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Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

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Abstract

The use of probabilistic (PNN) and multilayer feedforward (MLFNN) neural networks is investigated for the calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of network are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this paper.

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Authors and Affiliations

  1. Raytheon STX/MSFC/NASA, Huntsman, AL, USA, USA

    Subbiah Baskaran

  2. Universities Space Research Association, NASA, Huntsville, AL, USA, USA

    Narayanan Ramachandran

  3. National Aeronautics and Space Administration, George C. Marshall Space Flight Center, Huntsville, AL, USA, USA

    David Noever

Authors
  1. Subbiah Baskaran
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  2. Narayanan Ramachandran
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  3. David Noever
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Additional information

Received: 1 October 1998¶Received in revised form: 12 December 1998¶Accepted: 16 December 1998

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Baskaran, S., Ramachandran, N. & Noever, D. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition. Pattern Analysis & Applications 2, 92–98 (1999). https://doi.org/10.1007/s100440050018

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  • DOI: https://doi.org/10.1007/s100440050018

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