Imaging Of Temperature Fields Research Articles

Application Research of Infrared Technology in Safety Detection in Ship Field

Abstract. This paper studies infrared technology and its application in the field of ship detection, mainly studying infrared thermal imaging of failure equipment refueling oil spill and ship safety problems because infrared technology is a high precision contact thermal measurement method through infrared camera infrared objects, the infrared spectrum wavelength range of infrared radiation heat, on the receiver into electronic signals, through electronic attenuation and electronic scanning form the corresponding heat map or temperature field image, to intuitively reflect the temperature distribution and heat focus of the object. In addition, the infrared detection device can be reasonably installed in the ship without affecting the work of other equipment, and it has been found that infrared technology has significantly improved the solution to the safety problems of ships.

Optimization of Pipeline Leakage Detection System in Utility Tunnel Based on Finite Element Method

In addressing the problems of delayed detection, inefficient identification, and coverage blind spots in optical fiber‐based pipeline leakage detection within pipe galleries, this study proposes a leakage detection strategy utilizing distributed optical fiber temperature measurement technology. Finite element method is employed to analyze the temperature influence radius around the pipeline leakage hole and a model test is conducted as a validation. The results show that the temperature field image at the leakage site is elliptical, influenced by temperature differences between ambient and liquid. An increase in this temperature difference accelerates changes in the temperature field’s range. Adjustments to the optical fiber’s winding angle and pitch demonstrated that an optimal pitch is 1/24 of the pipeline’s length, with a 45° winding angle. This configuration maximizes the optical fiber’s distribution in detection while maintaining its cost‐effectiveness. When the leakage site is constant, and only the winding mode is altered, it is observed that when the ambient temperature exceeds the liquid temperature in the pipeline, the temperature of the escaping liquid impacts the temperature‐measuring fiber due to gravity, registering approximately 2°C higher than the temperature measured directly at the leakage site. The temperature anomaly from field diffusion is significantly less than that caused by the water flow from the leakage impacting the fiber due to gravity. Conversely, when the ambient temperature is lower than the pipeline’s liquid temperature, the opposite occurs. These research findings offer a novel approach for distributed detection in water supply and drainage pipeline leakage.

Investigation of SrB4O7:Tm2+ luminescence for temperature imaging with high sensitivity based on time-resolved luminescence.

In recent years, with the growing demand for non-contact and real-time optical temperature measurements, it has become imperative to develop new luminescent thermometry materials as well as novel temperature detection schemes with higher sensitivity. In this work, a series of Sr1-xB4O7:xTm2+ (x = 0.001-0.01) polycrystalline powder samples were prepared using a high temperature solid state reaction under ambient atmospheric conditions. The emission spectra and the luminescence decay curves of the red emission corresponding to the 4f125d → 4f13(2F7/2) transition of Tm2+ were recorded at intervals of 10 K from 280 K to 380 K. Both the emission intensity and the fluorescence lifetime exhibited remarkable temperature correlation in the studied temperature range, which reached the best relative temperature sensitivities of 3.55% K-1 and 3.86% K-1 at 363 K and 346 K, respectively. Furthermore, taking into account the highly sensitive variation of the fluorescence lifetime of Tm2+ in the SrB4O7 matrix as the temperature increased near room temperature, a time-resolved temperature measurement scheme was performed to realize real-time temperature field imaging. By utilizing the intensified charge coupled device (ICCD) camera with a time gate to acquire the integral intensity of distinct time intervals and calibrate the relationship between the ratio and the temperature, the experimental results demonstrated that this temperature measurement scheme can achieve a maximum relative sensitivity of 9.39% K-1 at 313 K, which significantly surpasses the relative sensitivity of the other two conventional temperature measurement schemes.

A Deep Learning-Based Surrogate Model for Complex Temperature Field Calculation With Various Thermal Parameters

AbstractSurrogate models of temperature field calculation based on deep learning have gained popularity in recent years because it does not need to establish complex mathematical models. However, the existing models cannot generate the temperature field for different boundary conditions or thermal parameters. In addition, it is also challenging to generate the details of the complex temperature field. In this paper, we propose the Parameters-to-Temperature Generative Adversarial Network (PTGAN) to generate temperature field images with high-quality details for different thermal parameters. The PTGAN model mainly includes the temperature field generation module and the thermal parameter encoding module. Additionally, we use a joint loss function to improve the quality of the generated temperature field image. The temperature field of the armored vehicle is calculated by the computational fluid dynamics method to obtain data set to verify the proposed PTGAN. The results show that the temperature images generated by the PTGAN has high accuracy, and the average relative error is only 0.205%. The attempt to integrate thermal parameters into the temperature field image generation is successful. The temperature field database can be generated quickly and accurately, which is of great significance for the further integration of deep learning and heat transfer.

Research on gearbox temperature field image fault diagnosis method based on transfer learning and deep belief network

This paper applies thermal imaging technology to gearbox fault diagnosis. The temperature field calculation model is established to obtain the temperature field images of various faults. A deep learning network model combining transfer learning of convolutional neural network with supervised training and unsupervised training of deep belief network is proposed. The model requires one-fifth of the training time of the convolutional neural network model. The data set used for training the deep learning network model is expanded by using the temperature field simulation image of the gearbox. The results show that the network model has over 97% accuracy for the diagnosis of simulation faults. The finite element model of gearbox can be modified with experimental data to obtain more accurate thermal images, and this method can be better used in practice.

Imaging and detection method for static interferometric high-temperature temperature field

Imaging and detection method for static interferometric high-temperature temperature field

Experimental research on temperature field construction of nuclear waste barrel based on RDTS with improved spatial resolution

In this paper, a 3D temperature field construction method based on Raman Distributed Temperature Sensor (RDTS) with improved spatial resolution is proposed to monitor the side surface temperature of Nuclear Waste Barrel (NWB). This method includes two aspects: the spatial resolution enhancement of RDTS and the signal dimension conversion. Firstly, the spatial resolution improvement method combining Fully-Connected Neural Network (FCNN) and Total Variation Deconvolution (TVD) is used to improve the spatial resolution of RDTS from 1.6 m to 0.4 m. Then, the one-dimensional temperature signal is converted into 2D temperature field by matrix laying method. Finally, by establishing the coordinate system of the NWB, the 2D temperature field can be converted into a 3D one. The experimental results of the simulation device showed that when the circular thermal area with diameter of 0.4 m, 0.6 m and 0.8 m appeared on the side surface of NWB, the temperature accuracy of the temperature field image constructed by this method can reach 80.91 %, 72.09 % and 85.97 % respectively, and the shape was accurate. The method proposed in this paper can accurately present the surface temperature field of NWB and provide technical support for the safe operation of the nuclear waste disposal repository during its life cycle.

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ∼ 0.1 to ∼ 0.001 without sacrificing the true positive rate (∼0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

A deep learning method for estimating thermal boundary condition parameters in transient inverse heat transfer problem

Estimating thermal boundary condition parameters in the transient inverse heat transfer problem (IHTP) is characterized by instability and non-uniqueness of the solution. This study formulated a simple method based on deep learning, which can realize multi-dimensional real-time prediction of thermal boundary condition parameters. The proposed model combining convolutional neural network (CNN) and long short-term memory networks (LSTM) allowed estimating multiple time-varying parameters based on the time-varying temperature field image of the target. The data-driven model used the computational fluid dynamics (CFD) method to obtain the numerical data, and the influence weight of the parameters was introduced in the training process to improve the generalization ability of the model. An experiment on the cubic cavity was made to verify the reliability of the proposed model to estimate time-varying parameters. The studies we have performed showed that the proposed hybrid models outperformed the standalone models (CNN, LSTM) in estimating multiple time-varying parameters. In the experimental results, the relative errors of air temperature and humidity were only 2.33% and 4.33%, respectively. These attempts of introducing the deep learning method to the IHTP in the present study were successful and it was significant for the study of the transient inverse heat transfer problem.

Imaging of liquid temperature distribution during microwave heating via thermochromic metal organic frameworks

Microwave has been widely used as an efficient and clean method for energy supply in chemical engineering, due to its unique heating characteristics. The industrialization of microwave is obstructed by the problem of uneven heating; the inhomogeneous temperature distribution results in inaccurate prediction of reaction kinetics, uncertain product yields, and unstable energy utilization efficiencies. With the aim to overcome this problem, we use a non-contact nano-thermometer to achieve in situ imaging of liquid temperature distribution during microwave heating. A zinc-based metal organic framework material (ZMOF) is employed as the temperature indicator in microwave field, due to its microwave transparency, thermal stability and good dispersibility in solvents. A case study was conducted by dispersing the ZMOF particles in a 95% ethanol solution, where the color of ZMOF changes from yellow to orange with the increase of temperature in the range of 30–120 °C with the sensitivity of 1.512%/°C. By linearly correlating the hue value of ZMOF suspension with the temperature, the color images of liquid heated in microwave cavity were transformed into temperature distribution maps. ZMOF also has shown the advantages of good dispersion, cyclic temperature measurement, continuous linear discoloration, and visible light excitation. This facile route of temperature field imaging can provide experimental validation for numerical simulation and guide the equipment optimization in microwave-assisted chemical engineering.

Monitoring of back bead penetration based on temperature sensing and deep learning

The monitoring of back bead penetration has always been an important topic in welding field, the vision inspection and arc sound recognition are main methods to monitor welding penetration state. In this paper, an innovative method based on temperature sensing and deep learning is proposed to monitor weld bead penetration. Firstly, it is verified that the distribution of welding temperature field is separable under various welding penetration states. Secondly, a region of interest (ROI) in the welding heat affected zone near molten pool is identified for temperature field detection. Taking the temperature field image of ROI as the input, the prediction model for weld bead penetration is built on the basis of deep residual network. Finally, the generalization performance of the proposed method is verified. The experimental results indicate that detection precision of the model for weld bead penetration is higher than 99%, and the generalization performance is strong.

(Invited) Millisecond Annealing by Atmospheric Pressure Thermal Plasma Jet and Direct Imaging of Temperature Distribution Using Optical Interference Contactless Thermometry (OICT)

A non-contact temperature measurement technique on the basis of optical interference has been developed [1,2]. In Optical Interference Contactless Thermometry (OICT), the transient reflectivity of a substrate during rapid annealing is measured and reproduced by thermal simulation and optical analysis, whereupon the temperature profile is obtained from the simulation results. This method allows us to obtain a temperature distribution inside the wafer because it has depth resolution, unlike other interferometric methods. The time resolution of OICT is dependent on the photodetector, therefore, a millisecond detection of temperature variation is possible. By introducing a proper probe laser, this method can be applied to various substrates such as silicon, silicon carbide, glass, and so on [3-5]. OICT is useful to monitor transient temperature variation during rapid thermal processing. This helps us to correlate the annealing history to the characteristics of annealed samples. For instance, we have demonstrated the direct measurement of crystallization temperature of amorphous silicon, the role of heating and cooling speed on the yield of impurity activation in shallow junction, and the densification of gate SiO2 films and its effect on reliability of transistors under operation [6-9]. In this study, OICT has been extended to temperature field imaging. The principle is the same, and a high-speed camera replaced the photodetector to capture the 2-dimensional interference contour images. The images are analyzed to transfer them to temperature distributions. In the experiment, semiconductor wafers were annealed by atmospheric-pressure thermal plasma jet (TPJ) and the optical measurement was performed from the backside of the wafers. Probe lasers with the wavelengths of 1310nm for silicon wafers, and 633nm for silicon carbide wafers, were used. The laser lights went through a beam expander and irradiated the samples via a beam splitter and the reflected lights were introduce to high-speed cameras. Clear contour movies were obtained, where the contour lines correspond to isothermal lines. This temperature imaging method enables fast analysis of temperature, which can be used for the feedback to the annealing conditions as the real-time monitoring technique.[1] T. Okada, Jpn. J. Appl. Phys., 45 (2006) 4355.[2] T. Okada, Thin Solid Films, 515 (2007) 4897.[3] H. Furukawa, ECS Trans., 13 (1) (2008) 31.[4] K. Maruyama, Jpn. J. Appl. Phys., 54 (2015) 06GC01-1.[5] A. Kameda, J. Appl. Phys., 127 (2020) 203302-1.[6] Y. Mizukawa, Appl. Phys. Express, 13 (2020) 015507-1.[7] K. Matsumoto, Jpn. J. Appl. Phys., 49 (2010) 04DA02-1.[8] K. Matsumoto, Jpn. J. Appl. Phys., 50 (2011) 04DA07-1.[9] S. Higashi, Jpn. J. Appl. Phys., 50 (2011) 03CB10-1.

Measurement of Temperature Distribution Within Steel Box Girder of Vistula River Bridge in Central Europe

Uneven temperature distribution in a structural element constitutes one of its load factors. Temperature fields occurring in structural elements lead to stresses and strains. The values of internal stresses are directly related to temperature distribution and degree of freedom for element deformation. The best way to get information about temperature distribution in an element is to take measurements on a real object. Such measurements have been run or are still taken over decades in various parts of the world, e.g. in Western Europe, USA, China, South America. In a number of cases, such examinations were carried out for objects constructed in warm or hot climate. It is a lot harder to find the results of measurements made in the countries with moderate and transitional climate, like in the Central or East-Central Europe. This paper presents measurement methodology and results gained for a large steel bridge located in the East European Plain, about 52.5° N northern latitude. Permanently installed contact sensors, temporary sensors, as well as pyrometry and thermography were used. An attempt was made to determine temperature distribution in a steel box girder of a bridge using thermovision technique. Approximate temperature distribution patterns in the main span girder were determined from thermal photographs taken. The most important results of examinations are the images of temperature fields in the main span girder related to solar radiation that first and foremost directly affects the bridge deck.

The thermal model of winter aggregation of bees

For successful competition of the Russian beekeepers in the world honey market it is necessary to concentrate their attention on production of separate grades of this product. The most expensive honey is produced in spring and therefore it is necessary that the bees were successful in winter. The use of electric heating of bee hives does not always have a positive effect on the safety of bees in winter. In this regard, it is necessary to study more carefully the complex biological formation - winter bee hives. There were the basic equations for determining the physical parameters of the bee family in winter, including the energy characteristic. The established equations were included in the mathematical models describing the parameters of the microclimate. Mathematical processing of the obtained thermophysical model was carried out in the Comsol software product, which confirmed the validity of the use of the main functional dependencies and allowed to see the critical zones of the hive in temperature and humidity. Despite the large temperature changes outside the hive, it is maintained a fairly stable positive temperature at the level of +25°C to + 32°C inside. Such temperatures are not dangerous for bees. The images of temperature fields obtained in the course of mathematical processing confirm the assumptions that the lowest temperatures are in the lower part of the hive, and the higher ones are in the upper part. The results of the simulation will be used to determine the optimal power supply to electric heaters and to the place of their installation.

Study on Mechanism of Heating Temperature Field of Aluminum Foil Seal

The theoretical basis for non-contact sealing detection is lacking for the sealing performance of aluminum foil sealing. First, simulation and analysis of heat transfer characteristics of aluminum foil seals in heating temperature field using ANSYS software. The temperature field image sealed by aluminum foil is a closed and uniform annular region with high temperature. In the temperature curve, the temperature is concentrated at 70~80 celsius. It has symmetry characteristics, and there are 2 high temperature peaks; the other 5 are unlooped or uneven thermal image aluminum foil seal failed. A comparative experiment was then carried out to collect infrared thermal images of different types of aluminum foil seals under electromagnetic induction heating. Experiments show that the surface temperature field distribution calculated by the three-dimensional finite element simulation is consistent with the experimental temperature-field distribution. According to the distribution characteristics of the thermal temperature field, it can be evaluated the state of the sealability of the aluminum foil seal, and it can provide theoretical support for further optimizing the automation degree of the non-contact sealability test.

Two-dimensional temperature field imaging in laminar sooting flames using a two-line Kr PLIF approach

A two-line Kr PLIF approach is presented for thermometry in moderately sooting flames. This technique leverages the spectral line-broadening phenomenon to choose the two excitation wavelengths whose Kr PLIF signal ratio effectively cancels out the composition dependence while retaining the temperature dependence. Furthermore, the Kr PLIF ratio for the chosen wavelengths also exhibits a monotonic trend with temperature, and span a wide range of values to ensure adequate dynamic range on the measurements. The technique is evaluated in the near field of an ethylene laminar jet flame where the peak soot loading was about $$0.15~\mathrm{ppm}$$ . Krypton gas was added in small amounts to both fuel mixture and air co-flow. Comparing the Kr PLIF fields with the LII fields showed that the main source of interference to Kr PLIF signal is from the soot interference, which contributed to a maximum of $$20-50\%$$ of the total signal at different axial locations. Interestingly, the interference from PAH molecules was observed to be less than $$1\%$$ of the total signal. The soot interference was retained during data processing to obtain an evaluation of the measurement uncertainty caused by the soot interference and the maximum soot loading that could be tolerated. The temperature in the regions away from soot layers exhibit very consistent values with literature, where the value extended from close to $$300~\mathrm{K}$$ in the fuel core and air co-flow through about $$2200~\mathrm{K}$$ in the reaction zone. The presence of soot, however, caused a noticeable depreciation in the measured temperature by about $$200~\mathrm{K}$$ at the peak sooting location. It is further noted that the mean systematic error of $$50~\mathrm{K}$$ is expected at $$f_v = 60~\mathrm{ppb}$$ . This limit is observed to be a strong function of the fractional contribution of the soot interference to the overall signal and can be substantially extended by subtracting the soot interference and using higher excitation energies.

Simultaneous imaging of temperature field and flow field around a micro-heating sphere by applying the near infrared temperature imaging method

Simultaneous imaging of temperature field and flow field around a micro-heating sphere by applying the near infrared temperature imaging method

Adaptive optimization on ultrasonic transmission tomography-based temperature image for biomedical treatment**Project supported by the DoD/BCRP Idea Award BC095397P1, the National Natural Science Foundation of China (Grant No. 61201425), and the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.

Hyperthermia has proven to be beneficial to treating superficial malignancies, particularly chest wall recurrences of breast cancer. During hyperthermia, monitoring the time–temperature profiles in the target and surrounding areas is of great significance for the effect of therapy. An ultrasound-based temperature imaging method has advantages over other approaches. When the temperature around the tumor is calculated by using the propagation speed of ultrasound, there always exist overshoot artifacts along the boundary between different tissues. In this paper, we present a new method combined with empirical mode decomposition (EDM), similarity constraint, and continuity constraint to optimize the temperature images. Simulation and phantom experiment results compared with those from our previously proposed method prove that the EMD-based method can build a better temperature field image, which can adaptively yield better temperature images with less computation for assistant medical treatment control.

The Research of Temperature Fields in the Proximity of a Bundle of Heated Pipes Arranged Above Each Other

The paper deals with the research of temperature fields in the proximity of heated pipes arranged above each other in a natural air convection. The holographic interferometry method was used for the visualization of temperature fields. The experiments were made with pipes, diameter of 20 mm, length 200 mm, spacing two-dimensional (2D) at surface temperatures of 40 °C, 50 °C, and 60 °C, with the vertical arrangement of the pipes as well as with the horizontal shift of their centers by 1/4D and 1/2D (on a surface temperature of 50 °C). Temperature profiles were determined from the experimentally obtained images of temperature fields, and local parameters of heat transfer were calculated. Under the same marginal and geometric conditions, computational fluid dynamics (CFD) simulations of temperature fields were performed as well, while the results (temperature fields, local and mean parameters of heat transfer) were also calculated for various distances between the pipe centers (1D, 2D, and 3D). From the obtained experimental results and CFD simulation results, it is possible to observe the impact of the arrangement and spacing of pipes on heat transfer parameters. The achieved results imply the change in the spacing of the pipes has a greater impact on heat transfer parameters in the bundle of heated pipes located above each other than a moderate horizontal shift of their centers.

Possibilities of Use of the Thermographic Measurement as a Tool for Detecting Defects and Improving the Production Process

Infrared radiometric long-waved systems are widely used in various industries as well as in research and development. This long-waved system is the perfect tool for quick diagnosis of the state of the equipment, easy defect detection, which is reflected by higher temperatures due to increased friction and wear. Infrared radiometric long-waved systems are used especially in civil engineering, electrical engineering, metallurgy and many other industries. They are also used to optimize and improve of the production processes. The series of measurements using the infrared radiometric long-waved system in steel plant were carried out due to prepared research project. Images of temperature fields of ladles with liquid steel, cooled exhaust knee of electric arc furnace and ingot mould were obtained during solidification of steel ingot. Information about the state of wear of the refractory lining of the ladle, exhaust knee can be gained from obtained images. The obtained results can be used for more accurate numerical simulations of the process of casting and solidification of steel ingots.