Real-time predictive model for reactivity controlled compression ignition marine engines

Model-based design is proven to be essential for the development of control systems. This paper presents a real-time predictive control-orientated model (COM) for low-temperature combustion (LTC), dual-fuel, reactivity-controlled compression ignition (RCCI) engines. A comprehensive model-based design methodology must be capable of constructing an RCCI control-orientated model with high accuracy, high noise immunity, good response, predictivity in governing mechanisms, and low computation time. This work attains all of these for the first time for a cutting-edge RCCI marine engine. The real-time model (RTM) captures the key sensitivities of RCCI by controlling the total fuel energy and the blend ratio (BR) of two fuels, while also considering uncertainties arising from variations of inlet temperature and the gas exchange process. It provides not only the cycle-wise combustion indicators but also the crank-angle-based cylinder pressure trend. The RTM is derived by direct linearisation of a physics-based model and is successfully validated against experimental results from a large-bore, RCCI engine and the previously acknowledged UVATZ (University of Vaasa Advanced Thermo-kinetic Multi-zone) model. Validation covers both steady-state and transient modes. With high accuracy in several case studies representing typical load transients and air-path disturbance rejection tests, the model predicts maximum cylinder pressure (Pmax), crank-angle of 5 % burnt (CA5), crank-angle of 50 % burnt (CA50) and indicated mean effective pressure (IMEP) with root means square (RMS) errors of 8.6 %, 0.3 %, 0.6 %, and 0.6 % respectively. The average simulation time without any code optimisation is around 5 ms/cycle, offering sufficient real-time surplus to incorporate a semi-predictive emission submodel within the current approach.© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Radar—CubeSat Transionospheric HF Propagation Observations: Suomi 100 Satellite and EISCAT HF Facility

Radio waves provide a useful diagnostic tool to investigate the properties of the ionosphere because the ionosphere affects the transmission and properties of high frequency (HF) electromagnetic waves. We have conducted a transionospheric HF-propagation research campaign with a nanosatellite on a low-Earth polar orbit and the EISCAT HF transmitter facility in Tromsø, Norway, in December 2020. In the active measurement, the EISCAT HF facility transmitted sinusoidal 7.953 MHz signal which was received with the High frEquency rAdio spectRomEteR (HEARER) onboard 1 Unit (size: 10 × 10 × 10 cm) Suomi 100 space weather nanosatellite. Data analysis showed that the EISCAT HF signal was detected with the satellite's radio spectrometer when the satellite was the closest to the heater along its orbit. Part of the observed variations seen in the signal was identified to be related to the heater's antenna pattern and to the transmitted pulse shapes. Other observed variations can be related to the spatial and temporal variations of the ionosphere and its different responses to the used transmission frequencies and to the transmitted O- and X-wave modes. Some trends in the observed signal may also be associated to changes in the properties of ionospheric plasma resulting from the heater's electromagnetic wave energy. This paper is, to authors' best knowledge, the first observation of this kind of “self-absorption” measured from the transionospheric signal path from a powerful radio source on the ground to the satellite-borne receiver

Model-based on-board post-injection control development for marine diesel engine

Funding Information: Funding information: This work is part of the INTENS (Integrated Energy Solutions to Smart and Green Shipping) project. The authors would like to express their gratitude to Business Finland for funding support. Publisher Copyright: © 2021 Xiaoguo Storm et al., published by De Gruyter.The increasing demands for reducing fuel consumption and emissions in contemporary technology solutions lead to the use of more sensors, actuators, and control applications. With this increasing engine complexity, the feedback design is complex due to the coupling between inputs and combustion parameters. To be able to design the controller systematically, model predictive control (MPC) comes to the scope because of its advantages in the design of multi-input multi-output (MIMO) systems, especially with its constraints handling ability and performance in simultaneously optimizing the engine fuel efficiency and emission reduction. Multi-injection is one of the promising techniques for achieving better engine performance. In this work, post-injection control is implemented utilizing MPC MIMO strategy with the target of exploring the possibility of reducing emissions and improving engine efficiency by controlling post-injection duration and injection timing. The workflow of the MPC controller design from control-oriented model (COM) establishing to MPC problem formation and solution methodology is discussed in this work. Moreover, one contribution from this work is the different implementation angle when compared with the state-of-the-art approaches, where the MPC controller is implemented purely by Matlab Simulink to enable the rapid control prototyping design. The simulation result demonstrated the ability of the controller's tracking performance and showed a preliminary step towards the nonlinear combustion model-based multi-injection MPC design. The systematic model-based controller framework developed in this work can be applied to other control applications and enables a fast path from design to test.Peer reviewe

Simulation Environment for Analysis and Controller Design of Diesel Engines

Novel combustion concepts and multi injection cylinder-wise control methods are needed in large marine diesel engines for increased performance and to reduce the green house gas emissions. Even though diesel technology in cars might be reducing there is no replacement of dual fuel diesel technology in large marine engines to be seen in the near future. The paper discusses a rapid grey-box modelling technique, which can be used to predict cylinder pressure and heat release in engine cylinders. The model can be used to design effective cylinder-wise control algorithms which increase the engine performance and save fuel under constraint of emissions.Peer reviewe

Model predictive control for a multiple injection combustion model

In this work, a model predictive controller is developed for a multiple injection combustion model. A 1D engine model with three distinct injections is used to generate data for identifying the state-space representation of the engine model. This state-space model is then used to design a controller for controlling the start of injection and injected fuel mass of the post injection. These parameters are used as inputs for the engine model to control the maximum cylinder pressure and indicated mean effective pressure.Peer reviewe

A Neural Network Approach for Reconstructing In-Cylinder Pressure from Engine Vibration Data

In this work neural network models are used to reconstruct in-cylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer. Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility. The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints. A radial basis function (RBF) neural network model was first trained with a series of two operating points at low loads with data of 1000 consecutive combustion cycles, to build the needed nonlinear mapping. The results show that the developed neural network model is capable of reconstructing in-cylinder pressure at low loads with good accuracy. The error for combustion parameter such as maximum cylinder pressure did not exceed 5%. The approach is further validated with another series of operating points consisting of both low loads and high loads. However, the results in this case deteriorated. Changing the neural network model to generalized regression (GR) improved the in-cylinder pressure reconstruction quality. The performance of the models was also considered in terms of combustion parameters, such as maximum pressure and mass burned fraction. The paper concludes that vibration signal carries sufficient information to estimate combustion parameters independently on the engine platform or combustion concept.Peer reviewe

Low Temperature Combustion Modeling and Predictive Control of Marine Engines

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines

Machine Learning Methods for Emissions Prediction in Combustion Engines with Multiple Cylinders

The increasing demand of lowering the emissions of the combustion engines has led to the development of more complex engine systems. This paper presents artificial neural network (ANN) based models for estimating nitrogen oxide (NOx) and carbon dioxide (CO2) emissions from in-cylinder pressure of a maritime diesel engine. The architecture of the models is that of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) network. The data utilized to train and test the models are obtained from a four-cylinder marine engine. The inputs of the models are chosen as the first principal components of the in-cylinder pressure and engine parameters with highest correlation to aforementioned greenhouse gases. Generalization is performed on the models during the training to avoid overfitting. The estimation result of each model is then compared. Additionally, contribution of each cylinder to the production of emissions is investigated. Results indicate that MLP has a higher accuracy in estimating both NOx and CO2 compared to RBF network. The emission levels of each cylinder for both NOx and CO2 are mostly even due to the nature of the conventional diesel engine.Peer reviewe