The dataset is prepared and intended as a data source for development of a stress analysis method based on machine learning. It consists of finite element stress analyses of randomly generated mechanical structures. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples. The zip file contains all the files in the dataset.

The synthetic microbundle dataset consists of 60 200x256x256 ".tif" files generated based on textures extracted from real data and warped according to experimentally-informed Finite Element (FE) simulations. More details about generating this dataset can be found on the dedicated GitHub repository.

Modal interference is reflected in the speckle pattern that can be obtained at the output of a multimode fiber. This pattern contains important information, since the interference between the modes propagating through the fiber will be affected depending on some perturbations along the fiber [1]–[3]. There are different variables that can be measured by means of these specklegrams that affect the propagation of the modes, among them is the temperature. This dataset contains different speckles that vary according to a thermal change in the fiber acquired by finite element simulation (FEM).

This augmented dataset contains a total of 138230 specklegrams. This is composed of the 601 simulated images (of the previous dataset), from each of these 45 random rotations are performed, and from each original or rotated image 4 new ones are created with random Gaussian noise (this has a random variance value between 1 and 15). Figure 1 shows the nomenclature of the dataset files.

https://drive.google.com/uc?export=view&id=1CKMlssmoy2b2Ck-m4_cgAehkx19UH9sX" alt="">

Fig. 1. Nomenclature of the tiff images in the dataset. Note that the letter in red and blue corresponds to the only elements that change in the file names (data index and temperature). 'type' refers to one of three image types: S: simulated (original). SR: simulated and rotated. SRN: simulated, rotated and with Gaussian noise.

[1] A. Hoyos, N. D. Gómez, and J. A. Gómez, “Fiber specklegram sensors (FSS) for measuring high frequency mechanical perturbations,” Nov. 2013, p. 8785BH, doi: 10.1117/12.2026075. [2] Y. Liu, G. Li, Q. Qin, Z. Tan, M. Wang, and F. Yan, “Bending recognition based on the analysis of fiber specklegrams using deep learning,” Opt. Laser Technol., vol. 131, Nov. 2020, doi: 10.1016/j.optlastec.2020.106424. [3] J. D. Arango et al., “Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone,” Comput. Opt., vol. 45, no. 4, pp. 534–540, 2021, doi: 10.18287/2412-6179-CO-852.

This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.

V-

This data set includes synthetic microstructure instantiations generated using Dream.3D and their crystal plasticity finite element method (CPFEM) derived homogenized properties (elastic stiffness and yield strength determined using the 0.2% offset method). Eight different crystallographic textures were used with different volume fraction combinations of primary alpha phase and colony phase, with 30 instantiations per configuration. This data set is associated with the work by Stopka, Kalidindi and McDowell.

This study utilized a conventional Burger’s model, incorporating a nonlinear power-law dashpot. A new load-pass approach enabled a reduction in computational domain and cost. A graph neural network was established to extend the framework.

Abstract This study investigates the free vibration characteristics of an adhesively bonded double-strap joint with viscoelastic adhesive layer. To simplify the spatial finite element mesh generation and efficiently model the adhesively bonded joint, a layerwise plate finite element was extended to accommodate to the modeling of the joint, where the joint structure is treated as a special sandwich laminate. The proposed method was validated by three-dimensional finite element analysis and then applied to generate sampling points for training artificial neural networks (ANNs). The effects of the adhesive material properties and joint geometrical parameters on the joint dynamic characteristics were investigated in detail using the trained ANNs. The optimum design problem is defined as a multi-objective optimization problem considering maximizing the first natural frequency and corresponding loss factor while minimizing the total structural weight. The nondominated sorting genetic algorithm combined with the ANNs were employed to tackle the problem. The proposed method provides a computationally efficient alternative for analyzing and optimizing the adhesive double-strap joints.

In order to investigate the hot deformation behaviors of Invar36 alloy, isothermal compressive tests were conducted on a Gleeble 1500 thermo-mechanical simulator at the temperatures of 873, 948, 1023, 1098 and 1173 K and the strain rates of 0.01, 0.1, 1 and 10 s−1. The effects of strain, temperature and strain rate on flow stress were analyzed, and a dynamic recrystallization type softening characteristic with unimodal flow behavior is determined. An artificial neural network based on back-propagation algorithm was proposed to handle the complex deformation behavior characteristics. The ANN model was evaluated in terms of correlation coefficient and average absolute relative error. A comparative study was performed on ANN model and constitutive equation by regression method for Invar36 alloy. Finally, the ANN model was applied to the finite element simulation, and an experimental study on trial hot forming of a V-shaped part was conducted to demonstrate the precision of the finite element simulation based on predicted flow stress data by ANN model. The results have sufficiently showed that the well-trained ANN model with BP algorithm is able to deal with the complex flow behaviors of Invar36 alloy and has great application potentiality in hot deformation.

ABSTRACT Objectives: To compare the results of a simulated fall on the greater trochanter in the proximal portion of a synthetic femur before and after femoral reinforcement with tricalcium phosphate bone cement (TP) and polymethyl methacrylate (PMMA), using finite element analysis (FEA). Methods: Using two synthetic proximal femurs, a FEA simulating a fall on the greater trochanter was performed, using the Bi-directional Evolutionary Structural Optimization (BESO) program. For this analysis, the femurs were filled with TP and PMMA after perforations were created in the trochanteric region and neck. The results were compared with the strength values obtained from testing the control specimen, a synthetic bone without reinforcement. Results: FEA showed a value of 600 N prior to reinforcement. After cementing with PMMA, the load increased by 57.5% (945 N), and by 53% (920 N) after cementing with TP. Conclusion: Synthetic femurs gained resistance to fracture-causing forces in a simulated fall on the trochanter after bone reinforcement with PMMA and TP. Level of Evidence III; Experimental study.

All necessary files are included in this dataset to obtain the complete geometry of a human jaw, which includes the 14 teeth and the corresponding 14 periodontal ligaments. Models of the alveolar and cortical bones, as well as the teeth and ligaments, have been attached individually at the bone level. These models are accessible in the following formats:.iges,.stl, and.sedoc. Furthermore, a file containing the entire assembly of all of these geometries is included, and it is prepared for use in Space Claim. This configuration provides the advantage of allowing the models to be utilized in any finite element analysis software to investigate stresses and deformations in maxillofacial surgery or orthodontic research. This dataset contains STL and IGES files for finite element model simulations of the mandible, teeth, and ligaments in environments where it is desirable to understand the stresses or deformations of these structures for biomechanical assessment. The dataset is divided into three primary folders. The initial folder, "Bones," contains two subfolders: "Cancellous" and "Cortical." Each of these subfolders contains three files that represent models in distinct formats:.iges,.stl, and.sedoc. The latter is a native format of the Space Claim software from ANSYS R1 2020. The second folder, "PDL," contains the periodontal ligament models for teeth 37 to 47 in the same three formats as previously mentioned. These models are identified by a nomenclature that commences with the letter L and is followed by the corresponding tooth number. The third folder, "Teeth," contains the models of the mandibular teeth 37 to 47 in the same formats, with a nomenclature that begins with the letter D followed by the tooth number. Furthermore, the "Assembly Lower Jaw Dataset Space Claim" file is included, which contains the geometry of the entire lower jaw assembly, including its 14 teeth and 14 periodontal ligaments.

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Supplemental Dataset: Synthetic Vascular Model Generation, Multiscale CFD Simulation, and In Vitro Viability Analysis

This repository accompanies the article “Rapid, model-driven design of organ-scale vascular networks for perfused bioprinted tissues.”
It contains every geometry, computational-fluid-dynamics (CFD) result, image analysis and post-processing script required to reproduce the figures in the main text and Supplementary Information. The data trace the complete workflow—from algorithmic vascular-tree construction with svv, through 0-D/3-D CFD analysis, to quantitative figure generation—and offer ready-to-use models for new design studies.

Table of Contents

1. Data & File Structure
2. Materials & Methods
3. Sharing & Access
4. Code & Software

Data & File Structure

The archive unpacks into a single root folder:
Selected folders a files (input files and python code) are shown here:

adj6152_data/
Main/
Main Figures/
Figure 1/
cube/
heart/
Figure_1c.py
Figure_1d.py
Figure_1e.py
Figure_1f.py
Figure_1g.py
Figure 2/
Fig. 2C/
Fig. 2F/
Fig. 2H-I/
Figure 3/
anulus_0d_simulation/
timeseries/
timeseries_for_flow_gif/
timeseries_for_pressure_gif/
timeseries_for_wss_gif/
inflow.flow
plot_0d_results_at_slices.py
plot_0d_results_to_3d.py
run.py
solver_0d.in
wave.flow
cube_0d_simulation/
timeseries/
timeseries_for_flow_gif/
timeseries_for_pressure_gif/
timeseries_for_wss_gif/
inflow.flow
plot_0d_results_at_slices.py
plot_0d_results_to_3d.py
run.py
solver_0d.in
wave.flow
gyrus_0d_simulation/
timeseries/
timeseries_for_flow_gif/
timeseries_for_pressure_gif/
timeseries_for_wss_gif/
inflow.flow
plot_0d_results_at_slices.py
plot_0d_results_to_3d.py
run.py
solver_0d.in
wave.flow
heart_0d_simulation/
timeseries/
timeseries_for_flow_gif/
timeseries_for_pressure_gif/
timeseries_for_wss_gif/
inflow.flow
plot_0d_results_at_slices.py
plot_0d_results_to_3d.py
run.py
solver_0d.in
wave.flow
Figure 4/
Figure (Data)/
Fig 4.A/
Model 10000/
Model 100000/
Model 1000000/
Fig 4.B/
Model 10000/
Model 100000/
Model 1000000/
Fig 4.C - Biventricular Model/
Processed/
Raw/
Fig 4.D - Annulus Model/
Processed/
Raw/
Figure 5/
Figure (Data)/
Fig. 5D-F/
raw data.zip
Fig. 5G-J/
Vessel_Printing_65/
Images/
Meshes/
Models/
Paths/
ROMSimulations/
Segmentations/
Simulations/
cross_sections/
cross_sections_2/
cross_sections_3/
cross_sections_12/
cross_sections_22/
post_results_1/
post_results_2/
pulsatile_deformable_wall/
pulsatile_elastic_wall/
pulsatile_flow/
centerlines.txt
color_branches.py
create_graph.py
read_centerlines.py
slice_data.py
visualize_deformation.py
svFSI/
inflow.flow
simvascular.proj
multimaterial_test_network_65_vessels (1).txt
Fig. 5K/
Photos/
Fig. 5L/
Processes/
Fig. 5M/
Analysis/
Data (Raw, Processed)/
Annulus/
Raw, Reconstructed/
Annulus_Vasculature/
Photos/
Vasculature/
Vascular 1/
Raw/
Reconstructed/
Vascular 2/
Fig. 5O/
Photos/
Renderings/
Fig. 5P/
Fig. 5P i/
Photos/
Fig. 5P ii/
Analysis/
MATLAB/
Processed Data/
Fig. 5P iii/
Analysis/
Fig. 5P iv/
Analysis/
Fig. 5P v/
Analysis/
MATLAB/
Processed Data/
Re-Scaled Data (per cell counts)/
Fig. 5P vi/
Analysis/
MATLAB/
Processed Data/
Supplement Materials/
Supplementary Figures/
Fig. S1/
cube_opposite_diagonal_source_sink/
cube_same_side_diagonal_source_sink/
timeseries/
timeseries_for_flow_gif/
timeseries_for_pressure_gif/
timeseries_for_wss_gif/
inflow.flow
plot_0d_results_to_3d.py
README.txt
SF2.py
solver_0d.in
Fig. S2/
svcco/
array_vs_linked_list.py
Fig. S3/
BFGS/
COBYLA/
L-BFGS-B/
Nelder-Mead/
Newton-CG/
Powell/
SLSQP/
TNC/
trust-ncg/
get_data.py
test_optimizers.py
Fig. S4/
svcco/
global_pu_time.py
implicit_accuracy_number_patches.py
implicit_condition_number.py
Fig. S5/
brain_reconstructions/
brain_regions/
engineering_shapes/
svcco/
figure_code.py
figure_writer.py
Fig. S6/
svcco/
corner_recovery.py
patch_functions.py
Fig. S7/
svcco/
point_enclosure.py
Fig. S8/
brain_data/
convex_data/
svcco/
build_times_figure.py
Fig. S9/
Anterior Commissure/
output/
generate_tree.py
Branchium of Left Inferior Colliculus/
output/
generate_tree.py
Commissure of Fornix of Forebrain/
output/
generate_tree.py
Fourth Ventricle/
output/
generate_tree.py
Hypothalamus/
output/
generate_tree.py
Lamina Terminalis/
output/
generate_tree.py
Left Globus Pallidus/
output/
generate_tree.py
Left Inferior Frontal Gyrus/
output/
generate_tree.py
Left Internal Capsule/
output/
generate_tree.py
Left Olfactory Tract/
output/
generate_tree.py

The dataset presents is a collection of fatigue test data obtained from artificially notched specimens with weld characteristics. The data was used to investigate the relation between crack initiation and propagation in welded joints of different notch acuity (different radii and opening angle) by excluding the effect of geometrical variation along weld seams. The experiments show that the investigated relationship basically depends on the notch acuity, the load level and the stress ratio.

For detailed information about the tests and the assessment please refer to the article:

Braun M, Fischer C, Baumgartner J, Hecht M, Varfolomeev I. Fatigue Crack Initiation and Propagation Relation of Notched Specimens with Welded Joint Characteristics. Metals. 2022; 12(4):615. https://doi.org/10.3390/met12040615

In this dataset, data from experimental testing of fibre-reinforced shotcrete is presented. The dataset contains results from four different fibres made of steel (Dramix 3D and Dramix 4D), synthetic (BarChip R54) and basalt (MiniBar). Preparation of specimens and testing were performed by Vattenfall R&D in Älvkarleby, Sweden. This second version of the dataset contains the results for shotcrete specimens prepared through casting and spraying. The same shotcrete mix was used for all specimens, and three different dosages were used for each of the fibres. The dataset contains the shotcrete mix and standard output for the test of compressive strength according to EN 12390-3, residual flexural strength according to EN 14488-3 and energy absorption according to ASTM C1550. Raw data, i.e. force-displacement curves from testing, are also available.

This dataset can be used to study how the structural performance of fibre-reinforced shotcrete is affected by the dosage of fibre. Moreover, the data can be used to verify and tune non-linear material models using the finite element method. The data also provide a foundation to select a reasonable dosage of fibres to fulfil the structural requirements put on shotcrete in the design phase.

A data paper entitled "Dataset for evaluation and numerical modelling of structural performance of fibre-reinforced shotcrete with fibres of steel, synthetic and basalt" is currently under review in Data in Brief and, if accepted, a link to the paper will be added here for a more thorough description.

Four data files contain processed tsunami magnetic variations at ESA, B14, NWP, and CBI. Please read Section 4.3 in the main article for the detail of the processing. The contents in each file are composed of ten columns;
time min since 05:46 UTC on March 11, 2011, high-pass filtered XYZ at the target site (three columns), synthesized XYZ at the target site based on eq. (14) (three columns), and cleaned XYZ at the target site, i.e. data left after subtraction of synthesized data from high-pass filtered data (three columns). For a seafloor site, B14, not the high-pass filtered but just raw time-series are contained in the second to forth columns because of the shortage of the original time series at B14. Readers can easily plot the four data files by using the script, plot_CTS.sh, where CTS stands for Cleaned Time Series. The four postscript files were generated by this script.

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For use of data at B14, please also refer Ichihara et al. (2013), which first reported tsunami magnetic signals at B14 during the 2011 Tohoku tsunami event.

Ichihara, H., Hamano, Y., Baba, K., & Kasaya, T. (2013). Tsunami source of the 2011 Tohoku earthquake detected by an ocean-bottom magnetometer. Earth and Planetary Science Letters, 382, 117-124.

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For use of data at NWP, please also refer Minami et al. (2013), which first reported tsunami magnetic signals at NWP during the 2011 Tohoku tsunami event.

Minami, T., & Toh, H. (2013). Two‐dimensional simulations of the tsunami dynamo effect using the finite element method. Geophysical Research Letters, 40(17), 4560-4564.

Dataset Description: We present a database of four-chamber heart models derived from a statistical shape model (SSM) suitable for electro-mechanical (EM) simulations. Our database consists of 1000 four-chamber heart models generated from end-diastolic CT-derived meshes (available in the repository called ("Virtual cohort of adult healthy four-chamber heart meshes from CT images"). These meshes were used for EM simulations. The weights of the SSM are also provided.

Cardiac meshes: To build the SSM, we rigidly aligned the CT cohort and extracted the surfaces, representing them asdeRham currents. The registration between meshes and computation of the average shape was done using a Large Deformation Diffeomorphic Metric Mapping method. The deformation functions depend on a set of uniformly distributed control points in which the shapes are embedded, and on the deformation vectors attached to these points. It is in this spatial field of deformation vectors (one per each control point) where the Principal Component Analysis (PCA) is applied. Case #20 of the CT cohort was not included. More information on the details can be found in Supplement 3 of the reference paper. We created this cohort by modifying the weight of the modes explaining 90%of the variance in shape (corresponding to modes 1 to 9) within 2 standard deviations (SD) of each mode added to the average mesh. The elements of all the meshes are labelled as follows:

1. Left ventricle myocardium
2. Right ventricle myocardium
3. Left atrium myocardium
4. Right atrium myocardium
5. Aorta wall
6. Pulmonary artery wall
7. Mitral valve plane
8. Tricuspid valve plane
9. Aortic valve plane
10. Pulmonary valve plane
11. Left atrium appendage "inlet"
12. Left superior pulmonary vein inlet
13. Left inferior pulmonary vein inlet
14. Right inferior pulmonary vein inlet
15. Right superior pulmonary vein inlet
16. Superior vena cava inlet
17. Inferior vena cava inlet
18. Left atrial appendage border
19. Right inferior pulmonary vein border
20. Left inferior pulmonary vein border
21. Left superior pulmonary vein border
22. Right superior pulmonary vein border
23. Superior vena cava border
24. Inferior vena cava border

Each zipped folder contains 25 meshes and the weights of modes used to construct them for each mesh, A VTK file for each mesh (in ASCII) contains an UNSTRUCTURED GRID with the following fields:

• POINTS, with the coordinates of the points in mm.
• CELL_TYPES, having all of the points the value 10 since they are tetrahedra.
• CELLS, with the indices of the vertices of every element.
• CELL_DATA, corresponding to the meshing tags.

In addition, three descriptive files are included:

• Normalized_explained_variance.csv contains the percentages of variance explained by each of the 18 modes generated from PCA.
• Mode_standard_deviation.csv contains absolute standard deviations of each of the 18 modes.
• Eigenvectors.csv contains the directions of maximum shape variability within the shape population.

The CarHoods10k data set comprises a set of over 10,000 3D mesh geometries for variants of car hood frames, generated through an automated, industry-grade Computer Aided Design (CAD) workflow described in Ramnath (2019). The data set provides realistic designs that were validated by experts with respect to realism, manufacturability, variability, and performance. Variations in geometries were generated by a feature-based approach that varies parameter values describing design features on 109 parameterized base geometries ('skins'). Parameters describe feature patterns such as cut-outs or ribs on the hood frame as well as their properties, for example, rib location and height, or cut-out location. Geometries are represented as surface meshes (STL files) and are provided with the corresponding design parameter values and performance metrics from structural mechanics, generated through finite element analysis (FEA). The data set provides realistic and validated designs for the evaluation a...

This dataset comprises numerical investigation data for Concrete-Filled Steel Tube (CFST) T- and K-joints, sourced from studies by Zheng et al. (2018) and Zheng et al. (2019). The dataset includes key geometric and material parameters influencing the Stress Concentration Factor (SCF). Additionally, a sample Python script implementing an Artificial Neural Network (ANN) model is provided to predict the SCF at the Brace Saddle (BS) of a CFST T-joint subjected to compressive loading in the brace.

This dataset is useful for researchers conducting numerical and machine learning-based studies on SCF behavior in CFST joints.

Keywords Concrete Filled Steel Tubular K joints, Concrete Filled Steel Tubular T joint , Stress Concentration Factor, Finite Element Analysis, Artificial Neural Network, Multiple Regression Analysis

The geometric and physical parameters of the marine-sediment model.