Estimation Of Material Properties Research Articles

In Vivo Material Properties of Human Common Carotid Arteries: Trends and Sex Differences.

In vivo estimation of material properties of arterial tissue can provide essential insights into the development and progression of cardiovascular diseases. Furthermore, these properties can be used as an input to finite element simulations of potential medical treatments. This study uses non-invasively measured pressure, diameter and wall thickness of human common carotid arteries (CCAs) acquired in 103 healthy subjects. A non-linear optimization was performed to estimate material parameters of two different constitutive models: a phenomenological, isotropic model and a structural, anisotropic model. The effect of age, sex, body mass index and blood pressure on the parameters was investigated. Although both material models were able to model in vivo arterial behaviour, the structural model provided more realistic results in the supra-physiological domain. The phenomenological model predicted very high deformations for pressures above the systolic level. However, the phenomenological model has fewer parameters that were shown to be more robust. This is an advantage when only the physiological domain is of interest. The effect of stiffening with age, BMI and blood pressure was present for women, but not always for men. In general, sex had the biggest effect on the mechanical properties of CCAs. Stiffening trends with age, BMI and blood pressure were present but not very strong. The intersubject variability was high. Therefore, it can be concluded that finding a representative set of parameters for a certain age or BMI group would be very challenging. Instead, for purposes of patient-specific modelling of surgical procedures, we currently advise the use of patient-specific parameters.

Experimental and analytical investigation of the tensile behavior of 3D-printed composites based on micro-CT analysis

Mechanical properties of short fiber reinforced composites manufactured with fused deposition modeling is affected by variables such as printing properties and internal structure of the composite. In this study, the relation between the tensile properties of 3D-printed short fiber reinforced composites, printing orientation and internal structure is inspected. Samples of different printing orientations are manufactured. These samples are inspected under X-ray micro computed tomography (micro-CT) and their fiber orientation distributions and fiber properties are determined. Then, these internal structure properties are applied to analytical material models to estimate the tensile properties of composites. Estimated material properties are compared with tensile test results. It turns out that analytical models that consider internal structure properties provide more accurate estimations than other analytical models that does not take these into account.

An Estimation of Clayey-Oriented Rock Mass Material Properties, Sited in Koropi, Athens, Greece, through Feed-Forward Neural Networks

Rock mechanics and the estimation of their material properties through field tests are important aspects and challengees in civil and geotechnical engineering. However, this procedure is expensive and difficult to attain, while the machine learning and neural network theory provide a computational tool for estimating the material properties with limited data. In this work, an estimation of the Young Modulus and the cohesion of a clayey-originated rock through feed-forward neural networks constructed from in situ data measurements is given. The input values come from the Geological Strength Index (GSI) proposed values of the point load index Is50, the uniaxial compression strength σs, as well as the specific gravity γ of the rock mass. The convergence analysis revealed that the convergence occurs at approximately 2000 epochs, with the largest L2 mean square error norm being no greater than 10−5. In addition, it is demonstrated that augmenting γ results in the estimation of rock that is stiffer and stronger. The aforementioned increase in the specific site may be up to 20% for the stiffness and up to 25% for the cohesion. This model, aside from readability and accuracy, offers the convenience of enriching it with more in situ data, thereby enhancing the flexibility of the proposed numerical tool proposed. However, its applicability is limited to the specific data acquired from the particular site, so a more general estimation requires a substantially larger dataset. Finally, the justification of the proposed model has been carried out based on suggestions from the literature for common values of clayey-oriented rock, which is fairly disintegrated as seen in the field.

Effect of Arrhenius activation energy on two-phase nanofluid flow and heat transport inside a circular segment with convective boundary conditions: Optimization and sensitivity analysis

This paper analyzes the heat transport phenomena of two-phase nanofluid within a permeable circular segment with an interaction of Arrhenius activation energy and, examines the interaction of convective boundary conditions affecting the flow characteristics. The pre-exponential factor of the Arrhenius equation deals with the collision between molecules, and it has significant applications for getting the rate of a chemical reaction and is used to estimate material properties with changes in temperature and energy. To a greater extent, the external uniform heat source encourages thermal properties on account of the behavior of Brownian and thermophoresis parameters. The transformation of the standard nondimensional form of the governing equations is obtained for the assumption of appropriate similarity variables and stream function. The set of these transformed equations is tackled numerically with the shooting technique. The characteristics of physical parameters on the flow phenomena are presented graphically and the validation with earlier research displays a strong correlation for the specific case. The special attraction of the present investigation is the regression analysis for the simulated results of heat and solutal transfer rates using Response Surface Methodology considering various characterizing parameters like heat source, thermal radiation, curvature, activation energy, thermal and solutal Biot numbers within certain range.

Thermo-mechanical analysis of additive manufacturing for material properties estimation of layered polymer composite

This paper proposes a numerical approach for predicting residual stresses induced during the additive manufacturing (AM) of polymer composites and their effect on the deformation behavior of the composites. A finite element (FE) model was developed on Abaqus/Standard for 3D printing of layered polymer composite using three polymers: HDPE, PVC, and ABS. The material properties of pure polymers and their blends were measured through tensile testing of injection-molded specimens. These properties were then used as input data in the FE model for the regions of pure polymers and their interfaces. An uncoupled thermo-mechanical analysis was performed, where a heat transfer analysis was realized first using elements birth (activation/deactivation) technique and a moving cylindrical volumetric heat source as a Fortran subroutine (DFLUX). With temperature input from thermal simulation, a mechanical analysis was carried out to deform the polymer composites along and across the print direction, i.e. under isostrain and isostress conditions, respectively. It was observed that the theoretical models existing in the literature underestimate the material properties as they do not consider the residual stresses developed during fabrication. The FE simulations predicted that the estimated material properties were 10 to 50% different from those calculated by the theoretical models, depending upon the residual stress level and the print direction.

Microscopic computed tomography aided finite element modelling as a methodology to estimate hygroexpansion coefficients of wood: a case study on opposite and compression wood in softwood branches

Abstract Microscopic X-ray computed tomography (XµCT) aided finite element (FE) modelling is a popular method in material science to relate material properties to heterogeneous microstructures. Recently, a methodology was developed for the XµCT aided FE modelling of wood, which characterises the process from specimen preparation to estimation of material properties. In the current research, this methodology is tested on branches of Norway spruce (Picea abies (L.) Karst.) to estimate the hygroexpansion coefficients of opposite (OW) and compression wood (CW). These properties are largely unknown and have engineering implications. The study is complemented by measurements of density, moisture content (MC) and elastic moduli. Results showed that the methodology assisted in the design of an integrated process and the identification of bottlenecks. It was seen that the level of detail of the numerical model had a strong influence on the obtained hygroexpansion properties. CW from branches showed higher density and longitudinal shrinkage coefficients, and elastic moduli less affected by MC. These differences are unlikely caused by MC, but more likely by the characteristics of the microstructure.

Design of a low-velocity impact framework for evaluating space-grade materials

Material deformation and failure under impact loading is a subject of active investigation in space science and often requires very specialized equipment for testing. In this work, we present the design, operational analysis and application of a low-velocity (∼100m/s) projectile impact framework for evaluating the deformation and failure of space-grade materials. The system is designed to be modular and easily adaptable to various test geometries, while enabling accurate quantitative evaluation of plastic flow. Using coupled numerical methods and experimental techniques, we first establish an operating procedure for the system. Following this, its performance in two complementary impact configurations is demonstrated using numerical and experimental analysis. In the first, a Taylor impact test is performed for predicting the deformed shape of a cylindrical projectile impinging on a rigid substrate. In the second, deformation of a plate struck by a rigid projectile is evaluated. In both cases, physics-based models are used to interpret the resulting fields. We present a discussion of how the system may be used both for material property estimation (e.g., dynamic yield strength) as well as for failure evaluation (e.g., perforation and fracture) in the same projectile impact configuration.

Numerical analysis, experimental tests and non-linear analytical models in the study of bent timber beams reinforced with FRP strips

The paper presents selected methods for modelling bent timber beams strengthened with fibre-reinforced polymers (FRP). The assumptions are described, and the main analytical models representing the non-linear bending behaviour of wood are shown. Based on the models presented, analytical approaches considering reinforcement in the tension zone are proposed. The problem of determining the appropriate material parameters of the wood used in analytical calculations and numerical models is discussed. The main numerical methods used in timber analyses are presented. The Hill yield criterion and the generalised Hill yield criterion are discussed in more detail. A method for retrospective estimation of material properties of wood taking into account non-linear material models and fitting the generalised Hill yield criterion is described. Experimental bending tests of technical-scale beams reinforced with composites are presented. The numerical analysis was carried out and discussed. The generalised Hill yield criterion was applied to represent the behaviour of the wood, distinguishing the different yield stresses in compression and tension. The suitability of using non-linear models and advanced strength criteria in the numerical analysis of timber elements is discussed.

Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.

Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity. In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity Vz. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances. By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas' autocorrelation algorithm with an improved SNR of 4.47dB for the Vz signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data. The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes.

Germ-Free C57BL/6 Mice Have Increased Bone Mass and Altered Matrix Properties but Not Decreased Bone Fracture Resistance.

The gut microbiome impacts bone mass, which implies a disruption to bone homeostasis. However, it is not yet clear how the gut microbiome affects the regulation of bone mass and bone quality. We hypothesized that germ-free (GF) mice have increased bone mass and decreased bone toughness compared with conventionally housed mice. We tested this hypothesis using adult (20- to 21-week-old) C57BL/6J GF and conventionally raised female and male mice (n = 6-10/group). Trabecular microarchitecture and cortical geometry were measured from micro-CT of the femur distal metaphysis and cortical midshaft. Whole-femur strength and estimated material properties were measured using three-point bending and notched fracture toughness. Bone matrix properties were measured for the cortical femur by quantitative back-scattered electron imaging and nanoindentation, and, for the humerus, by Raman spectroscopy and fluorescent advanced glycation end product (fAGE) assay. Shifts in cortical tissue metabolism were measured from the contralateral humerus. GF mice had reduced bone resorption, increased trabecular bone microarchitecture, increased tissue strength and decreased whole-bone strengththat was not explained by differences in bone size, increased tissue mineralization and fAGEs, and altered collagen structure that did not decrease fracture toughness. We observed several sex differences in GF mice, most notably for bone tissue metabolism. Male GF mice had a greater signature of amino acid metabolism, and female GF mice had a greater signature of lipid metabolism, exceeding the metabolic sex differences of the conventional mice. Together, these data demonstrate that the GF state in C57BL/6J mice alters bone mass and matrix properties but does not decrease bone fracture resistance. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Example framework for evaluation of synthetic rock-like materials as applied to a commercial gypsum cement

Estimating material properties is an essential part of any engineering field. In rock mechanics, obtaining an infinite number of samples and/or samples at desired orientations is often not possible. Thus, replica materials have been extensively used to serve in the laboratory as analogues of natural rock. In this note, a framework is established to assess the suitability of a replica material to replicate natural rock for research purposes. The framework involves the evaluation of the mechanical properties in terms of the modulus ratio and the strength ratio. This framework is used to evaluate the adequacy and suitability of a plaster-like material, commercially known as “Flowstone”. The replica material formation process most closely resembles that of sedimentary rocks. From a physical and mechanical perspective, the modulus ratio, strength ratio, Poisson's ratio, and density fall within ranges that define sedimentary rock, indicating that this replica material is a suitable analogue to rock (i.e., ‘rock-like materials' or ‘synthetic rocks').

A Novel In-Line Polymer Melt Viscosity Sensing System of Integrated Soft Sensor and Machine Learning

Real-time monitoring of melt viscosity is one of the many challenges faced in the polymer extrusion process. Viscosity is one of the important metric reflecting material properties during plastic process. However, viscosity is an indicator that can be evaluated by calculating the evaluation value through temperature, pressure, screw rotation speed, etc., but cannot be directly measured by physical sensors. Melt viscosity should be calculated at the exact melt temperature. Temperature sensors cannot accurately measure the temperature of the melt in the barrel. Soft sensing technique is the best solution for estimating material properties. It just needs some physical sensors and physical formulas. The proposed viscosity soft sensor consists of physical sensors, a temperature estimator, and a simulation analysis software for calculating material properties. Four physically temperature signals, one physically pressure signal, and the simulation properties data are used as the dataset for the temperature estimator. An ensemble machine learning model of temperature estimators consisting of Random Forests (RF) and Convolutional Neural Networks (CNN). Melt viscosity, shear stress and shear rate are calculated by physical formulas. Experimental results show that the proposed temperature estimator predicts that the temperature will reduce the MAE from 6.08 to 2.86. Compared with the current work, the prediction error rate of the soft sensor is also reduced from 4% to 1.1%. The proposed soft sensor can be used to better predict polymer melt temperature. Finally, according to the predicting results, the material property scatter chart of viscosity can be precisely plotted under the specific melt temperature. In the past, the melt viscosity could only be measured offline. The proposed method can be added as a plug-in to the existing process to achieve real-time viscosity monitoring.

Comparing methods for permeability computation of porous materials and their limitations

AbstractEfficient numerical simulations of fluid flow on the pore scale allow for the numerical estimation of effective material properties of porous media, e.g. intrinsic permeability or tortuosity. These parameters are essential for various applications where hydro‐mechanical properties on larger scales have to be known. Numerical tools based intrinsically on pore scale simulations are known e.g. as Digital Rock Physics in geosciences and have even more and more replaced physical experiments. For these reasons, the validation of numerical methods as well as the establishment of clear limits regarding the application areas play an important role. Here, we compute single‐phase flow through a porous matrix, e.g. irregular sphere packings, sandstones, artificially created thin porous media, on the pore scale. Therefore we implement on the one hand a Smoothed Particle Hydrodynamics algorithm for solving the Navier‐Stokes equations and on the other hand a Finite Difference solver for the Stokes equations. Both methods work directly and seamlessly on voxel data of porous materials which are generated by µXRCT‐scans or by microfluidic experiments that have undergone segmentation and binarization. We compare both solvers from a parallel performance point of view as well as their results for flows in the Darcy regime. In addition, we investigate the limitations of the solvers using the example of a porous material whose pore geometry changes over time and precipitation affects the flow conditions.

Kriging, Polynomial Chaos Expansion, and Low-Rank Approximations in Material Science and Big Data Analytics.

In material science and engineering, the estimation of material properties and their failure modes is associated with physical experiments followed by modeling and optimization. However, proper optimization is challenging and computationally expensive. The main reason is the highly nonlinear behavior of brittle materials such as concrete. In this study, the application of surrogate models to predict the mechanical characteristics of concrete is investigated. Specifically, meta-models such as polynomial chaos expansion, Kriging, and canonical low-rank approximation are used for predicting the compressive strength of two different types of concrete (collected from experimental data in the literature). Various assumptions in surrogate models are examined, and the accuracy of each one is evaluated for the problem at hand. Finally, the optimal solution is provided. This study paves the road for other applications of surrogate models in material science and engineering.

Probability of unsatisfactory performance of monopile foundations for offshore wind turbines considering variations in seabed properties

Safe design of monopiles for Offshore Wind Turbines (OWT) requires an accurate estimation of the foundation soil material properties. Uncertainty in soils is mainly due to their inherent variability which involves various spatial and temporal processes they have been exposed to over their natural geological process. The limited amount of information from geotechnical investigations in the seabed, considering the size of OWT farms, adds to the uncertainty. Commonly used deterministic design approaches idealize soil layers by assigning average values to the mechanical soil properties and make use of empirical correlations that can significantly differ from offshore conditions. Probabilistic design approaches have been suggested in the literature to account for the effect of uncertainties on the foundation’s performance by applying a coefficient of variation (COV) to soil properties. Such methods include theoretical investigations by applying selected COV values to predetermined mean CPT profiles for sands and undrained shear strength for clays. This study calculates the probability of unsatisfactory performance, Pfsys, of monopiles in sands by implementing variation in friction angle and unit weight using current API and DNV-GL design guidelines. Monte Carlo Simulation is employed to generate random fields using lognormal, normal, and uniform distributions. Pfsys is presented for a case study found in the literature and a design exercise for which local COV for the Belgian coast is also proposed.

Integration of NDT, 3D parametric modelling, and nonlinear numerical analysis for the seismic assessment of a vaulted stone-masonry historical building

This paper presents the integration of parametric modelling with NDT (NDT) and advanced numerical simulations for assessing the seismic response of an emblematic stone-masonry vaulted construction, the 16th-century church of the Society of Jesus located in Cusco, Peru. Revit parametric families allowed the creation of complex curved elements such as vaults and 3D geometrical models of the church based on a hybrid point cloud. The onsite survey results indicated that the main anomalies correspond to the presence of biological agents and cracking. In contrast, NDT allowed the estimation of the masonry material properties and the modal identification primarily of the towers. Finally, the application of pushover analyses allowed the estimation of the structure's maximum load capacity and post-peak behavoir. It was possible to determine that some collapse mechanisms (overturning of main façade and bell towers, and the generation of hinges in curved elements) can be activated in medium-strong earthquakes.

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

AbstractDensity is one of the most commonly measured or estimated material properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science, and sustainable cements. Here, three types of machine learning models (i.e., random forest [RF], artificial neural network [ANN], and Gaussian process regression [GPR]) have been developed to predict the room‐temperature density of glasses in the compositional space of CaO–MgO–Al2O3–SiO2–TiO2–FeO–Fe2O3–Na2O–K2O–MnO (CMASTFNKM), based on ∼2100 data points mined from ∼140 literature studies. The results show that the RF, ANN, and GPR models give accurate predictions of glass density with R2 values, root mean square error (RMSE), and mean absolute percentage error (MAPE) of ∼0.95–0.97, ∼0.03–0.04 g/cm3 and ∼0.63%–0.83%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ∼0.28–0.91, ∼0.05–0.15 g/cm3, and ∼1.40%–4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO–Al2O3–SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ∼0.90) with the average metal–oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density–composition relationships from these models (for selected compositional subspaces) suggests that the single‐layer ANN and GPR models exhibit a certain level of transferability (i.e., ability to extrapolate to compositional spaces not [or less] covered in the database) and capture some known features, including the mixed alkaline earth (or alkali) effects.

A Two-Step FE Model Updating Approach for System and Damage Identification of Prestressed Bridge Girders

This study presents a two-step FE model updating approach for health monitoring and damage identification of prestressed concrete girder bridges. To reduce the effects of modeling error in the model updating process, in the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, a time-domain model updating is carried out using acceleration data to refine parameters accounting for the nonlinear response behavior of the bridge. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on the first-step model updating results. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of full-scale decommissioned and deteriorated prestressed bridge I-girders. In the first step, parameters related to boundary conditions, including stiffness of supports and coupling beams, as well as material properties, including initial stiffness of concrete material, are estimated. In the second step, concrete compressive strength and damping properties are updated. The final estimates of the concrete compressive strength are used to infer the extent of damage in the girders. The obtained results agree with the literature regarding the extent of reduction in concrete compressive strength in deteriorated concrete structures.

SHP-1 Protein Tyrosine Phosphatase Affects Early Postnatal Bone Development in Mice.

The Src homology region 2 domain-containing phosphatase-1 (SHP-1) is an intracellular tyrosine phosphatase that plays a negative regulatory role in immune cell signaling. Absent or diminished SHP-1 catalytic activity results in reduced bone mass with enhanced bone resorption. Here, we sought to investigate if Shp1 overexpression leads to increased bone mass and improved mechanical properties. Male and female wildtype (WT) and SHP1-transgenic (Tg) mice at 28, 56, and 84days of age were compared. We applied microcomputed tomography to assess femoral cortical bone geometry and trabecular architecture and 3-point mechanical bending to assess mid-diaphyseal structural and estimated material properties. Serum OPG, RANKL, P1NP, and CTX-1 concentrations were measured by enzyme-linked immunoassay. The majority of transgene effects were restricted to the 28-day-old mice. Trabecular bone volume per total volume, trabecular number, and connectivity density were greater in 28-day-old female SHP1-Tg mice when compared to WTs. SHP1-Tg female mice showed increased total and medullary areas, with no difference in cortical area and thickness. Cortical tissue mineral density was strongly genotype-dependent. Failure load, yield load, ultimate stress, and yield stress were all lower in 28-day-old SHP1-Tg females. In 28-day-old SHP1-Tg females, circulating levels of OPG and P1NP were higher and RANKL levels were lower than WT controls. Our study demonstrates a role for SHP-1 in early postnatal bone development; SHP-1 overexpression negatively impacted whole bone strength and material properties in females.

Evaluation of Materials and Structures with a Multistatic Ultra-Wideband Impulse Radar: A Concept Validation

This document describes the results of the study contributing to the methods and tools applicable in plastic waste sorting systems that exploit the multistatic ultra-wideband impulse radar enforced with a deep learning signal processing back-end. The novelty of the research is the use of synthetic data for the development of a trained neural network before real data are available, and the use of a multistatic radar for the improvement of the training data set. The study results are described in multiple publications; the current paper shows the applicability of the described approach. The main results are as follows: a monostatic impulse radar can be used for the determination of material properties, such as thickness, dielectric permittivity, and losses, with limited accuracy; multistatic radar configuration increases the accuracy of the material property estimation; an open source finite difference time domain simulator can be used to simulate electromagnetic wave propagation in dielectric structures in order to generate synthetic data for development of optimized artificial neuron network structures used for the estimation of dielectric material properties, and the developed network can successfully be used for multistatic radar data processing.