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Flight tests and wind tunnel experiments face difficulties in investigating the impact of aircraft carrier air-wake on the landing process. Meanwhile, numerical methods generally exhibit low overall computational efficiency in solving such problems. To address the computational challenges posed by the disparate spatiotemporal scales of the ship air-wake and aircraft motion, a domain precursor inflow method is developed to efficiently generate unsteady inflow boundary conditions from precomputed full-domain air-wake simulations. This study investigates the aerodynamic variability of carrier-based aircraft during landing through the turbulent air-wake generated by an aircraft carrier, employing a hybrid RANS-LES methodology on dynamic unstructured overset grids. The numerical framework integrates a delayed detached-eddy simulation (DDES) model with a parallel dynamic overset grid approach, enabling high-fidelity simulations of coupled aircraft carrier interactions. Validation confirms the accuracy of the precursor inflow method in reproducing air-wake characteristics and aerodynamic loads compared to full-domain simulations. Parametric analyses of 15 distinct landing trajectories reveal significant aerodynamic variability, particularly within 250 m of the carrier, where interactions with island-generated vortices induce fluctuations in lift (up to 25%), drag (18%), and pitching moments (30%). Ground effects near the deck further amplify load variations, while lateral deviations in landing paths generate asymmetric forces and moments. The proposed methodology demonstrates computational efficiency for multi-scenario analysis, providing critical insights into aerodynamic uncertainties during carrier operations.

This study introduces vector autoregression (VAR) as a linear procedure that can be used for synthesizing turbulence time series over an entire plane, allowing them to be imposed as an efficient turbulent inflow condition in simulations requiring stationary and cross-correlated turbulence time series. VAR is a statistical tool for modelling and prediction of multivariate time series through capturing linear correlations between multiple time series. A Fourier-based proper orthogonal decomposition (POD) is performed on the two-dimensional (2-D) velocity slices from a precursor simulation of a turbulent boundary layer at a momentum thickness-based Reynolds number, $Re_{\\theta }=790$. A subset of the most energetic structures in space are then extracted, followed by applying a VAR model to their complex time coefficients. It is observed that VAR models constructed using time coefficients of 5 and 30 most energetic POD modes per wavenumber (corresponding to $66\\,\\%$ and $97\\,\\%$ of turbulent kinetic energy, respectively) are able to make accurate predictions of the evolution of the velocity field at $Re_{\\theta }=790$ for infinite time. Moreover, the 2-D velocity fields from the POD–VAR when used as a turbulent inflow condition, gave a short development distance when compared with other common inflow methods. Since the VAR model can produce an infinite number of velocity planes in time, this enables reaching statistical stationarity without having to run an extremely long precursor simulation or applying ad hoc methods such as periodic time series.

The performance of six subgrid-scale (SGS) models is analyzed for large-eddy simulations (LES) of wind-farm flows under stable (SBL) and conventionally-neutral (CNBL) atmospheric conditions. A precursor–concurrent technique is employed to provide fully developed turbulent inflow for simulations of a 40-turbine wind farm. Turbines are represented using the actuator-disc method, employing a baseline grid of 12 cells across the turbine diameter. The SBL precursor flow poses a challenge for LES, as it may not be able to resolve the small turbulent scales featured in this flow if the grid is coarse. For these precursor flows, the baseline grid results of all six SGS models are assessed relative to coarser and finer grids, with 6 and 45 cells across the diameter, respectively. The wall-adapting local eddy-viscosity (WALE) and Lagrangian-averaged scale-dependent dynamic (LASDD) models exhibit high grid sensitivity, while the standard Smagorinsky (Smag.), anisotropic minimum-dissipation (AMD), one-equation turbulent kinetic energy (TKE), and stability-dependent Smagorinsky (SDS) models show low sensitivity. For the wind-farm simulations conducted with the baseline grid, the AMD and SDS models predict similar wind-farm performance. In contrast, the WALE and LASDD models predict nearly 30% less power output, primarily due to their prediction of lower inflow wind speeds. CNBL simulations on the baseline grid show reduced sensitivity to the SGS model due to larger atmospheric turbulence and length scales compared to the SBL flow. Among the six models, the AMD model demonstrates ease of implementation, the least sensitivity to grid size for the SBL precursor flow, and predictions that are consistent with other models and higher-order pseudo-spectral LES solvers, making it a suitable choice for LES of wind-farm flows under both stable and conventionally-neutral conditions.

The wake characteristics of a wind turbine in a turbulent atmospheric boundary layer under different thermal stratifications are investigated by means of large-eddy simulation with the geophysical flow solver EULAG. The turbulent inflow is based on a method that imposes the spectral energy distribution of a neutral boundary-layer precursor simulation, the turbulence-preserving method. This method is extended herein to make it applicable for different thermal stratification regimes (convective, stable, neutral) by including suitable turbulence assumptions, which are deduced from velocity fields of a diurnal-cycle precursor simulation. The wind-turbine-wake characteristics derived from simulations that include the parametrization result in good agreement with diurnal-cycle-driven wind-turbine simulations. Furthermore, different levels of accuracy are tested in the parametrization assumptions, representing the thermal stratification. These range from three-dimensional matrices of the precursor-simulation wind field to individual values. The resulting wake characteristics are similar, even for the simplest parametrization set-up, making the diurnal-cycle precursor simulation non-essential for the wind-turbine simulations. Therefore, the proposed parametrization results in a computationally fast, simple, and efficient tool for analyzing the effects of different thermal stratifications on wind-turbine wakes by means of large-eddy simulation.

In this work, we present Large Eddy Simulation (LES) results of atmospheric boundary layer (ABL) flow over complex terrain with neutral stratification using the OpenFOAM-based simulator for on offshore wind farm applications (SOWFA). The complete work flow to investigate the LES for the ABL over real complex terrain is described including meteorological-tower data analysis, mesh generation and case set-up. New boundary conditions for the lateral and top boundaries are developed and validated to allow inflow and outflow as required in complex terrain simulations. The turbulent inflow data for the terrain simulation is generated using a precursor simulation of a flat and neutral ABL. Conditionally averaged met-tower data is used to specify the conditions for the flat precursor simulation and is also used for comparison with the simulation results of the terrain LES. A qualitative analysis of the simulation results reveals boundary layer separation and recirculation downstream of a prominent ridge that runs across the simulation domain. Comparisons of mean wind speed, standard deviation and direction between the computed results and the conditionally averaged tower data show a reasonable agreement.

Pentachlorophenol (PCP) and its derivatives are considered to be the precursors of dioxins, thus their concentrations in environmental compartments remain relatively correlated. Unlimited production and usage of PCP in recent decades may have posed a potential ecological threat to marine ecosystems due to uncontrolled discharge of this contaminant into the Vistula River and finally into the Gulf of Gdańsk. Since there are no data on PCP concentration in sediments of the southern part of the Baltic Sea, the level of contamination has been examined and possible influence of sediment properties in the Gulf of Gdańsk on the accumulation intensification has been investigated. The study has resulted in the evaluation of an efficient analytical procedure characterized by a low detection limit (LOD<1 ng g −1 d.w.). Instrumental analyses have been supplemented with Microtox® bioassay in order to assess the sediment toxicity. The obtained concentrations in collected samples varied from below the LOD in sandy sediments to 179.31 ng g −1 d.w. in silty sediments, exceeding the PNEC value of 25 ng g −1 d.w. (Predicted No Effect Concentration) estimated for the Baltic Sea (Muir & Eduljee 1999). It has been proven that properties of sediments from the Gulf of Gdańsk, including pH, Eh of bottom water, the content of water and organic matter, affect the rate of PCP accumulation. High toxicity has been recorded in the bottom sediments of the Gdańsk Deep but no statistically significant correlation between PCP concentration and the sediment toxicity has been observed. Analysis of PCP concentration distribution in sediment cores revealed that the surface layer is the most polluted one, which indicates a continuous inflow of PCP from the Vistula River. Horizontal PCP distribution in the sediment from the Gdańsk Deep reveals variability similar to that observed for highly chlorinated dioxins (Niemirycz & Jankowska 2011).

Using the IRAM 30 m telescope, a mapping survey in optically thick and thin lines was performed towards 46 high mass star-forming regions. The sample includes UC H{\\sc ii} precursors and UC H{\\sc ii} regions. Seventeen sources are found to show \"blue profiles\", the expected signature of collapsing cores. The excess of sources with blue over red profiles ([\\(N_{\\rm blue}\\) -- \\(N_{\\rm red}\\)]/\\(N_{\\rm total}\\)) is 29% in the HCO\\(^+\\) \\(J\\)=1--0 line, with a probability of 0.6% that this is caused by random fluctuations. UC H{\\sc ii} regions show a higher excess (58%) than UC H{\\sc ii} precursors (17%), indicating that material is still accreted after the onset of the UC H{\\sc ii} phase. Similar differences in the excess of blue profiles as a function of evolutionary state are not observed in low mass star-forming regions. Thus, if confirmed for high mass star-forming sites, this would point at a fundamental difference between low- and high-mass star formation. Possible explanations are inadequate thermalization, stronger influence of outflows in massive early cores, larger gas reserves around massive stellar objects or different trigger mechanisms between low- and high- mass star formation.