The wake topology behind a wall-mounted square cylinder immersed in a turbulent boundary layer is investigated using high-resolution large-eddy simulations (LES). The boundary-layer thickness at the obstacle location is fixed, with a Reynolds number based on cylinder height ℎ and free-stream velocity 𝑢∞ of 10,000 while the aspect ratio (AR), defined as obstacle height divided by its width, ranges from 1 to 4. The mesh resolution is comparable to DNS standards used for similar wall-mounted obstacles, though with relatively lower Reynolds numbers. The effects of AR on wake structures, turbulence production, and transport are analyzed via Reynolds stresses, anisotropy-invariant maps (AIM), and the turbulent kinetic energy (TKE)budget. In particular, the transition from ‘‘dipole’’ to a ‘‘quadrupole’’ wake is extensively examined as AR increases. With increasing AR, the wake shrinks in both the streamwise and spanwise directions, attributed to the occurrence of the base vortices (AR = 3 and 4). This change in the flow structure also affects the size of the positive-production region that extends from the roof and the flank of the obstacle to the wake core. The AIMs confirm three-dimensional wake features, showing TKE redistribution in all directions (Simonsen and Krogstad, 2005). Stronger turbulence production in AR = 3 and 4 cases highlights the role of tip and base vortices behind the cylinder. The overall aim is to refine the dipole-to-quadrupole transition as a function of AR and accounting for the incoming TBL properties. The novelty relies on proposing the momentum-thickness-based Reynolds number Re𝜃 as a discriminant for assessing TBL effects on turbulent wake structures.

The environmental impact of air travel, largely driven by fossil-fuel consumption, remains a critical subject of debate. Addressing this challenge requires immediately adopting sustainable practices to mitigate its environmental footprint. While hydrogen and hybrid-electric propulsion technologies show promise for the future, current efforts focus on Sustainable Aviation Fuels (SAF) as a viable near-term solution to reduce aviation emissions while ensuring compatibility with existing aviation infrastructure. This paper examines the environmental impact of air travel, focusing on the emissions associated with conventional fuel and SAF. Using two methodologies, namely the subsonic fuel flow method (SF2) and an improved version of it, the emissions corrected subsonic fuel flow method (EC-SF2), non-CO2 emissions trends are analyzed along a flight trajectory from Stockholm to Bordeaux. The comparison between the two methods underscores the importance of accurate emission modeling, particularly for SAF correction on emission index. The SF2 method reveals that SAF fuels with higher calorific value than conventional fuel increased total HC and CO emissions while decreasing NOx emissions. Conversely, the EC-SF2 method resulted in a more homogeneous emissions reduction trend. Our proposed methodology, which corrects both fuel flow and emission index based on SAF-specific data, could, therefore, offer a more reliable estimation of emissions behavior for SAF. These findings highlight the sensitivity of emissions modeling on environmental assessment.

A unique series of flyover tests was conducted during the pandemic to study how variations commonly found in standard approach procedures primarily affect the noise level on the ground. The pandemic created unique conditions as the reduced air traffic provided the opportunity to perform these tests and collect noise measurements that are almost clear from background noise. The tests were performed with two A321neo aircraft one morning in April 2021 at Arlanda Airport, Stockholm. This work takes advantage of this database to develop an understanding of the influence of variations in configuration, speed, and altitude on the environmental impact. Interdependencies between noise, CO2, and non-CO2 emissions are closely studied, and the significant findings are presented and discussed. It is shown that, for early configurations, before landing gear deployment, there is a substantial tradeoff between CO2 and non-CO2 emissions, and careful consideration of the latter should be taken to avoid areas of critically low idle power. Noise in these configurations is mainly affected by the impact of the aircraft-microphone distance on the measurements. For the configurations that follow, CO2 emissions become more relevant as the fuel flow increases due to the increase in drag, which also results in an increased noise level that, in these configurations, shows a significant dependency on aircraft speed with an increase of 1 dB for every 10 knots. Based on these findings, some actions are proposed to minimize the impact of each configuration.

We present an application of a mixed-integer programming (MIP) framework for automatic traffic synchronization, providing safe separation between the arriving traffic within the terminal maneuvering area (TMA) of an airport implementing point merge (PM) procedures. Additionally, the proposed methodology ensures conflict-free operations when departures and arrivals share a common runway. Based on real traffic scenarios for two European airports, we model realistic descent profiles and assume all the arrivals are performing the most fuel-efficient continuous descent operations (CDOs). We compare two scenarios: in the first, the arriving aircraft are strictly forced to adhere to the published arrival route structures, meaning that a turn towards the merge point may not be initiated prior to reaching the point merge system (PMS), while in the second scenario, aircraft may be assigned a shortcut from a published waypoint along the arrival route. We evaluate the resulting arrival flight efficiency and compare it to that of the actual flights, arriving during the hour selected for our optimization, noticing varying benefits for the two airports and whether shortcuts are allowed or not. Given the correct setting for the specific airport, we demonstrate that our approach provides significant benefits, including increased vertical performance as well as reduced time and distance, contributing to lower levels of noise and fuel savings, accompanied by reduced emissions.

 Accurate regional climate projection calls for high-resolution downscaling of Global Climate Models (GCMs). This work presents a deep-learning-based multi-model evaluation and downscaling framework ranking 32 Coupled Model Intercomparison Project Phase 6 (CMIP6) models using a Deep Learning-TOPSIS (DL-TOPSIS) mechanism and refines outputs using advanced deep-learning models. Using nine performance criteria, five Köppen-Geiger climate zones—Tropical, Arid, Temperate, Continental, and Polar—are investigated over four seasons. While TaiESM1 and CMCC-CM2-SR5 show notable biases, ranking results show that NorESM2-LM, GISS-E2-1-G, and HadGEM3-GC31-LL outperform other models. Four models contribute to downscaling the top-ranked GCMs to 0.1^o resolution (Vision Transformer (ViT), Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoSTANet), CNN-LSTM, CNN-Long Short-Term Memory (ConvLSTM)). Effectively capturing temperature extremes (TXx, TNn), GeoSTANet achieves the highest accuracy (Root Mean Square Error (RMSE) = 1.57^oC, Kling-Gupta Efficiency (KGE) = 0.89, Nash-Sutcliffe Efficiency (NSE) = 0.85, Correlation (r) = 0.92), so reducing RMSE by 20% over ConvLSTM. CNN-LSTM and ConvLSTM do well in Continental and Temperate zones; ViT finds fine-scale temperature fluctuations difficult. These results confirm that multi-criteria ranking improves GCM selection for regional climate studies and transformer-based downscaling exceeds conventional deep-learning methods. This framework offers a scalable method to enhance high-resolution climate projections, benefiting impact assessments and adaptation plans. The present study has the following limitations, which will be addressed in future works: (i) temperature-only focus, (ii) unquantified scenario uncertainty, and (iii) higher computational cost of transformer models.

An open-source simulation model for aircraft noise prediction is presented and validated using backpropagated noise measurements for a state-of-the-art engine and aircraft. The validation is focused on approach procedures and was performed using ground-based noise measurements that were taken at 17 recording stations for a total of 18 consecutive flights carried out during the morning of 8 April 2021. The flights were performed using two A321neo aircraft with LEAP-1A engines. It is demonstrated that the presented noise model provides a satisfactory estimation of the source noise for varying approach configurations and flight conditions. Configurations using a greater number of high-lift devices are particularly well predicted in the mid- and high-frequency regions, whereas the lower configuration settings show greater spectral deviations, which are partly attributed to measurement uncertainties caused by the increased aircraft–microphone distance. The model can predict the overall mean total sound intensity level within a 2 dB accuracy for all configurations, while the average predicted level at each microphone differs by less than 3 dB from the measurement average, for all cases except one. Variation in aircraft speed showed to have a strong impact on the predicted total noise, which matches the well-recognized sixth-power Mach number far-field sound intensity scaling law for airframe noise models, while the measurements indicated a less significant dependency. This is mainly due to installation effects and noise reduction measures that are not included in the models. Nevertheless, the variations in the spectra of the predicted and measured noise showed similar patterns.

Aviation’s non-CO2 effects, especially the impact of aviation-induced contrails, drive atmospheric changes and can influence climate dynamics. Although contrails are believed to contribute to global warming through their net warming effect, uncertainties persist due to the challenges in accurately measuring their radiative impacts. This study aims to address this knowledge gap by investigating the relationship between aviation-induced contrails, as observed in Meteosat Second Generation (MSG) satellite imagery, and their impact on radiative forcing (RF) over a two-week study. Results show that while daytime contrails generally have a cooling effect, the higher number of nighttime contrails results in a net warming effect over the entire day. Net RF values for detected contrails range approximately from -8 TW to 2.5 TW during the day and from 0 to 6 TW at night. Our findings also show a 41.03 % increase in contrail coverage from January 24–30, 2023, to the same week in 2024, accompanied by a 128.7 % rise in contrail radiative forcing (CRF), indicating greater warming from the added contrails. These findings highlight the necessity of considering temporal factors, such as the timing and duration of contrail formation, when assessing their overall warming impact. They also indicate a potential increase in contrail-induced warming from 2023 to 2024, attributable to the rise in contrail coverage. Further investigation into these trends is crucial for the development of effective mitigation strategies.

Thunderstorms can be a large source of disruption for European air-traffic management causing a chaotic state of operation within the airspace system. In current practice, air-traffic managers are provided with imprecise forecasts which limit their ability to plan strategically. As a result, weather mitigation is performed using tactical measures with a time horizon of three hours. Increasing the lead time of thunderstorm predictions to the day before operations could help air-traffic managers plan around weather and improve the efficiency of air-traffic-management operations. Emerging techniques based on machine learning have provided promising results, partly attributed to reduced human bias and improved capacity in predicting thunderstorms purely from numerical weather prediction data. In this paper, we expand on our previous work on thunderstorm forecasting, by applying convolutional neural networks (CNNs) to exploit the spatial characteristics embedded in the weather data. The learning task of predicting convection is formulated as a binary-classification problem based on satellite data. The performance of multiple CNN-based architectures, including a fully-convolutional neural network (FCN), a CNN-based encoder–decoder, a U-Net, and a pyramid-scene parsing network (PSPNet) are compared against a multi-layer-perceptron (MLP) network. Our work indicates that CNN-based architectures improve the performance of point-prediction models, with a fully-convolutional neural-network architecture having the best performance. Results show that CNN-based architectures can be used to increase the prediction lead time of thunderstorms. Lastly, a case study illustrating the applications of convection-prediction models in an air-traffic-management setting is presented.

Traveling and possible impact on climate and environment are currently under intense debate, and air travel in particular is often in question due to the use of fossil fuels. Electric propulsion has therefore become very popular but the energy sources for electricity generation should as well be taken into consideration. On the other hand, the social aspect of traveling is usually forgotten and should be also included for a complete sustainability analysis. In this study, the business trip from Stockholm to Bordeaux experienced by airplane and train is analyzed. Though the journey by airplane generated six and a half times more CO2 emissions than the journey by train on a per-passenger basis, this latter resulted in a 35-h journey compared to seven, and a cost up to eight and a half times more expensive than the airplane. The trip is defined as an optimization problem with focus on environmental, economic, and social impact to define acceptable trade-offs. The critical criteria for transportation mode choice were identified as the environment, time and comfort, and a value model for business travel mode optimization is proposed, integrating as well a personal value.

Noise abatement procedures are one of the main actions implemented to reduce noise pollution around airports. In this study, the focus is turned on approach operations and their environmental impact. The assessment starts from standard optimized procedures, namely the Continuous Descent Approach (CDA) and the Low Drag Low Power (LDLP) and the aim is to look into more advanced procedures, such as a Steep and a Segmented CDA, an Advanced LDLP and an optimized trajectory for the specific flight conditions. The procedures are designed for an A321neo and compared and evaluated for noise and emissions. It is demonstrated that multidisciplinary design and adaptation to specific conditions are required for the assessment of these interdependencies for flight procedures.