Experimental Investigation of Ion Formation for Auto-Ignition Combustion in a High-Temperature and High-Pressure Combustion Vessel

One of the main challenges in internal combustion engine design is the simultaneous reduction of all engine pollutants like carbon monoxide (CO), total unburned hydrocarbons (THC), nitrogen oxides (NOx), and soot. Low-temperature combustion (LTC) concepts for compression ignition (CI) engines, e.g., premixed charged compression ignition (PCCI), make use of pre-injections to create a partially homogenous mixture and achieve an emission reduction. However, they present challenges in the combustion control, with the usage of in-cylinder pressure sensors as feedback signal is insufficient to control heat release and pollutant emissions simultaneously. Thus, an additional sensor, such as an ion-current sensor, could provide further information on the combustion process and effectively enable clean and efficient PCCI operation. This study performed experiments in a high-temperature, high-pressure, constant-flow combustion vessel to verify the ion-current application for premixed charge compression ignition (PCCI) engine control approaches. In this vessel, a metallic plate has been installed with a 40° orientation in front of the injector. A positively charged ion-current probe has been positioned close to the plate in the region where the fuel is injected. The electrons formed in the combustion process are drained to the probe because of the generated electrical field between the probe and the plate. The number of electrons is quantified as an ion-current signal. N-dodecane, representing a single-component surrogate fuel, has been used in the measurements to facilitate model validation. Additionally, diesel and a corresponding surrogate fuel formulation for diesel fuel have been investigated to validate the concept for a more complex fuel. The ion-current signal is measured at various conditions. These ion-current measurements will then serve as validation targets to correlate the combustion process with pollutant formation. Additionally, the local inhomogeneity of the mixture around the ion-current sensor head is analyzed regarding its impact on the measured ion-current signal. The results show promising evidence that ion-current sensors can control PCCI.</p

Development of a Fast-Running Injector Model with Artificial Neural Network (ANN) for the Prediction of Injection Rate with Multiple Injections

The most challenging part of the engine combustion development is the reduction of pollutants (e.g. CO, THC, NOx, soot, etc.) and CO2 emissions. In order to achieve this goal, new combustion techniques are required, which enable a clean and efficient combustion. For compression ignition engines, combustion rate shaping, which manipulates the injected fuel mass to control the in-cylinder pressure trace and the combustion rate itself, turned out to be a promising opportunity. One possibility to enable this technology is the usage of specially developed rate shaping injectors, which can control the injection rate continuously. A feasible solution with series injectors is the usage of multiple injections to control the injection rate and, therefore, the combustion rate. For the control of the combustion profile, a detailed injector model is required for predicting the amount of injected fuel. Simplified 0D models can easily predict single injection rates with low deviation. However, the prediction of injection rates with multiple injections is more challenging, because of the impact of past injections on future ones. In this work, an advanced 0D injector model is presented, which takes into account the effect of injection history. In order to develop and calibrate the model, an injection rate testbench has been used to generate an extensive and suitable database. This database is used to train an artificial neural network (ANN), which is integrated in the model. The developed multi-injection model predicts with high accuracy (R2&gt;0.85) the experimental injection rate up to four injections. Additionally, the model is real-time capable and therefore usable for controller application.</p

A Numerical Investigation of Potential Ion Current Sensor Applications in Premixed Charge Compression Ignition Engine

Simultaneous reduction of engine pollutants (e.g., CO, THC, NOx, and soot) is one of the main challenges in the development of new combustion systems. Low-temperature combustion (LTC) concepts in compression ignition (CI) engines like premixed charged compression ignition (PCCI) make use of pre-injections to create a partly homogenous mixture. In the PCCI combustion regime, a direct correlation between injection and pollutant formation is no longer present because of long ignition delay times. In LTC combustion systems, the in-cylinder pressure sensor is normally used to help the combustion control. However, to allow the control of PCCI engines, new sensor concepts are investigated to obtain additional information about the PCCI combustion for advanced controller structures. In LTC combustion systems like gasoline-controlled autoignition (GCAI) concepts, the application of ion current sensors enables additional monitoring of the combustion process with real-time capability. In analogy to GCAI, the use of an ion current sensor for the control of PCCI combustion in diesel engines could allow effective pollutant and combustion control. To investigate the potential of the application of an ion current sensor for controlling a PCCI engine, numerical engine investigations have been performed and are presented in this work. Experimental data of a single cylinder engine (SCE) are used to validate a RANS 3D-CFD simulation framework focusing on the prediction of engine-out emissions. The assembled chemical kinetic model accounts for ion and NOx formation inside the combustion chamber. After model validation, operating conditions with varying pre-injection patterns were analyzed to find correlations between pollutant and ion formation. The simulation results show a correlation between NOx and ion formation, suggesting that engine controls relying on ion current measurements potentially allow for a reduction of NOx emissions. Applying ion current sensors to control PCCI combustion seems promising to reduce pollutant emissions and improve the engine's overall performance through real-time in-cycle control strategies.</p

3D-CFD RANS Methodology to Predict Engine-Out Emissions with Gasoline-Like Fuel and Methanol for a DISI Engine

Renewable fuels, such as bio- and e-fuels, are of great interest for the defossilization of the transport sector. Among these fuels, methanol represents a promising candidate for emission reduction and efficiency increase due to its very high knock resistance and its production pathway as e-fuel. In general, reliable simulation tools are mandatory for evaluating a specific fuel potential and optimizing combustion systems. In this work, a previously presented methodology (Esposito et al., Energies, 2020) has been refined and applied to a different engine and different fuels. Experimental data measured with a single cylinder engine (SCE) are used to validate RANS 3D-CFD simulations of gaseous engine-out emissions. The RANS 3D-CFD model has been used for operation with a toluene reference fuel (TRF) gasoline surrogate and methanol. Varying operating conditions with exhaust gas recirculation (EGR) and air dilution are considered for the two fuels. The laminar flame speed for the fuels has been tabulated by means of detailed chemistry 1D-flame calculations. The G-equation model is used to simulate flame propagation in combination with chemical kinetics to estimate the emission species correctly. The resulting G-equation calibration is compared between the two fuels. Emission results are analyzed, validated with experiments, and compared between the two fuels. The methodology shows a good overall predictivity regarding trends and absolute values. Simulated carbon dioxide (CO2) shows to be mainly within a 4 % deviation from the measurements. Oxygen (O2) deviations are within 3 % at lean operation and higher at stoichiometric conditions due to the low overall oxygen content. The maximum nitrogen oxides (NOx) deviation for the TRF is 22 %, while higher deviations are observed for methanol up to 42.3 %. Total hydrocarbon (THC) emissions are mainly below 20 % deviation. Higher carbon monoxide (CO) deviations are observed due to high CO sensitivity to mixture formation prediction, even if the trends with EGR and air dilution are correctly reproduced. Overall, the methodology shows good potential for virtual pollutant evaluation, assessment of emission reduction strategies, and development of engines with methanol fuel.</p

Clinical applications of the Medipix detector

In this thesis a recently developed energy resolving x-ray detector (Medipix) is used to investigate potential medical applications of spectral x-ray imaging. Computed Tomography (CT) is one of the most important medical imaging modalities. Recent developments in CT techniques include dual-energy CT, where images are taken with two different x-ray spectra by either using two x-ray tubes operated at different voltages, or modulating the operating voltage of a single tube. These techniques provide spectral information in the CT dataset but are limited to what can be achieved by manipulating the x-ray source, since the detectors used in current CT systems are unable to provide spectral information about the detected x-rays. A preliminary investigation of the use of the Medipix detectors for two different medical applications is presented. The first, applications is imaging of blood vessels for diagnosis of vascular diseases, and the second, characterising and measuring the energy dependence of x-ray attenuation in fat and liver tissue using the Medipix2 detector. This second investigation is part of work towards (eventually) quantifying the fat content of liver tissue in vivo, which is important for the early diagnosis of fatty liver disease. While an early attempt to identify iron fluorescence x-rays in a Monte-Carlo simulation of blood vessel x-ray image was not successful, the potential for improving image contrast using the changes in x-ray attenuation at the iodine k-edge iodine have been investigated in a series of further simulations and appears to be feasible. The potential use of spectral imaging to differentiate and quantify tissues without the need for added contrast material has been investigated by using a Medipix2 detector to measure the energy dependence of x-ray absorption in fat and liver tissue. The results of this experimental work show significant differences in x-ray attenuation between these two tissues that suggest this form of spectral imaging may be useful in practice

3D-CFD RANS Methodology to Predict Engine-Out Emissions with Gasoline-Like Fuel and Methanol for a DISI Engine

Renewable fuels, such as bio- and e-fuels, are of great interest for the defossilization of the transport sector. Among these fuels, methanol represents a promising candidate for emission reduction and efficiency increase due to its very high knock resistance and its production pathway as e-fuel. In general, reliable simulation tools are mandatory for evaluating a specific fuel potential and optimizing combustion systems. In this work, a previously presented methodology (Esposito et al., Energies, 2020) has been refined and applied to a different engine and different fuels. Experimental data measured with a single cylinder engine (SCE) are used to validate RANS 3D-CFD simulations of gaseous engine-out emissions. The RANS 3D-CFD model has been used for operation with a toluene reference fuel (TRF) gasoline surrogate and methanol. Varying operating conditions with exhaust gas recirculation (EGR) and air dilution are considered for the two fuels. The laminar flame speed for the fuels has been tabulated by means of detailed chemistry 1D-flame calculations. The G-equation model is used to simulate flame propagation in combination with chemical kinetics to estimate the emission species correctly. The resulting G-equation calibration is compared between the two fuels. Emission results are analyzed, validated with experiments, and compared between the two fuels. The methodology shows a good overall predictivity regarding trends and absolute values. Simulated carbon dioxide (CO2) shows to be mainly within a 4 % deviation from the measurements. Oxygen (O2) deviations are within 3 % at lean operation and higher at stoichiometric conditions due to the low overall oxygen content. The maximum nitrogen oxides (NOx) deviation for the TRF is 22 %, while higher deviations are observed for methanol up to 42.3 %. Total hydrocarbon (THC) emissions are mainly below 20 % deviation. Higher carbon monoxide (CO) deviations are observed due to high CO sensitivity to mixture formation prediction, even if the trends with EGR and air dilution are correctly reproduced. Overall, the methodology shows good potential for virtual pollutant evaluation, assessment of emission reduction strategies, and development of engines with methanol fuel.</p

Experimental-Based Laminar Flame Speed Approximation Formulas of Efficiency-Optimized Biofuels for SI-Engine Modeling

The transition towards sustainable mobility encourages research into biofuels for use in internal combustion engines. For these alternative energy carriers, high-fidelity experimental data of flame speeds influenced by pressure, temperature, and air-fuel equivalence ratio under engine-relevant conditions are required to support the development of robust combustion models for spark-ignition engines. E.g., physicochemical-based approximation formulas adjusted to the fuel provide similar accuracy as high fidelity chemical kinetic model calculations at a fraction of the computational cost and can be easily adopted in engine simulation codes. In the present study, a workflow to enable predictive combustion engine modeling is applied first for a gasoline reference fuel and two biofuel blends recently proposed by Dahmen and Marquardt [Energy Fuels, 2017]. They identified one promising high-octane rating biofuel blend, expected to be optimized for SI combustion engines, and one promising low carbon high energy density blend with an optimized production pathway. The first blend consists of ethanol, 2-butanone, cyclopentane, and cyclopentanone, and the second blend consists of 1-butanol, ethanol, and cyclopentane. In the present study, the reference fuel RON95 E10 and both biofuel blends were experimentally examined for their flame speed in RWTH-ITV's closed combustion chamber at 423 K and 2.5 bar, with equivalence ratios (φ) ranging from 0.8 to 1.3. Then, pressure (1 atm and 5 bar) and temperature variations (398 K and 450 K) were conducted for the blends at φ = 1.1. Due to its good agreement with the experimental results, a detailed kinetic mechanism was selected and used for comprehensive flame speed calculations at engine conditions. The approximation formula was parametrized in the next step, showing good agreement with the detailed calculations. Finally, the flame speed model is adopted for engine simulations, and the 0-2% burn duration of gasoline is used as a benchmark against engine data, showing the improved predictability of the newly derived approximation compared to a standard correlation. The biofuels' burn durations indicate slight improvements due to higher flame speeds.</p

Soil Water Movement in Vapor and Liquid Phases

One of the well-known characteristics of arid region soils is that the temperature undergoes wide fluctuations throughout the day and throughout the season. These temperature variations induce thermal gradients and temperature differences between locations in the surface soil. Although the existence of these effects is well known, their effect on the processes that occur in the soil is not so well known. One of the problems that has been of considerable interest in recent years is the influence of temperature differences upon the movement of water in the soil. Early investigators of this problem discovered that there was a net water flux from warm to cold in soil materials subjected to a thermal difference, but they became involved in a controversy as to whether the movement was in the vapour or liquid phase. Some studies supported the concept of flow in the vapour phase, and others supported the concept of flow in the liquid phase. A net flux of vapour from warm to cold and liquid from cold to warm was demonstrated in an enclosed and sealed system of porous material, water, and air by Krischer and Rohnalter (1940). This was later confirmed for soil material, using a different technique, by Gurr, Marshall and Hutton (1952), but they obtained a vapour diffusion coefficient that was very much greater than expected on the basis of simple laws of diffusion. Similar studies in sealed systems, using still different techniques, were reported by Taylor and Cavazza (1954), who found that the apparent vapour diffusion coefficient was ten times larger than expected on the basis of simple diffusion laws. Subsequently,. there have been repeated observations of this effect, and several attempts to explain the phenomena based on various analyses of simultaneous movement of water and heat (Philip and Dc Vries. 1957; De Vries, 1958; Derjaguin and Melnikova, 1958)

Experimental-Based Laminar Flame Speed Approximation Formulas of Efficiency-Optimized Biofuels for SI-Engine Modeling

The transition towards sustainable mobility encourages research into biofuels for use in internal combustion engines. For these alternative energy carriers, high-fidelity experimental data of flame speeds influenced by pressure, temperature, and air-fuel equivalence ratio under engine-relevant conditions are required to support the development of robust combustion models for spark-ignition engines. E.g., physicochemical-based approximation formulas adjusted to the fuel provide similar accuracy as high fidelity chemical kinetic model calculations at a fraction of the computational cost and can be easily adopted in engine simulation codes. In the present study, a workflow to enable predictive combustion engine modeling is applied first for a gasoline reference fuel and two biofuel blends recently proposed by Dahmen and Marquardt [Energy Fuels, 2017]. They identified one promising high-octane rating biofuel blend, expected to be optimized for SI combustion engines, and one promising low carbon high energy density blend with an optimized production pathway. The first blend consists of ethanol, 2-butanone, cyclopentane, and cyclopentanone, and the second blend consists of 1-butanol, ethanol, and cyclopentane. In the present study, the reference fuel RON95 E10 and both biofuel blends were experimentally examined for their flame speed in RWTH-ITV's closed combustion chamber at 423 K and 2.5 bar, with equivalence ratios (φ) ranging from 0.8 to 1.3. Then, pressure (1 atm and 5 bar) and temperature variations (398 K and 450 K) were conducted for the blends at φ = 1.1. Due to its good agreement with the experimental results, a detailed kinetic mechanism was selected and used for comprehensive flame speed calculations at engine conditions. The approximation formula was parametrized in the next step, showing good agreement with the detailed calculations. Finally, the flame speed model is adopted for engine simulations, and the 0-2% burn duration of gasoline is used as a benchmark against engine data, showing the improved predictability of the newly derived approximation compared to a standard correlation. The biofuels' burn durations indicate slight improvements due to higher flame speeds.</p