Expert system application education project

Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems

Goal-based h-adaptivity of the 1-D diamond difference discrete ordinate method.

The quantity of interest (QoI) associated with a solution of a partial differential equation (PDE) is not, in general, the solution itself, but a functional of the solution. Dual weighted residual (DWR) error estimators are one way of providing an estimate of the error in the QoI resulting from the discretisation of the PDE. This paper aims to provide an estimate of the error in the QoI due to the spatial discretisation, where the discretisation scheme being used is the diamond difference (DD) method in space and discrete ordinate (SNSN) method in angle. The QoI are reaction rates in detectors and the value of the eigenvalue (Keff)(Keff) for 1-D fixed source and eigenvalue (KeffKeff criticality) neutron transport problems respectively. Local values of the DWR over individual cells are used as error indicators for goal-based mesh refinement, which aims to give an optimal mesh for a given QoI

An optimum organizational structure for a large earth-orbiting multidisciplinary space base

An optimum hypothetical organizational structure was studied for a large earth-orbiting, multidisciplinary research and applications space base manned by a crew of technologists. Because such a facility does not presently exist, in situ empirical testing was not possible. Study activity was, therefore, concerned with the identification of a desired organizational structural model rather than with the empirical testing of the model. The essential finding of this research was that a four-level project type total matrix model will optimize the efficiency and effectiveness of space base technologists

Adaptive mesh refinement techniques for diffusion-synthetic-accelerated discrete-ordinates neutral particle transport

An Adaptive Mesh Refinement (AMR) technique is presented for the one-group and the multigroup SN transport equations discretized using a Discontinuous Galerkin (DG) method. A diffusion synthetic accelerator, also based on a DG discretization and directly obtained from the discretized transport equations, is given. Numerical results are provided for 2D unstructured triangular meshes

Historical data and analysis for the first five years of KSC STS payload processing

General and specific quantitative and qualitative results were identified from a study of actual operational experience while processing 186 science, applications, and commercial payloads for the first 5 years of Space Transportation System (STS) operations at the National Aeronautics and Space Administration's (NASA) John F. Kennedy Space Center (KSC). All non-Department of Defense payloads from STS-2 through STS-33 were part of the study. Historical data and cumulative program experiences from key personnel were used extensively. Emphasis was placed on various program planning and events that affected KSC processing, payload experiences and improvements, payload hardware condition after arrival, services to customers, and the impact of STS operations and delays. From these initial considerations, operational drivers were identified, data for selected processing parameters collected and analyzed, processing criteria and options determined, and STS payload results and conclusions reached. The study showed a significant reduction in time and effort needed by STS customers and KSC to process a wide variety of payload configurations. Also of significance is the fact that even the simplest payloads required more processing resources than were initially assumed. The success to date of payload integration, testing, and mission operations, however, indicates the soundness of the approach taken and the methods used

Expert System Prototype Developments For Nasa-KSC Business And Engineering Applications

Artificial Intelligence (AI) technology, and in particular expert systems — a subset of AI which shows the strongest applicability to a wide variety of environments, has recently emerged from the realm of basic research into that of real-world applications. To further these advances, NASA-Kennedy Space Center (KSC) provided funding and other critical resources to the University of Central Florida (UCF) in support of instruction of expert systems technology. During the Fall 1987 semester, UCF\u27s Colleges of Business and Engineering concurrently offered courses in response to the increased interest in expert system applications and to satisfy the intent of this grant. This paper describes the prototype expert systems which evolved from this sponsorship and the development methods used