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Abstract The promise of redox flow batteries (RFBs) utilizing soluble redox couples, such as all vanadium ions as well as iron and chromium ions, is becoming increasingly recognized for large-scale energy storage of renewables such as wind and solar, owing to their unique advantages including scalability, intrinsic safety, and long cycle life. An ongoing question associated with these two RFBs is determining whether the vanadium redox flow battery (VRFB) or iron-chromium redox flow battery (ICRFB) is more suitable and competitive for large-scale energy storage. To address this concern, a comparative study has been conducted for the two types of battery based on their charge–discharge performance, cycle performance, and capital cost. It is found that: i) the two batteries have similar energy efficiencies at high current densities; ii) the ICRFB exhibits a higher capacity decay rate than does the VRFB; and iii) the ICRFB is much less expensive in capital costs when operated at high power densities or at large capacities.

Abstract One factor that significantly limits blade service life and reliability in GT-100 power turbines is sulphide-oxide metal corrosion of guide and rotating blades. Application of coatings is the most efficient method to prevent corrosion failure. An electron beam physical vapour deposition CoCrAlY coating is used for high and low pressure turbine first-stage rotating blades made from E1 893 alloy. Studies of CoCrAlY-coated blades after different service periods (from 390 to 5900 h) showed that this coating most efficiently protects rotating blades in turbines which operate in northern regions of the country. Because of low temperature hot corrosion leakage, corrosion failure is observed in turbines operating in southern regions of the country. In order to improve corrosion resistance, a ceramic coating has been developed. These coatings increase the endurance of equipment and make it possible to save energy.

Charging lithium–oxygen batteries with redox mediators suppresses singlet oxygen generation at rates orders of magnitude faster than quenchers.

Using an experiment embedded within a representative survey, this study examined the interactive effect of party identification and risk/benefit perception on public opinion about biofuels. Democrats tended to be more supportive of biofuels than Republicans. However, the effect of party identification on opinion about biofuels varied when individuals considered the risk/benefit of biofuels in different domains. Individuals who reported greater affiliation with the Democratic Party were likely to support funding biofuels research when primed with the economic risks or the social/ethical benefits of biofuels. For those who considered the social/ethical benefits of biofuels, more self-identified Democrats were likely to support biofuels production and use. However, more self-identified Democrats were less supportive of biofuels production and use when they considered the political risks of biofuels. Implications are discussed.

Abstract Explosion risk analysis (ERA) is an effective method to investigate potential accidents in hydrogen production facilities. The ERA suffers from significant hydrogen dispersion-explosion scenario-related parametric uncertainty. To better understand the uncertainty in ERA results, thousands of Computational Fluid Dynamics (CFD) scenarios need to be computed. Such a large number of CFD simulations are computationally expensive. This study presents a stochastic procedure by integrating a Bayesian Regularization Artificial Neural Network (BRANN) methodology with ERA to effectively manage the uncertainty as well as reducing the stimulation intensity in hydrogen explosion risk study. This BRANN method randomly generates thousands of non-simulation data presenting the relevant hydrogen dispersion and explosion physics. The generated data is used to develop scenario-based probability models, which are then used to estimate the exceedance frequency of maximum overpressure. The performance of the proposed approach is verified by analyzing the parametric sensitivity on the exceedance frequency curve and comparing the results against the traditional ERA approach.

Abstract The present study is aimed to investigate and compare effects of biodiesel-ethanol (BE) and biodiesel-n-butanol (BBu) blends on combustion, performance and emissions of a direct-injection diesel engine. Experiments were conducted on BE5 (5% ethanol and 95% biodiesel, v/v), BE10, BE15, BBu5, BBu10 and BBu15, at five engine loads and at 1800 rpm. In regard to combustion characteristics, effects on maximum heat release rate, maximum in-cylinder pressure, start of combustion, combustion duration and coefficients of variations (COVs) of IMEP and maximum increase rate of in-cylinder pressure were investigated. In regard to engine performance, effects on BSFC and BTE were investigated. The blended fuels have adverse effects on engine performance especially at low load, with the BE blends having more adverse effects than the BBu blends. Moreover, on average of the five engine loads, the BBu and BE blends increase CO emission by 13.7% and 22.8% and HC emission by 5.6% and 29.2%, respectively; but reduce NOx emission by 6.5% and 28.0%, particle mass concentration by 20.7% and 20.6% and particle number concentration by 22% and 21%, respectively. Overall, the BE blends are more effective in reducing particulate and NOx emissions but the BBu blends would lead to less increase in CO and HC emissions.

The paper proves the economic feasibility of using a local autonomous source of energy supply, which is formed from electrical equipment of other functional purposes based on an asynchronous machine with capacitive self-excitation. To assess the technical and economic indicators of the effectiveness of introducing a local autonomous source of energy supply, the discounted method is adopted, where an indicator of savings from possible losses due to the exclusion of labor resources from production is used as an additional influence factor.

Abstract Driven by wind and buoyancy effects in the urban environment, ventilation performance and pollutant transmission are highly related to human health. In order to investigate characteristics of the single-sided natural ventilation and interunit dispersion problem, this study conducted scaled outdoor experiments in summer and winter periods in two-dimensional street canyons. Tracer gas method was adopted to predict the ventilation rate and simulate the pollutant dispersion. It was found the ventilation performance of windward and leeward rooms showed different trends with wind velocities. Archimedes number Ar was used to examine the interactions of the buoyancy and the wind forces. It revealed that the non-dimensional ventilation rates of all rooms were generally smaller than the results of buoyancy effect only. It indicates that interactions between the buoyancy and wind effects were destructive, which reduced the ventilation rates. The interunit dispersion characteristics with the wind effect were highly dependent on source locations. The results of the tracer gas concentrations of the reentered rooms were not showing simple increasing or decreasing trends. This study provides authentic and instant airflow and pollutant dispersion information in an urban environment. The dataset of this experiment can offer validations for further numerical simulations.

Abstract Renewable energy utilisation, latent energy storage, optimal system design, and robust system operation are critical elements for carbon-free buildings and communities. Machine learning methods are effective to assist the energy-efficient renewable systems during multi-criteria design and multi-level uncertainty-based operation periods. However, the current literature provides little knowledge on this topic. In this study, a state-of-the-art-review on phase change materials for cooling applications is presented, in terms of smart ventilations, intelligent PCMs charging/discharging, deterministic parametrical analysis, stochastic uncertainty-based performance prediction and optimisation. Furthermore, technical effectiveness of machine learning methods in single and multi-objective optimisations has been presented, through hybrid PCMs integrated renewable systems. Multivariables involved in the review include thermo-physical, geometrical and operating parameters of PCMs. Multi-criteria employed in the review include heat transfer rate, cooling energy storage density, heat storage and release efficiency, and indoor thermal comfort. The literature review presents technical challenges, such as tradeoff solutions between computational accuracy and efficiency, generic methods for effective selection amongst multi-diversified optimal solutions along the Pareto front, the general methodology for multi-level uncertainty quantification, smart controllers with accurate predictions under high-level parameters’ uncertainty and stochastic occupants’ behaviors. The future outlook and recommendations of machine learning methods in PCMs integrated cooling systems have also been presented as avenues for upcoming research.