A basic study on the convective flame-wall heat transfer in diesel engines was performed with a fundamental experiment and simplified theoretical models. Based on the concept of flame tubes, a combustor was designed and optimized to support laminar, stable flame propagation at constant ambient pressure. Measurements of flame position and heat transfer during head-on quenching of premixed methane-air flames with varying mixture equivalence ratios at a metallic surface were made using flame luminosity videography and surface thermometry. Two models were developed to predict the magnitude of single-wall quenching layers and the flame-wall heat transfer at a variable temperature wall. One of the models was a quasi steady-state first law balance which utilizes an Arrhenius reaction equation to represent the temperature sensitivity of the chemical processes according to a single-step reaction mechanism. The second model was based on transient heat conduction theory; a planar, moving heat generating sheet simulated the heat release of a propagating flame front in a one-dimensional slab of gases at rest, bounded at the wall at which quenching occurs. Experimental and model results showed that flame-wall heat transfer is primarily dictated by the reaction rate of combustion and the thermal diffusivity of the gas mixture. The convective heat transfer coefficient was predicted to increase with rising wall temperature. Measured peak heat transfer rates were 25% higher than those reported in the literature. Recommendations are made for the design of an experimental apparatus with which conditions encountered in internal combustion engines can be simulated more closely. / Master of Science

A method using gas chromatography for the analysis of carbon dioxide, carbon monoxide, and nitric oxide in diesel exhaust was developed. A gas chromatograph containing a liquid phase column in series with a molecular sieve column, each of which eluted into thermal conductivity detectors, was utilized. Activation of the molecular sieve column was achieved by heat-treating and purging with nitric oxide. The chromatograph was calibrated by introducing sample mixtures of known concentration and measuring the responses. The exhaust gases of a diesel engine were analyzed while the engine operated at constant speed and load. Engine speed was kept at 1400 rpm while several different loads were applied. The results of these tests indicated that carbon dioxide, carbon monoxide, and nitric oxide concentrations all increased with load in the load range studied. Carbon monoxide exhibited a greater dependence on load than did the other compounds. Additionally, water was injected into the intake air stream to study its effect on nitric oxide concentration. Nitric oxide levels were reduced by 15% when a water/fuel mass ratio of 0.75 lb/lb was used. / Master of Science

M. Tech. Engineering Technology . / Demonstrates through design and experiments the heat transfer effectiveness of energy recovery from waste gases by using a heat exchanger. To use the heat exchanger to intercept the waste gases before they leave the process, extract some of the heat in the gases and use the same for preheating/heating the process water. The experiment is also intended to demonstrate whether or not waste heat unit has an effect on the emissions released to the environment. Diesel engines have been widely used in heavy-duty vehicles for their better fuel efficiency and higher power output than gasoline engines. However, the emissions of gas (CO, HC and NOx) and particulate matter (PM) pollutants from the diesel engine receive much concern from the general public and environmental researchers because of the epidemiological and toxicological investigations suggesting a relationship between exhaust pollutants exposure and adverse health effects.

This work focuses on the cooling of diesel engines. Facing heavy constraints such<p>as emissions control or fossil energy management, political leaders are forcing car<p>manufacturers to drastically reduce the fuel consumption of passenger vehicles. For<p>instance, in Europe, this fuel consumption has to reach 120 g CO2 km by 2012, namely 25 % reduction from today's level.<p>Such objectives can only be reached with an optimization of all engines components<p>from injection strategies to power steering. A classical energy balance of an internal<p>combustion engine shows four main losses: enthalpy losses at the exhaust, heat<p>transfer to the cylinder walls, friction losses and external devices driving. An<p>optimized cooling will improve three of them: the heat transfer losses by increasing<p>the cylinder walls temperature, the friction losses by reducing the oil viscosity and<p>the coolant pump power consumption.<p>A model is first built to simulate the engine thermal behavior from the combustion<p>itself to the temperatures of the different engine components. It is composed by two<p>models with different time scales. First, a thermodynamic model computes the in cylinder<p>pressure and temperature as well as the heat flows for each crank angle.<p>These heat flows are the main input parameters for the second model: the nodal<p>one. This last model computes all the engine components temperatures according<p>to the nodal model theory. The cylinder walls temperature is then given back to<p>the thermodynamic model to compute the heat flows.<p>The models are then validated through test bench measurements giving excellent<p>results for both Mean Effective Pressure and fluids (coolant and oil) temperatures.<p>The used engine is a 1.9l displacement turbocharged piston engine equipped with<p>an in-cylinder pressure sensor for the thermodynamic model validation and thermocouples<p>for the nodal model validation.<p>The model is then used to optimize the coolant mass flow rate as a function of<p>the engine temperature level. Simulations have been done for both stationary<p>conditions with effciency improvement up to 7% for specific points (low load, high<p>engine speed) and transient ones with a heating time improvement of about 2000s.<p>This gains are then validated on the test bench showing again good agreement. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

published_or_final_version / Public Administration / Master / Master of Public Administration

Thesis (D.Tech.: Mechanical Engineering)-Dept. of Mechanical Engineering, Durban Institute of Technology, 2005 xxi, 265 leaves / The aim of this study is to develop, using neural networks, a model to aid the performance monitoring of operational diesel engines in industrial settings. Feed-forward and modular neural network-based models are created for the prediction of the specific fuel consumption on any normally aspirated direct injection four-stroke diesel engine. The predictive capability of each model is compared to that of a published quadratic method. Since engine performance maps are difficult and time consuming to develop, there is a general scarcity of these maps, thereby limiting the effectiveness of any engine monitoring program that aims to manage the fuel consumption of an operational engine. Current methods applied for engine consumption prediction are either too complex or fail to account for specific engine characteristics that could make engine fuel consumption monitoring simple and general in application. This study addresses these issues by providing a neural network-based predictive model that requires two measured operational parameters: the engine speed and torque, and five known engine parameters. The five parameters are: rated power, rated and minimum specific fuel consumption bore and stroke. The neural networks are trained using the performance maps of eight commercially available diesel engines, with one entire map being held out of sample for assessment of model generalisation performance and application validation. The model inputs are defined using the domain expertise approach to neural network input specification. This approach requires a thorough review of the operational and design parameters affecting engine fuel consumption performance and the development of specific parameters that both scale and normalize engine performance for comparative purposes. Network architecture and learning rate parameters are optimized using a genetic algorithm-based global search method together with a locally adaptive learning algorithm for weight optimization. Network training errors are statistically verified and the neural network test responses are validation tested using both white and black box validation principles. The validation tests are constructed to enable assessment of the confidence that can be associated with the model for its intended purpose. Comparison of the modular network with the feed-forward network indicates that they learn the underlying function differently, with the modular network displaying improved generalisation on the test data set. Both networks demonstrate improved predictive performance over the published quadratic method. The modular network is the only model accepted as verified and validated for application implementation. The significance of this work is that fuel consumption monitoring can be effectively applied to operational diesel engines using a neural network-based model, the consequence of which is improved long term energy efficiency. Further, a methodology is demonstrated for the development and validation testing of modular neural networks for diesel engine performance prediction.

This thesis describes an investigation into the spatial species conversion profiles of a Cu-zeolite SCR under engine conditions at low exhaust gas temperatures; this was then compared with a CFD model that models the catalyst via a porous medium measuring 5 x 5 x 91 cells assuming a uniform cross-sectional flow distribution. Species conversion rates were sampled at fixed points in the axial direction. The analysis of the spatial conversion profiles is a more rigorous method in assessing the ability of a mathematical model to predict the experimental data. It can also assist in the optimisation of the catalyst size, minimising packaging requirements and manufacturing costs. The experiments were undertaken on a light-duty diesel engine at a speed of 1500rpm, and at a load of 6bar BMEP; this provided exhaust gas temeraqtures between 200 and 220°C. NO2:NOx ratios were controlled by changing the size and position of the diesel oxidation catalyst, the inlet NH3: NOx ratio was also also varied, ammonia gas was used instead of urea for the purposes of simlicity. The advantage of testing on an actual engine over lab-babed studies is that the conditions such as exhaust gas composition are more realistic. A 1D CFD model was constructed using the ‘porous medium approach’ with kinetics obtained from open literature. Results from the simulations were then compared with the experimental data for the same engine conditions. It was observed that the majority of the NOx conversion took place in the first half of the brick for all NH3: NOx ratios investigated, and that the formation of N2O via NO2 and ammonia had the same influence as the ‘fast’ SCR reaction just after the inlet, which the CFD model failed to predict for the base case analyses. The influence of the inlet ammonia on the model was also noticed to be greater than in the experiments. Simple transient analyses were also undertaken on the short SCR bricks for NO2: NOx ratios of 0.6 and 0.07, and it was observed that the response time to steady-state was noticeably higher in the experiments than in the model. Modifications made to the model, including decreasing the influence of the ‘fast’ SCR reaction, and the addition of an empirical term onto the ammonia adsorption provided a noticeably better agreement for different NH3: NOx injection ratios. The desorption kinetics in the model were also altered by increasing the strength of the bonding of the ammonia onto the adsorption sites. This improved the transient agreement between the model and the experiments, but reduced the steady-state concentrations at the exit of the brick for all NH3:NOx ratios investigated.

published_or_final_version / Urban Planning and Environmental Management / Doctoral / Doctor of Philosophy

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management