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An important challenge in developing the Scramjet engine is cooling of the walls. One possibility is circulating the fuel itself through the walls of the engine, with cooling achieved through the strongly endothermic decomposition of the fuel. This paper presents an experimental and modelling study of the pyrolysis of n-dodecane, a component of some jet fuels. The experiments have been performed in a stainless steel isothermal plug flow reactor at temperatures of 950, 1000 and 1050 K and atmospheric pressure, with GC analysis of the end products, mainly methane, ethane and alkenes from C2 to C10. A detailed kinetic model containing 1175 reactions has been produced using EXGAS software. This model is able to represent with reasonable accuracy the experimental results presented here, both for the conversion of n-dodecane and for the formation of products, as well as literature data obtained from 623 to 823 K.

While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.

AbstractAtomic resonance absorption spectroscopy has been used to investigate the thermal decomposition of N2O by monitoring the formation of O atoms behind reflected shock waves in the temperature range 1490–2490 K and at total pressures from 58 to 347 kPa, by using the mixtures of N2O highly diluted in Ar. For the chosen experimental conditions, the rate coefficient k1,0 for the reaction N2O + Ar → N2 + O + Ar had the greatest effect on the O atom concentration increase, so this reaction rate constant could be deduced by comparison between experiment and computed simulation. In the actual temperature range, we found k1,0 (cm3 mol−1s−1) = 7.2 × 1014 exp(−28878/T(K)), with an overall uncertainty evaluated to be less than 20%, by considering all the parameters, which contributed to uncertainties in the rate constant determination. The possible absorption at the O triplet emission line of N2O has been investigated. The absorption cross section of N2O at the O line has been estimated and taken into account for the determination of k1,0 at high concentrations of N2O and at temperatures lower than 1850 K. The effect of the presence of impurities like H2O on rate constant determination has been examined and was found to be negligible. The choice of the rate coefficient for the consumption of O atoms by reaction with N2O and that of the high‐pressure limiting rate coefficients k1,∞ were also discussed. The rate constant reported in the present study was compared with the literature values and was found to be overall higher than those determined experimentally by other teams in the last decade. Finally, the effect of the modified constant value on reaction rate of diluted Ar–N2O mixtures and H2–N2O–Ar systems was investigated. In the temperature range 1500–2500 K, the use of the rate constant deduced from this study has led to a better prediction of N2O decomposition and N2O reduction by H2 than with lower rate constants proposed in the literature. © 2009 Wiley Periodicals, Inc. Int J Chem Kinet 41: 357–375, 2009

The current energy transition imposes a rapid implementation of energy storage systems with high energy density and eminent regeneration and cycling efficiency. Metal hydrides are potential candidates for generalized energy storage, when coupled with fuel cell units and/or batteries. An overview of ongoing research is reported and discussed in this review work on the light of application as hydrogen and heat storage matrices, as well as thin films for hydrogen optical sensors. These include a selection of single-metal hydrides, Ti-V(Fe) based intermetallics, multi-principal element alloys (high-entropy alloys), and a series of novel synthetically accessible metal borohydrides. Metal hydride materials can be as well of important usefulness for MH-based electrodes with high capacity (e.g. MgH2 ~ 2000 mAh g-1) and solid-state electrolytes displaying high ionic conductivity suitable, respectively, for Li-ion and Li/Mg battery technologies. To boost further research and development directions some characterization techniques dedicated to the study of M-H interactions, their equilibrium reactions, and additional quantification of hydrogen concentration in thin film and bulk hydrides are presented at the end of this manuscript.

The present research work concerns development of regression models to predict the monthly heating demand for single-family residential sector in temperate climates, with the aim to be used by architects or design engineers as support tools in the very first stage of their projects in finding efficiently energetic solutions. Another interest to use such simplified models is to make it possible a very quick parametric study in order to optimize the building structure versus environmental or economic criteria. All the energy prediction models were based on an extended database obtained by dynamic simulations for 16 major cities of France. The inputs for the regression models are the building shape factor, the building envelope U-value, the window to floor area ratio, the building time constant and the climate which is defined as function of the sol-air temperature and heating set-point. If the neural network (NN) methods could give precise representations in predicting energy use, with the advantage that they are capable of adjusting themselves to unexpected pattern changes in the incoming data, the multiple regression analysis was also found to be an efficient method, nevertheless with the requirement that an extended database should be used for the regression. The validation is probably the most important level when trying to find prediction models, so 270 different scenarios are analysed in this research work for different inputs of the models. It has been established that the energy equations obtained can do predictions quite well, a maximum deviation between the predicted and the simulated is noticed to be 5.1% for Nice climate, with an average error of 2%. In this paper, we also show that is possible to predict the building heating demand even for more complex scenarios, when the construction is adjacent to non-heated spaces, basements or roof attics.

Demand response (DR) and renewable energy sources have opened new avenues for end-users to lower their energy expenses via energy management systems. Aggregators facilitate the participation of end-users by acting on their behalf and interacting with bulk electricity markets. In this paper, an energy management algorithm is presented to investigate the impact of distributed photovoltaic (PV) and central energy storage system (ESS) assets on the economic performance of an energy aggregator in the residential sector. To enable DR, the aggregator provides a competitive incentive price to end-users, and centrally optimizes the central ESS assets and schedule of committed customer elastic loads. Thus, customers reduce their daily electricity bill while the aggregator decreases the aggregated peak consumption and earns profits as a return for providing DR services. The scope of this paper pertains to the economic impact of distributed PV and central ESS assets on aggregator profits and customer savings resulting from DR, including ESS degradation. Simulation results showed that the central ESS increases the income of the aggregator, whereas residential PV reduces the impact of DR.