Material Parameters Research Articles

Photo-crosslinkable hydrogel incorporated with bone matrix particles for advancements in dentin tissue engineering.

The objective of this study was to create injectable photo-crosslinkable biomaterials, using gelatin methacryloyl (GelMA) hydrogel, combined with a decellularized bone matrix (BMdc) and a deproteinized (BMdp) bovine bone matrix. These were intended to serve as bioactive scaffolds for dentin regeneration. The parameters for GelMA hydrogel fabrication were initially selected, followed by the incorporation of BMdc and BMdp at a 1% (w/v) ratio. Nano-hydroxyapatite (nHA) was also included as a control. A physicochemical characterization was conducted, with FTIR analysis indicating that the mineral phase was complexed with GelMA, and BMdc was chemically bonded to the amide groups of gelatin. The porous structure was preserved post-BMdc incorporation, with bone particles incorporated alongside the pores. Conversely, the mineral phase was situated inside the pore opening, affecting the degree of porosity. The mineral phase did not modify the degradability of GelMA, even under conditions of type I collagenase-mediated enzymatic challenge, allowing hydrogel injection and increased mechanical strength. Subsequently, human dental pulp cells (HDPCs) were seeded onto the hydrogels. The cells remained viable and proliferative, irrespective of the GelMA composition. All mineral phases resulted in a significant increase in alkaline phosphatase activity and mineralized matrix deposition. However, GelMA-BMdc exhibited higher cell expression values, significantly surpassing those of all other formulations. In conclusion, our results showed that GelMA-BMdc produced a porous and stable hydrogel, capable of enhancing odontoblastic differentiation and mineral deposition when in contact with HDPCs, thereby showing potential for dentin regeneration.

Optimising synthetic datasets for machine learning-based prediction of building damage due to tunnelling

Assessment of tunnelling-induced building damage is a complex Soil-Structure Interaction (SSI) probelm, influenced by numerous geometric and material parameters of both the soil and structures, and is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture this complex behaviour. Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic datasets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reliance solely on parameter domains and ranges derived from case studies can lead to imbalanced data distributions and subsequently poor performance of models in less populated regions. In this paper, we introduce a strategy for designing optimal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI. Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, statistical modelling, and re-sampling in statistically significant regions. This evaluation allows us to refine the model’s input space, seeking scenarios that mitigate output distribution imbalances. The procedure is repeated until the datasets achieve a satisfactory balance for training metamodels, minimising bias effectively. Our findings highlight the success of this approach in identifying an optimal and feasible input space that significantly reduces imbalanced distributions of output features. This approach not only proves effective in our study but also offers a versatile methodology that could be adapted to other disciplines aiming to generate high-quality synthetic datasets.

Thermal modeling of porous medium integrated in PCM and its application in passive thermal management of electric vehicle battery pack

The integration of a composite of porous medium with phase change material (PCM) offers significant advantages in thermal management systems, enhancing heat transfer efficiency and addressing various thermal regulation challenges. This approach utilizes the PCM's latent heat absorption and the enhanced thermal conductivity provided by the porous medium, resulting in optimized system performance. Its applicability spans across electronics cooling and building insulation systems. However, predicting the thermal behavior of this composite material is challenging, necessitating computational tools to anticipate its response under different conditions and evaluate its influence on cooling strategies. The objective of this study is to create a computational tool specifically tailored to evaluate constitutive parameters of this composite material, thereby providing a comprehensive description of its thermal behavior. To achieve this goal, the multiscale homogenization principle is employed to assess the composite's effective thermophysical material properties using the representative volume element approach. The repeating unit cell of the aluminum lattice is incorporated into the PCM to define a representative volume element. The finite element method (FEM) is utilized to solve the three-dimensional homogenization problem, yielding an orthotropic effective thermal conductivity due to the inherent symmetry of the repeating material cell. Moreover, the study leverages the apparent heat capacity method to effectively manage the phase transitions within the PCM domain, utilizing smooth and temperature-dependent functions to accurately describe the thermophysical properties of the PCM. Integrating the composite into battery pack thermal management, this study thoroughly examines thermal dynamics by comparing outcomes with and without PCM integration. The transient thermal problem is accurately tackled using the FEM, employing the evaluated effective constitutive parameters of the homogenized composite to minimize computational effort. The results indicate a notable decline in the highest temperatures of the battery pack, leading to a reduction of about 14 °C at the specific moment when the phase change material fully transitions into its liquid form. The obtained results emphasize the effectiveness and practical feasibility of the proposed thermal management strategy. The modeling approach presented provides a robust tool with significant efficiency in reducing computational time for analyzing the thermal behavior of large models, as the utilization of the homogenization technique notably decreases the computational time.

Design and Control of the Natural Frequency of Brake Discs in the Aspect of the Gray Cast Iron Production Process

The results of research on the influence of the chemical composition of cast iron and its potential changes in the production cycle on the elastic properties and the correctness of numerical simulations of the natural frequency of ventilated brake discs are presented. The tests were carried out for three grades of gray cast iron with flake graphite with a eutectic saturation coefficient ranging from 0.88 to 1.01. A quantitative metallographic assessment of the pearlitic cast iron matrix and graphite precipitates was carried out, and the hardness and compressive/tensile strength of individual cast iron grades were determined, taking into account the limit contents of the alloying elements. Next, ultrasonic tests were performed, and the elastic properties of cast iron were determined. Based on the obtained data, a numerical modal analysis of brake discs was performed, the results of which were compared with the actual values of an FRF frequency analysis. The error of the computer simulations was estimated at approx. 1%, and it was found that the accuracy of the calculations of the first natural frequency did not depend on the dimensions (size) of the discs and the chemical composition of the cast iron from which they were cast. The functional relationships between the chemical composition of cast iron, its strength and elasticity and the first natural frequency of the disc vibrations were determined, and a database of the material parameters of the produced cast iron grades was developed. An implementation example showed the validation of the brake disc design with natural frequency prediction and demonstrated a high convergence of the experimental results with the simulated values. Using I-MR control cards, both the effectiveness of designing and predicting the natural vibrations of brake discs based on the implemented material database as well as the stability of the gray cast iron production and disc casting processes were confirmed.

Gravity-driven thin film flow of a third-grade fluid on a movable inclined plane with slip boundary condition

The prime aim and essence of this study are to present a closed-form solution of the unidirectional velocity field for thin film flow generated by a third-grade liquid moving over a fixed or movable inclined plane in the presence of partial slip boundary condition. Consideration of partial slip makes this problem different from other published research works. Here lies the novelty of this study. The nondimensional, unidirectional velocity profiles rely on the material parameter of third-grade fluid, slip parameter and initial velocity of the movable plane. Study discloses that the fluid’s velocity decelerates with raising the material property of grade-3 fluid and it accelerates with the slip parameter and initial velocity of the inclined plane. The outcomes of this study are deliberated physically at length.

Machine learning driven performance enhancement of perovskite solar cells with CNT as both hole transport layer and back contact

We propose a machine learning-based approach to enhance the performance of perovskite solar cells (PSCs) using carbon nanotubes as both hole transport layer (HTL) and back contact. Carbon-based PSCs offer low fabrication costs, long-term mechanical stability, high charge transport, and broad wavelength transparency. Our study estimates and enhances power conversion efficiency (PCE) of carbon-based PSCs through machine learning. PSC with single-walled carbon nanotube (SWCNT)-based HTL and back contact is investigated in simulations using SCAPS-1D and achieved 14.24% PCE, closely aligning with experimental data. Additionally, we explore the impact of various easily tunable fabrication parameters, such as the band gap and electron affinity of SWCNT in HTL, HTL thickness, active layer thickness, electron transport layer (ETL) thickness, HTL dopant concentration, absorber defect density, and ETL dopant concentration, on PCE. We generate a dataset of 20,480 samples to train classical and neural network-based machine learning models to predict performance parameters of PSCs, including short-circuit current density (JSC), open-circuit voltage (VOC), fill factor (FF), and PCE (η). The current density vs. voltage (J-V) characteristics curve is well-fitted by the predicted JSC, VOC, and FF, as evidenced by an average root mean squared error (RMSE) of 0.03 and a goodness of fit (R2) value of 0.99. The fully connected artificial neural network performs best, with RMSE of 0.05% and R2 of 0.99998 for predicting PCE. Genetic algorithm is used to optimize PCE, yielding a maximum PCE of 20.92%. Furthermore, an analysis of feature dimension and dataset size on prediction performance guides future modeling approaches.

Versatile data-adaptive hyperelastic energy functions for soft materials

Applications of soft materials are customarily linked to complex deformation scenarios and material nonlinearities. In the bioengineering field, soft materials typically mimic the low stiffness of biological matter subjected to extreme deformations. Computational frameworks surge as a versatile tool to assist the design of functional applications. The constitutive model lies at the core of such frameworks. In this regard, the customary extreme non-linear behavior of elastomers poses an additional challenge to thoroughly capture the material behavior. Here, data-driven methodologies hold considerable promise for enhancing constitutive modeling when contrasted with phenomenological approaches. In this investigation, we introduce a versatile data-adaptive method tailored to the modeling of hyperelastic soft materials at finite strains. Specifically, our method substitutes an a priori chosen model for the strain energy function by a flexible interpolant defined on a discretized invariant space. Within this framework, the interpolation values assume the role of material parameters and are determined through finite element model updating to conform to measured experimental data — comprising full-field displacements coming from Digital-Image-Correlation and global reaction forces. We validate the method on uniaxial experimental tests of soft elastomers, encompassing ELASTOSILTM, DOWSILTM, and V HBTM. Overall, we aim to establish a new route for the construction of hyperelastic energy functions, untethered from any predefined existing models or assumptions regarding the shape of the energy.

Traction-separation law parameters for the description of age-related changes in the delamination strength of the human descending thoracic aorta.

Aortic dissection is a life-threatening disease that consists in the development of a tear in the wall of the aorta. The initial tear propagates as a discontinuity leading to separation within the aortic wall, which can result in the creation of a so-called false lumen. A fatal threat occurs if the rupture extends through the whole thickness of the aortic wall, as blood may then leak. It is generally accepted that the dissection, which can sometime extend along the entire length of the aorta, propagates via a delamination mechanism. The aim of the present paper is to provide experimentally validated parameters of a mathematical model for the description of the wall's cohesion. A model of the peeling experiment was built in Abaqus. The delamination interface was described by a piecewise linear traction-separation law. The bulk behavior of the aorta was assumed to be nonlinearly elastic, anisotropic, and incompressible. Our simulations resulted in estimates of the material parameters for the traction-separation law of the human descending thoracic aorta, which were obtained by minimizing the differences between the FEM predictions and the delamination force given by the regression of the peeling experiments. The results show that the stress at damage initiation, Tc, should be understood as an age-dependent quantity, and under the assumptions of our model this dependence can be expressed by linear regression as Tc = - 13.03·10-4·Age + 0.2485 if the crack front advances in the axial direction, and Tc = - 7.58·10-4·Age + 0.1897 if the crack front advances in the direction of the aortic circumference (Tc [MPa], Age [years]). Other model parameters were the stiffness K and the separation at failure, δf-δc (K = 0.5MPa/mm, δf-δc = 0.1mm). The material parameters provided by our study can be used in numerical simulations of the biomechanics of dissection propagation through the aorta especially when age-associated phenomena are studied.

Сравнительный прочностной анализ алмазных сегментов канатных камнерезных станков

The paper discusses the different shapes of diamond segments for stone cutting machines. The purpose of this research is to find ways to improve the abrasion resistance of the diamond segments, reduce the vibration load and increase the cutting efficiency. The operating conditions of stone-cutting machines are analysed, as well as the ways of increasing the efficiency of diamond segments by changing their shape. A calculation of the loads arising during operation of the diamond segments is made, and the results are verified using an experimental setup. The initial shape is analysed and the stresses that exceed the material parameters are found. In order to reduce the stresses, a diamond segment with rounded edges is simulated. Segments with bevelled edges and conical shape are also considered. Based on the experimental results, it is found that one of the promising shapes is that with rounded edges.

Сравнительный прочностной анализ алмазных сегментов канатных камнерезных станков

The paper discusses the different shapes of diamond segments for stone cutting machines. The purpose of this research is to find ways to improve the abrasion resistance of the diamond segments, reduce the vibration load and increase the cutting efficiency. The operating conditions of stone-cutting machines are analysed, as well as the ways of increasing the efficiency of diamond segments by changing their shape. A calculation of the loads arising during operation of the diamond segments is made, and the results are verified using an experimental setup. The initial shape is analysed and the stresses that exceed the material parameters are found. In order to reduce the stresses, a diamond segment with rounded edges is simulated. Segments with bevelled edges and conical shape are also considered. Based on the experimental results, it is found that one of the promising shapes is that with rounded edges.

Finite Element Analysis of the Mechanical Response for Cylindrical Lithium-Ion Batteries with the Double-Layer Windings

The plastic properties for the jellyroll of lithium-ion batteries showed different behavior in tension and compression, showing the yield strength in compression being several times higher than in tension. The crushable foam models were widely used to predict the mechanical responses to compressive loadings. However, since the compressive characteristic is dominant in this model, it is difficult to identify distributions of the yield strength in tension. In this study, a simplified jellyroll model consisting of double-layer windings was devised to reflect different plastic characteristics of a jellyroll, and the proposed model was applied to an 18650 cylindrical battery under compressive loading conditions. One winding adopted the crushable foam model for representing the compressive plastic behavior, and the other winding adopted the elastoplastic models for tracking the tensile plastic behavior. The material parameters in the crushable foam model were calibrated by comparing the simulated force–displacement curve with the experimental one for the case where the cell was crushed between two plates when the punch was displaced by 7 mm. A specific cut-off value (10 MPa) was assigned to a yield stress limit in the elastoplastic model. Further, the computational model was validated with two more loading cases, a cylindrical rod indentation and a spherical punch indentation, as the punch was displaced by 6.3 mm and 6.5 mm, respectively. For three loading cases, deformed configurations and plastic strain distributions were investigated by finite element analysis. It was found that the proposed model clearly provides the plastic behavior both in compression and tension. For the crush simulation, the maximum compressive stress approached 222 MPa in the middle of the jellyroll, and the maximum effective plastic strain approached 60% in the middle of the layered roll. For indentation with the cylindrical and the spherical punch, the maximum effective plastic strain approached 52% and 277% in the layered roll, respectively. The local crack or location of a short circuit could be predicted from the maximum effective plastic strain.

Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach

This paper describes an application of a machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long short-term memory networks allowed a reasonable agreement of stress–strain curves to be obtained for cyclic deformation in a low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility of obtaining parameters for a new material without the necessity of conducting any further optimizations. As the power and robustness of the developed method was demonstrated for very challenging problems (cyclic deformation, crystal plasticity, self-consistent model and isotropic and kinematic hardening), it is directly applicable to other experiments and models.

Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper—A Machine Learning Approach

This paper describes an application of a machine learning approach for parameter optimization. The method is demonstrated for the elasto-viscoplastic model with both isotropic and kinematic hardening. It is shown that the proposed method based on long short-term memory networks allowed a reasonable agreement of stress–strain curves to be obtained for cyclic deformation in a low-cycle fatigue regime. The main advantage of the proposed approach over traditional optimization schemes lies in the possibility of obtaining parameters for a new material without the necessity of conducting any further optimizations. As the power and robustness of the developed method was demonstrated for very challenging problems (cyclic deformation, crystal plasticity, self-consistent model and isotropic and kinematic hardening), it is directly applicable to other experiments and models.

Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion

The alkali–silica reaction (ASR), a major cause of cracks in concrete, is a critical issue in the maintenance of social infrastructure. In this study, a concrete mesoscale model was meticulously developed, and a coupled stress–moisture model was also developed to reproduce ASR degradation. The aim was to investigate the effect of ASR degradation on the bending load-carrying capacity of RC beams. The concrete mesoscale model, specifically designed to reproduce ASR degradation, was modeled from three phases: coarse aggregate, mortar, and ITZ (interfacial transition zone). ASR was considered as the expansion of the coarse aggregate, and the objective was to reproduce expansion cracks with numerical analysis using the mesoscale model. Uniaxial compression tests were carried out on cylindrical specimens with ASR-accelerated deterioration to clarify the relationship between ASR deterioration and compressive properties, and the experimental results were used to identify material parameters in the mesoscale analysis model. The results showed that the model proposed in this study can reproduce the change in compressive properties due to expansion cracking. Finally, RC beams were constructed using the mesoscale model, and the effect of ASR degradation on the bending load-carrying capacity of the RC beams was investigated. The results showed that the presence of expansion cracks caused the initial stiffness of the load-displacement curves to decrease, but the bearing capacity tended to increase. This suggests that factors other than cracks, such as chemical prestress and boundary conditions in this model, have a strong influence on the load-bearing capacity of deteriorated RC beams.

An Optimized Strain-Compensated Arrhenius Constitutive Model of GH4169 Superalloy Based on Hot Compression

A precise constitutive model is essential for capturing the deformation characteristics of the GH4169 superalloy in numerical simulations of thermal plastic forming processes. Hence, the aim of this study was to develop a precise modified constitutive model to describe the hot deformation behavior exhibited by the GH4169 superalloy. The isothermal cylindrical uniaxial compression tests of the GH4169 superalloy were carried out at temperatures of 950~1100 °C and strain rates of 0.01~10 s−1 using a Thermecmastor-200KN thermal–mechanical simulator. The original strain–stress curves were corrected by minimizing the effects of plastic heat and interfacial friction. Based on the true stress–strain curves, the original strain-compensated Arrhenius constitutive model was constructed using polynomial orders of 3, 5, and 10, respectively. The results showed that once the polynomial order exceeds the 5th, further increasing the order has little contribution to the accuracy of the model. To improve prediction ability, a higher precision Arrhenius constitutive model was established by extending a series of material parameters as functions that depend on temperature, strain, and strain rate, in which the error can be reduced from 4.767% to 0.901% compared with the classic strain-compensated Arrhenius constitutive model.

Sensitivity analysis of fibrous thick-walled tubes with mechano-sensitive remodeling fibers in homeostasis

AbstractIn this article, we apply the sensitivity analysis method to capture the influence of various parameters on the inflation pressure, axial force, and the deformation for an inflated and axially stretched cylinder. The material consists of an isotropic ground substance material reinforced with fibers that undergo a continuous and mechano-sensitive remodeling process. The input parameters of the mechanical system are assumed to be distributed according to the uniform probability distribution function. In the sensitivity analysis, we apply the Sobol method to determine how the variations of input parameters affect the inflation as well as the axial force in the cylinder. Special attention is given to the fiber remodeling process associated with a homeostatic balance between the constant fiber creation process and the strain-stabilized fiber dissolution. The results may help to understand the importance of the effect of material parameter changes, for example, due to remodeling processes in the context of diseases or recovering processes, on the overall tissue behavior.

Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion

The alkali–silica reaction (ASR), a major cause of cracks in concrete, is a critical issue in the maintenance of social infrastructure. In this study, a concrete mesoscale model was meticulously developed, and a coupled stress–moisture model was also developed to reproduce ASR degradation. The aim was to investigate the effect of ASR degradation on the bending load-carrying capacity of RC beams. The concrete mesoscale model, specifically designed to reproduce ASR degradation, was modeled from three phases: coarse aggregate, mortar, and ITZ (interfacial transition zone). ASR was considered as the expansion of the coarse aggregate, and the objective was to reproduce expansion cracks with numerical analysis using the mesoscale model. Uniaxial compression tests were carried out on cylindrical specimens with ASR-accelerated deterioration to clarify the relationship between ASR deterioration and compressive properties, and the experimental results were used to identify material parameters in the mesoscale analysis model. The results showed that the model proposed in this study can reproduce the change in compressive properties due to expansion cracking. Finally, RC beams were constructed using the mesoscale model, and the effect of ASR degradation on the bending load-carrying capacity of the RC beams was investigated. The results showed that the presence of expansion cracks caused the initial stiffness of the load-displacement curves to decrease, but the bearing capacity tended to increase. This suggests that factors other than cracks, such as chemical prestress and boundary conditions in this model, have a strong influence on the load-bearing capacity of deteriorated RC beams.

Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using POD-based reduced-order modeling

AbstractThis study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem’s material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter.

NAVIER SOLUTION FOR DEFLECTION AND STRESSES ANALYSIS OF FGM SANDWICH PLATE RESTING ON PASTERNAK ELASTIC FOUNDATION

In this article, the first-order shear deformation theory (FSDT) and Navier’s solution are used to establish analytical solutions for analyzing the deflection and stresses of FGM sandwich plate structures resting on a Pasternak elastic foundation. The top and bottom layers consist of functionally graded materials (P-FGM) that vary according to the exponential law along the thickness direction, while the core layer is composed of a porous material. The boundary condition of the plate is simply supported on all the edges, and the plate is subjected to a sinusoidal distributed load perpendicular to the mid-surface. The reliability of the model is validated through comparison with findings published in reputable journals. The effects of material parameters, geometric dimensions, foundation coefficients, and face-core-face thickness ratios on deflection and stress components of rectangular FGM sandwich plates are investigated in detail in parametric studies.

RESEARCH ON APPLICATION OF FINITE ELEMENT METHOD TO ANALYZE LOAD TRANSFER EFFICIENCY ACCORDING TO DEFLECTION OF AIRPORT RIGID PAVEMENT

The load transfer between adjacent cement concrete slabs at expansion joint locations is affected by many different factors. The load-bearing capacity of the pavement will be limited and even lead to destruction if the load transmission ability of the dowel bars does not meet the requirements. Especially, for airports that have just been put into operation or have been in operation for a long time. In this article, the authors have researched and evaluated the effectiveness of load transfer according to deflection through the application of the 3D finite element method. The model utilizes material parameters and actual airport hard pavement structures and calculates for many types of aircraft commonly operating in Vietnam. Thereby calculating and evaluating the load transfer efficiency (LTE) according to deflection for each specific type of aircraft. The model also visually shows the deformation of the transmission dowel bars and concrete slabs at the expansion joint location. The authors tested the model's reliability through experiments conducted at Tan Son Nhat International Airport which is the largest-scale and oldest airport in Vietnam. The results of calculating LTE from the simulation and experimental models have a small error (3.39%) which shows that the model is highly reliable and can be used for similar research directions without being easily done through traditional methods.