Temperature Dependence Of Parameters Research Articles

Wetland CH4 and CO2 emissions show opposite temperature dependencies along global climate gradients

Methane (CH4) and carbon dioxide (CO2) are the two largest contributors to the anthropogenically-driven greenhouse effect, which are the dominant gaseous end-products of wetland C decomposition. However, given our limited understanding of the spatial heterogeneity of wetland CH4 and CO2 emissions, it is uncertain how future warming may impact the emissions of these dangerous emissions. Here, we show opposite temperature dependencies of CH4 and CO2 emissions along global climate gradients using data from 45 widely distributed wetlands in the FLUXNET-CH4 database. Specifically, the temperature dependence of CH4 emissions increased as mean annual temperature (MAT) rose, while the dependence of CO2 emissions decreased, suggesting that in warmer areas, there is a greater risk for increased wetland CH4 emissions accompanying lower CO2 emissions in response to global warming. The ratio of wetland CH4 to CO2 emissions increased linearly with increasing temperature only when MAT and mean annual precipitation (MAP) are greater than 4.7 °C and 483 mm, respectively. This response indicates that, compared with those in cold and dry climates, wetland ecosystems in warmer and wetter climates are more prone to methanogenesis as temperatures rise. Our results suggest that neglecting spatial variation of temperature dependencies in models may underestimate wetland CH4 and CO2 emissions compared to the use of a static and consistent temperature dependence parameter when only considering temperature effects. These findings highlight the importance of incorporating climate-related variation in the temperature dependencies of CH4 and CO2 emissions into models to improve predictions of wetland C-climate change feedbacks in the Anthropocene.

A data-driven intelligent learning algorithm for simultaneous prediction of aerodynamic heat and thermo-physical property parameters

It is of great importance and challenging to simultaneously determine time-varying aerodynamic heat and temperature-dependent thermo-physical property parameters with high accuracy, for the optimization of thermal protection systems of hypersonic vehicles. However, it is difficult to directly measure these parameters under high temperature conditions. It is an effective way to determine thermo-physical property parameters and aerodynamic heat by solving inverse problems, based on measurable or easily measured transient temperatures. However, the prediction error of these parameters may be too large, if the measurement error is large, due to the thermal inertia. To deal with this issue, an intelligent algorithm is proposed to simultaneously predict the aerodynamic heat and thermo-physical property parameters for the thermal protection systems of hypersonic vehicles, based on the temperature measurement information. It combines a genetic algorithm and a machine learning algorithm, and the genetic algorithm is employed to update the relevant parameters in the neural network. By training the neural network, the relationship among the predicted parameters and transient temperatures could be established. Thereafter, the aerodynamic heat subjected to the outer surface of the aircraft and the temperature-dependent non-linear thermo-physical property parameters could be predicted. Examples are given to verify the present algorithm. The results show that this work provides an accurate and efficient method for simultaneously determining the aerodynamic heat and thermo-physical property parameters for the thermal protection system of a hypersonic vehicle. The prediction errors of aerodynamic heat and thermo-physical property parameters are much smaller than the measurement errors, when there are relatively large measurement errors in the input data.

Balancing the elementary steps via photo-assisted thermal catalysis for boosting propane dehydrogenation

Balancing kinetics behavior is a significant priority in chemical reactions especially involving two or more elementary steps. Propane dehydrogenation (PDH), usually involves a slower 2nd C-H activation step at a low temperature, resulting in heat waste for overcoming a higher energy barrier of the 2nd dehydrogenation. Herein, we develop one Photo-assisted Thermal PDH process (PTPDH) based on PtZn clusters encapsulated in porous MFI zeolite, which effectively balances the kinetic elementary steps via exciting the ground state electrons of the active sites to the positively charged -C3H7 fragments, achieving a 23.4 % growth ratio (from 39.6 to 51.3·10−8 mol·g−1·s−1) with the assistance of ∼5 suns light intensity at 400 °C. The comparison between the temperature-dependent and light intensity-dependent kinetic parameters confirmed that the non-thermal effect instead of the photo-to-thermal effect dominates the PTPDH process. Furthermore, the Lewis acidity-activity correlation experiments, in-situ radiation mass spectrometry and the H2 co-feeds experiment indicate that the hot electrons instead of the holes play a more significant role in propylene production. Last, the in-situ Drifts-MS technique and DFT calculations demonstrate light radiation conduce to the electron transition from metal to the unpopulated electronic states of -C3H7 fragments, while not conducing to the adsorbed C3H8 molecule, thus selectively promoting the 2nd dehydrogenation process and achieving a balanced chemical kinetic condition. Our findings demonstrate that Photo-assisted Thermal PDH is a promising method for balancing the 1st and 2nd dehydrogenation processes.

Mixing properties of Fe–Si alloys in liquid state based on linear temperature dependent energy parameters

Mixing properties of Fe–Si alloys in liquid state based on linear temperature dependent energy parameters

Bayesian inference of hysteretic behavior of unfrozen water content and electrical conductivity in saturated frozen rocks

Electrical conductivity is a fundamental geophysical parameter of electrical and electromagnetic techniques. It is widely used to non-intrusively assess and monitor physical and chemical properties in cold regions (i.e., liquid water saturation, ice content, salinity, and temperature). However, the significant hysteretic behavior observed in the temperature-dependent physical properties of frozen porous media (i.e., unfrozen water saturation and electrical conductivity) remains difficult to interpret during freezing and thawing processes. Such hysteretic characteristics are mainly associated with the connectivity and irregularity of the pore network. In this study, a physically-based model is developed to consider micro-scale heterogeneities in finite-size percolation during the freezing and thawing processes using an upscaling procedure. We introduce two simple parameters to describe the constrictivity and irregularity of the pore network: the radial factor and length factor of the pore throats within the capillaries. The proposed model accounts for the hysteretic mechanisms, upscaling single-pore hysteresis at the individual pore scale and pore irregularity hysteresis at the pore network scale. Furthermore, we use a series of experimental datasets to test the proposed hysteretic model. Our findings show an excellent agreement between the predicted values and the experimental dataset, indicating that our new physically-based models can effectively predict and interpret hysteretic behaviors in the temperature-dependent physical parameters of frozen rocks during freezing and thawing processes.

Electro-Optic Kerr Response in Optically Isotropic Liquid Crystal Phases

The results of an experimental investigation of the temperature and wavelength dependence of the Kerr constant (K) of mixtures with an increasing amount of chiral dopant in an isotropic liquid crystal phase are reported. The material was composed of a nematic liquid crystal (5CB) and a chiral dopant (CE2), which formed non-polymer-stabilized liquid crystalline blue phases with an exceptionally large value of K∼2 × 10−9 mV−2. The measurements were performed on liquid and blue phases at several concentrations covering a range of temperatures and using three wavelengths: 532 nm, 589 nm and 633 nm. The work focused on changes caused by concentration and their impact on the increase in the value of K, and it was found that in the case of the 5CB/CE2 mixture these changes were significant and quite systematic with temperature and wavelength. It is shown that the dispersion relation based on the single-band birefringence model described K well in isotropic liquid crystal phases at all of the measured concentrations. In an isotropic fluid, both temperature-dependent parameters in the dispersion relation had a simple linear form and, therefore, the K-surface could be described by only four constants. In the blue phase, the expression reproducing the temperature variation of K depended on concentration, which could vary from being almost linear to quasi-linear and could be represented well by an inverse exponential analytic expression.

A Between-Host Cholera Mathematical Model Incorporating Temperature Dependence

This paper establishes a between-host cholera model with temperature dependent parameter. This is done using system of ODEs to analyse the effect of temperature change on cholera disease. The model analysis reveals that when R0 < 1, the disease free equilibrium point is locally and globally asymptotically. It is also noticed that if R0 > 1, the endemic equilibrium point is locally and globally asymptotically stable. The sensitivity analysis of model parameters shows that R0 ​depends intensively on infection rate of pathogen α1​ normalized with temperature. An increase in infection rate of pathogen α1​ that is dependent on temperature by 10% would increase R0​ by 10% and decreasing it by 10% reduces R0 by 10%; hence, increasing the temperature of the environment where the pathogen lives would help reduce the rate of infection of the pathogen, thus reducing the reproduction number R0​. We conducted numerical simulation of the model in response to temperature changes, and the results indicate that Vibrio cholerae pathogens multiply faster at 23°C but between 23°C < T ≤ 43°C the pathogen multiplication is hindered, therefore, at 23°C, more pathogens are active to cause infection compared to high temperatures.

Influence of space conjugate temperature varying non-uniform heat sink/source on hydromagnetic slip water-EG (50:50) nanofluid

Significant cooling is an essential requirement in the field of aeronautical and bio-medical engineering, nuclear reactor system, solar collectors, and in development of electronic chips etc. Involving rotating conical geometries. In view of this, flow and heat transfer over conical geometries subject to different constraints of motion are highly needed. Implementation of magnetic field subject to flow pattern controls the fluid motion thereby imparting better cooling. Consideration of nanofluid instead of regular fluid yields prominent cooling of the associated surface. Such relevance has motivated the authors to work on magnetohydrodynamics and heat transfer investigation of water-EG (50:50) mixture based Al2O3 and Fe3O4 past heated and rotating down-pointing upright cone subject to impact of space and temperature varying non-uniform heat source or sink. Numerical solution of dimensionless governing equations is accomplished by implementing Runge-Kutta method. The findings indicate that swirl and axial velocities peter out with rise in magnetic parameter while both exhibit opposite impact in response to slip parameter. Temperature profiles upgrade due to amplification of space and temperature dependent parameters. Skin friction and heat transportation upsurge with growth of solid volume fraction of nanoparticle.

Analysis of high temperature and strain amplitude effects on low cycle fatigue behavior of pitting corroded killed E350 BR structural steel

High-tension structural steels are prone to accelerated fatigue damage from pitting corrosion and high-temperature. Despite adverse effects, research on their low cycle fatigue (LCF) behavior is limited. Specifically, studies analyzing temperature-dependent pit sensitivity effects, considering pit-related material’s susceptibility to surface topographic features variation and stress concentration are lacking. This study conducts LCF tests on pitting corroded killed E350 BR structural steel at multiple strain amplitudes and high temperatures. It develops temperature-dependent parameters, such as the cyclic softening pit sensitivity factor, suitable for integration into existing approaches like total cyclic plastic strain energy density (CPSED), power-law, average strain energy density (SED), Coffin-Manson, and pit stress intensity factor (pit-SIF). Further, it proposes multiple linear regression-based prediction models relating total CPSED and average SED with strain amplitude and temperature. Corroded specimens show higher plastic deformation and reduced peak stress, fatigue life, and total CPSED compared to uncorroded ones. The developed parameters, integrated with average SED approach, predicts LCF life within an error band of ±1.5, while power-law relationship reduces it to ±1.2. Moreover, pit-SIF approach estimates fatigue life within an error band of ±1.5. The findings provide critical knowledge for enhanced component design, leading to structural safety, performance, and fire resilience.

A fractional time-stepping method for unsteady thermal convection in non-Newtonian fluids

We propose a fractional-step method for the numerical solution of unsteady thermal convection in non-Newtonian fluids with temperature-dependent physical parameters. The proposed method is based on a viscosity-splitting approach, and it consists of four uncoupled steps where the convection and diffusion terms of both velocity and temperature solutions are uncoupled while a viscosity term is kept in the correction step at all times. This fractional-step method maintains the same boundary conditions imposed in the original problem for the corrected velocity solution, and it eliminates all inconsistencies related to boundary conditions for the treatment of the pressure solution. In addition, the method is unconditionally stable, and it allows the temperature to be transported by a non-divergence-free velocity field. In this case, we introduce a methodology to handle the subtle temperature convection term in the error analysis and establish full first-order error estimates for the velocity and the temperature solutions and 1/2-order estimates for the pressure solution in their appropriate norms. Three numerical examples are presented to demonstrate the theoretical results and examine the performance of the proposed method for solving unsteady thermal convection in non-Newtonian fluids. The computational results obtained for the considered examples confirm the convergence, accuracy, and applicability of the proposed time fractional-step method for unsteady thermal convection in non-Newtonian fluids.

Molecular interactions in ethyl caprate and 2-alcohol: Extended hard sphere framework

BackgroundThe study investigates the liquid densities and viscosities of Ethyl caprate (EC) combined with various 2-alkanols across a temperature spectrum of 293.15 to 323.15 K, aiming to understand the intermolecular interactions and deviations from ideal behavior. MethodsExperimental density and viscosity for the mixtures were measured with the SVM Stabinger viscometer. A modified rough hard-sphere theory was employed to model the viscosity of pure substances and binary liquids, incorporating temperature-dependent parameters. Significant Findings: All examined mixtures exhibited positive excess molar volumes. The modified rough hard-sphere model demonstrated a maximum viscosity error of 4.12 % for 2-hexanol in the temperature range of 293 to 323 K. For binary mixtures, the calculated values closely matched experimental data, with a maximum deviation of 3.51 % observed for the EC + 2-butanol mixture, highlighting the model's predictive accuracy.

Multimode optical thermometry using CaWO4 emission band

Optical thermometry has emerged as a promising technique for remote temperature sensing in plenty of applications, where traditional contact methods are useless. To date, many sensing strategies have been developed and realized, but most of them are based on monitoring of the single temperature dependent parameter. Here, we report simple dual-center materials (Eu3+ and Sm3+-doped CaWO4) that provide multimode optical thermometry. In addition to standard ratiometric approach based on intensity ratio between host and lanthanide emission bands, spectral position and bandwidth of CaWO4 emission band were successfully utilized as temperature sensitive parameters for the first time. It was shown that multimode sensing leads to broadening of temperature working range and enhances reliability of thermal sensing. The best thermometric characteristics were achieved for CaWO4:Sm3+ sample: Sr = 1.29 % K−1@273–673 K and δT = 0.15 K.

Results and Discussion of Dengue Model with Temperature Effects in Interval Environment

We have investigated a dengue model with temperature effects under interval uncertainty in this work. This study observed the Aedes aegypti temperature-dependent entomological parameters that affect dengue illness transmission dynamics in Taiwan's subtropical zone. A vector-host transmission model was used to examine how temperature fluctuations influence the development of pre-adult mosquitoes, their egg-laying rates, adult mortality, and the incubation rate of viruses within them. This study showed that although estimations of entomological parameters were positively correlated with slow temperature rises, no such correlation was detected with mosquito mortality or maturation rates, underscoring the slow rate of maturation of pre-adult mosquitoes. The findings suggest that the dynamic modeling of vector-host interactions is significantly influenced by temperature. Additionally, our modeling indicates that a temperature range of about 32°C is ideal for dengue transmission. In the future, control measure modeling and cost-effectiveness assessments may benefit from these insights.

A New Method for Temperature Dependent COM Parameters Extraction and Its Application to Simulation of SAW Devices

Abstract Surface Acoustic Wave (SAW) filters are widely used in mobile communication systems due to their superior performance. Yet, application scenarios for these SAW devices can be limited by temperature stability. The coupling of modes (COM) model is proved to be an efficient modeling approach to design SAW devices at present. However, temperature dependent COM parameter extraction is still absent. This work proposed a new method combining COM model with quasi-3D periodic finite element method (FEM) model coupled with thermal field, which can accurately and rapidly simulate and optimize the temperature stability of SAW devices. The COM parameters extraction method used in this study mainly depends on harmonic admittance. For a given temperature (T), the thermal effect can be computed by incorporating the linear thermal expansion coefficients and temperature coefficients of elasticity into the model. For validation, the admittance responses of a finite-length SAW resonator and the S-parameter of a double-mode SAW (DMS) for 42º YX-LiTaO3 piezoelectric substrate are calculated by using P-Matrix with the extracted COM parameters. For validation, the admittance responses of a SAW resonator on 42º YX-LiTaO3 under different temperature conditions is calculated. Furthermore, the temperature behaviors of a DMS filter on 42º YX-LiTaO3 are also analyzed based on the proposed method. Accurate predictions of the temperature behaviors for both resonator and filter were achieved and discussed. This investigation provides guidelines for design of high-performance SAW devices with good temperature stability.

Temperature-dependent optical and dielectric properties of polyvinyl chloride and polystyrene blends in terahertz regime

Terahertz time-domain spectroscopy (THz-TDS) has been used to study the temperature-dependent refractive index, absorption coefficient and dielectric constant of polyvinyl chloride/polystyrene (PVC/PS) blends in frequency range of 0.2–1.8 THz. Moreover, the Sellmeier and thermo optic coefficients of PVC/PS blends with different weight ratios have been explored in the temperature range of 25–80 °C. These parameters are used to evaluate the dispersion properties of these blends in the observed frequency range. A clear indication of temperature dependence on the values of refractive index and the real dielectric constant of these blends have been observed. Their values decrease linearly with increasing temperature up to 80 °C. Whereas, no noticeable change has been observed in the imaginary dielectric constant and the absorption coefficient. These results provide a database of temperature-dependent optical and dielectric parameters of PVC/PS polymer blends for their efficient utilization for device fabrication in THz technology.

Fluid Phase Equilibria Carbon Dioxide + Decane + Hexadecane Ternary System

The interest in understanding reservoir fluids' phase behavior is to increase hydrocarbon production without any flow assurance issues. Due to its opacity, the evident complexity of determining phase equilibrium data revolves around the thermodynamic modeling of model systems. The article presents experimental phase equilibrium data and thermodynamic modeling for the CO2 + decane + hexadecane ternary system at 283.15, 298.15, and 323.15 K and pressures up to 20 MPa. The transitions observed during this study were liquid-liquid (LL), vapor-liquid (VL), and vapor-liquid-liquid (VLL). The Peng-Robinson equation of state was used to model this ternary system for various compositions. The temperature-dependent binary interaction parameters (kij) for the CO2 + n-alkane mixtures were adjusted to the experimental data. Additionally, a binary interaction parameter for the n-C16H34/n-C10H22 pair, independent of temperature, was obtained through the critical volume of the components. The results reveal complex behaviors as the mixture's composition of hexadecane progressively increases. Adding this longer-chain linear hydrocarbon influences the phase behavior, leading to the emergence of liquid-liquid transitions and barotropic inversion in the system. This study contributes valuable data on model systems representing crude oil, highlighting complex behaviors in ternary systems with high carbon dioxide content.

Thermophoresis & Soret-DuFour on MHD Mixed Convection of a Nano Fluid with a Porous Medium over a Stretching Sheet with a Non-Uniform Heat Source/Sink

This work aimed to examine the effects of thermal diffusion (Soret) and diffusion-thermo (DuFour) on MHD mixed convective flow of a viscous nanofluid across a stretching sheet under a magnetic field embedded in a porous medium in the presence of non-uniform heat source/sink and chemical reaction. The governing equations are transformed into a set of ordinary differential equations using the similarity transformation approach. They are subsequently resolved computationally by use of the efficient Keller box method. The effects of different physical factors on concentration, temperature, and velocity profiles are graphically shown. Increasing Du values decreases temperature, although concentration profiles indicate the reverse. As temperature rises, the chemical reaction parameter Kr values increase, while the concentration profile decreases. The temperature was found to rise when the space-dependent (A1) and temperature-dependent (B1) parameters for heat source/sink increased. Additionally, a tabular presentation of the skin friction coefficient, Nusselt number, and Sherwood number behaviour is provided. Mixed convection heat and mass transfer flows are very important in manufacturing for designing reliable equipment, nuclear power plants, gas turbines, and various propulsion devices for aircraft, rockets, satellites, and spacecraft. The effects of non-uniform heat sources/sinks, thermophoresis, and chemical reactions on mixed convection flow play an important role in space technology and high-temperature processes.

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

Optical Study on Temperature-Dependent Absorption Edge of γ-InSe-Layered Semiconductor

We have studied the variations in the temperature-dependent absorption edge of a bulk InSe-layered semiconductor using photoconductivity (PC) measurements. From both the X-ray diffraction (XRD) and Raman experimental results, the structural phase of the as-prepared InSe sample was confirmed to be γ-polytype. Upon heating from 15 K to 300 K, the absorption edge of PC spectra was found to shift significantly toward lower energy, and the absorption edge as a function of temperature was further analyzed by the Varshni’s relationship and Bose–Einstein empirical equation. The Urbach energy as a function of temperature was obtained by fitting the absorption tail below the absorption coefficient of the PC spectrum, and the effective phonon energy can be derived from the temperature-dependent steepness parameter associated with Urbach energy. Our study indicates that the broadening of the absorption edge in the as-synthesized bulk γ-InSe is caused by a combination of electron/exciton–phonon interactions and thermal/structural disorder.

Analysis of the tri-hybrid Maxwell nanofluid flow with temperature-dependent thermophysical characteristics on static and dynamic surfaces employing Levenberg-Marquardt artificial neural networks

Using the Levenberg-Marquardt Artificial Neural Network (LM-ANN) model, this study applies computational neuroscience to the analysis of flow, heat, and mass transport characteristics. The novel characteristics of MHD Maxwell tri-hybrid nanofluid passing through static and moving wedge with temperature-dependent thermophysical properties (thermal conductivity, viscosity, diffusivity, and electrical field) in permeable, radiative, and mixed convection processes are investigated in this paper. Nonlinear PDEs are transformed into nonlinear ODEs using similarity variables. A dataset for the LM-ANN is generated across various scenarios by varying parameters such as the temperature-dependent viscosity parameter ( 0.07 − 0.1 ) , variable diffusivity parameter ( 0.4 − 2.5 ) , thermal buoyancy parameter ( 0.1 − 0.5 ) , nonlinear thermal buoyancy parameter ( 0.2 − 0.5 ) , thermal radiation ( 1 − 1.5 ) , buoyancy ratio parameter ( 0.2 − 0.5 ) , chemical reaction parameter ( 1 − 3 ) , and Schmidt number ( 1 − 3 ) using the Bvp5c numerical algorithm. The testing, training, and validation procedure of LM-ANN is utilized to examine the estimated solutions of specific situations, and the proposed algorithm is then validated. After that, the proposed LM-ANN is validated using regression analysis, MSE, and histogram analysis. The prescribed approach impeccably mirrors the recommended and cited findings, evincing a precision level within the realm of 10−9. Results indicate that increasing the permeability parameter from 1 to 1.2 and the temperature-dependent thermal conductivity parameter from 0.4 to 2.5 enhances the Nusselt number. Additionally, increasing the chemical reaction parameter from 1 to 3 leads to an increase in the Sherwood number. The significance of this study lies in its potential applications in industries such as aerospace, chemical processing, and energy systems, where efficient heat and mass transfer processes are crucial. For instance, the findings can be applied to enhance cooling techniques in high-performance aerospace components, optimize reactor designs in chemical plants, and improve thermal management in power generation systems.