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A comparison of four different control-oriented models has been carried out in this paper for the simulation of the main combustion metrics in diesel engines, i.e., combustion phasing, peak firing pressure, and brake mean effective pressure. The aim of the investigation has been to understand the potential of each approach in view of their implementation in the engine control unit (ECU) for onboard combustion control applications. The four developed control-oriented models, namely the baseline physics-based model, the artificial neural network (ANN) physics-based model, the semi-empirical model, and direct ANN model, have been assessed and compared under steady-state conditions and over the Worldwide Harmonized Heavy-duty Transient Cycle (WHTC) for a Euro VI FPT F1C 3.0 L diesel engine. Moreover, a new procedure has been introduced for the selection of the input parameters. The direct ANN model has shown the best accuracy in the estimation of the combustion metrics under both steady-state/transient operating conditions, since the root mean square errors are of the order of 0.25/1.1 deg, 0.85/9.6 bar, and 0.071/0.7 bar for combustion phasing, peak firing pressure, and brake mean effective pressure, respectively. Moreover, it requires the least computational time, that is, less than 50 s when the model is run on a rapid prototyping device. Therefore, it can be considered the best candidate for model-based combustion control applications.

In this paper, an integrated and automated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive diesel engine, considering different working points, including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable assessing pollutant formation properly, specifically to estimate NOx concentrations. Soot formation trends were also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new diesel engines, due to its ability to provide reliable engine performance and emissions estimations from the early stage of a new engine development.

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This work proposes the integration of a data driven model into a multilayer pattern analytical simulator. This model allows quantifying areal distribution injection in each layer and models the delay in the producer's response through two kind of parameters used in the simulation. This paper deals with several problems appearing in the parameters’ identification process in real waterflood projects with large amount of unknowns and practical situations that lead to ill conditioned matrices. The waterflood simulator consists of multi - layer patterns. These injection patterns contain a series of flow elements that link each injector well with the neighboring producing wells in each layer. The patterns’ construction takes into account the geometry involved in the injection and producer wells configuration in each layer. Patterns change in time as wells/layers are opened or shut in or wells are converted. A complete algorithm able to support the dynamic changes of the patterns was developed to identify the set of parameters in the data driven model (distribution coefficients and time constants) to fit the total production/layer injection history. Then, each flow element, characterized by its pore volume and mobile oil saturation, can be swept by the water injected in the layer, areally prorated by the distribution coefficients towards each producer well. The developed identification algorithm proved to be fast and robust to identify distribution coefficients and time constants in several synthetic cases and real multilayer reservoirs. The algorithm was able to handle the high number of unknowns generated in big multilayer multistage waterflood projects. Problems related to ill conditioned matrices, such as collinearities due to multilayer injection and insufficient data in the identification process, were successfully solved using different strategies. The data driven model reduces the effort and uncertainty associated with the proper distribution of injection each time a pattern changes simultaneously modeling the delay in producers’ response, improving the whole simulation process. Besides, it is capable to detect low connection zones highlighting the presence of useless flow elements due to low permeability regions, sealing faults or operational problems. The impact of the integration of the data driven model into the simulation process was evaluated quantifying history matching and computational run time for real waterflood projects. A complete methodology to perform simulations in mature fields is presented. For the first time, a data driven model is integrated into a waterflood pattern simulator in order to quantify connectivities between wells once the patterns have been constructed in time. In this way, the data driven model incorporates geometric considerations and geological information. The methodology is especially useful when production comes from multilayer reservoirs or from old fields where the lack of information impedes performing traditional numerical simulations.