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Abstract. Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities. In this study, river discharge data from the Global Flood Awareness System (GloFAS 2.1) and observed data at 58 gauging stations are used to examine whether positive or negative phases of several Pacific and Atlantic indices significantly alter the characteristics of river flows throughout the Amazon basin (1979–2015). Results show significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative El Niño–Southern Oscillation (ENSO) phases when the sea surface temperature (SST) anomaly is positioned in the central tropical Pacific. This response is not identified for the eastern Pacific index, highlighting how the response can differ between ENSO types. Although flood magnitude and duration were found to be highly correlated, the impacts of large-scale climate variability on these characteristics are non-linear; some increases in annual flood maxima coincide with decreases in flood duration. The impact of flood timing, however, does not follow any notable pattern for all indices analysed. Finally, observed and simulated changes are found to be much more highly correlated for negative ENSO phases compared to the positive phase, meaning that GloFAS struggles to accurately simulate the differences in flood characteristics between El Niño and neutral years. These results have important implications for both the social and physical sectors working towards the improvement of early warning action systems for floods.

This work (and the companion paper, Part II) presents new experimental data for the combustion of n-C3–C6 alcohols (n-propanol, n-butanol, n-pentanol, n-hexanol) and a lumped kinetic model to describe their pyrolysis and oxidation. The kinetic subsets for alcohol pyrolysis and oxidation from the CRECK kinetic model have been systematically updated to describe the pyrolysis and high- and low-temperature oxidation of this series of fuels. Using the reaction class approach, the reference kinetic parameters have been determined based on experimental, theoretical, and kinetic modeling studies previously reported in the literature, providing a consistent set of rate rules that allow easy extension and good predictive capability. The modeling approach is based on the assumption of an alkane-like and alcohol-specific moiety for the alcohol fuel molecules. A thorough review and discussion of the information available in the literature supports the selection of the kinetic parameters that are then applied to the n-C3–C6 alcohol series and extended for further proof to describe n-octanol oxidation. Because of space limitations, the large amount of information, and the comprehensive character of this study, the manuscript has been divided into two parts. Part I describes the kinetic model as well as the lumping techniques and provides a synoptic synthesis of its wide range validation made possible also by newly obtained experimental data. These include speciation measurements performed in a jet-stirred reactor (p = 107 kPa, T = 550–1100 K, φ = 0.5, 1.0, 2.0) for n-butanol, n-pentanol, and n-hexanol and ignition delay times of ethanol, n-propanol, n-butanol, n-pentanol/air mixtures measured in a rapid compression machine at φ = 1.0, p = 10 and 30 bar, and T = 704–935 K. These data are presented and discussed in detail in Part II, together with detailed comparisons with model predictions and a deep kinetic discussion. This work provides new experimental targets that are useful for kinetic model development and validation (Part II), as well as an extensively validated kinetic model (Part I), which also contains subsets of other reference components for real fuels, thus allowing the assessment of combustion properties of new sustainable fuels and fuel mixtures.

Abstract The use of electric bus fleets has become a topical issue in recent years. Several companies and municipalities, either voluntarily or to comply with legal requirements, will transition to greener bus fleets in the next decades. Such transitions are often established by fleet electrification targets, which dictate the number of electric buses that should be in the fleet by a given time period. In this paper we introduce a comprehensive optimization-based decision making tool to support such transitions. More precisely, we present a fleet replacement problem which allows organizations to determine bus replacement plans that will meet their fleet electrification targets in a cost-effective way, namely considering purchase costs, salvage revenues, operating costs, charging infrastructure investments, and demand charges. We account for several charging infrastructure options, such as slow and fast plug-in stations, overhead pantograph chargers, and inductive (wireless) chargers. We refer to this problem as the electric bus fleet transition problem, and we model it as an integer linear program. We apply our model to conduct computational experiments based on several scenarios. We use real data provided by a public transit agency in order to draw insights into optimal transition plans.

With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.

Organised by: Cranfield University Starting from the framework of Product Lifecycle Management (PLM), sustainability should be provided by continuous sharing of information among the different product lifecycle phases. A PLM system provides lifecycle knowledge generated by PLM systems through product lifecycle activities. The paper aims at presenting how PLM systems represent a very important foundation for achieving a more sustainable paradigm for life, a more sustainable development, engineering, manufacturing, use and disposal of products. Mori Seiki – The Machine Tool Company

Abstract Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method.