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Generative Representations for Automated Design of Robots

Compact representations circumvent the computational obstacle to complexity. A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term “generative representations” as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity.

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Temperature-Corrected Model of Turbulence in Hot Jet Flows

A standard turbulence model is corrected for total-temperature gradient and compressibility. An improved correction has been developed to increase the accuracy with which certain formulations of computational fluid dynamics predict mixing in shear layers of hot jet flows. The CFD formulations in question are those derived from the Reynolds-averaged Navier-Stokes equations closed by means of a two-equation model of turbulence, known as the k−ε model, wherein effects of turbulence are summarized by means of an eddy viscosity. The need for a correction arises because it is well known among specialists in CFD that two-equation turbulence models, which were developed and calibrated for room-temperature, low Mach-number, plane-mixing-layer flows, underpredict mixing in shear layers of hot jet flows. The present correction represents an attempt to account for increased mixing that takes place in jet flows characterized by high gradients of total temperature. This correction also incorporates a commonly accepted, previously developed correction for the effect of compressibility on mixing.

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Hypothetical Scenario Generator for Fault-Tolerant Diagnosis

This is a means of performing diagnostic reasoning when data are missing. The Hypothetical Scenario Generator for Fault-tolerant Diagnostics (HSG) is an algorithm being developed in conjunction with other components of artificial- intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. By incorporating prognostic capabilities along with advanced diagnostic capabilities, these developments hold promise to increase the safety and affordability of the affected engineering systems by making it possible to obtain timely and accurate information on the statuses of the systems and predicting impending failures well in advance.

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Algorithm Would Enable Robots to Solve Problems Creatively

A control architecture is based on hypotheses concerning natural intelligence. A control architecture and algorithms to implement the architecture have been conceived to enable a robot to learn from its experiences and to combine knowledge gained from prior experiences in such a way as to be able to solve new problems. The architecture is an abstraction of an interacting system of relatively simple components that, when properly interconnected, should enable the spontaneous emergence of behaviors from the complete system that would not necessarily be expected from the individual components. These emergent behaviors should enable a robot to interact robustly and intelligently with a complex, dynamic environment.

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A Robustly Stabilizing Model Predictive Control Algorithm

The algorithm can be applied to industrial and automotive systems. A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a recedinghorizon implementation. Given a feasible solution to the finite-horizon optimal control problem at an initial time, resolvability implies the ability to solve the optimal control problem at subsequent times.

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Modeling Evaporation of Drops of Different Kerosenes

One model applies to all three classes of hydrocarbon constituents. A mathematical model describes the evaporation of drops of a hydrocarbon liquid composed of as many as hundreds of chemical species. The model is intended especially for application to any of several types of kerosenes commonly used as fuels. Like evaporating- multicomponent- fuel-drop models described in several previous NASA Tech Briefs articles, the present model invokes the concept of continuous thermodynamics, according to which the chemical composition of the evaporating multicomponent liquid is described by use of a probability distribution function (PDF). However, as described below, the present model is more generally applicable than is its immediate predecessor.

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Bayesian-Augmented Identification of Stars in a Narrow View

An adaptive threshold guides acceptance or rejection of a tentative identification. An algorithm for the identification of stars from a charge-coupled-device (CCD) image of a star field has been extended for use with narrower field-of-view images. Previously, the algorithm had been shown to be effective at a field of view of 8°. This work augments the earlier algorithm using Bayesian decision theory. The new algorithm is shown to be capable of effective star identification down to a field of view of 2°. The algorithm was developed for use in estimating the attitude of a spacecraft and could be used on Earth to help in the identification of stars and other celestial objects for astronomical observations.

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