Open Access

Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movement s and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.

Combined with the use of renewable energy sources for its production, Hydrogen represents a possible alternative gas turbine fuel for future low emission power generation. Due to its different physical properties compared to other fuels such as natural gas, well established gas turbine combustion systems cannot be directly applied for Dry Low NOx (DLN) Hydrogen combustion. This makes the development of new combustion technologies an essential and challenging task for the future of hydrogen fueled gas turbines. The newly developed and successfully tested “DLN Micromix” combustion technology offers a great potential to burn hydrogen in gas turbines at very low NOx emissions. Aiming to further develop an existing burner design in terms of increased energy density, a redesign is required in order to stabilise the flames at higher mass flows and to maintain low emission levels. For this purpose, a systematic design exploration has been carried out with the support of CFD and optimisation tools to identify the interactions of geometrical and design parameters on the combustor performance. Aerodynamic effects as well as flame and emission formation are observed and understood time- and cost-efficiently. Correlations between single geometric values, the pressure drop of the burner and NOx production have been identified as a result. This numeric methodology helps to reduce the effort of manufacturing and testing to few designs for single validation campaigns, in order to confirm the flame stability and NOx emissions in a wider operating condition field.