Estimation Of Material Properties Research Articles

Small punch test size reduction to enable high flux irradiation

Screening and qualification of new nuclear materials with superior irradiation resistance requires expensive irradiation exposure and post-irradiation examination before consideration into industrial nuclear structural components. The development of miniaturized test methods is needed for enhanced cost/time efficient testing, while enabling comparative and robust material property estimation in a timely and high throughput manner. The Small Punch Test (SPT) is drawing much interest, especially since the publication of an European standard. Motivated by this standard, a new SP test sample geometry is proposed in order to reduce the material volume to be tested, hences increasing the number of samples that can be introduced in an irradiation rig is proposed. The modified sample diameter has been reduced from 8 to 5.35 mm. The qualification of this new design is described here together with a detailed description of the SPT setup modification as compared to the reference setup of the EN standard. Finite element analyses and a series of experiments have been conducted using both standard and reduced size SP samples to demonstrate that the modified test gives estimates of the basic mechanical properties with the same magnitude of error and validates the modified sample geometry for usage in irradiated material testing.

Probing the Relationship between Material Categorization and Material Property Estimation using Ambiguous Visual Stimuli

Probing the Relationship between Material Categorization and Material Property Estimation using Ambiguous Visual Stimuli

Dynamic Soft Tissue Artifacts during Impulsive Loads: Measurement Errors Vary With Wearable Inertial Measurement Unit Sensor Design.

Characterize and model Inertial Measurement Unit (IMU) errors due to transient dynamic soft tissue artifacts excited by impulsive loads, such as foot strikes during running and jumping. We instrumented 10 participants (5 female, 5 male) with IMUs on the dominant leg. An ankle IMU measured reference vertical accelerations during impulsive loads and was cross-validated against vertical force measures. Two IMUs on the posterior shank and anterior shank were used to characterize errors caused by dynamic soft tissue artifacts with respect to the reference. Shank sensors' masses were varied to explore their effect on dynamic soft tissue artifacts. Both the posterior IMU and anterior IMU overestimated peak vertical accelerations during the impulsive load (gain of 2.18 ± 0.63 and 1.55 ± 0.35 respectively). The post- impulsive load oscillation duration and natural frequency varied with sensor mass according to an underdamped second-order system, with posterior IMU and anterior IMU durations of 326 ± 75ms and 151 ± 50ms respectively and natural frequencies of 9.79 ± 2.68Hz and 18.22 ± 12.10Hz respectively. Low-pass filtering reduced overestimation of peak vertical accelerations, but also attenuated the reference measure. Our study suggests dynamic soft tissue artifacts result in transient, but substantial measurement errors that may not be appropriately mitigated through low-pass filtering. However, these dynamic soft tissue artifacts can be modeled using an underdamped second-order system and used to estimate material properties of underlying soft tissue. We demonstrate that dynamic soft tissue artifacts can be modeled and potentially mitigated to improve accuracy in applications necessitating measurement of impulsive loads such as foot strikes.

On the evaluation of surface tension of biodiesel

Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw1), between 200 and 300 (Mw2), and greater than 300 (Mw3). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

Stochastic mesoscale characterization of ablative materials for atmospheric entry

This study addresses the estimation of material properties at a mesoscopic level using the PuMA software from an uncertainty quantification perspective. Stochastic simulation with PuMA is primarily related to the random distribution of fibers, which is an intrinsic source of uncertainty. Additionally, the selection of certain physical parameters, such as the fiber's thermal conductivity, introduces further uncertainties. The first contribution of this study is to propose a low-cost surrogate-based methodology with an unequal allocation scheme, applied for the first time to the stochastic mesoscale characterization of ablative materials. The second contribution is a study on uncertainty propagation and sensitivity analysis of material properties, providing a systematic assessment of the choice of voxel resolution for both the fibers and the domain. Specifically, the convergence of quantities of interest can be monitored, thus identifying the minimal reference elementary volume.

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

AbstractThe development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibration of a large number of parameters, which may be space-dependent, challenging their clinical translation. In this work, we propose a new approach, which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models, capable of reconstructing displacement fields and estimating heterogeneous patient-specific biophysical properties and secondary variables such as stresses and strains. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting, and combines it with the physics of the problem, represented by a mathematical model based on partial differential equations, to regularise the problem and improve its convergence properties. Several benchmarks are presented to show the accuracy and robustness of the proposed method with respect to noise and model uncertainty and its great potential to enable the effective identification of patient-specific, heterogeneous physical properties, e.g. tissue stiffness properties. In particular, we demonstrate the capability of PINNs to detect the presence, location and severity of scar tissue, which is beneficial to develop personalised simulation models for disease diagnosis, especially for cardiac applications.

Optical constants and thickness determination of La[formula omitted]Sr[formula omitted]MnO3 thin films on Nb:SrTiO3 substrates by spectro-ellipsometry: Combination of optical and X-ray techniques

40 nm thick La2/3Sr1/3MnO3 (LSMO) thin films were epitaxially grown by Pulsed Laser Deposition (PLD) onto niobium doped SrTiO3 (Nb:STO) substrates, with different Nb concentration from 0.01%wt to 0.5%wt. The optical characterization of the heterostructures by spectroscopic ellipsometry enables us to extract the optical constants of the manganite heteroepitaxial layer at room temperature. Performing spectrophotometry in the same wavelength range brings a useful cross-validation of the extracted results. In addition, the thickness evaluation of the LSMO layer by spectro-ellipsometry is further validated by both High Resolution X-ray diffraction and X-ray reflectivity, as well as a Transmission Electron Microscopy cross section, taken as a physical reference. This study validates quantitatively the spectro-ellipsometry as a suitable routine tool to measure accurately both thickness and complex refractive index of the LSMO thin film, picturing their peculiar electrical behaviour between both metallic and insulating phases. The relative error on the thickness measurement between X-ray and ellipsometry is less than 5%. The LSMO complex refractive index enabled also a simultaneous estimation of further material properties, such as the optical gap ωg or the mass density ρm, determined with less than 1.5% relative error compared to X-ray reflectivity results.

Performance prediction and analysis of engineered cementitious composites based on machine learning

This study presents the implementation of machine learning (ML) techniques for mechanical properties prediction and analysis of polyethylene fiber-reinforced ECC (PE-ECC). A comprehensive database including different mechanical properties of PE-ECC was first constructed, with total 50 compressive strengths, 123 tensile strengths and 123 tensile strain capacities being assembled. Grey relational analysis was used to investigate the sensitivity of the critical parameters of PE-ECC’s mechanical properties. The evaluation results showed that the supplementary cementitious materials-to-binder ratio, water-to-binder ratio, sand-to-binder ratio, and fiber reinforcing index have significant effects on the mechanical properties of PE-ECC. Three representative ML techniques were utilized and demonstrated good predictive performance. A parametric study was further undertaken to quantify the effects of the selected parameters on the mechanical properties of PE-ECC based on the ML models. This study aims to help researchers and engineers estimate material properties of PE-ECC more effectively and provide supports for ECC design.

Experimental full waveform inversion for elastic material characterization with accurate transducer modeling

For quality assurance and in-service safety, sufficient characterization of the materials is essential during the development, manufacture, and processing of a material in any industrial setting. Evaluation of elastic coefficients, material microstructures, morphological features, and related mechanical properties are all part of the process of characterizing a material. Ultrasonic signals are sensitive to material properties such as wave speeds, attenuation, diffusion backscattering, microstructural variation, and elastic characteristics (e.g., elastic modulus, hardness, etc.). Ultrasonic computed tomography (USCT) is an emerging imaging method that can be implemented to obtain a quantitative estimation of material properties. In this study, a source estimation technique was initially proposed to obtain the source time function for accurate forward modeling by constructing a linear inverse problem for the unknown transducer modeling. Finally, a material characterization approach was proposed with accurate source estimation to extract wave speed distribution from an elastic material by employing a wave-based method, known as full waveform inversion (FWI). Systematic performance analysis of the proposed FWI model with accurate source estimation was assessed using experimental and synthetic data obtained from a 6061 aluminum sample. Overall, the proposed FWI technique has successfully reconstructed the wave speed distribution, exhibiting the potential of the proposed method of material characterization in various engineering applications.

A 3D viscoelastic–viscoplastic behavior of carbon nanotube‐reinforced polymers: Constitutive model and experimental characterization

AbstractDue to the widespread use of carbon nanotube (CNT)‐reinforced polymers in various industries, investigating the dynamic mechanical response of these materials under impact loading is of significant importance. A three‐dimensional viscoelastic (VE)–viscoplastic (VP) constitutive model for CNT/epoxy nanocomposites is developed. A proper rheological model is established using the generalized Maxwell model to represent the VE behavior and propose an exponential function to express the elasto‐VP response. Employing an empirical logarithmic relationship to estimate material properties at different strain rates, the VP behavior is modeled based on the overstress concept. The 3D numerical discretization of the proposed rate‐dependent material model is also carried out to develop a user‐defined material model (UMAT) for the commercial finite element (FE) software of Abaqus. The performed FE simulations based on the proposed rate‐dependent constitutive material can properly capture stress relaxation and hysteresis behaviors of the nanocomposites. Comparing the estimated tensile and shear stress–strain curves through developed material models with experimental measurements, an excellent agreement is observed.Highlights Tensile/shear properties of CNT/epoxy are measured at three strain rates. An empirical constitutive model is proposed for any arbitrary strain rates Viscoplastic behavior is captured based on over‐stress concept A 3D incremental form of a viscoelastic–viscoplastic material model is developed.

Sensitivity analysis for an effective transfer of estimated material properties from cone calorimeter to horizontal flame spread simulations

Predictive flame spread models based on temperature dependent pyrolysis rates require numerous material properties as input parameters. These parameters are often derived by optimisation and inverse modelling using data from bench scale experiments such as the Cone Calorimeter. The estimated parameters are then transferred to flame spread simulations, where self-sustained propagation is expected. A fundamental requirement for this transfer is that the simulation model used in the optimisation is sufficiently sensitive to the input parameters that are important to flame spread. Otherwise, the estimated parameters will have an increased associated uncertainty that will be transferred to the flame spread simulation. This is investigated here using a variance-based global sensitivity analysis method, the Sobol indices. The sensitivities of a Cone Calorimeter and a horizontal flame spread simulation to 15 effective properties of polymethyl methacrylate (PMMA) are compared. Results show significant differences between the setups: the Cone Calorimeter is dominated by strong interaction effects between two temperature dependent specific heat values, whereas the flame spread is influenced by several parameters. Furthermore, the importance of some parameters for the Cone Calorimeter is found to be time-varying, suggesting that single-value cost functions may not be sufficient to account for all sensitive parameters during optimisation.

Material Design of Porous Hydroxyapatite Ceramics via Inverse Analysis of an Estimation Model for Bone-Forming Ability Based on Machine Learning and Experimental Validation of Biological Hard Tissue Responses.

Hydroxyapatite and β-tricalcium phosphate have been clinically applied as artificial bone materials due to their high biocompatibility. The development of artificial bones requires the verification of safety and efficacy through animal experiments; however, from the viewpoint of animal welfare, it is necessary to reduce the number of animal experiments. In this study, we utilized machine learning to construct a model that estimates the bone-forming ability of bioceramics from material fabrication conditions, material properties, and in vivo experimental conditions. We succeeded in constructing two models: 'Model 1', which predicts material properties from their fabrication conditions, and 'Model 2', which predicts the bone-formation rate from material properties and in vivo experimental conditions. The inclusion of full width at half maximum (FWHM) in the feature of Model 2 showed an improvement in accuracy. Furthermore, the results of the feature importance showed that the FWHMs were the most important. By an inverse analysis of the two models, we proposed candidates for material fabrication conditions to achieve target values of the bone-formation rate. Under the proposed conditions, the material properties of the fabricated material were consistent with the estimated material properties. Furthermore, a comparison between bone-formation rates after 12 weeks of implantation in the porcine tibia and the estimated bone-formation rate. This result showed that the actual bone-formation rates existed within the error range of the estimated bone-formation rates, indicating that machine learning consistently predicts the results of animal experiments using material fabrication conditions. We believe that these findings will lead to the establishment of alternative animal experiments to replace animal experiments in the development of artificial bones.

Flexural Behavior of High-Strength Steel and Ultra-High-Performance Fiber-Reinforced Concrete Composite Beams

The flexural performance of steel and concrete composite beams can be further enhanced by utilizing advanced construction materials such as ultra-high performance fiber-reinforced concrete (UHPFRC) and high-strength steel. In this paper, the concept of critical elastic moment resistance is proposed and the equation for its estimation is derived. It was found that the high yield strength of steel calls for a narrow UHPFRC top layer to reach the critical state, whereas this ideal condition is not realistic for composite beams with normal-strength steel and UHPFRC. Small-scale composite beams composed of both high-strength and low-strength steel materials were tested under four-point bending to verify the critical state and performance of different types of connectors. The headed studs and plate connectors were first tested through small-scale push-out tests and then implemented in the composite beam with different spacing. The connection utilizing headed studs with 150 mm spacing performed the best among the three tested specimens in helping reach the critical elastic moment resistance. Finite element analyses of the composite beam were performed based on the estimated material properties under axial and biaxial stress conditions and the results align with the experiment results.

Experimental determination of strain in thin aluminum plate with central hole subjected to far-field tensile loading using digital image correlation (DIC)

A flat Aluminum specimen with a geometric discontinuity, which allows testing of the applicability of 2D and 3D Digital Image Correlation Strain measurements, has been considered for this research since it is prone to high stress concentration via Addis Ababa Institute of Technology research interest. Experimental strain using digital image correlation and geometry measurements should be measured with estimated material properties and compare the results with theoretical model predictions. Aluminum plate with central hole were subjected for far field stress in the Machine shop of School of Mechanical and Industrial Engineering at Addis Ababa University in order to test the agreement between DIC’s strain analysis, strain gauge strain analysis and calculated empirical formulas of strain analysis and for the stress distribution of the plate elastically deformed by using VIC-3D and strain gauge. The aim is to measure vertical strain field, Vertical strain along horizontal line through hole center as function of applied loading and vertical strain using theoretical formula along same line as measurements in a plate with central hole subjected to far field and near field tensile loading using visual image coloration (VIC-3D) software, Destensometere device and solid mechanics equations to compare strain results.

NUMERICAL DESIGN OF ASYMMETRIC POROUS MATERIALS WITH TARGET PROPERTIES

Numerical tools have become ubiquitous in design of manufactured porous materials. Many methods have been developed for imaging, reconstruction, material property estimation, and generation of materials in a virtual environment with the ultimate goal of understanding the connection between the synthesis process, material microstructure, and material properties. In previous works, we presented a new random field-based generation technique called adjustable level cut filtered Poisson field (ALCPF). We paired the ALCPF technique with a flow simulation method, the pore topology method (PTM), to compute material properties and verify that targets have been attained. Building on our earlier work where we demonstrated the ability of ALCPF to efficiently generate a wide variety of homogeneous microstructures, we pursue three new goals. First, we extend ALCPF to produce heterogeneous asymmetric porous materials with a target pore size gradient. Second, we demonstrate the capability of asymmetric-ALCPF to control both solid and void spaces by generating virtual asymmetric materials with different types of solid matrix geometries and void space pore size gradients. Third, we use these materials to assess the accuracy of PTM results in comparison with the solution from a direct numerical simulation. This work demonstrates that the ALCPF method successfully generates porous microstructures with desired asymmetric geometry with less than 4% error compared to target pore size gradient. Also, PTM estimates permeability with an average error of less than 7% compared to direct numerical simulation results.

Estimating viscoelastic, soft material properties using a modified Rayleigh cavitation bubble collapse time

Accurate determination of high strain rate (>103 1/s) constitutive properties of soft materials remains a formidable challenge. Albeit recent advancements among experimental techniques, in particular inertial microcavitation rheometry (IMR), the intrinsic requirement to visualize the bubble cavitation dynamics has limited its application to nominally transparent materials. Here, in an effort to address this challenge and to expand the experimental capability of IMR to optically opaque materials, we investigated whether one could use the acoustic signature of the time interval between the bubble's maximum radius and first collapse time point, characterized as the bubble collapse time, to infer the viscoelastic material properties without being able to image the bubble directly in the tissue. By introducing a modified Rayleigh collapse time for soft materials, which is strongly dependent on the stiffness of the material at hand, we show that, in principle, one can obtain an order of magnitude or better estimate of the viscoelastic material properties of the soft material under investigation. Using a newly developed energy-based theoretical framework, we show that for materials stiffer than 10 kPa the bubble collapse time during a single bubble cavitation event can provide quantitative and meaningful information about the constitutive properties of the material at hand. For very soft materials (i.e., shear modulus less than 10 kPa), our theory shows that unless the collapse time measurement has very high precision and low uncertainties, the material property estimates based on the bubble collapse time only will not be accurate and require visual resolution of the full cavitation kinematics.

How machine learning can extend electroanalytical measurements beyond analytical interpretation.

Electroanalytical measurements are routinely used to estimate material properties exhibiting current and voltage signatures. Analysis of such measurements relies on analytical expressions of material properties to describe the experiments. The need for analytical expressions limits the experiments that can be used to measure properties as well as the properties that can be estimated from a given experiment. Such analytical relations are essentially solutions of the physics-based differential equations (with properties as coefficients) describing the material behavior under certain specific conditions. In recent years, a new machine learning-based approach has been gaining popularity wherein the differential equations are numerically solved to interpret the electroanalytical experiments in terms of corresponding material properties. Since the physics-based differential equations are solved, one can additionally estimate underlying fields, e.g., concentration profile, using such an approach. To exemplify the characteristics of such a machine learning assisted interpretation of electroanalytical measurements, we use data from the Hebb-Wagner test on a magnesium spinel intercalation host. As compared to the traditional analytical expression-based interpretation, the emerging approach decreases experimental efforts to characterize relevant material properties as well as provides field information that was previously inaccessible.

Non-linear stability characteristics of randomly distributed CNT-reinforced fiber composite beams under thermo-mechanical loadings

The aim of the present study is to elucidate the non-linear stability characteristics of randomly distributed carbon nanotube-reinforced fiber composite (RD-CNTRFC) beams subjected to thermo-mechanical loading. Effective material properties of RD-CNTRFC are estimated using the Eshelby-Mori-Tanaka approach and Halpin–Tsai micromechanical techniques. The material properties are considered to be temperature-dependent, and the influence of carbon nanotube (CNT) agglomeration is also incorporated in material properties estimation of hybrid matrix. The strain-displacement relations are assumed by incorporating the higher-order shear deformation theory and geometrical nonlinearity (von Kármán). The governing partial differential equations are obtained by minimizing the total potential energy of the beam. Buckling loads of beam subjected to in-plane compressive loads are estimated using Eigen value approach. Similarly, buckling temperatures are also determined by solving Eigenvalue problem iteratively due to temperature-dependent material properties. The non-linear equilibrium paths under thermal and mechanical loadings are traced through iterative Eigenvalue approach. Numerical results are presented to elucidate the influence of various parameters like CNT agglomeration, ply sequences, span-to-depth ratio, boundary conditions, and mass fraction of CNTs.

Redesigning elastic full‐waveform inversion on the new Sunway architecture

AbstractIFOS3D is a three‐dimensional elastic full‐waveform inversion (EFWI) tool designed for high‐resolution estimation of the Earth's material properties within 3D subsurface structures. However, due to the significant computational costs associated with 3D EFWI, leveraging the computing power of a supercomputer for implementation is a logical choice. In this article, we introduce several innovative process‐level and thread‐level optimizations based on heterogeneous many‐core architectures in the new Sunway supercomputer, which is a powerful system globally. These optimizations encompass a process‐level communication overlapping strategy, thread‐level data partitioning and layout approaches, a remote memory access optimized master‐slave communication scheme, and a thread‐level data reuse and overlapping strategy. Through these optimizations, we achieve significant improvements in each iteration, with a kernel function speedup of approximately 59 and an overall program speedup of about 14. Our findings demonstrate the ability of our proposed optimization strategies to overcome the computational challenges associated with 3D EFWI, providing a promising framework for future advancements in the field of subsurface imaging.

On the Material Constitutive Behavior of the Aortic Root in Patients with Transcatheter Aortic Valve Implantation

BackgroundTranscatheter aortic valve implantation (TAVI) is a minimally invasive procedure used to treat patients with severe aortic valve stenosis. However, there is limited knowledge on the material properties of the aortic root in TAVI patients, and this can impact the credibility of computer simulations. This study aimed to develop a non-invasive inverse approach for estimating reliable material constituents for the aortic root and calcified valve leaflets in patients undergoing TAVI. MethodsThe identification of material parameters is based on the simultaneous minimization of two cost functions, which define the difference between model predictions and cardiac-gated CT measurements of the aortic wall and valve orifice area. Validation of the inverse analysis output was performed comparing the numerical predictions with actual CT shapes and post-TAVI measures of implanted device diameter.ResultsA good agreement of the peak systolic shape of the aortic wall was found between simulations and imaging, with similarity index in the range in the range of 83.7% to 91.5% for n.20 patients. Not any statistical difference was observed between predictions and CT measures of orifice area for the stenotic aortic valve. After TAVI simulations, the measurements of SAPIEN 3 Ultra (S3) device diameter were in agreement with those from post-TAVI angio-CT imaging. A sensitivity analysis demonstrated a modest impact on the S3 diameters when altering the elastic material property of the aortic wall in the range of inverse analysis solution.ConclusionsOverall, this study demonstrates the feasibility and potential benefits of using non-invasive imaging techniques and computational modeling to estimate material properties in patients undergoing TAVI.