Variation Of Material Properties Research Articles

Space–time method for analyzing transient heat conduction in functionally graded materials

In this study, a novel approach is presented for simulating transient heat conduction in functionally graded materials (FGM) with varying material properties only in the z direction. The proposed collocation method employs a meshless localized space–time radial basis function (LSTRBF), which yields a linear sparse matrix system, offering advantages over the global method in terms of scalability and suitability for multidimensional problems. Additionally, a novel shape parameter strategy, known as variable leave-one-out cross-validation (LOOCV), is introduced to enhance accuracy while overcoming conventional LOOCV limitations. Four benchmark numerical examples are utilized to demonstrate the simplicity, efficiency, accuracy, and stability of the method. Overall, the proposed LSTRBF method presents a promising alternative for efficiently and accurately simulating transient heat conduction in FGMs.

An Investigation into the Free Vibration of Intact and Cracked FGM Plates

In this study, the linear free vibration of intact and cracked functionally graded material plates is investigated numerically and experimentally. The experimental work is limited to the isotropic materials. The numerical work is based on finite element, where a code is developed to obtain the natural frequencies of intact plates based on the first-order shear deformation theory (FSDT) using MATLAB software. Also, a model of through-cracked FGM plate is developed using ANSYS Workbench with the help of APDL coding. The material properties of the plates under study are graded in one, two, and three directions. The novelty of this study emerges through its examination of the synergistic impacts resulting from variations in FGM material properties, crack length, crack orientation, and crack location. These effects are comprehensively discussed in the results section. The result of the present model shows that the use of three-directional FGM reduces the natural frequency compared with the other cases of two-directional and unidirectional FGM. Also, the results show that the effect of FGM gradient on the frequency of intact and cracked plate is high when the gradient index n < 3 . The present paper results are useful for the design of FGM plates especially when cracks exist.

Local and global sensitivity analysis of a coupled heat and moisture transfers model: effect of the variability of cob material properties

Local and global sensitivity analysis of a coupled heat and moisture transfers model: effect of the variability of cob material properties

Fabrication of polymer-based stents: Impact of test specimen manufacturing protocol

Abstract Various manufacturing techniques are available for polymer stent fabrication. Polymer semi-finished products can be prepared using solvent based methods, e.g. dip coating, as well as thermal processes, e.g. extrusion. These different methods may lead to an altered polymer crystal structure, resulting in a different deformation mechanism during mechanical stress. For the material property characterization needed for implant development, the test specimens usually are prepared using laser-cut or die-cut methods. Due to these different preparation protocols, a change in polymer microstructure, causing a material property variation, may also result. For this purpose, comparison of laser-cut versus die-cut of PLLA films has been performed. PLLA films have been prepared by dip-coating and were evaluated with respect to structural, thermal, and mechanical properties. In this study, a combination of uniaxial tensile tests, SEM and DSC studies was used. The results of the mechanical tests showed drastic differences in the elongations at break of die-cut specimens compared to laser-cut ones. The results point out formation of complex crystal structures during the manufacturing process. By the use of SEM imaging and DSC measurements, we were able to attribute these changes to the different plastic deformation mechanisms.

Optimal structures for focusing and energy accumulation: mathematical models and intuition

Metamaterials and composite structures are able to manipulate waves and focus fields and currents in desirable directions. Designs based on spatial and temporal variation of material properties create structures forcing fluxes into specified parts of the domain or concentrating energy into arrays of progressively sharpening pulses. The paper discusses examples of focusing structures, mathematical and intuitive considerations that influence optimal design theory. The optimality requirement introduces zones of optimal composites with variable microgeometry. The observed absence of classical solutions motivates the extension of the class of optimal partitions to composites. Such materials also provide a solution to the problem of optimal design of a thermal lens focusing thermal fluxes when the incoming fluxes are not completely known in advance. An extension of designs to dynamical materials such as space–time checkerboard composites introduces metamaterials with additional capabilities that control the accumulation of energy in the propagating waves. The discussed mathematical methods of focusing and suitable properties alternation targeted on optimality are illustrated by physical examples.

Dynamic Crack-Front Deformations in Cohesive Materials.

Crack fronts deform due to heterogeneities, and inspecting these deformations can reveal local variations of material properties, and help predict out-of-plane damage. Current models neglect the influence of a finite dissipation length scale behind the crack tip, called the process zone size. The latter introduces scale effects in the deformation of the crack front, that are mitigated by the dynamics of the crack. We provide and numerically validate a theoretical framework for dynamic crack-front deformations in heterogeneous cohesive materials, a key step toward identifying the effective properties of a microstructure.

Equivalent initial damage sizes (EIDS) for type 1C anodised aluminium alloy 7085-T7452 under variable amplitude loading

Aluminium alloy (AA) 7085-T7452 is a high strength, low weight alloy used in large forgings in primary airframe applications. For corrosion protection, airframe components are anodised and this study looks at the influence of type 1C anodising on the fatigue crack growth behaviour in AA 7085-T7452 specimens that were fatigue tested with fighter wing spectrum loading. Surface etch pits formed during essential anodising preparatory processes were the primary cause of fatigue crack nucleation. The severity of the early crack growth caused by those etch pits that nucleated the worst crack in each specimen was evaluated using quantitative fractography-based fatigue crack growth measurements and the equivalent initial damage size (EIDS) of those etch pits was determined. Characterising the EIDS distribution of aircraft alloys with representative surface treatments is a fundamental input into Linear Elastic Fracture Mechanics (LEFM) based fleet failure analysis of aircraft structures. In this study, a notable increase in the average EIDS value for the worst crack present was observed whenever material or loading condition changes led to a significantly increased number of crack nucleation sites. In particular, an increase in the cyclic stress spectrum’s severity increased the number of nucleation sites in a specimen and significantly increased the average EIDS. It appears that due to variable nucleating discontinuity properties (e.g. size, shape and orientation) and material property variations, an increased number of nucleation sites increases the probability that the worst crack present will nucleate at a location that is conducive for relatively fast early crack growth. This will potentially cause shorter overall airframe component fatigue lives and so both the mechanisms and conditions of etch pit fatigue nucleation in this material and material finish shall be discussed for the benefit of failure analysts evaluating unexpected fleet fatigue cracking.

Study of the geometry and structure of a thermoelectric leg with variable material properties and side heat dissipation based on thermodynamic, economic, and environmental analysis

Thermoelectric generator (TEG) without moving parts, refrigerants, and direct carbon emissions is the most promising way to reduce carbon dioxide. The economy and thermal efficiency of TEG are seriously inhibited by geometry and structure, while existing studies have not investigated the effect of geometric and structure on output characteristics when the model considers the effect of side leg dissipation and variable properties. This paper proposes a one-dimensional model considering the effect of Peltier heat, conductive heat, Joule heat, Thomson heat, side leg dissipation, and temperature dependence for variable and conventional geometric and structures for 8 different materials. The effects of operation parameters, geometry parameters, and structures on thermodynamic, economic, and environmental performances are studied. The higher hot source temperature and shorter leg length benefit comprehensive performance. Increasing side leg dissipation decreases efficiency. When the fin parameter (L(heP/λA)0.5) is below a specific value, increasing side leg dissipation can promote the output power, economic and environmental performance. Compared to the traditional structure, the output power and efficiency of new configurations are increased by 1.01–3.59 and 1.03–4.57 times, respectively; The cost and CO2 emission reduce by 1.0–72.1% for low hot source temperature and figure of merit (ZT) material.

Performance analysis of functionally graded multifunctional piezoelectric energy harvesting microgyroscopes

Functionally graded materials (FGMs) are composite materials with varying material properties in one or more directions. These materials possess distinct characteristics compared to their constituent components. The ability to control material distribution and compositions in FGMs offers improved constraints over the natural frequencies of the system, making them highly suitable for energy harvesting applications. In this study, we focus on a piezoelectric FGM composed of Platinum (Pt) and Lead Titanate Zirconate (PZT). By utilizing FGMs for energy harvesting, we simplify the system from a multilayer structure to a single-layer system, thereby increasing power density by reducing volume. In this work, a power law distribution is used to model the material variation throughout the thickness of the single-layered beam. The governing equations of motion and boundary conditions for FG energy harvesting microgyroscope are derived using Euler-Bernoulli beam theory, the constitutive piezoelectric principle, and the extended Hamilton's principle. To simulate the nonlinear motion of the FG energy harvester, we employ the differential quadrature method (DQM). Subsequently, an investigation is carried out to examine the effects of various factors including material distribution, platinum percentage, base rotation, DC voltage, and electrical load resistance on the energy harvesting microgyroscope with functionally graded materials. The findings suggest that functionally graded energy harvesting gyroscopes can be adjusted to obtain effective energy harvesting and sensing capabilities by carefully selecting the material distribution, input voltage, and electrical load resistance.

Comparative analysis of photovoltaic thermoelectric systems using different photovoltaic cells

The photovoltaic-thermoelectric (PV-TE) system has emerged as a focal point in research endeavors aimed at harnessing the full spectrum of solar energy and enhancing the efficacy of solar power generation. Owing to the variations in bandgap and inherent material properties across diverse photovoltaic cells, the capacity to utilize the solar spectrum of PV-TE systems can be significantly affected when using different photovoltaic cells. Historically, investigations into the influence of photovoltaic cells on PV-TE systems have been predominantly rooted in theoretical simulations. These examinations have primarily concentrated on the holistic system efficiency under varying temperature conditions. In this study, we integrated three distinct types of photovoltaic cells into PV-TE systems. Both simulation and experimental methodologies were employed to evaluate the impact of these photovoltaic cell types on the PV-TE systems' performance. Additionally, we compared the back temperatures of standard PV systems with those of PV-TE systems. The average photovoltaic conversion efficiencies of PV-TE systems equipped with CIGS, CdTe, and a-Si photovoltaic cells were 21.9%, 19.7%, and 12.7%, respectively. Meanwhile, the average efficiencies of TEG were 0.256%, 0.102%, and 0.083% respectively, with average backplate temperatures of 39.3 °C, 44.0 °C, and 40.5 °C. The temperature disparities between the back of standard photovoltaic systems and PV-TEG-PCM systems stood at 4.70 °C, 2.32 °C, and 3.43 °C, respectively. Notably, CIGS photovoltaic cells, which harness a specific range of the solar spectrum more effectively, showcased superior performance. Furthermore, a broader usable solar spectrum band for a PV cell doesn't always translate to enhanced performance. These findings offer valuable insights for optimizing the power generation capabilities of photovoltaic-thermoelectric systems leveraging full-spectrum solar energy.

Automatically Predicting Material Properties with Microscopic Images: Polymer Miscibility as an Example.

Many material properties are manifested in the morphological appearance and characterized using microscopic images, such as scanning electron microscopy (SEM). Polymer miscibility is a key physical quantity of polymer materials and is commonly and intuitively judged using SEM images. However, human observation and judgment of the images is time-consuming, labor-intensive, and hard to be quantified. Computer image recognition with machine learning methods can make up for the defects of artificial judging, giving accurate and quantitative judgment. We achieve automatic miscibility recognition utilizing a convolutional neural network and transfer learning methods, and the model obtains up to 94% accuracy. We also put forward a quantitative criterion for polymer miscibility with this model. The proposed method can be widely applied to the quantitative characterization of the microstructure and properties of various materials.

Structural and optoelectronic properties of LiYP (Y = Ca, Mg, and Zn) half-Heusler alloy under pressure: A DFT study

By adjusting the lattice geometry through pressure engineering, it is possible to further enhance and customize various properties of materials even at zero pressure. The utilization of pressure allows for the modification of the electronic structure of materials, presenting a relatively new approach to creating essential features for advanced technological applications. These intriguing applications have served as the driving force behind this research, which investigates the behavior of the LiYP (Y = Ca, Mg, and Zn) Half-Heusler (HH) semiconductor. We shed light on the effects of pressure on the optoelectronic and structural characteristics of LiYP (Y = Ca, Mg, and Zn) using the DFT-based Wien2K code. HHs belong to the F 4‾ 3 m space group (No. 216) and are denoted by the chemical symbol XYZ. The calculations were performed by fitting the computed total energy and atomic volume to the Murnaghan equation of state, allowing us to obtain the bulk modulus, first derivatives, and lattice constant. Through the analysis of the partial density of states (PDOS), we evaluated the electronic band structure of LiYP (Y = Ca, Mg, and Zn) under pressures ranging from 0 to 10 GPa. Finally, we examined the optical properties of LiYP and relevant parameters (real part, imaginary part, absorption coefficient and reflectivity coefficient) at different pressure levels. The results obtained from the analysis of optoelectronic properties demonstrate that LiYP (Y = Ca, Mg, and Zn) is suitable for applications in renewable energy devices.

Surrogate recycling for structures with spatially uncertain stiffness

This study expands the existing methods for non-destructively identifying the spatially varying material properties of a structure using modal data. It continues a recently published approach to this inverse problem that employed Bayesian inference in conjunction with the Karhunen-Loève expansion to solve it. Here, we present two developments. Firstly, eigenvectors are used instead of eigenvalues, improving the results significantly. Secondly, a generalized polynomial chaos surrogate accelerates the inversion procedure. Finally, we develop a methodology for reusing the surrogate model across inversion tasks. We demonstrate the efficacy and efficiency of this methodology via the field of additive manufacturing and the fused deposition modeling process. The good results promise profound computational cost saving potential for large-scale applications.

Correlation of Microstructure and Nanomechanical Properties of Additively Manufactured Inconel 718

Abstract Additive manufacturing (AM) has emerged as a crucial technology in recent decades, particularly within the aerospace industry. However, the thermally cyclic nature of these processes introduces significant variations and defects in microstructure, which can adversely affect final part performance and hinder the widespread adoption of the technology. Traditionally, characterization of AM parts has relied on conventional bulk testing methods, which involve analyzing many samples to gather sufficient data for statistical analysis. Unfortunately, these methods are unable to account for local nanoscale variations in material properties caused by the microstructure, as they measure a single averaged property for each tested sample. In this work, we use AM Inconel 718 as a model system in developing a novel approach to correlate nanomechanical properties obtained through nanoindentation with microstructure obtained through electron backscatter diffraction (EBSD). By associating mechanical properties obtained from each indent with the corresponding crystallographic direction, we calculate the weighted average hardness and modulus for each orientation. This enables us to generate inverse pole figure maps depicting the relationship between mechanical properties and crystallographic direction. Our method yields results in good agreement with literature when calculating the part modulus and hardness, while effectively capturing nanoscale variations in properties across the microstructure. The key advantage of this methodology is its capability to rapidly test a single AM part and generate a large dataset for statistical analysis. Complementing existing macroscale characterization techniques, this method can help improve AM part performance prediction and contribute to the wider adoption of AM technologies in the future.

A study on the vibration characteristics of functionally graded cylindrical beam in a thermal environment using the Carrera unified formulation

The displacement function was constructed using Carrera unified formulation (CUF) in combination with Taylor polynomials and the improved Fourier series method (IFSM). This study investigated the vibration characteristics of cylindrical beam made of functionally graded materials (FGM) in a thermal environment. The accuracy of the theoretical model was verified by comparing the calculation results with those obtained using the finite element method. Subsequently, the influential factors were investigated. The variations in material properties with temperature and volume rate index were studied. Additionally, the effects of temperature changes and volume rate index on the thermoelastic vibration of the FGM cylindrical beam were examined. The results indicate that in a uniform temperature field, temperature and volume rate index variations result in changes in the material’s physical properties. The modal frequency of the FGM cylindrical beam decreases gradually with increasing temperature and volume rate index.

HTS Tape Mechanical Behavior Sensitivity on Material Properties and Thickness of Material Layers

Multilayer 2G high-temperature superconductor (HTS) tapes undergo various mechanical loading steps as part of a superconducting magnet operation. The mechanical loading steps include cool-down to cryogenic temperatures and Lorentz forces when powering up the magnets. Studying the mechanical behavior of the constituent layers in a HTS tape can reveal the strain or stress level in each layer and help in predicting the probability of mechanical failure or critical current degradation. A detailed finite element method (FEM) -based simulation models enable us to estimate the mechanical behavior in each layer. However, defining the model parameters for material mechanical properties or thickness of each material layer can have consid-erable uncertainty due to variation in manufacturing processes and missing measurements of material properties within these tapes. The material properties in thin layers may not exactly comply with bulk materials of the same kind. In this paper we present a sensitivity analysis for mechanical behavior dependence on material properties variation in the constituent layers of a HTS tape. The results give important insight about the accuracy and reliability of mechanical simulation results and guide the priorities in future material characterization. In addition, we will investigate the effect of the thickness of Hastelloy layer on effective macroscopic mechanical behavior of the tape. A nonlinear elastoplastic FEM model is used for performing the simulations.

Fast Multigrid Simulations of Pinning in REBCO With Highly Resistive Nanorods

A geometric multigrid method is applied to the frozen-field time-dependent Ginzburg–Landau equations for anisotropic superconductors with spatially varying material properties. This reduces the simulation time for critical current determination in large 3D systems by up to two orders of magnitude compared with a single grid. Critical current density calculations are presented for a model of REBCO tape with nanorod artificial pinning centers. The model consists of an anisotropic superconducting matrix with embedded nanorods which are treated as isotropic highly resistive cylinders. The effects of varying the density, splay, and conductivity of the nanorods are considered. Increasing the resistivity of the nanorods profoundly affects the critical current density in high fields, where current paths are percolative in the vicinity of matrix-pin interfaces and persist well above the upper critical field of the superconducting matrix.

Materialistic: Selecting Similar Materials in Images

Separating an image into meaningful underlying components is a crucial first step for both editing and understanding images. We present a method capable of selecting the regions of a photograph exhibiting the same material as an artist-chosen area. Our proposed approach is robust to shading, specular highlights, and cast shadows, enabling selection in real images. As we do not rely on semantic segmentation (different woods or metal should not be selected together), we formulate the problem as a similarity-based grouping problem based on a user-provided image location. In particular, we propose to leverage the unsupervised DINO [Caron et al. 2021] features coupled with a proposed Cross-Similarity Feature Weighting module and an MLP head to extract material similarities in an image. We train our model on a new synthetic image dataset, that we release. We show that our method generalizes well to real-world images. We carefully analyze our model's behavior on varying material properties and lighting. Additionally, we evaluate it against a hand-annotated benchmark of 50 real photographs. We further demonstrate our model on a set of applications, including material editing, in-video selection, and retrieval of object photographs with similar materials.

Sub-Micrometer Phonon Mean Free Paths in Metal-Organic Frameworks Revealed by Machine Learning Molecular Dynamics Simulations.

Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which require a proper understanding of the thermal transport properties. Molecular dynamics (MD) simulations play an important role in characterizing the thermal transport properties of various materials. However, due to the complexity of the structures, it is difficult to construct accurate empirical interatomic potentials for reliable MD simulations of MOFs. To this end, we develop a set of accurate yet highly efficient machine-learned potentials for three typical MOFs, including MOF-5, HKUST-1, and ZIF-8, using the neuroevolution potential approach as implemented in the GPUMD package, and perform extensive MD simulations to study thermal transport in the three MOFs. Although the lattice thermal conductivity values of the three MOFs are all predicted to be smaller than 1 W/(m K) at room temperature, the phonon mean free paths (MFPs) are found to reach the sub-micrometer scale in the low-frequency region. As a consequence, the apparent thermal conductivity only converges to the diffusive limit for micrometer single crystals, which means that the thermal conductivity is heavily reduced in nanocrystalline MOFs. The sub-micrometer phonon MFPs are also found to be correlated with a moderate temperature dependence of thermal conductivity between those in typical crystalline and amorphous materials. Both the large phonon MFPs and the moderate temperature dependence of thermal conductivity fundamentally change our understanding of thermal transport in MOFs.

Investigation of Non-Metallic Material Laser Processing Modes

One of the relevant problems when processing materials using laser is to provide a certain level of surface roughness of the product. This is especially important for materials of natural origin (wood, leather, plastics, bones, stones, and etc.). One approach to select laser processing modes is to pre-test modes on product samples. This leads to significant time costs, as well as the use of a large number of samples (up to several dozen). The article proposes to process the product sample study results using probabilistic and statistical data processing. The example of using this approach in the wood product processing is given. The results of the products laser treatment with surface roughness assessment of the product using probabilistic processing algorithms of the experimental results are shown. The experiment contained three stages. First, a product sample model is prepared, representing a stepped wedge with different roughness, then the model is installed on a laser and laser processing of the model takes place. At the third stage, surface roughness is measured and statistical processing of experimental results is performed on the computer. The use of this approach accelerates the development of laser processing modes for wood products by 3-4 times in comparison with known approaches. The main algorithms of probabilistic and statistical experiment result processing are presented in the work, diagrams of roughness with respect to various laser processing modes for different materials (wood, glass, and leather) are shown: ablation depth, dispersion of profile irregularities, and relation of dispersion of irregularities on laser radiation power. For the experiment, a Trotec Speedy 300 laser, a microdensitometer, an MII 4 microinterferometer, and a photoelectric glare meter FB-2 were used. For data processing, a regression model was built linking laser processing modes and surface properties of various materials. The article will be useful for students applying laser processing of materials of natural origin.