Search Results (5)

The aim of this study was to determine the influence of boom height above a crop stand and the spacing between nozzles and diffusers in an over-row sprayer on the uniformity of the horizontal spray distribution and the uniformity of the air velocity distribution. The experimental setup involved a prototype over-row sprayer equipped with a boom with a working width of 8 m and ten air diffusers with spray nozzles. Air diffusers were connected to one or two nozzles each, and they were installed on the boom at intervals of 60, 80, and 90 cm. Terminal airflow velocity at a canopy is determined by the height of a sprayer boom and the diffuser spacing, ranging from around 2 m s^–1 to around 27 m s^–1. The sprayer boom should be positioned at a height of 50 cm above a crop stand due to the difference between the minimum and maximum airflow velocities. The horizontal spray distribution was more uniform when the sprayer was equipped with hollow-cone nozzles instead of flat-fan nozzles; hollow-cone nozzles should be applied if the distance between nozzles needs to be adjusted to the row width and row spacing. The analyzed coefficients did not exceed 10% when the boom was positioned 50 cm above the crop stand and when the nozzles were spaced 80 cm apart, which suggests that, in this configuration, sprayers equipped with hollow-cone nozzles can also be applied to close-grown crops. Full article

In the pursuit of sustainable aviation, this paper presents an innovative approach that adopts a swarm division strategy to enhance and refine the velocity obstacle (VO) method, guided by a low-carbon principle. A dynamic elliptical protection zone model forms the core of this innovative approach. Specifically, this dynamic elliptical protection zone is created based on the difference in aircraft velocity, and a swarm division strategy is introduced in this process. Initially, aircraft that share the same route and type, and have similar velocities and distances, are grouped into swarms. Then, the characteristics of the swarms, such as mass points, velocities, and protection zones, are recorded. Second, the collision cone (CC) between swarms is established, and planar geometrical analysis is used to determine the optimal relief velocity and heading of aircraft on the low-carbon objective while ensuring a safe interval between aircraft in the swarm during the relief period. Additionally, a swarm control algorithm is utilized to adjust the velocity of the aircraft by a small margin. Finally, simulation experiments are conducted using Python, revealing that the swarm relief efficiency of the enhanced VO method sees a notable increase of over 33%. Concurrently, the need for adjustments decreases by an average of 32.78%, while fuel savings reach as high as 70.18%. The strategy is real-time and operational, significantly reduces the air traffic controller (ATC) workload, improves flight efficiency and safety, and contributes positively to the reduction in carbon emissions, which is beneficial for the environment. Full article

Energy efficiency and air quality in residential buildings have aroused intensive interest. Generally speaking, the heating, ventilation and air conditioning (HVAC) system is widely used to regulate indoor environmental spaces. Meanwhile, mixed-mode ventilation has been proven to reduce energy consumption and introduce fresh air effectively. This study aims to discuss the correlations between air velocity, temperature and indoor thermal comfort and establish corresponding statistical models based on the ASHRAE_db II database and the Predicted Mean Vote (PMV). On this basis, the air-velocity adjustment strategy, including determining adjustability and establishing adjustable intervals, is optimized based on support vector machine and envelope curve methods. The results show that the recognition accuracy of the adjustability determination model is over 98%, and the air-velocity adjustable interval in the envelope is increased, facilitating control of mixed-mode ventilation. The case shows that interval adjustment increases the sample points by 18.6% (18.1% above 20 °C and 4.5% above 28 °C). Therefore, further research can be supported on improving thermal comfort by air-velocity adjustment to take advantage of the mixed-mode ventilation mode, which is beneficial to building energy efficiency. Full article

The significance of the water-side gas transfer velocity for air–sea CO₂ gas exchange (k) and its non-linear dependence on wind speed ($\mathrm{U}$) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f($\mathrm{U}$), where f(.) stands for an arbitrary function of $\mathrm{U}$), the distributional properties of $\mathrm{U}$ (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from $\mathrm{U}$. To address the latter issue, a Taylor series expansion is applied to separate f($\mathrm{U}$) into a term derived from time-averaging wind speed (labeled as $⟨\mathrm{U}⟩,$ where $⟨.⟩$indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of $\mathrm{U}$. The method was explored for nine widely used f($\mathrm{U}$) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σ${\mathsf{\sigma }}_{\mathrm{U}}$ around $⟨\mathrm{U}⟩$ or, more preferably, a dimensionless coefficient of variation ${\mathrm{I}}_{\mathrm{u}}$= σ${\mathsf{\sigma }}_{\mathrm{U}}$/$⟨\mathrm{U}⟩$. The proposed correction outperforms previous methodologies that adjusted k when using $⟨\mathrm{U}⟩$ only. An unexpected outcome was that upon setting ${\mathrm{I}}_{\mathrm{u}}^2$ = 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating ${\mathrm{I}}_{\mathrm{u}}^2$ to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO₂, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models. Full article

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (h_c) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of h_c in both drying systems with a range 12.4 and 20.8 W/m² °C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated h_c values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model’s accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated h_c values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the h_c values. Full article