Variation Of Material Properties Research Articles

Predicting the elastic properties of Norway spruce by its morphology

Models for the elastic material properties of wood aim to predict the mechanical response of wooden structures to external loading. Traditionally, the variability of these properties in trees is described by a taxonomy of growth defects that is typically based on visual indicators in the material. This includes curvature, knots, and spiral grain models. Existing meso- and macro-scale models fail to describe the uncertainty connected to the local heterogeneity of the material. In this paper, we propose a novel meso-scale model that describes the natural variability of Norway spruce morphology and material properties based on random field theory. Our approach removes the need for a taxonomy of growth defects and enables uncertainty quantification of the stiffness and density in a straightforward fashion using simulations. This may enhance confidence for stiffness-graded applications, where the dynamic resonant behavior of wood structures is relevant and growth anomalies are present. Further, our stochastic models can generate images that realistically mimic wood patterns, which is relevant for applications like synthetic wood panels and flooring.

Thermal vibration analysis of bi-directionally stepped porous functionally graded plates with segment-specific material property variation supported by Kerr foundation

This paper presents a comprehensive analysis of the nonlinear dynamic response of stepped rectangular plates made of functionally graded porous material (FGPM), supported by the Kerr foundation, in a thermal environment. A novel analytical framework is developed to explore geometric and material variations. The study investigates structural variations in plate thickness with abrupt changes in uni- or bi-directional orientations, examining both single and double stepped thickness profiles. Material properties vary with thickness, featuring distinct horizontal discontinuities across plate segments. Porosity distributions, both even and uneven, are addressed using modified mixture rules. Nonlinear kinematic relationships are established using Reddy’s third-order shear deformation plate theory and von Kármán’s nonlinear geometric assumptions, with equations of motion solved via Galerkin’s technique. This improved model effectively addresses non-continuous thickness variation through integral calculus, enhancing computational efficiency. Validation is achieved by comparing outcomes with published literature and Finite Element Analysis (FEA). The study investigates the influence of material properties, elastic foundation, boundary conditions, and geometric parameters on the free vibration and nonlinear behaviors of the plates. Some of the key findings include: increasing the thickness of the stepped segment significantly heightens the fundamental frequency while reducing vibrational amplitudes; optimizing the location of the stepped segment directly impacts the plate’s fundamental frequency and vibrational amplitudes; and as the load factor increases, the difference between linear and nonlinear deflection becomes evident. Therefore, accurate FGPM stepped plate design requires incorporating nonlinear terms in the strain–displacement relationships. Suggestions for future model modifications are also discussed, contributing to advancements in structural design and analysis.

Influence of Spatially Varying Boundary Conditions Based on Material Heterogeneity

When conducting numerical analyses, boundary conditions are generally applied homogeneously, neglecting the inherent heterogeneity of the material being represented. Whilst the heterogeneity is often considered within the medium, its influence on the response at the boundary should also be accounted for. In this study, A novel approach to applying heterogeneous boundary conditions in the simulation of physical systems is presented, particularly focusing on moisture transport in unsaturated soils. The proposed method divides the surface into blocks or “macro-elements” and scales the boundary conditions based on the variation of material properties within these blocks. The principle of using overlapping kernel functions allows local effects to be considered, impacting neighbouring regions. To demonstrate the efficacy of the approach, a set of analyses were conducted that considered infiltration into a body of unsaturated soil, with various configurations of material properties and boundary conditions. The numerical simulations indicate that the application of scaled boundary conditions leads to a more natural and realistic response in the system. The applied method is independent on the numerical techniques employed in the simulation process, making it adaptable to existing computational codes, offering flexibility in capturing complex behaviours, and providing insights into how heterogeneity influences the system’s overall response.

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Machine learning techniques for predictive modelling and uncertainty quantification of the mechanical properties of woven carbon fibre composites

Carbon fibre composites owe their exceptional mechanical properties to the characteristics of their constituents; fibres and resin. However, uncertainty in these individual constituent properties can introduce significant variations in the macroscale mechanical performance of composites. These variations are amplified in the presence of various plies in the composite and pose a challenge to quantify during manufacturing stages. In this study, uncertain constituent material properties are propagated to the macroscale composite through multiscale homogenisation to compute the varied resultant thin-shell stiffness matrices. An in-house-built automated simulation environment has been used to create a physics-conforming trainable database for model training, testing, validation and uncertainty quantification using Gaussian Process Regression (GPR), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR). Through rigorous comparative analyses, the superior performance of GPR is demonstrated when compared to alternative techniques with R² ≥ 0.99 and NRMSE < 10⁻⁷. Further, the findings approve the validity of GPR for extrapolatory predictions. For instance, a randomly sampled [-5, 5] % variation of constituent material properties leads to extreme uncertainties in macroscale ± 0.067 %, for axial stiffness with excellent GPR predictive accuracy of R² = 0.99. This slightly increases to ± 0.14 % for a variation of [-10, 10] %. Moreover, the extreme uncertainties indicate that the direct axial and bending stiffnesses are more sensitive to variations in constituent properties than others. Additional investigations presented in the paper produce predictive models and uncertainty bounds for all stiffness entries that are of high importance for macroscale property control during manufacturing.

Probabilistic Finite Element Analysis of Human Rib Biomechanics: A Framework for Improved Generalizability.

In dynamic impact events, thoracic injuries often involve rib fractures, which are closely related to injury severity. Previous studies have investigated the behavior of isolated ribs under impact loading conditions, but often neglected the variability in anatomical shape and tissue material properties. In this study, we used probabilistic finite element analysis and statistical shape modeling to investigate the effect of population-wide variability in rib cortical bone tissue mechanical properties and rib shape on the biomechanical response of the rib to impact loading. Using the probabilistic finite element analysis results, a response surface model was generated to rapidly investigate the biomechanical response of an isolated rib under dynamic anterior-posterior load given the variability in rib morphometry and tissue material properties. The response surface was used to generate pre-fracture force-displacement computational corridors for the overall population and a population sub-group of older mid-sized males. When compared to the experimental data, the computational mean response had a RMSE of 4.28N (peak force 94N) and 6.11N (peak force 116N) for the overall population and sub-group respectively, whereas the normalized area metric when comparing the experimental and computational corridors ranged from 3.32% to 22.65% for the population and 10.90% to 32.81% for the sub-group. Furthermore, probabilistic sensitivities were computed in which the contribution of uncertainty and variability of the parameters of interest was quantified. The study found that rib cortical bone elastic modulus, rib morphometry and cortical thickness are the random variables that produce the largest variability in the predicted force-displacement response. The proposed framework offers a novel approach for accounting biological variability in a representative population and has the potential to improve the generalizability of findings in biomechanical studies.

Thermomechanical bending of functionally graded carbon nanotubes reinforced composite plate by meshless method

AbstractFunctionally graded (FG) carbon nanotubes (CNTs) reinforced composite possessing continuously varied material properties have received extensive concern in the area of aviation, automotive, military, and spaceflight. The nonlinear bending of FG CNT composite is performed by developing a novel meshless model on the base of Smoothed Particle Hydrodynamics (SPH) method. The equivalent temperature relative material properties of composite is established in the light of the extended rule of mixture model. Different gradient dispersions of CNTs are taken into account and modeled. The governing equations are explored using the Mindlin theory and discretized though a symmetric SPH method with high prediction ability. The developed model is verified by analyzed several examples and compared to the solution obtained in literature. Influences of CNT dispersion, content, plate aspect ratio, boundary conditions and lamination angle on the bending response are elaborated. The present study provides an alternative potential method to solve the mechanical performances of FG CNT composites based on the meshless SPH formulation.Highlights Thermo‐mechanics of FG‐CNTRC is firstly solved by SPH method. The model is verified by solving the benchmarks in literature. Nonlinear bending behaviors of FG‐CNTRC are demonstrated. The minimum deflection take place in FG‐X type composite. For laminate plate, the smallest deformation occurs in 0° CNTRC.

Batch-to-Batch Variation in Laser-Inscribed Graphene (LIG) Electrodes for Electrochemical Sensing.

Laser-inscribed graphene (LIG) is an emerging material for micro-electronic applications and is being used to develop supercapacitors, soft actuators, triboelectric generators, and sensors. The fabrication technique is simple, yet the batch-to-batch variation of LIG quality is not well documented in the literature. In this study, we conduct experiments to characterize batch-to-batch variation in the manufacturing of LIG electrodes for applications in electrochemical sensing. Numerous batches of 36 LIG electrodes were synthesized using a CO2 laser system on polyimide film. The LIG material was characterized using goniometry, stereomicroscopy, open circuit potentiometry, and cyclic voltammetry. Hydrophobicity and electrochemical screening (cyclic voltammetry) indicate that LIG electrode batch-to-batch variation is less than 5% when using a commercial reference and counter electrode. Metallization of LIG led to a significant increase in peak current and specific capacitance (area between anodic/cathodic curve). However, batch-to-batch variation increased to approximately 30%. Two different platinum electrodeposition techniques were studied, including galvanostatic and frequency-modulated electrodeposition. The study shows that formation of metallized LIG electrodes with high specific capacitance and peak current may come at the expense of high batch variability. This design tradeoff has not been discussed in the literature and is an important consideration if scaling sensor designs for mass use is desired. This study provides important insight into the variation of LIG material properties for scalable development of LIG sensors. Additional studies are needed to understand the underlying mechanism(s) of this variability so that strategies to improve the repeatability may be developed for improving quality control. The dataset from this study is available via an open access repository.

Surface Interaction and Wear Due to Sliding Electrical Contact of Materials

This article reports a research study on sliding electrical contacts of copper–diamond materials, involving modeling and validation, contact status simulations, and parametric studies. The simulation model includes the analyses of both mechanical and electrical heat sources, evaluations of heat partition and temperature, and the capture of material thermal softening, plastic deformation, and material removal. The simulation model is validated through result comparison with experimental average temperature and measured wear. With this model, temperature variation and surface wear accumulation were numerically predicted under the influences of material property variations. Factors influencing wear of the copper surfaces were explored, and parameter sensitivity was investigated by means of the Taguchi L18 matrix. The results reveal that thermally related parameters of the contact interface strongly affect the wear of the copper pin, and thermal conductivity of its mating surface material is of critical importance. The research also suggests that the electromagnetic field change due to switching the polarity of the power supply does not affect the friction and wear results; however, the polarity change influences material electrochemistry, which results in a difference in friction and wear.

Parametric study on the effect of material properties, tool geometry, and tolerances on preform quality in wind turbine blade manufacturing

Increasing throughput in wind turbine blade production can be achieved by separately manufacturing pre-shaped binder-stabilised dry preforms, and subsequently placing them in the blade mould. To avoid manufacturing defects, a trade-off between the formability and the handleability of the preform is necessary. In this paper, an experimentally validated preform model is used to study how variations in material properties, tool geometry, and placement tolerances influence defect generation. The results from three studies are presented. In the first study, a preform is formed over a ramp transition with variations in geometry. The results from this study indicate that a short ramp promotes transverse shearing of the preform. In the second study, the material properties of the preform are varied. The results indicate that a high mode I cohesive law of the binder and a high bending stiffness of the fabric promote transverse shearing and remove wrinkles. In the last study, placement tolerances for a pre-shaped preform are studied. The results show that if the preform can shear between the preform edge and the tool edge, it can conform to the mould even with large placement offsets. Process engineers and blade designers can readily use these results to help reduce forming-induced wrinkles.

Bending and Buckling Analysis of Porous 2D Functionally Graded Beams with Exponential Material Property Variation

Bending and Buckling Analysis of Porous 2D Functionally Graded Beams with Exponential Material Property Variation

Investigating the Impact of Blunt Force Trauma: A Probabilistic Study of Behind Armor Blunt Trauma Risk.

Evaluating Behind Armor Blunt Trauma (BABT) is a critical step in preventing non-penetrating injuries in military personnel, which can result from the transfer of kinetic energy from projectiles impacting body armor. While the current NIJ Standard-0101.06 standard focuses on preventing excessive armor backface deformation, this standard does not account for the variability in impact location, thorax organ and tissue material properties, and injury thresholds in order to assess potential injury. To address this gap, Finite Element (FE) human body models (HBMs) have been employed to investigate variability in BABT impact conditions by recreating specific cases from survivor databases and generating injury risk curves. However, these deterministic analyses predominantly use models representing the 50th percentile male and do not investigate the uncertainty and variability inherent within the system, thus limiting the generalizability of investigating injury risk over a diverse military population. The DoD-funded I-PREDICT Future Naval Capability (FNC) introduces a probabilistic HBM, which considers uncertainty and variability in tissue material and failure properties, anthropometry, and external loading conditions. This study utilizes the I-PREDICT HBM for BABT simulations for three thoracic impact locations-liver, heart, and lower abdomen. A probabilistic analysis of tissue-level strains resulting from a BABT event is used to determine the probability of achieving a Military Combat Incapacitation Scale (MCIS) for organ-level injuries and the New Injury Severity Score (NISS) is employed for whole-body injury risk evaluations. Organ-level MCIS metrics show that impact at the heart can cause severe injuries to the heart and spleen, whereas impact to the liver can cause rib fractures and major lacerations in the liver. Impact at the lower abdomen can cause lacerations in the spleen. Simulation results indicate that, under current protection standards, the whole-body risk of injury varies between 6 and 98% based on impact location, with the impact at the heart being the most severe, followed by impact at the liver and the lower abdomen. These results suggest that the current body armor protection standards might result in severe injuries in specific locations, but no injuries in others.

Analytical solutions and material tailoring for combined radial expansion and twisting of functionally graded orthotropic hollow cylinders

Assuming that material properties are described by the same function of the radius, we first analytically find the displacement field in a functionally graded, orthotropic and linearly elastic hollow cylinder undergoing combined radial expansion and twisting. Subsequently, either for a desired axisymmetric displacement field or a stress distribution, we determine the required variations of the material properties to produce them in an inhomogeneous orthotropic cylinder. Numerical results presented for four example problems reveal the possibility of suitably varying material properties in the radial direction to simultaneously minimize the structural mass and the maximum circumferential stress on the inner surface of a hollow cylinder. It is found that out of the three heterogeneous cylinders of the same geometric and material parameters, the one having the least mass also has the smallest value of the maximum circumferential stress on the cylinder's inner surface. It occurs when the material moduli essentially vary affinely through the cylinder thickness. The analytical solutions presented here can serve as benchmarks for others to verify their numerical algorithms.

A single mesh approximation for modeling multiphase flow in heterogeneous porous media

The control volume finite element (CVFE) approach is based on the discretization of primary flow and transport unknowns on two different meshes. The element mesh, used to represent the physical properties of the medium, differs from the control volume mesh necessary to ensure a mass conservative solution. The inherent two mesh feature of the CVFE approximation introduces inconsistency in the transport solution due to the averaging of physical and computed quantities between neighboring elements in the mesh. In this work, we present a consistent approach for modeling multiphase flow and transport in heterogeneous porous media. The approach is applied in the CVFE framework by enabling the discretization of primary unknowns on a single mesh. We combine the discretization of an element-wise, discontinuous pressure approximation with a first-order, discontinuous velocity approximation to resolve the elliptic or parabolic flow problem. The effectiveness of the formulation is achieved by exploiting the same finite element mesh as the flow problem, when updating the saturation solution. This direct mapping between the flow and transport mesh is simple yet effective for establishing a consistent solution in addition to circumventing the non-physical mass leakage exhibited in the classical CVFE method. We describe the interface approximations and the discontinuous terms needed for consistent solutions. The method is well suited to model flow in complex geometrical subsurface domains and is shown to be numerically stable while providing locally and globally mass conservative solutions. We apply the approach to several domains with complex geometrical features to emphasize the superiority of the approximation over conventional methods. The analysis shows over two orders of magnitude reduction in solution error compared to the classical CVFE. The new formulation effectively captures accurate transport solutions, even in the presence of varying material properties, without the need for mesh refinement.

Roentgenoscopy of laser-induced projectile impact testing.

Laser-induced projectile impact testing (LIPIT) based on synchrotron imaging is proposed and validated. This emerging high-velocity, high-strain microscale dynamic loading technique offers a unique perspective on the strain and energy dissipation behavior of materials subjected to high-speed microscale single-particle impacts. When combined with synchrotron radiation imaging techniques, LIPIT allows for in situ observation of particle infiltration. Two validation experiments were carried out, demonstrating the potential of LIPIT in the roentgenoscopy of the dynamic properties of various materials. With a spatial resolution of 10 µm and a temporal resolution of 33.4 µs, the system was successfully realized at the Beijing Synchrotron Radiation Facility 3W1 beamline. This innovative approach opens up new avenues for studying the dynamic properties of materials in situ.

A 3D computational model for ground motion simulation in Peninsular India

Due to the gradual and constant accumulation of seismic energy, Peninsular India (PI) is typically considered seismically stable with low to moderate seismicity. The seismic studies in Peninsular India always resorted to synthetic ground motion simulations, because of the limited instrumentation and hence lack of recorded data. In the absence of a well-defined medium model for PI, the usual practice is to use simple site proxies or one-dimensional velocity structures for ground motion simulations. However, the region consists of multi-scale geometric complexities, significant topography, and sedimentary basins and is surrounded by deep oceans. Thus, the radiated seismic wave field in the region is influenced by the medium properties and in the absence of a well-defined tomography model the reliable estimation of seismic hazard is a challenging problem in PI. Therefore, the seismic wave propagation in PI can be investigated using numerical simulation with reliable 3D computational model for PI, incorporating the knowledge of the underlying Earth structure. Hence, the present study attempts to develop a sophisticated three-dimensional (3D) medium model of Peninsular India for physics-based ground motion simulations for regional earthquakes. This is aided by the availability of one-dimensional (1D) velocity models and the crustal structure from the receiver function analysis which provides valuable insight into the variation of material properties in the region. In the present study, >100 s of 1D velocity profiles are collected from various literature, which is then grouped under 23 different geological regions identified in PI (as per GSI (2000)). The averaged material properties are assigned per each geological region and the information on sediment depths, basin geometry, topography, and bathymetry are incorporated. We use the spectral element method (SEM) to calibrate our 3D computational model by simulating synthetic seismograms and comparing them to recorded ground motions for two past earthquakes: the 2001 Mw 7.6 Bhuj earthquake and the 1997 Mw 5.8 Jabalpur earthquake. Further, the seismic waveforms at the near field of 2001 Mw 7.6 Bhuj event are simulated using a refined regional model. The spatial variability of associated seismic intensities and peak ground velocity (PGV) amplification are investigated. In addition, a study of the impact of model depth truncation and sphericity on ground motion is also conducted. The implemented medium model is the first of its kind for Peninsular India and can reliably be used in seismic wave propagation studies in the region. The simulated outcomes from the model are of engineering importance as these results can be used for seismic hazard assessment of the region.

Why Do Small Earth Dams Deteriorate: Insights from Physical Investigations in the West African Sahel

In West Africa, the construction of small earth dams is common against water scarcity. Burkina Faso, an inland country in West Africa, is home to 1001 dams that serve agricultural and pastoral needs. These embankments are predominantly made of compacted laterite, a cost-effective material abundant in over 2/3 of the country. However, these dams degrade over time, hindering their functionality. This study aims to establish a catalog of typical degradation occurring on small dams in Burkina Faso, which is virtually non-existent in the region while identifying and analyzing the potential causes. The study uses a diagnostic analysis followed up with technical visits on a representative sample of 24 dams in the Centre and Centre-South regions as a basis for future studies. The results reveal that these dams were constructed between 1965 and 2018, with capacities ranging from 150,000 to 4,740,000 m3. 33% of these dams have undergone total failure, likely attributed to factors such as internal erosion, pore overpressures, settlement, and deformation. Although 67% of the dams remain functional, their structural integrity could be improved. Erosion observed in riprap indicates vulnerability during high flood periods. Additionally, the absence of proper maintenance, as shown by the vegetation development weakening embankments, contributes to deterioration. The analysis also suggests that variability in construction techniques and lateritic material properties across time and regions may further exacerbate degradation. These findings inform infrastructure improvements and policy development for sustainable water resource management in Burkina Faso and similar regions.

Probability distributions of geotechnical properties for heterogeneous lignite mine spoils

ABSTRACT During surface coal mining, large amounts of overburden waste materials, known as spoils, are excavated and dumped, creating massive heaps. The sustainable management of these areas is a global priority, but a comprehensive geotechnical characterization has not yet been achieved due to limited research on the spoils’ geotechnical properties. This study conducts an advanced statistical analysis of lignite mine spoil materials from a large heap in Greece to determine suitable probability distributions. Five distributions - normal, lognormal, Weibull (2 and 3 parameters), and Gamma - were evaluated, considering the high variability of spoil material properties. The lognormal and Weibull distributions were found to best fit most geotechnical parameters, except for cohesion, which showed an uncommon distribution. In some cases, the normal distribution can be used for a simplified analysis. The findings can guide the analysis of spoil materials, as advanced numerical and stochastic methods are essential for robust geotechnical design.

Voxel Design of Grayscale DLP 3D-Printed Soft Robots.

Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D-printed structures due to the complexities arising from the voxel-level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP-printed soft robots is developed by combining a grayscale-dependent hyperelastic constitutive model and a voxel-based finite-element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel-based finite-element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion(DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3Dprinting.

Channeled spectroscopic ellipsometry enabled by physics-informed tandem untrained neural networks

Ellipsometry is a powerful metrology technique for characterizing the optical properties of various materials. Channeled spectroscopic ellipsometry (CSE) has shown great promise among the different types of ellipsometry due to its simple setup and rapid performance. Furthermore, CSE modulates the polarization parameters of thin films into a spectrum, thus transforming the measurement process into a demodulation problem. However, conventional CSE faces challenges in measurement accuracy and computational efficiency, with strict hardware and calibration requirements. Inspired by physics-informed machine learning, we propose CSE enabled by the physics-informed tandem untrained neural networks (PITUNN), which does not require training, exhibits high computational efficiency and partially alleviates the strict requirements for hardware and calibration accuracy. We also demonstrate the effectiveness of CSE enabled by the PITUNN and its ability to handle system errors and random noise through simulations and experiments on thin films of different thickness and materials.