Postprocessing and storage of large data sets represent one of the main computational bottlenecks in computational fluid dynamics. We assume that the accuracy necessary for computation is higher than needed for postprocessing. Therefore, in the current work we assess thresholds for data reduction as required by the most common data analysis tools used in the study of fluid flow phenomena, specifically wall-bounded turbulence. These thresholds are imposed a priori by the user in L₂-norm, and we assess a set of parameters to identify the minimum accuracy requirements. The method considered in the present work is the discrete Legendre transform (DLT), which we evaluate in the computation of turbulence statistics, spectral analysis and resilience for cases highly-sensitive to the initial conditions. Maximum acceptable compression ratios of the original data have been found to be around 97%, depending on the application purpose. The new method outperforms downsampling, as well as the previously explored data truncation method based on discrete Chebyshev transform (DCT).

The implicit lower–upper symmetric Gauss–Seidel (LU-SGS) solver is combined with the line-implicit technique to improve convergence on the very anisotropic grids necessary for resolving the boundary layers. The computational fluid dynamics code used is Edge, a Navier–Stokes flow solver for unstructured grids based on a dual grid and edge-based formulation. Multigrid acceleration is applied with the intention to accelerate the convergence to steady state. LU-SGS works in parallel and gives better linear scaling with respect to the number of processors, than the explicit scheme. The ordering techniques investigated have shown that node numbering does influence the convergence and that the orderings from Delaunay and advancing front generation were among the best tested. 2D Reynolds-averaged Navier–Stokes computations have clearly shown the strong efficiency of our novel approach line-implicit LU-SGS which is four times faster than implicit LU-SGS and line-implicit Runge–Kutta. Implicit LU-SGS for Euler and line-implicit LU-SGS for Reynolds-averaged Navier–Stokes are at least twice faster than explicit and line-implicit Runge–Kutta, respectively, for 2D and 3D cases. For 3D Reynolds-averaged Navier–Stokes, multigrid did not accelerate the convergence and therefore may not be needed.

Many attempts have been made to reduce aviation’s environmental impact, as aviation traffic has grown exponentially in recent decades. While some approaches focus on technology and fuel alternatives, others strive to develop improved operational measures within air traffic management as a short-term action to mitigate aviation-induced climate change, as well as air pollution. In this work, different flight procedures are analyzed in terms of emissions and noise impact to define optimal trade-offs. The investigation is based on flight data recorders, emissions, and noise prediction models. An aircraft trajectory simulation code with flight procedure optimization is also implemented to define an environmentally optimal trajectory. The results show that while noise and the emissions proportional to the burned fuel may be reduced for some trajectories, other non-${\mathrm{CO}}_2$ emissions could drastically increase if too low idle-thrust levels are reached. Therefore, a minimum threshold for idle thrust is suggested as a key factor to define a truly optimal trajectory in terms of ${\mathrm{CO}}_2$ emissions, non-${\mathrm{CO}}_2$ emissions, and noise.

An implicit lower–upper symmetric Gauss–Seidel (LU-SGS) solver has been implemented as a multigrid smoother combined with a line-implicit method as an acceleration technique for Reynolds-averaged Navier–Stokes (RANS) simulation on stretched meshes. The computational fluid dynamics code concerned is Edge, an edge-based finite volume Navier–Stokes flow solver for structured and unstructured grids. The paper focuses on the investigation of the parameters related to our novel line-implicit LU-SGS solver for convergence acceleration on 3D RANS meshes. The LU-SGS parameters are defined as the Courant–Friedrichs–Lewy number, the left-hand side dissipation, and the convergence of iterative solution of the linear problem arising from the linearisation of the implicit scheme. The influence of these parameters on the overall convergence is presented and default values are defined for maximum convergence acceleration. The optimised settings are applied to 3D RANS computations for comparison with explicit and line-implicit Runge–Kutta smoothing. For most of the cases, a computing time acceleration of the order of 2 is found depending on the mesh type, namely the boundary layer and the magnitude of residual reduction.

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 CO₂emissions 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.