Temperature Curves Research Articles

Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive Particle Swarm Optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of Particle Swarm Optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature closely follows the preset temperature curve. Experimental results demonstrate that the actual output value of the system closely aligns with the original preset temperature curve, with a root mean square error of only 0.74. This designed identification and control system effectively achieves the desired outcome.

Can rheometer be used for determination of baking stability of cocoa based filling creams

Bake stable filling creams are important for some products such as biscuits, cakes, cookies, etc. In the present study, a rheometer-based method for determining the baking stability of the cream fillings was developed considering the consistency and fluidity of the fillings after baking. For this aim, the heating and cooling process was simulated depending on the temperature of the filling reached during the baking process of cookies. Three bake-stable and three bake-unstable filling creams were obtained from the market. Rheological, textural, and baking stability analysis were performed on the samples. Change in the apparent viscosity values of the samples was observed at temperature values from 20 °C to 105 °C and from 105 °C to 30 °C using rheometer. The percentage of viscosity change at 30 °C before and after the heating process was calculated. This value for stable samples was 67%, 73% and 9%. Fluctuation observation during the measurement is also important for stability. Unstable fillings have many fluctuations in the temperature versus apparent viscosity curve as their viscosity change value was lower. The absence of fluctuations on the temperature versus viscosity graph indicates the stability of the filling creams. The findings of the study indicated that a rheometer can be used as an effective tool for determining the baking stability of the creams with minimal effort, labor, sample requirement, and time.

Fire Resistance Performance of Constrained H-Shaped Steel Columns with Uneven Vertical Temperature Distributions

Steel columns, which are widely used in building frameworks and spatial structures, are susceptible to capacity degradation during fires, potentially leading to the overall collapse of buildings. Existing research on the fire resistance of steel columns assumes that the temperature loads encountered by steel columns are evenly distributed vertically. However, real-world fire scenarios often feature significant vertical temperature differences. Therefore, this study comprehensively investigated these variations to derive a temperature curve that accurately represents real fire conditions. Subsequently, the fire resistance limits of steel columns were studied using this temperature curve, leading to a revised calculation formula for the critical fire resistance temperatures of steel columns. The following conclusions were drawn: (1) Nonuniform longitudinal temperature distributions are influenced by parameters such as the fire source distance, the heights of ventilation openings, and the distance between a vent and the temperature measurement point. Among them, the fire source distance has the greatest impact, with the maximum longitudinal temperature difference reaching over 500 °C. (2) Variations in the load ratio and longitudinal temperature differences alter the failure positions of steel columns, reducing their critical temperatures by up to 200 °C. (3) The revised critical fire resistance temperature formula is more accurate and safer compared with that outlined in the “Technical Code for Fire Protection of Steel Structures” (GB51249-2017). These findings offer valuable insights for the fire designs of steel columns.

Improving recognition of early clinical decline: Determining reliability and validity of the Garvey clinical warning curves.

New graduate nurses have difficulty recognizing and managing the early signs of clinical decline, resulting in a Failure to Rescue (FTR) event and preventable patient death. To address this gap, Garvey developed a series of Clinical Warning Curves as an instructional tool for new graduate nurses in an academic medical center. The Garvey Clinical Warning Curve models depict the progression of clinical changes in six body systems that occur before cardiac arrest. The purpose of this study was to establish the content validity, reliability, and usability of the Garvey Clinical Warning Curves among healthcare experts and new-graduate nurses. The current study was a cross-sectional, observational, validation survey design. Content experts used the content validity index (CVI) to evaluate the Curves. All but the temperature curves were rated as "acceptable" (CVI >0.60) for relevance, clarity, and ambiguity. The new graduate nurses who reviewed the case studies and placed patients onto the Clinical Warning Curves did so similarly, generating high intraclass correlation (ICC) scores. The usability survey components measured the perceptions of acceptability, appropriateness, and feasibility for the use of the six Clinical Warning Curves in practice settings. All components of the Curves had an average score of 4.0 or greater except for the level of complexity which scored 3.88. The Garvey Clinical Warning Curves emerged as valid and reliable tools that aid new graduate nurses in recognizing subtle signs of clinical decline. Because timely recognition and communication of clinical decline are key to preventing FTR events and avoiding patient deaths, it would be beneficial to provide the Clinical Warning Curves as a bedside resource for new graduate nurses during their orientation to the unit or within a nurse residency program.

Research on performance of porous scaffold-based getter activated by induction heating and its application in MEMS device

Non-evaporable getter (NEG) is widely used in vacuum packaging. However, there are two main contradictions. Firstly, the contradiction between bonding temperature of the device and activation temperature of the getter results in the getter being activated first and then packaged, and cannot be repeatedly activated, which seriously reduces the adsorption capacity and life of the getter. Secondly, the contradiction between the small package volume and the large adsorption capacity, how to integrate large adsorption capacity getter in a small space is a tricky problem. In view of the above contradictions, a porous scaffold-based getter activated by induction heating was proposed in this paper, results show that when nickel plating is used as the induction heating layer, the temperature can quickly rise to 700 °C within 10 s, and 1 μm Ti getter can be fully activated for 7 min. The good periodicity of the temperature curves proved that the nickel layer has good repeatable heating characteristics. In addition, the adsorption performance of the getter was tested and characterized at device level. The device level characterization of optical deflection method proves the effectiveness of induction heating to activate getter, which lays a foundation for the application of getter on MEMS devices.

Finite Element Method Simulation Study on the Temperature Field of Mass Concrete with Phase Change Material

Phase change materials can be converted between solid, liquid, and gaseous states, absorbing or releasing a large amount of heat. PCM is incorporated into concrete to adjust the temperature difference between inside and outside of concrete, which can reduce cracking. In this paper, the finite element analysis method is used to establish the model of an ordinary concrete structure, doped with phase change materials, on the basis of mechanical properties and a temperature regulation test performed by calculating the adiabatic temperature rise of concrete with different contents of composite phase change material, comparing the experimental and simulation results of the ordinary concrete structures with phase change materials, and analyzing the change in temperature field of the concrete structure with the content of self-prepared composite phase change materials. It is found that the addition of self-prepared composite phase change materials reduces the temperature peak of the concrete structure in the stage of hydration heat and delays the time taken to reach the temperature peak. Then, the temperature field of the phase change mass concrete structure is established, and the influence law of composite phase change material admixture on the temperature field of mass concrete is summarized through the time–temperature curves of different admixture amounts and positions so as to predict the possibility of cracks in mass concrete.

Operando monitoring of gelation kinetics of polyacrylamide hydrogel using in-fiber dual-MZI

Operando monitoring of gelation kinetics and exploring the gelation law are crucial for optimizing the performance of hydrogels. In this paper, we propose a novel superposition sensing structure based on an integrated dual-Mach–Zehnder interferometer (MZI), which can simultaneously sense temperature and refractive index (RI), with an RI sensitivity of −10,173 nm/RIU and a temperature sensitivity of 8.14 nm/ °C. For the first time, its application for the operando monitoring of gelation kinetics of polyacrylamide hydrogel (PAH) is demonstrated. By establishing the kinetic curves of temperature and RI information in the gelation process, it is confirmed that the gelation process can be divided into three stages: initial lag, rapid gelation, and final saturation. In addition, two gelation laws are verified through experiments: (1) As the concentration of monomers and crosslinkers increases, RI changes, and the amount of heat released gradually increases. (2) As the concentration of catalysts increases, the gelation time decreases significantly. This superposition sensing technique based on dual-MZI can be further applied to monitor other complex dynamic biochemical processes, thereby providing insights into the laws and mechanisms of reaction processes.

Modeling the melting temperature of semiconductor nanocrystals

Exploring the thermal stability of semiconductor crystals at the nanoscale is of great significance for the design, fabrication, and application of modern quantum devices. In this paper, we propose a thermodynamic model to predict the melting temperature of semiconductor nanocrystals, which is in good agreement with the experimental results of Si, Bi, CdS, and CdSe. In addition, when the size decreases, the drop of melting temperature curves tends to be synchronized with the size-dependent solid/liquid interface energy and surface stress ratio γsl(D)/f(D), which reveals the physical origin of the decrease in the melting temperature of the semiconductor nanocrystals.

Comparative Evaluation of Falling Weight Deflectometer, Traffic Speed Deflectometer and Rapid Pavement Tester in Deflection Measurement

This study intends to compare deflections obtained from traffic speed deflection devices (TSDDs) with the deflections of the falling weight deflectometer (FWD). For this purpose, deflections were measured on five sections of three roads in the Norwegian road network using traffic speed deflectometer (TSD) and rapid pavement tester (Raptor). Deflections were also measured using FWD at three different temperatures and the curves of FWD deflections versus temperature (FWD temperature-dependent deflection curves) were obtained. These curves were used to correct the effect of temperature difference. It was shown that both TSD and Raptor have the potential to detect structural deficiencies; however, TSD had better consistency with FWD with regard to deflection values and deflection basin parameters. A refinement was then made to make the Raptor data more consistent with FWD data. Calculating bearing capacity before and after refinement revealed that refining Raptor data can substantially increase the consistency between Raptor and FWD.

Influence of ice properties on wave propagation characteristics in partially frozen soils and rocks: A temperature-dependent rock-physics model

A better understanding of the temperature effects on the propagation characteristics of elastic waves in frozen soils and rocks is imperative for accurately quantifying their freezing degrees. Although the existing rock-physics models based on the three-phase Biot (TPB) theory adeptly interpret observed velocity variation with temperature (VVT) curves, they often lack a comprehensive understanding of the mechanisms underlying attenuation variation with temperature (AVT) curves. In this study, we first extend the TPB theory to incorporate the temperature-dependent properties of ice, such as changes in volumetric fraction, morphology, and viscoelasticity, by integrating the relevant thermodynamic laws. Model parameters related to ice properties and interactions, such as rigidity, shear moduli, density, and friction, are redefined. Then, using a numerical rock-physics modeling approach, we examine influential factors and modes of the wave VVT and AVT responses. Our results indicate that the P- and S-wave velocities increase with source frequency, consolidation degree, and frame-supporting ice content, whereas they decrease with temperature and pore-floating ice content. The P- and S-wave attenuation factors increase with the frame-supporting ice content and decrease with the consolidation degree. Rising temperatures tend to amplify the peak magnitude of the P-wave attenuation factors and shift the central frequency of the S-wave attenuation factors. Finally, within a temperature-controlled laboratory environment, we conduct ultrasonic wave transmission testing on brine-saturated sediment and rock specimens. The results demonstrate that as the temperature increases from −15°C to −3°C, the P- and S-wave velocities decrease, whereas the P-wave attenuation factors decrease and the S-wave attenuation factors initially rise before declining. Our viscoelastic TPB theory outperforms the existing ones in interpreting the S-wave AVT observations. This temperature-dependent rock-physics model holds promise for interpreting the sonic logging data in the time-lapse monitoring of permafrost, glaciers, and Antarctica.

Molecular modeling of cryogenic hydrogen behavior in vapor–liquid equilibria and confinement

We report the effect of Feynman–Hibbs corrections on the intermolecular potential to take into account the quantum effects of hydrogen at low temperature and high density for vapor–liquid equilibria (VLE) and confinement into carbon nanotubes. Density against temperature, pressure against temperature and isochoric curves are displayed for VLE, while adsorption isotherms for different pore sizes and different temperatures are showcased to see the effect under confinement. Besides, 5 different models for hydrogen are employed; one atomistic and 4 coarse-grained models, covering both classical and quantum approximations. For the case of VLE we observed that the quantum corrected models lead to more accurate and reliable results in good agreement with the experimental data. The adsorption isotherms produced with the different models showed that the difference between quantum and classical approximations are significant for maximum confinement (smallest pore) and lowest temperature (77K). As we increase both the temperature and the pore size, the difference between the different models become less noticeable.

Accuracy of 3D real-time MRI temperature mapping in gel phantoms during microwave heating

BackgroundInterventional magnetic resonance imaging (MRI) can provide a comprehensive setting for microwave ablation of tumors with real-time monitoring of the energy delivery using MRI-based temperature mapping. The purpose of this study was to quantify the accuracy of three-dimensional (3D) real-time MRI temperature mapping during microwave heating in vitro by comparing MRI thermometry data to reference data measured by fiber-optical thermometry.MethodsNine phantom experiments were evaluated in agar-based gel phantoms using an in-room MR-conditional microwave system and MRI thermometry. MRI measurements were performed for 700 s (25 slices; temporal resolution 2 s). The temperature was monitored with two fiber-optical temperature sensors approximately 5 mm and 10 mm distant from the microwave antenna. Temperature curves of the sensors were compared to MRI temperature data of single-voxel regions of interest (ROIs) at the sensor tips; the accuracy of MRI thermometry was assessed as the root-mean-squared (RMS)-averaged temperature difference. Eighteen neighboring voxels around the original ROI were also evaluated and the voxel with the smallest temperature difference was additionally selected for further evaluation.ResultsThe maximum temperature changes measured by the fiber-optical sensors ranged from 7.3 K to 50.7 K. The median RMS-averaged temperature differences in the originally selected voxels ranged from 1.4 K to 3.4 K. When evaluating the minimum-difference voxel from the neighborhood, the temperature differences ranged from 0.5 K to 0.9 K. The microwave antenna and the MRI-conditional in-room microwave generator did not induce relevant radiofrequency artifacts.ConclusionAccurate 3D real-time MRI temperature mapping during microwave heating with very low RMS-averaged temperature errors below 1 K is feasible in gel phantoms.Relevance statementAccurate MRI-based volumetric real-time monitoring of temperature distribution and thermal dose is highly relevant in clinical MRI-based interventions and can be expected to improve local tumor control, as well as procedural safety by extending the limits of thermal (e.g., microwave) ablation of tumors in the liver and in other organs.Key PointsInterventional MRI can provide a comprehensive setting for the microwave ablation of tumors.MRI can monitor the microwave ablation using real-time MRI-based temperature mapping.3D real-time MRI temperature mapping during microwave heating is feasible.Measured temperature errors were below 1 °C in gel phantoms.The active in-room microwave generator did not induce any relevant radiofrequency artifacts.Graphical

Furnace Temperature Curve Optimization Model Based on Differential Evolution Algorithm

Furnace Temperature Curve Optimization Model Based on Differential Evolution Algorithm

Exploring mesophase formation: Structural characterization approaches in a soft sphere model

This study employs Molecular Dynamics simulations to enhance the structural characterization of sphere mixture systems, focusing on local order parameters to identify order-disorder transitions within lamellar and hexagonal mesophases. The structural characterization included the use of neural networks and the search for clusters with topological order. We have identified the order-disorder transition at a characteristic temperature TOD, effectively captured by the structural parameters analyzed. Below TOD, the system organizes into an ordered mesophase, while above this temperature, it transitions into an isotropic liquid state. Moreover, within the isotropic region, we observe that the liquid begins to form clusters. This clustering phenomenon becomes evident below a specific temperature, denoted as T⁎. At T⁎, there are noticeable changes in the volume versus temperature curves, indicating a process of micellization or clustering in the system. The structural parameters used in our study also register this behavioral change at T⁎, further supporting the occurrence of this phenomenon. Additionally, our analysis of the overlap in detecting ordered particles between the two structural parameters using the Sørensen-Dice similarity coefficient reveals a significant spatial correlation in the isotropic phase, with a correlation higher than what would be expected in a random case. This indicates that both neural networks and topological cluster classification detect statistically similar ordered regions in the isotropic phase, highlighting the clustering. Our findings reveal that the structural parameters effectively capture both the order-disorder transition at TOD and the clustering process at T⁎. These results provide valuable insights into the self-assembly mechanisms and phase behavior in this soft-sphere model, advancing our understanding of self-assembly processes in material science, particularly in exploring phase behavior in mesogenic nanoparticle systems.

Study on crack propagation characteristics of rocks with different lateral pressure based on joint monitoring of DIC and AE

During the process of rock failure, the characteristics of crack propagation affect the fracture characteristics and macroscopic mechanical behavior of rocks, indirectly affecting the safety and stability of rock engineering. In order to study the evolution characteristics of cracks during rock failure under different lateral pressure, based on an improved digital image correlation (DIC) and acoustic emission (AE) signal recognition method, a visual biaxial servo loading device was developed to conduct biaxial compression tests on mudstone with prefabricated cracks of the same inclination angle. The research results indicate that the stages of crack propagation include microcracks propagation, crack tip formation, stable macroscopic cracks propagation, and unstable macroscopic cracks propagation. As the lateral pressure increased, the initiation frequency of cracks decreased, the quantity of propagation decreased, and the propagation path shortened, indirectly increasing the bearing strength of rocks. The initiation stress, peak stress, and elastic modulus of pre-cracked rocks with lateral pressure ≤ 2 MPa were lower than those of pre-cracked rocks with lateral pressure > 3 MPa, with the minimum reduction amplitude of 14.1%, 21.2%, and 12.6%, respectively. As the lateral pressure decreased, the dispersion of the AE main frequency distribution increased and accelerated its downward expansion. The surface temperature curves of rocks were prone to fluctuations and rapid upward evolution characteristics corresponding to crack tip formation and crack propagation, respectively. The research results provide theoretical and engineering references for the mining of weak coal seams.

Temperature-dependent acoustic emission characteristics and statistical constitutive model of granite under uniaxial compression

Understanding the temperature-dependent mechanical behavior and fracture characteristics of granite is crucial for many engineering projects. In this study, the real-time temperature curves of granite specimens were obtained during the heating and cooling process, and the thermal treatment tests were conducted. The physical properties of the specimen before and after thermal treatment, including mass, volume, and P-wave velocity, were measured. The acoustic emission (AE) signal in the uniaxial compression is monitored. The results indicate that the physical properties of granite deteriorate with temperature, while the mechanical properties show two effects of thermal strengthening and thermal weakening. This phenomenon is comprehensively analyzed by literature statistical data and optical microscopic observation. Furthermore, the AE characteristic is strongly dependent on temperature. High temperature induces more AE ring count to appear in the early stage of loading. As the temperature increases, the crack initiation stress decreases and the table crack propagation stage becomes longer. The attenuation of high-frequency signals and the enhancement of low-frequency signals are related to the development and interaction mechanism of thermally-induced crack and stress-induced crack. At 600 °C, the global b-value increases significantly. Meanwhile, the evolution of dynamic b-value helps explain the failure process of granite under axial load after thermal treatment. In addition, a new thermo-mechanical damage statistical constitutive model of granite considering temperature effects is proposed by introducing AE parameters. The main advantages of this model can well fit the nonlinear behavior of granite in the early loading stage after thermal treatment, and reflect the failure process of granite before the peak value.

Experimental and Modeling Study of Arc Fault Induced Thermal Runaway in Prismatic Lithium-Ion Batteries

With the widespread application of electrochemical energy storage technology, the safety issues of lithium-ion batteries have garnered significant attention. The issue of arc faults resulting from electrical failures is especially critical, as it can lead to catastrophic battery disasters. Therefore, this paper first established an arc testing platform and conducted experiments on top cover and body of prismatic lithium-ion batteries to analyze the thermoelectric characteristics between arc and battery. Under experimental conditions of 300 V and 15 A, it was found that arcs can induce thermal runaway in batteries. Subsequently, based on the experimental conditions, a mathematical model was established to induce thermal runaway in batteries through an equivalent method of arc heat source. By comparing the temperature curves of model and experiment, the RMSE of temperature at the center point of large surface was 5.09 °C (with a maximum temperature of 212 °C), indicating the accuracy of the model. This paper’s research on arc faults in battery systems revealed the evolution pattern and realized that arcs can trigger thermal runaway in batteries. The model for arc-triggered thermal runaway in batteries is highly accurate, capable of reducing the number of experiments, accelerating experimental progress, and is of significant importance for guiding the design of arc experiments about batteries.

Decomposition behavior of supercritical ethylene under ultra-high temperature and ultra-high pressure

The gas-phase raw materials in many industrial scenarios currently exist in the reactor in a supercritical state. Unlike gas phase molecules, the supercritical molecules usually exhibited dynamic motion patterns in the system, which made them more susceptible to decomposition. Previous researches mainly focused on low-temperature and low-pressure systems, and few research on decomposition behavior of supercritical molecules under ultra-high temperature and ultra-high pressure were studied. This study focused on the decomposition behavior of supercritical ethylene under ultra-high temperature and ultra-high pressure, the effect of triggering conditions on supercritical ethylene decomposition was investigated. In addition, the temperature and pressure change curves during ethylene decomposition were analyzed to obtain evolution time nodes to determine energy accumulation in supercritical states. Moreover, the gas product distribution and derived explosive carbon were characterized for better evaluating the consequences of supercritical ethylene decomposition. This article aimed to provide theoretical basis and data guidance for energy utilization and conversion in ethylene supercritical systems.

Adaptive Kriging-Based Heat Production Performance Optimization for a Two-Horizontal-Well Geothermal System

Optimizing heat generation capacity is crucial for geothermal system design and evaluation. Computer simulation is a valuable approach for determining the influence of various parameter combinations on a geothermal system’s ability to produce heat. However, computer simulation evaluations are often computationally demanding since all potential parameter combinations must be examined, posing significant hurdles for heat generation performance evaluation and optimization. This research proposes an adaptive Kriging-based heat generation performance optimization method. Firstly, a two-horizontal-well geothermal system with rectangular multi-parallel fractures is constructed. The heat production performance optimization problem is then established, and the temperature and enthalpy of the outlet water are calculated using computer simulation and Kriging. A parameterized lower confidence bounding sampling scheme (PLCB) is developed to adaptively update Kriging in order to strike a compromise between optimization accuracy and computation burden. The outcomes of the optimization are compared to those of the Kriging-based optimization approach and other common infill options to demonstrate the efficiency of the proposed method. The outlet temperature curve obtained with PLCB-AKO-1 rose for a longer time and the heat generation power curve reached a stable output without a downward trend. According to the Friedman and Wilcoxon signed ranks tests, the PLCB-1-AKO technique is statistically superior to alternative strategies.

Peristaltic transport with multiple solutions of heat and mass transfer using modified Buongiorno nanofluid model over tapered channel with long wave‐length at small Reynolds number

AbstractThis research paper aims to investigate the peristaltic transport of a nanofluid (NF) in a tapered asymmetric channel. Initially, the governing equations for the balance of mass, momentum, temperature, and volume fraction for the NF using Reiner–Philippoff (RP) based NF are formulated. Subsequently, these equations are employed to analyze long wavelength and small Reynolds number scenarios. The numerical results for various flow features are thoroughly examined and discussed. Dual solutions have been examined for some factors. So, stability assessment is implemented to find stable solution. Novelty of the existing is to investigate the peristaltic motion of Buongiorno's NF model and its stability which has not investigated in the previous literatures. It has been demonstrated that modifying the RPF parameter leads to a transition in the fluid's velocity, changing it from a dilatant liquid to a Newtonian fluid and from Newtonian to pseudoplastic. The findings indicate that the temperature curves rise as Brownian motion and thermophoretic factors increase, while they decrease as the Prandtl number increases. Furthermore, a concise mathematical and graphical analysis is carried out to examine the impact of each key parameter on the flow characteristics.