https://www.cfd-online.com/W/index.php?title=J-FLO&feed=atom&action=historyJ-FLO - Revision history2017-11-18T20:12:24ZRevision history for this page on the wikiMediaWiki 1.16.5https://www.cfd-online.com/W/index.php?title=J-FLO&diff=7039&oldid=prevJola: removed commercial advertising2006-12-15T15:00:52Z<p>removed commercial advertising</p>
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<td colspan='2' style="background-color: white; color:black;">Revision as of 15:00, 15 December 2006</td>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>J-FLO <del class="diffchange diffchange-inline">Revolutionary Technology </del>for Optimal Aerodynamic Design</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>J-FLO <ins class="diffchange diffchange-inline">is a tool for </ins>for Optimal Aerodynamic Design. CFD needs to be combined with search and optimization procedures that can automatically design components with ideal aerodynamic, structural and acoustic characteristics.</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><del class="diffchange diffchange-inline">Aerodynamic shape selection largely remains a trial-and-error process consisting of testing, via fast computers, a large number of aircraft shapes based on the intuition and experience of the designer</del>. <del class="diffchange diffchange-inline">Such use of CFD as a virtual wind tunnel will not necessarily lead to a truly optimal design. To examine a larger design space, </del>CFD needs to be combined with search and optimization procedures that can automatically design components with ideal aerodynamic, structural and acoustic characteristics.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div></div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The simplest traditional approach is Optimization, defining the geometry through a set of design parameters, with a cost function such as drag, lift/drag ratio, or range. The sensitivities are then estimated by making a small variation in each design parameter in turn, and each time recalculating the flow to obtain the change in the cost function. The main disadvantage of this approach is obvious: a number of flow calculations proportional to the number of design variables to estimate the gradient. The computational costs become prohibitive as the number of design variables is increased.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>The simplest traditional approach is Optimization, defining the geometry through a set of design parameters, with a cost function such as drag, lift/drag ratio, or range. The sensitivities are then estimated by making a small variation in each design parameter in turn, and each time recalculating the flow to obtain the change in the cost function. The main disadvantage of this approach is obvious: a number of flow calculations proportional to the number of design variables to estimate the gradient. The computational costs become prohibitive as the number of design variables is increased.</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>A higher-level approach would be Inverse, casting the problem as a search for the shape that will generate a desired pressure distribution. This approach anticipates the designer to know the pressure distribution that will lead to the desired performance. Inverse design may lead to an intractable problem where the shape to achieve a desired pressure distribution does not exist and it incurs the same computational costs associated with optimization procedures.</div></td><td class='diff-marker'> </td><td style="background: #eee; color:black; font-size: smaller;"><div>A higher-level approach would be Inverse, casting the problem as a search for the shape that will generate a desired pressure distribution. This approach anticipates the designer to know the pressure distribution that will lead to the desired performance. Inverse design may lead to an intractable problem where the shape to achieve a desired pressure distribution does not exist and it incurs the same computational costs associated with optimization procedures.</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><del class="diffchange diffchange-inline">The most cost efficient approach as well as the trend of the future, is </del>Optimal Control<del class="diffchange diffchange-inline">. The </del>design of a wing can be regarded as an optimal control of the flow equations by variation of the shape of the boundary, regarded as completely arbitrary point-by-point. Using techniques of control theory, the gradient can be determined indirectly by solving an adjoint equation that has coefficients defined by the solution of the flow equations and whose solution cost is comparable to that of solving the flow equations. Thus, the gradient can be determined with roughly the computational costs of two flow solutions, independently of the number of design variables, which may be very large when the boundary is regarded as a free surface.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div><ins class="diffchange diffchange-inline">With </ins>Optimal Control <ins class="diffchange diffchange-inline">the </ins>design of a wing can be regarded as an optimal control of the flow equations by variation of the shape of the boundary, regarded as completely arbitrary point-by-point. Using techniques of control theory, the gradient can be determined indirectly by solving an adjoint equation that has coefficients defined by the solution of the flow equations and whose solution cost is comparable to that of solving the flow equations. Thus, the gradient can be determined with roughly the computational costs of two flow solutions, independently of the number of design variables, which may be very large when the boundary is regarded as a free surface.</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div>The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI)<del class="diffchange diffchange-inline">, a leading light of CFD algorithmic development and a pioneer of Optimal Control Aerodynamics. NTI is thus very proud to be the first in bringing such comprehensive technology to the market and to associate itself in its further development with Professor Jameson. Users that base their aircraft designs on JFLO will distinguish themselves by the speed of their design, its accuracy, and the drastic decrease in design team size needed</del>.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI).</div></td></tr>
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<tr><td class='diff-marker'>-</td><td style="background: #ffa; color:black; font-size: smaller;"><div><del class="diffchange diffchange-inline">JFLO applications will be broadened rapidly to more aerospace and fluid flow application areas. JFLO is piloted by NTI advanced graphical environment, including the ability to visualize the evolving 3D geometry to be meshed automatically by JFLO. </del>Variants of JFLO include analysis and optimal control capabilities using Euler or Navier-Stokes equations, with automatically generated structured or unstructured body-conforming grids.</div></td><td class='diff-marker'>+</td><td style="background: #cfc; color:black; font-size: smaller;"><div>Variants of JFLO include analysis and optimal control capabilities using Euler or Navier-Stokes equations, with automatically generated structured or unstructured body-conforming grids.</div></td></tr>
</table>Jolahttps://www.cfd-online.com/W/index.php?title=J-FLO&diff=7037&oldid=prevMartin nti: J-FLO software description2006-12-14T21:10:32Z<p>J-FLO software description</p>
<p><b>New page</b></p><div>J-FLO Revolutionary Technology for Optimal Aerodynamic Design<br />
<br />
Aerodynamic shape selection largely remains a trial-and-error process consisting of testing, via fast computers, a large number of aircraft shapes based on the intuition and experience of the designer. Such use of CFD as a virtual wind tunnel will not necessarily lead to a truly optimal design. To examine a larger design space, CFD needs to be combined with search and optimization procedures that can automatically design components with ideal aerodynamic, structural and acoustic characteristics.<br />
<br />
The simplest traditional approach is Optimization, defining the geometry through a set of design parameters, with a cost function such as drag, lift/drag ratio, or range. The sensitivities are then estimated by making a small variation in each design parameter in turn, and each time recalculating the flow to obtain the change in the cost function. The main disadvantage of this approach is obvious: a number of flow calculations proportional to the number of design variables to estimate the gradient. The computational costs become prohibitive as the number of design variables is increased.<br />
<br />
A higher-level approach would be Inverse, casting the problem as a search for the shape that will generate a desired pressure distribution. This approach anticipates the designer to know the pressure distribution that will lead to the desired performance. Inverse design may lead to an intractable problem where the shape to achieve a desired pressure distribution does not exist and it incurs the same computational costs associated with optimization procedures.<br />
<br />
The most cost efficient approach as well as the trend of the future, is Optimal Control. The design of a wing can be regarded as an optimal control of the flow equations by variation of the shape of the boundary, regarded as completely arbitrary point-by-point. Using techniques of control theory, the gradient can be determined indirectly by solving an adjoint equation that has coefficients defined by the solution of the flow equations and whose solution cost is comparable to that of solving the flow equations. Thus, the gradient can be determined with roughly the computational costs of two flow solutions, independently of the number of design variables, which may be very large when the boundary is regarded as a free surface.<br />
<br />
The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI), a leading light of CFD algorithmic development and a pioneer of Optimal Control Aerodynamics. NTI is thus very proud to be the first in bringing such comprehensive technology to the market and to associate itself in its further development with Professor Jameson. Users that base their aircraft designs on JFLO will distinguish themselves by the speed of their design, its accuracy, and the drastic decrease in design team size needed.<br />
<br />
JFLO applications will be broadened rapidly to more aerospace and fluid flow application areas. JFLO is piloted by NTI advanced graphical environment, including the ability to visualize the evolving 3D geometry to be meshed automatically by JFLO. Variants of JFLO include analysis and optimal control capabilities using Euler or Navier-Stokes equations, with automatically generated structured or unstructured body-conforming grids.</div>Martin nti