Prediction of gaseous pollutant emissions from a spark-ignition direct-injection engine with gas-exchange simulation

With the further tightening of emission regulations and the introduction of real driving emission tests (RDE), the simulative prediction of emissions is becoming increasingly important for the development of future low-emission internal combustion engines. In this context, gas-exchange simulation can be used as a powerful tool for the evaluation of new design concepts. However, the simplified description of the combustion chamber can make the prediction of complex in-cylinder phenomena like emission formation quite challenging. The present work focuses on the prediction of gaseous pollutants from a spark-ignition (SI) direct injection (DI) engine with 1D–0D gas-exchange simulations. The accuracy of the simulative prediction regarding gaseous pollutant emissions is assessed based on the comparison with measurement data obtained with a research single cylinder engine (SCE). Multiple variations of engine operating parameters – for example, load, speed, air-to-fuel ratio, valve timing – are taken into account to verify the predictivity of the simulation toward changing engine operating conditions. Regarding the unburned hydrocarbon (HC) emissions, phenomenological models are used to estimate the contribution of the piston top-land crevice as well as flame wall-quenching and oil-film fuel adsorption-desorption mechanisms. Regarding CO and NO emissions, multiple approaches to describe the burned zone kinetics in combination with a two-zone 0D combustion chamber model are evaluated. In particular, calculations with reduced reaction kinetics are compared with simplified kinetic descriptions. At engine warm operation, the HC models show an accuracy mainly within 20%. The predictions for the NO emissions follow the trend of the measurements with changing engine operating parameters and all modeled results are mainly within ±20%. Regarding CO emissions, the simplified kinetic models are not capable to predict CO at stoichiometric conditions with errors below 30%. With the usage of a reduced kinetic mechanism, a better prediction capability of CO at stoichiometric air-to-fuel ratio could be achieved.</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

Development of Phenomenological Models for Engine-Out Hydrocarbon Emissions from an SI di Engine within a 0D Two-Zone Combustion Chamber Description

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms. In addition, phenomenological models can be applied within simplified simulation environments, e.g., 0D-1D engine simulations, to enable predictions of HC emissions for a wide range of operating conditions. In this work, the development of phenomenological models to account for HC emissions from piston top-land crevices, wall flame quenching, and oil-film adsorption/desorption mechanisms is explained in detail. The model development is based on measurements and models from a single cylinder direct injection (DI) spark ignition (SI) research engine. Common modeling hypotheses and approaches from literature have been verified and further developed with 3D-CFD simulations. In particular, assumptions regarding local temperature and air-fuel ratio, which are necessary for HC modeling, have been developed on the basis of a zone post-processing of the 3D-CFD results. Additionally, a novel approach to describe HC post-oxidation, which is based on 0D-chemistry calculations, has been developed. The HC models have been implemented within a GT-POWER model of the engine in conjunction with a 0D two-zone combustion chamber description. The accuracy of the developed models has been tested against a large experimental database with varying engine load, speed, air to fuel ratio, valve timing, and oil/coolant temperature. The deviation in the HC emission prediction is mainly within 20% at warm engine operation. Higher deviations are observed at cold engine conditions because of the absence of secondary HC models which have not been considered in the present work.</p

Enzymatic Strategies to Detoxify Gluten: Implications for Celiac Disease

Celiac disease is a permanent intolerance to the gliadin fraction of wheat gluten and to similar barley and rye proteins that occurs in genetically susceptible subjects. After ingestion, degraded gluten proteins reach the small intestine and trigger an inappropriate T cell-mediated immune response, which can result in intestinal mucosal inflammation and extraintestinal manifestations. To date, no pharmacological treatment is available to gluten-intolerant patients, and a strict, life-long gluten-free diet is the only safe and efficient treatment available. Inevitably, this may produce considerable psychological, emotional, and economic stress. Therefore, the scientific community is very interested in establishing alternative or adjunctive treatments. Attractive and novel forms of therapy include strategies to eliminate detrimental gluten peptides from the celiac diet so that the immunogenic effect of the gluten epitopes can be neutralized, as well as strategies to block the gluten-induced inflammatory response. In the present paper, we review recent developments in the use of enzymes as additives or as processing aids in the food biotechnology industry to detoxify gluten

Development of Phenomenological Models for Engine-Out Hydrocarbon Emissions from an SI di Engine within a 0D Two-Zone Combustion Chamber Description

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms. In addition, phenomenological models can be applied within simplified simulation environments, e.g., 0D-1D engine simulations, to enable predictions of HC emissions for a wide range of operating conditions. In this work, the development of phenomenological models to account for HC emissions from piston top-land crevices, wall flame quenching, and oil-film adsorption/desorption mechanisms is explained in detail. The model development is based on measurements and models from a single cylinder direct injection (DI) spark ignition (SI) research engine. Common modeling hypotheses and approaches from literature have been verified and further developed with 3D-CFD simulations. In particular, assumptions regarding local temperature and air-fuel ratio, which are necessary for HC modeling, have been developed on the basis of a zone post-processing of the 3D-CFD results. Additionally, a novel approach to describe HC post-oxidation, which is based on 0D-chemistry calculations, has been developed. The HC models have been implemented within a GT-POWER model of the engine in conjunction with a 0D two-zone combustion chamber description. The accuracy of the developed models has been tested against a large experimental database with varying engine load, speed, air to fuel ratio, valve timing, and oil/coolant temperature. The deviation in the HC emission prediction is mainly within 20% at warm engine operation. Higher deviations are observed at cold engine conditions because of the absence of secondary HC models which have not been considered in the present work.</p

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

Drawingvoice 2.0: classroom joint designing and Facebook interactions to develop reflexivity and awareness

Drawingvoice 2.0 is an instructional method of collaborative pencil and paper drawing to use in the school classroom, followed by Facebook interaction on the drawing produced in class. It is based on a participatory and meta reflective approach, explicitly aimed at deconstructing, negotiating, and reconstructing the meaning that students attribute to themselves regarding their professional expectations and educational pathways. In particular, the collaborative pencil and paper drawing allows for the student’s emotional symbolisation processes underlying their educational pathway. Drawingvoice 2.0 induces a multidimensional cognitive and meta-cognitive process further supported by the following interaction on Facebook. Therefore, the World Wide Web is the added resource for sharing and deepening the classmates’ discussion. Finally, Drawingvoice 2.0 supported structural group interaction and was an important supportive and instructional method to bring about transformational and developmental training practices. As the main result, in our experience, psychology students increased their reflectivity about their strengths and threats in being psychologists within their cultural contexts and potential positive resources underlying their choice. Drawingvoice 2.0 thus enhanced their self-awareness about the lights and shadows of their training and future professional career.info:eu-repo/semantics/publishedVersio

Experimental comparison of combustion and emission characteristics between a market gasoline and its surrogate

Mixtures of few representative hydrocarbon species are often used in computational studies as surrogates of market petroleum fuels, which contain hundreds of components. While this simplification is imperative for computational costs of reaction kinetics, it introduces unavoidably uncertainties in simulations. Differences between the real and the surrogate fuels regarding mixture formation, oxidation chemistry, and fuel composition contribute to such uncertainties. An evaluation of the underlying concept of a surrogate fuel model in terms of engine performance is thus of high interest. This paper presents an experimental study with a spark-ignition (SI) single-cylinder engine (SCE) to compare the combustion and emission characteristics between a market gasoline fuel and its corresponding four-component surrogate (iso-octane, n-heptane, toluene, ethanol). The measurements cover a wide range of operating points in terms of engine load, speed, air-to-fuel ratio, and operating conditions. Together with standard performance and emission measurements, a non-standard hydrocarbons (HC) analysis has been performed with a fast flame ionization detector and an ion-molecule-reaction mass spectrometer. The comparison reveals very good agreement between the market gasoline and the surrogate fuel regarding combustion and global gaseous emission behaviors, with an average deviation for almost all of the analyzed quantities below 2%. The comparison of CO emissions in stoichiometric operation presents a higher scatter, due to the high sensitivity of the CO emissions on mixture formation and fuel volatility. The different compositions of the two fuels also lead to deviations of speciated-HC emissions, which is confirmed by mass spectrometry. Additionally, the sooting tendency of the surrogate fuel is found to be more than 10 times lower compared to the market gasoline fuel.</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