What is nTop Fluids?

Ajay Prasad

June 09, 2025 20:31

nTop Fluids is a new computational fluid dynamics (CFD) toolset integrated directly inside nTop. Eliminating the meshing and solving bottlenecks created by traditional tools, nTop Fluids brings CFD into the iterative computational design loop. We have a new nTop Learn course on nTop Fluids, which dives deep into the technology and setup in nTop.

What solver is used in nTop Fluids?

nTop Fluids uses the Lattice Boltzmann Method (LBM), a numerical approach that simulates fluid flow by evolving particle distribution functions. It recovers the Navier-Stokes equations with a mesoscopic model where particle distribution functions propagate and collide on a voxel grid. The method operates directly on implicit geometry definitions without generating body-fitted meshes.

Key Principles
Advantages
nTop Fluids (LBM) vs Traditional CFD
Common Applications

Key Principles

Lattice Boltzmann equation (LBE):

$f_{i} \left( \mathbf{x} + \mathbf{c}_{i} , t + 1 \right) = f_{i} \left(\mathbf{x} , t \right) \, + \Omega_{i},$

f: discrete particle distribution function
c: discrete velocity
x: position
t: time
Ω: collision operator

The discrete distribution function f(x,t) represents the density of fluid particles moving in a discrete velocity direction c at position x and time t (e.g. this means that 20% of the particles are moving to the right, 30% are moving up, 10% are moving diagonally, and so on).
LBE tracks these distribution functions and evolves them over time.
The spatial discretization uses a Cartesian grid, where each point consists of a finite set of discrete particle distribution functions arranged according to a lattice velocity set.
Lattice velocity set: DdQq, where d is dimensions and q is discrete velocities

D3Q19: A typical lattice velocity set for fluid simulations

Advantages

Simplicity and Scalability: LBM, like direct time stepping schemes, introduces artificial compressibility, simplifying the solution process and improving scalability.
No Poisson Equation: Unlike traditional methods, LBM does not require solving the Poisson equation, which is computationally expensive due to its non-local nature.
Highly Parallelizable: The LBM algorithm is characterized by very simple parallelization and high numerical efficiency, so that time-resolved flow simulations can be carried out significantly faster than with other solution methods.
Easy Meshing: Generating Cartesian grids for LBM is much simpler and more automatable, removing the meshing bottleneck of many other approaches.

nTop Fluids (LBM) vs Traditional CFD

	Traditional (Navier-Stokes)	nTop Fluids (LBM)
Solver Method	Complex partial differential equations (PDEs) are computationally expensive.	Linear equations are solved on a lattice set that is far less computationally intensive.
Model Representation	High-density body-fitted mesh that requires manual, time-consuming work by a meshing expert.	Voxel mesh is far easier and less time-consuming to create.
Accuracy	High accuracy for a wide range of problems with documented validation in various publications.	It is a relatively newer methodology and is less validated for some applications.
Compute Parallelization	Most CFD is still run on CPUs. However, CFD solvers are gradually being ported to run on GPUs, with massive improvements to solver speed. These often require additional HPC licenses at a significant cost to the customer.	Simple to parallelize on a GPU solver that is 100 - 1000x faster than traditional CFD solvers running on CPUs.

Common Applications

Pressure drop prediction across complex geometries to minimize energy losses and optimize system designs.
Simulation of laminar, turbulent, and transitional flow regimes for comprehensive and accurate flow behavior analysis.
Identification of unequal flow distributions, recirculation zones, and vortex structures to improve flow efficiency and system reliability.
Calculation of maximum flow velocity to detect critical high-speed zones and prevent erosion or mechanical failure.
Detection of blockages and inefficiencies in flow paths to enhance fluid distribution and reduce pressure losses.