Search Results (3)

Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade conditions (PSCs). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPTs) may become locked in one of the LPs, significantly reducing the PV system’s generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms’ key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA’s convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA’s superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time was reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, the best initialization of search agents, and the optimal design of the dc–dc converter are introduced for optimal MPPT performance. Full article

Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO₂ average concentrations of approximately 25 ppbV and SO₂ average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO₂ and SO₂ concentrations in the airport area were very low, and the maximum hourly average NO₂ and SO₂ concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO₂ and SO₂ emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO₂ and SO₂ aircraft emissions during routine airport operations without interfering with the normal operation of the airport. Full article

The long-path differential optical absorption spectroscopy (LP-DOAS) technique was deployed in Shanghai to continuously monitor ozone (O₃), formaldehyde (HCHO), nitrogen dioxide (NO₂), nitrous acid (HONO), and nitrate radical (NO₃) mixing ratios from September 2019 to August 2020. Through a clustering method, four typical clusters of the O₃ diurnal pattern were identified: high during both the daytime and nighttime (cluster 1), high during the nighttime but low during the daytime (cluster 2), low during both the daytime and nighttime (cluster 3), and low during the nighttime but high during the daytime (cluster 4). The drivers of O₃ variation for the four clusters were investigated for the day- and nighttime. Ambient NO caused the O₃ gap after midnight between clusters 1 and 2 and clusters 3 and 4. During the daytime, vigorous O₃ generation (clusters 1 and 4) was found to accompany higher temperature, lower humidity, lower wind speed, and higher radiation. Moreover, O₃ concentration correlated with HCHO for all clusters except for the low O₃ cluster 3, while O₃ correlated with HCHO/NO_x, but anti-correlated with NO_x for all clusters. The lower boundary layer height before midnight hindered O₃ diffusion and accordingly determined the final O₃ accumulation over the daily cycle for clusters 1 and 4. The interactions between the O₃ diel profile and other atmospheric reactive components established that higher HONO before sunrise significantly promoted daytime O₃ generation, while higher daytime O₃ led to a higher nighttime NO₃ level. This paper summarizes the interplays between day- and nighttime oxidants and oxidation products, particularly the cause and effect for daytime O₃ generation from the perspective of nighttime atmospheric components. Full article