This paper presents a holistic Reynolds-averaged Navier–Stokes (RANS) turbulence modeling framework for the computational fluid dynamics (CFD) simulations of complex wall-bounded turbulent flows. Based on the elliptic relaxation idea, the deployed eddy viscosity turbulence model reconstructs the near-wall stress anisotropy and nonviscous effects. The appropriate selection of the turbulent quantities that are being solved for, together with the zero value wall boundary condition for the related turbulent quantities, renders the model less sensitive to the near-wall grid nonuniformities and resolution. The unified near-wall velocity profile, obtained based on the boundary layer theory, is used to devise the compound near-wall treatment that ensures the robustness of the numerical simulation. The proposed turbulence modeling framework is implemented into the general-purpose open-source CFD code and validated against the generic test cases with satisfactory agreement.

The adjoint method is a very promising gradient-based optimisation framework for computational fluid dynamics (CFD), because of its independence of the computation cost from the number of design variables. With the aim of improving the robustness of the continuous adjoint topology optimization, this paper presents a direct method for the design variable update. Based on the intrinsic feature of the topology optimization, this straightforward update method explicitly controls the design variable target volume, precluding any case-dependent parameters. The details of its implementation are discussed with regard to an existing open-source continuous adjoint topology optimization solver. The performance of this alternative method is tested on complex 3D engineering problems with duct configuration, and no impact on the computational demands or numerical stability has been observed in the simulations.

This paper presents a data-driven surrogate model for predicting cabin airflow and its integration into system-level electric vehicle simulations for energy management analysis. The model employs a graph-based neural network with a mirror-symmetric predictor–corrector architecture and is trained on a dataset generated using computational fluid dynamics (CFD) covering a defined range of inlet velocities and temperatures. The surrogate appropriately reconstructs temperature fields and captures the dominant airflow structures at significantly lower computational cost than CFD. Quantitative evaluation shows high accuracy in passenger-relevant regions, while localized discrepancies remain confined mainly to shear-layer zones. The model enables near-real-time inference and is coupled with a system-level modeling framework for control-oriented simulations that are impractical with CFD. The study is tailored to a specific geometry and operating range, showing that targeted training strategies and physics-based extensions improve robustness, particularly under limited data conditions.

To reduce emissions in the existing transportation system and lower carbon dioxide (CO₂) output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system can help save energy and improve passenger comfort. This study investigates an advanced thermal management system for an electric truck cabin with heating panels and added insulation. A one-dimensional (1D) cabin thermal model was also developed and validated with experimental data. The model integrates insulation, heating panels, and a 1D comfort simulation. It is functional mock-up unit (FMU) compatible and connects to larger system simulations and real-time applications. The results show that energy consumption can be reduced by up to 50% with these thermal measures. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions.

To reduce emissions in the existing transportation system and lower CO₂ output, battery electric vehicles (BEVs) offer a promising approach due to their higher energy efficiency. However, their driving range still falls short compared to conventional vehicles. Optimizing the heating, ventilation, and air conditioning (HVAC) system helps to save energy and improve passenger comfort. This work investigates an advanced thermal management system for an electric truck cabin, equipped with heat panels and additional insulation. First, a 1D model of the thermal management system was developed to investigate and properly size the thermal measures. The simulations enabled initial sizing and provided a first evaluation of the concept truck cabin. Subsequently, the prototype truck cabin was tested in different configurations and under varying ambient conditions in a climatic chamber and laboratory. Using the experimental data, the 1D model was optimized and validated, making it possible to simulate real-world scenarios. In the future, further research and new approaches will be necessary to identify even more efficient subsystems and cost-effective solutions.