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59 results for “Material optimization”
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Dataset of optimal cement-based panels enhanced with microencapsulated phase change material for EnergyPlus simulations
<p>This dataset includes:<br> - A series of EnergyPlus models of the BESTEST - Case 900 - from ANSI/ASHRAE Standard 140-2011 for the original (Baseline_Case900) and three enhanced designs (Opt-1, Opt-2, Opt-5) by using a cement-based panel containing microencapsulated phase change material.<br> - All the optimal solutions (parameters and corresponding performance) in XLSX format, which were obtained for two multiobjective optimization studies of the thermophysical properties (ParetoFront_CaseA and ParetoFront_CaseB).<br> - The typical meteorological year (TMY) of Sofia city employed to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/)</p>
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>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations
<p>Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.</p> <p>For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.</p>
Supplementary Material for "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations"
<p>This is the supplementary material for our publication "General Principles for Yield Optimization of Nucleoside Phosphorylase-Catalyzed Transglycosylations".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Conflict of Interest, Sample analysis by HPLC, Table S1 and S1, Figure S1 and a suggested workflow for NPase-catalyzed nucleoside synthesis.</p> <p>The .xlsx file contains an implementation of the simplified formula for yield prediction (equation (4) ), using previously reported thermodynamic data (10.5281/zenodo.3459298).</p> <p>The Python code and all data calculated from numerical solutions of the system of equilibrium constraints is available elsewhere (10.5281/zenodo.3522588).</p>
AiNU data for Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
<p>This file contains the AiNU data used for the article entitled by <em>Physics-based material parameters extraction from perovskite experiments via Bayesian optimization</em> (https://arxiv.org/abs/2402.11101).</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>
Supporting materials for the methodology for optimal combinations of agroecological practices (AEPs)
<p><span>The assessment framework is developed by first reviewing existing agroecological sustainability assessment tools. Indicators are collected based on literature review. Then they are synthesized into the holistic agroecology assessment framework. New indicators are developed during the project and are also used to address any context-specific data needs or cover gaps of the existing tools. </span></p>
Fig. 3. Agarose gel image Fig. 4 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 3. Agarose gel image Fig. 4. Agarose gel image (successful PCR amplification) (failed PCR amplification) M: marker (bp) M: marker (bp) A1: Agonum fuliginosum Panzer, 1809 A: Agonum fuliginosum Panzer, 1809 A2: Agonum thoreyi Dejean, 1828 O: Omophron aequale aequale Morawitz, 1863 O: Omophron aequale aequale Morawitz, 1863 N: Notiophilus semistriatus Say, 1823 N: Notiophilus semistriatus Say, 1823 Nk: negative control. Nk: negative control.
Fig. 1 in Optimization Of Dna Extraction Protocol For Dna Isolation From Air-Dried Collection Material For Further Phylogenetic Analysis (Coleoptera: Carabidae)
Fig. 1. Photo of Omophron aequale jacobsoni Fig. 2. Photo of Omophron aequale jacobsoni Semenov, 1922 before incubation. Semenov, 1922 after 16 h (56°C) incubation time in tissue lysis buffer with proteinase K.
Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"
<div> <div> <div> <p>This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation.</p> </div> </div> </div>
Fig. 3 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 3. Resulting order after multiple sorting according to the columns typeStatus, country, state, city and locality, in that order. This sequence of column names corresponds to the variable Sorting Order in the Gridit software.
Fig. 2 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 2. Columns swapped according to the desired order for the information in the final text. The order of the column names (scientificName, typeStatus, sex, country, etc.) corresponds to the variable Display Order in the Gridit software.
Fig. 1 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 1. Spreadsheet data from some of the specimens of Distictus tibialis (Brullé, 1846) cited in Supeleto et al. (2019). Mandatory columns and column names in the Gridit software marked in red.
Fig. 5 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 5. Data from the spreadsheet in Fig. 4, excluding header and the column scientificName, copied and pasted into a text editor; resulting tabs (cells) replaced with comma. Sequences of "ib" and "?" in each row were grouped together with a preceding number (e.g., "11ib" in row 2) that indicates the total of subsequent repeats. Each row represents a unique collecting event. The final text generated from this file is shown in Table 2.
Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications
Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).
Performance analysis & optimization of inverted inorganic CsGeI3 perovskite cells with carbon/copper charge transport materials using SCAPS‑1D
<p>Hybrid perovskite solar cells (PSC) have achieved efficiencies (PCE) of more than 25%. However, the organic compound is causing structural degradation due to heat and moisture. This has led to the exploration of inorganic perovskites. Inorganic-PSC such as cesium has seen a breakthrough by achieving highly stable PSC with PCE exceeding 15%. In this work, the inorganic non-toxic PSC of cesium germanium tri-iodide (CsGeI<sub>3</sub>) is numerically modeled in SCAPS-1D with two carbon-based and two copper-based charge transport layers(CTL). This study introduces in-depth modelling and analysis of CsGeI<sub>3</sub> through continuity and Poisson equations. Cu-CTL are selected to increase the electric conductivity of the cell, while carbon-CTL is used to increase the thermal conductivity. Four structures are designed and presented. A systematic approach is adopted to obtain the optimized design parameters for maximum performance. From the results it is observed that the C<sub>60</sub>/CsGeI<sub>3</sub>/CuSCN structure has the highest performance, with open-circuit voltage of 1.0169V, short-circuit current of 19.653 mA/Cm<sup>2</sup>, fill factor of 88.13% and PCE of 17.61%. Moreover, the effect of quantum efficiency, electric field, interface recombination, interface defects, layer thickness, defect density, doping concentration, working temperature and reflection coating on the cell performance are studied in detail.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.