Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
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Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described. The network is designed for two-way communications with unmanned spacecraft traveling approximately 16,000 km (10,000 miles) from earth to the farthest planets and to the edge of our solar system.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep Space Station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network are summarized; deep space station, ground communication, and network operations control capabilities are described.
Renzetti, N. A.↗
Network functions and facilities
The objectives, functions, and organization of the Deep Space Network (DSN) are summarized: tracking complexes, ground communications, and network operations control capabilities are described.
Amorose, R. J.↗
A methodology for commonality analysis, with applications to selected space station systems
The application of commonality in a system represents an attempt to reduce costs by reducing the number of unique components. A formal method for conducting commonality analysis has not been established. In this dissertation, commonality analysis is characterized as a partitioning problem. The cost impacts of commonality are quantified in an objective function, and the solution is that partition which minimizes this objective function. Clustering techniques are used to approximate a solution, and sufficient conditions are developed which can be used to verify the optimality of the solution. This method for commonality analysis is general in scope. It may be applied to the various types of commonality analysis required in the conceptual, preliminary, and detail design phases of the system development cycle.
Thomas, Lawrence Dale↗
Constrained optimization for image restoration using nonlinear programming
The constrained optimization problem for image restoration, utilizing incomplete information and partial constraints, is formulated using nonlinear proramming techniques. This method restores a distorted image by optimizing a chosen object function subject to available constraints. The penalty function method of nonlinear programming is used. Both linear or nonlinear object function, and linear or nonlinear constraint functions can be incorporated in the formulation. This formulation provides a generalized approach to solve constrained optimization problems for image restoration. Experiments using this scheme have been performed. The results are compared with those obtained from other restoration methods and the comparative study is presented.
Yeh, C.-L.↗
Numerical Optimization Using Computer Experiments
Engineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented.
Trosset, Michael W.↗
PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization
We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.
97 MATHEMATICS AND COMPUTING↗
Viscous aerodynamic design using the adjoint variable approach
The use of classical optimal control methods, in particular variational methods, to solve the airfoil optimization problem, by deriving a set of adjoint (costate) equations and boundary conditions has already been done for inviscid (potential and Euler flows) and two dimensional, steady state, incompressible flow governed by the Navier-Stokes equations. The interior and boundary terms of the volume integral have been derived (in this work) for the steady Navier-Stokes equations in three dimensions for a viscous, compressible heat conducting fluid. This can be used to derive the adjoint equations and numerical boundary conditions for general classes of problems and hence paves the way for a solution to the aerodynamic optimization problem for compressible viscous flows. The next steps to the realization of that goal are projected as below. The usual square integral pressure functional as an objective function is being replaced by a more realistic drag functional subject to a lift constraint. The feasibility of attempting the more difficult time dependent problem is being investigated. It remains to get the full system of adjoint equations and boundary conditions with the new functional. The state and adjoint equations must be discretized and coded. An appropriate optimization program must be used (steepest descents seems inadequate) and various known airfoil shapes should be recovered in test cases of the computer program.
DeRise, George↗