Photogrammetry as a finite element modelling geometry builder

Results from laboratory specimens and finite element modeling are being used to better understand the apparent surface resistivity values obtained from field measurements. Concrete specimens were prepared with no rebar and also specimens with two different rebar arrangements. Most of the specimens were moist cured for 60 days, and then transferred to lab environment for the following 60 days; thereafter, exposure was outdoor cyclic ponding with sea water. The last two exposures are expected to produce multi-layered concrete resistivity close to the surface. An analytical method was used to study multi-layered resistivity close to concrete surface. Finite element methods were used to assess rebar presence, multi-layered resistivity close to the surface, and the combined effect of these two parameters. It was found that the finite geometry also affects the resistivity measurements.

Finite element models (FEMs) for simple shapes such as spheres, cylinders and pipes can be used to uncover potential features for classification by sonar systems. Analyses were conducted of the simulated, bistatic returns over a wide range of aspects using FEMs for a solid steel cylinder and a cement pipe all in free space. Salient characteristics noted in this way were then sought in field data collected from the same or similar objects during the SAX04 experiment and two other data collections conducted in a test pond at the Naval Surface Warfare Center in Panama City, Florida. Characteristics robust enough to appear in both simulated data and field data are candidates to serve as classification features for similarly shaped objects. It is found that FEMs can provide important pointers to classification features that are closely tied to the physics of reflection. As such, the variability of these features in the face of object burial and multipath can be more easily assessed than statistical spectral features. [This work was supported by the U.S. Navy Office of Naval Research.]

Source localization of focal electrical activity from scalp electroencephalogram (sEEG) signal is generally modeled as an inverse problem that is highly ill-posed. In this paper, a novel source localization method is proposed to model the EEG inverse problem using spatio-temporal long-short term memory recurrent neural networks (LSTM). The network model consists of two parts, sEEG encoding and source decoding, to model the sEEG signal and receive the regression of source location. As there does not exist enough annotated sEEG signals correspond to specific source locations, simulated data is generated with forward model using finite element method (FEM) to act as a part of training signals. A framework for source localization is proposed to estimate the source position based on simulated training data. Experiments are done on simulated testing data. The results on simulated data exhibit good robustness on noise signal, and the proposed network solves the EEG inverse problem with spatio-temporal deep network. The result show that the proposed method overcomes the highly ill-posed linear inverse problem with data driven learning.

Uncertainties in finite element (FE) model updating arise from two main sources: measurement noise and modeling errors. The latter includes model parameter uncertainty and model uncertainty itself. Among these sources of uncertainty, model uncertainty has been proven to be the most influential source of error in FE model updating, which is particularly important when using the updated model for damage identification (DID) purposes. This paper investigates the effects of model uncertainty when updating mechanics-based nonlinear FE models of building structures subjected to earthquake excitation. To solve the parameter estimation problem, the unscented Kalman filter is used as parameter estimation tool. Numerically simulated response data of two state-of-the-art nonlinear FE models of building structures designed according to modern design codes are used as application examples. A two-dimensional steel building and a three-dimensional reinforced concrete building, both subjected to seismic base excitation, are analyzed for different types and levels of model uncertainty. The results show that model uncertainty may have significant detrimental effects when using the updated FE model for DID, chiefly in the case of large modeling uncertainty. Although a good match between the measured (observed) and FE predicted responses is usually achieved, unobserved responses at global and local levels often show significant errors.

Carbon/carbon composites (C/Cs) are extensively utilized as structural materials and functional materials in the aerospace industry. Laser processing technology is an effective means of precision manufacturing C/Cs parts, with the advantages of no mechanical impact and high efficiency. Accurately predicting the material removal of C/Cs during laser processing is of great significance for the precision manufacturing of C/Cs parts. However, the numerical simulation models that can directly display the microstructure of C/Cs are still inadequate, and measuring the sublimation temperatures of two different phases of carbon is challenging. This paper establishes a three-dimensional microscopic heterogeneous finite element (FE) model of C/Cs, and the FE simulation of quasi-continuous wave (QCW) laser ablation of C/Cs is optimized using the restart method taking into account the residual temperature. Combining the optimized FE model, the material parameters of C/Cs are inverted using response surface methodology and genetic algorithm, resulting in the sublimation temperatures of the fiber phase being 4029.01 K and the matrix phase being 3481.86 K. After these parameters are substituted into the FE model, the resulting simulations are then compared with the experiments of QCW laser processing C/Cs, which reveals high correspondence between simulated morphology and experimental data, with the relative error of predicted ablation depth not exceeding 6.169%. The revised FE model can guide the laser processing of C/Cs, and the inverted material parameters can provide references for the theoretical study of the laser processing of C/Cs.

Surrogate models have been used as a substitute for traditional engineering simulation to get target simulation results faster. These surrogate models usually need sufficient data to fulfill their optimum performance. However, the acquisition of high quality datasets for cases like welding residual stress is both difficult and costly. This work developed a workflow that can rapidly develop machine learning based surrogate models to predict post-weld residual stresses using finite element (FE) simulation data. The workflow comprises five steps: (i) baseline FE model creation, (ii) FE model processing (iii) scripting (iv) data generation and (v) surrogate model development. The workflow presented in the paper automates all the steps involved in generating, calculating, and extracting results from FE simulations, with the aim of reducing the time and cost required for data acquisition. In addition, to minimize the possibility of FE analyses not completing successfully during the automated data generation process and to ensure that the training data can be obtained without any manual intervention, the FE model was simplified without significant loss of accuracy. A single weld bead-on-plate structure, taken from the NeT project Task Group 1, was used as a case study to validate the developed workflow. The overall aim of this work was to validate the feasibility of developing a surrogate model for specific welding conditions with no experimental dataset and with the aid of simulations, and to investigate the predictive performance of an Artificial Neural Network based surrogate model. Four welding parameters, arc advance speed, net energy input rate, arc travel length and position, were considered as the input for the surrogate model to predict the longitudinal stress along the line running across the sample thickness in the mid-length of the specimen. The relative root mean square error of the predictive surrogate model for the test dataset was 0.0024. Residual stress prediction based on the NeT-TG1 benchmark welding case met the R6 validation criteria and the developed model generates predictions that are in line with other methods used by contributors to the NeT-TG1 round robin.

The main goal of this work is to investigate if finite element (FE) model techniques with special applications of material properties accurately estimate the parameters of flexible ring tire models. It is known that commercially available ring tire models are used as standard tools for simulating and predicting vehicle ride and durability, e.g., rigid ring MF-Swift [1] and flexible ring Flexible Structure Tire Model (FTire) [2–5]. Despite wide acceptance of these models, difficulty in model parameterization limits their application in the vehicle development process. For estimation of tire dynamic stiffnesses and inertial properties, rolling tire cleat test data are required for most ring models. Although this test method produces reliable models, the parameterization is not time and cost effective as it requires measurement and processing of cleat data at multiple speeds and loads and is prone to test rig dynamic compliance variations. This approach also limits the ability to evaluate tire performances during the virtual stages of tire design. The objective of this work is to develop virtual data using time and cost effective FE-based methods towards the estimation of flexible ring model parameters rather than relying on measured cleat data on physical tires. Commercial product ABAQUS is used for the FE simulations and FTire for tire flexible ring model simulations. Two FE modeling techniques are utilized in this work. Firstly, it is shown that the dynamic stiffness of a rolling tire can be estimated from a steady state eigensolution modal analysis of a static tire using material properties characterized for a rolling tire. Secondly, a method of separation of the sidewall from the tread band is developed for the estimation of mass and bending properties of the tread band. The estimated stiffnesses, inertias, and dimensions from the FE model results are converted into FTire model parameters. Finally, to validate the virtually generated FTire model, simulated dynamic cleat data response trends at multiple inflation pressures and velocities are compared with measurements. The virtual FE based techniques presented in this work can be applied to other ring based models as well.

To predict the response of masonry buildings to various types of loads, engineers use finite element models, specifically solid-element and macro-element models. For predicting masonry responses to seismic events in particular, equivalent frame models—a subcategory of macro-element models—are a common choice because of their low computational cost. However, an existing bottleneck in modeling pipelines is generating the geometry of the model, which is currently a slow and laborious process that is done manually using computer-aided design tools. In this paper, we address this by automating the modelling process using recent advancements in computer vision and machine learning. We present an image-based end-to-end pipeline that automatically generates finite element meshes for solid-element and equivalent-frame models of the outer walls of free-standing historical masonry buildings. As the input, our framework requires RGB images of the buildings that are processed using structure-from-motion algorithms, which create 3D geometries, and convolutional neural networks, which segment the openings and their corners. These layers are then combined to generate level of detail models. We tested our pipeline on structures with irregular surface geometries and opening layouts. While generating the solid element mesh from the level of detail model is straightforward, generating equivalent frame models required algorithms for segmenting the façade and the meshing. Experts in the field analyzed the generated equivalent frame models and determined them to be useful for numerical modeling. These finite element geometries will be invaluable for future predictions of the seismic response of damaged and undamaged buildings. The codes and dataset are publicly available for future studies and benchmarking (https://github.com/eesd-epfl/FEM_buildings and https://doi.org/10.5281/zenodo.8094306).

Structural safety assessment of bowstring type RC arch bridges using ambient vibration testing and finite element model calibration

While injuries sustained from body armour backface deformation (BFD) have not been well-documented in military injury trauma registries, data from US law enforcement officers, animal tests and currently available data pertaining to military combatants has shown that BFD can not only cause minor injuries, but also result in serious trauma. However, the nature and severity of injuries sustained depends on a multitude of factors including the projectile type, the impact location and velocity, and the specific type of body armour worn. The difficulties involved in current measurement techniques for ballistic testing has led researchers to seek alternative techniques to evaluate the level of protection from body armour, such as the finite element (FE) method. In the current study, a systematic review of the open literature was undertaken using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. The aim was to summarise the literature pertaining to the development and application of FE models to investigate body armour BFD and behind armour blunt trauma (BABT), and included FE models representing the projectile, clay-based mediums, ballistic gelatine and the human torso. Using the keywords ‘behind armour*’, ‘ballistic blunt trauma’, ‘BABT’, ‘backface signature’, ‘backface deformation’, ‘BFS’, ‘BFD’, ‘wound ballistic’, ‘ballistic impact testing’, ‘body armour’, ‘bullet proof vest’, ‘ballistic vest’, ‘Finite Element*’ and ‘FE’, an electronic database search of EBSCOhost, Google Scholar, ProQuest, Scopus, Standards, Web of Science and PubMed was conducted, and included peer-reviewed journal articles, review papers, research reports, conference papers, and MSc or PhD theses. While this research demonstrates the potential of FE analysis for recreating realistic blunt impact scenarios and enhancing the current understanding of BABT mechanisms, a common limitation in most studies is the lack of validation. Thus, in order to address this issue, it is proposed that injury predictions from FE models be correlated with trauma data from soldiers who have sustained BABT. Consequently, pressure and energy distributions within the organs can be used to interpret the effects of non-penetrating ballistic impacts on the human torso. Bridging the gap between simulation and real-world data is essential in order to validate FE models and enhance their utility in optimising body armour design and employing injury mitigation strategies.

Piezoelectric fans typically consist of one piezoelectric patch bonded on one side of a thin, flexible elastic beam. The fan is excited at resonance and used to drive unsteady flows to cool microelectronics components. Field equations of the coupled structure governing the coupled longitudinal and bending motions of the fan are derived using linear constitutive equations, slender beam approximations, and Hamilton’s principle. Analytical solutions are found to the coupled eigenvalue problem. Eigenvalues and eigenfunctions for closed and open circuited configurations are predicted analytically and are found to be in excellent agreement with three-dimensional finite element results. Electromechanical Coupling Factors (EMCF) are computed using the analytical and finite element models and optimal fan geometries are identified for maximal EMCF. The analytical solution provides a convenient tool for the optimal design of such fans.

FastBlade, a leading centre for testing composite materials and structures, specialises in conducting experimental fatigue testing on tidal turbine blades and composite structures under various load conditions. These experimental fatigue campaigns can sometimes run for months and hence generate vast datasets from numerous sensors used in testing. Traditional grid-based sensor deployment often results in redundant data, complicating data processing and analysis. Hence, optimising sensor placement is crucial for efficient data collection and cost-effectiveness. This paper addresses the challenge of optimising sensor placement on the FastBlade reaction frame, essential for full-scale tidal turbine testing, by integrating finite element (FE) simulation data with machine learning (ML) algorithms. Our approach leverages validated finite element (FE) models to identify critical stress regions and simulate mechanical responses, generating high-resolution strain datasets. ML algorithms then determine optimal sensor locations, focusing on areas with high strain gradients. The optimised sensor network is validated using calibrated FE models, which provides continuous deformation data across the frame. Results show a significant reduction in the quantity of stain gauges or sensors while maintaining accurate monitoring of critical areas, demonstrating the effectiveness and cost-efficiency of our innovative sensor placement strategy.

The ultrasonic guide wave (UGW) has good application prospects in steel rail damage diagnosis, but the features of the rail damage implied in the UGW are complex. Deep learning enables an end-to-end approach to fault diagnosis. Nevertheless, a large amount of diversity data is needed for training, whereas the ultrasonic wave guide signals of simulation and repeated experiments lack diversity. Therefore, in this paper, a diagnostic framework based on simulation and transfer learning for rail damage is developed to tackle the problems mentioned above. The proposed framework is based on deep learning with a simulation pretraining strategy to build convolutional neural network (CNN) models through parameter fine-tuning for damage diagnosis. Specifically, for the problem that the simulation data lacks diversity, a damage mechanism-based data diversity augmentation method is proposed; this obtains the diagnostic high-value simulation data including supporting features, and expanded the diversity of the simulation data. Adopting the proposed method of data augmentation and transfer learning (TL), a diagnostic model for rail damage utilizing augmented UGW signals is constructed. The finite element simulation data of UGW with damages at different locations and depths of rails are augmented to achieve the pretraining of CNN models, and the model transfer is performed with the experimental data of rails. Ultimately, through comparative studies it can be concluded that (1) The TL diagnostic framework makes full use of the finite element simulation data to realize the model pretraining. (2) The proposed data augmentation method realizes the diversity expansion of simulation data containing supporting features and ensures the efficient application of simulation data in model pretraining.

Skull stiffness of children is of great significance to protect brain tissues during traffic accidents. In this paper, the effects of skull stiffness on traumatic brain injury (TBI) are investigated by using a validated 6-year-old child head finite element (FE) model according to Chinese standard GB/T 24550-2009 Results showed that the HIC value, intracranial pressure, Von Mises stress and shear strain of brain tissue increased with the decrease of skull stiffness during the impact of FE model with engine hood. Therefore, the injury risk of the child head decreases with the increase of skull stiffness. And the skull with higher stiffness can provide a better protection for brain tissues during traffic accidents.

In order to solve the existing problems such as the less-plump corners and wrinkle during the forming process of a double curvature thin-wall part. Material parameter formulas are calculated based on Hill 1948 anisotropic yield criterion and flow rule. The material mechanical property parameters of 2A12 are obtained using uniaxial tensile experiment. Single factor test method is used and LS-DYNA is applied to conduct finite element analysis. So the influence law of holding force, blank shape and blank size imposed on the finite element model are calculated in sequence. On the basis of these laws, that the blanking force, blank dimension and blank shape are decided. Stamping die for the experiment model is designed and made, deformation measurement technology of coordinate grid circle is used to carry out strain analysis. From the comparison result between experiment and simulated data, it can be seen that Hill 1948 yield criterion and flow law can better solve the formability of 2A12 sheet.