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207 results for “friction”
Data and MATLAB Code for the paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow"
<p>This link includes the data and MATLAB code files for the research paper entitled "A modified Chezy formula for one-dimensional unsteady frictional resistance in open channel flow" by Zhou, J.W.; Bro, W.M.; Tick*, G.R.; Mofatakari, H.; Li, Y.; and Cheng, L., which has been submitted to the Journal of Fluids Engineering. These files are edited under the GB18030 character set standard.</p>
Friction Map for Brazil in 2014
<p><strong>Summary</strong></p> <p>This map shows friction values on a grid of 90x90 meters for Brazil. Friction values represent the average travel time by car, boat, train or foot (depending on the surface) to cross one grid-cell in horizontal or vertical space. Friction maps are fundamental for calculating accumulated costs maps that can serve as a substitute for infrastructure data in spatial modelling.</p> <p><strong>The base-data</strong> used for creating this friction map comprises:</p> <ol> <li>A layer of road-infrastructure data obtained from the Brazilian Ministry of transportation (Ministério dos Transportes e Departamento Nacional de Infraestrutura de Transportes). The data is available at http://pnlt.imagem-govfed.opendata.arcgis.com/ . Planned roads where filtered out of the dataset.</li> <li>A layer of rail-roads in Brazil from the same source as (1).</li> <li>A layer of oficial hidroways in Brazil from the same source as (1).</li> <li>A layer of land-use classes in 2014 obtained from Mapbiomas. The data is available for download here: http://mapbiomas.org/</li> <li>A layer of hydrological data that was composed from two different datasets from the Brazlian Water Agency (Agencia Nacional das Águas). The source data can be downloaded here: http://www.ana.gov.br/bibliotecavirtual/redeHidrografica.asp. The datasets from ANA are Drainage Network models based on SRTM data (2000) in the scale of 1:100.000 and in the scale of 1:250.000. We used the lower resolution data (1:250.000) to identify mayor rivers. However the spatial accuracy of this data is not sufficient for our purpose and affluent rivers to the main river are not included. We therefore developed a model to buffer the low resolution data-set with a 10km buffer on each side of the river. We than used this buffer areas to crop out the higher resolution data (1:100.000) thereby including affluent rivers of up to 10 km on each side of the main river. Furthermore the higher resolution data is spatially more accurate if compared to satellite imagery. </li> <li>A layer of sloped data based on global SRTM elevation data from 2000 available at: https://urs.earthdata.nasa.gov/</li> </ol> <p><strong>Description of the model</strong>:</p> <p>First all data was reprojected to WGS84, than the data was reclassified to hold travel times to cross one grid-cell for each class in seconds. The following assumptions on travel speed had been used for this purpose:</p> <ul> <li>Paved Roads: 60km/h</li> <li>Unpaved Roads: 40 km/h</li> <li>Waterbodies or navigable stretches in the flooding season: 10 km/h</li> <li>Hidroways: 20km/h</li> <li>Railways: 40km/h</li> <li>Forest: 3 km/h (comprises in the original data: forest, planted forest, coastal zone forest)</li> <li>Non-forest: 12 km/h (comprises: Agricultural areas, non-forest vegetation, pastures, others)</li> </ul> <p>Afterwards, all data-sources where rasterized at a resolution of 90x90meters and stacked in a rasterstack. Than the highest value of each grid-cell in the stack was extracted into a new summary raster. The summary layer is the raw friction map that needs to be corrected for the effect of slope. Slope was calculated from the SRTM1 data and rescaled to the same extent and resolution as the raw friction map. Slope was included as a constant factor with the following assumptions:</p> <ul> <li>slope between 0 and 5 degrees: slope constant 1 (travel speed is not impacted)</li> <li>slope between 5-10 degrees: slope constant 2 (travel speed is reduced by half)</li> <li>slope between 10-15 degrees: slope constant 3 ( travel speed is reduced to a third of the original speed)</li> <li>slope above 15 degrees: slope constant is 10 (travel speed is reduced to 10% of the original speed)</li> </ul> <p>Slope effects where not applied on water bodies. The corrected friction map was exported as a GeoTiff (WGS84) in a resolution of 90x90 meters.</p> <p><strong>Additional Information</strong>:</p> <p>The friction map was created using the R-software environment and GDAL. The script to replicated the results or modify the model with other base data can be obtained on request. The friction map can be used to calculate accumulated cost maps with GIS software (costdistance function in ArcGIS and r.cost function in QGIS).</p>
Metal on Ceramic Friction Surfacing Data for Printing Electronics
<p>This repository is for data for an upcoming paper that presents work using micro friction surfacing for applying in-situ maskless metallizations and robust seed layers for electroless plating on demand to substrates like, aluminum oxide, aluminum nitride, and as fired LTCC, for fabrication of next generation power module and other high reliability electronic substrates. </p> <p>An adjoining youtube playlist, with unique video identifiers that correspond to data in the provided excel data sheets, of all raw video footage of the friction surfacing process can be found <a title="Metal on Ceramic Friction Surfacing playlist" href="https://youtube.com/playlist?list=PLxlbqMdRe6OVbtT3ehHzCJgsyfsY8-2mJ&si=QhrZ0YftJCpKQ3uq" target="_blank" rel="noopener">here:</a><br><br></p> <p>New generation power modules provide compact form factors while achieving multi kilovolt drive potentials at kiloamp currents.[1] However, their typical packaging and substrate metallization methods, such as thick film, direct bond copper, and active metal braze, limits attachment options and other manufacturing process requirements while incurring large processing costs and extended lead times for researchers and industry.[2]–[5] High speed micro friction surfacing allows for directly writing pure metal conductors and integrated passives, onto common insulating high reliability electronics substrates, supports additional layers of metallization and provides direct device interconnect before or after die fabrication and bonding, without bulk thermal annealing and without damaging the underlying substrate. Thus, making the next generation of power devices more tenable at the prototype level, and with further process refinements, at industrial scale.[6]–[13] This work highlights the importance of rapid and flexible prototyping for next generation power modules and high reliability electronics, and how finding new ways to use existing tooling can enhance fabrication options and potentially shore up semiconductor prototyping supply chain stability</p> <h2>1. Introduction</h2> <p>Current generation power modules and high-reliability electronics require rapid and flexible prototyping, but current fabrication methods using thin and thick film, ultrasonic soldering, direct oxide bonding and active metal brazing, have limitations due to exotic interface metallization, atmosphere control, and thermal cycling requirements during fabrication and deployment [2], [3], [5]. These limitations particularly apply to silicon carbide devices, where typical wire bondable aluminum, active metal brazed gold-titanium and direct bond copper substrate metallization schemes incur large fabrication costs and lead times while inhibiting rework of as fabricated substrates due to deep vacuum/ high temperature requirements and a substantial need for skilled manual labor [14], [15]. </p> <p>In this work, High Speed Micro-Friction Surfacing(HSMFS) is used to metallize substrates of aluminum oxide, aluminum nitride, and as fired LTCC, with millimetric to sub-millimeter, traces made of, copper, and gold. HSMFS enables relatively automated, single step fabrication of single layer electronic circuits with bond strengths that exceed thin and thick film methods and ultrasonic soldering, at a cost and lead time 20-50X less, without need for skilled labor. HSMFS is a downscaled extension of a broader class of methods known as "friction surfacing" wherein a rod or powder of a material to be coated onto a substrate, is stirred by rotating a tool, or "mechtrode" against the substrate, trapping the material to be deposited between the mechtrode and substrate surfaces.[1]–[3] The mechtrode can be either a wire of material that is consumed as deposition proceeds, or a non-consumable tool made of a hard material that resists wear during deposition. Heat is generated due to friction between mechtrode and substrate, and forging pressure is applied from a CNC motion platform. The combination of heat from friction, mechano-chemical activation, and forging pressure induced plastic deformation results in the shearing, viscoplastic flow and chemical and mechanical bonding of material from the mechtrode to the substrate being coated. </p> <p>While there have been previous examples of friction surfacing metals onto ceramic substrates[4], [5], none have been used in electronics applications, and no characterization of relevant electro-thermal properties and endurance has been carried out. Additionally, the typically centimeter or larger deposit size scale of the mechtrode and consequently large supporting machinery in previous work has meant that the technique would be unsuitable for fabricating modern electronics. This large mechtrode scale results in excessive, evolved heat at the interface and thus high probability of heat shock damage to ceramic materials. Further, the relatively low mechtrode rotational speeds used in most prior works, results in very high forging pressures (hundreds of MPa), which typically far exceed the fracture toughness of common ceramic substrates. We have overcome these limitations and managed to obtain near bulk metallic electronic properties in as deposited track widths as small as 0.5mm, and metallization thicknesses from nanometers to 10's of microns on frangible substrates without damaging the substrate or compromising its electro-thermo-mechanical endurance. </p> <h2>2. Materials and Methods</h2> <p> </p> <h2>2.1 Materials and tools</h2> <p>For this study the raw materials used to produce the printed prototype as fired circuits were provided by Tommy's Watch and Jewelry via Stuller Precious Metals, (1.6mm copper, #43-6421:100000:T and 0.6mm gold wire, #WIRE:9698:P) and The University of Arkansas High Density Electronics Center (HiDEC), (Dupont 1mm thick 951 LTCC, Stellar Industries 0.5mm thick 99% aluminum nitride, and 0.5mm thick 96% alumina ceramics). </p> <p>The process parameters for printing tracks of copper and gold on the three substrates of interest were explored using a genmitsu 1610 minimill with a Dremel "multipro" 30,000 RPM rotary tool as it's spindle, and a 26 gauge 1070 spring steel sheet covering the mill bed between the aluminum t-slotbed and the ceramic substrate being printed on, purchased on amazon. Each substrated was held in place with a set of binder clips to keep it firmly in position nad flat against the spring steel sheet during deposition. Each deposition process was recorded in thermal video(Flir-T300) (courtesy of Dr. Darin Nutter) with a microscope camera(Opti-Tekscope OT-HD) and in real time macro video (Nikon D750). Subsequent profilometry (Dektak3030) electrical resistance (Fluke 77), current handling testing, taklife and ACS723 current sensor, and Flir-T300 camera (courtesy of Dr. Darin Nutter), and film strength (Kapton pull tests) measurements were performed with tooling available at HiDEC. <br>Temperature data were extraced via optical character recognition using the script here:<br>https://github.com/mahydraal/OCRDataExtractor<br>it deploys tesseract OCR and relatively simple python script with tkinter to provide a graphical user interface to select a region of a video, scrub it for noise, convert it to black and white, and then read character data from the user selected region. </p> <h2>2.2 Determination of printing parameters</h2> <p>Metals, copper and gold, were deposited on substrates of 96% alumina, 99% aluminum nitride(Al-N) and fired 951 LTCC, from wires of 1.6mm and 0.6mm OD respectively, via high speed micro friction surfacing (HSMFS). Spindle RPM was set open-loop constant to 30K RPM, and surface feed velocity was varied between 15, 45 and 75 mm/minute at a constant ratio of X-Z feed distance of 80 to approximate a constant normal force at the stall torque of the Z axis motor of the motion frame in open loop mode. Each surface feed velocity set point was tested 3 times for each metal substrate combination. Each metal and substrate combination were cleaned with 90% IPA and 90% Acetone and Di rinsed then blown dry with nitrogen before deposition.</p> <p>Friction surfacing is a solid-state joining process that involves rubbing two surfaces together at high speeds under pressure, creating a bond between the two surfaces without melting them, stereotypically shown in figure. The process can be used to join similar or dissimilar metals and alloys, metals and ceramics, and organics, and is particularly useful for joining materials with high melting points, such as titanium and nickel-based alloys without obtaining fusion and melting temperatures and without protective atmosphere. This process generates significant waste heat from friction and plastic deformation, which is useful for monitoring and controlling deposition consistency, thus real time thermographic videos during each test were collected using a FLIR T-300 thermal camera, and optical character recognition on it's display to obtain insight into the deposition temperature trends at the substrate-feedstock interface and better tune the surface feed-velocity at constant RPM to obtain electronic continuity in the as deposited metallic tracks on each ceramic substrate type. Real time macro videography was performed on each test to provide post-facto analysis and record any anomalies that would not be representative of typical performance. </p> <p>An appropriate spindle speed for deposition must be selected as well as appropriate vertical and linear feeds and speeds for the mini mill in micro friction surfacing. This is generally due to the need for a specific surface energy threshold associated with frictional heating and mechanical surface activation to be obtained between the feedstock and the substrate. This surface energy must exceed the free energy of reaction for diffusion and bonding to occur between the atoms of the substrate and those of the feedstock. A list of energies of formation for various transition metal carbides and oxides, necessary for bonding of metals to carbide and nitride sub-states by friction surfacing is shown. </p> <p>In short, by controlling spindle speed surface feed rate, and providing a constant down force by constant Z-X feed rate ratio on the minimill, it is possible to set a constant rate of heat evolved at the friction interface between the feedstock and substrate. If this heat evolved exceeds the heat of formation of a bonding compound of interest for long enough, the reaction of interest can proceed and a tenacious bond between metal and substrate can form. The details of accurately modeling heat evolved in friction surfacing, given the details of a specific deposition system and feed stock geometry are elucidated well elsewhere, [29], [30] so we will not go into them here. The primary point being that one can approximate appropriate deposition parameters for almost any material combination, knowing the free energy of formation of an appropriate bonding phase, and or the pressure-temperature phase diagram for the material pair of interest.</p> <h2>2.3 Characterization and measurement of test films</h2> <p>Bond strength of the HSMFS deposited films of copper and gold were tested initially by simple kapton tape pull testing, thereby assigning a minimum failure stress on film bond strengths. Temperature trends recorded during the deposition via thermography were correlated with resultant film resistivities and average height profiles and cycling performance for each set of parameters, each metal and each substrate; the most consistent and robust parametrization results were used in subsequent experiments to fabricate basic current carrying tracks with a mix of soldered and wire bonded terminals to demonstrate feasibility of HSMFS for rapid prototyping of electronics. </p> <h3>2.3.1 Electrical resistivity extraction and profilometry</h3> <p>Each material deposition was followed by profilometry (Dektak3030) at 3 points along each track, averaging the resultant maximum heights to determine film thickness and calculate sheet resistivity from resistance measurements on the multimeter(Fluke 77).</p> <h3>2.3.2 Maximum ampacity testing</h3> <p>Each printed specimen was terminated with copper tape, and soldered/wire bonded respectively. A taklife DC benchtop power supply was used to supply DC 31 volt power at up to 11 amps of current. An Arduino and high current shunt resistor current sensor measured the current flowing through the printed track, and acted to provide automatic control of current ramp up time. The current through the printed track was stepped up by the Arduino in steps of 25 milliamps every 60 seconds to provide time for thermal equilibration and avoid substrate fracture. This process continued until the track failed due to shorting, thermal breakdown, or electromigration failure. </p>
Frictional properties of natural granite fault gouge under hydrothermal conditions: A case study of strike-slip fault from Anninghe Fault zone, southeastern Tibetan Plateau
<p>We performed friction experiments on natural granite gouge under hydrothermal conditions to investigate roles of the Anninghe Fault (ANHF) on seismogenesis in the continental crust. In this dataset, we report processed data after correction. Detailed information about the files in the zip-files is given in the explanatory file Lei-et-al-2023-Data-Description.pdf.</p>
Supporting Data for Figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide"
<p>Supporting data for figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide" by Preston S. Spicer, Parker MacCready, and Zhaoqing Yang. The manuscript is being considered for publication in Journal of Geophysical Research: Oceans (2024). The article analyzes the effect of a tidal turbine farm on incident and reflected tidal energy fluxes in the Salish Sea. Files are in MATLAB data and .m format with some .txt and shape files. Files named figX.m create the corresponding Figure X using provided .mat and other files. Variable names and units correspond to graphed data of each figure in the journal article.</p>
Numerical simulation of friction extrusion: Process characteristics and material deformation due to friction
<p>This study employs a finite element thermo-mechanical model, using a Lagrangian incremental setting to investigate friction extrusion (FE) under varying process conditions. The incorporation of rotation in FE generates substantial frictional heat, leading to significantly reduced process forces in comparison to conventional extrusion (CE). The model reveals the interplay between temperature, strain, and strain rate across different microstructural zones of the resulting wire. Specifically, the sticking friction condition in FE enhances initial shear deformation, aligning with a homogeneous spatial strain distribution and predicting complete grain refinement in the extruded wire, as per Zener-Hollomon calculations. On the other hand, under the sliding friction condition in FE, the shear deformation is reduced which results in an inhomogeneous microstructure in the extruded wire. The analysis of material flow in the workpiece reveals distinct transitions from the base material to the thermo-mechanically affected zones. The simulated process force, thermal history, and microstructure during sliding friction conditions align well with the findings from performed friction extrusion experiments.</p>
Data from: Effect of ambient conditions in friction surfacing
<p>This dataset contains the data for the publication "Effect of ambient conditions in friction surfacing".</p>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
Frictional Properties of Opalinus Clay: Influence of Humidity, Normal Stress and Grain-size on Frictional Stability
<p>We designed frictional experiments to characterize the effect exerted by humidity, grain size and normal stress on frictional behaviour of the Opalinus clay fault gouge. We explored a wide range of normal stresses, ranging from 5 to 70 MPa performing velocity up-steps from 1 to 300 μm/s and slide-hold-slide from 1 to 3000s. Our experiments confirms that the OPA clay is weak, with friction coefficients at steady-state of ~0.35 and ~0.41, for 100% RH and 25% RH experiments, respectively. The OPA clay is velocity strengthening over the entire range of applied normal stress. We observe a direct relationship between frictional parameter <em>(a-b)</em> and slip velocity up to 35 MPa where, from there on, <em>(a-b)</em> parameter seems to be velocity independent. As evidenced by the microstructural analysis, we suggest that this behaviour is due to the progressive transition with normal stress, from strain localization and grain size reduction to distributed deformation on well-developed phyllosilicate networks. The amount of relative humidity does not affect deformation mechanisms (i.e. localized or distributed), whereas decreases fault strength and increases fault stability. We hypothesize that this is due to a possible interplay of OPA clay swelling and lubrication, caused by the weakening of chemical bonds between phyllosilicate foliae. Notably, the initial grain size (< 63 µm or 63 < g.s. < 125 µm) does not affect either the frictional strength or stability, with similar values of dilation upon velocity up-step. Collectively, our mechanical and microstructural observations have allowed us to build a conceptual model that summarizes the main mechanical features of the OPA clay fault gouge. In the context of deep geological repositories (DGR), our results confirm that slow aseismic slip is the most likely slip behaviour for a fault gouge hosted in the OPA clay, with similar mineralogical composition and clay fabric as our samples. Beyond the context of deep geological repositories, this study has also implications for carbon capture and geological storage in the deep subsurface. Indeed, OPA has the characteristics of a low permeability caprock, but faulted, and the integrity of a sealing caprock overlying a storage reservoir can evolve after fault reactivation, potentially generating undesired seismicity and new hydraulic pathways.</p> <p>The data are uploaded are structured as follow:</p> <p>1) A .txt file of the datafile that is recorded from the machine (raw data)</p> <p>2) A file in .txt format containing the elaborated data (data_rp) </p> <p>The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Nico Bigaroni at nico.bigaroni@uniroma1.it</p> <p> </p>
Data from: Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing
<p>This dataset contains the data for the publication "Experimental Investigation of Efficiency and Deposit Process Temperature during Multi-Layer Friction Surfacing"</p>
Supporting Dataset for Fault Friction Derived from Fault Bend Influence on Coseismic Slip During the 2019 Ridgecrest Mw 7.1 Mainshock
<p>Supporting Dataset for Fault Friction Derived from Fault Bend Influence on Coseismic Slip During the 2019 Ridgecrest M<sub>w</sub> 7.1 Mainshock, JGR</p> <p>See Readme text files for details. </p>
Costa Rica Friction Experiments
<p>Intermediate (10-2 m/s) and high (1m/s) velocity experiments on IODP samples collected offshore Costa Rica during Exp. 334 and 344</p>
Pin-on-disc tests with friction and wear depth data at increasing temperatures: dry sliding between 100Cr6 sphere against 100Cr6 ring.
<p>This dataset has been collected during 6 ball-on-disc tests. The objective of the tests was to assess the influence of temperature on wear and friction between steel-to-steel counterfaces.</p> <p>Materials: 100Cr6 sphere against 100Cr6 ring. Both components have been extracted from a FAG axial bearing. Surface roughness: sphere Ra: 0.04 microns; ring: 0.42 microns.</p> <p>test conditions (test 1/2/3/4/5/6):</p> <p>vertical load: 10N</p> <p>sliding speed: 0.1 m/s</p> <p>sliding distance: 500 m</p> <p>temperature: 23/50/80/110/140/170 °C</p> <p>wear track radius: 27.8/22.6/29/27/24/22 mm</p>
Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" II: Cascade Volcanic Arc
<p>Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Cascades used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Lesser Antilles used in the same manuscript (doi:10.5281/zenodo.10912445). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator). </p>
Supporting data: "How collective asperity detachments nucleate slip at frictional interfaces"
<p>This repository supports:</p> <p><strong>T.W.J. de Geus, M. Popović, W. Ji, A, Rosso, M. Wyart. How collective asperity detachments nucleate slip at frictional interfaces. Proc. Natl. Acad. Sci. U.S.A. 2019. <a href="https://dx.doi.org/10.1073/pnas.1906551116">doi: 10.1073/pnas.1906551116</a>, <a href="http://arxiv.org/abs/1904.07635">arXiv: 1904.07635</a></strong></p> <p>In particular, it provides all used data, all codes used to produce this data (including clones to all the used open-source libraries), and simple functions to plot the data. All data and code is free to use under the CC-BY-4 license, but: <em>Please cite the above research article</em> when using code or data (inspired) from this repository (or the open-source projects <a href="https://www.github.com/tdegeus/GooseFEM">GooseFEM</a> and <a href="https://www.github.com/tdegeus/GMatElastoPlasticQPot">GMatElastoPlasticQPot</a>), in addition to this dataset (<a href="https://dx.doi.org/10.5281/zenodo.3477938">doi: 10.5281/zenodo.3477938</a>).</p> <p>(c) T.W.J. de Geus | 2019 | contact: <a href="/Volumes/data/dataset/Geus_PNAS/tom%40gems.me">tom@geus.me</a>, <a href="http://www.geus.me">www.geus.me</a></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contents</strong></p> <ol> <li>In brief</li> <li>Data files</li> <li>Code</li> <li>Plots</li> </ol> <p><strong>1. In brief</strong></p> <p>All codes (<code>codes/</code>) are written in C++ using a number of open-source libraries (<code>libraries/</code>). All data (<code>data/</code>) is stored in the HDF5 format. All plots (<code>data/</code>) are generated using Python and a number of open-source libraries.</p> <p>All codes are developed and tested on macOS and Linux. The notation used here is consistent with these Unix-based platforms. Windows based compilation and use might differ from the description here.</p> <p><strong>2. Data files ("data/")</strong></p> <p>The different ensembles (datasets) are included in different directories in <code>data/</code>. They are distinguished through their directory name that comprises the system size (denoted <code>nx=...</code>) and the shape factor of the Weibull distribution from which the yield strains are drawn (denoted <code>weibull=...</code>).</p> <p>Each ensemble consists of a number of realisations of the random yield strains at the frictional interface. Each realisation is stored in a separate file (<code>id=xxx.hdf5</code>). This file serves as input for the event-driven code (<code>code/Run/main.cpp</code>). This code stores the displacement field at the end of each event-driven step (for which it may take significant time for energy to be minimised). With these displacement fields, all other quantities (stress, strain, plastic strain, ...) can be reconstructed. The relevant reconstructed data for the entire ensemble is collected in <code>EnsembleInfo.hdf5</code>.</p> <p>For the manually triggered avalanches at different stresses (and fixed relative strain increment w.r.t. the last system spanning event) only selected output is stored to limit storage usage (<code>code/AvalancheAfterPush...</code>). Please note that the simulations are stopped when an event becomes system spanning to save on computation time, for this case the output thus does not correspond to a state of mechanical equilibrium. By contrast, any simulation that was not system-spanning does correspond to a state of mechanical equilibrium.</p> <p><strong>2a. Realisation ("data/.../id=xxx.hdf5")</strong></p> <p>See code <code>code/Run/main.cpp</code> and generation <code>code/Generate/generate.py</code></p> <ul> <li>Mesh (input)<br> <ul> <li><code>/coor</code>: Nodal coordinates <code>[nnode, ndim]</code> (<code>ndim == 2</code>)</li> <li><code>/conn</code>: Connectivity <code>[nelem, nne]</code> (<code>nne = 4</code>)</li> <li><code>/dofs</code>: Degrees-of-freedom (DOF) per node <code>[nnode, ndim]</code></li> <li><code>/iip</code>: Prescribed DOFs <code>[n_iip]</code><br> </li> </ul> </li> <li>Material model (input)<br> <ul> <li><code>/elastic/elem</code>: Elastic elements <code>[n_elasic]</code></li> <li><code>/elastic/G</code>: Shear modulus <code>[n_elasic]</code></li> <li><code>/elastic/K</code>: Bulk modulus <code>[n_elasic]</code></li> <li><code>/cusp/elem</code>: Elasto-plastic elements <code>[n_cusp]</code></li> <li><code>/cusp/G</code>: Shear modulus <code>[n_cusp]</code></li> <li><code>/cusp/K</code>: Bulk modulus <code>[n_cusp]</code></li> <li><code>/cusp/epsy</code>: Yield strains <code>[n_cusp, n_potentials]</code></li> <li><code>/uuid</code>: Unique identifier for the realisation<br> <br> Note that <code>n_elasic + n_cusp == nelem</code><br> </li> </ul> </li> <li>Simulation (input)<br> <ul> <li><code>/alpha</code>: Background damping coefficient <code>[nelem]</code> (homogeneous)</li> <li><code>/rho</code>: Mass density <code>[nelem]</code> (homogeneous)</li> <li><code>/run/dt</code>: Time-step</li> <li><code>/run/epsd/kick</code>: Size of the strain kick</li> <li><code>/run/epsd/max</code>: Local strain at which to stop<br> </li> </ul> </li> <li>Output<br> <ul> <li><code>/completed</code>: Completion signal, emitted when <code>/run/epsd/max</code> was reached locally</li> <li><code>/stored</code>: Stored event-driven step numbers <code>[n_event]</code></li> <li><code>/t</code>: Time at the end of each event-driven step <code>[n_event]</code></li> <li><code>/kick</code>: Strain kick (yes/no) per event-driven step <code>[n_event]</code></li> <li><code>/disp/...</code>: Nodal displacements per event-driven step <code>[nnode, ndim]</code></li> </ul> </li> </ul> <p><strong>2b. Simulation output ("data/.../EnsembleInfo.hdf5")</strong></p> <p>See code and help <code>code/EnsembleInfo/main.cpp</code>.</p> <p><strong>2c. Distribution P(x) ("data/.../EnsembleYieldDistance*.hdf5")</strong></p> <p>See code and help <code>code/EnsembleYieldDistance_stressControl/main.cpp</code> and <code>EnsembleYieldDistance_strainControl/main.cpp</code>.</p> <p><strong>2d. Manual triggering of events ("data/.../AvalancheAfterPush*.hdf5")</strong></p> <p>See code and help <code>code/AvalancheAfterPush_stressControl/main.cpp</code> and <code>code/AvalancheAfterPush_strainControl/main.cpp</code>.</p> <p><strong>3. Code ("code/")</strong></p> <p>The relevant codes to generate the datasets are referenced above. All non-standard libraries have been cloned under <code>libraries/</code>. Please note that they are subject to evolution: their cloned versions allow one to rerun the code in this dataset, however, for further development one is strongly encouraged to use the latest version. Please check out the development of:</p> <ul> <li><a href="https://github.com/tdegeus/GooseFEM.git">GooseFEM (v0.2.3)</a></li> <li><a href="https://github.com/tdegeus/GMatElastoPlasticQPot.git">GMatElastoPlasticQPot (v0.2.1)</a></li> <li><a href="https://github.com/tdegeus/cpppath.git">cpppath (v0.0.7)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor.git">xtensor (v0.20.8)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor-blas.git">xtensor-blas (v0.16.1)</a></li> <li><a href="https://github.com/xtensor-stack/xtl.git">xtl (v0.6.5)</a></li> <li><a href="https://github.com/xtensor-stack/xsimd.git">xsimd (v7.2.5)</a></li> <li><a href="https://github.com/BlueBrain/HighFive.git">highfive (master)</a></li> <li><a href="https://github.com/docopt/docopt.git">docopt (master)</a></li> <li><a href="https://github.com/fmtlib/fmt.git">fmt (master)</a></li> <li><a href="https://github.com/tdegeus/pyxtensor.git">pyxtensor (v0.0.5)</a></li> <li><a href="https://github.com/tdegeus/GooseMPL.git">GooseMPL (v0.2.24)</a></li> <li><a href="https://github.com/tdegeus/GooseEYE.git">GooseEYE (v0.2.0)</a></li> <li><a href="https://github.com/h5py/h5py.git">h5py (master)</a></li> </ul> <p>To compile code, follow the following structure:</p> <pre>cd code/... mkdir build cmake .. make</pre> <p>Then to run use:</p> <pre>./Run ...</pre> <p>(use <code>./Run --help</code> for help, and/or read the code). For some codes a support function generates commands. They can be generated and run as follows:</p> <pre>python makeJob.py source commands.txt</pre> <p><strong>4. Plots ("data/.../*.py")</strong></p> <p>Basic plot functions are included with the datasets. Note that all scripts require <code>numpy</code>, <code>matplotlib</code>, <code>h5py</code>, and <code>GooseMPL</code> to be installed. The latter two are included here, the other two are considered standard.</p>
Data from: Insight into layer formation during friction surfacing: Relationship between deposition behavior and microstructure
<p>This dataset contains the data for the publication "Insight into layer formation during friction surfacing: Relationship between deposition behavior and microstructure".</p>
VALID - Rods friction test register
<p><span>This dataset provides the inputs, measurements and evaluation of friction levels with quarter-scale rods performed during VALID Project (funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101006927). The aim of the tests was to analyse friction levels and Stribeck profile estimations for a subscale component of the Wave Energy Converter (WEC). </span></p> <p><span>This dataset contains the measurements about speed, positions, temperature and friction force of tests.</span></p>
Length of day residuals after the removal of tidal friction, glacial isostatic adjustment, and climatic effects: 720 BC to 2020
<p>LOD residuals after the removal of tidal friction, glacial isostatic adjustment, and climatic effects.<br>Time range 720 BC to 2020 AD.<br>Data are with respect to 2020.<br>First column: time in year (negative years mean BC)<br>Second column: LOD residuals in milliseconds<br>Third column: uncertainty of the LOD residuals in milliseconds</p> <p>If you use the data, please cite the following references:<br>1. The increasingly dominant role of climate change on length of day variations: Kiani Shahvandi et al. 2024 published in PNAS, https://doi.org/10.1073/pnas.2406930121<br>2. Length of day variations explained in a Bayesian framework: Kiani Shahvandi et al. 2024 published in GRL<br>3. Addendum 2020 to ‘Measurement of the Earth’s rotation: 720 BC to AD 2015’: Morrison et al. 2021 published in Proceedings of the Royal Society A, https://doi.org/10.1098/rspa.2020.0776</p>
Experimental investigation of composite materials for sliding friction dampers: data, plots, photos and videos of the tests
<p><strong>Folder DATA</strong></p> <p>This folder contains the data acquired by testing the friction pads M1, M2, M3, M4 and M5 under the following loading protocols:</p> <ul> <li>Linear static loading (M);</li> <li>Cyclic loading with constant amplitude (CA);</li> <li>Cyclic loading with decreasing amplitude at low rate (DA);</li> <li>Cyclic loading with increasing amplitude at low rate (IA);</li> <li>Cyclic loading with increasing amplitude at moderate rate (IA-H);</li> <li>Cyclic loading with increasing amplitude at high rate (IA-HH);</li> <li>Pulse-like loading protocol (PL);</li> <li>Mainshock-aftershock protocol (MS-AS): mainshock (MS), first aftershock (AS1) and second aftershock (AS2).</li> </ul> <p>The data include:</p> <ul> <li><em>Time</em>: time (unit: second);</li> <li><em>F</em>: axial force experienced by the sliding friction damper (unit: kN);</li> <li><em>N_bolt</em>: bolt preload (unit: kN);</li> <li><em>mu</em>: friction coefficient of the considered pad (unit: dimensionless);</li> <li><em>delta</em>: axial displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. delta</em>: total cumulative displacement experienced by the sliding friction damper (unit: mm);</li> <li><em>Cum. E</em>: total cumulative energy dissipated by the sliding friction damper (unit: kJ);</li> <li><em>max Tin</em>: maximum temperature tracked close to the sliding interface (unit: Celsius);</li> <li><em>Tout</em>: temperature tracked at the surface of the inner slotted plate (unit: Celsius).</li> </ul> <p> The data are organized as follows:</p> <ul> <li>Folder <strong>T100</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 100 Nm. Each EXCEL file <strong>T100_M_Y</strong> saved in the folder <strong>T100</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>This folder contains the data acquired under the linear static loading protocol (M) for a tightening torque of 200 Nm. Each EXCEL file <strong>T200_M_Y</strong> saved in the folder <strong>T200</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol M.</p> <ul> <li>Folder <strong>Fs150</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 150 kN. Each subfolder <strong>Fs150_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H). Each EXCEL file <strong>Fs150_X_Y</strong> saved in the subfolder <strong>Fs150_X</strong> contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <ul> <li>Folder <strong>Fs300</strong></li> </ul> <p>This folder contains the data acquired for an expected slip load of 300 kN. Each subfolder <strong>Fs300_X</strong> contains the data obtained under the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2). Each EXCEL file <strong>Fs300_X_Y</strong> saved in the subfolder <strong>Fs300_X </strong>contains the data obtained by testing the friction pad Y (Y = M1, M2, M3, M4, M5) under the loading protocol X.</p> <p><strong>Folder PHOTOS</strong></p> <p>This folder contains the following photos:</p> <ul> <li>Folder <strong>01_FrictionDamper</strong>: photos of the sliding friction damper and its components.</li> <li>Folder <strong>02_Instrumentation</strong>: photos of the instrumentation used for the data acquisition during the experimental campaign.</li> <li>Folder <strong>03_FrictionPads</strong>: <ul> <li>Subfolder <strong>BeforeTesting</strong>: photos of the friction pads before the experimental campaign.</li> <li>Subfolder <strong>AfterTesting</strong>: photos of the friction pads at the end of each loading protocol. The photo <strong>Fs150vs300_X_Y</strong> shows the condition of the pad Y (Y = M1, M2, M3, M4, M5) at the end of the loading protocol X (X = M, CA, DA, IA, IA-H, IA-HH, PL, MS, AS1, AS2) performed for an expected slip load of 150 kN and 300 kN (the pads shown at the top of each photo are those tested for an expected slip load of 150 kN). Similarly, the photo <strong>Fs300_X_Y</strong> shows the condition of the pad Y at the end of the loading protocol X performed for an expected slip load of 300 kN.</li> </ul> </li> <li>Folder <strong>04_Tests</strong>: photos taken from the east and north side of the sliding friction damper during the loading protocols that caused the fracture of the pads <ul> <li>Subfolder <strong>Fs150</strong>: photos taken during the tests conducted for an expected slip load of 150 kN. Each folder <strong>Fs150_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, DA, IA, IA-H).</li> <li>Subfolder <strong>Fs300</strong>: photos taken during the tests conducted for an expected slip load of 300 kN. Each folder <strong>Fs300_X_Y</strong> contains the photos taken by testing the pad Y (Y = M1, M2, M4, M5) during the loading protocol X (X = CA, IA, IA-H). A video was recorded live during the loading protocols IA-HH, PL, MS, AS1 and AS2 (see folder <strong>VIDEOS</strong>).</li> </ul> </li> </ul> <p><strong>Folder PLOTS</strong></p> <p>This folder contains the following MATLAB plots:</p> <ul> <li><em>Force-Disp</em>: axial force – axial displacement response of the sliding friction damper;</li> <li><em>Preload-CumDisp</em>: bolt preload as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>FrictionCoeff-CumDisp</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper;</li> <li><em>Temp-CumDisp</em>: rise in temperature as a function of the total cumulative displacement experienced by the sliding friction damper (the temperature values reported for the expected slip load of 150 kN correspond to “max Tin”, whereas those reported for the expected slip load of 300 kN correspond to “Tout”);</li> <li><em>FrictionCoeff-LoadingHistoryEffect</em>: friction coefficient of the considered pad as a function of the total cumulative displacement experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-RateEffect</em>: friction coefficient of the considered pad as a function of sliding velocity experienced by the sliding friction damper under different loading protocols;</li> <li><em>FrictionCoeff-TempEffect</em>: friction coefficient of the considered pad as a function of the rise in temperature tracked during different loading protocols;</li> <li><em>FrictionCoeff-PressureDependency</em>: mean and standard deviation of the friction coefficient of the considered pad obtained for different expected slip loads and loading protocols;</li> <li><em>FrictionCoeffStaticDynamic-PressureDependency</em>: mean of the static and dynamic friction coefficient of the considered pad obtained for different expected slip loads and loading protocols.</li> </ul> <p>The MATLAB plots are organized as follows:</p> <ul> <li>Folder <strong>T200</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the linear static loading protocol (M) for a tightening torque of 200 Nm.</p> <ul> <li>Folder <strong>Fs150 and Fs300</strong></li> </ul> <p>The MATLAB plots saved in this folder illustrate the data obtained by testing the friction pads M1, M2, M3, M4 and M5 under the considered loading protocol (M, CA, DA, IA, IA-H, IA-HH, PL, MS-AS) for an expected slip load of 150 kN and 300 kN.</p> <p><strong>Folder VIDEOS</strong></p> <p>This folder contains the following videos:</p> <ul> <li>Folder <strong>T100</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 100 Nm under the linear static loading protocol (M). The videos <strong>T100_M_Y_East</strong> and <strong>T100_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>T200</strong>: videos created from the photos taken during the tests conducted for a tightening torque of 200 Nm under the linear static loading protocol (M). The videos <strong>T200_M_Y_East</strong> and <strong>T200_M_Y_North</strong> show the test conducted on the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs150</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 150 kN. The videos <strong>Fs150_X_Y_East</strong> and <strong>Fs150_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively.</li> <li>Folder <strong>Fs300</strong>: videos created from the photos taken during the tests conducted for an expected slip load of 300 kN. The videos <strong>Fs300_X_Y_East</strong> and <strong>Fs300_X_Y_North</strong> show the loading protocol X (X = M, CA, DA, IA, IA-H) applied to the pad Y (Y = M1, M2, M3, M4, M5) from the east and north side of the sliding friction damper respectively. The videos obtained for the loading protocols IA-HH, PL, MS, AS1 and AS2 were recorded live during each test.</li> </ul>
Physical state of water controls friction of gabbro-built faults
<p>Experimental data</p> <p>six columns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, axial displacement (mm), pore pressure (MPa) and temperature (℃). </p>
ScienceDex guides
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.