There exists a common perception that mechanical engineers tend to minimize their involvement in programming, opting to steer clear of it whenever possible. If they cannot automate a task using Excel, they often choose to avoid programming entirely. In the day-to-day routine of mechanical engineers, programming typically assumes a secondary role or sometimes no role at all. Their primary focus revolves around machine design, dynamics, and material science, with programming not being a prominent aspect.
What is frequently overlooked, though, is the fact that acquiring programming skills can actually enhance efficiency. Shunning programming due to a dislike for it or a fear of not mastering it well enough can ultimately hinder long-term progress.
Fortunately, there are programming languages accessible enough for mechanical engineers to grasp the fundamentals relatively quickly. Python stands out as one such language, and personally, it has become an indispensable tool for various tasks within the field of mechanical engineering.
In 2020 and 2021, Python consistently held the top spot as the most popular programming language. This status persists as of July 2023 and has even earned Python a place in the TIOBE Index Programming Language Hall of Fame.
With this surge in popularity, an increasing number of employers are incorporating Python into their list of required skills. However, this is just one incentive to embrace this programming language. Python stands out for its user-friendliness; its syntax is designed to resemble plain English as closely as possible, enabling individuals to read and write Python code within a matter of hours. More significantly, mastering Python opens the door to a multitude of opportunities, expanding your horizons in the world of programming.
Python can streamline workflow, accelerate data simulation on a massive scale, and, most importantly, automate mundane and time-consuming tasks, transforming them into quick, efficient processes.
In a world where Artificial Intelligence has become a hot topic, especially around generative design, generative engineering,… Python brings an answer to the creation of low-code models to automate engineering process or at least bring design efficiency.
Let's delve into some illustrative examples.
Python offers an array of packages, such as NumPy and SciPy, equipped with built-in functions that streamline numerical analyses, making them faster and more accessible than ever before. SciPy, for instance, allows you to perform integration, plotting, and even sparse calculations without the need for manual computations, resulting in significant time savings. With a minimal initial setup, Python can be harnessed to create programs that execute complex calculations instantly.
At Dessia we enable our users to leverage a low-code engineering platform to navigate this custom code, use it for design support applications, integrate it in model based engineering practices, and much more !
For example, the PYro tool facilitates the straight forward calculation of various thermodynamic properties based on temperature and pressure, including density, enthalpy, entropy, internal energy, molecular weight, specific heats, specific heat ratio, and specific volume.
In this context, PyCFD employs Python to write and solve partial differential equations, compute fluid volumes, address Navier-Stokes equations for fluid mechanics, and utilize the boundary element method for solving boundary value partial differential equations.
These applications yield three primary advantages for mechanical engineers within their organizations. Firstly, the scripts you develop can be utilized by your colleagues, just as you can use theirs, fostering efficiency and saving time for the entire team. Secondly, Python enables further automation by scripting customized features on platforms like Onshape. This facilitates the rapid generation of tailored features for project use, including custom beams, airfoils, spur gears, ray tracers, and wiring. Lastly, Python empowers you to automate extensive calculations that might otherwise be unwieldy or time-consuming. The language excels in floating-point arithmetic and supports calculations with up to 17 significant digits, making it a foundation for mathematically intensive machine learning (ML) frameworks like PyTorch.
Excel and Google Sheets are widely adopted tools in the business world for a good reason. Spreadsheets offer user-friendly features, making it relatively easy for newcomers to initiate substantial projects using these applications.
Nonetheless, Excel and similar tools have their limitations, particularly when dealing with extensive datasets. Python, on the other hand, stands as the preferred choice for handling large-scale data science tasks. Python not only accelerates code development with its high-level features but also enhances problem-solving by providing an intuitive approach. Furthermore, the scientific community has developed an abundance of readily available libraries for Python, extending its capabilities beyond imagination.
These libraries encompass scientific packages like NumPy and SciPy, as previously mentioned, but this merely scratches the surface. Matplotlib, for instance, serves as a 2D graph plotting library ideal for scientific and engineering endeavors, while SymPy specializes in symbolic computing tasks, encompassing domains from calculus to quantum physics. There's even Mechpy, a Python toolbox tailored exclusively for mechanical engineers.
Comparatively, when we assess Python against MATLAB, distinct differences emerge. Both are interpreted languages, yet they diverge in both technical and philosophical aspects. Apart from variations in MATLAB and Python syntax, Python outshines MATLAB in versatility, boasts a more vibrant support community, and encompasses most of the same functionalities, with the notable exception of MATLAB's Simulink for simulation and model-based design.
These technical distinctions largely emanate from their distinct development approaches: MATLAB is proprietary and closed-source software, whereas Python is an open-source project bolstered by a vast developer community.
In essence, this translates into several significant advantages favouring Python. Firstly, Python's versatility enables us to achieve more with fewer resources. A single program can seamlessly integrate functions from various libraries, streamlining workflows and reducing the need for extensive user input. This, in turn, mitigates the complexity often associated with managing numerous spreadsheets in a corporate setting, as Python programming enables us to consolidate much of this work within a unified platform.
While computer programming may not be our primary focus as mechanical engineers, it has become an indispensable skill in today's digital landscape. Acquiring proficiency in a user-friendly language like Python can yield significant rewards. Python, equipped with a comprehensive set of scientific and mathematical tools, facilitates its expansion into various applications as your mastery of it deepens.
The remarkable surge in Python's popularity is well-founded. It stands as one of Google's official languages and serves as the foundation for major platforms like Dropbox and Netflix. Much like these web services, our mechanical designs can greatly benefit from expertise in Python.
Curious about how engineers can get started on using python without much training? Explore further details on Dessia Technologies Generative engineering platform here.
