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Modeling and identification of the c...

Connolly, Francis T.

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Modeling and identification of the combustion pressure process in internal combustion engines.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Modeling and identification of the combustion pressure process in internal combustion engines./
Author:	Connolly, Francis T.
Description:	216 p.
Notes:	Co-Chairs: Andrew E. Yagle; Giorgio Rizzoni.
Contained By:	Dissertation Abstracts International53-10B.
Subject:	Engineering, Automotive. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9303721

Modeling and identification of the combustion pressure process in internal combustion engines.
Connolly, Francis T.

Modeling and identification of the combustion pressure process in internal combustion engines. - 216 p.

Co-Chairs: Andrew E. Yagle; Giorgio Rizzoni.

Thesis (Ph.D.)--University of Michigan, 1992.

This research bridges the gap between physical spark-ignited engine modeling and advanced signal processing to solve the problem of reconstructing combustion pressure from noisy observations of angular velocity at steady-state. Cyclic variability in pressure is estimated from fluctuations in velocity; they are recognized as a fundamental limitation of engine operation, especially as mixtures are leaned to optimize fuel economy and emission control.Subjects--Topical Terms:

1018477
Engineering, Automotive.

Modeling and identification of the combustion pressure process in internal combustion engines.
LDR:03295nam 2200313 a 45 001 929692
005 20110427
008 110427s1992 eng d
035 $a (UnM)AAI9303721
035 $a AAI9303721
040 $a UnM $c UnM
100 1 $a Connolly, Francis T. $3 1253179
245 1 0 $a Modeling and identification of the combustion pressure process in internal combustion engines.
300 $a 216 p.
500 $a Co-Chairs: Andrew E. Yagle; Giorgio Rizzoni.
500 $a Source: Dissertation Abstracts International, Volume: 53-10, Section: B, page: 5356.
502 $a Thesis (Ph.D.)--University of Michigan, 1992.
520 $a This research bridges the gap between physical spark-ignited engine modeling and advanced signal processing to solve the problem of reconstructing combustion pressure from noisy observations of angular velocity at steady-state. Cyclic variability in pressure is estimated from fluctuations in velocity; they are recognized as a fundamental limitation of engine operation, especially as mixtures are leaned to optimize fuel economy and emission control.
520 $a This thesis makes several contributions in order to solve this inverse problem. First, a new model for cyclic pressure variability is proposed, using a Bernoulli-Gaussian random sequence indexed by combustion number. Second, this model amplitude-modulates a template of pressure deviation from mean pressure to form a model for continuous pressure, parametrized by the sample modulating sequence. Third, this pressure model is used as the input to a linear, crankshaft-angle-varying engine model which relates combustion pressures to the square of angular velocity, defined in the crankshaft-angle-domain--i.e., crankshaft angle is the independent variable. This model is cascaded with a square root operator to solve the forward problem, computing angular velocity from pressures. It is mathematically equivalent to nonlinear models in the time-domain.
520 $a Fourth, the pressure-to-velocity model is used to develop a recursion relating samples of the square of angular velocity in every combustion to the Bernoulli-Gaussian sequence. A Kalman-filter stochastic deconvolution algorithm is applied to this recursive model with noisy observations of the square of angular velocity to solve the inverse problem. The simplicity of the problem solution makes it implementable on-line.
520 $a Computer simulations of algorithms for solving the forward and inverse problems were developed. These simulations show that simulated angular velocity (the forward problem) shows behavior much like real data and that the inverse problem may be solved fairly well at low and moderate noise levels. Experimental verification of these algorithms is made using data acquired from a production six-cylinder engine. The results show that the inverse problem solution is able to detect total misfires and poor combustions, indicating that it is able to classify the severity of misfires as well.
590 $a School code: 0127.
650 4 $a Engineering, Automotive. $3 1018477
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0540
690 $a 0544
710 2 0 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 53-10B.
790 $a 0127
790 1 0 $a Rizzoni, Giorgio, $e advisor
790 1 0 $a Yagle, Andrew E., $e advisor
791 $a Ph.D.
792 $a 1992
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9303721