Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles

Development of a Framework for Internal Combustion Engine Simulations in OpenFOAM

High-accuracy simulations of internal combustion engines (ICE) allow deep insight into the physical processes of the different phases of the engine cycle: gas exchange, mixture formation, compression, combustion and emission formation. The commercial solvers for ICE simulations provide a full package which covers these areas. However, the user of such software is unable to look into the source code, making it impossible to implement new models or investigate possible implementation errors in the code, and costs arise due to licensing requirements for commercial solvers. Although the open source framework OpenFOAM already includes multiple classes and two solvers dedicated to internal combustion engine simulations, there is no way to move engine valves and piston simultaneously with its standard tools. Thus, this paper presents a new engine library for ICE simulations written for OpenFOAM. The new framework is capable of simulating a complete fired engine cycle. The piston and the valves are moved simultaneously. To address large deformations in the mesh, a methodology to avoid insufficient mesh quality was developed. Ignition and combustion is modeled with standard tools from OpenFOAM. To validate the method, the simulation results for the averaged in-cylinder quantities pressure, temperature and mass are compared with experimental data

Test Rig for Fundamental Investigations of Ignition System Characteristics under Severe Flow Conditions

supply concepts. To achieve further improvements in efficiency and to decrease emissions, engine operating strategies with very lean air-fuel mixtures and high turbulence levels are required. However, these severe conditions have a significant impact on the inflammability of the mixture and compromise combustion stability. Reliably igniting the mixture and keeping cycle-to-cycle variation of the combustion process at a low level is challenging and requires deeper understanding of the fundamentals of the ignition process. The electric arc, which transfers the electric energy to the air-fuel mixture and initiates the inflammation, plays a central role in the ignition process. Thus, the paper at hand presents a test rig that was developed for detailed investigations of electric arc behavior under flow conditions similar to those in spark ignited large gas engines. The test rig consists of a closed loop flow circuit. Flow velocities at the spark plug up to 30 m/s, pressures up to 60 bar and temperatures up to 80 °C can be achieved under non-combustible conditions. The centerpiece of the test rig is the test cell, which provides excellent optical access from three sides for high-speed imaging of the arc without disturbing the flow field at the spark plug. A sufficiently long stabilizing path upstream of the test cell guarantees defined and fully developed turbulent pipe flow conditions at the spark plug. Sophisticated post-processing algorithms were developed that automatically extract relevant data from the high-speed images (e.g., arc length) and compare the information with electrical signals such as current and voltage on both the primary and secondary sides of the electronic ignition system. The results provide a deeper understanding of the ignition process and serve as basis for model validation. Finally, measurement results of a pressure variation are presented and discussed. The results show greater arc stretching and increased cycle-to-cycle variation in arc length at higher pressures

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified

Optimal design and operation of maritime energy systems based on renewable methanol and closed carbon cycles

The phasing out of fossil fuels in the shipping sector is of key importance for reducing greenhouse gas emissions. Synthetic fuels based on renewable energy are a promising option for a sustainable maritime sector, with renewable methanol being one of the most widely considered energy carriers. However, the availability of renewable methanol is still limited and the costs associated with it are significantly higher than for conventional fuels, also because fuel synthesis must rely on carbon dioxide as a resource. Through the use of onboard carbon capture, the release of carbon dioxide during combustion can be avoided, and this closed cycle reduces the need for carbon sources. This paper investigates such a scenario by analyzing overall ship energy systems that use internal combustion engines with connected pre-combustion and post-combustion carbon capture technologies. The effect of these technologies on the techno-economic performance of a fully renewable energy system is investigated by setting up a mixed-integer optimization framework for the optimal design and operation of ship propulsion systems. The propulsion demand for the chosen case study consists of a typical operational profile of a ferry operating in the Baltic Sea. Comparison of the capture cases to a system solely based on renewable methanol reveals significant cost advantages of the closed carbon cycle systems. The baseline scenario has nearly 20% lower annual costs, with total capture rates of 90% in the post-combustion case and around 40% in the pre-combustion case. An extensive sensitivity analysis shows that these cost advantages are robust against various technological and economic boundary conditions. In the pre-combustion case, process heat demand reduction in combination with increased engine heat supply might enable higher capture rates beyond 90%. The results indicate that combining renewable fuels with onboard carbon capture creates opportunities for cost-effective, sustainable shipping

Dimeric chlorite dismutase from the nitrogen-fixing cyanobacterium Cyanothece sp. PCC7425

It is demonstrated that cyanobacteria (both azotrophic and non-azotrophic) may 34 contain heme b oxidoreductases that can convert chlorite to chloride and molecular oxygen (incorrectly denominated chlorite “dismutase”, Cld). Beside the water-splitting manganese complex of photosystem II this metalloenzyme is the second known enzyme that catalyzes the formation of a covalent oxygen-oxygen bond. All cyanobacterial Clds have a truncated N-terminus and are dimeric (i.e. clade 2) proteins. As model protein, Cld from Cyanothece sp. PCC7425 (CCld) was recombinantly produced in E. coli and shown to efficiently degrade chlorite with an activity optimum at pH 5 (kcat 1144 ± 23.8 s-1, KM 162 ± 10.0 μM, catalytic efficiency (7.1 ± 0.6) × 106 M-1 s-1). The resting ferric high-spin axially symmetric heme enzyme has a standard reduction potential of the Fe(III)/Fe(II) couple of -126 ± 1.9 mV at pH 7. Cyanide mediates the formation of a low-spin complex with kon = (1.6 ± 0.1) × 105 M-1 s-1 and koff = 1.4 ± 2.9 s-1 (KD ~ 8.6 μM). Both, thermal and chemical unfolding follows a non-two state unfolding pathway with the first transition being related to the release of the prosthetic group. The obtained data are discussed with respect to known structure-function relationships of Clds. We ask for the physiological substrate and putative function of these O2-producing proteins in (nitrogen-fixing) cyanobacteria

Transformation-based regression

acceptance rate = 33%status: publishe

Development and Validation of 3D-CFD Injection and Combustion Models for Dual Fuel Combustion in Diesel Ignited Large Gas Engines

This paper focuses on improving the 3D-Computational Fluid Dynamics (CFD) modeling of diesel ignited gas engines, with an emphasis on injection and combustion modeling. The challenges of modeling are stated and possible solutions are provided. A specific approach for modeling injection is proposed that improves the modeling of the ballistic region of the needle lift. Experimental results from an inert spray chamber are used for model validation. Two-stage ignition methods are described along with improvements in ignition delay modeling of the diesel ignited gas engine. The improved models are used in the Extended Coherent Flame Model with the 3 Zones approach (ECFM-3Z). The predictive capability of the models is investigated using data from single cylinder engine (SCE) tests conducted at the Large Engines Competence Center (LEC). The results are discussed and further steps for development are identified