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This article draws from a database of asset-level emissions to identify key methane-emitting coal, oil and gas facilities in Southeast Asia while taking stock of the methane commitments of their owners. Coal mines account for around a third of fossil fuel methane emissions globally, but in Southeast Asia they make up more than half of tracked fossil fuel methane emissions. Over half of emissions from the coal mining subsector is traced to its top ten emitters, mostly in East Kalimantan, Indonesia; while some coal mines in North Vietnam have high emissions intensities. Though the global discourse on fossil methane focuses on oil and gas, coal mine methane remains crucial for Southeast Asia due to the region's lack of decisive coal phaseout plans. As countries begin to tackle coal emissions at the power generation stage, a gap still remains when it comes to coal mining emissions. Methane monitoring and abatement actions are urgently needed for coal mines that will continue to operate, as well as those slated for closure. More clarity is needed on how private sector commitments in the oil and gas sector will translate to action under complex and changing ownership arrangements. These gaps and uncertainties in methane abatement are ripe opportunities for closer partnership in the region, including within the private sector.
Malaysia has traditionally adopted an intensive automotive industrialization model and created its own vehicles under national brands. The national car project started with Proton in 1983, and the national motorcycle project with Modenas in 1995. While policies and scholarship have focused on national car projects, the two-wheeler sector has stood in their shadow. Modenas witnessed early growth and remains a popular brand after Yamaha and Honda; it has however failed to hit export targets, owing to limited technology transfer and the inability to scale. Recently, there has been renewed interest in the two-wheeler sector, focusing on phasing out combustion motorcycles in favour of electric two-wheelers (E2Ws). Still nascent, Malaysia's electric two-wheeler (E2W) sector appears to prioritize an extensive model of assembly and distribution rather than the protection of home-grown brands. Still in its infancy, E2W adoption rates remain low at under 1 per cent, albeit there has been high year-over-year growth since 2022. Interviews with E2W manufacturers, regulators, dealers, and consumers reveal challenges beyond common issues like price, range, charging time, maximum speed, absence of servicing infrastructure and a second-hand market. Notably, Malaysia lacks a suitable product for Malaysian roads and lifestyle due to insufficient institutional support for Research and Development (R&D) and talent matching. With aspirations to be an E2Ws regional manufacturing hub, the government and businesses should step up on public education to bridge the information gap, rethink the R&D support model for the electric vehicle industry, and develop clarity surrounding what a 'Made in Malaysia' motorcycle entails.
We investigate the motion of weakly negatively buoyant spheres settling in surface gravity waves using laboratory experiments. The trajectories of the settling spheres are tracked over most of the water depth with simultaneous measurements of the background fluid flow. These experiments are conducted in the regime relevant for environmental and geophysical applications where both particle inertia and fluid inertia are important. Using these data, we show that the sphere motion is well described by the kinematic sum of the undisturbed fluid velocity and the particle terminal settling velocity as long as the fluid inertia is not too large. We show how this result can be understood in the context of an ad hoc Maxey–Riley–Gatignol-type equation where the drag on the particle is given by the Schiller–Naumann drag correlation. We also evaluate whether inertial particles experience enhanced settling in waves, finding that measurement uncertainties in the particle terminal settling velocity and the presence of Eulerian-mean flows do not allow the small percentage increase in the settling velocity to be measured. When the fluid inertia becomes large enough, we observe path instabilities caused by particle wake effects in both quiescent and wavy conditions. However, the particle velocity fluctuations associated with the path instabilities are unaffected by the background flow. The minimal influence of the wavy flow on the particle path instabilities is thought to be due to the large-scale separation between the waves and the particle.
This study investigates the strong influence of a splitter plate on two- and three-dimensional wake transitions of a circular cylinder. Direct numerical simulations and Floquet analyses are conducted over a parameter space including Reynolds numbers (Re) of 10–480 and non-dimensional plate lengths (L/D) of 0–6. With the increase in L/D, the critical Re for the onset of vortex shedding (Recr2D) increases monotonically. The delayed onset of vortex shedding with elongation of the body is physically explained. The critical Re for the onset of three-dimensionality (Recr3D) and the three-dimensional wake instability modes and structures are also significantly altered by the splitter plate. Compared with an isolated cylinder, the Recr3D for L/D = 1 is significantly reduced via a long wavelength mode, whereas the Recr3D for L/D = 2–6 is significantly increased via other modes. For each L/D, with increasing Re over the wake transition process, the spanwise wavelength of the wake structure gradually decreases, and the wake structure becomes increasingly chaotic. The strong influence of the splitter plate on the formation of the primary vortices and three-dimensional wake structures alter the hydrodynamic characteristics strongly. In particular, optimal lift reduction is achieved at L/D ∼ 1. In addition, the existence/absence of a hysteresis effect at the onset of three-dimensionality is identified by three methods. Among which, the method involving the Landau equation may be contaminated by initial transients induced by stable Floquet modes and may thus lead to a false conclusion on the existence/absence of hysteresis.
Flight Data Monitoring (FDM) programmes have become a key part of every major airline’s safety management system. They are primarily based on learning from unwanted deviations in flight parameters encountered during normal flight operations. Owing to its unique nature, anomaly detection of FDM presents distinct problem complexities from the majority of analytical and learning tasks. This methodology, while useful, concentrates only on a small part of the operation, leaving most of the data unprocessed, and does not allow for analysing events that had the potential to go wrong but were recovered in time by the crews. This research focused on analysing an FDM dataset of 1332 approaches between January 2018 and July 2022 at Tenerife South Airport (Spain), where there is a known phenomenon of increasing headwinds during the final approach. The flights were clustered using self-organising maps (SOM) by patterns of increasing headwinds, and the clusters were assessed in terms of clustering performance. The clusters were well differentiated. A further comparison between the results from the airline showed that 88 flights were affected by wind shifts, while 27 flights were picked up by the airline. The results demonstrate that SOMs are a meaningful tool for clustering flight data and can complement the current FDM analysis methodology. Combining both methodologies could shift FDM data analysis to look beyond exceedances into what went well, thus shifting the FDM paradigm towards a more safety-II-based method.
Technological developments and affordable price structures have increased the usage of unmanned aerial vehicles (UAVs) across almost all sectors, hence increasing demand. Since UAVs can fly and perform various tasks without requiring a human operator, the most dangerous and time-consuming tasks previously performed by humans in many sectors are now accomplished by using UAVs. The increased use of UAVs has also introduced critical safety and security risks, including airspace congestion, collisions and malicious use, and therefore, identifying and assessing the risks associated with UAVs and finding ways to mitigate them is of great importance. This qualitative study investigates the safety and security risks posed by the increased use of UAVs and discusses ways to mitigate these risks. Semi-structured interviews with aviation professionals, including pilots, air traffic controllers and academicians, were conducted, and the collected data were analysed by using MAXQDA 24 qualitative analysis software. The results indicate that 86% of participants emphasised air traffic density as a major safety concern, while 71% underlined the need for dedicated air corridors and robust legal frameworks to reduce collision risks. These insights suggest that the safe integration of UAVs into current aviation systems demands a multifaceted strategy involving regulatory interventions, such as clearly defined UAV flight zones and essential technological enhancements. Overall, the study underscores the urgent need for coordinated efforts–legal, technological, and inter-institutional–to ensure the secure incorporation of UAVs into national airspace.
Le Liang, Southeast University, Nanjing,Shi Jin, Southeast University, Nanjing,Hao Ye, University of California, Santa Cruz,Geoffrey Ye Li, Imperial College of Science, Technology and Medicine, London
Sub-convective wall pressure fluctuations play a critical role in vibroacoustic and noise analyses of vehicle structures as they serve as the primary forcing function. However, measuring these fluctuations is challenging due to their weak pressure magnitudes, typically $10^{-3}{-}10^{-5}$ of convective fluctuations. This study introduces a non-intrusive measurement technique using an array of multi-pore Helmholtz resonator sensors to capture sub-convective fluctuations with high resolution. The array features large-area, spanwise-oriented sensors arranged linearly for optimal sampling. Results provide a continuous streamwise wavenumber–frequency spectrum, resolving sub-convective fluctuations with sufficient range and accuracy. Convergence analysis indicates that long sampling durations, $\mathcal{O}(10^6 \delta ^*/U_\infty )$, $\delta^*$ is the displacement thickness of the boundary layer. $U_\infty$ is the freestream velocity are necessary to capture true sub-convective levels. Comparisons with four existing wall pressure models, which account for sensor area averaging, reveal discrepancies in predicted levels, convection speed relations and convective ridge characteristics. Notably, the measured data align most closely with the Chase (1980, J. Sound Vib., vol.70, pp. 29–67) model at convective peak levels and in the sub-convective domain. However, the observed roll-off at wavenumbers exceeding the convective wavenumber decays more slowly than predicted, giving the convective ridge an asymmetric profile about the convective line. These findings underscore the need for improved wall pressure models that incorporate frequency-dependent convective speed relations, ridge asymmetry, and more accurate sub-convective levels. Further validation using a microphone array from Farabee & Geib (1991) confirms the accuracy of our measurements, which indicate sub-convective pressure levels lower than reported previously.
Rotorcraft engines are highly complex, nonlinear thermodynamic systems operating under varying environmental and flight conditions. Simulating their dynamics is crucial for design, fault diagnostics and deterioration control, requiring robust control systems to estimate performance throughout the flight envelope. Numerical simulations provide accurate assessments in both steady and unsteady scenarios through physics-based and mathematical models, although their development is challenging due to the engine’s complex physics and strong dependencies on environmental conditions. In this context, data-driven machine-learning techniques have gained significant interest for their ability to capture nonlinear dynamics and enable online performance estimation with competitive accuracy. This work explores different neural network architectures to model the turboshaft engine of Leonardo’s AW189P4 prototype, aiming to predict engine torque. The models are trained on a large database of real flight tests, covering a variety of operational manoeuvers under different conditions, thus offering a comprehensive performance representation. Additionally, sparse identification of nonlinear dynamics (SINDy) is applied to derive a low-dimensional model from the available data, capturing the relationship between fuel flow and engine torque. The resulting model highlights SINDy’s ability to recover underlying engine physics and suggests its potential for further investigations into engine complexity. The paper details the development and prediction results of each model, demonstrating that data-driven approaches can exploit a broader range of parameters compared to standard transfer function-based methods, enabling the use of trained schemes to simulate nonlinear effects in different engines and helicopters.
For Stokes waves in finite depth within the neighbourhood of the Benjamin–Feir stability transition, there are two families of periodic waves, one modulationally unstable and the other stable. In this paper we show that these two families can be joined by a heteroclinic connection, which manifests in the fluid as a travelling front. By shifting the analysis to the setting of Whitham modulation theory, this front is in wavenumber and frequency space. An implication of this jump is that a permanent frequency downshift of the Stokes wave can occur in the absence of viscous effects. This argument, which is built on a sequence of asymptotic expansions of the phase dynamics, is confirmed via energetic arguments, with additional corroboration obtained by numerical simulations of a reduced model based on the Benney–Roskes equation.
Le Liang, Southeast University, Nanjing,Shi Jin, Southeast University, Nanjing,Hao Ye, University of California, Santa Cruz,Geoffrey Ye Li, Imperial College of Science, Technology and Medicine, London
This paper explores the construction of quadratic Lyapunov functions for establishing the conditional stability of shear flows described by truncated ordinary differential equations, addressing the limitations of traditional methods like the Reynolds–Orr equation and linear stability analysis. The Reynolds–Orr equation, while effective for predicting unconditional stability thresholds in shear flows due to the non-contribution of nonlinear terms, often underestimates critical Reynolds numbers. Linear stability analysis, conversely, can yield impractically high limits due to subcritical transitions. Quadratic Lyapunov functions offer a promising alternative, capable of proving conditional stability, albeit with challenges in their construction. Typically, sum-of-squares programs are employed for this purpose, but these can result in sizable optimisation problems as system complexity increases. This study introduces a novel approach using linear transformations described by matrices to define quadratic Lyapunov functions, validated through nonlinear optimisation techniques. This method proves particularly advantageous for large systems by leveraging analytical gradients in the optimisation process. Two construction methods are proposed: one based on general optimisation of transformation matrix coefficients, and another focusing solely on the system’s linear aspects for more efficient Lyapunov function construction. These approaches are tested on low-order models of subcritical transition and a two-dimensional Poiseuille flow model with degrees of freedom nearing 1000, demonstrating their effectiveness and efficiency compared with sum-of-squares programs.