SAMICS: Input data preparation

The Solar Array Manufacturing Industry Costing Standards (SAMICS) provide standard formats, data, assumptions, and procedures for estimating the price that a manufacturer would have to charge for the product of a specified manufacturing process sequence. A line-by-line explanation is given of those standard formats which describe the economically important characteristics of the manufacturing processes and the technological structure of the companies and the industry. This revision provides an updated presentation of Format A Process Description, consistent with the October 1978 version of that form. A checklist of items which should be entered on Format A as direct expenses is included

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings

Finite Element Modeling of Ultrasonic Inspection of Weldments

High performance weldments for critical service applications require 100% inspection. Balanced against the adaptability of the ultrasonic method for automated inspection are the difficulties encountered with nonhomogeneous and anisotropic materials. This research utilizes crystals and bicrystals of nickel to model austenitic weld metal, where the anisotropy produces scattering and mode conversion, making detection and measurement of actual defects difficult. Well characterized samples of Ni are produced in a levitation zone melting facility. Crystals in excess of 25 mm diameter and length are large enough to permit ultrasonic measurements of attenuation, wave speed, and spectral content. At the same time, the experiments are duplicated as finite element models for comparison purposes. Finite element models permit easy description of boundary conditions, geometry, and loading. Direct integration of the wave equation is done with the Newmark-Beta and Wilson-Theta Methods. The usual problem with the large number of degress of freedom can be alleviated with the use of Guyan reduction. Two-dimensional comparisons showing mode conversion and a plate with a flaw are made. The continued development of this computational tool should increase understanding of quantitative ultrasonic inspection

Risk of cancer following primary total hip replacement or primary resurfacing arthroplasty of the hip : A retrospective cohort study in Scotland

Acknowledgements: We are grateful to Lee Barnsdale, Doug Clark, and Richard Dobbie for advice and assistance with data preparation before analysis, and to the three anonymous referees for their helpful comments and suggestions.Peer reviewedPublisher PD

Automatic data generation scheme for finite-element method /FEDGE/ - Computer program

Algorithm provides for automatic input data preparation for the analysis of continuous domains in the fields of structural analysis, heat transfer, and fluid mechanics. The computer program utilizes the natural coordinate systems concept and the finite element method for data generation

Master control data handling program uses automatic data input

General purpose digital computer program is applicable for use with analysis programs that require basic data and calculated parameters as input. It is designed to automate input data preparation for flight control computer programs, but it is general enough to permit application in other areas

Geometrically nonlinear static and dynamic analysis of arbitrarily loaded shells of revolution

Computer program on axisymmetric response of shells with other meridional geometries and response of shells subjected to asymmetric loads is described. Description of theory, method of solution, instructions for preparing input data, and two sample problems to illustrate data preparation and output format are included

BiSON data preparation: A correction for differential extinction and the weighted averaging of contemporaneous data

The Birmingham Solar Oscillations Network (BiSON) has provided high-quality high-cadence observations from as far back in time as 1978. These data must be calibrated from the raw observations into radial velocity and the quality of the calibration has a large impact on the signal-to-noise ratio of the final time series. The aim of this work is to maximise the potential science that can be performed with the BiSON data set by optimising the calibration procedure. To achieve better levels of signal-to-noise ratio we perform two key steps in the calibration process: we attempt a correction for terrestrial atmospheric differential extinction; and the resulting improvement in the calibration allows us to perform weighted averaging of contemporaneous data from different BiSON stations. The improvements listed produce significant improvement in the signal-to-noise ratio of the BiSON frequency-power spectrum across all frequency ranges. The reduction of noise in the power spectrum will allow future work to provide greater constraint on changes in the oscillation spectrum with solar activity. In addition, the analysis of the low-frequency region suggests we have achieved a noise level that may allow us to improve estimates of the upper limit of g-mode amplitudes.Comment: Accepted for publication in MNRAS; 10 pages, 7 figure