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A previously developed CFD-based optimization tool is utilized to find optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses of a split injection system, the duration of each pulse, the exhaust gas recirculation rate, the boost pressure and the compression ratio.

This paper compares 4 different EGR systems by means of simulation in GT-Power. The demands of optimum massive EGR and fresh air rates were based on experimental results. The experimental data were used to calibrate the model and ROHR, in particular. The main aim was to investigate the influence of pumping work on engine and vehicle fuel consumption (thus CO2 production) in different EGR layouts using optimum VG turbine control. These EGR systems differ in the source of pressure drop between the exhaust and intake pipes. Firstly, the engine settings were optimized under steady operation - BSFC was minimized while taking into account both the required EGR rate and fresh air mass flow. Secondly, transient simulations (NEDC cycle) were carried out - a full engine model was used to obtain detailed information on important parameters. The study shows the necessity to use natural pressure differences or renewable pressure losses if reasonable fuel consumption is to be achieved.

One-dimensional simulation methods for unsteady (transient) engine operations have been developed and published in previous studies. These 1-D methods utilize heat release and emissions results obtained from 3-D CFD simulations which are stored in a data library. The goal of this study is to improve the 1-D methodology by optimizing the control strategies. Also, additional independent parameters are introduced to extend the 3-D data library, while, as in the previous studies, the number of interpolation points for each parameter remains small. The data points for the 3-D simulations are selected in the vicinity of the expected trajectories obtained from the independent parameter changes, as predicted by the transient 1-D simulations. By this approach, the number of time-consuming 3-D simulations is limited to a reasonable amount.

The paper describes experience obtained with a GT-Power code used for a downsized turbocharged gasoline engine modeling. The steady-performance model, calibrated by preliminary experiments, has been modified to the transient response one. Knock limit prediction has been used for compression ratio and boost pressure optimization. New authors′ models have been developed for extrapolation of compressor and turbine maps to cover the field of operation modes during a typical transient response. GT-Power control elements ensured a realistic engine response to accelerator, brake or clutch positions. The Driver element could drive various speed schedules such as maximum acceleration mode, engine braking mode or the European fuel-consumption/emission test.

This paper introduces a research work on the air loop system for a downsized two-stroke two-cylinder diesel engine conducted in framework of the European project dealing with the POWERtrain for Future Light-duty vehicles - POWERFUL. The main objective was to determine requirements on the air management including the engine intake and exhaust system, boosting devices and the EGR system and to select the best possible technical solution. With respect to the power target of 45 kW and scavenging demands of the two-cylinder two-stroke engine with a displacement of 0.73 l, a two-stage boosting architecture was required. Further, to allow engine scavenging at any operation, supercharger had to be integrated in the air loop. Various air loop system layouts and concepts were assessed based on the 1-D steady state simulation at full and part load with respect to the fuel consumption.

The objective of this study is the development of a computationally efficient CFD-based tool for finding optimal engine operating conditions with respect to fuel consumption and emissions. The optimization algorithm employed is based on the steepest descent method where an adaptive cost function is minimized along each line search using an effective backtracking strategy. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space. The application of this optimization tool is demonstrated for the Sulzer S20, a central-injection, non-road DI diesel engine. The optimization parameters are the start of injection of the two pulses, the duration of each pulse, the duration of the dwell, the exhaust gas recirculation rate and the boost pressure.

Diesel fuel injection process calculations have been performed by means of in-house developed mathematical models. An Eulerian multidimensional code for in-cylinder two-phase flow computations is used in conjunction with a hydrodynamic one-dimensional model of a fuel injection system. The multidimensional model comprehends all basic processes, which play a role in spray formation. The compressible gaseous flow with transport of species is solved together with the flow of dispersed liquid phase using the Eulerian reference frame for both phases. The two-way coupling between the phases in mass, momentum, and energy balances is considered. A detailed description of liquid phase is present, taking into account drop size distribution in terms of the multi-continua approach. The hydrodynamic model capable of simulating common fuel injection systems is used for the rate-of-injection computations to provide realistic boundary conditions to the spray model.

The objective of this paper is to present the results of the GT Power calibration with engine test results of the air loop system technology down selection described in the SAE Paper No. 2012-01-0831. Two specific boosting systems were identified as the preferred path forward: (1) Super-turbo with two speed Roots type supercharger, (2) Super-turbo with centrifugal mechanical compressor and CVT transmission both downstream a Fixed Geometry Turbine. The initial performance validation of the boosting hardware in the gas stand and the calibration of the GT Power model developed is described. The calibration leverages data coming from the tests on a 2 cylinder 2-stroke 0.73L diesel engine. The initial flow bench results suggested the need for a revision of the turbo matching due to the big gap in performance between predicted maps and real data. This activity was performed using Honeywell turbocharger solutions spacing from fixed geometry waste gate to variable nozzle turbo (VNT).

Current vehicles, especially the electric ones, are complex mechatronic devices. The pickup vehicles of small sizes are currently used in transport considerably. They often operate within a repeating scheme of a limited variety of tracks and larger fleets. Thanks to mechatronic design of vehicles and their components and availability of high capacity data connection with computational centers (clouds), there are many means to optimize their performance, both by planning prior the trip and recalculations during the route. Although many aspects of this opportunity were already addressed, the paper shows an approach developed to further increase the range of e-vehicle operation. It is based on prior information about the route profile, traffic density, road conditions, past behaviour, mathematical models of the route, vehicle and dynamic optimization. The most important part of the procedure is performed in the cloud, using both computational power and rich information resources.