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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.

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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.

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Key Principles

Lattice Boltzmann equation (LBE): The Lattice Boltzmann equation that is used to calculate fluid flow
  • 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 

A typical lattice velocity set for fluid simulations

D3Q19: A typical lattice velocity set for fluid simulations

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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.

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nTop Fluids (LBM) vs Traditional CFD

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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.
An example of fluid flow through a pipe that has a secondary inletExample of fluid flow through a cold plateExample fluid flow through a manifold

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Keywords:

analysis ntop flow cfd fluids lbm