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Abstract Secondary concentrators are used in solar concentrating systems to redirect solar beams reflected by the primary concentrators to the focal point or line. These components allow to increase the concentrated solar flux density and hence to lower thermal radiation losses. Solar reflectors for secondary concentrators are permanently exposed to environmental conditions, high radiation fluxes and elevated temperatures that potentially cause stress and degradation throughout the time. Therefore, analyzing solar reflectors of secondary concentrators by simulating these conditions is crucial. No previous research works about the durability of solar reflector materials for secondary concentrators have been reported. The present work is focused on studying the degradation of the reflector materials by simulating accelerated aging, caused by several ambient parameters and the effect of concentrated radiation. Both cooled and uncooled systems for secondary concentrators are included in this study. According to results obtained, aluminum reflectors and thin silvered-glass reflectors glued to an aluminum structure showed minimum reflectance losses and structural degradation under the operation conditions of cooled 3D secondary concentrators (tower systems). Following critical aspects to avoid reflector degradation were identified: to select a suitable adhesive material to glue the thin silvered-glass reflector to the support aluminum structure, to properly protect reflectors edges, to design a suitable cooling system and to avoid the combination of high radiation fluxes with mechanical stress. In addition, laminated silvered-glass reflectors have shown to be suitable for uncooled 2D secondary concentrators (Fresnel collectors). Furthermore, a comparison with naturally aged secondary concentrators using silvered-glass reflectors glued to an aluminum structure revealed that the simulated degradation under accelerated conditions performed in this work did reproduce the most frequent degradation patterns suffered in real operating conditions.

Abstract Limiting the increase of global temperature requires electrification of the railway transport, which is relatively a distinct mode of transport in terms of economy, business and socialization of a country. However, battery complemented railway transport systems such as fuel cell hybrid railway propulsion systems are constrained by the high cost of the battery, and suboptimal performance of the fuel cell under varying loads. Optimized selection of battery can reduce its costs associated with sizing, life and utilization. Similarly, managing the load distribution between the battery and fuel cell can reduce the load variations concern for fuel cell. In this study, model-based selective characterization of the battery and fuel cell is done to identify the optimal features and constraints. Following characterization, the load allocation to battery and fuel cell is determined with the targets of collaborative utilization of optimal features and mitigation of constraints. Subsequently, considering this load allocation and results of characterization, conditions are proposed for reliable sizing and optimized selection of battery. The significance of the proposed conditions in reliable sizing is explored. Based on these conditions, certain commercially developed batteries are evaluated for optimized selection of battery. The results signify this study as a foundation for research on the reliable sizing and optimized selection of battery in fuel cell hybrid railway propulsion systems.

This paper introduces a model predictive control (MPC) strategy for the purpose of fuel-optimal operation of a range-extender hybrid vehicle. The modern navigation system nowadays can provide abundant road information. Using this information, the proposed controller solves a global optimization problem offline in order to determine a preset trajectory of the state of charge (SoC). The online MPC uses the resulting SoC trajectory as set-points for the terminal state in every moving horizon. Repeating this process, the optimal energy management along the trip to be traveled can thus be calculated. This proposed control strategy is implemented in the commercial vehicle simulation environment IPG CarMaker. From the first simulation results, the proposed strategy shows a promising fuel saving potential with real-time capability.

AbstractIn this paper a complete vehicle system simulation tool chain which applies a Multi Objective Optimization (MOO) methodology for designing the Electric Powertrain (ePT) of Battery Electric Vehicles (BEV) is presented. Optimization scope includes all relevant electric powertrain components from battery, inverter, electric machine to gear box. In addition to cost, vehicle dynamics, energy consumption and range are further optimization targets. For an overall minimal system cost design a multiplicity of interactions between all powertrain components has to be taken into account. High system complexity prevents an expert to consider all relevant correlations without the support of numeric simulation tools. The presented simulation tool chain enables fast identification of the best cost/benefit trade off regarding system cost while considering all defined system performance requirements. The approach enables experts to find unconventional solutions which would have been overlooked applying a classical straight forward approach and, thus, helps to sharpen the expert's knowledge in cause-effect relationships on the system level. Typical use cases are given and illustrated by several practical examples.

The current status and challenges associated with the production and utilization of cellulosic ethanol in Korea are reviewed in this paper. Cellulosic ethanol has emerged as a promising option for mitigating Korea's CO(2) emissions and enhancing its energy security. Korea's limited biomass resources is the most critical barrier to achieving its implementation targets for cellulosic ethanol. Efforts to identify new suitable biomass resources for cellulosic ethanol production are ongoing and intensive. Aquatic biomasses including macroalgae and plantation wastes collected in the Southeast Asia region have been found to have great potential as feedstocks for the production of cellulosic ethanol. R&D explorations into the development of technologies that can convert biomass materials to ethanol more efficiently also are underway. It is expected that cellulosic ethanol will be in supply from 2020 and that, by 2030, its use will have effectively reduced Korea's total gasoline consumption by 10%.

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

In order to exploit renewable energies from tidal stream, tandem propellers of a unique counter-rotating type horizontal-axis tidal turbine was firstly designed based on the blade element momentum (BEM) theory. And then a multi-objective numerical optimization method coupled the response surface method (RSM) with the genetic algorithm (GA) was employed to obtain desirable blade profiles. The front blade pitch angle distribution was taken as optimization variable in this paper, as it plays an important role in affecting the inlet conditions of the rear blade. The numerical results show that both optimization objectives of power coefficient and thrust coefficient can be significantly improved. It was verified that the performance of the power unit with the optimized blades increases obviously by optimizing the pitch angle.