39 workloads slower or faster than real time while generating repeatable results. In addition to accurately recreating real-world driving conditions, the simulation environment must also render vehicle sensor data in the exact same way that cameras, radars and Lidars take in data from the physical world. The digital twin also has to work with more sophisticated simulation tools, particularly solvers for computational fluid dynamics. These tools can model an entire vehicle and dive down into the different components, and understand drag and range. The models could be as simple as a spreadsheet or full-blown 3D systems. This is particularly popular with battery pack developers for simulating thermal runaway. Developers run a bench test and use that data to input into the modelling software. This could be as simple as a look-up table, but it could be replaced with full details of all the chemistry if test data is not available. One advantage is that solvers are moving to add GPU acceleration, which is seeing an increase in performance of about 600% when moving from running on a processor to a GPU. These solvers are also using machine learning (ML) and even genAI in the optimisation software for mapping heat, building response surface models and design exploration. This helps to predict the next design changes. This is automating the building of force maps for aerospace, capturing the control surface deflections, and building a whole model of the aircraft to put the data into the control system algorithm. This enables a full wind-tunnel test in a simulation to test many parameters that couldn’t be achieved in a physical scenario. It generates the responses that could occur in a control algorithm for the extreme attitude and forces acting to provide the control algorithm with the model, so before a first prototype flight you gain the basis for flight testing. However, all of this complexity increasingly requires an ecosystem of tools that can interact, so the applications programming interface (API) of the tools is key (see below). Computational Fluid Dynamics (CFD) is coupled to the structures, design and mission profiles, so a wide range of tools need to interact effectively and accurately. Rendering Validating automated driving software requires millions of test kilometres. This not only implies long system development cycles with continuously increasing complexity, but it also brings the problem that real-world testing is resource intensive, and safety issues may also arise. Closed-loop evaluation requires a 3D environment that accurately represents real-world scenarios. One of the key aspects is to create a 3D environment that is as realistic as possible so that a digital twin can be a virtual environment in which to perform tests. This has moved from 3D artists creating high-density maps with stationary objects to a variety of locations and scenarios. The locations are brought into the virtual scenes using neural reconstructions with neural network technologies such as NeRFs (neural radiance fields), and 3D Gaussian Splatting provides scalability. A NeRF is a neural network that can reconstruct complex 3D scenes from a partial set of 2D images. Images in 3D are required in various simulations, gaming, media and Internet of Things (IoT) applications to make digital interactions more realistic and accurate. The Simulation and testing | Focus One advantage is that solvers are moving to add GPU acceleration, which is seeing an increase in performance of about 600% when moving… to a GPU Uncrewed Systems Technology | October/November 2024 Computational fluid dynamics is a key simulation technology for uncrewed systems (Image courtesy of TotalSim)
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