SSME parameter modeling with neural networks

Atam Dhawan; Kevin R. Wheeler; Timothy F. Doniere

doi:10.4271/941221

SSME parameter modeling with neural networks

Atam Dhawan, Kevin R. Wheeler, Timothy F. Doniere

Research output: Contribution to conference › Paper › peer-review

Abstract

The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

Original language	English (US)
DOIs	https://doi.org/10.4271/941221
State	Published - Dec 1 1994
Externally published	Yes
Event	Aerospace Atlantic Conference and Exposition - Dayton, OH, United States Duration: Apr 18 1994 → Apr 22 1994

Other

Other	Aerospace Atlantic Conference and Exposition
Country/Territory	United States
City	Dayton, OH
Period	4/18/94 → 4/22/94

All Science Journal Classification (ASJC) codes

Automotive Engineering
Safety, Risk, Reliability and Quality
Pollution
Industrial and Manufacturing Engineering

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10.4271/941221

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title = "SSME parameter modeling with neural networks",

abstract = "The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.",

author = "Atam Dhawan and Wheeler, {Kevin R.} and Doniere, {Timothy F.}",

year = "1994",

month = dec,

day = "1",

doi = "10.4271/941221",

language = "English (US)",

note = "Aerospace Atlantic Conference and Exposition ; Conference date: 18-04-1994 Through 22-04-1994",

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TY - CONF

T1 - SSME parameter modeling with neural networks

AU - Dhawan, Atam

AU - Wheeler, Kevin R.

AU - Doniere, Timothy F.

PY - 1994/12/1

Y1 - 1994/12/1

N2 - The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

AB - The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.

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U2 - 10.4271/941221

DO - 10.4271/941221

M3 - Paper

T2 - Aerospace Atlantic Conference and Exposition

Y2 - 18 April 1994 through 22 April 1994

ER -

SSME parameter modeling with neural networks

Abstract

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All Science Journal Classification (ASJC) codes

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