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5 results for “heliostat”
Heliostat and receiver efficiency calculated with DLR's software HFLCAL
<p>The heliostat field and the receiver design efficiency was estimated for the design point by optimizing for lowest LCOE.</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
Heliostat field positions after optimization with DLR's software HFLCAL
<p>The dataset provides the design of the heliostat field layout, in terms of number and positions. The filed layout is optimized by minimizing the estimated LCOE of the system, using the DLR tool Visual HFLCAL software</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
Odeillo's big solar furnace: heliostats field plan
<p>Sketch by Bruno Rivoire of the heliostats field of the CNRS big solar furnace in Odeillo, France:</p> <ul> <li>heliostats layout</li> <li>general heliostats geometry</li> </ul> <p>English translation <em>heliostats01.en.pdf</em> by Emmanuel Guillot.</p> <p>First outside publication (in French) date lost.</p> <p><strong>Check also: </strong></p> <ol> <li>F. Trombe, A. Le Phat Vinh, <em>Thousand kW solar furnace, built by the National Center of Scientific Research, in Odeillo (France)</em>, Solar Energy; Volume 15, Issue 1, May 1973, Pages 57–61. <a href="https://doi.org/10.1016/0038-092X(73)90006-6">https://doi.org/10.1016/0038-092X(73)90006-6</a></li> <li>E. Guillot, R. Rodriguez, N. Boullet, J-L Sans, <em>Some details about the third rejuvenation of the 1000 kWth solar furnace in Odeillo: Extreme performance heliostats</em>, AIP Conf. Proc. 2033, 040016 (2018). <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> </ol>
Flux measurements at the CNRS big solar furnace in Odeillo with heliostat scan (L2 data)
<p><strong>Flux measurements at the big solar furnace in Odeillo.</strong></p> <p>Data levels description:</p> <ul> <li>Level 0 data: raw datasets from the different acquisition systems (cameras, radiometers, motion controllers…).</li> <li>Level 1 data: area of interest extracted from flux camera images and converted as HDF file (grayscale).</li> <li><strong>=> Level 2 data: All data synchronised, converted and supersampled at millisecond resolution in HDF files: DNI, radiometer, heliostat aiming location from ARGOS camera, image maps at focal plane on the receiver in suns normalised for DNI 1000 W/m2.</strong></li> <li>Level 3 data: data interpolated and smoothed for regular heliostat aiming positions.</li> <li>Level 4 data: idem, but spatially subsampled at various resolutions.</li> </ul> <p>One HDF file per heliostat.</p> <p>Heliostats chosen randomly among the 26 available.</p> <p>HDF file are compressed in zstandard. In python 3, requires the packages: pandas, hdf5plugin, tables, h5py, blosc, zstd.</p> <p>For each heliostat : the aiming position of the heliostats was moved in S patterns.</p> <p>Data from experiment started 2021-12-22 120618Z.</p> <p><strong>References:</strong></p> <ul> <li>Related to SolarPaces 2022 communication: <strong>Building a 5D Database of Heliostats Flux Distributions: toward High Flexibility High Accuracy Flux Control for Odeillo's Big Solar Furnace </strong></li> <li>Solar furnace description and more: <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> <li>Cameras settings description and more: <a href="https://doi.org/10.1063/1.5067202">https://doi.org/10.1063/1.5067202</a></li> </ul>
Flux measurements at the CNRS big solar furnace in Odeillo with heliostat scan (L4 data)
<p><strong>Flux measurements at the big solar furnace in Odeillo.</strong></p> <p>Data levels description:</p> <ul> <li>Level 0 data: raw datasets from the different acquisition systems (cameras, radiometers, motion controllers…).</li> <li>Level 1 data: area of interest extracted from flux camera images and converted as HDF file (grayscale).</li> <li>Level 2 data: All data synchronised, converted and supersampled at millisecond resolution in HDF files: DNI, radiometer, heliostat aiming location from ARGOS camera, image maps at focal plane on the receiver in suns normalised for DNI 1000 W/m2.</li> <li>Level 3 data: data interpolated and smoothed for regular heliostat aiming positions.</li> <li><strong>=> Level 4 data: idem, but spatially subsampled at various resolutions. Provided here: 10mm resolution.</strong></li> </ul> <p>One HDF file per heliostat.</p> <p>Heliostats chosen randomly among the 26 available.</p> <p>HDF file are compressed in zstandard. In python 3, requires the packages: pandas, hdf5plugin, tables, h5py, blosc, zstd.</p> <p>For each heliostat : the aiming position of the heliostats was moved in S patterns.</p> <p>Data from experiment started 2021-12-22 120618 GMT.</p> <p>Data processing completed 2022-03-02 1459 GMT.</p> <p><strong>References:</strong></p> <ul> <li>Related to SolarPaces 2022 communication: <strong>Building a 5D Database of Heliostats Flux Distributions: toward High Flexibility High Accuracy Flux Control for Odeillo's Big Solar Furnace </strong></li> <li>Solar furnace description and more: <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> <li>Cameras settings description and more: <a href="https://doi.org/10.1063/1.5067202">https://doi.org/10.1063/1.5067202</a></li> </ul>
ScienceDex guides
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