The design and uncertainty analysis of an improved total hemispherical emittance transient calorimeter
Design and performance prediction of calorimetric apparatus for measuring total hemispherical emittance of materials
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Design and performance prediction of calorimetric apparatus for measuring total hemispherical emittance of materials
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A study is made of the variability in the nonlinear response of three stiffened composite panels associated with variations in their geometric and material parameters. The three panels have a cylindrical skin with either four or five T-shaped stiffeners. Two of the panels have a notch and the third panel has a circular cutout. Hierarchical sensitivity analysis is used to identify the major parameters at the micro-mechanical, layer, laminate and sub-component levels. The major parameters are then taken to be fuzzy parameters, and a fuzzy set analysis is used to determine the range of variation of the response associated with pre-selected variations in the major parameters.
A two-phase approach and a computational procedure are presented for predicting the variability in the nonlinear response of composite structures associated with variations in the geometric and material parameters of the structure. In the first phase, hierarchical sensitivity analysis is used to identify the major parameters, which have the most effect on the response quantities of interest. In the second phase, the major parameters are taken to be fuzzy parameters, and a fuzzy set analysis is used to determine the range of variation of the response, associated with preselected variations in the major parameters. The effectiveness of the procedure is demonstrated by means of a numerical example of a cylindrical panel with four T-shaped stiffeners and a circular cutout.
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Recent introduction of Coaxial Thermocouple type calorimeters into the NASA Ames arc jet facilities has inspired an analysis of 2D conduction effects internal to this type of calorimeter. The 1D finite slab inverse analysis (which is typically used to deduce the heat transfer to the calorimeter) relies on the assumption that lateral conduction (i.e., 2D effects) is negligible. Most calorimeter bodies have a spherical nose, which in itself is a violation of the 1D finite slab analysis assumption. Secondly most calorimeters experience a variation in heating across the face of the body which is also a violation of the 1D finite slab analysis assumption. It turns out that these two effects tend to cancel each other to some extent. This paper shows the extent to which error exists in the analysis of the Coaxial Thermocouple type calorimeters, and also offers analysis strategies for reducing the errors.
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This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.