Compression Ignition Engine Technology Research Articles

Enhancement of plasma assisted ignition by multi-voltage pulse discharges: A theoretical study

Currently, compression ignition engine technology is becoming less competitive compared to spark ignition engine technology due to emission standards. Lean combustion strategies are preferable for spark ignition engines, but they require enhanced ignition strategies compared to current stoichiometric spark ignition engines. In this paper, we lay physical foundations for such enhanced ignition strategies that can support lean combustion strategies in spark ignition engines. In this theoretical study, we conduct a preliminary foray into the possibility of controlling the species composition of a cold-plasma kernel as a precursor for an ignition process. We consider a multi-pulse electric source system coupled to a DBD (Dielectric Barrier Discharge) electrode immersed in a dry air medium. The coupled system is designed such that the medium in the electrodes’ gap is subjected to a pulse structure which is composed of a peak voltage followed by a secondary low-voltage pulse. Use is made of a comprehensive mathematical model that includes the electric field, potential and current, with the relevant conservation equations for 24 species and 168 kinetic reactions, while considering different energy modes (rotational, vibrational, electronic excitation, dissociation, and ionization) within the electrodes’ gap. The model was validated against independent published results, and was used to understand the mechanism of energy conversion to species that play a central role in terms of the ultimate ignition dynamics. Various pulse repetition frequencies and different pulse constructions were considered. Special attention was given to the amount of deposited energy, and to the energy channeled to known ignition supportive modes and species. The present results show that the energy deposition can be divided into two main stages which are characterized by high and low voltage levels, respectively. We propose that for more successful ignition, the amount of energy deposition during the second (low voltage) stage should be higher than that during the first (high voltage) stage. During the second stage, the deposition of energy into specific modes can be fine-tuned by setting appropriate voltage levels. Based on these findings, we demonstrate how control of the sequence of voltage pulses can further increase enhancement of ignition and combustion promoting processes.

Approaches and solutions to HCCI - a review energy systems: fuel and combustion, injection and atomization

SI and CI engines are entities in which conditions of internal combustion was triumphantly implemented. In a gasoline engine both efficiency and emissions are less; on the other hand in a diesel engine both are relatively high. It is hard to achieve a win-win situation where fuel consumption and adverse impact on the environment were diminished. Over the past few decades researchers have assigned themselves to strike a balance in this regard, creating Homogeneous charge compression ignition engine technology, which perfectly amalgamates the principles of Spark Ignition and Compression Ignition engines, making it the holy grail of engines. Homogeneous charge compression ignition engines do not employ spark plug or fuel injector to initiate combustion, the lean homogeneous mixture auto-ignites in multitudinous spots once it is compressed to attain its chemical activation energy. Unlike its contemporaries the combustion is flameless, propelled the fuel efficiency along with an enormous decrease in emissions. This proposed work digs deep into conceptual description of the fundamentals of HCCI engine and various approaches to attain HCCI, along with the challenges encountered related to the same being elaborated. Depending on inferences arrived from the challenges different controlling strategies are proposed in this paper.

Autoignition of straight-run naphtha: A promising fuel for advanced compression ignition engines

Naphtha, a low-octane distillate fuel, has been proposed as a promising low-cost fuel for advanced compression ignition engine technologies. Experimental and modelling studies have been conducted in this work to assess autoignition characteristics of naphtha for use in advanced engines. Ignition delay times of a certified straight-run naphtha fuel, supplied by Haltermann Solutions, were measured in a shock tube and a rapid comparison machine over wide ranges of experimental conditions (20 and 60 bar, 620–1223 K, ϕ = 0.5, 1 and 2). The Haltermann straight-run naphtha (HSRN) has research octane number (RON) of 60 and motor octane number (MON) of 58.3, with carbon range spanning C3–C9. Reactivity of HSRN was compared, via experiments and simulations, with three suitably formulated surrogates: a two-component PRF (n-heptane/iso-octane) surrogate, a three-component TPRF (toluene/n-heptane/iso-octane) surrogate, and a six-component surrogate. All surrogates reasonably captured the ignition delays of HSRN at high and intermediate temperatures. However, at low temperatures (T < 750 K), the six-component surrogate performed the best in emulating the reactivity of naphtha fuel. Temperature sensitivity and rate of production analyses revealed that the presence of cyclo-alkanes in naphtha inhibits the overall fuel reactivity. Zero-dimensional engine simulations showed that PRF is a good autoignition surrogate for naphtha at high engine loads, however, the six-component surrogate is needed to match the combustion phasing of naphtha at low engine loads.

Compression Ignition Engines – Revolutionary Technology That has Civilized Frontiers all Over the Globe from the Industrial Revolution into the Twenty-First Century

The history, present and future of the compression ignition engine is a fascinating story that spans over 100 years, from the time of Rudolf Diesel to the highly regulated and computerized engines of the 21st Century. The development of these engines provided inexpensive, reliable and high power density machines to allow transportation, construction and farming to be more productive with less human effort than in any previous period of human history. The concept that fuels could be consumed efficiently and effectively with only the ignition of pressurized and heated air was a significant departure from the previous coal-burning architecture of the 1800s. Today, the compression ignition engine is undergoing yet another revolution. The equipment that provides transport, builds roads and infrastructure, and harvests the food we eat needs to meet more stringent requirements than ever before. How successfully 21st Century engineers are able to make compression ignition engine technology meet these demands will be of major influence in assisting developing nations (with over 50% of the world’s population) achieve the economic and environmental goals they seek.

Real-Time Self-Learning Optimization of Diesel Engine Calibration

Compression ignition engine technologies have been advanced in the past decade to provide superior fuel economy and high performance. These technologies offer increased opportunities for optimizing engine calibration. Current engine calibration methods rely on deriving static tabular relationships between a set of steady-state operating points and the corresponding values of the controllable variables. While the engine is running, these values are being interpolated for each engine operating point to coordinate optimal performance criteria, e.g., fuel economy, emissions, and acceleration. These methods, however, are not efficient in capturing transient engine operation designated by common driving habits, e.g., stop-and-go driving, rapid acceleration, and braking. An alternative approach was developed recently, which makes the engine an autonomous intelligent system, namely, one capable of learning its optimal calibration for both steady-state and transient operating points in real time. Through this approach, while the engine is running the vehicle, it progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria. The major challenge to this approach is problem dimensionality when more than one controllable variable is considered. In this paper, we address this problem by proposing a decentralized learning control scheme. The scheme is evaluated through simulation of a diesel engine model, which learns the values of injection timing and variable geometry turbocharging vane position that optimize fuel economy and pollutant emissions over a segment of the FTP-75 driving cycle.