Search Results (987)

Agricultural producers need affordable tools to estimate reference evapotranspiration (ET₀) in field conditions, especially in regions with limited access to complete weather data. In this study, the ET₀ for an alfalfa (Medicago sativa) crop was estimated using the SEG-SRM-V1 electronic system, based on the surface renewal method and validated using the FAO-56 Penman–Monteith method. The estimated ET₀ values for alfalfa ranged from approximately 4.0 to 8.5 mm day⁻¹ under the prevailing climatic conditions: periods of high temperature (21 °C to 33 °C), as measured by the system in the experimental area, with cloud cover, wind (1 to 8 m/s) and net radiation of 664 W/m² to 910 W/m². Comparisons between the two methods yielded determination coefficients of between 0.65 and 0.85, and the values of the errors (MSE, RMSE and MAE) tend to 0, which indicates that the estimates of ET₀ measured by the system (SEG-SRM-V1) are close to those obtained using the Penman–Monteith method. Similarly to the performance of open-field systems operating under atmospheric and vegetation cover conditions, these results demonstrate that high-frequency (10 Hz) air temperature measurements provide sufficient physical information to support the estimation of ET₀ in alfalfa, while the open architecture of the SEG-SRM-V1 system allows for flexibility and scalability for irrigation management applications in other crop types. Full article

Vernacular settlements in hot–humid regions preserve climate-responsive spatial knowledge, yet evidence on how linked outdoor, transitional, and indoor spaces jointly shape microclimate and thermal comfort remains limited. This study investigates a compact Hakka settlement in southern Jiangxi, China, by integrating field measurements, calibrated simulation, PET-based thermal-comfort assessment, and parametric scenario comparison to examine microclimatic differentiation across cold alleys, patios, halls, semi-open interfaces, and interior rooms. The results reveal clear microclimatic gradients across the linked vernacular spatial sequence. During the summer afternoon peak, cold alleys reduced air temperature by approximately 2.5 °C and PET by approximately 8.5 °C relative to ordinary streets, while semi-enclosed spaces adjacent to patios reduced air temperature by approximately 4.0 °C but increased relative humidity by 8–12%, indicating a cooling–moisture trade-off. Measured and simulated air temperature and wind speed showed satisfactory agreement and reproduced the main thermal and ventilation hierarchy across the connected spaces. Parametric comparison further identified case-based geometry-performance tendencies under the tested boundary conditions: within the tested cold-alley scenarios, widths of approximately 0.8–1.4 m combined with an H/W ratio close to 3:1 showed relatively favorable airflow-temperature performance in terms of shading continuity, moderated airflow, and reduced summer thermal exposure. The findings suggest that thermal comfort in compact hot–humid vernacular settlements depends on radiant-load reduction, moderated ventilation, and thermal buffering rather than on ventilation enhancement alone. Beyond the case-specific evidence, this study contributes a sequence-based, locally calibratable approach for preliminary retrofit appraisal in comparable compact hot–humid vernacular settlements. Full article

The integration of artificial intelligence (AI), particularly deep reinforcement learning (DRL), promises to enhance the autonomy and adaptability of drones in complex environments. This research explores the implementation of a cooperative counter-drone swarm solution using multi-agent DRL, such as Multi-Agent Proximal Policy Optimization (MAPPO), and the aim is to enhance public security. In this paper, an open-source simulation platform, AirSim, is utilized to train and test the proposed method. A centralized critic architecture within a multi-agent reinforcement learning (MARL) framework using Proximal Policy Optimization (PPO) is implemented. A PettingZoo–Ray RLlib integration provides scalable multi-agent training using shared policies to encourage collaboration. A centralized critic is trained by observing the joint state and action space of all drone agents, while drone agents execute decentralized policies during deployment. We observed that increasing the number of cooperative drones improves performance, achieving a 66.7% increase in episode reward, a 42% improvement in team success rate, and a 65% reduction in geofence violations compared to the two-drone configuration. The proposed framework provides a scalable foundation for real-world cooperative counter-unmanned aerial system (C-UAS) operations using deep reinforcement learning. Full article

Within the framework of European Union-funded Clean Aviation and TheMa4HERA (Thermal Management for the Hybrid Electric Regional Aircraft) projects, a preliminary performance prediction and design tool for centrifugal compressors has been developed, targeting the turbomachinery components used in environmental control systems (ECS) in short/medium-range types of aircraft. This tool is an integral part of the objective to establish a complete optimization methodology for the performance assessment and sizing of air generation systems for next-generation aircraft. The methodology is based on mean-line analysis for the impeller, vaneless and vaned (including variable-vaned) diffusers, and volute, with a two-zone approach for the flow analysis in the vaned diffuser passage. The results of the model are validated against experimental data related to two different open-source compressor designs with both diffuser types. It is concluded from these cases that, for the purpose of the design tool, the model provides accurate results for the impeller and both diffuser types. Extreme conditions such as stall and choke remain difficult to accurately predict due to the complex three-dimensional nature of these phenomena. Future developments of the tool will include modeling capabilities for radial turbines and heat exchangers. Full article

High-speed train aerodynamics have mainly been improved by passive design methods, such as streamlined noses, local fairings, and surface smoothing. These methods have achieved clear benefits, but several important aerodynamic problems remain difficult to solve by geometry optimization alone. Open-air drag is still affected by tail flow separation, base-pressure recovery, and disturbances around bogies and the underbody; crosswind safety is influenced by unsteady leeward-side separation and wake asymmetry; slipstream behavior depends on wake vortices, boundary-layer development, and complex near-ground underbody flow; and tunnel-related pressure transients arise from compression-wave generation, propagation, and reflection. These coupled effects mean that one fixed train shape cannot perform optimally in all operating conditions. For this reason, this review proposes that active flow control (AFC) should not be regarded only as a drag-reduction or stability-improvement technique for high-speed trains. Instead, it should be understood as a mission-adaptive aerodynamic control framework, in which different control actions are used for different operating scenarios. This paper first clarifies that passive optimization is increasingly subject to diminishing returns under multi-objective and engineering constraints. It then reviews AFC studies on drag reduction, base-pressure recovery, wake and slipstream control, underbody flow conditioning, crosswind mitigation, and tunnel pressure-wave suppression. Related AFC studies on bluff bodies, road vehicles, and other separated flows are included only when their physical relevance to trains is clear. The review further distinguishes gross aerodynamic improvement from net energy gain and identifies actuator power, durability, maintainability, acoustic impact, validation level, and full-scale transferability as decisive feasibility factors. Current research is still dominated by open-loop numerical studies with simplified actuation. Future work should therefore move toward multi-objective, closed-loop, energy-aware, sensor–actuator-integrated, and explainable machine-learning-assisted AFC. The main message is that the next step in train aerodynamics is not simply a better fixed shape, but a control-enabled train that can selectively redistribute aerodynamic authority across its mission profile. Full article

To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open-cathode air-cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan speed regulation. A three-dimensional single-cell multiphysics model is developed and validated using a 300 W experimental platform. The effects of atomization frequency and water temperature on stack performance and internal hydrothermal distribution are systematically investigated. Results show that ultrasonic atomization provides inlet precooling, latent heat absorption, and active region humidification, thereby improving hydrothermal uniformity within the stack. Under the optimal condition of 100 kHz and 55 °C, the peak stack power increases by 21.0% to 319.00 W, while voltage consistency and surface temperature uniformity are also improved. Analysis based on the Stokes number and Dalton’s law of partial pressures indicates that the optimum results from a balance between suppressing droplet agglomeration and inertial deposition, and limiting oxygen dilution caused by excessive water vapor. The proposed strategy provides a compact and practical approach for improving the stability, uniformity, and efficiency of air-cooled PEMFCs. Full article

In long-haul flights, cold non-insulated structural zones within aircraft cabins can lead to discomfort for passengers and crew, particularly during cruise phases. Moreover, during ground operations in cold weather, maintaining the thermal conditioning of the cabin becomes challenging, especially with open doors. This article presents the development of an active air curtain designed to address these issues by isolating significant cold zones and enhancing cabin comfort. The conceptual design is based on redirecting conditioned air to form a controlled barrier, which reduces thermal gradients and air mixing. The cold stream infiltrating from non-insulated structures was characterized under typical cruise scenarios using flight test data, while the open-door scenario on the ground was characterized analytically. A CFD analysis was performed to optimize nozzle geometry, airflow rate, and placement. Based on simulation results, a prototype was manufactured and tested in a controlled laboratory environment. The experimental validation confirmed the effectiveness of the air curtain in minimizing heat loss and improving thermal comfort. This paper discusses design trade-offs, thermal performance, and integration considerations, highlighting the potential of air curtains as a lightweight and low-impact solution for environmental control systems in modern transport cargo aircraft. Full article

Bio-inspired flapping-wing micro air vehicles (FWMAVs) are a research hotspot in micro air vehicles due to their high maneuverability and hovering capabilities. Accurate motion damping modeling is a prerequisite for their attitude disturbance rejection and control law design. Addressing the key issues in existing research—namely, the low computational efficiency of high-fidelity flexible-wing aerodynamic simulations and the inability of efficient rigid-wing assumptions to capture dynamic deformation of flexible wings—this paper investigates motion damping modeling for FWMAVs and its application to lateral flight stability analysis. First, an aerodynamic damping model under lateral motion parameters is established by approximating the flexible-wing surface using the spatial topology of the spar and veins. Second, numerical simulations of the flapping trajectory and motion damping are conducted. Subsequently, the validity and reliability of the model are verified through wind tunnel and turntable experiments. Finally, leveraging this model, lateral flight dynamics equations are derived to perform lateral stability analysis. The results effectively address the gap in assessing flapping-induced aerodynamic damping for flexible wings, providing an accurate analytical damping model, an efficient simulation framework, and an effective open-loop dynamics assessment method for the rapid design iteration and control algorithm development of FWMAVs. Full article

In this work, a dynamic polymer electrolyte membrane (PEM) fuel cell system is modelled in Modelica using the in-house developed, open-source library ThermoFluidStream. The focus lies on the fuel cell stack, the hydrogen fuel supply and the air supply. Additionally, the thermal management and the power electronics are considered in a simplified manner. Dynamic simulations are carried out for this system over an exemplary aircraft gate-to-gate mission. Simultaneously, a baseline control scheme is developed to provide the fuel cell with sufficient product gases in a suitable state regarding the temperature, pressure and relative humidity. The results indicate that the fuel cell system performs well with standard PI controllers. Only when strong dynamics occur, such as when going from taxi to take-off, does the control scheme show some weaknesses, as expected. This fuel cell system together with its control is a powerful baseline for future investigations. Full article

This study investigates the environmental and energy performance of rice straw-based energy pathways in Egypt, combining life cycle assessment (LCA) with supply chain optimization to improve system efficiency. The analysis covers thirteen governorates producing over 4.45 million tons of rice straw annually. It examines the whole supply chain from paddy farming, straw collection, and transport to electricity generation and ash disposal. Total energy consumption was 11,287 TJ, dominated by farming (5673 TJ) and transport (5490 TJ). Greenhouse gas (GHG) emissions were estimated at 12,007.5 million kg CO₂-eq, with significant contributions from farming (5158 million), combustion (3630 million), and natural gas use (3039 million). Gross electricity output was 5525 GWh, yielding a net of 4973 GWh, equivalent to 1116.5 kWh per ton of straw. Scenario analysis highlighted that the optimized multi-hub system, prioritizing Cluster 1 in the Nile Delta, which contributes over 92% of straw production and 4607 GWh of net electricity, achieved a reduction of more than 25% in transport distances and an 18% decrease in diesel consumption and related emissions. Sensitivity analysis further indicated that delivered electricity and GHG intensity are more sensitive to conversion efficiency and transmission and distribution losses than to moderate changes in transport assumptions. In addition to environmental improvements, the optimized scenario indicates potential social co-benefits, including rural employment generation, additional income opportunities for farmers, and improved air quality associated with reduced open-field burning. These outcomes are presented as indicative qualitative insights. Findings confirm rice straw as a strategic, scalable, and sustainable energy resource aligned with Egypt’s Vision 2030 and the UN Sustainable Development Goals (SDGs). Full article

In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression [SVR]) to estimate daily ET₀ based only on four independent variables: 2 m air temperature, vapor pressure deficit, 10 m wind speed, and sunshine duration. We used a total of 9132 daily values (2000–2025) from the Open-Meteo Historical Weather API (2000–2025) at 10 stations in the Tlemcen province of northwest Algeria. The dataset was divided into training, validation, and testing sets using a chronological split of 70/15/15. We estimated the performance of each algorithm by using several statistics (RMSE, MAE, R², NSE, RSR, and Willmott Index) as well as some statistics to evaluate the potential of overfitting and the ability to reproduce the behavior observed during the training phase. CatBoost had the highest overall accuracy and the most generalized performance, with an RMSE of approximately 0.292 mm day⁻¹, MAE of approximately 0.208 mm day⁻¹, R² of 0.971, and NSE of 0.971 in the test set, suggesting an extremely low risk of overfitting. The optimal CatBoost model was also used to estimate the spatial and temporal variations of monthly ET₀. The results showed high interannual variability (changes from year to year from −12.815 to +8.707 mm month⁻¹) in the semi-arid region of Tlemcen but no significant long-term trends (cumulative net change of approximately −0.021 mm month⁻¹ over 2000–2025). Therefore, the use of CatBoost is recommended as a robust, efficient, and reliable emulator of the FAO-56 Penman–Monteith equation (ET₀) for estimating ET₀ in semi-arid environments with limited climate data availability, and could be particularly useful in northwestern Algeria and other semi-arid Mediterranean regions. Full article

This study presents a combined numerical and experimental investigation of transient multiphase compressible flow inside a profiled-rotor rotary volumetric compressor. While most existing studies rely on high-fidelity CFD approaches, a low-order thermodynamic chamber-based model implemented in MATLAB Release 2023a is proposed to predict the temporal evolution of pressure, temperature, and vapor volume fraction during the compression cycle. The model is based on mass and energy conservation applied to variable-volume control chambers and incorporates a simplified cavitation criterion derived from local pressure relative to saturation vapor pressure. An open-loop experimental test bench was developed to measure air mass flow rate, suction and discharge pressures, temperatures, torque, and shaft power under controlled operating conditions. These measurements are used to validate the numerical predictions. The results show good agreement between measured and simulated pressure levels and global performance indicators, with deviations quantified using mean absolute percentage error values remaining below 5% over the investigated operating range. The numerical analysis further reveals the occurrence of localized low-pressure zones during the suction phase, indicating incipient cavitation or microbubble formation at specific rotor positions. The proposed modeling approach provides a computationally efficient alternative to full CFD simulations and enables rapid parametric analysis of rotor geometry and operating conditions. The cavitation formulation does not aim to resolve detailed bubble dynamics or erosion mechanisms, but rather to identify cavitation tendency based on thermodynamic pressure thresholds. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

This study evaluates the performance of evaporative cooling (EC)-assisted residential HVAC systems within the broader context of improving energy efficiency in U.S. housing. Using whole-building energy simulation in OpenStudio, a representative single-family house was analyzed across multiple climate zones under three configurations: (1) a baseline air-source heat pump, (2) EC applied at the outdoor air intake, and (3) EC applied at the heat pump inlet. Annual energy use, indoor temperature and humidity, thermal comfort (PMV), water consumption, and economic performance were assessed. Results indicate that system configuration exerts a stronger influence on performance than climate variability. Specifically, the EC at the heat pump inlet configuration reduced annual energy consumption by up to 5.1%, whereas the EC at the outdoor air intake configuration yielded negligible or inconsistent savings (generally within ±1%). The heat pump inlet EC configuration consistently reduced annual energy consumption and showed favorable economic performance in 10 of 16 climate zones, whereas outdoor air intake configuration yielded limited energy savings and was not economically viable. Indoor temperature control remained stable across all cases, while relative humidity increased with EC operation but remained within acceptable limits under appropriate control strategies. The findings indicate that EC integration can improve residential HVAC performance when properly configured, with system placement and humidity control being critical determinants of effectiveness. Full article