Development and Evaluation of In-Flight Icing Index Forecast for Aviation

<p>Hindcasts from the United Kingdom Met Office weather model are used as inputs to an in-flight icing index from the literature. This index uses information about model-predicted temperature, relative humidity, vertical velocity and cloud liquid water content. Parts of the icing index formulation are then modified slightly, in the light of comparisons between hindcast model data and ground-based remote sensing observations. Firstly, the link to relative humidity is replaced with a link to model-predicted cloud cover. Secondly, although super-cooled liquid water icing is due to cloud condensate in the liquid phase, the model may not always correctly partition its condensate into the correct phase. So the second modification is to consider all condensate irrespective of phase when calculating the icing index. The skill of the original and new index are then assessed quantitatively against satellite-derived icing potential. We show that the new indices have substantially better reliability than the operational index used up until recently. Finally, we present a case study, when icing was reported, and discuss ways of presenting the likelihood and severity information.</p>

Dynamic Prediction and Optimization of Energy Efficiency Operational Index (EEOI) for an Operating Ship in Varying Environments

The demands for lower Energy Efficiency Operational Index (EEOI) reflect the requirements of international conventions for green shipping. Within this context it is believed that practical solutions for the dynamic optimization of a ship’s main engine and the reduction of EEOI in real conditions are useful in terms of improving sustainable shipping operations. In this paper, we introduce a model for dynamic optimization of the main engine that can improve fuel efficiency and decrease EEOI. The model considers as input environmental factors that influence overall ship dynamics (e.g., wind speed, wind direction, wave height, water flow speed) and engine revolutions. Fuel consumption rate and ship speed are taken as outputs. Consequently, a genetic algorithm is applied to optimize the initial connection weight and threshold of nodes of a neural network (NN) that is used to predict fuel consumption rate and ship speed. Navigation data from the training ship “YUMING” are applied to train the network. The genetic algorithm is used to optimize engine revolution and obtain the lowest EEOI. Results show that the optimization method proposed may assist with the prediction of lower EEOI in different environmental conditions and operational speed.

The Cost to Quality Ratio in Terms of Voyage Energy Efficiency

Shipping is permanently engaged in efforts to regulate the voyage energy efficiency and to control the marine GHG emissions. In order to achieve this, the International Maritime Organization (IMO) has developed a series of technical and operational measures. The Energy Efficiency Operational Index is one of the operational measures that can be used as a monitoring tool for the voyage optimization and represents the mass of CO2 emitted per unit of transport work. The purpose of this study is to analyze the competitiveness of using different types of marine fuels during the voyage and also to emphasize their influence over the Energy Efficiency Operational Index. The emissions from ships are directly proportional to the bunker consumption and with its quality, and this paper presents the Energy Efficiency Operational Index value for one complete voyage, varying the type of fuel for different legs for the main consumers: main engine, diesel generators, boiler and inert gas generator. The results consist in the cost to quality ratio, where the cost is the sum of money spent for different types of fuel and the quality is the ships Energy Efficiency Operational Index. The cost-to-quality ratio is presented in graphs in order to allow the ship-owner to choose the solution of protecting the marine environment, acting over the EEOI, based on the cost involved.