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An engine exhaust manifold undergoes repeated thermal expansion and contraction due to temperature variation. Thermomechanical fatigue (TMF) arises due to the boundary constraints on thermal expansion so that mechanical strain is introduced. Therefore, TMF evaluation is very important in engine design. In this work, the mechanical properties important for TMF assessment and modeling of a silicon (Si)- and molybdenum (Mo)-containing ductile cast iron used for exhaust manifold have been evaluated. Tensile, creep, isothermal low cycle fatigue (LCF), and TMF tests have been conducted. Parameters for material modeling, such as the viscoplastic constitutive model and the Neu-Sehitoglu TMF damage model, have been calibrated, validated, and used to evaluate the TMF life of the exhaust manifold.

In the present paper optimization tools are used to recommend low-emission engine combustion chamber designs, spray targeting and swirl ratio levels for a heavy-duty diesel engine operated at high-load. The study identifies aspects of the combustion and pollution formation that are affected by mixing processes, and offers guidance for better matching of the piston geometry with the spray plume geometry for enhanced mixing. By coupling a GA (genetic algorithm) with the KIVA-CFD code, and also by utilizing an automated grid generation technique, multi-objective optimizations with goals of low emissions and fuel economy were achieved. Three different multi-objective genetic algorithms including a Micro-Genetic Algorithm (μGA), a Nondominated Sorting Genetic Algorithm II (NSGA II) and an Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA) were compared for conducting the optimization under the same conditions.

A multi-objective genetic algorithm coupled with the KIVA3V release 2 code was used to optimize the piston bowl geometry, spray targeting, and swirl ratio levels of a high speed direct injected (HSDI) diesel engine for passenger cars. Three modes, which represent full-, mid-, and low-loads, were optimized separately. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. High throughput computing was conducted using the CONDOR software. An automated grid generator was used for efficient mesh generation with variable geometry parameters, including open and reentrant bowl designs. A series of new spray models featuring reduced mesh dependency were also integrated into the code. A characteristic-time combustion (CTC) model was used for the initial optimization for time savings. Model validation was performed by comparison with experiments for the baseline engine at full-, mid-, and low-load operating conditions.

A computational study was performed to evaluate the effects of bowl geometry, fuel spray targeting and swirl ratio under highly diluted, low-temperature combustion conditions in a heavy-duty diesel engine. This study is used to examine aspects of low-temperature combustion that are affected by mixing processes and offers insight into the effect these processes have on emissions formation and oxidation. The foundation for this exploratory study stems from a large data set which was generated using a genetic algorithm optimization methodology. The main results suggest that an optimal combination of spray targeting, swirl ratio and bowl geometry exist to simultaneously minimize emissions formation and improve soot and CO oxidation rates. Spray targeting was found to have a significant impact on the emissions and fuel consumption performance, and was furthermore found to be the most influential design parameter explored in this study.

An experimental study was carried out to characterize the exhaust flow structure inside the closed-coupled catalytic converter, which is installed on a firing four-cylinder 12-valve passenger car gasoline engine. Simultaneous velocity and pressure measurements were taken using cycle-resolved Laser Doppler anemometer (LDA) technique and pressure transducer. A small fraction of titanium (IV) iso-propoxide was dissolved in gasoline to generate titanium dioxide during combustion as seeding particles for the LDA measurements. It was found that the velocity is highly fluctuating due to the pulsating nature of the engine exhaust flow, which strongly depends on the engine operating conditions and the measuring locations. The pressure oscillation is correlated with the transient exhaust flow characteristics. The main exhaust flow event from each cylinder can only be observed at the certain region in front of the monolith brick.

A multi-objective genetic algorithm methodology was applied to a heavy-duty diesel engine at three different operating conditions of interest. Separate optimizations were performed over various fuel injection nozzle parameters, piston bowl geometries and swirl ratios (SR). Different beginning of injection (BOI) timings were considered in all optimizations. The objective of the optimizations was to find the best possible fuel economy, NOx, and soot emissions tradeoffs. The input parameter ranges were determined using design of experiment methodology. A non-dominated sorting genetic algorithm II (NSGA II) was used for the optimization. For the optimization of piston bowl geometry, an automated grid generator was used for efficient mesh generation with variable geometry parameters. The KIVA3V release 2 code with improved ERC sub-models was used. The characteristic time combustion (CTC) model was employed to improve computational efficiency.

The present work proposes a methodology for diesel engine development using multi-dimensional CFD and computer optimization. A multi-objective genetic algorithm coupled with the KIVA3V Release 2 code was used to optimize a high speed direct injection (HSDI) diesel engine for passenger car applications. The simulations were conducted using high-throughput computing with the CONDOR system. An automated grid generator was used for efficient mesh generation with 11 variable piston bowl geometry parameters. The first step in the procedure was to search for an optimal nozzle and piston bowl design. In this case, spray targeting, swirl ratio, and piston bowl shape were optimized separately for two full-load cases using simpler efficient combustion models (the characteristic time scale model and the shell ignition model). The optimal designs from the two optimizations were then validated using a combustion model with detailed chemistry (KIVA-CHEMKIN model and ERC n-heptane mechanism).

The recently developed KIVA-GA computer code was used in the current study to optimize the combustion chamber geometry of a heavy -duty diesel truck engine and a high-speed direct-injection (HSDI) small-bore diesel engine. KIVA-GA performs engine simulations within the framework of a genetic algorithm (GA) global optimization code. Design fitness was determined using a modified version of the KIVA-3V code, which calculates the spray, combustion, and emissions formation processes. The measure of design fitness includes NOx, unburned HC, and soot emissions, as well as fuel consumption. The simultaneous minimization of these factors was the ultimate goal. The KIVA-GA methodology was used to optimize the engine performance using nine input variables simultaneously. Three chamber geometry related variables were used along with six other variables, which were thought to have significant interaction with the chamber geometry.

A study of the combined use of split injections, EGR, and flexible boosting was conducted. Statistical optimization of the engine operating parameters was accomplished using a new response surface method. The objective of the study was to demonstrate the emissions and fuel consumption capabilities of a state-of-the-art heavy -duty diesel engine when using split injections, EGR, and flexible boosting over a wide range of engine operating conditions. Previous studies have indicated that multiple injections with EGR can provide substantial simultaneous reductions in emissions of particulate and NOx from heavy-duty diesel engines, but careful optimization of the operating parameters is necessary in order to receive the full benefit of these combustion control techniques. Similarly, boost has been shown to be an important parameter to optimize. During the experiments, an instrumented single-cylinder heavy -duty diesel engine was used.

A micro-genetic algorithm (μGA) optimization method was applied to a heavy-duty, direct-injected diesel engine via an automated test bed system. The goal of this application was to demonstrate the feasibility and advantages of automated optimization experiments. With the genetic algorithm, no user input was required other than the factors of interest and their allowable ranges. This means that once the routine was initiated, it was essentially run undisturbed until a preset objective level was reached or a preset number of generations had been run. The automated μGA was successfully demonstrated at all points of the six-mode transient cycle simulation, excluding idle. To accomplish the automated experiments, an automated testing system was developed around a Caterpillar single-cylinder diesel engine.

An optimization study combining multidimensional CFD modeling and a global, evolutionary search technique known as the Genetic Algorithm has been carried out. The subject of this study was a 2-stroke, spark-ignited, direct-injection, single-cylinder research engine (SCRE). The goal of the study was to optimize the part load operating parameters of the engine in order to achieve the lowest possible emissions, improved fuel economy, and reduced wall heat transfer. Parameters subject to permutation in this study were the start-of-injection (SOI) timing, injection duration, spark timing, fuel injection angle, dwell between injections, and the percentage of fuel mass in the first injection pulse. The study was comprised of three cases. All simulations were for a part load, intermediate-speed condition representing a transition operating regime between stratified charge and homogeneous charge operation.

A modification of the computational fluid dynamics code KIVA-II is presented that allows computations to be made in complex engine geometries. An example application is given in which three versions of KIVA-II are run simultaneously. Each version considers a separate block of the computational domain, and the blocks exchange boundary condition information with each other at their common interfaces. The use of separate blocks permits the connectedness of the overall computational domain to change with time. The scavenging flow in the cylinder, transfer pipes (ports), and exhaust pipe of a ported two-stroke engine with a moving piston was modeled in this way. Results are presented for three engine designs that differ only in the angle of their boost ports. The calculated flow fields and the resulting fuel distributions are shown to be markedly different with the different geometries.

The present work proposes a methodology for diesel engine development using scaling laws and computational optimization with multi-dimensional CFD tools. A previously optimized 450cc HSDI diesel engine was down-scaled to 400cc size using recently developed scaling laws. The scaling laws were validated by comparing the performance of these two engines, including pressure, HRR, peak and averaged temperature, and pollutant emissions. A novel optimization methodology, which is able to simultaneously optimize multiple operating conditions, was proposed. The method is based on multi-objective genetic algorithms, and was coupled with the KIVA3V Release 2 code to further optimize the down-scaled diesel engine. An adaptive multi-grid chemistry model was used in the KIVA3V code to reduce the computational cost of the optimization. The computations were conducted using high-throughput computing with the CONDOR system.

RNG k-ε closure turbulence dissipation equations are evaluated employing the CFD code KIVA-3V Release 2. The numerical evaluations start by considering simple jet flows, including incompressible air jets and compressible helium jets. The results show that the RNG closure turbulence model predicts lower jet tip penetration than the "standard" k-ε model, as well as being lower than experimental data. The reason is found to be that the turbulence kinetic energy is dissipated too slowly in the downstream region near the jet nozzle exit. In this case, the over-predicted R term in RNG model becomes a sink of dissipation in the ε-equation. As a second step, the RNG turbulence closure dissipation models are further tested in complex engine flows to compare against the measured evolution of turbulence kinetic energy, and an estimate of its dissipation rate, during both the compression and expansion processes.

A new search technique, called Non-Gradient Step-Controlled algorithm (NGSC), is presented. The NGSC was applied independently from pre-selected starting points and as a supplement to a Genetic Algorithm (GA) to optimize a HSDI diesel engine using split injection strategies. It is shown that the NGSC handles well the challenges of a complex response surface and factor high-dimensionality, which demonstrates its capability as an efficient and accurate tool to seek “local” convergence on complex surfaces. By directly tracking the change of a merit function, the NGSC places no requirement on response surface continuity / differentiability, and hence is more robust than gradient-dependent search techniques. The directional search mechanism takes factor interactions into consideration, and search step size control is adopted to facilitate search efficiency.

Optimizations were performed on a single-cylinder heavy-duty Caterpillar SCOTE 3401E engine for NOx, PM and BSFC reductions. The engine was equipped with a Caterpillar 300B HEUI fuel injection system capable of up to four injections with timings from 90 BTDC to 90 ATDC. The engine was operated at a medium load (57%), high speed (1737 rev/min) operation point. A micro-genetic algorithm was utilized to optimize a hybrid, double-injection strategy, which incorporated an early, premixed pilot injection with a late main injection. The fuel injection parameters, intake boost pressure, and EGR were considered in the optimization. The optimization produced a parameter set that met the 2007 and 2010 PM emissions mandate of 0.0134 g/kW-hr, and was within the 1.5x not to exceed NOx + HC mandate of 2.694 g/kW-hr. Following the optimization exercise, further parametric interaction studies were performed to reveal the underlying interactions and phenomena.

Current automotive diesel engine research is motivated by the need to meet more-and-more strict emission regulations. The major target for future HSDI combustion research and development is to find the most effective ways of reducing the soot particulate and NOx emissions to the levels required by future emission regulations. Recently, a variety of statistical optimization tools have been proposed to optimize engine-operating conditions for emissions reduction. In this study, a micro-genetic algorithm technique, which locates a global optimum via the law of “the survival of the fittest”, was applied to a high-speed, direct-injection, single-cylinder (HSDI) diesel engine. The engine operating condition considered single-injection operation using a common-rail fuel injection system was at 1757 rev/min and 45% load.

Particle Image Velocimetry (PIV) was used to make gas velocity and turbulence measurements in a motored diesel engine. The experiments were conducted using a single-cylinder version of the Caterpillar 3406 production engine. One of the exhaust valves and the fuel injector port were used to provide optical access to the combustion chamber so that modifications to the engine geometry were minimal, and the results are representative of the actual engine. Measurements of gas velocity were made in a plane in the piston bowl using TiO2 seed particles. The light sheet necessary for PIV was formed by passing the beam from a Nd:YAG laser through the injector port and reflecting the beam off a conical mirror at the center of the piston. PIV data was difficult to obtain due to significant out-of-plane velocities. However, data was acquired at 25° and 15° before top dead center of compression at 750 rev/min.

Progress on the development and validation of a CFD model for diesel engine combustion and flow is described. A modified version of the KIVA code is used for the computations, with improved submodels for liquid breakup, drop distortion and drag, spray/wall impingement with rebounding, sliding and breaking-up drops, wall heat transfer with unsteadiness and compressibility, multistep kinetics ignition and laminar-turbulent characteristic time combustion models, Zeldovich NOx formation, and soot formation with Nagle Strickland-Constable oxidation. The code also considers piston-cylinder-liner crevice flows and allows computations of the intake flow process in the realistic engine geometry with two moving intake valves. Significant progress has been made using a modified RNG k-ε turbulence model, and a multicomponent fuel vaporization model and a flamelet combustion model have been implemented.

An experimental study was performed, using cycle-resolved laser Doppler velocimetry (LDV) technique, to characterize the exhaust flow structure inside a catalytic converter retro-fitted to a firing four-cylinder gasoline engine over different operating conditions. A small fraction of titanium (IV) isopropoxide was dissolved in gasoline to generate titanium dioxide during combustion as seeding particles for LDV measurements. It was found that in the front plane of the catalytic monolith, the velocity is highly fluctuating due to the pulsating nature of the engine exhaust flow, which strongly depends on the engine operating conditions. Under unloaded condition, four pairs of major peaks are clearly observed in the time history of the velocity, which correspond to the main exhaust events of each individual cylinder.