Infrared Temperature Measurement Research Articles

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Optimized Machine Learning Models for Predicting Core Body Temperature in Dairy Cows: Enhancing Accuracy and Interpretability for Practical Livestock Management.

Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R2) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO-XGBoost model surpasses others in predictive accuracy with an R2 value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s-approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.

Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

The precise non-contact temperature measurement method is essential for studying gas turbine component heat transfer. While infrared thermal imaging provides high-resolution two-dimensional temperature fields, errors arise from the measurement environment's complexity, emissivity variations, and reflection radiation effects. This study utilizes BP neural networks to correct infrared radiation on gas film cooled plates. By modeling background radiation and analyzing infrared emission processes, factors influencing temperature distribution are identified. Experimental data from uncooled plate are used for network training. Experimental measurements were conducted on uncooled plates to obtain training data. A BP neural network was established based on Planck's law and the radiation transmission process to relate the mainstream temperature to the background radiation. Calibration analysis was performed based on experimental data from the cooled plate, achieving the restoration of the two-dimensional temperature field under multiple operating conditions and significantly reducing measurement errors. This method comprehensively considers the entire process of radiation transmission, thereby eliminating background radiation embedded in the infrared images. The obtained measurement results vividly depict the real temperature distribution, offered significant support for gas turbine engineering applications.

A temperature measurement compensation method for industrial rotary kilns based on infrared multi-feature fusion under dynamic water mist interference

Accurate temperature measurement is vital for assessing the reaction atmosphere of industrial rotary kilns. However, dynamic water mist interference imposes significant challenges to accurate infrared temperature measurement. To this end, this study proposes a temperature measurement compensation method based on infrared multi-feature fusion. First, we developed an industrial system to collect infrared data from the kiln. To address the issue of unclear water mist in infrared images caused by its low resolution and weak texture features, we designed an artificial feature based on infrared images and temperature differences named water mist level (WML). This feature effectively represents the size of the water mist in the image. Additionally, a classification model is established to identify the WML automatically. Subsequently, a novel multi-scale feature fusion network called efficient regression feature pyramid network (ERFPN) is proposed to acquire multi-scale image features. Finally, we propose a grouped feature fusion network (GFFN) to fuse multi-scale image features, WML, and interfered temperature. Experimental results show that the proposed compensation method achieves satisfactory infrared temperature measurement results and significantly reduces the temperature measurement error caused by water mist.

A method for processing multispectral radiometric thermometry data based on BP-Alpha constraints

Multispectral radiometric thermometry is a commonly used method for infrared temperature measurement. The biggest challenge in processing multispectral data is the unknown emissivity. The traditional method of modeling emissivity assumptions is inadequate in dealing with emissivity instability, particularly when there is a rapid change in emissivity due to varying degrees of oxidation during metal heating. To address the temperature measurement challenge, this paper converts the multispectral temperature measurement problem into a constrained optimization problem. This is achieved by constructing a new type of constraints, using a BP neural network to establish the emissivity range constraints, and constructing an Alpha spectral model to establish the emissivity shape constraints. The improved Lichtenberg algorithm is then used to solve the problem, resulting in significantly improved temperature inversion accuracy. The method’s effectiveness was verified through simulations and GH3044 alloy experiments. The temperature inversion’s maximum absolute and relative errors were 9.5 K and 0.92%, respectively.

Noncontact infrared thermometer measurements offer a reasonable alternative to rectal temperature measurement in afebrile horses.

To assess the repeatability of infrared thermometer temperature readings and evaluate the correlation between digital rectal temperature and infrared thermometer temperatures taken at different locations in healthy afebrile horses. 101 afebrile horses ≥ 1 year old. Digital rectal temperatures and infrared temperatures from the eye, gingiva, neck, axilla, and perineum were obtained in a climate-controlled environment and at 2 outdoor ambient temperatures (study period, November 1, 2021, to April 30, 2023). Infrared temperature measurements were well tolerated by horses, including those resistant to rectal temperature. There was significant correlation between rectal temperature and infrared temperature taken at the perineum (R = 0.57; P < .001) and eye (R = 0.37; P < .001). Infrared temperature measurements were highly repeatable, allowing for calculation of reference ranges for the perineum (36.0 to 37.8 °C) and eye (35.7 to 37.1 °C) in climate-controlled conditions. There was increased variance in outside temperatures compared to climate-controlled conditions for the eye (P = .002), gingiva (P = .047), and perineum (P = .005). While infrared thermometer temperatures were not numerically the same as rectal temperature using a digital thermometer, measurements at the perineum and eye were correlated with rectal temperature readings. Further, the repeatability of infrared readings allows for computation of reference ranges that make the infrared thermometer a viable alternative for the practicing veterinarian when obtaining a temperature in uncooperative horses. The infrared thermometer was reliable outdoors for the eye, but not the perineum. Additional validation of infrared temperature reference ranges in febrile horses and warmer ambient temperatures is warranted.

Impact-induced energy release characteristics of Ti-Zr-Hf-Ta high-entropy alloys by using a temperature-pressure synchronous measurement method

Energetic structural materials (ESMs) are a new type of materials with structural strength and reactivity, and their impact-induced energy release characteristics are greatly influenced by the fragmentation behavior. In this study, the dynamic fragmentation behavior and energy release characteristics of Ti-Zr-Hf-Ta high-entropy alloys (HEAs) with different precipitate content were investigated based on split Hopkinson pressure bars (SHPB) tests. To quantitatively evaluate the reactivity of ESMs, a temperature-pressure synchronous measurement method combined with a modified calculation model for quasi-sealed chamber was proposed. The results showed that the precipitates were distributed at the grain boundaries. As the precipitate content increased, the quasi-static yield strengths increased from 1004 to 1132 MPa, the dynamic yield strengths increased from 1672 to 1874 MPa, while the fracture strains decreased from 0.47 to 0.29, and correspondingly, the released energy increased from 14±2–511±154 J/g. The results demonstrated that the impact-induced energy release characteristics of the materials are closely related to the fracture strain. It can be confirmed that the alloys with lower plasticity are more likely to fracture under impact conditions, and the combustion reaction will be more violent, which is well consistent with the measurement of the energy-release amounts. High-speed infrared temperature measurement and microstructural photography also provided the underlying mechanism: the fragment size of low plasticity materials is smaller and the local hot spots are more dispersed, both of which are conducive to the combustion reaction. By using this method, the relationship among mechanical properties, fragmentation behavior, and energy release characteristics can be established, and the results will provide new insights into the development of ESMs with superior energy release under impact loading and applicable mechanical properties for potential applications.

Smart Health Monitoring with Power-Aware IoT Using a Wireless Body Area Network

Smart health monitoring systems have become increasingly popular in recent years due to the growth of the Internet of Things (IoT) technology and its applications in healthcare. This project proposes an IoT-based smart health monitoring system that utilises an ESP32 Microcontroller, MAX30100 Pulse Oximeter Heart Rate Sensor Module, MLX90614 ESF Non-Contact Human Body Infrared Temperature Measurement Module, LCD 16*2, 3.7v 2000mAh Lithium Battery, TP4056 1A Li-Ion Lithium Battery charging Module, and some buttons. The system is designed to monitor the user's heart rate, blood oxygen level, and body temperature in real-time and display the readings on an LCD screen as well as an IoT web page. The project aims to provide an affordable, portable, and user-friendly smart health monitoring solution that can be used at home, in hospitals, or other healthcare settings. The project methodology involved a combination of hardware and software components, including circuit design and programming using the Arduino IDE. The system was tested and evaluated for its performance, accuracy, and usability using a sample population. The results showed that the system was effective in monitoring the user's vital signs and displaying the readings in real-time. The system was found to be accurate and reliable, with a high level of user satisfaction and ease of use. Overall, the IoT-based smart health monitoring system proposed in this project has significant implications for healthcare, particularly in terms of remote monitoring, telemedicine, and personalised healthcare. The system has the potential to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services. The project also highlights the potential of IoT technology in healthcare and the need for further research and development in this area

High-temperature transient-induced thermomechanical damage of fiber-reinforced ceramic-matrix composites in supersonic wind tunnel

This article is based on the supersonic directly connected wind tunnel. Through a specially designed experimental chamber, combined with infrared temperature measurement, high-speed camera, etc., in-situ monitoring of composite materials under airflow at Ma 3.0 with a total temperature of 950 ∼ 1473K was carried out. The dimensional analysis method was used to propose dimensionless parameters to characterize the thermal coupling caused by high-speed airflow thermal shock. Research has shown that the thermal coupling effect of supersonic airflow causes uneven temperature inside the material, and the thermal stress caused by temperature gradient changes (including increasing and decreasing processes) is the main reason for material damage. The damage of ceramic matrix composites under thermal shock mainly manifests as a decrease in surface roughness, surface fiber fracture and a decrease in elastic modulus. In addition, the study also found that there are damage thresholds for the thermal shock effect of airflow at different total temperatures, which helps to further understand the thermomechanical damage mechanism and degradation law of composite structure under high-temperature transient conditions.

Design of CuS composite carbon-based Ni Al-LDH multifunctional phase change composite with electromagnetic shielding performance and heat storage capacity

CuS-based composites are widely used in the fields related to photothermal energy storage and electromagnetic shielding and are excellent candidates for composites with phase change materials. To meet the requirements of micro-integration and electromagnetic resistance of electronic products and optimize the thermal management of electronic products, it is necessary to prepare new flexible composite phase change materials. In this paper, through the design of MPC@Ni Al-LDH/CuS double-layer structure, two-step vacuum filtration method is used, and the highly conductive materials CuS and MPC are introduced into the manufacture of PCM films. With the help of Ni Al-LDH, flame retardancy is introduced. The electromagnetic wave absorption performance is enhanced, and the existence of nano-cellulose (CNF) makes the film have excellent mechanical properties. Ni Al-LDH/MPC and CuS substrates with stable and high mechanical properties are prepared by ultrasonic treatment. According to scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform-infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), differential scanning calorimetry (DSC) and thermogravimetry (TG) characterizations, PEG successfully entered the MPC/CuS skeleton by vacuum filtration, and flexible phase change composite bilayers (MLPC) were obtained. Due to the synergistic effect of structure and components, the obtained composite film has many different excellent functions. Specifically, when MPC@Ni Al-LDH is 15 wt%, the phase transition enthalpy, thermal conductivity, electromagnetic shielding, and electrical conductivity are 146.8 J/g, 1.07 W/m·K, 38.8 dB and 126.4 S/m, respectively. The 3600 s infrared temperature measurement results show that the sample has stable thermal properties. Due to the presence of CuS, when MPC@Ni Al-LDH is 5 wt%, its absorbance can be kept at 1.51 L/(g·cm), and its photothermal conversion efficiency can reach 90.6 %. The MPC/CuS structure in the composite film provides many heat transfer channels, which reduces the phonon scattering in the heat transfer process and significantly increases thermal conductivity. Therefore, this multifunctional flexible phase change film can be used in photothermal conversion devices and for thermal management of electronic equipment, and it has broad application prospects in wearable equipment design due to its excellent electromagnetic shielding ability and infrared thermal stealth ability.

A face recognition and temperature compensation system based on Raspberry Pi

Abstract In the post-epidemic era, for quickly and accurately screening people with abnormal body temperature in small crowds and providing efficient methods for epidemic control, a multi-functional identification thermometer was designed and produced. The device uses the Raspberry PI GIS camera to accurately locate the face and identify the identity information of the measured person. The device uses the AMG8833 infrared array thermal sensor for infrared temperature measurement and the HC-SR04 ultrasonic ranging module for temperature compensation. Through multiple temperature compensation experiments, the measurement error is maintained between ±0.2°C. Based on the mathematical model of temperature compensation, the device can match the identity of people with abnormal body temperature through the database. The research results show that the device is of great significance for improving the efficiency of epidemic prevention and management cost control.

Extension mechanism of fracturing in liquid nitrogen fractured coal rock dominated by phase change expansion pressure

In this paper, the liquid nitrogen (LN2) fracturing test research using LN2 phase expansion instead of N2 pressurization was carried out in response to the neglected high phase expansion rate characteristic of LN2, an anhydrous fracturing medium. Expansion pressure measurements, infrared temperature measurements, DIC full-field strain measurements and acoustic emission (AE) signal measurements were used as measurement means. The phase change gas expansion pressure, specimen end-face field, internal fracture signal and end-face temperature changes during the fracturing process were explored. The experimental results show that there was a significant difference in the performance of LN2 cold shock damaged specimens and undamaged specimens during fracturing. The change rule of LN2 phase change expansion pressure under no peripheral pressure went through linearly increasing first and then stabilizing, the slope of the linear phase was 0.029 MPa/s, the expansion pressure in the stabilizing phase was 1.2 MPa and lasted for 12s, and then the specimen ruptured. The initiation pressure of the gas fractured specimen was 2.2 MPa. The surface strain of the specimen was significantly increased during the stabilization stage of expansion pressure, and the maximum values of transverse strain and longitudinal strain were 0.059% and 0.071%, respectively, which were increased by 43.9% and 73.2% within 12 s. At the same time, the number of AE localization points increased from 12 to 78, and the location of the localization points gradually expanded from the two sides of the horizontal slot to the top of the horizontal slot and the outer wall of the specimen. There was a low-temperature zone with obvious temperature gradient in the extended area and no such change existed in the gas fractured specimens. Therefore, the fracture initiation pressure of the damaged specimen is lower than that of the undamaged specimen, and the cracks are more likely to expand and become continuous during the fracturing process, thus producing complex cracks. The use of LN2 with high phase change expansion rate can replace the high-pressure pumping of N2, which is of great significance for the reduction of the implementation time and cost of the LN2 fracturing process.

Temperature field calculation of rail flash welding

The forging stage of rail flash welding has a decisive influence on joint strength, and the study of the temperature distribution in the process has an important role in further improving joint strength. In this paper, three calculation methods for the temperature field are given. First, the finite element model of the temperature field before forging rail flash welding is established by using the transient heat module of Ansys software and verified by infrared temperature measurement. Second, the temperature distribution of different parts of the rail before flash welding is obtained by using infrared thermal imaging equipment. Third, Matlab software is used to calculate the temperature of the non-measured part. Finally, the temperature distribution function along the rail axis is fitted through the temperature measurement data. The temperature distribution before the top forging of the rail flash welding can be used to analyze the joint and heat-affected zone organization and properties effectively and to guide the parameter setting and industrial production.

Effects of nano-metal oxide additives on ignition and combustion properties of MICs-boron rich fuels

Boron has been considered a promising powdered metal fuel for enhancing composite propellants' energy output due to its high energy density. However, the high ignition temperature and low combustion efficiency limit the application of boron powder due to the high boiling point of the boron oxide layer. Much research is ongoing to overcome these shortcomings, and one potential approach is to introduce a small quantity of metal oxide additives to promote the reaction of boron. This study prepared boron-rich fuels with 10 wt% of eight nano-metal oxide additives by mechanical ball milling. The effect of metal oxides on the thermo-oxidation, ignition, and combustion properties of boron powder was comprehensively studied by the thermogravimetric analysis (TG), the electrically heated filament setup (T-jump), and the laser-induced combustion experiments. TG experiments at 5 K/min found that Bi2O3, MoO3, TiO2, Fe2O3, and CuO can promote thermo-oxidation of boron. Compared to pure boron, Tonset can be reduced from 569 °C to a minimum of 449 °C (B/Bi2O3). Infrared temperature measurement in T-jump tests showed that when heated by an electric heating wire at rates from 1000 K/s to 25000 K/s, the ignition temperatures of B/Bi2O3 are the lowest, even lower than the melting point of boron oxide. Ignition images and SEM for the products further showed that the high heating rate is beneficial to the rapid reaction of boron powder in the single-particle combustion state. Fuels (B/Bi2O3, B/MoO3, and B/CuO) were mixed with the oxidant AP and ignited by laser to study the combustion performance. The results showed that B/CuO/AP has the largest flame area, the highest BO2 characteristic spectral intensity, and the largest burn rate for powder lines. To combine the advantages of CuO and Bi2O3, binary metal oxide (CBO, mass ratio of 3:1) was prepared and the test results showed that CBO can very well improve both ignition and combustion properties of boron. Especially B/CBO/AP has the highest burn rate compared with all fuels containing other additives. It was found that multi-component metal-oxide additive can more synergistically improve the reaction characteristics of boron powder than unary additive. These findings contribute to the development of boron-rich fuels and their application in propellants.

GA-BP Optimization Using Hybrid Machine Learning Algorithm for Thermopile Temperature Compensation

Thermoelectric pile, which uses non-contact infrared temperature measurement principle, is widely used in various precision temperature measuring instruments. This paper analyzes environmental temperature's influence on thermoelectric piles' measurement accuracy and proposes a environment temperature compensation based on GA-BP (Genetic Algorithm-Back Propagation) neural network. The GA algorithm makes up for the slow iterative speed and easy to fall into local optimization of BP algorithm. The experimental simulation results show that environment temperature compensation based on GA-BP can accurately correct the measurement error caused by environmental temperature and other factors.

Development of new pocket distribution network inspection equipment

This paper aims to explore a new intelligent inspection mode of pocket distribution network equipment, which realizes rapid and comprehensive inspection and inspection by using terminal +N intelligent sensors and partial discharge detection and infrared temperature measurement technology as the core. First, this paper introduces the background and motivation of the new mode, and emphasizes the limitations of the existing traditional inspection methods and the problems that need to be improved. Then, the design and manufacture of pocket devices are described in detail, including key aspects such as sensor integration, portability and user-friendliness. Finally, the paper summarizes the research results and emphasizes the potential benefits and future research directions of this new intelligent inspection model.

Non-destructive investigation of thermophysical properties on the China's space station: In-orbit experiment measurements and analysis

This paper discusses the current state of non-destructive research on the density, surface tension and viscosity of molten samples using the electrostatic levitation furnace (ELF) aboard the China Space Station (CSS). To address the challenges posed by the microgravity environment during the non-destructive testing process of containerless technology, we have developed several solutions. These include a temperature correction for infrared temperature measurement, a dual-camera position correction technique for density testing, a double electrode synchronous excitation, semi-frequency excitation and fine-sweeping approach for surface tension testing, and a signal demodulation method for viscosity testing. The uncertainties of the three thermophysical properties were 0.51 %, 3.10 %, and 9.78 %, respectively. This study highlights the feasibility and accuracy of measurements conducted in the ELF on the CSS, offering valuable insights into the behavior of molten materials under microgravity conditions. The findings from this study significantly contribute to our understanding of the thermophysical properties of molten materials.

Ignition mechanism of near α high temperature titanium alloy

The risk of titanium fire increases significantly with the development of future aero-engine, however, the burning mechanisms of titanium alloys remain uncertain. Therefore, the ignition behavior and mechanism of near α high-temperature titanium alloy are studied in this work by an integrated experiment method, including laser-oxygen concentration ignition method, infrared temperature measurement and observation of molten metal by high-speed camera. Based on this, the ignition boundary curve is determined and the ignition temperature of the alloy is found to decrease from 1595 to 1527 ℃ with the laser power increasing from 200 to 325 W and oxygen concentration increasing from 21% to 60%. The ignition microstructure is characterized by FIB and TEM to study the evolution of reaction products. Pores are found to form beneath the TiO&lt;sub&gt;2&lt;/sub&gt; surface layer, which can be attributed to the instablity of TiO. The failure mechanism of protective oxide layer is further analyzed according to the thermal stress caused oxide layer damage model. When the temperature approaches the ignition temperature, which is below the melting point, the high vapor pressure of TiO leads to the formation of porous defects beneath the TiO&lt;sub&gt;2&lt;/sub&gt; surface, thus accelerating the fracture and failure of the TiO&lt;sub&gt;2&lt;/sub&gt; layer under thermal stress. It is revealed that critical conditions of temperature and instantaneous temperature change rate are needed to realize ignition. Based on this, an ignition model is further constructed to discuss the relationship among ignition temperature, laser power and oxgyen concentration. According to the experimental data fitting, the reaction activation energy of TA19 alloy during the ignition stage is calculated to be about 280 kJ/mol, and the function for calculating ignition temperature is given as follows: &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ 1.2 \times {10^{10}}{{\mathrm{e}}^{\frac{{ - 280000}}{{R{T_{{\text{ig}}}}}}}}{c^{\frac{1}{2}}} + $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="8-20240003_M2.jpg"/&gt;&lt;graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="8-20240003_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;&lt;inline-formula&gt;&lt;tex-math id="M2-1"&gt;\begin{document}$ 0.52{P_{\mathrm{L}}} - 315 = 0 $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="8-20240003_M2-1.jpg"/&gt;&lt;graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="8-20240003_M2-1.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;. This provides a theoretical reference for predicting the ignition temperatures of near α high temperature titanium alloy and other types of titanium alloys under complex airflow conditions in aircraft engines.

Infrared measurements of fluid temperature in a polymeric Pulsating Heat Pipe

Pulsating heat pipes are two-phase passive heat transfer devices partially filled with a working fluid in saturation conditions. During operation, supplying heat to one end of the system (named evaporator) results in a local increase in temperature and pressure, which drives the fluid through a transport section (named adiabatic section) towards the cooled, opposite end (named condenser) for effective heat dissipation. The local thermo-fluid dynamic state of the working fluid is sometimes assessed by means of non-intrusive techniques, such as infrared thermography. In this case, the radiative properties of the systems in the infrared spectrum must be known a priori. Nevertheless, since pulsating heat pipes may be manufactured with different materials, wall thicknesses and channel geometries, the radiative properties of the walls and the confined flow are not always known or assessable by means of the available literature. Hence, the work proposes to design a straightforward calibration procedure for quantitative infrared fluid temperature measurements in a polymeric pulsating heat pipe charged with FC-72 and having unknown radiative properties. The emissivity and transmissivity of the walls and confined fluid are estimated with good accuracy. The results will allow repeatable and reliable fluid temperature measurements in future experimentations on the mentioned device.