Suppression or reduction of soot emissions is an important goal in the development of automotive engines for environmental and human health purposes. A better understanding at the molecular level of the formation process of soot particles resulting from collision and aggregation of smaller particles made of Polycyclic Aromatic Hydrocarbon (PAH) is needed. In addition to experiments, computational methods are efficient and valuable tools for this purpose. As a first step in our detailed computational chemistry study, we applied Ultra-Accelerated Molecular Dynamics (UAQCMD) and Canonical Monte-Carlo (CMC) methods to investigate the nucleation process. The UA-QCMD can calculate chemical reaction dynamics 107 times faster than conventional first principle molecular dynamics methods, while CMC can calculate equilibrium properties at various temperatures, pressures, and chemical compositions.
This research paper builds onto the wealth of technical information that has been published in the past by engineers such as Flick, Radlinski, and Heusser. For this paper, the pushrod force versus chamber pressure data published by Heusser are supplemented with data taken from brake chamber types not reported on by Heusser in 1991. The utility of Heusser's braking force relationships is explored and discussed. Finally, a straightforward and robust method for calculating truck braking performance, based on the brake stroke measurements and published heavy truck braking test results, is introduced and compared to full-scale vehicle test data.
The APTS ability to detect abdominal loading in sled tests was also confirmed, with peak pressures typically below 1 bar when the belt loaded only the pelvis and the thorax (appropriate restraint) and values above that level when the abdomen was loaded directly (inappropriate restraint). Then, accidentreconstructions performed as part of CASPER and previous EC funded projects were reanalyzed.
A new method for processing data from time-accurate point measurements has been developed in order to investigate periodic elements of unsteady flow fields. The technique synchronizes the phase of measurements taken at different locations using a reference signal and collapses the spectral peak of interest onto a single frequency. The technique has been applied to data gathered using a time-accurate 5-hole probe behind a two dimensional body exhibiting vortex shedding. It has been possible to generate a sequence of instantaneous pressure and velocity fields which show the shedding of vorticity and total pressure loss to form a vortex street.
Many researchers have studied and developed methods for on-board engine combustion misfire detection in production vehicles. Misfiring can damage the catalytic converter within a short time and can lead to increased emission levels. For that reason, the on-board detection of engine misfire is one requirement of the On Board Diagnosis II (OBDII) Regulation and a recent interest for many researchers. One object in this paper is to propose a misfire detection method for multi-cylinder SI engines. The detection is achieved by examining the estimated cylinder pressures and combustion heat release rates in engine cylinders. The Sliding Observer methodology is applied in these estimations. This detection method provides a reliable and low-cost way to diagnose engine misfires. The other object of the paper is to eliminate large estimation errors due to system unobservability and reconstruct cylinder pressures.
Knocking combustions decrease the efficiency of an SI engine and can cause damages in combustion chambers. The in-cylinder pressure signal provides an insight into the combustions. Due to cost and installation reasons, vibration signals are used for knock analysis in conventional engines; however, vibration signal analysis is less effective than pressure signal analysis. The aim of this paper is to approximate the pressure signal, which is more suitable for knock investigation, from structure-borne sound. Therefore, new models for pressure and vibration signals, related to each other, are presented. These models consist of constructional specific signal components and combustion specific random parameters. The signal components need to be determined only once for an engine by using ordinary mathematical tools. A least-squares method, applied on vibration signals, leads to estimates of the random parameters.
The identification of the propulsion noise of turbofan engines plays an important role in the design of low-noise aircraft. The noise generation mechanisms of a typical turbofan engine are very complicated and it is not practical, if not impossible, to identify these noise sources efficiently and accurately using numerical or experimental techniques alone. In addition, a major practical concern for the measurement of acoustic pressure inside the duct of a turbofan is the placement of microphones and their supporting frames which will change the flow conditions under normal operational conditions. The measurement of acoustic pressures on the surface of the duct using surface-mounted microphones eliminates this undesirable effect. In this paper, a generalized acoustical holography (GAH) method that is capable of estimating aeroacoustic sources using surface sound pressure is developed.
There are growing demands for condition monitoring of IC engines, and therefore any method in order to improve the performance of the engines ought to be evaluated. This paper proposes a new approach for the prediction and optimisation of noise and exhaust emissions in IC engines. The idea is to reconstruct the cylinder pressure from vibration measurements on the engine surface by using the complex cepstrum method [3, 4]. The reconstructed cylinder pressure is further used as input in Multivariate models, based on cylinder pressure, for estimating noise and exhaust emissions. This paper demonstrates the applicability of the method for modelling of noise and exhaust emissions
This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.
In order to optimize the performance and emission of engines, advanced control and diagnostic systems require detailed feedback information about the combustion process. In this context, cost-effective solutions are of interest. The contribution describes a method for reconstructing cylinder-individual features of each combustion cycle by processing the instantaneous fluctuations of the engine speed and the in-cylinder pressure of one cylinder. Model-based torque estimation, analyzing both of the signals simultaneously, provides an accurate estimation of the mean indicated pressure. Using this method, a new algorithm for advanced misfire detection is presented. Furthermore, a new pressure model with a feasible number of parameters is proposed. It is combined with the torque estimation in order to reconstruct the unknown pressure traces of the cylinders not equipped with sensors.
Individual pressure fluctuations were simulated as a white noise using a random number generator with a Gaussian distribution of known standard deviation. MonteCarlo experiments were performed perturbing different steady total pressure patterns on the AIP with random signals of different RMS values.
In our paper we analyze the effect of input uncertainty on the accuracy of engine-out NOx estimates via a numerical MonteCarlo simulation and show that this effect can be significant. Even though our model is based on an in-cylinder pressure sensor, this sensor is limited in its capability to reduce the effect of other measured inputs on the model.
Propagating those uncertainties using a MonteCarlo simulation and Bayesian inference methods then allows for estimation of uncertainties of the mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity.
Under certain conditions, many can also record parameters that are useful in accidentreconstruction. Cummins engines are commonly found on highway tractors on the road today. ...Since this data reports second-by-second speed information, it can be very useful in the analysis of an accident. Establishing the reliability of this information is important, if it is to be used by an accident investigator. ...Establishing the reliability of this information is important, if it is to be used by an accident investigator. This paper explores the data recorded on a Cummins engine ECM created during a series of acceleration, cruising, and braking tests.
To address head protection and understand the head injury mechanisms, in-depth accident investigation and accidentreconstructions were conducted. A total of 6 passenger-cars to adult-pedestrian accidents were sampled from the in-depth accident investigation in Changsha China. ...A total of 6 passenger-cars to adult-pedestrian accidents were sampled from the in-depth accident investigation in Changsha China. Accidents were firstly reconstructed by using Multi-bodies (MBS) pedestrian and car models. ...The head impact conditions such as head impact velocity; position and orientation were calculated from MBS reconstructions, which were then employed to set the initial conditions in the simulation of a head model striking a windshield using Finite Element (FE) head and windshield models.
The initial droplet size distribution is obtained from a χ-squared distribution using a MonteCarlo sampling technique. The initial ambient air pressure and temperature are taken to be 45 bar and 800 K respectively.