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Journal ArticleDOI

Three-Way Catalytic Converter Modeling as a Modern Engineering Design Tool

TLDR
In this paper, the extents and limitations of applicability of state-of-the-art mathematical models of catalytic converter performance are discussed, and extensive comparisons between measured and computed instantaneous emissions over full cycles are presented.
Abstract
The competition to deliver ultra low emitting vehicles at a reasonable cost is driving the automotive industry to invest significant manpower and test lab resources in the design optimization of increasingly complex exhaust aftertreatment systems. Optimization can no longer be based on traditional approaches, which are intensive in hardware use and lab testing. This paper discusses the extents and limitations of applicability of state-of-the-art mathematical models of catalytic converter performance. In-house software from the authors' lab, already in use during the last decade in design optimization studies, updated with recent, important model improvements, is employed as a reference in this discussion. Emphasis is on the engineering methodology of the computational tools and their application, which covers quality assurance of input data, advanced parameter estimation procedures, and a suggested performance measure that drives the parameter estimation code to optimum results and also allows a less subjective assessment of model prediction accuracy. Extensive comparisons between measured and computed instantaneous emissions overfull cycles are presented, aiming to give a good picture of the capabilities of state of the art engineering models of automotive catalytic converter systems.

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Journal ArticleDOI

One-dimensional automotive catalyst modeling

TL;DR: In this article, the bulk gas equations of automotive catalysts are reformulated based on the governing equations of inviscid flow, while retaining full transient capability, which can be used to calibrate the simpler models by determining the actual residence time within the catalyst.
Journal ArticleDOI

Detailed surface reaction mechanism for Pt-catalyzed abatement of automotive exhaust gases

TL;DR: In this article, a Pt-catalyzed conversion of automotive exhaust gases is presented based on a newly developed surface reaction mechanism consisting of 73 elementary-step like reactions among 22 surface and 11 gas-phase species.
Journal ArticleDOI

Mathematical modeling of monolith catalysts and reactors for gas phase reactions

TL;DR: In this article, a general monolith this article model for gas phase reactions is proposed and discussed for various modeling applications, with the focus on mass and heat transfer in monoliths, transient/dynamic modeling, gas flow uniformity and chemical kinetics effects on monolith reactor performance.
Journal ArticleDOI

Recent advances in numerical modeling of catalytic monolith reactors

TL;DR: In this paper, the development and improvement of numerical tools for the simulation of catalytic monolith reactors is discussed, and three numerical studies are shown: a catalytic combustion device with alternating active and inactive channels, a catalytically combustion monolith with spatially varying inlet conditions, and a NO x storage catalyst with alternating inlet condition.
Journal ArticleDOI

Kinetic Parameters Estimation for Three Way Catalyst Modeling

TL;DR: The emphasis in this work is restricted to predicting the light-off (cold-start emissions) in TWC, which is calibrated using 4 vehicle data sets (which use the FTP drive cycle) using iSIGHT software package.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book

Genetic Algorithms

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Linear and nonlinear programming

TL;DR: Strodiot and Zentralblatt as discussed by the authors introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.
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Nonlinear Regression Analysis and Its Applications

TL;DR: This book offers a balanced presentation of the theoretical, practical, and computational aspects of nonlinear regression and provides background material on linear regression, including the geometrical development for linear and nonlinear least squares.
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Genetic Algorithms and Grouping Problems

TL;DR: This book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Falkenauer's new class of algorithms.
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