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6 results for “hydraulic system”
Dataset related to the article "Laboratory-scale hydraulic fracturing dataset for benchmarking of Enhanced Geothermal System simulation tools"
<p>Experimental results from hydraulic fracturing experiments performed in granite and marble samples of size 30 cm × 30 cm × 45 cm under well-defined boundary conditions.</p> <p>Datasets include:</p> <ul> <li>pressure versus flow-rate response</li> <li>acoustic emission data from a dense network of 32 seismic sensors</li> <li>detailed description of the experimental set-up and adopted test protocol</li> <li>mechanical and petrophysical properties of the samples</li> <li>python code for seismic data processing</li> </ul> <p>This complete collection of data, obtained within the framework of European Union’s Horizon 2020 project GEMex, is rare in its kind and indispensable for verification of model assumptions and constitutive relationships of numerical codes used for designing field-scale hydraulic fracturing experiments.</p>
Map of the Roman mining hydraulic system - Las Médulas, León, Spain
<p>This resource is related with the Roman hydraulic system linked to Las Médulas gold mining complex in northwest Iberia. The dataset includes a detailed digital cartography of the hydraulic network, which extends over 1100km. It identifies 41 canals distributed between La Cabrera and El Bierzo regions, (33 and 8, respectively), with 14 canals supplying water to Las Médulas. Additionally, the dataset includes the locations of the Roman mining sites.</p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://earth.google.com/earth/d/1argDbqGaEMdlE7Uomb4z70LGYce9pCBb?usp=sharing">View on Google Earth (3D)</a></p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://www.google.com/maps/d/edit?mid=12yHtyAN3qJcmrt-P_3abv3rd6PaI_ZQ&usp=sharing">View on Google Maps (2D)</a></p> <p> </p>
Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system
<p>This database includes the data used to produce the results for the following article:</p> <p>Bellos, V., Kossieris, P., Efstratiadis, A., Papakonstantis, I., Papanicolaou, P., Dimas, P., Makropoulos, C. 2022. Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system. 7th IAHR Europe Congress, September 7th – 9th, 2022, Athens, Greece (accepted paper for oral presentation, in press).</p>
Data from: The PDI model system for parameterizing soil hydraulic properties
<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>
Data from: The PDI model system for parameterizing soil hydraulic properties
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Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
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