Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

The authors discuss the parallelization of an implicit solver for the 2d Euler equations on a structured grid. The spatial distribution
involves the MUSCL scheme, a Total Variation Diminishing scheme. It is shown that an implicit solver that is based on quasi-
Newton iteration and approximate factorization to solve the linear system of equations resulting from the Euler Backward scheme,
has favorable properties for both multigrid acceleration and parallelization as compared to explicit Runge-Kutta time stepping.
to preserve data locality, the authors apply domain decomposition to obtain a parallelizable code. Although the domain decomposition
does affect the efficiency of the approximately factorization method, the results show that this hardly affects the convergence
rate as obtained with a single block code. the accuracy with which the linear system of equations is solved is found to be
an important parameter. The combination of parallel execution and implicit time integration provides an interesting perspective
for time dependent problems in computational fluid dynamics.
NTIS
Algorithms; Inviscid Flow; Computational Fluid Dynamics
20000064608 NASA Ames Research Center, Moffett Field, CA USA
NASA’s Aero-Space Technology
Milstead, Phil, NASA Ames Research Center, USA; February 2000; In English; See also 20000064579; No Copyright; Abstract
Only; Available from CASI only as part of the entire parent document
This presentation reviews the three pillars and the associated goals of NASA’s Aero-Space Technology Enterprise. The three
pillars for success are: (1) Global Civil Aviation, (2) Revolutionary Technology Leaps, (3) Advanced Space Transportation. The
associated goals of the first pillar are to reduce accidents, emissions, and cost, and to increase the aviation system capacity. The
goals of the second pillar are to reduce transoceanic travel time, revolutionize general aviation aircraft, and improve development
capacity. The goals associated with the third pillar are to reduce the launch cost for low earth orbit and to reduce travel time for
planetary missions. In order to meet these goals NASA must provide next-generation design capability for new and or experimental
craft which enable a balance between reducing components of the design cycle by up to 50% and or increasing the confidence
in design by 50%. These next-generation design tools, concepts, and processes will revolutionize vehicle development. The presentation
finally reviews the importance of modeling and simulation in achieving the goals.
CASI
Simulation; Models; NASA Programs; Space Programs; Technology Utilization
20000064627 Boeing Co., Phantom Works, Long Beach, CA USA
Viscous Design Optimization Using ADJIFOR - An HPCCP Perspective
Sundaram, P., Boeing Co., USA; Agrawal, Shreekant, Boeing Co., USA; Hager, James O., Boeing Co., USA; Carle, Alan, Rice
Univ., USA; Fagan, Mike, Rice Univ., USA; February 2000; In English; See also 20000064579; No Copyright; Abstract Only;
Available from CASI only as part of the entire parent document
The accurate computation of objective function sensitivity to design variable (DV) perturbations is the most crucial and
expensive element of gradient-based optimization. In a nonlinear aerodynamic optimization problem, where the objective functions
are computed based on Euler/Navier-Stokes codes, the computation of sensitivities becomes a computational challenge even
for today’s large parallel systems. The situation gets even worse in constrained aerodynamic shape optimizations where the number
of DVs tend to be rather large. The finite-difference method of calculating gradients for these problems is ruled out for two
reasons: prohibitive cost and approximation error. Adjoint methods are essential for calculating the gradients. Primary among the
adjoint methods is the method of deriving the adjoints by posing the original continuous form of the problem as a calculus of variations
problem. This method requires long and tedious analytical derivations and hand-differentiation of the underlying partial differential
equations. Furthermore, turbulence models present in Navier-Stokes equation solvers complicate the construction of
adjoint codes. Consequently, few commercial Navier-Stokes adjoint codes are available, and those that are available cannot be
easily adapted for use in the desired design environment. Automatic differentiation (AD) using ADIFOR has been known for
sometime to be an accurate method of calculating analytical sensitivities of a FORTRAN function code. The forward-mode ADI-
FOR and adjoint-mode ADJIFOR tools (both components of the soon to be released ADIFOR 3.0 System) automatically enhance
function codes with code to compute the required derivatives. In the past year, sensitivity calculations performed by ADIFOR
and ADJIFOR-differentiated codes have shown great promise. In last year’s HPCCP/CAS workshop, we presented initial results
using an ADJIFOR-differentiated version of the CFL3D/Euler code on a shape design optimization problem. The present paper
provides further details on the use of the derivative-enhanced CFL3D code as the basis of an automated design environment. In
addition, the paper presents a successful HSCT configuration design discovered using this environment on the NAS Origin 2000
parallel system. This success on the CFL3D/Euler code has led to the application of ADJIFOR to compute flow sensitivities for
the CFL3D/Navier-Stokes code. The paper compares gradient accuracy and time requirements for computing Navier-Stokes flow
sensitivities using both ADIFOR and ADJIFOR-differentiated codes and describes additional steps taken to improve the effi-
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