In R&D, the systems we want to model are rarely clean or convenient. You have irregular boundaries, shifting phases, soft solids, and chaotic flows and this is quite often the norm!

While traditional Computational Fluid Dynamics (CFD) has its place, it’s not always a comfortable fit, especially when the system doesn’t want to behave like a neat little mesh. And this is where Smoothed Particle Hydrodynamics (SPH) offers something genuinely useful in such cases. 

SPH was originally developed for astrophysics, but is now being applied across engineering, biophysics, and even food science. It’s a mesh-free computational method that treats matter as a collection of discrete particles. These particles interact through smoothing kernels, allowing the method to capture the nuances of deformable materials and complex flows, without the constraints of a predefined grid.

Many of the problems that industrial R&D teams face involve free-surface flows, splashing, or breakup; multiphase systems like slurries, emulsions, or suspensions, soft, gel-like, or granular materials that don’t behave "neatly" and flow regimes that are non-Newtonian or highly localised. In other words, R&D teams increasingly face challenges of systems that are difficult to model using Computational Fluid Dynamics (CFD) approaches. 

Hence one can explore SPH in contexts where flexibility and physical intuition matter more than rigid formulations or high-fidelity turbulence models, such as for instance in food science and technology with pastes, emulsions and powder-liquid interactions. It can also be used in Materials Science such as in soft solids, gels and composites or in bioprocessing with slow flows, yield-stress fluids and phase interactions as well as in environmental processes such as in sedimentation, erosion and pollutant spread. These are just a few of the applications that SPH can be used for. 

Notwithstanding the above, SPH is not a silver bullet. For large-scale simulations, SPH can be computationally heavy. Furthermore, it takes experience to tune things like kernel size and particle density effectively. But when the goal is to gain insight into complex, deformable, and dynamic systems, it often outperforms more conventional options, especially when you want models that reflect the system’s quirks rather than smoothing them away.

A lot of R&D teams have a deep understanding of their processes, empirical knowledge, pattern recognition, hands-on experience but don’t always have tools that can express that complexity. SPH offers a bridge between what people know intuitively and what can be represented computationally. It’s just versatile enough to model what really matters.