Thermal Machine Research Articles

Application of Metal-based, Polymer-based and Ceramic-Based Composites in Thermal Engine

Abstract. As a matter of fact, thermal engines have been widely implemented in various fields especially in recent years. In reality, the operation of the thermal engine produces numerous uncontrollable factors, which have a negative impact on the thermal engine machinery itself and negatively feedback the output effect of the thermal engine. Among various factors, the composites of coatings have a huge impact on the performances for the thermal engines. With this in mind, by analyzing various types of coatings in recent years and understanding the application of some coatings in other fields, this study will compare 3 typical types coting material, i.e., metal-based, polymer-based as well as ceramic-based composites. According to the analysis, Lenovo conceives whether it has suitable performance and function on the thermal machine. Introduce the feasibility of composite materials, and comprehensively analyze the specificity of various coatings. On the basis of effective implementation technology, the composite is envisaged.

A quasi-in-situ EBSD study on abnormal grain growth in 2219 aluminum alloy friction stir welded joints during post-weld heat treatment

Abnormal grain growth occurs in the friction stir welds of 2219 aluminum alloy during the solution treatment. To further investigate abnormal grain growth, the quasi-in-situ electron backscatter diffraction (EBSD) experiment, including the EBSD testing and heating by thermal simulation testing machine, was proposed to observe the grain growth process in the weld. The results show that the abnormal grain growth behavior in the stir zone and the thermo-mechanically affected zone is different, and different microstructures at different positions in the weld promote the growth of abnormal grains. In the stir zone, the modified Humphreys’ model is used to analyze the abnormal grain growth behavior. The grains with an advantage size and low strain are more likely to grow abnormally. The non-uniform pinning caused by the dissolution of second-phase particles further promotes abnormal grain growth. In the thermo-mechanically affected zone, the abnormal grains are formed by unstrained equiaxed grains near the stir zone or recrystallized sub-grains. The growth of abnormal grains in the thermo-mechanically affected zone is a strain-induced grain boundary migration process. The research is helpful to understand the abnormal grain growth in friction-stir welds during post-weld heat treatment.

Study on hot deformation behavior and dynamic recrystallization mechanism of recycled Al–Zn–Mg–Cu alloy

This study utilized Al–Zn–Mg–Cu alloy chips as raw materials to prepare recycled billets through a solid-state recycling process. Isothermal hot compression tests were conducted using a thermal simulation machine to establish an Arrhenius-type constitutive equation and the hot processing map based on the dynamic materials model. The microstructure of the recycled Al–Zn–Mg–Cu alloy after hot compression was characterized and analyzed using SEM, EBSD, and TEM, exploring the microstructural evolution mechanisms of the recycled alloy at different temperatures and strain rates. The results indicate that the optimal hot deformation parameters for the recycled Al–Zn–Mg–Cu alloy are 300–360 °C at a strain rate of 0.01–0.05 s⁻1 and 400–450 °C at a strain rate of 0.05–0.3 s⁻1. At the same strain rate, as the deformation temperature increases, low-angle grain boundaries gradually disappear, and the grain structure becomes more stable. Dynamic recrystallization replaces dynamic recovery as the dominant mechanism. At the same deformation temperature, low strain rates allow more time for the migration of low-angle grain boundaries, thereby promoting the growth of dynamically recrystallized grains and ultimately forming new, undistorted grains.

Optimization method of parameters inverse identification for hot deformation constitutive model of 2Cr13 martensitic stainless steel using genetic algorithm

Constitutive models are considered a key prerequisite to investigate the thermal deformation behavior of materials. Accurate identification of their parameters is crucial for enhancing the predictive precision of the models. A novel optimization method for accurate inverse identification of parameters using the genetic algorithm (GA) in the Modified Zerilli-Armstrong (MZA) model was proposed in this work. Under conditions of temperatures ranging from 1223 K to 1473 K at intervals of 50 K and strain rates of 0.01, 0.1, 1, and 5 s−1, uniaxial isothermal hot compression tests were performed on 2Cr13 martensitic stainless steel (MSS) employing a Gleeble-1500D thermal simulation test machine. Based on the experimental data, the conventional linear regression method was utilized to solve the MZA model of 2Cr13 MSS. Initial values for the material constants I1, S1, C5, and C6 to be optimized were assigned drawing from the calculated results. The GA-based iteratively optimized MZA (G-ZA) model was established by minimizing the mean squared error as an objective function between the experimental and predicted flow stress values. Compared to the MZA model, a significant enhancement in predictive performance was achieved with the G-ZA model, while good generalization was also demonstrated. Both models were successfully integrated into the Forge® finite element analysis software through the secondary development of user subroutines. Numerical simulation results indicated that the G-ZA model demonstrated a better agreement with the experimental data in predicting the load-displacement curve. This validated that a more accurate constitutive model for the hot deformation of 2Cr13 MSS can be effectively constructed using the GA-based parameter inverse identification strategy.

Hot deformation behavior and dynamic recrystallization mechanism of GH2132 superalloy

In order to study the hot deformation and dynamic recrystallization behavior of annealed GH2132 superalloy, this paper conducted hot compression tests of GH2132 superalloy at different temperatures (950°C–1150°C) and different strain rates (0.01/s–10/s) using Thermocmaster Z thermal simulation testing machine, and made an in-depth analysis of recrystallization behavior via electron backscattered diffraction (EBSD). The results indicate that the flow stresses of GH2132 alloy first increase and then decrease at high temperature, among which the curves present a more obvious flow softening at 10/s. Continuous dynamic recrystallization (CDRX) is dominant at high temperature and low strain rate, while discontinuous dynamic recrystallization (DDRX) plays the major role at low temperature and high strain rate. The type of recrystallization has a significant impact on the formation and volume fraction of twinning. In addition, high-temperature constitutive model, hot processing map, and an unified dynamic recrystallization kinetic model of GH2132 superalloy were established. Thus, the optimal range of hot working parameters was obtained. The research results provide guidance for determining the hot working process window of GH2132 superalloy.

Thermal performance assessment of traditional Japanese wooden houses with short-term measurements: Machine learning approaches

Traditional Japanese wooden houses are sustainable and vernacular in nature; however, energy consumption owing to poor insulation is inevitable in the winter. These wooden houses in Japan are an integral part of historical landscapes and are protected from the potential risks of retrofitting. To continue using these houses without bearing additional energy burdens, it is necessary to better understand their actual thermal performance. Therefore, this study proposes a combined approach to assess the thermal performance of traditional Japanese-style rooms using in-situ thermal measurements and machine learning predictive methods. The in-situ thermal measurements evaluated the actual thermal environment of traditional Japanese-style rooms with and without air conditioners (AC) during winter. Similarly, two machine learning models, support vector regression (SVR) and random forest regression (RFR) were developed to predict the indoor temperature based on the external factors and their adjacent rooms temperature. The result revealed strong thermal interaction between adjacent rooms, likely driven by heat transfer dynamic. To further investigate these interactions, thermal images of each room were analyzed after the AC was in use. The result revealed the presence of hot and cold spot within the gap of sliding fusuma (thick paper made door) and ranma (transom or indoor ventilation), indicating significant heat transfer between the AC and non-AC rooms. Consequently, the prediction model for the AC room temperature was notably influenced by the thermal conditions of adjacent non-AC room. Thus, this study proposes a simple yet reliable approach that enables resident to prioritize thermal improvements for efficient and cost-effective renovations.

Research on hot workability and deformation mechanism of as-extruded Ti-6554 alloy over a broad temperature range

The favorable mechanical characteristics of Ti-6554 offer potential uses in the aerospace industry. To broaden the potential uses of as-extruded Ti-6554 alloy, it is essential to conduct additional assessments on its hot workability and deformation mechanisms. This study, through a series of hot compression tests conducted over a broad temperature range using a thermal simulation testing machine, established the activation energy map and the hot processing map, while examining the evolution of microstructures and deformation mechanisms. The findings indicate that the activation energy of the Ti-6554 alloy reduces as strain rises. The ideal conditions for hot processing are 850-900 °C and 10-3-10-2s-1. The precipitation of the α phase occurs when deforming in the two-phase region, some maintain burgers orientation relationship (BOR) with the matrix, and some maintain random orientation with the matrix. In addition, the α phase precipitation significantly promotes dynamic recrystallization (DRX), resulting in obvious flow softening. The examination of misorientation angles reveals that DRX mechanisms of the β phase. At a slow strain rate, DDRX gradually dominates as the temperature rises. At constant temperatures, CDRX increasingly takes precedence with the increase of strain rate.

Effect of a quantum thermal machine on an entangled atomic system

In this contribution, we examine the effect of a quantum thermal machine on the behavior of two entangled atoms. It is shown that sudden changes (death/rebirth) in entanglement are predicted when increasing either the average rate of interaction coupling or the relative coupling. The maximum bounds of re-birthed entanglement decrease as machine parameters increase. Entanglement survival time can be increased as one reduces the average rate of interaction coupling. Due to the interaction, there will be an exchange of energies from the baths which may be a cool or hot flux. Therefore, the machine parameters can be used as controllers to switch between heating and refrigeration processes.

Study on the heating process, high temperature mechanical properties and fatigue properties of a low-carbon microalloyed steel

The dynamic continuous cooling transformation (CCT) experiments of a low-carbon microalloyed building structure steel were conducted on a Gleeble-3500 thermal simulation test machine, and the corresponding dynamic CCT curve was drawn. According to the dynamic CCT curves, different austenitization temperatures were designed to clarify the influence of austenitization temperature on the transformation and microstructure of the tested steel. The results showed that when the austenitization temperatures were set as 1050, 1150 and 1250 °C, respectively, the microstructure mainly consisted of ferrite and granular bainite. With the increase of austenitization temperature, the starting temperature of ferrite transformation gradually decreased, but the starting and ending temperatures of bainite transformation gradually increased, leading to the increase of the volume fraction of granular bainite. In addition, the tensile properties of the tested steel at different temperatures were determined. The tensile strength gradually decreased with the increase of the tensile temperature, and the highest tensile strength was 502 MPa for the room-temperature sample, while the elongation showed different trends. It was the largest elongation (57.8%) at the tensile temperature of 500 °C, while the smallest value of 19.2%–23.2% was obtained in the 200–300 °C tensile samples. Finally, the fatigue property of the experimental steel was detected by plotting the corresponding fatigue curves. The fatigue limit strength under 1 million cycles was about 376.2 MPa. Results provide a theoretical reference for the setting of heating process in the real industrial production and the application of the low-carbon microalloyed building structured steel.

Continuous cooling transformation behavior of V microalloyed hot-bent X80 pipeline steel

Abstract Continuous cooling transformation (CCT) behavior of a vanadium-microalloyed hot bent pipeline steel was studied by using Gleeble 3800 thermal simulation machine, and the hot-rolling technique of the tested steel was designed. Acicular ferrite and granular bainite can be observed with the cooling speeds ranging from 1~ 40 °C/s. Higher cooling speed leads to grain refinement. The yield strength is 570 MPa. The ultimate tensile strength (UTS) is 757 MPa. The fracture elongation is 22.50 %. The low temperature toughness at -40 °C is 325 J. The tested steel has both high strength and good low temperature toughness, and meets the mechanical performance requirement of API Spec 5L specification for X80 pipeline steel.

Prediction of flow stresses for a typical Nickel-Based superalloy during hot deformation based on constitutive equation

Abstract Utilizing the Gleeble-3500 thermal simulation testing machine, a series of isothermal constant strain rate thermal compression tests were executed to study the high-temperature rheological properties of the GH4169 alloy. These tests were carried out under deformation temperatures between 900°C and 1100°C, with strain rates from 0.001 to 1 s−1, and involved a deformation of 60%. The results reveal that the peak stress of the GH4169 alloy decreases as the temperature increases, and the strain rate decreases during hot deformation. The activation energy for thermal deformation is calculated to be 806.39 kJ/mol. Through regression analysis and polynomial fitting of true stress-strain curves from thermal compression tests, an Arrhenius constitutive equation integrating strain compensation for high-temperature deformation was established. The model exhibits an average relative error of 8.86% between predicted and experimental values, indicating the model’s good accuracy in predicting the alloy’s rheological behavior during thermal deformation. By adjusting the strain rate and deformation temperature, this model can be utilized to control the stress level in the hot working process.

The flow stress prediction of TiB2/2024 aluminum matrix composites based on modified Arrhenius model and gene expression programming model

The high temperature flow data of TiB2/2024 aluminum matrix composites (referred to as TiB2/2024 alloy) was investigated using a Gleeble-3500 thermal simulation testing machine. The experiments were conducted at various deformation temperatures (573 K, 623 K, 673 K, and 723 K), strain rates (0.01s−1, 0.1s−1, 1s−1, and 10s−1), and a maximum deformation of 60%. By comprehensively accounting for the deformation conditions, the relationships between the material parameters α, n, S, f of TiB2/2024 alloy and the deformation temperature, strain, and strain rate were determined, leading to the modification of the Arrhenius model. A constitutive model for TiB2/2024 alloy was constructed using the Gene expression programming (GEP) approach. The flow stress of TiB2/2024 alloy during the compression process was predicted using both the modified Arrhenius model and the GEP model. The statistical analysis was performed to evaluate the prediction accuracy of the two models, and the extended stress-strain data was implemented in finite element simulations of the hot compression process. The results indicate that the flow stress of TiB2/2024 alloy is significantly affected by the strain rate and temperature during the deformation process. The flow stress decreases with increasing temperature and increases with increasing strain rate. Both the modified Arrhenius model and the GEP model can effectively predict the alloy's flow stress. However, the modified Arrhenius model exhibits greater prediction accuracy than the GEP model.

Thermal modeling and Machine learning for optimizing heat transfer in smart city infrastructure balancing energy efficiency and Climate Impact

The paper proposes a framework based on deep learning, transfer learning, and multi-objective optimisation to model and optimise heat transfer in smart city infrastructure to make them energy efficient and thermally comfortable. The framework in the paper contains a building thermal dynamics prediction model developed using hybrid CNN-LSTM on an extensive dataset (12.56 metric tonnes) of Indian buildings covering various characteristics, which is then fine-tuned with data from five major Indian cities. This predictive framework has a high generalisation capability of energy consumption and predicting indoor temperature profiles with the mean absolute errors (MAE) of building energy consumption ranging from 8.7 to 12.3 kWh and indoor temperature as 0.6 to 1.1 °C, respectively. Transfer learning is considerably improving the performance of the proposed model in newly added cities, which improved the MAE in the training cities (New Delhi and Mumbai) by 3.6 % and reduced the R^2 to 10.7 %. The multi-objective optimisation involving decision-making processes resulted in energy savings of 15.7 % to 22.3 % and improved comfort levels by 21.8 % to 28.5 % in the evaluated cities. The paper significantly contributes to developing a data-driven, generalisable, and interpretable framework, which can usher how to optimise heat transfer using deep learning to make smart city infrastructure resilient and comfortable. It also provides a novel solution to addressing the problems posed by energy efficiency and climate change in Indian cities. Policymakers and urban planners can utilise these key policy recommendations suggested in the paper to design new, liveable and self-sustaining urban environments in India.

Analysis of extrusion characterization of Nd-Fe-B permanent magnet ring based on Normalized Cockcroft & Latham fracture criterion

The hot deformation behavior of hot-pressed Nd-Fe-B material was investigated using a Gleeble-3500 thermal simulation machine, with deformation of 50 %, temperatures ranging from 700 to 850 °C, and strain rates ranging from 0.01 to 1 s−1. A hyperbolic sine model was employed to establish a constitutive equation suitable for this material. In addition, the chosen fracture criterion and damage threshold in the simulations were validated against the experimental results. Then, a three-dimensional thermo-mechanical coupled model was developed to simulate the damage evolution of the material during the extrusion process. The effects of different extrusion processes and positions of the back pressure tool on the magnetic performance and forming quality of the material were investigated. The simulation results indicate that both the combined extrusion process and the back pressure tool located at the half of the workpiece will contribute to improve the uniformity of the magnetic properties, and effectively ensuring the regularity of the forming process at the top of the magnetic ring and suppressing the occurrence of cracks. The results of this paper will help to a better understanding of the deformation mechanism of Nd-Fe-B permanent magnet and provide a valuable theoretical guidance for the preparation of high-performance Nd-Fe-B magnetic rings.

Performance analysis of quantum harmonic Otto engine and refrigerator under a trade-off figure of merit

Abstract We investigate the optimal performance of the quantum Otto engine and refrigeration cycles of a time-dependent harmonic oscillator under a trade-off figure of merit for both adiabatic and nonadiabatic (sudden-switch) frequency modulations. For heat engines (refrigerators), the chosen trade-off figure of merit is an objective function defined by the product of efficiency (coefficient of performance) and work output (cooling load), thus representing a compromise between them. We obtain analytical expressions for the efficiency and coefficient of performance of the harmonic Otto cycle for the optimal performance of the thermal machine in various operational regimes. Particularly, in the sudden-switch regime, we discuss the implications of the nonadiabatic driving on the performance of the thermal machine under consideration and obtain analytic expressions for the maximum achievable efficiency and coefficient of performance of the harmonic Otto thermal machine. Particularly, we show that the quantum harmonic Otto cycle driven by sudden-switch protocol cannot work as a heat engine or refrigerator in the low-temperature limit. Finally, we show that in the high-temperature limit, the frictional effects give rise to a richer structure of the phase diagram of the harmonic Otto cycle. We identify the parametric regime for the operation of the Otto cycle as a heat engine, refrigerator, accelerator, and heater.

Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning

Linking Precursors and Volatility of Ambient Oxygenated Organic Aerosols Using Thermal Desorption Measurement and Machine Learning

Effect of high welding heat input on the microstructure and low-temperature toughness of heat affected zone in magnesium-treated EH36 steel

In the present study, the welding thermal cycle of magnesium-treated EH36 steel was simulated by a thermal simulation experiment machine. The effects of welding heat input on the microstructure and low temperature toughness of the welded joint were studied, and the mechanism of acicular ferrite nucleation induced by MgAl2O4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm { MgAl_2O_4}$$\\end{document} inclusion was revealed. The results showed that, when the welding heat input of 150 kJ/cm, 250 kJ/cm and 350 kJ/cm was utilized, the microstructure of heat affected zone in the experimental EH36 steel consisted of grain boundary ferrite, acicular ferrite, granular bainite, and a small amount of pearlite, but the volume fraction and grain size of each independent microstructure were different. With the increase of welding heat input, the content of acicular ferrite and granular bainite decreased, the content of grain boundary ferrite increased, and the average grain size increased, whereas the low temperature toughness of the experimental EH36 steel welded joint reduced from 174 to 67 J. Furthermore, MgAl2O4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {MgAl_2O_4}$$\\end{document} inclusions can induce acicular ferrite nucleation, whereas the nucleation mechanism is a combination of low lattice mismatch and low interfacial energy.

Nonequilibrium fluctuations of a quantum heat engine

The thermodynamic properties of quantum heat engines are stochastic owing to the presence of thermal and quantum fluctuations. We here experimentally investigate the efficiency and nonequilibrium entropy production statistics of a spin-1/2 quantum Otto cycle in a nuclear magnetic resonance setup. We first study the correlations between work and heat within a cycle by extracting their joint distribution for different driving times. We show that near perfect correlation, corresponding to the tight-coupling condition between work and heat, can be achieved. In this limit, the reconstructed efficiency distribution is peaked at the deterministic thermodynamic efficiency, and fluctuations are strongly suppressed. We further successfully test the second law in the form of a joint fluctuation relation for work and heat in the quantum cycle. Our results characterize the statistical features of a small-scale thermal machine in the quantum domain, and provide means to control them.

Research on the Dynamic Softening Mechanism of Dual‐Phase Structure During Warm Forming of Fe–8.5Mn–1.5Al Medium‐Manganese Steel

In this article, Fe–8.5Mn–1.5Al medium‐manganese steel is taken as the research object, and Gleeble 3500 thermal simulation testing machine is used to conduct isothermal compression at different deformation temperatures, strain rates, and strains in its austenite ferrite two‐phase zone. A constitutive model and a dynamic recrystallization volume fraction model for materials are established, and finite‐element method is used to simulate the temperature, equivalent strain, and dynamic recrystallization volume fraction distribution in different regions of the workpiece under different deformation conditions. By comparing and analyzing the microstructure, finite‐element simulation results, and hot working diagrams obtained from the experiment, the microscopic mechanism of dynamic softening of the material when ferrite and austenite coexist is explained; the transformation of the dynamic softening mechanism of the two phases with the hot deformation process is also explained. Early deformation mainly occurs in softer ferrite, which preferentially undergoes continuous dynamic recrystallization. Austenite has a lower stacking fault energy, and discontinuous dynamic recrystallization (DDRX) occurs when the dislocation density reaches a certain critical value. At the same time, when the strain reaches a certain level, the dynamic softening behavior of ferrite will also transform into DDRX. The optimal deformation conditions for Fe–8.5Mn–1.5Al medium‐manganese steel during warm processing in the two‐phase zone are a temperature of 720 °C, a strain rate of 0.01 s−1, and a large deformation zone.

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.