Scientific and Technical Aerospace Reports Volume 38 October 6 ...

Contract(s)/Grant(s): DAAG55-98-1-0221
Report No.(s): AD-A379403; ARO-37661.6-EV; No Copyright; Avail: CASI; A01, Microfiche; A03, Hardcopy
Mathematical models for simulating contaminant fate and transport often include mass transfer, nonlinear reactions, multiple
species, and multiple dimensions. These problems can require significant computational effort. Split operator numerical methods
can be used to significantly reduce the computational demands of simulating some of these problems. We demonstrate a split operator
numerical method on a model for sorption and degradation processes.
DTIC
Mathematical Models; Numerical Analysis; Contaminants; Degradation; Sorption; Environment Effects
20000093956 Defence Science and Technology Organisation, Joint Systems Branch, Salisbury, Australia
Underlying Principles for Joint Systems Studies of a New Aerospace Combat Capability
Moon, Terry, Defence Science and Technology Organisation, Australia; OBrien, Michael, O’Brien (M. G.) and Associates Pty
Ltd., Australia; May 2000; 36p; In English
Report No.(s): DSTO-GD-0241; AR-011-449; Copyright; Avail: Issuing Activity
The purpose of this paper was to identify the underlying principles that may be used to guide joint systems studies of a new
aerospace combat capability for the ADF that is being considered under Project AIR 6000. From these principles broad systems
attributes are identified. Assessments of these attributes are considered important for subsequent investment decisions and development
of capabilities from a broad systems perspective. Methods for assessing the broad systems attributes identified will, however,
be the subject of subsequent work.
Author
Combat; Military Operations; Strategy
20000094298 Brown Univ., Providence, RI USA
Model Acquisition for Markov Decision Problems Final Report, 1 Dec. 1996 - 31 Jul. 1998
Dean, Thomas; Oct. 31, 1998; 6p; In English
Contract(s)/Grant(s): F49620-97-1-0031
Report No.(s): AD-A380049; AFRL-SR-BL-TR-00-0320; No Copyright; Avail: CASI; A01, Microfiche; A02, Hardcopy
The original focus of this work was on the automatic acquisition (learning) of stochastic models. The motivation was the lack
of such models for military problems, specifically air-campaign planning, and the existence of new algorithms that could, if the
appropriate models were available, considerably improve the accuracy and efficiency of military planning. This final report
describes the course of our investigations, some unanticipated turns, and the direction that our research has taken as a consequence
of what we have learned.
DTIC
Markov Processes; Mathematical Models; Systems Analysis
20000094387 Rice Univ., Dept. of Computational and Applied Mathematics, Houston, TX USA
Managing the Choice of Surrogate Variables and the Use of Approximation Models to Optimize Expensive Functions Final
Report, 1 Mar. 1995 - 28 Feb. 1998
Dennis, J. E.; Torczon, Virginia; May 1998; 28p; In English; Prepared in cooperation with College of William and Mary, Dept.
of Computer Science, Williamsburg, VA.
Contract(s)/Grant(s): F49620-95-1-0210
Report No.(s): AD-A380051; AFRL-SR-BL-TR-00-0318; No Copyright; Avail: CASI; A01, Microfiche; A03, Hardcopy
We propose research in three basic areas. First, it is standard in engineering practice to use approximation models of expensive
simulations to drive nonlinear programming algorithms. An open question, which we will investigate using well-established
notions from the literature on trust-region methods, is how to manage the interplay between optimization and the fidelity of the
approximation models to insure that the process converges to a reasonable solution of the original design problem. It is also standard
in engineering design to reduce the dimension of the optimization problem to be solved by using a technique known as variable
linking. We plan to generalize this concept using some recent ideas from the optimization community to design robust ways
to decompose a single iteration of any optimization method into a multi-phase process. First, an algorithm is applied to solve subproblems
posed on subspaces of lower dimension, and then to solve the full problem on the affine hull defined by the current iterate
and the solutions found for the subproblems. Crucial questions remain about the convergence of a practical implementation of
this process. Finally, we will continue our research to extend and analyze pattern search methods. Pattern search methods can be
successfully applied when only ranking (ordinal) information is available and when derivatives are either unavailable or unreli-
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