Full-field Approach Research Articles

Cryogenic Digital Image Correlation as a Probe of Strain in Iron-Based Superconductors

Abstract Uniaxial strain is a powerful tuning parameter that can control symmetry and anisotropic electronic properties in iron-based superconductors. However, accurately characterizing anisotropic strain can be challenging and complex. Here, we utilize a cryogenic optical system equipped with a high-spatial-resolution microscope to characterize surface strains in iron-based superconductors using the digital image correlation method. Compared with other methods such as high-resolution X-ray diffraction, strain gauge, and capacitive sensor, digital image correlation offers a non-contact, full-field measurement approach, acting as an optical virtual strain gauge that provides high spatial resolution. The results measured on detwinned BaFe2As2 are quantitatively consistent with the distortion measured by X-ray diffraction and neutron Larmor diffraction. These findings highlight the potential of cryogenic digital image correlation as an effective and accessible tool for probing the isotropic and anisotropic strains, facilitating the application of uniaxial strain tuning in the study of quantum materials.

A full-field approach for precipitation in metallic alloys. Comparison with a mean-field model

Modeling precipitation in metallic alloys is a topic of great importance in physical metallurgy as the resulting strengthening strongly depends on the precipitate microstructure. We propose here a numerical full-field model for precipitation that describes precipitates with shape functions, thereby allowing to bridge scales between phase-field approaches - that accurately describe the precipitate evolution but require a fine discretization grid - and mean-field approaches - that are computationally very efficient but rely on strong assumptions. Our results demonstrate the capability of the full-field approach to model the different stages of precipitation during isothermal treatments. The comparison with mean-field results allow to discuss the influence of solutal impingement and precipitate coagulations on the evolution of the precipitate microstructure.

A Unified Multiscale Framework for Simulating Electrochemical Gas Evolving Reactions

The presence of gas bubbles in electrochemical systems, such as water-splitting, significantly increases overpotential and diminishes energy efficiency. High-fidelity simulation holds significant promise to gain mechanistic understanding of bubble dynamics and guide high-performance electrolytic cell design. However, the extreme length scales involved into electrochemical gas evolving systems, from sub-nanometer dictated by the electrical double layer (EDL) to a few centimeters featured by the electrolytic cell, have posed a huge challenge to enable sufficient numerical accuracy and superior computational efficiency. As a result, state-of-the-art numerical approaches either can only simulate a nanoscale computational domain or neglect the electrochemical kinetics within the EDL, impeding an in-depth understanding of how bubbles could intervene with the electrochemical process.In this work, we demonstrate a full-field simulation approach of electrochemical gas evolving reactions that can capture the electrochemical kinetics of the EDL in a centimeter-scale electrolytical cell with the presence of micro-to-millimeter scale bubbles. To resolve all characteristic length scales, the EDL is geometrically decoupled from the electrolytical cell but physically coupled with the bulk electrolyte and gas bubble through a quasi-1D treatment. As a result, the Nernst-Planck-Poisson-Boltzmann model can be rigorously solved in both the EDL and the rest of the electrolytical cell with highly affordable computational cost. Taking hydrogen evolution reaction as an example, we validated our approach by comparing with a variety of existing numerical and experimental results. For the first time, the impact of bubbles on the increase of overpotential observed in experiments can be quantitatively confirmed through numerical simulation. More notably, compared to state-of-the-art high-fidelity simulations, our approach exhibits a remarkable computational efficiency, which reduced the computational time by a factor of 100,000. This work provides a viable solution to simulate electrochemical gas evolving reactions with highly desirable numerical accuracy and unprecedented computational efficiency, which can serve as an effective tool to understand bubble dynamics and guide the design of next-generation electrolytical cells.

Microstructure generation and full-field multi-scale analyses for short fiber reinforced thermoplastics: Application to PA66GF composites

Short Fiber Reinforced Thermoplastics (SFRTs) like PA66GF composites are highly heterogeneous materials with a rather complex microstructure such that the characterization and the prediction of their mechanical behavior remain quite challenging. So far, most of the research efforts have employed phenomenological and mean-field multi-scale models to deal with SFRTs. The present contribution rather focuses on a full-field multi-scale approach that incorporates advanced techniques in terms of microstructural representation and material modeling, allowing a deep insight of the dominating deformation mechanisms occurring in PA66GF composites. The proposed approach is based on an automatic periodic mesh generation algorithm for matrix-inclusion Representative Volume Elements (RVE) with randomly positioned fibers that follow a given Orientation Distribution Function (ODF). At the microscopic scale, while the fibers are assumed to be elastic, the behavior of the thermoplastic matrix is described by a phenomenological multi-mechanism constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. It results an advanced multi-scale model that enables to visualize the local deformation and degradation mechanisms occurring at the microscopic scale while simultaneously analyzing their influence on the macroscopic response of the composite upon monotonic, persistent and cyclic loading. The potential of the proposed approach is further evaluated by comparing the predicted responses against experimental data.

Design of Cold-Formed Z-Shaped purlin-to-rafter connections subject to pull-through failure

The current study is focused on understanding the pull-through behaviour of Z-Shaped Cold-Formed Steel (CFS) purlin-to-rafter connection. A total of forty tests were conducted using small-scale testing methods to investigate the influence of parameters such as thickness, depth, and flange width of the purlin section along with the size of the screw head diameter. The results indicated that the test specimens exhibited two modes of pull-through failure (i) rupture-type failure and (ii) bearing-type failure. A transition from rupture-type to bearing-type was observed with an increase in thickness from 1 mm to 3 mm. Both the failure modes were accompanied by tilting of the screw due to eccentricity in the applied load for Z-Shaped purlins. A full-field measurement approach called Three-Dimensional Digital Image Correlation (3D-DIC) technique was employed to better understand the failure mechanism. The DIC strain contours adequately captured the tilting of the screw that can be attributed to the increased strain and out-of-plane deformation at the vicinity of the screw head. A comparison of the test with existing design standards indicates that the design guidelines are unconservative for Z-shaped purlin sections. A modified design equation is proposed to determine the pull-through capacity of Z-shaped purlin-to-rafter connection. Further, reliability study was carried out to determine the resistance and safety factors for the preliminary design equations proposed.

A Unified Full-Field Deformation Measurement Approach for Plates With Different Thickness

A Unified Full-Field Deformation Measurement Approach for Plates With Different Thickness

Unravelling the extra-hardening in chemically architectured high entropy alloys

Chemically architectured high entropy alloys are a new concept of multi-scale microstructure originally including a 3D network of composition fluctuations, named interphase. To unravel the strengthening contribution of each entity of the microstructure, chemically architectured alloys were processed, their microstructure and mechanical properties were characterized and then they were modelled by the finite element method. Nanoindentation measurements reveal a local extra-hardening at the interphase. Conventional modelling, even when considering three phases in a full-field approach, could not reproduce the compression properties, indicating again the existence of an extra-hardening. Then, the chemical and plastic strain gradients effects were included in the model and an agreement was reached with experimental data. Both experimental and modelling results prove that chemical gradients are at the origin of a new strengthening mechanism. This chemical gradient strengthening depends on the many microstructural parameters of chemically architectured alloys and opens the way for tuning and optimization of mechanical properties.

Particle Size Inversion from Spectrally Resolved Full-Field Forward Scattering.

Ensemble particle sizing has traditionally relied on inversion of extinction measurements for the characterization of the particle size distribution (PSD) in particulate media. However, particulate media induce complex phase changes that contain valuable information about their structure. Here, we propose the use of coherent detection to derive particle size distributions in inhomogeneous samples from light scattering. This is achieved by exploiting THz waves, which allow for both extinction and refractive index information to be directly retrieved. A modified version of the iterative Twomey method is presented in order to take into account this information. Additionally, by using a forward model based on the Waterman-Truell formula for the complex refractive index, samples with absorption in both the matrix medium and the particulate phase can be measured. The inversion needs neither a priori assumptions nor constraints regarding the PSD shape. Numerical simulations show that this full-field approach reduces the error of the inversion process potentially up to 65% compared with inversion using only extinction data. Experimental validation of the technique is provided by measuring calibrated spherical glass particles inside a PTFE matrix and retrieving the PSD in the case of monodisperse and polydisperse samples showing an enhancement of up to 32% in comparison to inversion from extinction data.

Prediction of fracture evolution in the TiN/Al thin films based on a full-field modelling approach

The classical approach to fracture simulation in a thin film for bioengineering applications neglects the underlying complex morphology of the deposited layer, leading to inaccurate numerical predictions. That may carry high risks during product development and its further exploitation, especially in biomedical applications. Therefore, here, we demonstrate the new approach to numerical investigation of the fracture evolution in pulsed laser-deposited (PLD) titanium nitride (TiN) thin films under loading conditions based on the full-field model of layer columnar morphology. We use the TiN film deposited on the aluminium (Al) substrate as a case study. Based on the nanoindentation test, we first determine the flow stress characteristics of the Al substrate and TiN/Al structure. An inverse analysis technique allowed us to precisely recalculate measured load–displacement values into the required stress–strain curve. Then, we developed a full-field model based on the digital material representation (DMR) concept for further investigation. The DMR was generated to replicate major morphological features of the deposited thin film identified under transmission electron microscop (TEM). Finally, we incorporated the TiN/Al full-field model into the finite element analysis with a cohesive zone approach for local analysis of fracture behaviour. Inverse analysis based on the developed direct model and experimental measurements allowed us to identify the parameters of the fracture model. As a result, we proved that the full-field model based on the digital material representation concept could be used for reliable predictions of local fracture development along the morphological features of the deposited thin film.

Symmetry breaking in core-valence double ionisation of allene

Conventional electron spectroscopy is an established one-electron-at-the-time method for revealing the electronic structure and dynamics of either valence or inner shell ionized systems. By combining an electron-electron coincidence technique with the use of soft X-radiation we have measured a double ionisation spectrum of the allene molecule in which one electron is removed from a C1s core orbital and one from a valence orbital, well beyond Siegbahns Electron-Spectroscopy-for-Chemical-Analysis method. This core-valence double ionisation spectrum shows the effect of symmetry breaking in an extraordinary way, when the core electron is ejected from one of the two outer carbon atoms. To explain the spectrum we present a new theoretical approach combining the benefits of a full self-consistent field approach with those of perturbation methods and multi-configurational techniques, thus establishing a powerful tool to reveal molecular orbital symmetry breaking on such an organic molecule, going beyond Löwdins standard definition of electron correlation.

Bayesian approach to micromechanical parameter identification using Integrated Digital Image Correlation

Micromechanical parameters are essential in understanding the behavior of materials with a heterogeneous structure, which helps to predict complex physical processes such as delamination, cracks, and plasticity. However, identifying these parameters is challenging due to micro-macro length scale differences, required high resolution, and ambiguity in boundary conditions, among others. The Integrated Digital Image Correlation (IDIC) method, a state-of-the-art full-field deterministic approach to parameter identification, is widely used but suffers from high sensitivity to boundary data errors and is limited to identification of parameters within well-posed problems. This article employs Bayesian approach to estimate micromechanical shear and bulk moduli of fiber-reinforced composite samples under plane strain assumption, and to improve handling of boundary noise. The main purpose of this article is to quantify the effect of uncertainty in the boundary conditions in the stochastic setting. To this end, the Metropolis–Hastings Algorithm (MHA) is employed to estimate probability distributions of bulk and shear moduli and boundary condition parameters using IDIC, considering a fiber-reinforced composite sample under plane strain assumption. The performance and robustness of the MHA are compared to two versions of deterministic IDIC method, under artificially introduced random and systematic errors in kinematic boundary conditions. Although MHA is shown to be computationally more expensive and in certain cases less accurate than the recently introduced Boundary-Enriched IDIC, it offers significant advantages, in particular being able to optimize a large number of parameters while obtaining statistical characterization as well as insights into individual parameter relationships. The paper furthermore highlights the benefits of the non-normalized approach to parameter identification with MHA (leading, within deterministic IDIC, to an ill-posed formulation), which significantly improves the robustness in handling the boundary noise.

Multiscale identification and NTFA reduction of the elasto-viscoplastic polycrystalline behaviour of uranium dioxide (UO2) for wide range of loading conditions

This numerical study presents a micromechanical modelling of the behaviour of uranium dioxide fuel (UO2), a polycrystalline ceramic used in nuclear power plants, for loading condition typical of a reactivity initiated accident (RIA). Above a specific temperature, UO2 has an elasto-viscoplastic behaviour with strain hardening depending on both temperature and macroscopic strain rate (T,ε¯˙).In a first step, via a full-field approach, an inverse identification of the local single crystal evolution law is performed based on experimental data obtained at the macroscopic scale [4,5]. Beyond the macroscopic behaviour of the fuel pellet, it is sometimes necessary to go down in scale to study local phenomena. A first approach would be to perform a Finite Element Method squared to two (FEM2) resolution [37]. It consists to solve a thermomechanical problem on a first mesh defined at the pellet scale where at each integration point is solved another thermomechanical problem on a Representative Volume Element. The full-field approach guarantees access to the local field at the expense of relatively high computational time. It is not conceivable within industrial codes.In a second step it appeared necessary to replace the second full-field resolution by a model reduction method based on the Nonuniform Transformation Field Analysis (NTFA) approach. This method allows access to macroscopic quantities and local fields while drastically reducing the computational time. The NTFA model [21] with tangent second order (TSO) approximation [24,21] is developed and applied to the problem at hand. Two decompositions of the hardening variable are studied. It is taken as in the reference [21], as uniform per grain or taken as decomposed, like modes associated with the viscoplastic strain tensor. Then, work is done to integrate the specific loading conditions, i.e. temperatures and strain rates of the RIA, into the NTFA model while keeping the number of internal variables as low as possible. Finally, the results from the experimental, full-field and NTFA TSO models are compared. It is checked that the numerical results are not deteriorated either macroscopically or locally at the macroscopic and local scales for uniaxial and triaxial loading.

Sparse accelerometer-aided computer vision technology for the accurate full-field displacement estimation of beam-type bridge structures

Computer vision-based sensors are considered effective means to monitor full-field displacements; nevertheless, several limitations prevent their applications in full-scale structures. First, the accuracy of computer vision-based sensors is affected by measured distances. Second, due to the limited frame rate of cameras, computer vision-based sensors cannot record high-frequency responses. To effectively address these issues, a novel full-field displacement estimation approach based on the data fusion of camera-based measurements and limited accelerations was proposed. Full-field pseudo-static displacements were extracted from camera-based measurements, and full-field dynamic displacements were expanded by accelerations at sparse locations based on the modal superposition method. Subsequently, high-fidelity full-field displacements were obtained by data fusion. The proposed approach was numerically and experimentally verified. Further, the full-field displacements of an experimental-scale beam model and a full-scale bridge were estimated by a consumer-grade camera and several accelerometers.

Nitrogen dynamics in soils fertilized with digestate and mineral fertilizers: A full field approach

Highly stabilized digestate from sewage sludge and digestate-derived ammonium sulphate (RFs), were used in a comparison with synthetic mineral fertilizers (SF) to crop maize in a three-year plot trial in open fields. RFs and SF were dosed to ensure the same amount of mineral N (ammonia-N). In doing so, plots fertilized with digestate received much more N (+185 kg ha−1 of organic N) because digestate also contained organic N. The fate of nitrogen was studied by measuring mineral and organic N in soil at different depths, ammonia and N2O emissions, and N uptake in crops. Soil analyses indicated that at one-meter depth there was no significant difference in nitrate content between RF, SF and Unfertilized plots during crop season indicating that more N dosed with digestate did not lead to extra nitrate leaching. Ammonia emissions and N content in plants and grains measured were also similar for both RF and SF. Measuring denitrification activity by using gene makers resulted in a higher denitrification activity for RF than SF. Nevertheless, N2O measurements showed that SF emitted more N2O than RF (although it was not statistically different) (7.59 ± 3.2 kgN ha−1 for RF and 10.3 ± 6.8 kgN ha−1 for SF), suggesting that probably the addition of organic matter with digestate to RF, increased the denitrification efficiency so that N2 production was favoured. Soil analyses, although were not able detecting N differences between SF and Rf after three years of cropping, revealed a statistical increasing of total carbon, suggesting that dosing digestate lead to carbon (and maybe N) accumulation in soil. Data seem to suggest that N2O/N2 emission and organic N accumulation in soil can explain the fate of the extra N dosed (organic-N) in RF plots.

Target-free 3D tiny structural vibration measurement based on deep learning and motion magnification

• A novel target-free tiny vibration displacement measurement approach is proposed. • It is based on deep learning and motion magnification techniques. • Binocular vision system is used for measuring 3D vibration measurement. • Deep learning techniques are used for key-points detection, matching and tracking. • Experimental and in-field studies are conducted to validate the accuracy. In the field of structural health monitoring (SHM), computer vision based methods have been usually developed for 2-dimensional (2D) vibration displacement measurement and crack identification of civil engineering structures. However, the accurate measurement of tiny 3-dimensional (3D) vibrations for real civil engineering structures remains a very difficult task. To overcome this challenge, this paper proposes a target-free full-field 3D tiny vibration measurement approach for civil engineering structures by using a binocular vision system. The proposed approach is based on deep learning and motion magnification. A phase-based video motion magnification algorithm is employed to achieve a high measurement accuracy of tiny vibrations at the submillimeter level. The advanced key point detection, matching and tracking algorithms via deep learning techniques are employed to achieve target-free tiny vibration displacement measurement. The accuracy and performance of the proposed approach are evaluated through experimental tests on a steel cantilever beam in the laboratory. In-field experimental tests are conducted on a pedestrian bridge on a university campus to investigate the accuracy of the proposed approach in practical applications. The measured 3D tiny vibration displacement from the proposed approach is compared with those measured by laser displacement sensors, and the derived acceleration responses are compared with those measured from the installed accelerometers on the testing structures. The results demonstrate that the 3D tiny vibration measurements are obtained accurately by the proposed approach.

Multi-Camera Digital Image Correlation in Deformation Measurement of Civil Components with Large Slenderness Ratio and Large Curvature.

To address the limitations of conventional stereo-digital image correlation (DIC) on measuring complex objects, a continuous-view multi-camera DIC (MC-DIC) system and its two forms of camera arrangement are introduced. Multiple cameras with certain overlapping field of view are calibrated simultaneously to form an overall system for measuring the continuous full-surface deformation. The bending experiment of coral aggregate concrete beam and the axial compression experiment of timber column are conducted to verify the capability of continuous-view MC-DIC in deformation measurement of civil components with large slenderness ratio and large curvature, respectively. The obtained deformation data maintain good consistency with the displacement transducer and strain gauge. Results indicate that the continuous-view MC-DIC is a reliable 3D full-field measurement approach in civil measurements.

Analysis for Full-Field Photoacoustic Tomography with Variable Sound Speed.

Photoacoustic tomography (PAT) is a non-invasive imaging modality that requires recovering the initial data of the wave equation from certain measurements of the solution outside the object. In the standard PAT measurement setup, the used data consist of time-dependent signals measured on an observation surface. In contrast, the measured data from the recently invented full-field detection technique provide the solution of the wave equation on a spatial domain at a single instant in time. While reconstruction using classical PAT data has been extensively studied, not much is known for the full field PAT problem. In this paper, we build mathematical foundations of the latter problem for variable sound speed and settle its uniqueness and stability. Moreover, we introduce an exact inversion method using time-reversal and study its convergence. Our results demonstrate the suitability of both the full field approach and the proposed time-reversal technique for high resolution photoacoustic imaging.

Influence of the Grain Boundary Curvature Model on Cellular Automata Static Recrystallization Simulations

Determination of the influence of grain boundary curvature model type on the cellular automata (CA) static recrystallization (SRX) simulation predictions is the primary goal of the research. The developed CA model is a full-field approach that captures local heterogeneities in grain morphology, crystallographic orientation, and distribution of stored deformation energy. The main driving force of the model is the value of the stored energy; however, the curvature of the grain boundary may also play a role during the recrystallization. Therefore, different variants of grain boundary curvature calculations within the discrete computational domain during recrystallization and additionally subsequent grain growth are compared within this work.

4D characterisation of void nucleation, void growth and void coalescence using advanced void tracking algorithm on in situ X-ray tomographic data

An advanced tracking algorithm based upon 4D X-ray tomographic data was developed to enable a more robust and full field approach to ductile damage characterisation. By following each individual void, the three ductile damage mechanisms (void nucleation, void growth and void coalescence) could be separated and be studied in more detail, with enriched 4D information available for each of the three damage mechanisms. Here, a successful application of the developed tracking algorithm on a pure iron sample is presented. Two different notch geometries were chosen for the demonstration of analysis technique to further study the influence of triaxiality on ductile damage.

Nitrogen Dynamics in Soils Fertilized with Digestate and Mineral Fertilizers: A Full Field Approach

Nitrogen Dynamics in Soils Fertilized with Digestate and Mineral Fertilizers: A Full Field Approach