https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Morozova_et_al_2022a Morozova et al 2022a - Revision history 2026-04-25T18:28:03Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=262754&oldid=prev Move page script: Move page script moved page Morozova et al 1970a to Morozova et al 2022a 2022-11-25T15:06:35Z <p>Move page script moved page <a href="/public/Morozova_et_al_1970a" class="mw-redirect" title="Morozova et al 1970a">Morozova et al 1970a</a> to <a href="/public/Morozova_et_al_2022a" title="Morozova et al 2022a">Morozova et al 2022a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 15:06, 25 November 2022</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> Move page script https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=259998&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 570244440 to Morozova et al 1970a 2022-11-22T13:07:19Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_570244440" class="mw-redirect" title="Draft Sanchez Pinedo 570244440">Draft Sanchez Pinedo 570244440</a> to <a href="/public/Morozova_et_al_1970a" class="mw-redirect" title="Morozova et al 1970a">Morozova et al 1970a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 13:07, 22 November 2022</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=259997&oldid=prev JSanchez at 13:07, 22 November 2022 2022-11-22T13:07:13Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:07, 22 November 2022</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l6" >Line 6:</td> <td colspan="2" class="diff-lineno">Line 6:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Abstract ==</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>== Abstract ==</div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>&lt;pdf&gt;Media:Draft_Sanchez Pinedo_5702444401686_abstract.pdf&lt;/pdf&gt;</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; 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border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_5702444401686_paper.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:259995:newid:259997 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=259995&oldid=prev JSanchez at 13:07, 22 November 2022 2022-11-22T13:07:12Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:07, 22 November 2022</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarseand fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model's output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-kriging approach demonstrates the best trade-off between computational cost and accuracy.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarseand fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model's output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-kriging approach demonstrates the best trade-off between computational cost and accuracy.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Abstract ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_5702444401686_abstract.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:259993:newid:259995 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=259993&oldid=prev JSanchez at 13:07, 22 November 2022 2022-11-22T13:07:10Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:07, 22 November 2022</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Summary==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">In this work, we present a multi-fidelity machine learning surrogate model, which predicts comfort-related flow parameters in a ventilated room with a heated floor. The model uses coarseand fine-grid CFD data obtained using LES turbulence models. The dataset is created by changing the width aspect ratio of the rooms, inlet flow velocity, and temperature of the hot floor. The surrogate model takes the values of temperature and velocity magnitude at four different cavity locations as inputs. These probes are located such that they could be replaced by actual sensor readings in a practical case. The model's output is a set of comfort-related flow parameters. We test two multi-fidelity approaches based on Gaussian process regression (GPR), among them GPR with linear correction (LC GPR), and multi-fidelity GPR (MF GPR) or cokriging. The computational cost and accuracy of these approaches are compared with GPRs based on single-fidelity data. All of the tested multi-fidelity approaches successfully reduce the computational cost of dataset generation compared to high-fidelity GPR while maintaining the required level of accuracy. The co-kriging approach demonstrates the best trade-off between computational cost and accuracy.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:259992:newid:259993 --> </table> JSanchez https://www.scipedia.com/wd/index.php?title=Morozova_et_al_2022a&diff=259992&oldid=prev JSanchez: Created blank page 2022-11-22T13:07:08Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez