Information Science

Automated Knowledge Discovery From Simulators

Active learning process efficiently explores simulator input space. A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the “landscape characterization problem.” In particular, SimLearn tries to determine which regions in “input space” lead to a given output from the simulator, where “input space” refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output.

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Protecting Against Faults in JPL Spacecraft

A paper discusses techniques for protecting against faults in spacecraft designed and operated by NASA’s Jet Propulsion Laboratory (JPL). The paper addresses, more specifically, faultprotection requirements and techniques common to most JPL spacecraft (in contradistinction to unique, mission specific techniques), standard practices in the implementation of these techniques, and fault-protection software architectures. Common requirements include those to protect onboard command, data-processing, and control computers; protect against loss of Earth/spacecraft radio communication; maintain safe temperatures; and recover from power overloads. The paper describes fault-protection techniques as part of a fault-management strategy that also includes functional redundancy, redundant hardware, and autonomous monitoring of (1) the operational and “health” statuses of spacecraft components, (2) temperatures inside and outside the spacecraft, and (3) allocation of power. The strategy also provides for preprogrammed automated responses to anomalous conditions. In addition, the software running in almost every JPL spacecraft incorporates a generalpurpose “Safe Mode” response algorithm that configures the spacecraft in a lower-power state that is safe and predictable, thereby facilitating diagnosis of more complex faults by a team of human experts on Earth.

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Enhanced Elliptic Grid Generation

Decay parameters that govern grids near boundaries are determined automatically. An enhanced method of elliptic grid generation has been invented. Whereas prior methods require user input of certain grid parameters, this method provides for these parameters to be determined automatically.

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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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