## 1 Introduction  
Digitization, machine learning, and AI (Artificial Intelligence) techniques [1, 2, 3, 4], along with the increasing computational and management availability of big data [5], are revolutionizing every area of engineering. The availability of HPC (High Performance Computing) infrastructure and the increasing reliability of software for high-fidelity simulations make it possible to greatly reduce the time required to collect and store data. HPC evolution [6, 7, 8, 9] has revolutionized the field of CFD, enabling faster and more accurate simulations. The implementation of parallel computing has allowed the maximum utilization of computational resources, distributing workloads over multiple processors. In particular, the use of GPUs [10, 11] (Graphics Processing Units) has shown significant performance advantages due to their ability to perform a large number of operations in parallel. GPUs are designed to handle large-scale parallel computing operations, making them ideal for CFD simulations. Unlike traditional CPUs, which are optimized to execute a series of instructions sequentially, GPUs can perform thousands of operations simultaneously. This makes them particularly effective for the intensive numerical calculations required in CFD simulations. The integration of GPUs into HPC systems has opened up new possibilities for CFD simulations, allowing increasingly complex problems to be tackled with greater precision and in less time. The future of CFD computing in parallel with the use of GPUs promises further advances, with the development of new hardware architectures and algorithms optimized to make the most of GPU capabilities. The development of HPC is leading to two trends: on the one hand, increasingly complex (dynamic analysis of critical maneuvers, fluid-structure coupling, higher velocities, etc.) [12; 13; 14; 15] and precise (denser computational meshes) analyses can be tackled [16]. On the other hand, large datasets for optimization algorithms can be obtained in reasonable times. It is evident how having this computing power, without an automatic method for generating these datasets, greatly reduces the potential. Many steps forward have been made on both the automatic generation of CAD, and thus mesh [17; 18; 19; 20], and on the mesh-morphing techniques [21; 22; 23; 24]. In general, it is evident that the automation of the creation process of each design point is indispensable. The development of synthetic data must be accompanied by the development of techniques for managing this big data. From this point of view, the trend is the application of machine learning and AI techniques to create smarter metamodels with which to interact in real time. ROMs are one of the most promising techniques in CFD [25; 26]. These models simplify complex problems by reducing the number of variables and the size of the system while still maintaining good accuracy. Another key aspect is the ability to easily interact with these models. FMU [27] is a free standard that defines a container and interface for exchanging models. This standard is supported by over 200 tools and is maintained as a project of the Modelica Association. It allows the integration of models developed with different simulation tools, facilitating collaboration between teams and organizations using different software. Thanks to standardization, models can be easily exchanged and reused, reducing development time and costs. The standard is suitable for the simulation of complex systems, allowing large models to be handled efficiently. Finally, increasingly advanced applications are being developed that take advantage of VR [28] viewers to have a more immersive and intuitive experience and to have a greater understanding of the physics involved in the problem being studied.