Search Results (630)

Static wake steering can increase wind-farm power production, but its application to floating offshore wind farms requires assessment of the coupled wake, platform, structural, and station-keeping response. This study evaluates whether power-maximizing static yaw setpoints identified using the steady, control-oriented FLORIS model retain their benefit when transferred without re-optimization to a coupled FAST.Farm floating wind-farm model. The reference farm comprises four IEA Wind 15 MW turbines mounted on VolturnUS-S semi-submersible platforms. Greedy and static wake-steering operations are compared at three below-rated wind speeds, three sea states, and five matched turbulent-inflow realizations, resulting in 90 farm-level FAST.Farm simulations. Wake behavior is characterized through wake-center deflection, meandering, and velocity-deficit profiles, while turbine and mooring fatigue responses are evaluated using paired damage-equivalent-load statistics. Static wake steering increases mean farm power under all nine investigated wind–wave conditions. The gains are approximately 5.1–$5.2\%$ at $7\mathrm{m}{\mathrm{s}}^{-1}$, $5.0$–$5.1\%$ at $8\mathrm{m}{\mathrm{s}}^{-1}$, and $4.0$–$4.2\%$ at $9\mathrm{m}{\mathrm{s}}^{-1}$, with all paired $95\%$ confidence intervals remaining above zero. The gain results from a power redistribution in which the intentionally yawed upstream turbine incurs a local loss that is exceeded by the combined recovery of the downstream turbines. The fatigue response is strongly component- and turbine-dependent. The paired farm-mean blade-root DEL decreases by $0.82$–$2.24\%$, whereas the tower-base DEL increases by $0.76$–$2.78\%$, and the FairTen1 response generally increases by $0.88$–$2.92\%$. The farm-mean yaw-bearing response is mixed, ranging from a $1.15\%$ reduction to a $4.32\%$ increase. Turbine-level analysis reveals larger localized penalties, reaching approximately $10.4\%$ for the yaw-bearing DEL and $12.8\%$ for FairTen1. Spectral analysis associates the yaw-bearing response with yaw-induced aerodynamic and structural excitation, while the tower-base response is strongly influenced by low-frequency wave–platform dynamics. A complementary FLORIS sensitivity analysis demonstrates that the optimized aerodynamic benefit depends strongly on wind direction, spacing, wind speed, and turbulence intensity. For a Tampen-derived 11-turbine layout, resource weighting over the modeled 4–$13\mathrm{m}{\mathrm{s}}^{-1}$ interval produces an annual energy-contribution increase of $3.653\mathrm{GWh}{\mathrm{year}}^{-1}$, or $0.921\%$. These results provide numerical evidence that static wake steering can retain a positive power benefit in a coupled floating wind-farm environment, but controller assessment must include turbine- and component-specific dynamic loads rather than farm power alone. Full article

Compared with single-swirl configurations, linearly arranged multi-swirl burners introduce complex inter-jet interactions that significantly alter flame dynamics; however, the underlying mechanisms remain poorly understood. In this study, direct numerical simulation (DNS) is employed to investigate a lean-hydrogen multi-swirl flame, aiming to elucidate its flame structure and dynamic evolution. The flame development region is divided into upstream (Region 1) and downstream (Region 2) regions, based on the critical location where the flame stretch transitions from positive to negative. The flow field, flame morphology, thickness, stretch, curvature, and their joint statistical relationships are systematically compared between the two regions. The results show that in Region 1, the flame stretch is dominated by positive strain rate, and the flame maintains a continuous structure and a small thickness. In Region 2, the curvature stretch becomes dominant, leading to severe flame wrinkling, local extinction and breakup, with the mean flame thickness increasing to about 1.5 times that of the laminar flame. Joint PDF analyses reveal that negative flame stretch is correlated with a large negative curvature in Region 2, whereas in Region 1, it is not affected by the curvature sign. The downstream flame also exhibits higher displacement speeds, indicating intensified turbulence–flame interaction. This study reveals the streamwise transition mechanism of the multi-swirl flame from strain-dominated to curvature-dominated dynamics, clarifies the distinct coupling modes of upstream stabilization and downstream fragmentation, and provides new theoretical guidance for stable combustion and wide-operability design of lean-hydrogen swirl combustors. Full article

To addressthe challenge of attitude control in quad-ducted fan unmanned aerial vehicles (UAVs) under coupled disturbances comprising thrust lag, ground effect and a composite wind field during near-ground flight and to mitigate the inherent trade-off between disturbance rejection and noise suppression in fixed-bandwidth extended state observers (ESOs), this paper proposes a robust attitude control method based on a Sigmoid law adaptive-bandwidth extended state observer (AB-ESO). An attitude dynamic model covering the above multi-source disturbances is established, with all uncertainties uniformly treated as lumped disturbances. An adaptive-bandwidth mechanism with filtering and rate-limiting modules is designed for smooth continuous bandwidth tuning. A composite control framework integrating disturbance feedforward, lag compensation and attitude feedback is constructed, and the uniform ultimate boundedness of the closed-loop system is proved. Comparative simulations are conducted against six baseline controllers, including a cascade proportional–integral–derivative (PID) controller, fixed-bandwidth ESOs, incremental nonlinear dynamic inversion (INDI), fast terminal sliding mode control (FTSMC) and a time-varying bandwidth ESO, in a near-ground composite wind scenario. Results show that the proposed method achieves improved comprehensive performance: the three-axis average tracking root mean square error (RMSE) is approximately 72% lower than of the PID controller and 15.8% lower than that of the high-bandwidth ESO, and the control output total variation is reduced by about 27.8%. Monte Carlo verification with 100 random turbulence groups further validates the strong statistical robustness of the proposed method. All validations in this work are based on numerical simulations. This study provides a technical reference for high-precision control of ducted fan UAVs in near-ground environments. Full article

The generation of realistic wind speed time histories is essential for wind engineering analysis but remains challenging due to the scarcity of high-quality measured data and the non-stationary, stochastic nature of atmospheric turbulence. While existing artificial intelligence-based data-driven methods in wind engineering mainly focus on conditional forecasting tasks, the unconditional generation of independent wind speed time histories has received limited attention. To address this gap, a novel spectrogram-based generative framework, called DiffWind, is proposed for wind speed time history generation based on denoising diffusion probabilistic models (DDPM). Wind speed time histories are transformed into magnitude spectrograms using the short-time Fourier transform (STFT), modeled in the spectral domain using a U-Net-based diffusion model, and reconstructed through the Griffin–Lim algorithm (GLA). Field-measured wind speed records were employed to tune the STFT-GLA hyperparameters and train the DDPM. The effectiveness and accuracy of the STFT-GLA combination were validated through numerical experiments, while the diffusion-based spectrogram generation was evaluated using quantitative metrics. The results indicate that the proposed framework can generate high-fidelity wind speed time histories that reproduce key statistical, temporal, and spectral characteristics of measured wind data while demonstrating the capability to generate longer-duration records, highlighting its potential for wind engineering applications. Full article

In response to issues such as disordered airflow distribution and prominent local hotspots in campus non-standard data centers, this study took a non-standard raised-floor air-supply data center at a university in Hangzhou as the research object, and used a combination of on-site measurements and computational fluid dynamics (CFD) numerical simulation to investigate the optimization of the thermal environment. The temperature and air velocity of the data center were measured using a handheld hot-wire anemometer, and a standard k-ε turbulence model was established on the 6SigmaDC platform (now Cadence Reality DC Design Pro, version 2024.1). Model accuracy was confirmed through grid independence verification with three mesh levels and statistical error metrics (MAE, MBE, RMSE) across multiple measurement zones. The results show that the mean absolute error of temperature does not exceed 0.9 °C in all zones and the mean absolute error of air velocity does not exceed 0.20 m/s, indicating that the model effectively reproduces the airflow distribution and thermal environment of the data center. On this basis, to address the uneven airflow distribution in the underfloor plenum, an optimization strategy was proposed that involved the installation of composite baffles and the coordinated adjustment of variable floor tile openings. Eight representative simulation scenarios were designed, with the coefficient of variation and air supply uniformity index as evaluation indicators. Results indicate that the combined effect of perforated baffles and variable floor tile openings is the optimal strategy, reducing the range of net airflow among air supply outlets from 0.100 to 0.077 m³/s, decreasing the coefficient of variation from 12.8% to 10.8%, and increasing the air supply uniformity index by 10.7%. Whole-room thermal environment verification shows that the optimal scheme reduces the supply heat index (SHI) from 0.42 to 0.35, with an estimated PUE reduction of about 0.03, achieving both airflow uniformity improvement and energy-saving benefits. By improving the cooling efficiency and reducing the PUE, this retrofit strategy contributes to the sustainable operation of small-to-medium-sized campus data centers, supporting energy efficiency and carbon footprint reduction goals under green campus and low-carbon initiatives. Full article

Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the article is primarily a conceptual framework paper accompanied by an illustrative single-site qualitative case study designed to probe the plausibility and diagnostic utility of the proposed architecture rather than to generate statistical generalization. The research combines theoretical development with participatory territorial diagnostics in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis indicates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture is proposed to address these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures and is grounded in a real cyber-physical deployment of around 90 monitoring stations across Albania, La Jagua de Ibirico and Algarrobo. The study also introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs may function as socio-technical infrastructures supporting coordinated, adaptive, and justice-centered energy transitions in the Global South, while comparative empirical evidence remains an agenda for future work. Full article

Accurate retrieval of high-resolution three-dimensional (3D) wind fields from Doppler radar observations is essential for understanding the dynamical structure of convective storms and the turbulent processes that govern their evolution. This study develops and evaluates a ground-based dual and multi-Doppler radar wind retrieval framework designed to reconstruct turbulent wind structures within severe convective environments, including a squall-line event and a hurricane, using an advanced phased-array radar configuration at our facility. The retrieval algorithm combines a weighted least-squares initialization followed by a three-dimensional variational (3DVAR) refinement constrained by the anelastic mass continuity equation. To rigorously evaluate retrieval performance, we develop an LES-based radar wind retrieval framework in which turbulence-resolving LES wind fields are used to generate synthetic radial velocity observations, enabling direct comparison between the retrieved winds and the LES truth. The experiments are idealized and focus on radar geometry, sampling, filtering, and retrieval behavior, rather than representing a full radar instrument simulator. Statistical evaluation shows that the multi-Doppler configuration retrieves the horizontal wind components with high accuracy when sufficient radar viewing-angle diversity is available, whereas the dual-Doppler configuration shows degraded performance, particularly for the $v$-wind component, due to limited viewing geometry. Vertical velocity remains more difficult to retrieve, particularly at low levels, although the idealized hurricane experiment shows improved skill aloft, with correlations reaching ~0.8. Radar-geometry diagnostics show that the multi-Doppler configuration increases effective azimuth diversity across the retrieval swath, improves the conditioning of the local retrieval matrix, and reduces pointwise 3D wind-vector errors relative to the dual-Doppler configuration. Turbulence diagnostics, including velocity variance, turbulent kinetic energy, and kinetic energy spectra, indicate that the retrieval framework preserves the dominant storm-scale and radar-resolvable energy-containing turbulent structures while partially smoothing smaller-scale (<1–2 km) fluctuations by radar sampling, influence-radius filtering, and Gaussian distance weighting. These results emphasize that accurate 3D wind retrieval requires not only sufficient observational coverage but also a well-conditioned radar viewing geometry and highlight the utility of the LES-based radar wind retrieval framework for designing advanced ground-based radar systems for observing turbulent wind structures within convective storms. Full article

Disentangling the relative roles of emissions and atmospheric processes in controlling air-pollutant concentrations remains a central challenge in air-quality management. Although machine-learning (ML) models can accurately predict pollutant concentrations, the temporal evolution of the importance of individual drivers is often difficult to interpret. Here, we introduce a framework for reconstructing time-resolved feature importance (FI) in ML air-quality models. Hourly NO₂ and PM_2.5 concentrations were simulated across clusters of observations, defined along concentration trajectories in a state–space spanned by source intensity and meteorological variables. Within each cluster, predictor importance is quantified and mapped back onto the corresponding time points, yielding continuous FI time series for all predictors. The framework is demonstrated using observations from the nationwide air-quality network in Israel, together with traffic-related source indicators and meteorological parameters. The dominant drivers differ markedly between the two pollutants: NO₂ variability is primarily associated with local emissions, mechanical transport, and turbulent mixing, whereas PM_2.5 variability reflects predictors that are related to nucleation, coagulation, hygroscopic growth, long-range transport, and chemical transformation. The feature importance exhibits pronounced seasonal, regional, and diurnal variability, including modulation around traffic rush hours. These results demonstrate the value of time-resolved interpretability for diagnosing drivers of air-pollutant variability and improving the representation of processes in statistical air-quality models. Full article

Construction dust is a major contributor to urban inhalable particulate matter (PM₁₀) pollution, posing severe respiratory and cardiovascular health risks to construction workers and nearby residents, severely undermining urban environmental sustainability. Construction fences are widely adopted as a primary dust mitigation measure, yet their underlying dispersion mechanisms and comprehensive impacts on vertical air quality remain poorly understood due to the limitations of traditional field monitoring and empirical models, creating critical barriers to site-level pollution control and long-term urban sustainability. In this study, a reliable computational fluid dynamics (CFD) method was developed to investigate the spatial distribution of construction dust and quantify the dust suppression performance of fences with heights ranging from 0 to 3 m. Three mainstream k-ε turbulence models (Standard, RNG, and Realizable) were evaluated using on-site measurement data, and the RNG k-ε model was found to provide the best agreement with field observations, with statistical metrics of q = 1, FB = 0.052, and NMSE = 0.028. The results show that construction fences effectively reduce dust dispersion into the surrounding environment, particularly in the pedestrian breathing zone (z < 1.5 m). Increasing the fence height from 1.5 m to 3 m improves the breathing-zone dust reduction rate from 39% to 55%, with the most significant mitigation effect observed within 50 m downwind of the fence. However, a critical dual effect was identified: while fences suppress near-ground pollution, they induce strong upward airflow and turbulence, leading to elevated dust concentrations in the upper part of the near-ground region (z = 1.5–9 m), a phenomenon absent in the no-fence scenario. These findings provide practical implications for urban construction site management, suggesting that fence height and configuration should be carefully designed not only to reduce pedestrian-level exposure but also to avoid unintended pollutant accumulation aloft, thereby improving overall air quality control strategies and delivering balanced, long-term environmental sustainability at construction sites. Full article

This article employs exploratory data-driven modeling to examine the relationships between selected macroeconomic indicators, residential property prices, and housing affordability pressures in Poland between 2020 and 2024. This turbulent period was selected for analysis because of the unprecedented volatility triggered by the COVID-19 pandemic and the geopolitical shocks associated with the war in Ukraine, both of which severely disrupted macroeconomic stability and construction supply chains. The study examines how key economic variables—including inflation, gross domestic product (GDP), unemployment, and average and minimum wage dynamics—are associated with residential property price dynamics within the framework of construction economics. Using statistical modeling techniques, including linear regression and Pearson correlation analysis, the study quantifies the strength, direction, and dynamics of these relationships across primary and secondary housing sectors. Our findings reveal a distinct comparative pattern of associations: average wage growth and inflation emerge as the macroeconomic indicators most strongly associated with property valuations, while macroeconomic growth and unemployment dynamics exhibit asymmetric associations across market segments. Notably, the findings suggest that the primary sector may be more sensitive to credit-related demand shocks and policy interventions, whereas the secondary sector appears to respond more directly to broader consumer trends and household purchasing capacity. By integrating macroeconomic data into a sectoral analysis, this study provides an exploratory empirical basis for discussing sustainable housing strategies. The results underscore the necessity of aligning investment and production cycles in the construction sector with macroeconomic stability to maintain long-term residential purchasing capacity and support resilient urban development. Full article

Atmospheric turbulence distorts the wavefront of propagating optical radiation, degrading image resolution in astronomical telescopes and reducing power density at the target in focusing applications. These effects can be studied under controlled laboratory conditions using turbulence-generating devices—such as fan heaters (rough control), phase plates, or active mirrors (fine control)—in combination with a wavefront sensor for measurements. To support this research, we developed a software simulator for reconstructing atmospheric phase fluctuations. The integrated software–hardware system can generate phase screens following Kolmogorov turbulence statistics, incorporating parameters for wind velocity and the D/r₀ ratio. Phase screens were produced with an average approximation error of 0.01 µm (less than 5%). The average reconstruction error was 0.017 µm, corresponding to approximately 8%. The newly developed phase screen simulator outperforms the fastest existing version in several key aspects. Its aperture size is doubled, increasing from 400 mm to 800 mm, while the phase screen generation resolution expands by half, from 700 × 700 pixels to 1024 × 1024 pixels. The operating wavelength range also broadens significantly—from a maximum of 2.2 µm in the existing tool to 10 µm in the new one. Additionally, the wind velocity range becomes 1.5 times wider, extending from 30 m/s to 50 m/s. The developed tool might be useful for the performance analysis of wireless links, particularly in the estimation of bit error rate and quantum efficiency using the wavefront root mean square error. Full article

Horizontal annular channels are widely encountered in fluid mechanics. Extensive research has explored how wall roughness alters total pressure loss in circular pipe flows. Meanwhile, existing literature has thoroughly discussed artificially enhanced rough structures (e.g., ribs, helical strips, and axial corrugations) for turbulence control and modulation. By contrast, relevant research on the inner cylinder roughness of annular channels remains insufficient. In practical engineering like petroleum drilling, inner pipes are usually treated as hydraulically smooth in theoretical and numerical models. However, machining inevitably generates tiny surface asperities on inner walls. Though this manufacturing micro-roughness is minimal in size, its true impact on flow characteristics is still unclear and requires a systematic numerical study. This study numerically explores the effects of practical inner-wall roughness on the hydrodynamic performance of annular flow. The simulation results reveal that compared with a fully smooth inner wall, surface roughness significantly rearranges the spatial distribution of major turbulent parameters (e.g., flow velocity, turbulence intensity, turbulent kinetic energy and Reynolds stress), and changes both the magnitude and position of their peak values. When the inner wall roughness is 0.4 mm, the near-wall Reynolds stress increases by 153.5%, the turbulence intensity increases by 52.4%, and the turbulent kinetic energy increases by 133.0%; the velocity peak shifts to the outer ring side, and the critical roughness is 0.06 mm. The findings confirm that considering inner tube roughness is critical for improving the prediction accuracy of both microscale turbulent behaviors and macro flow parameters in practical annular flow engineering. This work distinguishes itself from existing studies by systematically quantifying the response of full first- and second-order turbulent statistics to pipeline manufacturing micro-roughness, revealing the symmetry-breaking turbulent transport mechanism of annular flow, and quantitatively determining the critical inner-wall roughness that shifts the velocity peak to the annular geometric midpoint, which provides a targeted quantitative database for the correction of annular flow numerical simulations that ignore native micro-roughness. Full article

Reliable communication infrastructure is a fundamental component of Intelligent Transport Systems (ITSs), particularly in scenarios involving maritime corridors and emergency traffic management. In locations where optical fiber deployment is geographically constrained, unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay links provide a flexible and rapidly deployable alternative. However, atmospheric attenuation, turbulence-induced fading, and wind-induced UAV misalignment can severely degrade link reliability and disrupt real-time transport data streams. This study proposes a payload-efficient multiple-input multiple-output free-space optical (MIMO-FSO) relay architecture based on a multi-output/single-input (MOSI) uplink and a single-output/multi-input (SOMI) downlink. Here, MOSI denotes multiple ground-based transmit apertures directed toward a single UAV receiving aperture, whereas SOMI denotes one UAV transmitting aperture serving multiple ground-based receiving apertures. Unlike conventional symmetric UAV-assisted MIMO-FSO relays that may duplicate diversity hardware on the aerial node, the proposed design shifts the parallel optical branches to the ground stations and keeps only one optical receiver and one optical transmitter on board the UAV. Under the adopted 4 × 4 comparison assumption, this reduces the UAV-side optical branch count from eight to two, corresponding to a 75% branch-count reduction proxy. System performance is evaluated over a 1.54 km relay link. The analytical framework describes Beer–Lambert attenuation, log-normal/gamma–gamma turbulence, and statistical pointing errors; in the OptiSystem implementation, their combined effects are represented by equivalent aggregate losses of 25 dB/km for atmospheric absorption/scattering and 25.5 dB/km for turbulence- and pointing-related degradation. Comparative simulations for SISO, 2 × 2, and 4 × 4 configurations show that the proposed 4 × 4 architecture increases the Q-factor from 8.38 to 18.25 and changes the OptiSystem-reported minimum BER from 2.73 × 10⁻¹⁷ to 9.95 × 10⁻⁷⁵. Because a finite simulation cannot statistically validate error probabilities of this magnitude through raw error counting, values far below 10⁻¹² are interpreted primarily as comparative indicators of receiver decision margin. The findings provide simulation-based evidence that the proposed architecture is a scalable candidate for resilient optical wireless backhaul in smart transport corridors under adverse propagation conditions. Full article

In order to reduce aviation’s negative environmental effects and support international efforts to battle climate change, the International Civil Aviation Organization (ICAO) seeks to cut greenhouse gas (GHG) emissions. About 2–3% of the world’s CO₂ emissions come from aviation, and at high altitudes, the fraction of other GHGs that significantly alter the atmosphere is considerably greater. In this study, hourly wind speed data at 100 m height from ECMWF’s fifth-generation reanalysis (ERA-5) were used over a period of 40 years (1985–2025). Hourly assessments of wind speeds at 40 m and 80 m heights are conducted in ERA-5, with biases at specific ground locations rectified via the Global Wind Atlas (GWA). This research estimates and analyzes many factors, including Weibull statistical parameters, daily and monthly wind speed variations, cumulative distribution function (CDF), and atmospheric turbulence intensity. The energy generation from several wind turbine types at different elevations was assessed. The findings indicate that the examined location revealed fair potential for the construction of large-capacity wind energy units at heights equal to or above 80 m. Turbines that are less than 50 m tall are spread out at least 10 km around the airport runway. While turbines that are less than 150 m tall are spread out at least 15 km away from the airport runway. Full article

Spatial chromatic instability is currently one of the most robust methods for improving solutions proposed for image-based fire detection systems. Real flames exhibit erratic, turbulent local color variations, providing a more reliable discriminative signal than global color information alone, especially in visually ambiguous non-fire situations. This study proposes a generalizable feature representation based on the Spatial Chromatic Instability Index (ICCS) to measure local RGB variations (ICCS_R, ICCS_G, ICCS_B, and ICCS_T). Two public datasets comprising both fire image files and non-fire imagery were used. The Hilbert–Schmidt Independence Criterion (HSIC) and Silhouette coefficient analysis were used to quantify the statistical dependence between feature sets and the resulting cluster separation. To evaluate the practical discriminatory performance of spatial chromatic instability, three classifiers, i.e., Logistic Regression, Linear SVM, and Random Forest, were employed. To verify the proposed approach’s effectiveness, three deep learning models, Swin Transformer, MobileViT, and ViT-Base-16, were also employed for cross-checking. Performance metrics demonstrated that integrating ICCS features into global color features improved classification. Logistic Regression performed best overall on the Kaggle dataset when local ICCS features were included, achieving an accuracy of 0.935 and an F1-score of 0.958. For the Mendeley dataset, Linear SVM achieved an accuracy of 0.862 and an F1-score of 0.881. The ICCS is a robust, easy-to-understand, and fast approach for identifying fires. It has real potential in early warning systems, mainly due to its limited requirements for computing power. Full article