1,721,055 research outputs found
Development and assessment of a new methodology for end of combustion detection and its application to cycle resolved heat release analysis in IC engines
No full textThe heat release analysis has proved to be a powerful diagnostic tool for the analysis of the combustion process in spark ignition engines. Still, a fine tuning of the heat transfer correlations embedded in the heat release models is necessary for a correct diagnostic analysis of the pressure signal. To that end, a new methodology has been developed and assessed to properly locate the end of combustion on the basis of the heat release intensity. The results produced by the proposed method have been compared to those obtained by applying different methodologies available in the literature. The newly developed method has proved to be accurate and consistent and has allowed a reliable estimation of the end of combustion on a cycle-by-cycle basis. An extensive burn rate analysis has also been accomplished by means of a heat release model previously developed and purposely modified to embed the new end of combustion detection procedure. The main combustion related quantities have been considered for the experimental investigation to appropriately quantify the engine cyclic variability as a function of the relative air-to-fuel ratio. The experimental tests have been performed on a naturally aspirated 2L engine featuring a fast-burn combustion chamber and running on gasoline and natural gas as well as on a 1.2L turbocharged natural gas engine displaying a disk shaped combustion chamber. The diagnostic tool has proved to properly match the nonlinear behavior of the quantities related to the combustion duration in the cycle-resolved analysis and a general good agreement with previous works has emerged as far as the coefficient of variations of the main combustion parameters are concerned. Moreover, thanks to the automatic facet the proposed methodology retains, it is strongly recommended when an extensive cycle-by-cycle and cylinder-to-cylinder analysis needs to be performed. Finally, regardless of the considered fuel, the heat release model embedding the EOC detection procedure proved to be capable of properly detecting the combustion features induced by a fast-burn combustion chamber with respect to a traditional one. As a matter of fact, smaller Δθ10-90% values and an overall reduced cyclic dispersion were highlighted for the 2L engin
Development of a method for the estimation of the behavior of a CNG engine over the NEDC cycle and its application to quantify for the effect of hydrogen addition to methane operations
No full textThe current pollutant regulations and policies have set the need to consider the use of alternative fuels capable of complying with the emission limits still retaining appreciable engine performance. Natural gas has been considered as an effective alternative to gasoline but the drawbacks connected to its use forced the researchers to investigate into fuel additives, dual fuel solutions and innovative engine control strategies. The present work analyzes the use of hydrogen as an additive to CNG for a natural gas production engine of a C-segment vehicle and carries out a thorough investigation into the engine response over a selection of operating key points. The actual focus is set on the investigation into the vehicle as well as into the engine response and performance over driving cycles. Still, the simulation of real driving conditions would set the need to properly quantify for the effect of the hydrogen enriched blends on the full engine map over varying powers and speeds within the vehicle driving cycle. Such an approach often turns out to be too demanding in terms of time and costs and an alternative solution has been hereafter proposed by properly selecting a reduced number of operating points on the basis of the correspondent residence time and frequency over the NEDC. The selection has been performed by matching the actual engine map to the readings from the NEDC vehicle testing. Different selections have been considered and compared so as to assess for the one embedding the minimum number of working points. The key points are not meant to substitute the accomplishment of the NEDC cycle but are to be used as driving factors in the engine design so as to allow for detecting the optimal hardware and ECU configurations. The so considered engine key points have hence been extensively studied by reproducing the engine performance at the test bench and by performing a detailed heat release analysis. Different composition of the hydrogen-methane blends have been considered up to a 25% by volume of hydrogen in the mixture and specific attention has been paid to the main combustion parameters and to their optimization. As a matter of fact, such a study would allow for detecting major trends in the engine design and control strategies to compensate for the poor behavior of some of the considered points. As an example, low load and speed operations assessed for the need of a better control of the engine parameters to diminish the cylinder-to-cylinder as well as the cycle-to-cycle variabilit
Development and validation of a semi-empirical model for the estimation of particulate matter in diesel engines
No full textA semi-empirical correlation for the estimation of PM (particulate matter) emissions in diesel engines, as a function of significant engine operating variables, has been developed and validated on a GM (General Motors) Euro 5 diesel engine. The experimental data used in the present study have been acquired at the dynamic test bench of ICEAL-PT (Internal Combustion Engine Advanced Laboratory at the Politecnico di Torino), in the frame of a research activity with GMPT-E (General Motors PowerTrain-Europe) for the calibration of a Euro 5 prototype 2.0 liter diesel engine equipped with a twin-stage turbine and a piezo-driven Common Rail injection system. The experimental data were acquired for six key-points representative of the engine working conditions over a NEDC (New European Driving Cycle). The experimental tests have been carried out according to the Design of Experiment approach and for each point several variation lists of the main engine variables have been considered. As a first step, the main engine variables which are expected to be related to the formation and oxidation of PM have been identified. An exponential mathematical model has then been introduced and a detailed statistical analysis has been carried out for each key-point in order to identify the most robust combination of the input variables among all the possible ones. It was verified that PM emissions are correlated to a great extent to the value of the chemical heat release at the end of the injection of the main pulse. This quantity is in fact related to the mass of burned gases which is generated by the oxidation of the pilot pulses that precede the main injection. Such a mass can have a large impact on the local oxygen concentration and temperature of the charge in which the fuel of the main pulse is injected, with a consequent effect on PM formation. Additional quantities have also been considered in the investigation: the relative air-to-fuel ratio k, the intake charge oxygen concentration, the accumulated fuel mass, the equivalence ratio of the spray at the main pulse start of combustion and some combustion metrics related to the heat release rate. At the end of the statistical analysis, the most influencing parameters have been selected and a semiempirical model to predict the in-cylinder formed PM mass has been developed. The model has hence been tested under both steady-state and transient condition
Numerical Analysis of a Flow Control System for High-Pressure Turbine Vanes Subject to Highly Oscillating Inflow Conditions
No full textUnder the prism of introducing pioneering technologies in the propulsive field, the rotating detonation engine (RDE) continuously attracts the gas turbine (GT) research community. However, how to effectively couple an RDE with high pressure turbine (HPT) stages is still debated. In fact, time dependent flow conditions from the RDE greatly affect turbine performance, thus reducing the positive impact of pressure gain combustion (PGC) on the overall cycle efficiency. The present numerical work aims at analyzing both the impact of a pulsating inflow on the performance of a newly designed high pressure turbine vane and the effectiveness of a flow control system in governing the oscillations within the vane passage. First, a baseline vane capable of ingesting high enthalpy flow at an inlet Mach number of 0.6 is introduced. A total number of 297 samples are generated by varying the 18 geometrical parameters that characterize the vane endwalls and airfoil profile with the help of a Latin hypercube sampling method. An optimization strategy is then performed under steady inflow conditions to minimize the vane loss coefficient, thereby determining the final geometry of the new vane. In the second part of the work, a flow control system is proposed by placing a series of holes in the endwalls of the vane. Air at constant stagnation conditions is injected upstream of the vane leading edge. Unsteady calculations with and without flow control, including similar pulsating conditions from the RDE, provide an insight into the generation and evolution of the secondary flow structures inside the passage. The main outcome of this analysis is that the flow control system intensifies the passage vortices providing less oscillating flow at the vane exit section, which is beneficial for the aerodynamic performance of a subsequent blade row
Acceleration control strategy for Battery Electric Vehicle based on Deep Reinforcement Learning in V2V driving
Full text linkThe transportation sector is seeing the flourishing of one of the most interesting technologies, autonomous driving (AD). In particular, Cooperative Adaptive Cruise Control (CACC) systems ensure higher levels both of safety and comfort, enhancing at the same time the reduction of energy consumption. In this framework a real-time velocity planner for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain good results on standard cycles, such as WLTP, UDDS and AUDC, and on real-world driving cycles. Moreover, it displays great adaptability to driving cycles different from the training one
Numerical Study of Unsteady Rotating Structures in a Turbine Disk Cavity
No full textThe improvement of modern cooling systems in aero-engines has led to an increase in the turbine inlet temperature to improve cycle efficiency. In particular, the use of purge flow within the cavity between the stator rim and rotor platform has considerably reduced hot gas ingestion from the main flow and has ensured the maximum metal components operating life. The mechanisms governing axial turbine rim seal flow are complex and influenced by various factors, firstly by the geometry of the cavity. The resulting flow has a three-dimensional structure and a pattern that varies over time. For a wide axial cavity, hot gas ingestion zones can be mainly attributed to three phenomena. The first one is the disc pumping effect generated by the shaft rotation, which induces a radial outflow in the rotor disc boundary layer, creating a recirculation within the cavity that leads to ingestion in the stator disc area. The second one is the vane/blade relative position, which determines a circumferential pressure profile where the higher and lower pressure zones define the flow ejection or ingestion. The third phenomenon is the purge/mainstream flow interaction and its inherent instability that leads to large-scale unsteady flow features developing within the cavity. The formation of such rotating structures and their impact on the ingestion zones is a key research topic in this area. This study presents the results of unsteady numerical simulations (URANS) of an axial turbine's first-stage cavity with a focus on the analysis of 3D-unsteady flow structures. The simulations have been performed by imposing a circumferential pressure periodicity at the outlet, extracted from previous simulations of the stage. The static pressure values within the cavity have been validated by comparison with experimental data. Three purge flow rates have been tested, namely high purge, low purge, and ingestion flow. The paper provides a new perspective on the topic by performing a SPOD analysis on a characteristic plane to identify the main energy modes that dominate the flow and their influence on the ingestion phenomenon
Implementation of a high-order spatial discretization into a finite volume solver: Applications to turbomachinery test cases using an eddy-viscosity turbulence closure
Full text linkIn this study, the implementation of a high-order spatial discretization method into a Finite Volume solver is presented. Specific emphasis is put on the analysis of the performance over selected turbomachinery test cases. High-order numerical discretization is achieved by adopting the cell-centered Least-Square reconstruction, which is implemented in the in-house solver HybFlow. The validation of the adopted methodology is performed by assessing the solution of a turbulent flat plate with zero pressure gradient, using a eddy-viscosity transitional model. The test case also evidences the effect of the discretization of gradient-based source terms when a high-order reconstruction methodology is used. In the second part of the paper, the solver is used for the solution of relevant two-dimensional turbomachinery test cases, assessing the impact of 2nd and 3rd order reconstruction on the prediction of the aerodynamics and the heat transfer for respectively a low-pressure blade and a high-pressure turbine vane. It is shown how a high-order reconstruction allows for obtaining a better prediction of turbomachinery aerodynamics, with lower number of elements. The benefits over heat transfer predictions in high Reynolds number conditions are instead limited to the reduction of heat transfer coefficient spikes in under-resolved regions of the blade. Eventually, the methodology is validated for a three-dimensional low-pressure turbine cascade with realistic boundary layer inflow conditions
Models and Calibration Techniques for Heat Release Analysis of Pressure Data in SI Engine
No full textDevelopment and assessment of a random-forest model for real-time prediction of diesel engine-out particulate matter emissions
No full textThe containment of air pollution resulting from transportation sector is worldwide considered as one of the most important targets to be still achieved. The emissions of particulate matter (PM) assume a relevant role when Diesel engine-based vehicles are concerned. In the present paper, an innovative model for the prediction of engine-out emitted PM mass has been developed and assessed on two different Diesel engines. A semi-empirical approach is defined as reference model and initially validated through the ISO/IEC Guide 98-3:2008 procedure and its potentials of the being coupled to a predictive combustion model are demonstrated. Then, relying on the findings of the semi-empirical approach, a Random Forest (RF) algorithm has been thoroughly analysed and selected as a promising solution for real-time testing with on-line computed variables. Furthermore, an automatic feature selection and calibration procedure of the algorithm hyper-parameters has been developed. Very interesting performances were recorded as the reference model prediction accuracies were reproduced and comparable results were obtained when only ECU-measured variables were considered. The presented Random Forest model can be intended to be part of a pollution-oriented real-time powertrain control strategy that could on accurate and repeatable PM estimations
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