Lattice Boltzmann Simulation Engine

A kinetic-based computational framework for transient, multiscale fluid simulation using GPU-accelerated lattice methods.

Explore Specifications

8× V100

GPU COMPUTATIONAL PLATFORM

D3Q19/D3Q27

LATTICE FRAMEWORK

5.0 GLUPS

TARGET THROUGHPUT

Why Lattice Boltzmann?

The Lattice Boltzmann Method (LBM) provides a kinetic-based computational approach for studying transient fluid systems.

Traditional computational fluid dynamics methods have demonstrated excellent performance across many engineering applications. However, complex transient flows, geometry-driven effects, and multiscale turbulent structures remain active areas of research.

The LBM architecture explored here uses a different computational representation. Instead of directly advancing macroscopic pressure and velocity variables, the solver evolves discrete distribution functions whose moments recover the macroscopic conservation laws.

The objective is not to replace established CFD methods universally, but to develop a complementary high-performance framework for:

  • transient flow simulation

  • complex geometry interaction

  • GPU-based acceleration

  • multiscale turbulence analysis

  • reduced-order modeling

Computational Architecture

Geometry
→
Discrete Lattice
Representation
→
Kinetic
Evolution
→
Moment
Recovery
→
Multiscale
Analysis

Kinetic Formulation

The solver advances discrete lattice populations rather than directly solving for macroscopic fields.

The lattice evolution equation is:

\begin{equation*} f_i(\mathbf{x}+\mathbf{c}_i\Delta t,t+\Delta t) = f_i(\mathbf{x},t) - \frac{1}{\tau} \left(f_i-f_i^{eq}\right) \end{equation*}

The macroscopic density is recovered through the zeroth moment:

\begin{equation*} \rho=\sum_i f_i \end{equation*}

The velocity field is obtained from:

\begin{equation*} \rho\mathbf{u} = \sum_i f_i\mathbf{c}_i \end{equation*}

The viscosity relationship is:

\begin{equation*} \nu = c_s^2 \left(\tau-\frac12\right)\Delta t \end{equation*}

Solver Configuration

The simulation framework is designed around GPU-friendly lattice operations and scalable domain decomposition.

Configuration:

Lattice Models:
    D3Q19 / D3Q27

Collision Models:
    MRT / TRT / Cumulant LBM

Boundary Treatment:
    Bounce-back and interpolated schemes

Precision:
    FP64 numerical validation

Hardware:
    Multi-GPU CUDA acceleration

GPU Acceleration Strategy

The primary advantage of the lattice formulation is the locality of the computational operations.

Each lattice update requires information from neighboring discrete velocity states, allowing efficient parallel execution on modern GPU architectures.

The computational strategy includes:

  • domain decomposition across multiple GPUs

  • memory-aware lattice partitioning

  • CUDA accelerated collision and streaming kernels

  • performance tracking using lattice updates per second

The target development platform is:

8 × NVIDIA V100 GPUs
256 GB aggregate HBM2 memory
FP64 simulation capability
Distributed lattice domains

Multiscale Flow Analysis

Large transient simulations generate high-dimensional flow data. The challenge is not only producing accurate solutions, but extracting the physical structures that govern aerodynamic behavior across multiple length and time scales.

The Base Drag computational framework investigates methods for transforming complex flow fields into compact, physically meaningful representations. This includes analysis of turbulent structures, energy transfer pathways, and hierarchical organization within the flow.

Wavelet-Based Flow Decomposition

Traditional spectral analysis methods describe turbulent behavior primarily through frequency or wavenumber space. While powerful, these approaches can be limited when analyzing localized transient events, coherent structures, and geometry-driven flow interactions.

Wavelet analysis provides a complementary approach by decomposing flow fields simultaneously in both spatial scale and location.

For a velocity or energy field, a wavelet transform can separate features associated with different physical scales:

  • large-scale coherent flow structures

  • intermediate turbulent energy transfer

  • small-scale dissipative features

  • localized interactions near surfaces and wakes

This multiscale representation provides a framework for investigating how aerodynamic losses emerge from interactions between geometry, boundary layers, separation regions, and turbulent structures.

Hierarchical Flow Representation

The objective is not simply to visualize turbulence, but to identify relationships between structures at different scales.

The analysis framework investigates:

  • wavelet decomposition of turbulent dissipation fields

  • scale-dependent energy transfer

  • hierarchical correlation structures

  • reduced-order representations of transient flow behavior

  • connections between geometric features and flow organization

These methods provide a pathway toward identifying the physically relevant variables required for targeted aerodynamic optimization.

Energy Dissipation Analysis

The turbulent dissipation rate is evaluated through:

\begin{equation*} \epsilon = 2\nu S_{ij}S_{ij} \end{equation*}

where:

\begin{equation*} S_{ij} = \frac12 \left( \frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} \right) \end{equation*}

The resulting dissipation fields provide a measure of where kinetic energy is converted into heat through viscous processes.

By analyzing these fields across multiple scales, the framework investigates whether dominant aerodynamic loss mechanisms can be identified and represented in a reduced computational form.

Research Direction

The long-term objective is to connect high-resolution kinetic simulation with compact physics-informed models.

The LBM framework provides the numerical foundation for investigating:

  • complex surface interactions

  • turbulent energy transfer

  • multiscale flow organization

  • reduced computational representations

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