Applications & Pathways

Population growth and economic development have significantly increased global energy demand. Hence it has raised concerns about the increase in the consumption of fossil fuels and climate change. The present work introduced a new approach to using carbon-free energy sources such as nuclear and renewable to meet energy demand. The idea of using the Nuclear-Renewable Hybrid Energy System (N-R HES) is suggested as a leading solution that couples a nuclear power plant with renewable energy and hydrogen-based storage systems. For this purpose using a meta-heuristic method based on Newton’s laws the configuration of the N-R HES is optimized from an economic and reliability point of view. The optimal system is selected from among six cases with different subsystems such as wind turbine photovoltaic panel nuclear reactor electrolysis fuel cell and hydrogen storage tank. Furthermore the performance of hydrogen-based energy storage systems such as hightemperature electrolysis (HTE) and low-temperature electrolysis (LTE) is evaluated from technical and economic aspects. The results of this work showed that using nuclear energy to supply the base load increases the reliability of the system and reduces the loss of power supply probability to zero. More than 70 % of the power is produced by nuclear reactors which includes more than 80 % of the system costs. The key findings showed that despite HTE’s higher efficiency using LTE as a storage system in N-R HES is more cost-effective. Finally due to recent developments and the safer design of nuclear reactors they can play an important role in combination with renewable energies to support carbon-free energy sectors especially in remote areas for decades to come.

The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result calculation time and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380 a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 % both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems firefighting systems simulation and prototyping tools.

This study explores the performance and emissions characteristics of a dual-fuel internal combustion engine operating on a blend of hydrogen and gasoline. This research began with a baseline simulation of a conventional gasoline engine which was subsequently validated through experimental testing on an AVL testbed. The simulation results closely matched the testbed data confirming the accuracy of the model with deviations within 5%. Building on this validated model a hydrogen–gasoline dual-fuel engine simulation was developed. The predictive simulation revealed an approximately 5% increase in overall engine efficiency at the optimal operating point primarily due to hydrogen’s combustion properties. Additionally the injected gasoline mass and CO2 emissions were reduced by around 30% across the RPM range. However the introduction of hydrogen also resulted in a slight reduction (~10%) in torque attributed to the lower volumetric efficiency caused by hydrogen displacing intake air. While CO emissions were significantly reduced NOx emissions nearly doubled due to the higher combustion temperatures associated with hydrogen. This research demonstrates the potential of hydrogen–gasoline dual-fuel systems in reducing carbon emissions while highlighting the need for further optimization to balance performance with environmental impact.

To expedite the development of the infrastructural expansion for hydrogen applications the research project “THEWA” was founded. Within this project the development of hydrogen-refueling stations is being advanced so that the hydrogen strategy for mobility in Germany can move forward. One development point of the project is to develop an evaluation model that recommends a concept for hydrogen-refueling stations for initial individual situations. In this work an evaluation method is developed that provides an appropriate recommendation. For this purpose basics such as the general structure of hydrogen-refueling stations their classification into functional areas and alreadyexisting evaluation methods for multi-criteria decisions are shown. The method for the evaluation of hydrogen-refueling stations will be developed in a component-based manner for which a selection of influencing factors of hydrogen-refueling stations will be explained and categorized. With the help of an expert workshop these are scaled so that the result is an evaluation method based on an expert assessment and the consideration of individual customer requirements. In addition the method is implemented in a tool so that it can be used more easily.