Temperature-dependent Material Model Research Articles

Impact of the variability of material constitutive models on the thermal response of reinforced concrete walls

PurposeThis study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.Design/methodology/approachThe study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.FindingsIt is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.Originality/valueLimited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

Thermal-structural analysis for reusable thrust chamber using temperature-dependent CuZr alloy model adapted to cryogenic to elevated temperature

The inner liners of reusable regeneratively-cooled thrust chambers endure significant thermal and mechanical loads, resulting in doghouse deformation and even cracks during cyclic operations. To correctly reveal the failure mechanism of the thrust chamber inner liner, this study investigates the material properties for CuZr alloy and develops a temperature-dependent material model spanning from cryogenic to elevated temperatures. Firstly, we experimentally obtain the thermal and mechanical properties of the material in the temperature range from 77 K to 900 K. The experimental data reveal that the material properties vary considerably with temperature. Then, to precisely characterize the mechanical behavior of CuZr alloy under cyclic loading across a wide temperature range, a material model that combines Voce nonlinear isotropic hardening model with Chaboche nonlinear kinematic hardening model is proposed, and temperature-dependent parameters of this model are calibrated and verified. Finally, the developed material model is applied to perform the thermal-structural analysis on a reusable thrust chamber, with the aim of investigating its failure mechanism. The results demonstrate the capability of the developed model to accurately replicate the doghouse deformation of the inner liner and quantitatively capture the ratcheting effect.

Cracking mechanism analysis and experimental verification of encapsulated module under high low temperature cycle considering residual stress

Aiming at the cracking failure of the modified epoxy resin encapsulated module as a result of interface failure under high low temperature cycles, numerical simulation and experimental studies were carried out. Firstly, the residual stress field in the encapsulated module was reconstructed after measuring the curing residual stresses in epoxy resin using the hole-drilling method. Temperature-dependent material models were developed after testing the mechanical and thermal characteristic parameters of encapsulated module components, such as modified epoxy resin, in high and low temperature conditions. Then, a finite element model of a high-reduction encapsulated module with multiple components, multiple interfaces, and complicated contacts was established considering residual stress and temperature effects. To simulate the failure behaviour of the resin-embedded part interfaces, the cohesive zone model was utilized. Finally, the stress and strain of the encapsulated module under high and low temperature cycles were simulated, and their distribution features and cracking failure mechanism were analyzed. The results indicate that regardless of the heating/cooling process, significant due to a mismatch in thermal expansion coefficients between the resin and the embedded parts. As the temperature approaches the glass transition temperature Tg, the difference grows dramatically. The resulting thermal stress, together with the residual stress, led to the interface failure in encapsulated module. The numerical results were in good agreement with the high and low temperature cycle test results of the encapsulated module, which verified the effectiveness of the analysis method and the established finite element model. The investigation provides an important reference for the high-reliability design of the encapsulation module.

Characterization of the compaction and frictional behaviors of the green anode paste at various temperatures

The aim of this work was to investigate the effect of the green anode paste temperature on its compaction behavior and on its frictional behavior with a steel mold during the anode forming process. An experimental campaign composed of monotonic compaction tests and friction tests was carried out at temperatures ranging from 130 °C to 170 °C. The compaction tests were performed with an instrumented thin-walled mold to characterize the axial and radial behaviors of the anode paste. The results highlight that the nonlinear behavior of the anode paste is significantly affected by temperature. Friction tests were performed under various normal loads to evaluate the static and kinetic friction coefficients of the paste/steel at different interface temperatures. The results bring a new insight into the behavior of the anode paste and pave the way for the development of a temperature-dependent material model of the anode paste compaction process.

Behavior and Capacity of Moment-Frame Members and Connections during Fire

This paper focuses on investigating the structural behavior of members and connections that are part of moment frames under the combined effect of bending moment and thermally induced axial force during a compartment-fire event. The finite-element analysis method was employed to conduct this study using models benchmarked against experimental data from several past studies while utilizing temperature-dependent material models. A numerical parametric study on typical floor beams with slender elements for compression were conducted under combined bending and axial loading to develop interaction-capacity curves at temperatures representing fire events. The results were compared against the member-strength equations provided in the AISC Specification. The analysis results demonstrated that the AISC beam-column strength equations including the combined effects of axial-load and bending moment provided reasonable estimates for member-slenderness ratios greater than 60, but overestimated the strength of beams with slender elements for low member-slenderness ratios. Combined-load-strength studies were also conducted on a typical connection used in moment frames. The moment-connection behavior was governed by failure modes exhibited at the ends of floor beams. Therefore, the interaction equations available for beam columns resulted in conservative estimates and are recommended for calculating moment-connection capacity during compartment-fire scenarios.

Temperature-dependent model for ferroelectrics embedded into two-dimensional polygonal finite element framework

A two-dimensional constitutive model, which is based on micromechanical rotation of domains, is presented in this work to demonstrate the non-linear behavior of ferroelectric materials under different loading conditions. A temperature-dependent combined material model that incorporates both phases i.e., tetragonal and rhombohedral phases is embedded into two-dimensional polygonal finite element framework. The developed material model which has easily overcome shortcomings related to tetragonal model and rhombohedral model alone. This model is developed on the morphotropic phase boundary which has produced desired behavior based on macroscopic material parameters. Temperature effects on behavior of combined material model are obtained in the work. Furthermore, polarization rotation test and temperature effects on polarization rotation are also studied in present work. There are numerous studies conducted on ferroelectric materials. All of them incorporate behavior of ferroelectric material confined to specific conditions. Either tetragonal model or rhombohedral model has been used extensively to get the desired behavior based on macroscopic material parameters. This model has some limitations in either satisfying one or another condition related to experiments conducted to study the behavior of ferroelectric materials. The tetragonal material model is capable of producing results with microscopic material properties but in case of macroscopic material parameters the tetragonal model is insufficient to produce the desired behavior for PZT ceramics. The rhombohedral material model is sufficient to produce results based on macroscopic material parameters. Therefore, model is extended by rhombohedral phase, allowing a mixture of both phases with varying quantities inside the grain. Though, rhombohedral model is also not used to that extent. We have developed material model that gives desired behavior and overcomes the hurdles in the existing model.

Correction to: Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Correction to: Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Generalized temperature-dependent material models for compressive strength of masonry using fire tests, statistical methods and artificial intelligence

Masonry has superior fire resistance properties stemming from its inert characteristics, and slow degradation of mechanical properties. However, once exposed to fire conditions, masonry undergoes a series of physio-chemical changes. Such changes are often described via temperature-dependent material models. Despite calls for standardization of such models, there is a lack in such standardized models. As a result, available temperature-dependent material models vary across various fire codes and standards. In order to bridge this knowledge gap, this paper presents three methodologies, namely, regression-based, probabilistic-based, and the use of artificial neural (ANN) networks, to derive generalized temperature-dependent material models for masonry with a case study on the compressive strength property. Findings from this paper can be adopted to establish updated temperature-dependent material models of fire design and analysis of masonry structures.

A temperature-dependent material model for numerical simulation of steel fibre reinforced concrete

Steel fibre reinforced concrete (SFRC) has been increasingly used in super high-rise buildings, nuclear facilities and infrastructures in recent years. Fire is one of the inevitable threats to these structures during their service life, therefore it is of great significance to study the behaviour of SFRC at elevated temperatures. In this study, a new temperature-dependent constitutive model is proposed to describe the mechanical behaviour of SFRC at elevated temperatures. In the present model, the plastic damageof SFRC is considered using the sum of accumulated shear damage and compaction damage, and a temperature-dependent coefficient is introduced to evaluate the plastic strain at fracture. To predict the mechanical properties of SFRC at elevated temperatures, a set of equations are proposed based on the collected experimental data. Particularly, three indexes, i.e. water to binder ratio, moisture content and alumina content, are introduced to calibrate the normalised compressive strength of SFRC. Besides, the effects of fibre dosage and fibre shape are taken into account. The proposed model is used to predict the mechanical behaviour of SFRC subjected to elevated temperatures and is demonstrated to be effective.

Cyclic plasticity models and fatigue criteria for exhaust manifold life assessment in the context of limited material data available

ABSTRACT The work focuses on predictive capabilities of fundamental cyclic plasticity and fatigue life models, which can be calibrated using limited amount of experiments as specific ones needed for more advanced models are often absent. The analyses are conducted for the synthetic case of exhaust manifold made from cast iron. The thermal boundary conditions from the forced convection were obtained from the computational fluid dynamics considered as a conjugate heat transfer problem. Two rate-independent and temperature-dependent material models were calibrated for structural analyses. Both were validated with experiments on isothermal and anisothermal levels. Sequential thermal–mechanical finite element simulations were performed. Two fatigue life models were employed. The first was a temperature-dependent strain-based fatigue life criterion calibrated from uniaxial data. The second was a temperature-independent energy-based fatigue life criterion resulting in twice lower life than the strain-based criterion, while none of the plasticity models made a significant difference in that prediction.

Ceramic tiles as sustainable, functional and insulating materials to mitigate fire damage

ABSTRACT Owing to their lower costs and functional properties, the construction industry has been increasingly adopting synthetic organic polymer (SOP) materials into linings, interiors and non-load bearing structural components. While SOPs have favourable properties and characteristics at ambient conditions, the same materials often perform poorly under moderate-to-elevated temperatures such as that arising from a building fire. In fact, most SOPs tend to combust and decompose at elevated temperatures which, unlike traditional building materials such as concrete and ceramic tiles, is proven to not only contribute to the fire but also to adversely affect evacuation and firefighting operations. From a fire engineering perspective, this paper tests a hypothesis in which ceramic tiles (CTs) are expected to outperform SOPs and commonly used insulations as finishing and lining materials under fire conditions. As such, this study showcases a thorough comparison between the behaviour of commonly available CTs, SOPs and insulations in temperatures ranging between 25 and 1000°C. Then, this paper analyses published CT models to derive temperature-dependent material models with the aid of machine learning (ML). Findings of this work advocate the use of CTs as favourable finishing and interior lining materials to enable realising improved structural fire performance and fire response managements, as opposed to SOPs, composites or insulations. The outcome of this work is expected to be of interest to architects, first responders, building officials, fire and structural engineers.

Fire performance of masonry under various testing methods

Masonry, as a construction material, is known to perform well under elevated temperatures, which makes it an attractive choice for structural applications. This superior performance is a reflection of its inert thermal characteristics, good stability, and slow degradation of mechanical properties. Still, and similar to other construction materials, masonry undergoes a series of temperature-dependent physio-chemical and phase changes once exposed to high temperatures. Such changes are determined through temperature-dependent material models often obtained by means of physical tests on representative masonry specimens. A deep dive into the open literature shows that not only we lack standardized procedures for testing masonry under fire conditions, but existing researcher-derived methods vary significantly. As a result, available temperature-dependent material models also vary given their sensitivity to testing parameters (i.e., set-ups, heating history etc.). It is primarily due to the aforenoted observations that we continue to lack a holistic understanding of the fire behavior of masonry which also extends to limiting advancements in performance-based design of masonry structures. In order to bridge this knowledge gap, this paper reviews commonly adopted fire testing methods on masonry and the wide scatter of corresponding temperature-dependent material models to provide researchers and practitioners with much-needed knowledge that is currently missing in this domain. Findings from this review can then be used to develop modern and up-to-date temperature-dependent material models to facilitate the design of new masonry constructions or analysis of existing ones (including historical buildings).

Updating strategy of a domain decomposition preconditioner for parallel solution of dynamic fracture problems

Iterative methods used for analysis of multiphysics problems that result in localized solution features may be highly demanding from a computational standpoint and often require special treatment to be more efficient. Dynamic fracture of metals is one such example in which a nonlinear thermo-mechanical system leads to strain localization, resulting in shear bands and/or cracks, in which iterative solvers may have difficult time converging.In the current paper, we develop a novel updating domain decomposition preconditioner for parallel solution of dynamic fracture problems, in which shearbands are modeled using a temperature-dependent viscoplastic material model and fracture is modeled by the phase-field method. The domain decomposition method is based on the Additive Schwarz Method (ASM).The key idea is to decompose the computational domain into two subdomains, a localized subdomain that includes all localized features of the solution and a healthy subdomain for the remaining part of the domain. In this way, one can apply different solvers in each subdomain, i.e. focus more effort in the localized subdomain. In this work, an LU solver is applied in both subdomains, however, while the localized subdomain is solved exactly at every nonlinear iteration, the healthy subdomain LU operator is reused and only selectively updated. Hence, significant CPU time savings associated with the setup of the preconditioner can be achieved.To this end, we propose a strategy for updating the preconditioner in the healthy subdomain. The strategy is based on an idealized performance-based optimization procedure that takes into account machine on-the-fly execution time. Three dynamic fracture problems corresponding to different failure modes are investigated. Excellent performance of the proposed updating preconditioner is reported in serial and parallel computers.

Effect of constitutive material model on the finite element simulation of shear localization onset

One of the most challenging problems in the field of machining is to determine the onset of shear localization. The consequences of the emergence of shear localized chips are fluctuations in the machining forces, tool wear, deterioration of the surface quality and out-of-tolerance machined components. Several constitutive material models are developed for the simulation of shear localization during machining, especially for Ti6Al4V. However, the accuracy and capability of the proposed models for the prediction of shear localization onset have not been investigated yet. In this study, the effect of different constitutive material models in the prediction of shear localization onset has been investigated. Different material models are studied including the Johnson-Cook (J-C) material model with Cockcroft-Latham damage model, J-C material model with a J-C damage model, models based on modified J-C material models (MJ-C) with strain softening terms, and material model with power-law type strain hardening and strain rate sensitivity, with polynomial thermal softening and polynomial temperature-dependent damage. The results of the finite element models are verified using orthogonal cutting experiments in terms of chip morphology and machining forces. Metallography techniques are used along with SEM observations to elucidate the distinction between continuous and shear localized chips. The results of this study indicate that three models are capable of predicting shear localization onset. However, when compared to the experiments, where a critical cutting speed of 2.8 m/min is obtained for shear localization onset, the results revealed that the model proposed by Sima and Ozel (2016) which is a model based on MJ-C model with temperature-dependent overarching modifier and temperature-dependent material model parameters is more accurate for the prediction of shear localization onset during machining Ti6Al4V. This model is shown to reveal a good prediction for the machining forces as well.

Elasto-Viscoplastic Material Model of a Directly-Cast Low-Carbon Steel at High Temperatures.

A model-based process control of material production processes demands realistic material models describing the local evolution of the thermal and mechanical state variables, i.e., temperature, stress, strain, or plastic strain, for the relevant microstructure state. In the present work, a material model for the specific microstructure in a continuously cast strand shell, viable for reproducing cyclic viscoplastic effects, was developed for a 0.17 wt.% C steel. Experimental data was generated using directly-cast samples and a well-controllable testing facility to apply representative loading conditions. Displacement- and force-controlled experiments in the temperature range of 700–1100 °C were conducted, with a special focus on the relevant strain rates documented for the straightening operation. A temperature-dependent constitutive material model combining elastic, plastic, and viscoplastic effects was parameterized to fit the whole set of experimentally-determined material response curves. In order to account for the cyclic plastic material response, a combination of isotropic and kinematic hardening was considered. The material model sets a new standard for the material description of a continuously cast strand shell, and it can be applied in elaborate continuous casting simulations.

Determination of Directional Residual Stresses by the Contour Method

This study evaluated residual stresses in heat-treated specimens made of 316L stainless steel using FE analysis and compared them with stresses determined by the contour method. Contour method is usually used just for evaluation of residual stresses that are normal to the cut plan. In the current study this approach is extended and both normal and tangential stresses are determined. The specimens were cut using wire electrical discharge machine and the contours of the cut were measured using a coordinate measuring machine. The prior treatment of the specimens was simulated using the finite-element method. An appropriate boundary condition and temperature-dependent material model were employed. The finite-element model was validated against neutron-diffraction measurement data. The results showed a good agreement in normal and tangential directions of stress.

Properties and material models for common construction materials at elevated temperatures

Abstract Construction building materials experience physio-chemical and phase changes when subjected to elevated temperatures. These changes are often defined through temperature-dependent material models. A cross examination of adopted models reveals that such models markedly varies across open literature and fire guides (i.e. ASCE, Eurocodes etc.). This, not only complicates the process of fire analysis and design, but can also hinders ongoing standardization initiatives. In support of these initiatives, this paper leverages symbolic regression through artificial neural networks (ANN) and genetic programming (GP) to arrive at representative temperature-dependent thermal and mechanical material models for common building materials, namely: normal strength concrete, masonry, structural steel, stainless steel, cold-formed steel and wood. The proposed material models have the potential to regulate and modernize structural design under extreme loading conditions, i.e. fire. The result of this investigation demonstrates the value of utilizing artificial intelligence (AI) into comprehending the complex nature of temperature-induced effects on building materials; together with deriving associated temperature-dependent models.

Simplified temperature-dependent elasto-viscoplastic deformation and fracture modeling of a talcum-filled PP/PE co-polymer

Thermoplastic polymers are widely used in the automotive industry, however, for industrial crash applications there is no generally agreed constitutive theory for modeling large deformations of the rate- and temperature-dependent, non-linear, elasto-viscoplastic material behavior, especially for crash load cases around the glass transition temperature. Nevertheless, for simulating the material response, a prediction model capturing the polymer specific features is required. Therefore, in this work a temperature-dependent material model is presented for a talcum-filled polypropylene/polyethylene co-polymer. The model predicts the rate-dependent deformation and fracture behavior at different ambient temperatures and stress states. It has been implemented in a user material subroutine for an explicit crash code, based on a profound material characterization at −35∘C, 20∘C and 90∘C, covering the load cases uni-axial tension, bi-axial tension, uni-axial compression and shear. For the parameterization and calibration of the model, a parameter identification procedure is presented using a non-linear interpolation method, based on uni-axial tensile tests in between the aforementioned fully characterized ambient temperatures. These additional tests are utilized for obtaining the variation of the most important mechanical properties such as the elastic modulus, the strain hardening, the non-linear plastic Poisson's ratio and the fracture strain over the desired range of ambient temperatures. The model is validated utilizing bi-axial tension tests and glove box flap component punch tests at combinations of ambient temperatures and stress states which have not been used for characterization. The numerical predictions were in good agreement with the experimental observations.

Fire resistance evaluation through artificial intelligence - A case for timber structures

With the ever-growing surge of new technologies, there seems to be an ongoing inertia towards integrating automation and cognition into various engineering applications. Despite a number of initiatives, and oddly enough of all civil engineering sub-disciplines, the structural fire engineering and fire safety community continues to embrace a classical stance to tackle the problem of fire. In support of growing demands to adopt performance-based solutions, this paper showcases the potential of integrating Artificial Intelligence (AI) as a unique technology to assess performance and fire resistance of structures. More specifically, this study sheds light on the proper use of AI to derive temperature-dependent material models for wood, together with simple expressions that can be used to trace thermo-structural response of timber elements/components (i.e. floor assemblies, beams, columns, and connections). These expressions comprehend the naturally complex temperature-induced physio-chemical changes to timber properties, including creep and charring, and hence do not require input of such properties nor special computing software. The outcome of this study clearly shows the merit of utilizing AI to modernize fire resistance evaluation given that the developed AI-models have high degree of perception (i.e. learn from past behaviors) and ability to improve their prediction capability through independent and unsupervised learning.

Properties and material models for modern construction materials at elevated temperatures

The construction industry has adopted a number of new building materials over the past few years. While these materials are specifically designed to achieve improved strength and durability characteristics at ambient conditions, the performance of modern construction materials (MCMs) under extreme conditions such as fire is still not understood. Under elevated temperatures, MCMs not only undergo a series of physio-chemical degradations, but these degradations are often of a much severe magnitude than that in traditional construction materials (TCMs). Despite ongoing efforts, there continues to be a lack of guidance/provisions on how to account for such temperature-induced degradations in MCMs. This adds another dimension of complexity to researchers and engineers seeking to carry out fire resistance evaluation and also presents a major challenge towards promoting standardization and performance-based solutions for fire engineering applications. In order to bridge this knowledge gap, this paper presents a methodology to develop temperature-dependent material models for MCMs such as high strength/performance concrete (HSC/HPC), high/very high strength steels (HSS/VHSS), and fiber-reinforced polymer (FRP) composites, using two techniques of artificial intelligence (AI) namely: artificial neural networks (ANNs) and evolutionary genetic algorithms (GAs). The outcome of this study showcases the merit of integrating AI into understanding the complex behavior of MCMs under fire conditions as well as in deriving temperature-dependent material models for these materials.