This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.

Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

Combustion of fat and vegetable oil derived fuels in diesel engines

The RNG κ-e turbulence model derived by Yakhot and Orszag (1986) based on the Renormalization Group theory has been modified and applied to variable-density engine flows in the present study. The original RNG-based turbulence transport approximations were developed formally for an incompressible flow. In order to account for flow compressibility the RNG e-equation is modified and closed through an isotropic rapid distortion analysis. Computations were made of engine compressing/expanding flows and the results were compared with available experimental observations in a production diesel engine geometry. The modified RNG κ-e model was also applied to diesel spray combustion computations. It is shown that the use of the RNG model is warranted for spray combustion modeling since the ratio of the turbulent to mean-strain time scales is appreciable due to spray-generated mean flow gradients, and the model introduces a term to account for these effects. Large scale flow structures are predicted which ar...

Turbulence Modeling of Internal Combustion Engines Using RNG κ-ε Models

A phenomenological description, or “conceptual model,” of how direct-injection (DI) diesel combustion occurs has been derived from laser-sheet imaging and other recent optical data. To provide background, the most relevant of the recent imaging data of the author and co-workers are presented and discussed, as are the relationships between the various imaging measurements. Where appropriate, other supporting data from the literature is also discussed. Then, this combined information is summarized in a series of idealized schematics that depict the combustion process for a typical, modern-diesel-engine condition. The schematics incorporate virtually all of the information provided by our recent imaging data including: liquidand vapor-fuel zones, fuel/air mixing, autoignition, reaction zones, and soot distributions. By combining all these elements, the schematics show the evolution of a reacting diesel fuel jet from the start of fuel injection up through the first part of the mixing-controlled burn (i.e. until the end of fuel injection). In addition, for a “developed” reacting diesel fuel jet during the mixingcontrolled burn, the schematics explain the sequence of events that occurs as fuel moves from the injector downstream through the mixing, combustion, and emissions-formation processes. The conceptual model depicted in these schematics also gives insight into the most likely mechanisms for soot formation and destruction and NO formation during the portion of the DI diesel combustion event discussed.

A Conceptual Model of DI Diesel Combustion Based on Laser-Sheet Imaging*

Progress and recent trends in homogeneous charge compression ignition (HCCI) engines

1 Engine Types and Their Operations 2 Engine Design and Operating Parameters 3 Thermochemistry of Fuel-Air Mixtures 4 Properties of Working Fluids 5 Ideal Models of Engine Cycles 6 Gas Exchange Processes 7 SI Engine Fuel Metering and Manifold Phenomena 8 Charge Motion within the Cylinder 9 Combustion in Ignition Engines 10 Combustion in Compression Ignition Engines 11 Pollutant Formation and Control 12 Engine Heat Transfer 13 Engine Friction and Lubrication 14 Modeling Real Engine Flow and Combustion Processes 15 Engine Operating Characteristics Appendixes

John B. Heywood

Papers

Internal combustion engine fundamentals

Benefits of Octane-On-Demand in an E10-Gasoline Engine Vehicle Using an On-Board Fuel Separator

Breakthrough Gas Cleaning Technology – Clean Up Engine