China, People’s Republic

Deflagration-to-Detonation Transition Ratio (DDTR) is an important parameter in measuring the hazard of hydrogen detonation at given thermodynamic conditions. It’s among the major tasks to evaluate DDTR in the study of hydrogen safety in a nuclear containment. With CFD tools detailed distribution of thermodynamic parameters at each instant can be simulated with considerable reliability. Then DDTR can be estimated using related CFD output. Forstochastic or epistemic reasons uncertainty always exists in input parameters during computations. This lack of accuracy can finally be reflected in the uncertainty of computation results e.g. DDTR in our consideration. The analysis of the influence of the input uncertainty is therefore a key step to understand the model’s response on the output and possibly to improve the accuracy. The increase of computational power makes it possible to perform statistics-based sensitivity and uncertainty (SU) analysis on CFD simulations. This paper aims at presenting some ideas on the procedure in safety analysis on hydrogen in nuclear containment. A hydrogen recombiner case is constructed and simulated with CFD method. DDTR at each instant is computed using a semi-empirical method. RBD-FAST based SU analysis is performed on the result.

The pressure swing adsorption (PSA) system is widely applied to separate and purify hydrogen from gaseous mixtures. The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed. A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works. The effects of the adsorption pressure the P/F ratio the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied. A four-step two-bed PSA model is taken into consideration and the six-step PSA system shows higher about 13% hydrogen recovery than the four-step PSA system. The performance of the vacuum pressure swing adsorption (VPSA) system is compared with that of the PSA system the VPSA system shows higher hydrogen purity than the PSA system. Based on the validated PSA model a dataset has been produced to train the artificial neural network (ANN) model. The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated. Then the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification. Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model. The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.

It has been well known that doping nano-scale catalysts can significantly improve both the kinetics and reversible hydrogen storage capacity of MgH2. However so far it is still a challenge to directly synthesize ultrafine catalysts (e.g. < 5 nm) mainly because of the complicated chemical reaction processes. Here a facile one-step high-energy ball milling process is developed to in situ form ultrafine Ni nanoparticles from the nickel acetylacetonate precursor in the MgH2 matrix. With the combined action of ultrafine metallic Ni and expanded graphite (EG) the formed MgH2–Ni-EG nanocomposite with the optimized doping amounts of Ni and EG can still release 7.03 wt.% H2 within 8.5 min at 300 °C after 10 cycles. At a temperature close to room temperature (50 °C) it can also absorb 2.42 wt.% H2 within 1 h It can be confirmed from the microstructural characterization analysis that the in situ formed ultrafine metallic Ni is transformed into Mg2Ni/Mg2NiH4 in the subsequent hydrogen absorption and desorption cycles. It is calculated that the dehydrogenation activation energy of the MgH2–Ni-EG nanocomposite is also reduced obviously in comparison with the pure MgH2. Our work provides a methodology to significantly improve the hydrogen storage performance of MgH2 by combining the in situ formed and uniformly dispersed ultrafine metallic catalyst from the precursor and EG.

Green synthesis of crystalline porous materials for energy-related applications is of great significance but very challenging. Here we create a green strategy to fabricate a highly crystalline olefin-linked pyrazine-based covalent organic framework (COF) with high robustness and porosity under solvent-free conditions. The abundant nitrogen sites high hydrophilicity and well-defined one-dimensional nanochannels make the resulting COF an ideal platform to confine and stabilize the H₃PO₄ network in the pores through hydrogen-bonding interactions. The resulting material exhibits low activation energy (E_a) of 0.06 eV and ultrahigh proton conductivity across a wide relative humidity (10–90 %) and temperature range (25–80 °C). A realistic proton exchange membrane fuel cell using the olefin-linked COF as the solid electrolyte achieve a maximum power of 135 mW cm⁻² and a current density of 676 mA cm⁻² which exceeds all reported COF materials.

Hydrogen spillover phenomenon of metal-supported electrocatalysts can significantly impact their activity in hydrogen evolution reaction (HER). However design of active electrocatalysts faces grand challenges due to the insufficient understandings on how to overcome this thermodynamically and kinetically adverse process. Here we theoretically profile that the interfacial charge accumulation induces by the large work function difference between metal and support (∆Φ) and sequentially strong interfacial proton adsorption construct a high energy barrier for hydrogen transfer. Theoretical simulations and control experiments rationalize that small ∆Φ induces interfacial charge dilution and relocation thereby weakening interfacial proton adsorption and enabling efficient hydrogen spillover for HER. Experimentally a series of Pt alloys-CoP catalysts with tailorable ∆Φ show a strong ∆Φ-dependent HER activity in which PtIr/CoP with the smallest ∆Φ = 0.02 eV delivers the best HER performance. These findings have conclusively identified ∆Φ as the criterion in guiding the design of hydrogen spillover-based binary HER electrocatalysts

Imbalance costs caused by forecasting errors are considerable for grid-connected wind farms. In order to reduce such costs two onsite storage technologies i.e. power-to-hydrogen-to-power and lithium battery are investigated considering 14 uncertain technological and economic parameters. Probability density distributions of wind forecasting errors and power level are first considered to quantify the imbalance and excess wind power. Then robust optimal sizing of the onsite storage is performed under uncertainty to maximize wind-farm profit (the net present value). Global sensitivity analysis is further carried out for parameters prioritization to highlight the key influential parameters. The results show that the profit of power-to-hydrogen-to-power case is sensitive to the hydrogen price wind forecasting accuracy and hydrogen storage price. When hydrogen price ranges in (2 6) €/kg installing only electrolyzer can earn profits over 100 k€/MWWP in 9% scenarios with capacity below 250 kW/MWWP under high hydrogen price (over 4 €/kg); while installing only fuel cell can achieve such high profits only in 1.3% scenarios with capacity below 180 kW/MWWP. Installing both electrolyzer and fuel cell (only suggested in 22% scenarios) results in profits below 160 k€/MWWP and particularly 20% scenarios allow for a profit below 50 k€/MWWP due to the contradictory effects of wind forecasting error hydrogen and electricity price. For lithium battery investment cost is the single highly influential factor which should be reduced to 760 €/kWh. The battery capacity is limited to 88 kW h/MWWP. For profits over 100 k€/MWWP (in 3% scenarios) the battery should be with an investment cost below 510 €/kWh and a depth of discharge over 63%. The power-to-hydrogen-to-power case is more advantageous in terms of profitability reliability and utilization factor (full-load operating hours) while lithium battery is more helpful to reduce the lost wind and has less environmental impact considering current hydrogen market.

At present aero engine fault diagnosis is mainly based on the steady-state condition at the cruise phase and the gas path parameters in the entire flight process are not effectively used. At the same time high quality steady-state monitoring measurements are not always available and as a result the accuracy of diagnosis might be affected. There is a recognized need for real-time performance diagnosis of aero engines operating under transient conditions which can improve their condition-based maintenance. Recent studies have demonstrated the capability of the sequential model-based diagnostic method to predict accurately and efficiently the degradation of industrial gas turbines under steady-state conditions. Nevertheless incorporating real-time data for fault detection of aero engines that operate in dynamic conditions is a more challenging task. The primary objective of this study is to investigate the performance of the sequential diagnostic method when it is applied to aero engines that operate under transient conditions while there is a variation in the bypass ratio and the heat soakage effects are taken into consideration. This study provides a novel approach for quantifying component degradation such as fouling and erosion by using an adapted version of the sequential diagnostic method. The research presented here confirms that the proposed method could be applied to aero engine fault diagnosis under both steady-state and dynamic conditions in real-time. In addition the economic impact of engine degradation on fuel cost and payload revenue is evaluated when the engine under investigation is using hydrogen. The proposed method demonstrated promising diagnostic results where the maximum prediction errors for steady state and transient conditions are less than 0.006% and 0.016% respectively. The comparison of the proposed method to a benchmark diagnostic method revealed a 15% improvement in accuracy which can have great benefit when considering that the cost attributed to degradation can reach up to $702585 for 6000 flight cycles of a hydrogen powered aircraft fleet. This study provides an opportunity to improve our understanding of aero engine fault diagnosis in order to improve engine reliability availability and efficiency by online health monitoring.

The widespread penetration of hydrogen in mainstream energy systems requires hydrogen production processes to be economically competent and environmentally efficient. Hydrogen if produced efficiently can play a pivotal role in decarbonizing the global energy systems. Therefore this study develops a framework which evaluates hydrogen production processes and quantifies deficiencies for improvement. The framework integrates slack-based data envelopment analysis (DEA) with fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The proposed framework is applied to prioritize the most efficient and sustainable hydrogen production in Pakistan. Eleven hydrogen production alternatives were analyzed under five criteria including capital cost feedstock cost O&M cost hydrogen production and CO2 emission. FAHP obtained the initial weights of criteria while FTOPSIS determined the ultimate weights of criteria for each alternative. Finally slack-based DEA computed the efficiency of alternatives. Among the 11 three alternatives (wind electrolysis PV electrolysis and biomass gasification) were found to be fully efficient and therefore can be considered as sustainable options for hydrogen production in Pakistan. The rest of the eight alternatives achieved poor efficiency scores and thus are not recommended.

Minimising tailpipe emissions and the decarbonisation of transport in a cost effective way remains a major objective for policymakers and vehicle manufacturers. Current trends are rapidly evolving but appear to be moving towards solutions in which vehicles which are increasingly electrified. As a result we will see a greater linkage between the wider energy system and the transportation sector resulting in a more complex and mutual dependency. At the same time major investments into technological innovation across both sectors are yielding rapid advancements into on-board energy storage and more compact/lightweight on-board electricity generators. In the absence of sufficient technical data on such technology holistic evaluations of the future transportation sector and its energy sources have not considered the impact of a new generation of innovation in propulsion technologies. In this paper the potential impact of a number of novel powertrain technologies are evaluated and presented. The analysis considers heavy duty vehicles with conventional reciprocating engines powered by diesel and hydrogen hybrid and battery electric vehicles and vehicles powered by hydrogen fuel cells and freepiston engine generators (FPEGs). The benefits are compared for each technology to meet the expectations of representative medium and heavy-duty vehicle drivers. Analysis is presented in terms of vehicle type vehicle duty cycle fuel economy greenhouse gas (GHG) emissions impact on the vehicle etc.. The work shows that the underpinning energy vector and its primary energy source are the most significant factor for reducing primary energy consumption and net CO₂ emissions. Indeed while an HGV with a BEV powertrain offers no direct tailpipe emissions it produces significantly worse lifecycle CO2 emissions than a conventional diesel powertrain. Even with a de-carbonised electricity system (100 g CO2/kWh) CO₂ emissions are similar to a conventional Diesel fuelled HGV. For the HGV sector range is key to operator acceptability of new powertrain technologies. This analysis has shown that cumulative benefits of improved electrical powertrains on-board storage efficiency improvements and vehicle design in 2025 and 2035 mean that hydrogen and electric fuelled vehicles can be competitive on gravimetric and volumetric density. Overall the work demonstrates that presently there is no common powertrain solution appropriate for all vehicle types but how subtle improvements at a vehicle component level can have significant impact on the design choices for the wider energy system.

Nanostructured Fe₇S₈ was successfully synthesized and its catalytic effect on hydrogen absorption/desorption performance of MgH₂2 is systemically discussed. The MgH₂ + 16.7 wt% Fe7S8 composite prepared by ball-milling method offers a striking catalytic activity for hydrogenation kinetics and also reduces the initial decomposition temperature for MgH₂2. The composite of MgH₂–Fe₇S₈ can absorb 4.000 wt% of hydrogen within 1800 s at 473 K which is about twice that of pristine MgH₂ (1.847 wt%) under the same conditions. The onset hydrogen release temperature of Fe₇S₈-modified MgH₂ is 420 K which is 290 K lower than that of additive-free MgH₂ (710 K). Meanwhile the doped sample could release 4.403 wt% of hydrogen within 1800 s at 623 K as compared to 2.479 wt% of hydrogen by MgH₂. The activation energy for MgH2–Fe7S8 is about 130.0 kJ mol−1 approximately 36 kJ mol−1 lower than that of MgH₂. The hydriding process of MgH2 + 16.7 wt% Fe₇S₈ follows the nucleation and growth mechanism. The prominent hydrogen storage performances are related to the reactions between MgH₂ and Fe₇S₈. The newly formed MgS and Fe in the ball-milling process present a co-catalytic effect on the hydrogen storage performance of MgH₂2.