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Fire hazard during ship transportation of certain modern solid fuels is identified. A particular mechanism which may lead to catastrophic ship fire is discussed. A mathematical model predicting time to such fully developed fire (flashover time) I s proposed. An analytical solution is obtained for the developed model. An important part of this solution is a closed form analytical description of the smoke filling dynamics in a closed compartment. It is demonstrated that flashover time depends on a number of parameters. The relative importance of these parameters is discussed. Particular significance is attributed to fuel soot propensity (fuel soot yield) which is an intrinsic fuel property. This parameter controls the intensity of radiative heat exchange in the compartment, and its influence on flashover time is of paramount importance. It is suggested that fuels are ranked with respect to this parameter in order to describe their relative fire hazards. This ranking should be implemented into the regulatory framework governing fuel transportation by ships.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Due to global warming and shortage of fossil fuels, the grid-connected solar photovoltaic (PV) system has gained significant popularity all over the world. The modular multilevel cascaded (MMC) inverter is the natural choice for step-up transformer and line filter less direct medium voltage grid integration of solar PV systems. However, power quality and loss are the important issues while connecting the PV system to the medium voltage grid through MMC inverter. Modulation technique is the key to maintain output power quality, e.g., total harmonic distortion (THD) and to ensure low switching and conduction losses. In this paper, an advanced modulation technique named “triangle saturated common mode pulse width modulation (TSCMPWM)” control is proposed for a 3-phase 5-level MMC inverter-based grid-tied PV system. Compared to traditional modulation techniques, the proposed TSCMPWM control offers the lowest voltage THD as well as lower inverter power losses. Performance of the proposed modulation technique is evaluated in MATLAB/Simulink environment and tested with a reduced scale prototype test platform. Both simulation and experimental results show the effectiveness of the proposed modulation technique.

The aim of this study is to characterise the changes in mechanical properties and to provide a comprehensive micro-structural analysis of Harcourt granite over different pre-heating temperatures under two cooling treatments (1) rapid and (2) slow cooling. A series of uniaxial compression tests was conducted to evaluate the mechanical properties of granite specimens subjected to pre-heating to temperatures ranging from 25–1000 °C under both cooling conditions. An acoustic emission (AE) system was incorporated to identify the fracture propagation stress thresholds. Furthermore, the effect of loading and unloading behaviour on the elastic properties of Harcourt granite was evaluated at two locations prior to failure: (1) crack initiation and (2) crack damage. Scanning electron microscopy (SEM) analyses were conducted on heat-treated thin rock slices to observe the crack/fracture patterns and to quantify the extent of micro-cracking during intense heating followed by cooling. The results revealed that the thermal field induced in the Harcourt granite pore structure during heating up to 100 °C followed by cooling causes cracks to close, resulting in increased mechanical characteristics, in particular, material stiffness and strength. Thereafter, a decline in mechanical properties occurs with the increase of pre-heating temperatures from 100 °C to 800 °C. However, the thermal deterioration under rapid cooling is much higher than that under slow cooling, because rapid cooling appears to produce a significant amount of micro-cracking due to the irreversible thermal shock induced. Multiple stages of loading and unloading prior to failure degrade the elastic properties of Harcourt granite due to the damage accumulated through the coalescence of micro-cracks induced during compression loading. However, this degradation is insignificant for pre-heating temperatures over 400 °C, since the specimens are already damaged due to excessive thermal deterioration. Moreover, unloading after crack initiation tends to cause insignificant irreversible strains, whereas significant permanent strains occur during unloading after crack damage, and this appears to increase with the increase of pre-heating temperature over 400 °C.

Vietnam is widely considered as an energy-intensive economy. Renewable energy integration has been set as an important goal in the country’s revised Power Development Plan 7 (PDP7). This study first conducted a generation adequacy assessment using the basic probabilistic modeling approach to evaluate how the generation fleet, as foreseen in the PDP7, can meet the demand, despite the fast-changing renewable energy sources (RES) generation. The adequacy of the generation was measured using the Loss of Load Expectation (LOLE, expressed in hours) index. The study then conducted in-depth interviews with key stakeholders to identify and propose efficient strategic approach and policy implementations for integration of RES into the current power system in Vietnam. The results suggested that three major pillars should be considered to ensure the success of RES integration: strategic objectives, structural reforms and system transformation.

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport.

Green hydrogen is a promising energy carrier for the decarbonization of hard-to-abate sectors and supporting renewable energy integration, aligning with carbon neutrality goals like the European Green Deal. Iceland’s abundant renewable energy and decarbonized electricity system position it as a strong candidate for green hydrogen production. Despite early initiatives, its hydrogen economy has yet to significantly expand. This study evaluated Iceland’s hydrogen development through stakeholder interviews and a techno-economic analysis of alkaline and PEM electrolyzers. Stakeholders were driven by decarbonization goals, economic opportunities, and energy security but faced technological, economic, and governance challenges. Recommendations include building stakeholder confidence, financial incentives, and creating hydrogen-based chemicals to boost demand. Currently, alkaline electrolyzers are more cost-effective (EUR 1.5–2.8/kg) than PEMs (EUR 2.1–3.6/kg), though the future costs for both could drop below EUR 1.5/kg. Iceland’s low electricity costs and high electrolyzer capacity provide a competitive edge. However, this advantage may shrink as solar and wind costs decline globally, particularly in regions like Australia. This work’s findings emphasize the need for strategic planning to sustain competitiveness and offer transferable insights for other regions introducing hydrogen into ecosystems lacking infrastructure.

This paper studies the dynamic response of a wind turbine gearbox under different excitation conditions. The proposed 4 degree-of-freedom (DOF) dynamic model takes into account the key factors such as the time-varying mesh stiffness, bearing stiffness, damping, static transmission error and gear backlash. Both the external excitation due to wind and the internal excitation due to the static transmission error are included to represent the gearbox excitation conditions. With the help of the time history and frequency spectrum, the dynamic responses of wind turbine gearbox components are investigated by using the numerical integration method. This paper explains under which conditions the fretting corrosion, as one of the wind turbine gearbox failure modes, may occur. Furthermore, it is observed that the external excitation fluctuation has large influence on the dynamic responses of both the gears and bearings.

Empirical studies of public opinion on environmental protection have typically been grounded in Inglehart’s post-materialism thesis, proposing that societal affluence encourages materially-sated publics to look beyond their interests and value the environment. These studies are generally conducted within, or at best across, Western, democratic, industrialized countries. Absence of truly cross-cultural research means the theory’s limitations have gone undetected. This article draws on an exceptionally broad dataset—pooling cross-sectional survey data from 80 countries, each sampled at up to three different points over 15 years—to investigate environmental attitudes. We find that post-materialism provides little account of pro-environment attitudes across diverse cultures, and a far from adequate explanation even in the affluent West. We suggest that unique domestic interests, more than broad value systems, are driving emerging global trends in environmental attitudes. The environment’s future champions may be the far from ‘post-material’ citizens of those developing nations most at risk of real material harm from climate change and environmental degradation.