Search Results (33)

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

Fossil fuel depletion has increased interest in renewable alternatives such as biodiesel derived from non-edible plant oils. Droplet evaporation is a key process influencing fuel–air mixing and combustion efficiency in diesel engines. In this study, the evaporation characteristics of diesel and two non-edible biofuels, Jatropha and Castor, are investigated using computational fluid dynamics (CFD) under high-temperature and high-pressure conditions representative of engine environments. The numerical model incorporates the conservation equations of mass, momentum, and energy, together with the k–$\epsilon$ turbulence model and a discrete phase model to simulate droplet heating, motion, and mass transfer during evaporation. A comparative CFD analysis is performed to examine how fuel properties, ambient temperature, and droplet size affect the evaporation behaviour of diesel, Jatropha, and Castor droplets under identical engine-like conditions. The evolution of droplet diameter, temperature, velocity, and lifetime is analysed, and the applicability of the classical $D^2$-law is evaluated under different operating conditions. The results indicate that biofuel droplets generally evaporate faster than diesel droplets at lower temperatures, while evaporation trends become similar at higher temperatures. These findings provide insight into the evaporation behaviour of Jatropha and Castor fuels and their potential application in diesel engines. Full article

This review develops a unified geometric framework for synthesizing global asymptotic laws, termed classical entanglement. The central tool is the entanglement operator, a Minkowski–$L_a$ metric blend that couples asymptotic regimes through an index $a>1$, producing a nonlinear global state whose intermediate region is metrically non-separable and cannot be written as a linear combination of its limits. The framework reveals a universal transition knee whose curvature scales linearly with a, independent of amplitudes or local scales. We show that this geometric mechanism encompasses Orlicz norms, weighted Hölder metrics, and iterated Hölder constructions, the latter being structurally isomorphic to self-similar root approximants. A conceptual “Rosetta Stone” links practitioner terminology, geometric meta-language, and functional-analytic structures, clarifying how classical entanglement unifies empirical blending, metric curvature, and Calderón-type interpolation. Applications to turbulence (Darcy friction factor), fractional dynamics, and scale-dependent diffusion illustrate how classical entanglement provides stable, asymptotically consistent global states across multi-scale systems. Full article

Nowadays, the urban population is steadily increasing worldwide and, as a result, global construction output is expected to grow to more than 16 trillion EUR by 2030. This rapid urbanization has created a strong need for the successful selection and delivery of urban development projects to meet the challenges related to the provision of sustainable and resilient infrastructure, together with affordable residence solutions. Meanwhile, the dominance of the traditional capital budgeting discounted cash flow (DCF) technique has long been questioned for its inability to be effectively applied to the complex, uncertain, and turbulent current environment. The main cause of this stems from its deficiencies in recognizing and incorporating the value of managerial interventions through strategic decisions to delay, expand, or even abandon an investment. A real options analysis (ROA) is proposed in this paper as a dynamic “wait and see” alternative to the static “now or never” DCF methodology, which is based entirely on a positive net present value (NPV) output. Thus, the aim of the research is to explore whether the practical application of ROA for the assessment of the financial viability of urban development capital investment projects can be improved from the obtained managerial flexibility in the decision-making process. Spreadsheet-based mathematical models are developed for the analysis and implementation of both the Black–Scholes formula and the binomial lattice method. The results are discussed and compared with a classic DCF analysis. The main advantages of using ROA, i.e., determining alternative paths of urban development and providing a practical and flexible means to adapt to changing external conditions, are highlighted through the application of a common type of real option to an actual new multistorey office building project. Based on the DCF model and its negative NPV, the investment under study is not viable. However, when simply considering the delay strategic option, the project turns out to be highly valuable. For comparison reasons, future work is recommended on alternative types of real options, like the compound staging option, and towards the use of alternative ROA tools, like the Monte Carlo Simulation technique, non-recombining binomial lattices, and the dividend-based version of the Black–Scholes model. Full article

Corrugated pipes are widely used due to their mechanical flexibility; however, their corrugated internal geometry is associated with increased hydraulic losses. Previous studies have reported a non-classical increase in friction factors with pipe diameter at identical Reynolds numbers, although the underlying mechanisms and related energy implications have not been fully clarified. In this study, turbulent flow behavior and pumping power requirements in stainless-steel corrugated pipes are investigated using a validated three-dimensional Computational Fluid Dynamics (CFD) framework based on the SST k–ω turbulence model. The numerical predictions show good agreement with available experimental data, with maximum deviations remaining below approximately 12% across the validated range. The results indicate that both friction factor and pumping power increase systematically with pipe diameter under dynamically similar flow conditions, demonstrating that Reynolds-number similarity alone does not ensure flow similarity in corrugated geometries. From an energy perspective, an Energy Penalty Factor (EPF) is introduced to quantify corrugation-induced pumping requirements, and a surrogate correlation is developed to relate EPF to Reynolds number and selected dimensionless geometric parameters. The proposed formulation exhibits strong predictive performance within the investigated parameter space (R² = 0.972) and enables rapid, CFD-free estimation of energy penalties for preliminary design and comparative evaluation of corrugated piping systems. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

Axial fans are widely used in local and decentralized residential ventilation applications, such as bathroom and toilet exhausts and short-duct ventilation systems, but the turbulent swirling flow they generate can lead to increased hydraulic losses, reduced energy efficiency, and unstable fan operation. This study experimentally investigates the swirling flow produced by the axial fan operating in a straight duct, following the ISO 5801, case B. Original classical probes and one-component laser Doppler anemometry (LDA) were used to measure velocity components at multiple downstream locations. Results show a strong forced-vortex core (i.e., solid body profile) and a highly non-uniform axial velocity profile near the impeller (x/D = 3.35), which homogenizes downstream (x/D = 26.31), indicating significant energy loss. Circulation and swirl number decrease significantly downstream, but residual swirl remains throughout the duct, increasing pressure drops and leading to unstable fan performance. These findings demonstrate that swirl-induced velocity-profile transformations are a major source of inefficiency in residential ventilation systems employing axial fans without flow-straightening devices. Full article

The classical assumption of self-similarity in flow velocities and turbulence statistics has been successfully validated for fully developed flows in open channels, pipes, and boundary layers. However, its application in developing boundary-layer flows in channels with steep slopes and large roughness elements has not yet been thoroughly scrutinized. This study investigates whether turbulence statistics exhibit self-similar behavior when properly scaled in steep-stepped spillways. Specifically, it explores the influence of roughness height ($k_s$)—representing the cavity size of a steep-stepped spillway—on turbulence statistics in the non-aerated skimming flow region. Numerical simulations, extensively validated against experimental data, were conducted for a stepped spillway with a fixed slope angle of 51.34°, using five roughness heights ($k_s$ = 6.25, 3.12, 1.56, 0.78 and 0.39 cm), corresponding to step height-to-length ratios of 10:8, 5:4, 2.5:2, 1.25:1 and 0.625:0.5, respectively. The results show that the dimensionless profiles of turbulent kinetic energy (TKE) at the step edges collapse onto a single curve when rescaled by a factor of ${\left(\delta /k_s\right)}^n$ with $n~0.4$. Likewise, the dissipation rate of TKE follows a similar collapse with $n~0.3$. For the turbulent eddy viscosity, an exponent of $n~0.5$ was adopted based on dimensional analysis, although the values for the smoothest configuration deviate from the curve. Full article

This article aims to investigate the impact of sustainable assets on dynamic portfolio optimization under varying levels of investor risk aversion, particularly during turbulent market conditions. The analysis compares the performance of two portfolio types: (i) portfolios composed of non-sustainable assets such as fossil energy commodities and conventional equity indices, and (ii) mixed portfolios that combine non-sustainable and sustainable assets, including renewable energy, green bonds, and precious metals using advanced Deep Reinforcement Learning models (including TD3 and DDPG) based on risk and transaction cost- sensitive in portfolio optimization against the traditional Mean-Variance model. Results show that incorporating clean and sustainable assets significantly enhances portfolio returns and reduces volatility across all risk aversion profiles. Moreover, the Deep Reinforcing Learning optimization models outperform classical MV optimization, and the RTC-LSTM-TD3 optimization strategy outperforms all others. The RTC-LSTM-TD3 optimization achieves an annual return of 24.18% and a Sharpe ratio of 2.91 in mixed portfolios (sustainable and non-sustainable assets) under low risk aversion (λ = 0.005), compared to a return of only 8.73% and a Sharpe ratio of 0.67 in portfolios excluding sustainable assets. To the best of the authors’ knowledge, this is the first study that employs the DRL framework integrating risk sensitivity and transaction costs to evaluate the diversification benefits of sustainable assets. Findings offer important implications for portfolio managers to leverage the benefits of sustainable diversification, and for policymakers to encourage the integration of sustainable assets, while addressing fiduciary responsibilities. Full article

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

In current transportation hubs, passengers travelling with wheeled luggage or suitcases is a common phenomenon. Due to the fact that most luggage occupies a certain space in dense passenger crowds with high mass inertia, its abnormal motion, such as drifting, can frequently trigger unavoidable local disturbances and turbulence in the surrounding pedestrian flows, further increasing congestion risk. Meanwhile, there still is a lack of quantitative disturbance propagation analysis, since most state-of-the-art achievements rely on either scenario-based experiments or the spatial characteristics of crowd distribution assessed qualitatively. Therefore, this study considers the luggage-laden passenger as a deformable particle. The resulting disturbance on surrounding non-luggage-carrying passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-owned luggage is developed by adapting the classical Aw–Rascle traffic flow model with a pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding were conducted in Pathfinder 2022 Software. Experimental results showed that the disturbing force of a left-sided crowd can reach a peak of 238 N with a passenger density of 3.0 $\mathrm{p}/{\mathrm{m}}^2$, and the maximum force difference between the left- and right-sided disturbing force can reach 153 N, as confirmed by a case study in an L-shaped corridor of a transportation hub. Furthermore, it is recommended that the proposed model can be applied in crowd flow analysis and intelligent decision-making for passenger management in transportation hubs. Full article

This comprehensive overview presents our continued efforts in high-order finite difference method (FDM) development for adaptive numerical dissipation control in the long-time integration of direct numerical simulation (DNS), large eddy simulation (LES), and implicit LES (ILES) computations of compressible turbulence for gas dynamics and MHD. The focus is on turbulence with shock wave numerical simulations using the adaptive blending of high-order structure-preserving non-dissipative methods (classical central, Padé (compact), and dispersion relation-preserving (DRP)) with high-order shock-capturing methods in such a way that high-order shock-capturing methods are active only in the vicinity of shock/shear waves, and high-gradient and spurious high-frequency oscillation regions guided via flow sensors. Any efficient and high-resolution high-order shock-capturing methods are good candidates for the blending of methods procedure. Typically, the adaptive blending of more than one method falls under two camps: hybrid methods and nonlinear filter methods. They are applicable to unstructured finite volume, finite element, discontinuous Galerkin, and spectral element methods. This work represents the culmination of over 20 years of high-order FDM developments and hands-on experience by the authors and collaborators in adaptive numerical dissipation control using the “high order nonlinear filter approach”. Extensions of these FDM versions to curvilinear nonuniform, freestream-preserving moving grids and time-varying deforming grids were also developed. By examining the construction of these two approaches using the high-order multistage type of temporal discretization, the nonlinear filter approach is made more efficient and less CPU-intensive while obtaining similar accuracy. A representative variety of test cases that compare the various blending of high-order methods with standalone standard methods is illustrated. Due to the fact that our nonlinear filter methods are not well known in compressible turbulence with shock waves, the intent of this comprehensive overview is for general audiences who are not familiar with our nonlinear filter methods. For readers interested in the implementation of our methods into their computer code, it is hoped that the long overview will be helpful. Full article

In this work, we underscore the significance of selecting an appropriate scaling to derive dimensionless quantities that accurately reflect their dimensional counterparts, thereby enhancing the comprehension of the underlying physics. For the loss of head in a pipe flow, we argue that employing inertial force (or kinetic energy) to non-dimensionalized pressure force (or mechanical energy loss) lacks physical justification. As a result, an anomalous trend emerges for the classical friction factor: it decreases as the dimensionless flow rate (Reynolds number) increases, contrary to the behavior observed in the corresponding dimensional quantities. Conversely, by non-dimensionalizing the pressure force with the viscous force, a novel friction factor arises. In laminar flow, it is constant, while in turbulent flow, it is a monotonically increasing function of the Reynolds number, mirroring the behavior observed in the dimensional problem. Full article

The study of the conditions under which a stratified shear flow becomes turbulent is important, as turbulence is the source of mixing and dissipation in the atmosphere and can significantly influence the momentum and temperature structure of the atmospheric circulation. Oftentimes, the density structure of atmospheric flows is organized in thick layers of constant density separated by thin layers of sharp density gradients. It has been shown by previous studies that such multilayered flows can become unstable under shear. In this work, we investigate Taylor–Caulfield Instability (TCI), which occurs in a three-layer fluid moving with a constant shear flow. Previous studies examined the instability under the Boussinesq approximation, which is not expected to hold in cases of sharp density gradients. The non-Boussinesq limit is therefore investigated in this work. TCI is studied using the classical perturbation theory, that is by examining the evolution of small perturbations to the base flow. The wavelength of the waves expected to dominate the flow as well as the time in which these waves will emerge are calculated. In addition, the characteristics of the unstable waves are studied under a variety of conditions for the shear and the stratification. It is found that under the Boussinesq approximation, the wavelength of the instability waves is underestimated and the time for the evolution of the waves is overestimated. Full article

The aim of this work is to describe an efficient implementation of cubic and multiparameter real gas models in an existing discontinuous Galerkin solver to extend its capabilities to the simulation of turbulent real gas flows. The adopted thermodynamic models are van der Waals, Peng–Robinson, and Span–Wagner, which differ from each other in terms of accuracy and computational cost. Convective numerical fluxes across elements interfaces are calculated with a thermodynamic consistent linearized Riemann solver, whereas for boundary conditions, a linearized expression of the generalized Riemann invariants is employed. Transport properties are treated as temperature- and density-dependent quantities through multiparameter correlations. An implicit time integration is adopted; Jacobian matrix and thermodynamic derivatives are obtained with the automatic differentiation tool Tapenade. The solver accuracy is assessed by computing both steady and unsteady real gas test cases available in the literature, and the effect of the mesh size and polynomial degree of approximation on the solution accuracy is investigated. A good agreement with experimental and numerical reference data is observed and specific non-classical phenomena are well reproduced by the solver. Full article