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Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control.

Decision trees have been proposed as a basis for modifying table-based injection to reduce transient particulate spikes during the turbocharger lag period. It has been shown that decision trees can detect particulate spikes in real time. In well-calibrated electronically controlled diesel engines these spikes are narrow and are encompassed by a wider NO spike. Decision trees have been shown to pinpoint the exact location of measured opacity spikes in real time thus enabling targeted PM reduction with near zero NO penalty. A calibrated dimensional model has been used to demonstrate the possible reduction of particulate matter with targeted injection pressure pulses. Post injection strategy optimized for near stoichiometric combustion has been shown to provide additional benefits. Empirical models have been used to calculate emission tradeoffs over the entire FTP cycle.

A first law based regression model for estimating mean value engine torque on-board a diesel engine is presented. The model uses first law terms across the engine control volume in a regression built from least squares to predict engine torque. Torque information is often required by the engine ECM for torque based control and torque broadcast purposes. In the absence of real-time torque measurement torque estimation is usually achieved through look-up tables or empirical models. Given the increase in engine operating parameters as well as engine operating regimes as a result of emission control and exhaust aftertreatment technologies, accurate torque estimation has become more challenging as well as necessary.

A general combustion model, in the context of large eddy simulations, was developed to simulate the full range of combustion in conventional diesel-type and HCCI-type diesels. The combustion model consisted of a Chemkin sub-model and an Extended Flamelet Time Scale (EFTS) sub-model. Specifically, Chemkin was used to simulate auto-ignition process. In the post-ignition phase, the combustion model was switched to EFTS. In the EFTS sub-model, combustion was assumed to be a combination of two elementary combustion modes: homogeneous combustion and flamelet combustion. The combustion index acted as a weighting factor blending the contributions from these two modes. The Chemkin sub-model neglected the subgrid scale turbulence-chemistry interactions whereas the EFTS model took them into account through a presumed PDF approach. The model was used to simulate an early injection mode of a Cummins DI diesel engine and a mode of a Caterpillar DI diesel engine.

Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

A modeling study has been conducted in order to characterize the heat transfer in an automotive diesel exhaust system. The exhaust system model, focusing on 2 exhaust pipes, has been created using a transient 1-D engine flow network simulation program. Model results are in excellent agreement with experimental data gathered before commencement of the modeling study. Predicted pipe exit stream temperatures are generally within one percent of experimental values. Sensitivity analysis of the model was the major focus of this study. Four separate variables were chosen for the sensitivity analysis. These being the external convective heat transfer coefficient, external emissivity, mass flow rate of exhaust gases, and amplitude of incoming pressure fluctuations. These variables were independently studied to determine their contribution to changes in exhaust gas stream temperature and system heat flux. There are two primary benefits obtained from conducting this analysis.

The present study investigated HCCI combustion in a heavy-duty diesel engine both experimentally and numerically. The engine was equipped with a hollow-cone pressure-swirl injector using gasoline direct injection. Characteristics of HCCI combustion were obtained by very early injection with a heated intake charge. Experimental results showed an increase in NOx emission and a decrease in UHC as the injection timing was retarded. It was also found that optimization can be achieved by controlling the intake temperature together with the start-of-injection timing. The experiments were modeled by using an engine CFD code with detailed chemistry. The CHEMKIN code was implemented into KIVA-3V such that the chemistry and flow solutions were coupled. The model predicted ignition timing, cylinder pressure, and heat release rates reasonably well. The NOx emissions were found to increase as the injection timing was retarded, in agreement with experimental results.

Artificial neural networks (ANN) have been recognized as universal approximators for nonlinear continuous functions and actively applied in engine research in recent years [1, 2, 3, 4, 5, 6, 7 and 8]. This paper describes the methodology and results of using the ANN to model a turbocharged DI diesel engine. The engine was simulated using the CFD code (KIVA-ERC) over a wide range of operating conditions, and numerical simulation results were used to train the ANN. An efficient data collection methodology using the Design of Experiments (DOE) techniques was developed to select the most characteristic engine operating conditions and hence the most informative data to train the ANN. This approach minimizes the time and cost of collecting training data from either computational or experimental resources. The trained ANN was then used to predict engine parameters such as cylinder pressure, cylinder temperature, NOx and soot emissions, and cylinder heat transfer.

DPF regenerations involve a trade-off between fuel economy and DPF durability. High temperature regenerations of DPFs have fewer fuel penalties but simultaneously tend to give higher substrate temperatures, which can reduce thermal reliability. In order to weaken the trade-off, the integrated system-level model [1,2,3,4] is used to conduct optimization studies and explore novel regeneration strategies for DPF aftertreatment devices. The integrated model developed in the Engine Research Center (ERC) includes sub-models for engines, emissions, aftertreatment devices and controllers. Based on the engine and regeneration fuel economy, multiple and single cycle regeneration tests are performed and analyzed. The optimal soot loadings to initiate and terminate regenerations are discussed. A pulsed regeneration strategy, which is characterized by injecting multiple pulses of fuel (upstream of a DOC) during regenerations, is investigated.

Large Eddy Simulation (LES) with a flamelet time scale combustion model is used to simulate diesel combustion. The flamelet time scale model uses a steady-state flamelet library for n-heptane indexed by mean mixture fraction, mixture fraction variance, and mean scalar dissipation rate. In the combustion model, reactions proceed towards the flamelet library solution at a time scale associated with the slowest reaction. This combination of a flamelet solution and a chemical time scale helps to account for unsteady mixing effects. The turbulent sub-grid stresses are simulated using a one-equation, non-viscosity LES model called the dynamic structure model. The model uses a tensor coefficient determined by the dynamic procedure and the subgrid kinetic energy. The model has been expanded to include scalar mixing and scalar dissipation. A new model for the conditional scalar dissipation has been developed to better predict local extinction.

To help account for fuel distribution during combustion in diesel engines, a fuel film model has been developed and implemented into the KIVA-II code [1]. Spray-wall interaction and spray-film interaction are also incorporated into the model. Modified wall functions for evaporating, wavy films are developed and tested. The model simulates thin fuel film flow on solid surfaces of arbitrary configuration. This is achieved by solving the continuity, momentum and energy equations for the two dimensional film that flows over a three dimensional surface. The major physical effects considered in the model include mass and momentum contributions to the film due to spray drop impingement, splashing effects, various shear forces, piston acceleration, dynamic pressure effects, and convective heat and mass transfer.

The three-dimensional CFD codes KIVA-II and KIVA-3 have been used together to study the effects of intake generated in-cylinder flow structure on fuel-air mixing and combustion in a direct injected (DI) Diesel engine. In order to more accurately account for the effect of intake flow on in-cylinder processes, the KIVA-II code has been modified to allow for the use of data from other CFD codes as initial conditions. Simulation of the intake and compression strokes in a heavy-duty four-stroke DI Diesel engine has been carried out using KIVA-3. Flow quantities and thermodynamic field information were then mapped into a computational grid in KIVA-II for use in the study of mixing and combustion. A laminar and turbulent timescale combustion model, as well as advanced spray models, including wave breakup atomization, dynamic drop drag, and spray-wall interaction has been used in KIVA-II.

A turbulent combustion model based on the coherent flamelet model was developed in this study and applied to diesel engines. The combustion was modeled in three distinct but overlapping phases: low temperature ignition kinetics using the Shell ignition model, high temperature premixed burn using a single step Arrhenius equation, and the flamelet based diffusion burn. Two criteria for transitions based on temperature, heat release rate, and the local Damköhler number were developed for the progression of combustion between each of these phases. The model was implemented into the computational computer code KIVA-II. Previous experiments on a Caterpillar model E 300, # 1Y0540 engine, a Tacom LABECO research engine, and a single cylinder version of a Cummins N14 production engine were used to validate the cylinder averaged predictions of the model.

Previous studies have shown the importance of the in-cylinder flow field which exists prior to fuel injection on performance and emissions behavior of direct injected (DI) diesel engines. Key parameters in the flow field are the turbulence level and the resolved structures, such as swirl and tumble flow. These characteristics are known to have significant effects on the fuel vaporization, droplet break-up, and fuel-air mixing. The relative importance of these effects is investigated through simulation of injection into a stirred, heated, constant volume combustion bomb, using the computational fluid dynamics codes KIVA-3 [9] and KIVA-II [10]. Initial conditions for these simulations are based on in-cylinder conditions which exist in a heavy duty DI diesel engine immediately prior to fuel injection.

A fuel film model has been developed and implemented into the KIVA-II code to help account for fuel distribution during combustion in diesel engines. Spray-wall interaction and spray-film interaction are also incorporated into the model. The model simulates thin fuel film flow on solid surfaces of arbitrary configuration. This is achieved by solving the continuity and momentum equations for the two dimensional film that flows over a three dimensional surface. The major physical effects considered in the model include mass and momentum contributions to the film due to spray drop impingement, splashing effects, various shear forces, piston acceleration, and dynamic pressure effects. In order to adequately represent the drop interaction process, impingement regimes and post-impingement behavior have been modeled using experimental data and mass, momentum and energy conservation constraints. The regimes modeled for spray-film interaction are stick, rebound, spread, and splash.

Multidimensional computations were carried out to simulate the in-cylinder fuel/air mixing process of a direct-injection spark-ignition engine using a modified version of the KIVA-3 code. A hollow cone spray was modeled using a Lagrangian stochastic approach with an empirical initial atomization treatment which is based on experimental data. Improved Spalding-type evaporation and drag models were used to calculate drop vaporization and drop dynamic drag. Spray/wall impingement hydrodynamics was accounted for by using a phenomenological model. Intake flows were computed using a simple approach in which a prescribed velocity profile is specified at the two intake valve openings. This allowed three intake flow patterns, namely, swirl, tumble and non-tumble, to be considered. It was shown that fuel vaporization was completed at the end of compression stroke with early injection timing under the chosen engine operating conditions.

The flow through diesel fuel injector nozzles is important because of the effects on the spray and the atomization process. Modeling this nozzle flow is complicated by the presence of cavitation inside the nozzles. This investigation uses a two-dimensional, two-phase, transient model of cavitating nozzle flow to observe the individual effects of several nozzle parameters. The injection pressure is varied, as well as several geometric parameters. Results are presented for a range of rounded inlets, from r/D of 1/40 to 1/4. Similarly, results for a range of L/D from 2 to 8 are presented. Finally, the angle of the corner is varied from 50° to 150°. An axisymmetric injector tip is also simulated in order to observe the effects of upstream geometry on the nozzle flow. The injector tip calculations show that the upstream geometry has a small influence on the nozzle flow. The results demonstrate the model's ability to predict cavitating nozzle flow in several different geometries.

A hybrid calibration model combining dimensional and empirical modeling has been used to model transient smoke and demonstrate transient smoke reduction strategies during the turbocharger lag period of an electronically controlled heavy-duty diesel engine. This new hybrid approach termed the Non-Parametric Reduced Dimensionality approach (NPRD) uses GT-Power to transform the engine operating parameter model input space to a more fundamental, lower dimensional and less correlated model input space. A non-parametric nearest neighbor approach is then applied over this transformed model input space to make new predictions. The NPRD approach was used to predict transient FTP emissions of cumulative particulate matter (PM) within 7% of measured value, based solely on steady state training data.