Fuel cells : state of the art

Publication CIMNEThis report pretends to explain the state of the art of fuel cells and the applications focused on aviation, such as unmanned aerial vehicles (UAV). A fuel cell is an electromechanical device that ha the ability to convent chemical energy of a reactant directly into electricity with high efficiency. When the fuel reacts with the oxidant, the electromechanical reaction takes place and some energy is released, usually low-voltage DC electrical energy and heat. The former is used to do useful work directly and the latter is wasted or can be used in cogeneration applications. In the following sections, two concepts will be described: the unit cell and the fuel cell. The unit is the basic operating device that converts chemical energy into electricity. Multiple unit cells connected together in series make up the fuel cell, giving the desired voltage in a specific application.Preprin

Results Comparison

This is a compilation of the results presented by all the contributing teams. The main purpose of the presentation is to enable a direct comparison between results in order to understand the differences among strategies.&nbsp

Fuel-based flight inefficiency through the lens of different airlines and route characteristics A post-operational analysis for one day of traffic at the ECAC area.

In the light of the ambitious environmental targets for future air traffic management paradigms, there is a need in the enhancement of current (key) performance indicators, with the objective to facilitate the identification of different sources of environmental inefficiencies, and to enable large scale and systematic post-operational analyses. Based on a previously published methodological framework to compute fuel-based performance indicators, this paper aims at exploring these inefficiencies at different granularity of the results. For this purpose, a set of filters has been applied on a data-set of 24h of traffic within the ECAC (European Civil Aviation Conference) area, encompassing different airspace users categories, route length and flight frequencies. The results show that the carriers prone to low-cost business models have, on average, the highest value of total fuel inefficiency in absolute terms with a median around 530 kg (17%); compared to full-service carriers with a median around 432 kg (20%); observing as well that relative fuel inefficiency significantly drops as the stage length of the routes increases. Moreover, results reveal that the busiest the routes are, the higher fuel inefficiencies they accrue. For routes with less than 5 departures per day, the fuel inefficiency accounts for 19.1% in relative terms, if compared with the total fuel burnt; whereas for the routes from the category between 12 and 20 daily departures the relative fuel inefficiency rises to 22.6%. These figures are obtained when the reference trajectory used to derive fuel inefficiency is a full free route trajectory at maximum range operations and without considering en-route charges. The paper also explores other reference trajectories, constrained to the airway network in force and/or considering the (estimated) cost index chosen by the airspace users. It is acknowledged, however, that a larger data-set needs to be considered in the future to generalise the validity of the obtained results.Peer ReviewedPostprint (author's final draft

Multilevel Monte-Carlo methods applied to the stochastic analysis of aerodynamic problems

This paper demonstrates the capabilities of the Multi-Level Monte Carlo Methods (MLMC) for the stochastic analysis of CFD aeronautical problems with uncertainties. These capabilities are compared with the classical Monte Carlo Methods in terms of accuracy and computational cost through a set of benchmark test cases. The real possibilities of solving CFD aeronautical analysis with uncertainties by using MLMC methods with a reasonable computational cost are demonstrated.Postprint (published version

Probabilistic trajectory generation using uncertainty propagation model

This document establishes the basis for the work to be developed within Work Package 2 of the START project. The objective of this Work Package is to build a methodology that could allow for the obtainment of the probabilistic trajectories that would result from the propagation of the characterized micro-level uncertainties in the aircraft trajectory prediction process. This deliverable will be focused on implementing the models and processes required to capture the influence of the uncertainties that are present in the development of an aircraft trajectory. To this end, we will show how to propagate these uncertainties, using a stochastic trajectory predictor, that will allow us to obtain a set of probabilistic trajectories from an initial deterministic flight plan, which will encapsulate the effect of the inputs’ variability. First, an introduction to Polynomial Chaos Theory, which is the basis of the stochastic trajectory predictor developed in START, and our solution for introducing weather uncertainty into the trajectory prediction process will be exposed. Then, it will be presented how the integration of the advanced data assimilation models, introduced in the deliverable D2.1 [2], together with the stochastic trajectory predictor will lead to more robust airline operations. Additionally, the framework for the probabilistic trajectory generation will be introduced, showing how all different modules will be employed in START in a two-phase approach (first an off-line fitting phase to obtain the models for uncertainty propagation, and then an online phase where, making use of the fitted model, the probabilistic trajectories can be obtained from a deterministic flight plan). Finally, a study case will be presented, showing the application of the previously defined methodology to a specific scenario.Preprin

Contribution to the definition of non deterministic robust optimization in aeronautics accounting with variable uncertainties

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and Lewis (2001). Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes. This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions

Contribution to the Definition of Non Deterministic Robust Optimization in Aeronautics Accounting with Variable Uncertainties

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and Lewis (2001) Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes. This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions

On the application of the PFEM to droplet dynamics modeling in fuel cells

The Particle Finite Element Method (PFEM) is used to develop a model to study two-phase flow in fuel cell gas channels. First, the PFEM is used to develop the model of free and sessile droplets. The droplet model is then coupled to an Eulerian, fixed-grid, model for the airflow. The resulting coupled PFEM-Eulerian algorithm is used to study droplet oscillations in an air flowand droplet growth in a lowtemperature fuel cell gas channel. Numerical results show good agreement with predicted frequencies of oscillation, contact angle, and deformation of injected droplets in gas channels. The PFEM-based approach provides a novel strategy to study droplet dynamics in fuel cells.Postprint (published version