing cancer treatment.

Boiler Furnace Temperature Field Measurement and Reconstruction Error Elimination Based on Temperature Field Residual Correction Network

Boiler Furnace Temperature Field Measurement and Reconstruction Error Elimination Based on Temperature Field Residual Correction Network

An Axisymmetric Temperature Field Reconstruction Method Based on the Fan-Shaped Light Path Background Oriented Schlieren Method

An Axisymmetric Temperature Field Reconstruction Method Based on the Fan-Shaped Light Path Background Oriented Schlieren Method

A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack

A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack

Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements

The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D (x,y,z) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D (x,y) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0.97°C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.

Real-time temperature field reconstruction using a few measurement points and RPIM-AGQ6 interpolation

In thermal management systems, temperature field monitoring is vital for the structural safety of electronic equipment. However, there are practical challenges when it comes to installing temperature sensors on some monitoring points directly. To solve this problem, a novel temperature field reconstruction method is proposed. This strategy offers the advantage of using only a limited number of discrete values to predict temperature fields across an entire plane. Initially, the approach constructs a composite interpolation function, named RPIM-AGQ6, by coupling the mesh-free radial point interpolation method (RPIM) with the quadrilateral area coordinate method (AGQ6) The temperature field is discretized and represented as the product of the nodal temperatures and the RPIM-AGQ6. Next, ill-posed equations are established and solved using Bayesian Multivariate Linear Regression (BMLR) to obtain the temperature in the key points. Subsequently, a novel optimization strategy is proposed to achieve the cooperative optimization of the number of sensors and reconstruction accuracy. Finally, the simulated calculation and experimental tests are used to demonstrate the effectiveness and accuracy of the proposed approach. The results show that the proposed methodology can accurately reconstruct the plane temperature field of plate structure with one and two hot sources and the relative percentage error is within 2.6 %. Therefore, it can be utilized as an effective tool to real-time monitor structural surface temperature.

Efficient sound modeling for acoustic monitoring in complex temperature and wind fields

Active monitoring of atmospheric processes, combustion characteristics in burning furnaces, and various industrial applications relies on precise reconstruction of temperature and medium flow fields. This paper derives a novel trajectory equation for modeling sound wave trajectories within complex temperature and wind fields. Unlike traditional ray-equations widely used in dynamic media, the newly formulated trajectory equation is based on the propagation distance rather than the sound wave's time of flight. This alteration directly incorporates trajectory curvature, facilitating the development of efficient path tracing algorithms. In scenarios where wind speed is significantly lower than the Laplace sound speed and the wavefront's normal direction aligns closely with the sound wave's propagating direction, a simplified trajectory equation is derived. We develop a shooting method to numerically solve both the general and simplified trajectory equations. The fourth-order Runge-Kutta method (RK4) is employed, known for its high accuracy with a local fifth-order truncation error. A benchmark test, utilizing an analytically known path for the sound wave, confirms the shooting method's high precision. One notable strength of this approach lies in its computational efficiency. Tracing sound trajectories originating from a single source is achieved within seconds or tens of seconds on standard personal computers across various numerical examples. Furthermore, the shooting method effectively models sound trajectories in complex temperature and wind fields, such as those encountered in the near-surface atmosphere and high-temperature furnaces. Due to their efficiency and accuracy, the newly formulated trajectory equations and the accompanying shooting method are not only well suited for acoustic modeling, as demonstrated in this study, but are also poised for seamless integration into acoustic tomography systems in future studies.

A novel PoI temperature prediction method for heat source system based on graph convolutional networks

The ever-increasing functional density and complexity of the heat source system, the harsh chip cooling environment, as well as the cost reduction measures that require less sensor involvement are increasingly driving the need to develop new approaches for temperature monitoring and predicting. Most current researches investigate the temperature field reconstruction of the whole system. However, the reconstruction is not necessary or practical in some cases for computing consumption and unprocurable structure. The temperature prediction of the points of interest (PoIs) like the heat-sensitive area in the electronics is essentially important for function maintenance. Thus, a complete solution is proposed based on the graph convolutional networks (GCN) in this paper including the dimensional alignment, graph modeling and corresponding GCN construction. Moreover, various methods have been explored for edge modeling and node embedding obtained by Node2Vec has been integrated for better graph representation. After model training, the real-time temperature prediction of PoIs can be realized according to the corresponding temperature of monitoring points (MoPs) by the GCN. The results of experiments show that this method approach well prediction that the mean absolute error is less than 0.01K under the condition possessing diverse MoPs and PoIs. Moreover, the comparison experiments with the baseline methods further verify the validity of this GCN-based solution.