Search Results (16,723)

To address the challenge of model selection in large-scale traffic flow prediction tasks, this paper proposes a dynamic multi-model selection framework based on Deep Contextual Bandits (DCB). Centered on the optimal combination of sub-models, the framework leverages contextual information of road segments to select dynamically among candidate predictors, achieving more efficient and accurate traffic flow prediction. Several mechanisms are introduced to improve strategy learning and convergence, including a baseline network, experience replay, double-model estimation, and prioritized experience sampling. A clustering-based strategy is further designed to reduce the search space and enhance the generalization and transferability. Experiments on real-world traffic datasets demonstrate that the proposed framework significantly outperforms traditional static fusion methods, reinforcement learning (RL) baselines, and mainstream spatiotemporal prediction models. In particular, the framework yields a 1.0% improvement in R² and a 3.2% reduction in MAE compared to state-of-the-art baselines, while reducing inference time by 43.1%. Moreover, the proposed framework shows strong capability in adaptive model selection under varying contexts, with ablation studies confirming the effectiveness of its key components. Full article

This study advances the performance of a tandem-blade centrifugal compressor through a parametric Computational Fluid Dynamics (CFD) methodology integrated with Response Surface Methodology (RSM). Numerical simulations were executed by solving steady-state Reynolds-Averaged Navier–Stokes (RANS) equations utilising the Shear Stress Transport (SST) k-ω turbulence model on a validated structured hexahedral mesh. Local sensitivity analysis identified the hub outlet angle and hub inlet angle as the primary geometric parameters affecting pressure ratio and isentropic efficiency, respectively. Flow-field visualisations confirmed that the tandem configuration effectively re-energises the boundary layer, thereby reducing separation and enhancing pressure recovery. Using a Multi-Objective Genetic Algorithm (MOGA), an optimal blade design comprising 22 blades was determined, achieving a maximum isentropic efficiency of 95.23% and a total pressure ratio of 1.416. These findings provide valuable quantitative insights for the optimal design of tandem impellers and highlight the effectiveness of integrating CFD-based sensitivity analysis with multi-objective optimisation techniques. Full article

Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO₂,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints. Full article

To address the challenges of characterizing subway passenger flow fluctuations and overcoming the slow convergence and significant errors of existing intelligent optimization algorithms in tuning deep learning parameters for flow prediction, this study proposes a novel subway passenger flow fluctuation interval prediction model based on a Symmetry-Enhanced FIG-ICPO-XGBoost model. The core innovation is an Improved Cheetah Optimization Algorithm (ICPO), which incorporates enhancements including Circle mapping for population initialization, a hybrid strategy of dimension-by-dimension pinhole imaging opposition-based learning and Cauchy mutation to escape local optima, and adaptive variable spiral search with inertia weight to balance exploration and exploitation. The construction of this methodology embodies the concept of symmetry in algorithm design. For instance, Circle mapping achieves uniformity and ergodicity in the initial distribution of the population within the solution space, reflecting the symmetric principle of spatial coverage. Dimension-by-dimension pinhole imaging opposition-based learning generates opposite solutions through the principle of mirror symmetry, effectively expanding the search space. The adaptive variable spiral search strategy dynamically adjusts the spiral shape, simulating the symmetric relationship of dynamic balance between exploration and exploitation. Utilizing fuzzy-granulated passenger flow data (LOW, R, UP) from Harbin, the ICPO was employed to optimize XGBoost hyperparameters. Experimental results demonstrate the superior performance of the ICPO on 12 benchmark functions. The ICPO-XGBoost model achieves mean MAE, RMSE, and MAPE values of 10,291, 10,612, and 5.8%, respectively, for the predictions of the LOW, R, and UP datasets. Compared to existing models such as CPO-XGBoost, PSO-BiLSTM, GA-BP, and CNN-LSTM, these values represent improvements ranging from 4541 to 13,161 for MAE, 5258 to 14,613 for RMSE, and 2.6% to 7.2% for MAPE. The proposed model provides a reliable theoretical and data-driven foundation for optimizing subway train schedules and station passenger flow management. Full article

Conventional mixed-flow pump designs are typically developed under the assumption of uniform inflow. However, due to various operational constraints, these pumps often operate under non-uniform inflow conditions, which can significantly deteriorate hydraulic performance and operational stability. To investigate the influence of inflow conditions on parameter analysis and optimization results of mixed-flow pumps, this study conducted optimization design under both uniform and non-uniform inflow conditions. Four loading control parameters—LE (leading-edge preloading), K (slope of the intermediate linear segment), and NC and ND (first and second loading inflection positions)—were selected as design parameters, while the weighted hydraulic efficiency at 0.8Q_des, 1.0Q_des, and 1.2Q_des served as the optimization objective. Under uniform inflow conditions, the sensitivity ranking of the parameters for efficiency (subscripts s and h denote shroud and hub, respectively) is: LE_s > K_h > LE_h > ND_h > NC_h > K_s > NC_s > ND_s. The corresponding optimal parameter values are −0.2, 0, 0.2, 0.4, 0.25, 1.5, 0.1, and 0.6. Under non-uniform inflow, the ranking changes markedly to: NC_h > LE_s > K_h > K_s > ND_h> LE_h > ND_s > NC_s, with optimal values of 0.4, 0, 1.5, 0, 0.6, −0.2, 0.8, and 0.1, respectively. Compared with the baseline model, the optimized configurations achieve efficiency improvements of 0.61%, 2.94%, and 3.74% under uniform inflow, and 0.23%, 4.42%, and 6.78% under non-uniform inflow. Analysis of the internal flow field indicates that incorporating inflow conditions at the initial design stage significantly enhances the robustness of the optimized blade geometry when subjected to non-uniform inflow. These findings provide important implications for the optimization and practical design of mixed-flow pumps operating under complex inflow environments. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

EPDM is widely used as the polymer matrix for solid rocket motor (SRM) internal thermal protection because of its low density, chemical inertness, and ability to form carbonaceous residue. Practical performance is frequently limited by weak char integrity and barrier properties, char oxidation, mechanical stripping in gas-dynamic flow, and by the poor comparability of published results due to non-uniform test conditions and reporting. This review systematizes studies on 0D nanofillers in EPDM ablatives and harmonizes the key metrics, including linear and mass ablation rates (LAR, MAR), back-face temperature (T_back), and solid residue yield. The major 0D additives-nSiO₂, nTiO₂, nZnO, and carbon black (CB) are compared, and their dominant mechanisms are summarized: degradation-layer structuring, reduced gas permeability, thermo-oxidative stabilization, and effects on vulcanization. Several studies report larger improvements for hybrid systems, where CB enhances char cohesion and retention, while oxide nanoparticles improve barrier performance and resistance to oxidation. Finally, an application-oriented selection matrix is proposed that accounts for thermal protection efficiency, processability, agglomeration limits, and density penalties to support EPDM coating design and improve comparability. Full article

Unlike classical multi-layered micro-perforated panels (MPPs), which rely on sub-millimeter orifices for sound dissipation, we propose a multi-layered porous Helmholtz resonators absorber. It consists of alternately layered perforated porous material panels and perforated rigid panels with millimeter- to centimeter-scale orifices, primarily relying on porous materials for sound energy dissipation. Theoretically, perforated porous material panels are modeled as homogeneous fluid layers using double porosity theory, and the total surface impedance is derived through bottom-to-top impedance translation. A double-layered prototype was tested to validate the theoretical and numerical models, achieving near-perfect absorption peaks at 262 Hz and 774 Hz, with a subwavelength total thickness of 11 cm and a broadband absorption above an absorption coefficient of 0.7 from 202 Hz to 1076 Hz. Simulations of sound pressure, particle velocity, power dissipation, and sound intensity flow confirm that Helmholtz resonances in each layer enhance sound entry into resistive porous materials, causing absorption peaks. Parameter studies show this absorber maintains high absorption peaks across wide ranges of orifice diameters and panel thicknesses. Finally, an optimized triple-layer porous Helmholtz resonators absorber achieves an ultra-broadband absorption above a coefficient of 0.95 from 280 Hz to 1349 Hz with only 16.5 mm thickness. Compared with conventional MPPs, this design features significantly larger orifices that are easier to fabricate and less susceptible to blockage in harsh environments, offering an alternative solution for low-frequency and broadband sound absorption. Full article

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

Signage systems are an integral part of modern urban environments, and they influence both city aesthetics and information flow. But their growing use also adds to the embodied carbon footprint of urban infrastructure, a factor that is often overlooked in sustainable city planning. The present study investigates the environmental impact of signage within the context of urban development and climate-responsive design using two Australian case studies, including one installed at a national bank. The assessment is limited to the cradle-to-site (A1–A4) stages, focusing on material production and transportation impacts only. In each case study, one installed signage unit is used as the functional unit, with the results scaled to a nationwide-deployment scenario in Case Study 2. The results show that aluminium and steel dominate signage materials in both mass and embodied carbon. The study also proposes several mitigation strategies, including the use of low-carbon aluminium, higher-grade steel, and design optimization methods. A quantitative analysis also demonstrates the potential reductions in embodied carbon, ranging from 18% to 80.3%, with low-carbon material substitution achieving up to an 83.4% reduction in one case study. The findings also highlight that targeted material and design choices in the signage sector can significantly advance urban sustainability goals. Full article

The liner temperature distribution of an aero-engine combustor is a critical parameter for evaluating its cooling effectiveness. It provides essential guidance for designing the combustor cooling flow field, assessing combustion performance, identifying critical regions, and predicting service life. However, current research on surface temperature field measurements in real or model aero-engine combustors remains limited. Existing studies focus primarily on the liner temperature measurement under near-steady-state conditions, with less attention to its dynamic changes. This study employs Laser-Induced Phosphorescence (LIP) thermometry to measure the effusion-cooled liner temperature field of an aero-engine model combustor under various CH₄/Air swirling premixed flame conditions and varying blowing ratios. Based on the geometric characteristics of the effusion-cooled liner, an optimization method for matching phosphorescence images of different wavelengths is proposed. This enhances the applicability of phosphorescence intensity ratio-based LIP thermometry in high-vibration environments. The study specifically focuses on the dynamic response of LIP thermometry for monitoring combustor liner temperature. The instantaneous effects of blowing ratio variations on liner temperature rise rates were investigated. Additionally, the influence mechanisms of a broad range of combustion conditions and the blowing ratios on the combustor liner temperature distribution and cooling effectiveness were examined. These findings provide theoretical and technical support for cooling design and dynamic liner temperature field measurement in real aero-engine combustors. Full article

This study investigates the flow and heat transfer mechanisms of high-temperature air flowing across staggered lined fine tubes in a SABRE-type precooler. Large-Eddy Simulation (LES) was employed to model three-dimensional unsteady flow under constant-property and variable-property air models at inlet temperatures of 400–800 K. The results show that increasing temperature substantially enhances vorticity, turbulent kinetic energy, heat flux, and Nusselt number, while flow separation and pressure drop are intensified. However, when temperature-dependent air properties are incorporated, the wake width increases and the separated shear layers become thicker, while the turbulence/unsteadiness intensity decreases. Consequently, the near-wall shear is reduced and the heat transfer coefficients are lower. Compared with variable-property predictions, constant-property models overestimate the average Nusselt number by 20–40% and the local pressure drop by 40–65%, and they underestimate the air-side temperature drop along the tube rows. These findings demonstrate that real-gas effects significantly alter both aerodynamic resistance and thermal performance. Overall, accurate representation of temperature-dependent air properties is essential for the reliable design, evaluation, and optimization of micro-tube precoolers. Full article

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking “cutting edge micro-geometry—material separation behavior (separation point/minimum uncut chip thickness)—microstructural evolution of the machined surface.” This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel. Full article

The emerging concept of circular parks positions protected areas as active generators of shared value, as they integrate biodiversity conservation with natural resource optimization, local economies, and social cohesion. This perspective challenges traditional passive management by applying circular economy principles to parks as dynamic territorial organisms embedded within a regional socio-ecological metabolism. The research explores and tests circular park approaches starting from forest-related resource flows in areas where ecological richness coexists with socio-economic fragility. Focusing on the case study of the Madonie Regional Park (Sicily, Italy), the research investigates alternative pathways for the reuse of retrievable biomass by relating material flows to local social, economic, and cultural activities potentially involved in circular processes. This study supports the design of recycling, repurpose, and re-vision strategies to transform residual biomass into regenerative local value and strengthen the territorial resilience in inner areas characterized by demographic fragility despite being endowed with significant environmental and cultural capital. Through a design-oriented approach, the research experiments with alternative circular strategies in a case study, proposing a shift from extractive and mono-output models towards multi-output approaches and from an energy-centered towards a community-centered model. This perspective emerges not only as a cultural challenge but also as an opportunity to build an operational and replicable planning practice within the Italian and European park system, contributing to the debate on the ecological transition of fragile territories. Full article

Coronaviruses are worldwide-distributed RNA viruses with zoonotic potential and the ability to jump from one host species to another, including humans. Even after the COVID-19 pandemic, the search for new, biologically active substances with anti-coronavirus activity continues to be a critical milestone for human health protection. In the framework of a complex engineering strategy, we cultivated the microalgal species Scenedesmus acutus in two different innovative types of flat-plate photobioreactors (PBR1 and K1) for CO₂ utilization and biomass production with special features. Isolated extracts from the microalgal biomass of each one were compared for their anti-coronavirus potential. The design of both PBRs allows a hydrodynamic regime to achieve best fluid flow distribution in their sections, therefore providing the optimal so-called flashing light effect. Of course, this is achieved under well-controlled operational conditions. A strain of beta coronavirus 1 (BCoV, bovine coronavirus) replicated in MDBK cells was used as an in vitro model for the evaluation of the antiviral activity of both extracts. The cell viability, number of survived BCoV particles, and cytopathic effect were evaluated after pre-incubation of the virus with the extracts or direct treatment. The extracts’ samples exhibited evident antiviral activity—extract 1 (from PBR1) in concentrations ≥ 200 µg/mL and extract 2 (from K1) in concentrations ≥150 µg/mL. The ddPCR result revealed significant diminishment of the BCoV particles in samples treated with higher concentrations of the extracts. The phytochemical analysis for certain main groups of compounds (flavonoids, polyphenols, carotenoids, and lipids) showed some differences for both extracts, which could be a possible reason for the observed difference in the antiviral activity. In conclusion, the innovative PBRs are a good platform for studying microalgal growth kinetics by applying different stress conditions from hydrodynamics and mass transfer subsystems. Both extracts showed promising potential for the isolation of metabolites with antiviral activity against BCoV and could be an object for future pharmacological investigations. Full article