Agostino Gambarotta

A thermodynamic Mean Value Model of the intake and exhaust system of a turbocharged engine for HiL/SiL applications.

Nowadays requirements towards a reduction in fuel consumption and pollutant emissions of Internal Combustion Engines (ICE) keep on pushing Manufacturers to improve engines performance through the enhancement of existing subsystems (e.g., electronic fuel injection, VVT/VVA systems) and the introduction of specific devices (e.g., turbochargers, exhaust gas recirculation systems, after treatment components). Automotive engines have become very complex plants, and mathematical models are useful tools in the design of control systems. To define mathematical models for control oriented applications, an original library [1] has been developed in Simulink ® by the authors for the simulation of last generation automotive engines. Typical engine components and sub-systems are described through specific blocks that can be assembled to build up a whole engine model [2,3,4]. A “quasi-steady” (QS) approach was followed for non-volume components (as compressor, turbine, intercooler, EGR valve and cooler, etc.), while “filling-and-emptying” (F&E) techniques were used for volumes (e.g., intake and exhaust manifolds). Library blocks were then used to assembly a sub-model of the intake and exhaust system, fitted with a VGT turbocharger, intercooler, EGR circuit with cooler and throttle. The simulation procedure, which is based on a detailed physical modelling of the gas path, was then integrated in a HiL test bench from dSpace Gmbh at Magneti Marelli Powertrain facilities. After calibration and validation –with reference to a small Common Rail Diesel engine- it was widely used in SiL/HiL testing. In the paper the model is presented and several results obtained in the validation phase are reported, showing its capabilities to simulate the behaviour of the intake and exhaust system in “real-time” within HiL testing environments.

A LEARNING-MACHINE BASED METHOD FOR THE SIMULATION OF COMBUSTION PROCESS IN AUTOMOTIVE I.C. ENGINES

In automotive applications problems related to management and diagnostics play an important role to improve engine performance and to reduce fuel consumption and pollutant emissions. In the design of control systems the use of theoretical models for the simulation of engine behaviour proved to be very useful, and it is apparent from the literature. However, since automotive engines have become very complex plants, their modelling requires a comprehensive description of the behaviour of many processes and components. Combustion process has a strong influence on performance and emissions, but its theoretical description can be hardly combined with the requirements of control-oriented models (especially as regards “real-time” applications). Two simplified theoretical models are proposed in the paper, based on a thermodynamic and a simplified approach respectively. In the first case a single-zone method was followed with the introduction of an apparent heat release rate (HRR) described as a superposition of two Wiebe functions. Coefficients of these burning functions are estimated by means of Learning Machines (LM), i.e. Support Vector Machines (SVM), trained from experimental data and then embedded in a Simulink® block. In order to make calculation time shorter, a simpler and faster model based on the application of SVM was defined to describe combustion process. Starting from experimental data, the proposed SVM was trained and implemented in a Simulink® block to evaluate exhaust gas temperature and bmep directly from engine operating parameters. Both blocks were defined to be easily embedded in engine simulation models for control-oriented applications. Results were promising for both models, showing very short computation time. A comparison of theoretical outputs with experimental data is reported in the paper, together with an application of both calculation procedures to a comprehensive model of a modern automotive turbocharged Diesel engine.© 2003 ASME

Real-time simulation of the effects of catalyst on automotive engines performance

Today restrictions on pollutant emissions are forcing more and more the use of catalystbased after-treatment systems both in SI and in Diesel engines. The application of monolith cores with a honeycomb structure is an established practice: however, to overcome drawbacks such as poor flow homogenization, the use of ceramic foams has been recently investigated [1,2,3] as an alternative showing better conversion efficiencies (even if with higher pressure losses).

A switching Moving Boundary Model for the simulation of ORC plants in automotive applications

Waste heat recovery is a promising approach to improve fuel economy and emissions of thermal engines for stationary and mobile applications. Among recent solutions, Organic Rankine Cycles (ORC) seem to join effectiveness and technological readiness for the application to Internal Combustion Engines (ICE), both Spark Ignition (SI) and Diesel. Significant reductions in fuel consumption have been reported, but – especially in automotive applications – further improvements in the ORC plant matching and performance in transient operations are required. This paper presents a lumped-parameter model of an ORC system for exhaust waste heat recovery in automotive engines. The heat exchangers dynamics is accounted for by modeling the behavior of the working fluid through the Moving Boundary Method (MBM), which is based on a lumped-parameter representation of the conservation laws for single and two-phase fluid flows. An original switching technique has been implemented to account for variations in the fluid properties and heat transfer during transient operations of the ORC plant. Grey-box models for the pump and expander have been developed starting from steady-state characteristic maps. The behavior of the comprehensive model in transient operating conditions has been significantly improved also during start-up process, usually a threatening situation for mathematical models.

A Real Time Dynamic Model of a Micro-Gas Turbine CHP System With Regeneration

A model of a Micro Gas Turbine system for cogeneration is presented. The analyzed plant is based on an aero derivative Gas Turbine with a single staged centrifugal Compressor and an axial Turbine with two stages. The net power output is 260 kWe in simple cycle mode. Exhaust gases can be sent to a counter flow surface compact heat exchanger for thermal regeneration, which turns to be thermodynamically favourable in this range of power output. If a thermal load is required the system operates in CHP configuration and part, or the whole, of turbine exhaust gases are sent to a Heat Recovery Boiler for water heating. The HRB is, in analogy to the Regenerator, a counter flow surface heat exchanger. The mass of hot gases directed to each heat exchanger can be controlled by a regulation valve that allows, for a given fuel mass flow rate, to enhance the net power output or to privilege the thermal generation at the HRB. This degree of freedom allows the system to operate at different cogeneration degrees, thus covering many power-to-heat demand ratios. The whole system is modeled in the Simulink® environment, a powerful tool for dynamic system analysis. All components are studied and a mathematical representation for each of them is described. Equations are then implemented in Simulink® allowing to create customized blocks of different components which are then properly coupled, respecting the physical causality of the real system, by connections that may represent either mechanical or fluid dynamic links. Models are classified depending on whether state variables for the considered component can be defined or not. Compressor and turbine are represented as “Black Box” components without state, while the combustion chamber is modelled as a “white box” applying energy and mass conservation equations with three state variables. Heat exchangers are considered as “White Box” without state, and the physics of the heat exchange process is studied according to the Effectiveness-NTU method. A further dynamic equation is the shaft dynamic balance equation. Model results are reported in the paper in several transient conditions: in all cases the computational time proved to be lower than real time.Copyright