John Agudelo

Optimization of Raman Spectroscopy Parameters for Characterizing Soot from Different Diesel Fuels

Raman analysis of carbonaceous materials requires selecting a number of parameters, some of which alter the sample and affect the resulting spectra. Much literature has discussed about the most appropriate values of these parameters for analyzing graphite, coals, or more disordered materials, but no published studies have proposed appropriate values for comparing soot derived from diesel engines fueled with different fuels. This comparison is essential for the design of the regenerative diesel particulate traps that all new automotive diesel engines must incorporate. The effect of three Raman parameters was studied over the same spot to eliminate dispersions associated with the heterogeneity of the sample: laser wavelength, intensity of the incident laser, and exposure time. Among the tested laser wavelengths, 488 nm was preferred for comparing soot samples because it maximized the difference between their spectra due to better signal-to-noise ratio. The selection of the incident laser power is limited by altering/burning the focused area of the sample; this limit depends on the laser wavelength and the type of fuel used. Varying the exposure time was useful to show faster burning for the biodiesel soot sample than for the diesel soot one. The obtained results qualitatively indicate higher reactivity for biodiesel soot, enabling faster and less energy-consuming particulate trap regeneration.

Impact of rail pressure and biodiesel fueling on the particulate morphology and soot nanostructures from a common-rail turbocharged direct injection diesel engine

An investigation of the impact of rail pressure and biodiesel fueling on exhaust particulate agglomerate morphology and primary particle (soot) nanostructure was conducted with a common-rail turbocharged direct injection diesel engine. The engine was operated at steady state on a dynamometer running at moderate speed with both low (30%) and medium–high (60%) fixed loads, and exhaust particulate was sampled for analysis. The fuels used were ultra-low sulfur diesel and its 20% v/v blends with soybean methyl ester biodiesel. Fuel injection occurred in a single event around top dead center at three different injection pressures. Exhaust particulate samples were characterized with transmission electronic microscopy imaging, scanning mobility particle sizing, thermogravimetric analysis, Raman spectroscopy, and X-ray diffraction analysis. Particulate morphology and oxidative reactivity were found to vary significantly with both rail pressure and biodiesel blend level. Higher biodiesel content led to an increase in the primary particle size and oxidative reactivity but had no impact on nanoscale disorder in the as-received samples. For particulates generated with higher injection pressures, the initial oxidative reactivity increased, but there was no detectable correlation with primary particle size or nanoscale disorder.

Chemical Characteristics of the Soot Produced in a High-Speed Direct Injection Engine Operated with Diesel/Biodiesel Blends

The aim of this work is to study the influence of the molecular structure of fatty acid esters present in biodiesel and their blends with diesel on the chemical characteristics of the emitted particulate matter. Biodiesel produced from palm oil, jatropha, castor oil and sachainchi was blended at 5% and 20% by volume with diesel. These fuels were used for the operation of a four-cylinder direct injection automotive diesel engine. The equivalent ratio was kept between 0.36 and 0.40, and the engine was operated at the point of minimum air–fuel ratio and maximum smoke opacity. The amount of particulate matter emitted was evaluated by opacity index. A marked reduction in soot formation was observed when the concentration of unsaturated methyl esters in the fuel was at low concentrations. This was accompanied by a reduction of the aliphatic carbon content in the particulate matter.

Estudio del Efecto de la Altitud sobre el Comportamiento de Motores de Combustión Interna. Parte 2: Motores Diesel

En este trabajo se analiza el efecto de la altitud sobre los parametros caracteristicos de la combustion y sobre la formacion de oxidos de nitrogeno (NOx) en motores diesel. Se estudiaron motores de aspiracion natural y motores turboalimentados con diferentes grados de turboalimentacion. Al incrementar la altitud se modifica la composicion del aire atmosferico y disminuye su densidad debido a la disminucion de la presion barometrica. Esto afecta la relacion masica estequiometrica entre aire y combustible, por lo que el proceso de mezclado se modifica. Se encontro que las variaciones observadas sobre el desarrollo de la combustion en los motores turboalimentados son casi imperceptibles. Tambien se muestra que hay una reduccion de las emisiones de NOx con la altitud, debida principalmente a la disminucion de la temperatura adiabatica de combustion.

Estudio del Efecto de la Altitud sobre el Comportamiento de Motores de Combustión Interna. Parte 1: Funcionamiento

En este trabajo se estudia el efecto de la altitud sobre la potencia en motores de aspiracion natural y turbosobrealimentados sin sistemas correctores, en funcion de la presion ambiental. La altitud sobre el nivel del mar tiene un notable efecto sobre la densidad del aire y su composicion. Dado que los motores de combustion interna tienen sistemas de admision y de inyeccion de combustible volumetricos, la altitud modifica el ciclo termodinamico de operacion, y en consecuencia las prestaciones, asi como las condiciones locales de combustion, y por tanto la formacion de contaminantes. Se ha obtenido una expresion que permite calcular el incremento de relacion de compresion del turbogrupo, necesario para evitar cualquier perdida de potencia al aumentar la altitud.

LQR control for speed and torque of internal combustion engines

Abstract This paper presents a robust automation model for internal combustion engines test beds. A Linear Quadratic Regulator (LQR) allows setting the desired engine speed and torque on both compression and spark ignition engines. With this methodology, the user can change the engine by another one of different characteristics with few adjustments on the controller parameters. The controller was implemented using a microcontroller in order to guarantee operation in real time. The LQR controller performance has been validated in a wide range of engine operating modes, from low to high speeds and variable loads showing a good response. The description of the model using first order transfer functions with delay has proven to be a good approximation, despite of the nonlinearities caused by the turbocharger and the electronic control unit (ECU) incorporated in the engines. This low cost automation system has been tested for the last three years in a test rig at a university laboratory showing a good performance.

Prediction of NOx Emissions and Fuel Consumption of a City Bus under Real Operating Conditions by Means of Biharmonic Maps

AbstractThe prediction of pollutant emissions and fuel consumption under real operating conditions of any motor vehicle requires the use of complex mathematical models and experimental tools. In the present research, biharmonic maps (BM) were used to predict NOx (nitrogen oxides) emissions and relative fuel–air ratio (Fr) of a passenger city bus. For the collection of experimental data, an instrumented city bus was tested during real passenger transportation. The data were classified into four dynamic sequences: acceleration, idling, deceleration with fuel consumption, and deceleration without fuel consumption. Among them, the acceleration sequence was selected due to high NOx emissions and high fuel consumption. Experimental results were in good agreement with BM predictions. Significant parameters for predicting NOx concentration were vehicle velocity and relative fuel–air ratio (Fr). While for predicting Fr, significant parameters were the exhaust gas flow (EGF) rate, vehicle velocity, and NOx concentr...

A new criterion to validate and improve the classification process of LAMDA algorithm applied to diesel engines

This work proposes a new criterion to validate and improve the classification efficiency of the Learning Algorithm Multivariable and Data Analysis (LAMDA) fuzzy algorithm, which is an algorithm that combines the concepts of neural networks architecture and fuzzy clustering. LAMDA is based on finding the Global Adequacy Degree (GAD) of one data (individual) to a class (functional state), considering the contributions of each descriptor or variable. LAMDA is capable of generating new classes after the training stage and it uses probability density functions (PDF) for the estimation of similarity analysis between classes in order to determine the grouping criterion. The LAMDA algorithm was used here to identify new functional states that were not included during the training stage. However, this algorithm induced significant uncertainties when the recognized classes, corresponding to engine operating modes, exhibited similar membership degree values (MDV). To solve this, a new criterion to validate functional states after recognition (LAMDA-FAR), based on the minimum and maximum distances among MDV was developed. Both LAMDA and LAMDA-FAR algorithms were used in supervised learning mode to classify a historical database obtained from an experimental mapping methodology of an automotive diesel engine operating under several steady state conditions. For each engine operating mode the engine speed (rpm), exhaust gas temperature (C) and accelerator pedal position (%) were measured as the representative variables to carry out the classification. Both algorithms were trained with 70% of the historical database. The remaining 30% of the data, as well as new engine operating modes (not taken into account during the training stage), were used to validate classifier results. It was found that the LAMDA algorithm alone was unable to properly classify similar engine operating modes, while the LAMDA-FAR algorithm showed 100% efficiency for both known and unknown operating modes. This high efficiency and low computational cost tool can be used to improve engine control strategies based on experimental mapping methods, as well as to monitoring and controlling on-line vehicle performance.

Morphological Characteristics and Fractal Analysis of Diesel Particulate Matter From TEM Images Produced by Dual-Fuel N-Butanol Injection

This work analyzes the morphological characteristics and fractal dimension of diesel particulate matter (DPM) produced by multipoint-intake fumigation of n-butanol in a diesel engine. A novel methodology based on digital images processing (DIP) of micrographs from transmission and scanning electron microscopy (TEM and SEM) is presented. Two DIP algorithms were developed and compared for identification and cleaning of TEM images background: the semi-automatic (supervised), which uses the Watershed transform, morphological operators and edge detectors; and the automatic (non-supervised), which further includes adaptive threshold methods. Both algorithms performed successfully when compared with manual methods allowing a significant time saving (from 12 hours manual to 2 minutes automatic). Results showed that mean primary particle diameter (dp0), mean particulates agglomerates diameter, and fractal dimension of the agglomerate (Df) of DPM, which were around 30 nm, 70 nm, and 1.9 dimension respectively, were not affected by n-butanol fumigation in comparison with Ultra-low sulfur diesel (ULSD). The algorithms were sensible to the manual selection of the primary particles from the micrographs, strongly affected the determination of total number of primary particles (np0) and its diameter of gyration (dg); but the Df is not affected. Both algorithms performed successful avoiding the user subjectivity and providing significant time saving during the analysis.Copyright

Unsteady Forced Convection in Packed Beds: Computational and Experimental Analysis

In this work, a process of unsteady forced convection in a packed bed of spheres was experimentally and computationally analyzed. A device was designed and constructed in order to run the experiments in packed beds. It was used to carry out an experimental run in a packing of ten aluminum spheres, which tube-to-particle diameter ratio was 2,4. Methane-air combustion products were kept flowing into the packed bed at constant inlet conditions, 2,8 m/s and 369°C. Packed spheres were heated from 25°C to gases temperature. While heating, temperature of spheres, tube wall and gases at different positions were measured to follow unsteady process. On the other hand, computational simulation was carried out by modeling the ten-spheres packing under the same flow conditions of the experimental run. Physical properties of gases were kept constant and fluid flow profile was solved before heating process. Results of unsteady temperature variation in different positions showed good agreement with the experimental measures. This result allowed inferring that flow field calculations were a satisfactory representation of the actual flow field, since temperature field variation depends strongly upon flow field. In conclusion, it was found that the Computational Fluid Dynamics (CFD) simulation is an accurate tool to analyze unsteady forced convection in packed beds. The device designed is a flexible and powerful tool to measure unsteady forced convection in packed beds. The behavior of the gas-to-solid heat transfer coefficient is a fundamental question to solve, and CFD supported on experimental measures is the way to solve it.Copyright