Temperature-dependent Model Research Articles

Theoretical model to predict the fracture strength of fiber-reinforced ceramic matrix composites at elevated temperatures

The mechanical properties of ceramics and their composites in extreme environments, especially the fracture strength under high-temperature conditions, are key factors in assessing the safety and reliability of equipment. In this study, a temperature-dependent theoretical model is proposed with the aim of predicting the fracture strength for fiber-reinforced ceramic matrix composites at elevated temperature environments. This model encompasses not just the collective effects of various factors, including the Young's modulus of both fibers and matrix, the fiber volume fraction, and the melting point of the matrix, but also integrates the evolution of the Young's modulus of the fiber and matrix with temperature. Through the validation of multiple sets of experimental results, it is shown that the proposed model is able to predict the fracture strength of composites at elevated temperatures more accurately without conducting any high-temperature destructive tests. This model can help engineers and scientists to evaluate the mechanical properties of fiber-reinforced ceramic matrix composites at high temperatures more conveniently, thus guiding the optimal design and practical application of the materials, and thus improving the reliability and safety of the materials.

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Analytical temperature model for spindle speed selection in additive friction stir deposition

This paper describes a physics-based, analytical model for additive friction stir deposition (AFSD) spindle speed selection to achieve a desired deposition temperature. In the model, power input to the feedstock, which enables plastic flow and deposition, is related to the material temperature rise and subsequent flow stress reduction using Fourier’s conduction rate equation. Power input is modeled as frictional heating at the deposit-surface interface and adiabatic heating due to plastic deformation. The flow stress is predicted using the strain, strain rate, and temperature-dependent Johnson-Cook constitutive model for the selected feedstock alloy. Model predictions are compared to AFSD numerical simulation results available in the literature and experiments for aluminum alloys.

Characterizing Temperature-Dependent Acoustic and Thermal Tissue Properties for High-Intensity Focused Ultrasound Computational Modeling

High-intensity focused ultrasound (HIFU) thermal therapies utilize concentrated sound waves to ablate diseased tissue at precise locations within the body. Computational simulations of HIFU can assist clinicians by predicting the death of target tissues, identifying sensitive healthy tissues that risk thermal damage, and optimizing acoustic power delivery to minimize treatment times and maximize treatment efficacy. Accurate simulations require accurate inputs, and many computational solvers neglect property changes induced by tissue heating during treatment. Additionally, temperature-dependent tissue property data in the literature are relatively scarce. This study presents methodology for characterizing temperature-dependent acoustic and thermal properties in ex vivo porcine muscle tissue. From 20 – 50 °C, speed of sound is found to increase from approximately 1580 – 1620 m/s. The acoustic attenuation coefficient increases for 20 – 50 °C from 0.09 – 0.24 Np/cm at 0.5 MHz and 0.16 – 0.37 Np/cm at 1.6 MHz. Thermal conductivity and thermal diffusivity increase from 0.52 – 0.55 W/m °C and 0.147 – 0.158 mm2/s, respectively, over 20 – 60 °C. Specific heat capacity increases from approximately 3500 – 3800 J/kg °C, over 20 – 80 °C. Each property is consistent with data found in the literature, extends the literature to a larger temperature range, and, for acoustic properties, extends to unique frequencies. Temperature-dependent predictive models are also developed for each of the five properties. This study’s property measurement methodologies can be used to characterize other biological tissues, and the predictive models developed herein will facilitate future efforts in temperature-dependent modeling and uncertainty quantification of HIFU thermal therapies.

Temperature-Dependent Constitutive Model of Austenitic High-Strength A4L-80 Bolts after Furnace Fire

Temperature-Dependent Constitutive Model of Austenitic High-Strength A4L-80 Bolts after Furnace Fire

Experimental and analytical investigations on debonding response of embedded through-section ribbed GFRP bar-to-concrete joints at elevated temperatures

Embedded Through-Section (ETS) technique has gained a great deal of increasing interest in the shear strengthening of existing reinforced concrete members, which is based on using fibre-reinforced polymer (FRP) bars installed through predrilled holes into concrete via adhesive. One of the key issues associated with the bonding of ETS FRP bars to concrete is the debonding of FRP bar-to-concrete interfaces which leads to premature and brittle failure of strengthened members, particularly under elevated temperatures. However, little research has been conducted on the influence of elevated temperatures on the bond behavior between ETS FRP bars and concrete. This paper presents experimental and analytical evaluations of the bond response of ETS-ribbed glass FRP (GFRP) bar-to-concrete joints subjected to elevated temperatures. A total of 90 pullout specimens were tested to quantify the influences of the temperature, bar diameter, and concrete strength on the failure mode, load response and bond strength. An analytical model to reproduce the full-range debonding process of ETS-GFRP bar-to-concrete interfaces was presented, which allowed simultaneously considering both long and short embedded lengths, interfacial friction, and snap-back behavior. The load-slip response, stress field, and peak load were solved analytically, resulting in a closed-form solution for the critical length for snap-back and a non-closed-form solution for the effective embedment length. After an inverse analysis procedure was proposed for determining the bond parameters, the accuracy of the analytical solution was verified through comparisons with the self-conducted test results and existing experimental data. A new temperature-dependent bond strength model was also developed. The findings revealed a remarkable decline in the bond strength of up to 92.3 % as the temperature increased from 20 °C to 160 °C, with about 60–70 % of this degradation occurring close to the glass transition temperature (Tg) of the adhesive. At temperatures well above Tg, the bond strength decreased with the bar diameter and displayed insignificant improvement with higher concrete strength. The results also demonstrated that the proposed analytical approach and bond strength model can predict the bond response of ETS ribbed GFRP bars embedded in concrete with reasonable accuracy.

Modeling the interactive effects of sea surface temperature, fishing effort, and spatial closures on reef fish populations

Climate change can affect reef fish both directly (e.g., mortality, growth, fecundity) and indirectly (e.g., habitat degradation). The extent to which the effects of rising water temperature could drive changes in fish populations and if and how these effects may interact with potential management interventions remain unclear. The objective of this study was to test various hypothesized mechanisms by which sea surface temperature (SST) could affect reef fish population dynamics and explore these effects in combination with fishing effort restrictions and spatial closures. To do this, we modeled hypothesized relationships between SST and two governing parameters of the fish populations: intrinsic growth rate (r) and carrying capacity (K). We coupled these temperature-dependent fish population models with a fisheries harvest model and explored interactions between thermal effects, fishing effort level, and spatial closures. Under small closure scenarios, we found that the thermal effects models predicted substantially lower fish population biomass and harvest compared to the baseline (constant r and K) model. Under large closure scenarios, the thermal effects models more closely resembled the baseline. Generally, incorporating spatial closures mitigated some of the detrimental thermal effects on fish biomass and allowed for increased harvest under certain fishing effort levels. Whether intrinsic growth or carrying capacity most affected fish population levels also depended on the fishing effort and the spatial closure area. Overall, we described how fishing effort and spatial closures can influence the relative importance of key processes and the extent to which rising water temperatures affect fish populations and harvest.

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.

Analysis of Entropy Generation via Non-Similar Numerical Approach for Magnetohydrodynamics Casson Fluid Flow with Joule Heating.

This analysis emphasizes the significance of radiation and chemical reaction effects on the boundary layer flow (BLF) of Casson liquid over a linearly elongating surface, as well as the properties of momentum, entropy production, species, and thermal dispersion. The mass diffusion coefficient and temperature-dependent models of thermal conductivity and species are used to provide thermal transportation. Nonlinear partial differential equations (NPDEs) that go against the conservation laws of mass, momentum, heat, and species transportation are the form arising problems take on. A set of coupled dimensionless partial differential equations (PDEs) are obtained from a set of convective differential equations by applying the proper non-similar transformations. Local non-similarity approaches provide an analytical approximation of the dimensionless non-similar system up to two degrees of truncations. The built-in Matlab (Version: 7.10.0.499 (R2010a)) solver bvp4c is used to perform numerical simulations of the local non-similar (LNS) truncations.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

Microwave dielectric properties of normal, fibroelastotic, and malignant human lung tissue

Technological development of microwave treatment and detection techniques for lung cancer requires accurate and comprehensive knowledge of the microwave dielectric properties of human lung tissue. We characterize the dielectric properties of room temperature human lung tissue from 0.5 to 10 GHz for three lung tissue groups: normal, fibroelastotic, and malignant. We fit a two-pole Debye model to the measured frequency-dependent complex permittivity and calculate the median Debye parameters for the three groups. We find that malignant lung tissue is approximately 10% higher in relative permittivity and conductivity compared to normal lung tissue; this trend matches previously reported normal versus malignant data for other biological tissues. There is little contrast between benign lung tissue with fibroelastosis and malignant lung tissue. We extrapolate our data from room temperature to 37 °C using a temperature-dependence model for animal lung tissue and use the Maxwell-Garnett dielectric mixing model to predict the dielectric properties of inflation-dynamic human lung tissue; both approximations correspond with previously reported dielectric data of bovine and porcine lung tissue.

Improved Temperature-Scalable DC model for SiC power MOSFET including Quasi-Saturation effect

In this paper, accurate temperature-dependent static model for Silicon-Carbide (SiC) power MOSFET is presented. The proposed model is formed by two equations relating to linear and saturation operating regions. In this model, new formalism of the saturation drain current is introduced to consider the peculiar features observed in the I-V static characteristics of the SiC power MOSFET: a) moderate inversion region, or region of low gate voltages and b) quasi-saturation region, region of high gate voltages at which the drain current becomes less sensitive to the increase of gate voltage. In addition, the model captures with high-precision the transition region between linear and saturation region, pinch-off region, noticed in the output characteristics of the SiC power MOSFETs. It will be shown that the model equations ensure continuity and smooth transition between all operating regions. Temperature scaling of the model is carried out by its temperature scaling parameters. The proposed compact model is simple and efficient using reduced number of technology independent parameters. Simple parameter extraction procedure is described that uses an optimizer algorithm based on good experimental initial guess. Excellent agreement is obtained by comparing model to TCAD simulation and device measurement.

Temperature Dependent Growth Kinetics of Pd Nanocrystals: Insights from Liquid Cell Transmission Electron Microscopy.

Quantifying the role of experimental parameters on the growth of metal nanocrystals is crucial when designing synthesis protocols that yield specific structures. Here, the effect of temperature on the growth kinetics of radiolytically-formed branched palladium (Pd) nanocrystals is investigated by tracking their evolution using liquid cell transmission electron microscopy (TEM) and applying a temperature-dependent radiolysis model. At early times, kinetics consistent with growth limited is measured by the surface reaction rate, and it is found that the growth rate increases with temperature. After a transition time, kinetics consistent with growth limited by Pd atom supply is measured, which depends on the diffusion rate of Pd ions and atoms and the formation rate of Pd atoms by reduction of Pd ions by hydrated electrons. Growth in this regime is not strongly temperature-dependent, which is attributed to a balance between changes in the reducing agent concentration and the Pd ion diffusion rate. The observations suggest that branched rough surfaces, generally attributed to diffusion-limited growth, can form under surface reaction-limited kinetics. It is further shown that the combination of liquid cell TEM and radiolysis calculations can help identify the processes that determine crystal growth, with prospects for strategies for control during the synthesis of complex nanocrystals.

Temperature-Dependent Oviposition Models for Monochamus saltuarius (Coleoptera: Cerambycidae).

Monochamus saltuarius Gebler is a serious insect pest in Europe and East Asia regions, including Portugal, Spain, China, Japan, and Korea. It transfers the pine wood nematode Bursaphelenchus xylophilus to conifer trees, resulting in pine wilt disease (PWD). As temperature is a key factor influencing insect population dynamics, temperature-dependent models describing M. saltuarius oviposition could estimate population growth potential and evaluate outbreak risks. In this study, the longevity and fecundity of M. saltuarius females were measured under constant temperature conditions ranging from 20 to 32 °C, and temperature-dependent models were constructed. The longevity of M. saltuarius females ranged from 83.36 days to 22.92 days, with a total fecundity of 141 eggs and 52.77 eggs at 20 °C and 32 °C, respectively. To describe oviposition, we used a single-phase simulation describing oviposition as a single model and a two-phase simulation describing sexual maturation and oviposition as two separate models. These models effectively described M. saltuarius oviposition (r2 > 0.96) under constant temperature conditions, with the two-phase simulation demonstrating greater accuracy overall. Such models could facilitate assessments of PWD risks. The modeling framework of this study shows potential for predicting threats from various forestry and agricultural pests.

Effect of temperature-dependent and hysteretic sorption in computational mould risk analyses of wood fibreboard sheathing

Wood is known for its complex hygroscopic properties. Previous studies show that both the temperature-dependency and the hysteresis in sorption should be included in the physical models, if the most accurate results are desired from simulations of building assemblies with wood-based materials. Literature is scarce on the topic, where the significance of these aspects on the reliability of mould-risk assessments is inspected. Exterior walls with glass wool insulation and LDF-sheathing were studied by using two different models with previously measured material data. Over 300 hygrothermal simulations were carried out, where mould-indices were computed on the warm side of the sheathing. According to both conventional and more sophisticated model, the studied walls perform well in cold climate if no driving rain leaks in. Temperature-dependent and hysteretic model yields slightly smaller mould-indices in general, but for certain leaky cases the difference is significant, which indicates the importance of sophisticated sorption-properties in modelling.

Impact and Adhesion Mechanics of Block Copolymers in Cold Spray: Effects of Rubbery Domain Content

The impact and adhesion mechanics of two-phase block copolymers during high-velocity impacts are studied experimentally and computationally to understand the effect of the rubbery phase on bonding behavior in cold spray additive manufacturing. Micron-scale (10-20 μm) spherical particles of polystyrene-block-polydimethylsiloxane with varying rubbery phases are impacted on a silicon substrate by using a laser-induced projectile impact test setup with impact velocities in the range of 50-600 m/s. Experiments indicate that the minimum impact velocity for polymer particles adhering to the substrate decreases with increasing rubbery phase content. A strain rate- and temperature-dependent constitutive model and cohesive zone model are calibrated for each material by comparing the deformed and computed deformed particle shapes and coefficient of restitution values of the rebounding particles. Computational results show that increasing the rubbery phase content in block copolymers increases plastic energy dissipation from 89 to 96% and the critical strain energy release rate from 1.87 to 9.3 J/m2 at 140 m/s, and thus contributes to the observed decrease in the minimum impact velocity required for block copolymers to adhere to substrates. The discovered direct relationship between soft phase content and critical strain energy release rate implies that increased soft-rubbery PDMS content in block copolymers enhances adhesion through improved chain mobility, better surface asperities coverage, and enhanced wetting, due to its lower surface energy and greater adiabatic heating.

Nonlinear vibration characteristics of thermo-viscoelastic coupling of moving PET films with temperature-dependent elastic modulus

In order to improve the transport stability of roll-to-roll produced polyethylene terephthalate films (PET films) in high temperature environments, this paper investigates the nonlinear vibrational properties of moving PET films under a uniform temperature field by considering the temperature dependence of the elastic modulus. Firstly, constant tensile tests were conducted to test the elastic modulus values of PET films at different hot air temperatures, and the Wachtman model was improved to establish a temperature-dependent elastic modulus model for PET films. Secondly, based on the Kelvin-Voigt model, the nonlinear vibrational equilibrium differential equations of the system were established by applying the Bubnov-Galerkin method of discretization according to D’Alembert’s principle. Finally, numerical simulations based on the 4th-order Runge-Kutta method were carried out to analyze the effects of films width, external excitation frequency, external excitation amplitude, transfer velocity, ambient temperature, damping coefficient and viscoelasticity coefficient on the nonlinear vibration of the system. This provides a theoretical basis for the commissioning of stabilized transfer parameters for roll-to-roll production of PET films.

The emission mechanism of gamma-ray bursts from supernovae

The connection between gamma ray bursts and supernovae is studied using a temperature-dependent vacuum model. A harmonically bound particle–antiparticle system is consistent with both Hawking radiation and Casimir effect, therefore, the Maxwell–Sellmeier model correlates the speed of light to temperature. According to quantum field theory, Lorentz invariance is violated only for temperatures larger than [Formula: see text][Formula: see text]K. Introducing in the temperature distribution of a 2D simulation for a type Ia supernova the speed of light temperature dependence proposed in this paper, results a speed of light distribution. A theoretical snapshot of this distribution at an arbitrary distance is consistent with photon photo finish resulted from experiments. Variable speed of light shows that supernova could be accompanied by gamma-ray bursts.

A multi-horizon fully coupled thermo-mechanical peridynamics

This paper presents a fully coupled thermo-mechanical peridynamic model for simulating interactive thermo-mechanical material responses and thermally induced fracturing of solids. A temperature-dependent constitutive model and a deformation-dependent heat conduction model are derived for state-based peridynamic formulation. The dispersion relation and truncation error of the state-based peridynamic heat equation are analyzed for the first time. It is found that as non-locality becoming more pronounced, the dissipative rate of heat is reduced, and the truncation error becomes larger. A small horizon can effectively mitigate oscillation while reducing the error in the temperature field. For coupled thermo-mechanical modeling, a novel multi-horizon scheme is introduced where the thermal field is solved with a different horizon than that of the mechanical field. The multi-horizon scheme allows for the implementation of a distinct degree of non-locality for different physical field. Comparing with the constant-horizon scheme, we demonstrate through numerical examples that the multi-horizon scheme offers smoother and more accurate solutions and serves a promising option for peridynamics-based multi-physics simulations.

Development of temperature-controlled batch and 3-column counter-current protein A system for improved therapeutic purification

Maximizing product quality attributes by optimizing process parameters and performance attributes is a crucial aspect of bioprocess chromatography process design. Process parameters include but are not limited to bed height, eluate cut points, and elution pH. An under-characterized chromatography process parameter for protein A chromatography is process temperature. Here, we present a mechanistic understanding of the effects of temperature on the protein A purification of a monoclonal antibody (mAb) using a commercial chromatography resin for batch and continuous counter-current systems. A self-designed 3D-printed heating jacket controlled the 1 mL chromatography process temperature during the loading, wash, elution, and cleaning-in-place (CIP) steps. Batch loading experiments at 10, 20, and 30 °C demonstrated increased dynamic binding capacity (DBC) with temperature. The experimental data were fit to mechanistic and correlation-based models that predicted the optimal operating conditions over a range of temperatures. These model-based predictions optimized the development of a 3-column temperature-controlled periodic counter-current chromatography (TCPCC) and were validated experimentally. Operating a 3-column TCPCC at 30 °C led to a 47% increase in DBC relative to 20 °C batch chromatography. The DBC increase resulted in a two-fold increase in productivity relative to 20 °C batch. Increasing the number of columns to the TCPCC to optimize for increasing feed concentration resulted in further improvements to productivity. The feed-optimized TCPCC showed a respective two, three, and four-fold increase in productivity at feed concentrations of 1, 5, and 15 mg/mL mAb, respectively. The derived and experimentally validated temperature-dependent models offer a valuable tool for optimizing both batch and continuous chromatography systems under various operating conditions.