You don’t need to be a world-class coder or have the most powerful hardware, but neither will hurt.
CAE simulations require powerful hardware to handle computational demands effectively. Engineers need a well-configured computer with multi-core central processing units (CPUs), ample random access memory (RAM), high-performance graphics processing units (GPUs), fast storage, and reliable cooling and power supply systems. However, as more simulation software providers offer cloud-based software-as-a-service (SaaS) tools, engineers can perform complex studies with various devices.
High-performance computing (HPC) is an advanced option for larger, highly complex problems that yield higher-fidelity results. HPCs resemble server racks with tens, hundreds or even thousands of CPUs working in parallel to divide and conquer large computational tasks. Such environments use resource management tools and job schedulers to allocate resources and manage job queues efficiently.
On-premises HPC units can require extensive upfront investments and ongoing maintenance, impractical for many startups and small to medium-sized businesses. Even larger companies may grimace at the initial and long-term costs and effort of managing such resources on-premises or offsite, even if maintenance is outsourced. Therefore, cloud computing offers a flexible alternative for accessing high-performance hardware without significant upfront investment. With cloud-based HPC, multiple engineers can run complex simulations in minutes or hours instead of days or weeks and pay only for their usage. Alternatively, SaaS tools relieve engineers and IT departments of setting up and maintaining complex HPC cloud environments so they can run simulations around the clock.
While planning a simulation study, engineers must consider their available time and computational resources to solve a given problem. Defeaturing, refining and mesh optimization strategies help reduce the number of equations needed to represent the problem, yet computing capabilities significantly influence the study’s reliability and total costs.
Coding skills are not always necessary but can boost engineers’ capabilities for various simulation tasks. For example, when dealing with highly complex or custom simulation problems, engineers may need to write custom code to implement specific algorithms or models. They may also need to write scripts to automate repetitive tasks, integrate different software tools or customize workflows.
When using open-source simulation tools, engineers often need to modify or extend the existing code to fit their needs. However, many popular simulation software packages include GUIs for engineers to set up, run and analyze simulations with low or no code.
Nonetheless, it is never a bad idea for engineers to learn or brush up on coding skills. Commonly used languages in engineering simulation include Python, C, C++, Java, JavaScript and Matlab. The choice of programming languages in simulation depends on factors such as the engineering field, software used and available computing resources.
