NASA's Digital Transformation (DT) Initiative

NASA's Digital Transformation (DT) Initiative will dramatically enhance NASA's mission impact by reinventing mission and mission support processes, products, and capabilities, enabled by an innovation culture, digital-savvy workforce, and advanced digital technologies, building on a foundation of modern data management and IT security. This presentation, as part of a conference panel, provides an overview of NASA's DT Initiative, and describes how the Initiative supports an "antidisciplinary" future, where the greatest mission transformation opportunities will be found at the intersections and mergers of multiple disciplines

An expert system for choosing the best combination of options in a general-purpose program for automated design synthesis

An expert system was developed to aid a user of the Automated Design Synthesis (ADS) general-purpose optimization computer program in selecting the best combination of strategy, optimizer, and one-dimensional search options for solving a problem. There are approximately 100 such combinations available in ADS. The knowledge base contains over 200 rules, and is divided into three categories: constrained problems, unconstrained problems, and constrained problems treated as unconstrained problems. The inference engine is written in LISP and is available on DEC-VAX and IBM PC/XT computers

Development and application of optimum sensitivity analysis of structures

The research focused on developing an algorithm applying optimum sensitivity analysis for multilevel optimization. The research efforts have been devoted to assisting NASA Langley's Interdisciplinary Research Office (IRO) in the development of a mature methodology for a multilevel approach to the design of complex (large and multidisciplinary) engineering systems. An effort was undertaken to identify promising multilevel optimization algorithms. In the current reporting period, the computer program generating baseline single level solutions was completed and tested out

Sensitivity of optimum solutions to problem parameters

Derivation of the sensitivity equations that yield the sensitivity derivatives directly, which avoids the costly and inaccurate perturb-and-reoptimize approach, is discussed and solvability of the equations is examined. The equations apply to optimum solutions obtained by direct search methods as well as those generated by procedures of the sequential unconstrained minimization technique class. Applications are discussed for the use of the sensitivity derivatives in extrapolation of the optimal objective function and design variable values for incremented parameters, optimization with multiple objectives, and decomposition of large optimization problems

Structural optimization of an alternate design for the space shuttle solid rocket booster field joint

A structural optimization procedure is used to determine the shape of an alternate design for the shuttle solid rocket booster field joint. In contrast to the tang and clevis design of the existing joint, this alternate design consists of two flanges bolted together. Configurations with 150 studs of 1 1/8 in. diameter and 135 studs of 1 3/16 in. diameter are considered. Using a nonlinear programming procedure, the joint weight is minimized under constraints on either von Mises or maximum normal stresses, joint opening and geometry. The procedure solves the design problem by replacing it by a sequence of approximate (convex) subproblems; the pattern of contact between the joint halves is determined every few cycles by a nonliner displacement analysis. The minimum weight design has 135 studs of 1 3/16 in. diameter and is designed under constraints on normal stresses. It weighs 1144 lb per joint more than the current tang and clevis design

Development of a Multilevel Optimization Approach to the Design of Modern Engineering Systems

A general algorithm is proposed which carries out the design process iteratively, starting at the top of the hierarchy and proceeding downward. Each subproblem is optimized separately for fixed controls from higher level subproblems. An optimum sensitivity analysis is then performed which determines the sensitivity of the subproblem design to changes in higher level subproblem controls. The resulting sensitivity derivatives are used to construct constraints which force the controlling subproblems into chosing their own designs so as to improve the lower levels subproblem designs while satisfying their own constraints. The applicability of the proposed algorithm is demonstrated by devising a four-level hierarchy to perform the simultaneous aerodynamic and structural design of a high-performance sailplane wing for maximum cross-country speed. Finally, the concepts discussed are applied to the two-level minimum weight structural design of the sailplane wing. The numerical experiments show that discontinuities in the sensitivity derivatives may delay convergence, but that the algorithm is robust enough to overcome these discontinuities and produce low-weight feasible designs, regardless of whether the optimization is started from the feasible space or the infeasible one

Two-level relationships and Scale-Free Networks

Through the distinction between ``real'' and ``virtual'' links between the nodes of a graph, we develop a set of simple rules leading to scale-free networks with a tunable degree distribution exponent. Albeit sharing some similarities with preferential attachment, our procedure is both faster than a na\"ive implementation of the Barab\'asi and Albert model and exhibits different clustering properties. The model is thoroughly studied numerically and suggests that reducing the set of partners a node can connect to is important in seizing the diversity of scale-free structures