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The economic, political, strategic and cultural dynamism in Southeast Asia has gained added relevance in recent years with the spectacular rise of giant economies in East and South Asia. This has drawn greater attention to the region and to the enhanced role it now plays in international relations and global economics.
The sustained effort made by Southeast Asian nations since 1967 towards a peaceful and gradual integration of their economies has had indubitable success, and perhaps as a consequence of this, most of these countries are undergoing deep political and social changes domestically and are constructing innovative solutions to meet new international challenges. Big Power tensions continue to be played out in the neighbourhood despite the tradition of neutrality exercised by the Association of Southeast Asian Nations (ASEAN).
The Trends in Southeast Asia series acts as a platform for serious analyses by selected authors who are experts in their fields. It is aimed at encouraging policymakers and scholars to contemplate the diversity and dynamism of this exciting region.
This study investigates the interaction between a freely rising, deformable bubble and a freely settling particle of the same size due to gravity. Initially, an in-line configuration is considered while varying the Bond, Galilei and Archimedes numbers. The study shows that as the bubble and particle approach each other, a liquid film forms between them that undergoes drainage. The formation of the liquid film leads to dissipation of kinetic energy, and for sufficiently large bubble velocities, particle flotation takes place. Increasing the Bond number causes the bubble to deform more severely, which may allow the particle to pass through the bubble as it ruptures. This work also considers an offset configuration, which shows that the bubble slides away from the particle, affecting its settling trajectory.
Transonic aeroelasticity remains a significant challenge in aerospace. The coupling mechanism of aeroelastic problems involving the coexistence of fluid modes and multiple structural modes still needs further investigation. For this purpose, we analysed the dynamic characteristic of a two-degree-of-freedom (2DOF) NACA0012 airfoil in pre-buffet flow. First, we constructed an aeroelastic reduced-order model, which can represent near-unstable transonic flow using the dominant fluid mode. Then, the flutter mechanism was investigated by studying the main eigenvalues of the model that vary with the natural pitching frequency. The results revealed that the existence of the fluid mode transitions the transonic flutter type from coupled-mode flutter to single-DOF (SDOF) flutter, which leads to a reduction in the flutter boundary. Under the effect of the fluid mode, the system produces six aeroelastic phenomena at different structural natural frequencies, including SDOF heaving/pitching flutter, heaving/pitching instability within coupled-mode flutter, forced vibration and stable state. Moreover, we identified two types of SDOF flutter in the 2DOF system. The first type corresponds to the traditional SDOF flutter, where the coupling of other modes has a small impact on the system's stability in most cases. However, within specific ranges of natural frequencies, this type of SDOF flutter may disappear due to coupling with other modes. The second type of SDOF flutter is characterized by strong coupling dominated by the unstable mode. It arises from the interaction among the flow, heaving and pitching modes, and does not manifest in the absence of any of these modes.
The penetration of a spherical vortex into turbulence is studied theoretically and experimentally. The characteristics of the vortex are first analysed from an integral perspective that reconciles the far-field dipolar flow with the near-field source flow. The influence of entrainment on the vortex drag force is elucidated, extending the Maxworthy (J. Fluid Mech., vol. 81, 1977, pp. 465–495) model to account for turbulent entrainment into the vortex movement and vortex penetration into an evolving turbulent field. The physics are explored numerically using a spherical vortex (initial radius $R_0$, speed $U_{v0}$), characterised by a Reynolds number $Re_0(=2R_0U_{v0}/\nu$, where $\nu$ is the kinematic viscosity) of 2000, moving into decaying homogeneous turbulence (root-mean-square $u_0$, integral scale $L$) with turbulent intensity $I_t=u_0/U_{v0}$. When the turbulence is absent ($I_t=0$), a wake volume flux leads to a reduction of vortex impulse that causes the vortex to slow down. In the presence of turbulence ($I_t> 0$), the loss of vortical material is enhanced and the vortex speed decreases until it is comparable to the local turbulent intensity and quickly fragments, penetrating a distance that scales as $I_t^{-1}$. In the experimental study, a vortex ($Re_0\sim 1490\unicode{x2013}5660$) propagating into a statistically steady, spatially varying turbulent field ($I_{ve}=0.02$ to 0.98). The penetration distance is observed to scale with the inverse of the turbulent intensity. Incorporating the spatially and temporally varying turbulent fields into the integral model gives a good agreement with the predicted trend of the vortex penetration distance with turbulent intensity and insight into its dependence on the structure of the turbulence.
With global wind energy capacity ramping up, accurately predicting damage equivalent loads (DELs) and fatigue across wind turbine populations is critical, not only for ensuring the longevity of existing wind farms but also for the design of new farms. However, the estimation of such quantities of interests is hampered by the inherent complexity in modeling critical underlying processes, such as the aerodynamic wake interactions between turbines that increase mechanical stress and reduce useful lifetime. While high-fidelity computational fluid dynamics and aeroelastic models can capture these effects, their computational requirements limits real-world usage. Recently, fast machine learning-based surrogates which emulate more complex simulations have emerged as a promising solution. Yet, most surrogates are task-specific and lack flexibility for varying turbine layouts and types. This study explores the use of graph neural networks (GNNs) to create a robust, generalizable flow and DEL prediction platform. By conceptualizing wind turbine populations as graphs, GNNs effectively capture farm layout-dependent relational data, allowing extrapolation to novel configurations. We train a GNN surrogate on a large database of PyWake simulations of random wind farm layouts to learn basic wake physics, then fine-tune the model on limited data for a specific unseen layout simulated in HAWC2Farm for accurate adapted predictions. This transfer learning approach circumvents data scarcity limitations and leverages fundamental physics knowledge from the source low-resolution data. The proposed platform aims to match simulator accuracy, while enabling efficient adaptation to new higher-fidelity domains, providing a flexible blueprint for wake load forecasting across varying farm configurations.
We present the generation of high-repetition-rate strong-field terahertz (THz) pulses from a thin 4-N,N-dimethylamino-4’-N’-methyl-stilbazolium 2,4,6-trimethylbenzenesulfonate (DSTMS) organic crystal pumped by an ytterbium-doped yttrium aluminum garnet laser. The generated THz pulse energy reaches 932.8 nJ at 1 kHz repetition rate, with a conversion efficiency of 0.19% and a peak electric field of 819 kV/cm. At a repetition rate of 10 kHz, it is able to maintain a peak electric field of 236 kV/cm and an average THz power of 0.77 mW. The high-repetition-rate, strong-field THz source provides a convenient tool for the study of THz matter manipulation and THz spectroscopy.
When the first Malaysian national automaker Proton sold a 49.99 per cent share to the Chinese auto firm Geely in 2017, former Prime Minister of Malaysia Mahathir Mohamad who considers the national car project his brainchild said the acquisition was akin to him “losing a child”. If the national car project is a lost child, then the national motorcycle project can be likened to an orphan. Key players including Mahathir himself were once enthusiastic about the prospects of a national motorcycle “helping the country to achieve a highly motorized population”, with grandiose aspirations of exporting the national motorcycle to ASEAN, South Asia, Latin America and Africa. However, the motorcycle became a forgotten child—following the Second Industrial Master Plan, subsequent automotive policies and industrial plans offered few concrete policies for developing the two-wheeler (2W) sector.
As will become clear in the next section, throughout the 1980s and 1990s, the dominant narrative was to develop domestic manufacturing capabilities in small engines and eventually a national motorcycle, supported by the “lucrative Malaysian market”, the “huge potential” of the export market, and an association between a highly motorized population and development. Outlined in the Second Industrial Master Plan (1996–2005), the first national motorcycle project, Modenas, was implemented to “shift the development strategy of the sector from assembly and component parts manufacturing for the domestic market, to become an integrated motorcycle manufacturing sector supplying products at competitive prices for the export market”. This shift included “having an own design motorcycle being manufactured locally”. As with the national car project, Malaysia's aspiration for a national motorcycle is not just about having it made in Malaysia (that is, assembly alone), but also being “locally designed” and “develop[ing] components with own design & brand”, for which investments in Research and Development (R&D) are crucial. This focus on increases in local value addition based on input from national producers and on national technical capabilities is the essence of what Doner, Noble and Ravenhill (2021) term intensive growth. It is opposed to extensive growth which focuses on vehicles and components assembly and in some cases exports, primarily under the aegis of foreign producers operating in global value chains.
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of PEML methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate the application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different “genres” of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities.
We investigate the heterogeneous cavitation phenomenon in water when a spherical surface is abruptly separated from a nearby flat substrate, at a distance of approximately 10 nm. By tracking the surface separation using Newton ring positions, and capturing the bubble evolution with a high-speed camera on a microscope, we compare our experimental findings with hydrodynamic predictions at low Reynolds numbers. Upon upward movement of the spherical surface, the resulting bubble develops branched fingers through the Saffman–Taylor instability. Simultaneously, negative liquid pressures in the range $\sim$10 atm are observed. These large tension values occasionally lead to secondary nucleation events. The bubble sizes satisfy a predicted Family–Vicsek scaling law where the bubble area is proportional to the inverse bubble lifetime. The fact that creeping flow cavitation bubbles are more short-lived the larger they are separates them from bubbles that are governed by inertial dynamics.
This study comprehensively investigates the response of a combusting droplet during its interaction with a high-speed transient flow imposed by a coaxially propagating blast wave. The blast wave is generated using a specially designed miniature shock generator that produces blast waves using the wire-explosion technique, facilitating a wide range of Mach numbers (1.03 < Ms < 1.8). The experiments are performed in two configurations: open field and focused blast wave. The charging voltage and the configuration determine the Mach number (Ms) and flow characteristics. The flame is found to exhibit two major response patterns: partial extinction followed by reignition and full extinction. Increasing the Mach number (Ms > 1.1) makes the droplet flame more vulnerable to extinction. Additionally, the flame exhibits stretching and shedding, followed by reignition at lower Mach numbers (Ms < 1.06). In all cases, the flame base lifts off in response to the imposed flow, and the advection of the flame base interacting with the flame tip results in flame extinction. The entire interaction occurs in two stages: (i) interaction with the blast wave and the decaying velocity profile associated with it, and (ii) interaction with the induced flow behind the blast wave as a result of the entrainment (delayed response). Alongside the flame's response, the droplet also interacts with the flow imposed by the blast wave, exhibiting different response modes including pure deformation, Rayleigh–Taylor piercing bag breakup and shear-induced stripping.
In the present study, we propose a Reynolds analogy model for compressible wall turbulence. This model is demonstrated to be able to alleviate the defects of the generalized Reynolds analogy model (GRA) (Zhang et al., J. Fluid Mech., vol. 739, 2014, pp. 392–420), and maintains its success in describing the mean velocity–temperature relation. Furthermore, the present model is superior to the GRA in depicting the relationship between their fluctuating fields and also bridges the gap between the phenomenological model and the mathematical representation of the Reynolds analogy. The key points of the present model are validated by analysing the data of compressible wall-bounded turbulence with different Mach numbers, Reynolds numbers and wall thermal conditions.
The development of thermal boundary layers and plume near a section-triangular roof under different isothermal heating conditions has been the focus of numerous numerical studies. However, flow transition in this type of flow has never been observed experimentally. Here, phase-shifting interferometry and thermistor measurements are employed to experimentally observe and quantify the flow transitions in a buoyancy-driven flow over an isothermal section-triangular roof. Visualisation of temperature contours is conducted across a wide range of Rayleigh numbers from laminar at 103 to chaotic state at 4 × 106. Power spectral density of the temperature measurements reveals the type of bifurcations developing as the Rayleigh number is increased. This flow transition is characterised as a complex bifurcation route with the presence of two fundamental frequencies, a low and a high frequency. We found that the thermal stratification in the environment plays a significant role in the flow transition. The spatial development of flow is also quantitatively and qualitatively described. In addition to clarifying flow transition in experiments, the work demonstrates the implementation of phase-shifting interferometry and punctual temperature measurements for characterisation of near-field flow over a heated surface.
In recent years, the significance of terahertz (THz) and (sub-)THz communications has grown substantially due to its promising trade-off between higher capacity compared to microwave-based communication and better resilience against weather dependent influences (e.g., fog and rain). While electronic and optoelectronic techniques have been extensively explored, each offering distinct advantages and limitations, they have predominantly been demonstrated and discussed as individual experiments, making performance comparison challenging. This paper addresses this gap by systematically benchmarking electronic and optoelectronic signal generation approaches under comparable conditions. Our experiments incorporate various receiver types, revealing that best performance is achieved by combining optoelectronic signal generation techniques at the transmitter in combination with an all-electric intradyne receiver. This results in a remarkable line rate of 200 Gbit/s over a distance of 52 m. To our knowledge, this represents the highest line rate achieved for technically relevant transmission distances for indoor access or outdoor small cell networks.
The nonlinear waves in a sheared liquid film on a horizontal plate at small Reynolds numbers are examined by theoretical and numerical approaches. The analysis employs the long-wave approximation along with finite difference schemes. The results show that the surface tension can suppress disturbances and prevent the occurrence of singularities. While the film flow is driven by the shear stress on the interface, its instability highly depends on the magnitude and direction of gravity. Specifically, when the direction of gravity is opposite to the wall-normal direction, perturbations are stabilized by gravity. In contrast, when these two directions are the same, the gravitational force is destabilizing, and stationary travelling waves can exist if a balance is reached between the effects of gravity and surface tension. For the steady solitary waves, there are quasi-periodic oscillations occurring between two stationary points, indicating the presence of heteroclinic trajectories. For periodic waves, the evolutions are sensitive to several parameters and initial disturbances, while one steady-state wave exhibits a sine function-like behaviour.
This paper presents a comprehensive evaluation of reduced-order models (ROMs) for the determination of pressure coefficient distributions on supersonic and hypersonic bodies. The study investigates the limitations, aerodynamic precision and computational performance associated with various methodologies, ranging from simplistic Newtonian theory-based approaches to more advanced first and second-order shock-expansion theories. Validation is performed by comparing computed results with experimental and computational data for pressure distributions, drag and lift coefficients and centres of pressure for fundamental geometries and authentic vehicle design over a wide range of freestream conditions. The study also includes a comprehensive computational complexity analysis, demonstrating the superiority of finite-element ROM approaches over traditional finite-volume computational fluid dynamics (CFD) simulations. The primary objective of this paper is to scrutinise the extension of these methodological classes to the low supersonic regime. Hence, thermo-chemical reactions within the flow are disregarded, and the ideal gas law is adopted. A value of $\gamma = 1.4$ is chosen for consistency and comparability across the analyses. The proposed ROMs show remarkable potential for reducing high-speed simulation execution times by four orders of magnitude, maintaining accuracy within 20 per cent and as low as 1 per cent. The study unveils three key findings: first, the accuracy degradation of Newtonian-based theories for inclined elements, particularly around 45 degrees, and their reduced dependency on Mach number at large inclination. Secondly, the study presents novel insights into the impact of shock-wave-Mach-wave interactions on pressure distribution calculations, emphasising the Mach number as a crucial metric governing recompression effects. Lastly, the study demonstrates the exceptional accuracy of DeJarnette’s method, providing ${C_P}$ results within 2 per cent for a wide range of conditions, offering an attractive alternative to the Taylor-Maccoll equation.
The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.
Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.
Microorganism motility often takes place within complex, viscoelastic fluid environments, e.g. sperm in cervicovaginal mucus and bacteria in biofilms. In such complex fluids, strains and stresses generated by the microorganism are stored and relax across a spectrum of length and time scales and the complex fluid can be driven out of its linear response regime. Phenomena not possible in viscous media thereby arise from feedback between the swimmer and the complex fluid, making swimming efficiency co-dependent on the propulsion mechanism and fluid properties. Here, we parameterize a flagellar motor and filament properties together with elastic relaxation and nonlinear shear-thinning properties of the fluid in a computational immersed boundary model. We then explore swimming efficiency, defined as a particular flow rate divided by the torque required to spin the motor, over this parameter space. Our findings indicate that motor efficiency (measured by the volumetric flow rate) can be boosted or degraded by relatively moderate or strong shear thinning of the viscoelastic environment.
This paper presents a dual-band bandpass filter with regular harmonic suppression. The proposed third-order filter consists of two microstrip stepped impedance resonators and single substrate integrated waveguide resonator. The mismatches in their higher order resonant frequencies are utilized to suppress the regular harmonics. The passbands are centered at f1 = 2.4 GHz and f2 = 3.5 GHz with fractional bandwidths of 5% and 7.5%, respectively. The measured midband insertion and return losses are better than 2.55 and 14.5 dB for the first, whereas for the second band, they are better than 1.95 and 14 dB. The filter offers at least 33 dB suppression of first three higher order regular harmonics of f1 and f2.