Charles E. A. Finney

A REVIEW OF SYMBOLIC ANALYSIS OF EXPERIMENTAL DATA

This review covers the group of data-analysis techniques collectively referred to as symbolization or symbolic time-series analysis. Symbolization involves transformation of raw time-seriesmeasurements (i.e., experimental signals) into a series of discretized symbols that are processed to extract information about the generating process. In many cases, the degree of discretization can be quite severe, even to the point of converting the original data to single-bit values. Current approaches for constructing symbols and detecting the information they contain are summarized. Novel approaches for characterizing and recognizing temporal patterns can be important for many types of experimental systems, but this is especially true for processes that are nonlinear and possibly chaotic. Recent experience indicates that symbolization can increase the efficiency of finding and quantifying information from such systems, reduce sensitivity to measurement noise, and discriminate both specific and general classes of proposed models. Examples of the successful application of symbolization to experimental data are included. Key theoretical issues and limitations of the method are also discussed.

A simple model for cyclic variations in a spark-ignition engine

We propose a simple model that explains important characteristics of cyclic combustion variations in spark-ignited engines. A key model feature is the interaction between stochastic, small-scale fluctuations in engine parameters and nonlinear deterministic coupling between successive engine cycles. Prior-cycle effects are produced by residual cylinder gas which alters mean in-cylinder equivalence ratio and subsequent combustion efficiency. The model`s simplicity allows rapid simulation of thousands of engine cycles, permitting in-depth statistical studies. Additional mechanisms for stochastic and prior-cycle effects can be added to evaluate their impact on overall engine performance. We find good agreement with experimental data.

Symbolic Time-Series Analysis of Engine Combustion Measurements

We present techniques of symbolic time-series analysis which are useful for analyzing temporal patterns in dynamic measurements of engine combustion variables. We focus primarily on techniques that characterize predictability and the occurrence of repeating temporal patterns. These methods can be applied to standard, cycle-resolved engine combustion measurements, such as IMEP and heat release. The techniques are especially useful in cases with high levels of measurement and/or dynamic noise. We illustrate their application to experimental data from a production V8 engine and a laboratory single-cylinder engine.

Time Irreversibility and Comparison of Cyclic-Variability Models

We describe a method for detecting and quantifying time irreversibility in experimental engine data. We apply this method to experimental heat-release measurements from four sparkignited engines under leaning fueling conditions. We demonstrate that the observed behavior is inconsistent with a linear Gaussian random process and is more appropriately described as a noisy nonlinear dynamical process.

Spatio-temporal dynamics in a train of rising bubbles

Abstract It has been suggested that rising bubbles in dense fluids resemble an inverted dripping faucet and that they undergo analogues period-doubling bifurcations to chaos. We present experimental results that demonstrate that this analogy is weak because the dominant source of instability in the bubble train is inherently different — mutual interactions between spatially separated bubbles as opposed to nozzle dynamics. Unlike the dripping faucet, the initial instability in a bubble train develops at a location far from the injection nozzle and progresses toward the nozzle with increasing gas flow. From qualitative and rigorous quantitative observations, we conclude that rising-bubble dynamics are best described as ‘small-box spatio-temporal chaos’ with a flow instability. Such dynamics can superficially appear to be simple temporal chaos when considering spatially localized measurements. We show similarity between our experimental results and a bubble-interaction model that accounts for drag and coalescence effects without considering any nozzle dynamics.

Invited Review: A review of deterministic effects in cyclic variability of internal combustion engines

We review developments in the understanding of cycle–to–cycle variability in internal combustion engines, with a focus on spark-ignited and premixed combustion conditions. Much of the research on cyclic variability has focused on stochastic aspects, that is, features that can be modeled as inherently random with no short–term predictability. In some cases, models of this type appear to work very well at describing experimental observations, but the lack of predictability limits control options. Also, even when the statistical properties of the stochastic variations are known, it can be very difficult to discern their underlying physical causes and thus mitigate them. Some recent studies have demonstrated that under some conditions, cyclic combustion variations can have a relatively high degree of low–dimensional deterministic structure, which implies some degree of predictability and potential for real–time control. These deterministic effects are typically more pronounced near critical stability limits (e.g. near tipping points associated with ignition or flame propagation) such during highly dilute fueling or near the onset of homogeneous charge compression ignition. We review recent progress in experimental and analytical characterization of cyclic variability where low–dimensional, deterministic effects have been observed. We describe some theories about the sources of these dynamical features and discuss prospects for interactive control and improved engine designs. Taken as a whole, the research summarized here implies that the deterministic component of cyclic variability will become a pivotal issue (and potential opportunity) as engine manufacturers strive to meet aggressive emissions and fuel economy regulations in the coming decades.

Detailed Chemical Kinetic Modeling of Iso-octane SI-HCCI Transition

We describe a CHEMKIN-based multi-zone model that simulates the expected combustion variations in a single-cylinder engine fueled with iso-octane as the engine transitions from spark-ignited (SI) combustion to homogenous charge compression ignition (HCCI) combustion. The model includes a 63-species reaction mechanism and mass and energy balances for the cylinder and the exhaust flow. For this study we assumed that the SI-to-HCCI transition is implemented by means of increasing the internal exhaust gas recirculation (EGR) at constant engine speed. This transition scenario is consistent with that implemented in previously reported experimental measurements on an experimental engine equipped with variable valve actuation. We find that the model captures many of the important experimental trends, including stable SI combustion at low EGR (-0.10), a transition to highly unstable combustion at intermediate EGR, and finally stable HCCI combustion at very high EGR (-0.75). Remaining differences between the predicted and experimental instability patterns indicate that there is further room for model improvement.

Synchronization of Combustion Variations in a Multicylinder Spark Ignition Engine

Abstract : We report experimental observations of synchronization among combustion variations in different cylinders at fuel-lean conditions in an eight-cylinder spark ignition engine. Our results appear to confirm that synchronization readily occurs and that it becomes stronger as the overall equivalence ratio is reduced from stoichiometric. It also appears that the onset of synchronization is associated with bifurcation instabilities reported previously for combustion in single cylinders. We use both cross-correlation and symbolic time series analysis to quantify the apparent relationships between pairs of cylinders and multicylinder groups. Extension of a simple dynamic model for single-cylinder combustion variations to the multicylinder case appears to agree with the observations and provides a basis for further studies. The occurrence of significant cylinder-to-cylinder synchronization may have significant implications for engine diagnostics and control.

Neutron Imaging of Diesel Particulate Filters

This article presents nondestructive neutron computed tomography (nCT) measurements of Diesel Particulate Filters (DPFs) as a method to measure ash and soot loading in the filters. Uncatalyzed and unwashcoated 200cpsi cordierite DPFs exposed to 100% biodiesel (B100) exhaust and conventional ultra low sulfur 2007 certification diesel (ULSD) exhaust at one speed-load point (1500rpm, 2.6bar BMEP) are compared to a brand new (never exposed) filter. Precise structural information about the substrate as well as an attempt to quantify soot and ash loading in the channel of the DPF illustrates the potential strength of the neutron imaging technique.

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.