J-FLO
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| - | J-FLO | + | J-FLO is a tool for 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. |
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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. | 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. | ||
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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. | 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. | ||
| - | + | With 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. | |
| - | The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI) | + | The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI). |
| - | + | Variants of JFLO include analysis and optimal control capabilities using Euler or Navier-Stokes equations, with automatically generated structured or unstructured body-conforming grids. | |
Latest revision as of 15:00, 15 December 2006
J-FLO is a tool for 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.
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.
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.
With 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.
The JFLO aerodynamic design code is written by Professor Antony Jameson (Intelligent Aerodynamics Inc., IAI).
Variants of JFLO include analysis and optimal control capabilities using Euler or Navier-Stokes equations, with automatically generated structured or unstructured body-conforming grids.
