Many natural phenomena, such as a calming waterfall or an infuriating coffee splash on your computer keyboard, exhibit fluid flow. When you see the coffee stains on your keyboard, you may not immediately think, “Wow! That’s interesting!” Flowing water from a cliff, on the other hand, can create an atmosphere of awe because of its tranquillity while also being dynamic. In these cases, can we learn anything about fluid flow? Is it possible to predict the movement of a fluid in certain circumstances? What’s more, can we avoid more coffee spills in the future?
Performing experiments in the lab with natural fluids and analysing the flow properties with various imaging instruments can help answer these questions. This is what we mean by an experimental strategy. If you use a different approach, such as writing a set of equations to describe fluid flow and then plugging the appropriate values into the resulting governing equation, you can predict the flow dynamics. This method also involves simplifying assumptions and conditions and performing some mathematical magic. Here, we’re taking a more analytical stance.
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As computing power has increased, a third approach to answering these questions has emerged — the numerical one. There is currently no analytical solution for any arbitrary set of fluid flow equations, but their outputs can be computed using a powerful enough computer to do so. Computational Fluid Dynamics (CFD) is a term used to describe the study of fluid dynamics on a computer (CFD). There are several CFD courses available online that one can take if they wish to learn about this field.
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There are numerous numerical methods for solving Partial Difference Equations, each with its limitations. The easiest method is the Finite Difference method, which employs a low-order Taylor series approximation to transform PDEs into a set of algebraic equations.
While this is not the optimal method to model fluid flow in all circumstances, we will proceed with it because it simplifies other aspects of modelling a crystalliser, which is the purpose of this article series. You may want to employ the Finite Volume or Finite Element methods for more rigorous numerical analyses.
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The organisation of the Code
The code is separated into three distinct scripts or files. The first file, “FlowPy.py,” contains the code for solving PDEs using the finite difference method for various inputs. This script receives information via the “FlowPy Input.py” script, which functions as a user interface. After running the simulation, the “FlowPy Visualizer.py” script is used to animate the flow’s dynamics.
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The Simulation Code — FlowPy
Python’s Object-Oriented Programming (OOP) allows us to organise and simplify the code, an advantage of writing the code in Python. This will also make the addition of the heat and mass transfer extensions simple. The code is therefore organised into classes and functions that operate on objects of these classes.
After creating and validating FlowPy, we can proceed to the next step in modelling a crystalliser that adds heat and mass transfer to the solver. CFD is an intriguing career option. You can learn a CFD software course in Pune and start your CFD career now!
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