https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=El_Zaatari_et_al_2022a 2026-04-22T17:36:20Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=El_Zaatari_et_al_2022a&diff=262648&oldid=prev 2022-11-25T15:06:27Z

<p>Move page script moved page <a href="/public/El_Zaatari_et_al_1970a" class="mw-redirect" title="El Zaatari et al 1970a">El Zaatari et al 1970a</a> to <a href="/public/El_Zaatari_et_al_2022a" title="El Zaatari et al 2022a">El Zaatari 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=El_Zaatari_et_al_2022a&diff=260754&oldid=prev 2022-11-22T15:27:17Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_202305111" class="mw-redirect" title="Draft Sanchez Pinedo 202305111">Draft Sanchez Pinedo 202305111</a> to <a href="/public/El_Zaatari_et_al_1970a" class="mw-redirect" title="El Zaatari et al 1970a">El Zaatari 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 15:27, 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=El_Zaatari_et_al_2022a&diff=260753&oldid=prev 2022-11-22T15:27:08Z

<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 15:27, 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_202305111880_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; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>&lt;pdf&gt;Media:Draft_Sanchez Pinedo_202305111880_abstract.pdf&lt;/pdf&gt;</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;">== Full Paper ==</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_202305111880_paper.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:260751:newid:260753 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=El_Zaatari_et_al_2022a&diff=260751&oldid=prev 2022-11-22T15:27:06Z

<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 15:27, 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>Computational Fluid Dynamics is a viable tool in the field of aerodynamics enabling to reduce time, effort and budget required for experimental testing. Although powerful and established for various years, it remains a complex tool calling for experienced users to ensure consistent high-quality results. This complexity primarily stems from the underlying model, namely the Navier-Stokes equations typically combined with a set of equations resolving the effects of turbulence. Additionally, to obtain accurate high-fidelity result appropriate meshes are required. As a consequence, a substantial number of parameters needs to be selected carefully and the quality of a result often highly depends on individual knowledge and experience of a user. Hence, a strong desire exists to reduce the number of input parameters without causing a loss of accuracy and efficiency. Such reduction of parameters might be viewed as a prerequisite to CFD as a tool in process chains for multidisciplinary applications where typically no user interaction is possible. In this article we propose a machine-learned Expert System for CFD to provide guidance for users in selecting optimal or at least near optimal parameter combinations. The proposed Expert System is divided into two macro steps, the surrogate model and a genetic algorithm to determine from the surrogate model the parameters. Numerical examples are presented to demonstrate the approach.</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>Computational Fluid Dynamics is a viable tool in the field of aerodynamics enabling to reduce time, effort and budget required for experimental testing. Although powerful and established for various years, it remains a complex tool calling for experienced users to ensure consistent high-quality results. This complexity primarily stems from the underlying model, namely the Navier-Stokes equations typically combined with a set of equations resolving the effects of turbulence. Additionally, to obtain accurate high-fidelity result appropriate meshes are required. As a consequence, a substantial number of parameters needs to be selected carefully and the quality of a result often highly depends on individual knowledge and experience of a user. Hence, a strong desire exists to reduce the number of input parameters without causing a loss of accuracy and efficiency. Such reduction of parameters might be viewed as a prerequisite to CFD as a tool in process chains for multidisciplinary applications where typically no user interaction is possible. In this article we propose a machine-learned Expert System for CFD to provide guidance for users in selecting optimal or at least near optimal parameter combinations. The proposed Expert System is divided into two macro steps, the surrogate model and a genetic algorithm to determine from the surrogate model the parameters. Numerical examples are presented to demonstrate the approach.</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_202305111880_abstract.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:260749:newid:260751 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=El_Zaatari_et_al_2022a&diff=260749&oldid=prev 2022-11-22T15:27:04Z

<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 15:27, 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;">Computational Fluid Dynamics is a viable tool in the field of aerodynamics enabling to reduce time, effort and budget required for experimental testing. Although powerful and established for various years, it remains a complex tool calling for experienced users to ensure consistent high-quality results. This complexity primarily stems from the underlying model, namely the Navier-Stokes equations typically combined with a set of equations resolving the effects of turbulence. Additionally, to obtain accurate high-fidelity result appropriate meshes are required. As a consequence, a substantial number of parameters needs to be selected carefully and the quality of a result often highly depends on individual knowledge and experience of a user. Hence, a strong desire exists to reduce the number of input parameters without causing a loss of accuracy and efficiency. Such reduction of parameters might be viewed as a prerequisite to CFD as a tool in process chains for multidisciplinary applications where typically no user interaction is possible. In this article we propose a machine-learned Expert System for CFD to provide guidance for users in selecting optimal or at least near optimal parameter combinations. The proposed Expert System is divided into two macro steps, the surrogate model and a genetic algorithm to determine from the surrogate model the parameters. Numerical examples are presented to demonstrate the approach.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:260748:newid:260749 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=El_Zaatari_et_al_2022a&diff=260748&oldid=prev 2022-11-22T15:27:03Z

<p>Created blank page</p> <p><b>New page</b></p><div></div>

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