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Impact of flight speed distribution on efficiency of urban air traffic network
Get PDFThe emerging Urban Air Mobility (UAM) completes the current urban transportation landscape along with surface transport and relieves road traffic congestion significantly. This relief is not only to add one or more available ways in the air above each existing roadway, but to improve the operating efficiency of the entire traffic network by leveraging electric vertical take-off and landing (eVTOL) aircraft and exploiting advanced air traffic management technologies and procedures. The urban air traffic network is constructed in two different topological structures, namely generalized topology taking airway segments as edges and intersections between two airway segments as nodes, and dual topology considering airways as nodes and connection relationships between two airways as edges. Flight speed is assigned as weight to either airway segment or the entire airway in the two topology networks and network efficiencies are investigated with five common distributions of weight. A method of weight randomization is applied to further explore the impact of weight distribution on both networks, and the results demonstrate that the global and local network efficiencies of a regular network increase with various weight distributions to a certain degree, indicating that network efficiency can be improved by adjusting flight speed distribution while not changing the network structure. The comparison of the experiments on the two topology networks reveals that such adjustment is better-performed segment by segment instead of airway by airway, which provides a reference for stakeholders to manage the urban air traffic flow via better control of flight speed.IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC) 202
Distributed trajectory management for urban air mobility operations with ground-based edge intelligence
Get PDFTrajectory management is a critical undertaking in urban air mobility (UAM) to ensure safe, secure, and efficient operations. Cooperative targets have the capability to report their information while managing non-cooperative targets presents a challenge in the UAM operational environment (UOE). Consequently, ground-based non-cooperative surveillance assumes a vital role in monitoring anomalies. Given the difficulties associated with implementing centralized management in a large metropolitan area, this study proposes a distributed management architecture that leverages ground-based edge intelligence to enhance resilience in performing relevant tasks. It demonstrates that employing a developed edge computing system yields superior efficiency for heterogeneous sensors and their corresponding algorithms, such as detection, fusion, and tactical conflict management, compared to typical cloud servers. Furthermore, the proposed architecture incorporates an adaptive load balancing scheme, which monitors the real-time tasks and balances tasks among multiple edge devices to enhance the efficient resource management of the edge intelligence system. Ultimately, the distributed system offers energy-saving benefits and guarantees performance, making it suitable for providing services to diverse stakeholders involved in UAM.IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC) 202
Unmanned aerial vehicle positioning using 5G new radio technology in urban environment
Get PDFUnmanned aerial vehicles (UAVs) are becoming increasingly popular for various applications, including surveillance, monitoring, mapping, delivery, and inspection. However, their positioning capabilities in urban environments can be limited due to challenges such as Non-Line-of-Sight (NLOS) propagation, multi-path interference, and signal blockage caused by tall buildings, trees, and other obstacles, which can affect their positioning capabilities. The purpose of this paper is to provide a novel approach for UAV’s positioning based on Observed Time Difference of Arrival (OTDOA), combining 5G (NR) technology and an inertial measurement unit (IMU) to improve UAV positioning in urban environments. Integrating these technologies can improve UAV positioning and control systems by offering rapid, low-latency communication, a thorough and precise comprehension of the UAV’s surroundings and its own condition, and more accurate assessments of the UAV’s location, speed, and orientation. Simulation model shows the data from these sensors is then fused using an Extended Kalman Filter (EKF) to estimate the UAV’s position and orientation. The study shows that the proposed system delivers accurate and reliable UAV positioning in these environments, outperforming traditional methods.2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC
Using machine learning to expound energy poverty in the global south: understanding and predicting access to cooking with clean energy
Get PDFEfforts towards achieving high access to cooking with clean energy have not been transformative due to a limited understanding of the clean-energy drivers and a lack of evidence-based clean-energy policy recommendations. This study addresses this gap by building a high-performing machine learning model to predict and understand the mechanisms driving energy poverty - specifically access to cooking with clean energy. In a first-of-a-kind, the estimated cost of US14.5 to enable universal access to cooking with clean energy encompasses all the intermediate inputs required to build self-sufficient ecosystems by creating value-addition sectors. Unlike previous studies, the data-driven clean-cooking transition pathways provide foundations for shaping policy that can transform the energy and cooking landscape. Developing these pathways is necessary to increase people's financial resilience to tackle energy poverty. The findings also show the absence of a linear relationship between electricity access and clean cooking - evidencing the need for a rapid paradigm shift to address energy poverty. A new fundamental approach that focuses on improving and sustaining the financial capacity of households through a systems approach is required so that they can afford electricity or fuels for cooking.Engineering and Physical Sciences Research Council (EPSRC): EP/S023909/1Energy and A
Characterization and properties of a new insensitive explosive co-crystal composed of trinitrotoluene and pyrene
Get PDFA new energetic co-crystal of trinitrotoluene (TNT) and pyrene (PYRN) with a 1:1 molar ratio was prepared by a slow solvent evaporation technique. Co-crystal physicochemical properties have also been examined using optical microscopy, powder X-ray diffraction, single crystal X-ray diffraction, and differential scanning calorimetry. The results of single-crystal X-ray diffraction and non-covalent interaction calculations showed that non-covalent interactions (donor–acceptor π-π interaction) govern the structures of the TNT:PYRN co-crystal. The experimental and theoretical outcomes supported each other in the study. Thermal stability, impact sensitivity, and detonation performance of the co-crystal were investigated. DSC measurement indicates that the co-crystal has a melting point of 167 °C and a decomposition temperature of 293 °C, indicating outstanding thermal stability. The co-crystal was found to be less impact-sensitive than TNT using the BAM fall hammer instrument. Furthermore, the calculated detonation velocity and detonation pressure of the co-crystal are 5.29 km·s−1 and 8.48 G Pa, respectively. As an outcome, the TNT:PYRN co-crystal may be a promising intermediate energy explosive with low sensitivity and, as such, may be a desirable explosive alternative in the future instead of TNT for low-vulnerability formulations.Structural Chemistr
Symmetry-breaking-induced internal mixing enhancement of droplet collision
Get PDFBinary droplet collision is a basic fluid phenomenon for many spray processes in nature and industry involving lots of discrete droplets. It exists an inherent mirror symmetry between two colliding droplets. For specific cases of the collision between two identical droplets, the head-on collision and the off-center collision, respectively, show the axisymmetric and rotational symmetry characteristics, which is useful for the simplification of droplet collision modeling. However, for more general cases of the collision between two droplets involving the disparities of size ratio, surface tension, viscosity, and self-spin motions, the axisymmetric and rotational symmetry droplet deformation and inner flow tend to be broken, leading to many distinct phenomena that cannot occur for the collision between two identical droplets owing to the mirror symmetry. This review focused on interpreting the asymmetric droplet deformation and the collision-induced internal mixing that was affected by those symmetry breaking factors, such as size ratio effects, Marangoni Effects, non-Newtonian effects, and droplet self-spin motion. It helps to understand the droplet internal mixing for hypergolic propellants in the rocket engineering and microscale droplet reactors in the biological engineering, and the modeling of droplet collision in real combustion spray processes.Symmetr
GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present
Get PDFTerrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940–2022) and relatively high-resolution (i.e., 0.25∘) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine-learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land–ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10 168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability such as strong El Niño events. The GTWS-MLrec dataset consists of three reconstructions based on (a) mascons of the Jet Propulsion Laboratory of the California Institute of Technology, the Center for Space Research at the University of Texas at Austin, and the Goddard Space Flight Center of NASA; (b) three detrended and de-seasonalized reconstructions; and (c) six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://doi.org/10.5281/zenodo.10040927 (Yin, 2023).Earth System Science Dat
Vortex and core detection using computer vision and machine learning methods
Get PDFThe identification of vortices and cores is crucial for understanding airflow motion in aerodynamics. Currently, numerous methods in Computer Vision and Machine Learning exist for detecting vortices and cores. This research develops a comprehensive framework by combining classic Computer Vision and state-of-the-art Machine Learning techniques for vortex and core detection. It enhances a CNN-based method using Computer Vision algorithms for Feature Engineering and then adopts an Ensemble Learning approach for vortex core classification, through which false positives, false negatives, and computational costs are reduced. Specifically, four features, i.e., Contour Area, Aspect Ratio, Area Difference, and Moment Centre, are employed to identify vortex regions using YOLOv5s, followed by a hard voting classifier based on Random Forest, Adaptive Boosting, and Xtreme Gradient Boosting algorithms for vortex core detection. This novel approach differs from traditional Computer Vision approaches using mathematical variables and image features such as HAAR and SIFT for vortex core detection. The findings show that vortices are detected with a high degree of statistical confidence by a fine-tuned YOLOv5s model, and the integrated technique produces an accuracy score of 97.56% in detecting vortex cores conducted on a total of 133 images generated from a rotor blade NACA0012 simulation. Future work will focus on framework generalisation with a larger and more diverse dataset and intelligent threshold development for more efficient vortex and core detection.European Journal of Computational Mechanic
Rapid and effective removal of copper, nitrate and trichloromethane from aqueous media by aluminium alloys
Get PDFZero-valent iron (ZVI) has been extensively studied for its efficacy in removing heavy metals, nitrate, and chlorinated organic compounds from contaminated water. However, its limited effectiveness due to rapid passivation and poor selectivity is prompting for alternative solutions, such as the use of aluminium alloys. In this study, the efficacy of five distinct aluminium alloys, namely Al–Mg, Al–Fe, Al–Cu, and Al–Ni, each comprising 50 % Al by mass at a concentration of 10 g/L, was assessed using copper, nitrate and trichloromethane (TCM) as model contaminants. Results show that chemical pollutants reacted immediately with Al–Mg. On the contrary, the remaining three alloys exhibited a delay of 24 h before demonstrating significant reactivity. Remarkably, Al–Mg alloy reduced nitrate exclusively to ammonium, indicating minimal preference for nitrate reduction to N2. In contrast, the Al–Cu, Al–Ni, and Al–Fe alloys exhibited N2 selectivity of 3 %, 5 %, and 19 %, respectively. The removal efficiency of copper, nitrate and TCM reached 99 % within 24 h, 95 % within 48h and 48 % within 48h, respectively. Noteworthy findings included the correlation between Fe concentration within the Al–Fe alloy and an increased N2 selectivity from 9.3 % to 24.1 %. This resulted in an increase of Fe concentration from 10 % to 58 % albeit with a concurrent reduction in reactivity. Cu2+ removal by Al–Fe alloy occurred via direct electron transfer, while the removal of nitrate and TCM was facilitated by atomic hydrogen generated by the alloy's hydrolysis. Intriguingly, nitrate and TCM suppressed Cu2+ reduction, whereas Cu2+ improved nitrate reduction and TCM degradation. These findings demonstrate the great potential of Al–Mg and Al–Fe alloys as highly efficient agents for water remediation.Heliyo
Improving plastic pyrolysis oil quality via an electrochemical process for polymer recycling: a review
Get PDFElectrochemical hydrogenation (ECH) is a novel route for the upgradation of pyrolysis oil from both biomass and plastic feedstocks. Compared with conventional routes, including thermal cracking, ECH can be performed under mild conditions (o80 1C and 1 atm) and without the requirement of additional H2 supply. The successful demonstration of this application can be a critical step to enabling a circular plastic economy and low-carbon fuel production. In this review we provide a critical overview of the recent advancements in understanding the variables that influence the ECH process. In addition, we debate how this technology could be optimized and applied to plastic waste pyrolysis oil, assessing concerns such as the selection of cathode material, which needs to be resilient enough to address the complex nature of bio-oil. In addition, we present ideas on how to circumvent the challenge where the commonly used water-based electrolytes are unlikely to be suitable for pyrolysis oil treatment. Finally, we discuss the possible utilization of this product and scalability of this process.Energy Advance