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14 results for “topology optimization”
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers (Data)
<p>Auxiliary materials for ICPE 2016 paper titled:<br /> "Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers".</p> <p> </p>
Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"
<p>MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Supplementary material for the paper: "Mesh-based topology, shape and sizing optimization of ribbed plates"
<p>Source code and results, accompanying the paper "Mesh-based topology, shape and sizing optimization of ribbed plates", published in Structural and Multidisciplinary Optimization, 2024. </p>
Freeform topology optimized, dispersive nanostructures for field confinement
<p>Provides the 3D topology optimized nanostructures as STL files, as well as the Lumerical files used to validate their performance presented in the article "Time-Domain Topology Optimization of Arbitrary Dispersive Materials for Broadband 3D Nanophotonics Inverse Design" (https://doi.org/10.1021/acsphotonics.3c00572).</p>
Self-Directed Online Machine Learning for Topology Optimization
<p>Code and results of the paper "Self-Directed Online Machine Learning for Topology Optimization".</p> <p>See latest updates at https://github.com/deng-cy/deep_learning_topology_opt</p>
Data supporting the publication "Optimized Sandwich and Topological Structures for Enhanced Haptic Transparency"
<p>This dataset includes experimental modal responses of sandwich and topological structures, which are designed to enhance haptic transparency. </p>
Aerostructural Topology Optimization using High Fidelity Modeling
<p>The files included define shape and topology optimization problems with fluid-structure interaction (modelled with SU2) that were presented in:</p> <p>P. Gomes and R. Palacios, “Aerostructural Topology Optimization using High Fidelity Modeling,” in AeroBest 2021, July 2021.</p>
Topology optimization dataset
<p>Dataset of topology optimization problems in 50x140 2D meshes. It was used to train machine learning models to accelerate topology optimization. Each sample contains:</p> <ol> <li>Binary definition of supports</li> <li>Sparse definitions of position and components of two point loads</li> <li>Volume fraction</li> <li>Von Mises and strain energy density fields of initial FEA solution</li> <li>Final topology</li> </ol> <p>Other than the dataset, contains files related to data filtering, and checkpoints of the trained generative adversarial networks.</p>
Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol"
<p>Simulation input files and analysis scripts for "Optimal bond-constraint topology for molecular dynamics simulations of cholesterol".</p> <p>See ... for details.</p>
A General Optimization Model for Coupling Topology and Nodal Coordinates in Large-Scale Biorthogonal Tensegrity Structures
Open the record for dataset details and reuse information.
TinyTLA: Topological Landscape Analysis for Optimization Problem Classification in a Limited Sample Setting
<p>Topological features as well as the AutoGluon classification accuracies</p>
Topology Optimization of Piezoelectric Energy Harvesters for Enhanced Open-Circuit Voltage Subjected to Harmonic Excitations
<p>The figures of the manuscript.</p>
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