What is Non-linear CFD (Computational Fluid Dynamics)?

Clearly define the problem you aim to solve and establish the objectives of your simulation. This could involve optimizing airflow around a product, simulating heat transfer within a system, or analyzing fluid-structure interactions under complex conditions.

During this phase, identify key variables such as flow velocity, temperature gradients, pressure fields, and material properties that will influence the simulation. It’s essential to understand the specific challenges of your product development, such as turbulence or non-linear material behavior, which will guide the rest of the process.

Once the problem is defined, set up the boundary conditions for your simulation. Boundary conditions describe how the fluid interacts with its environment—such as inlet velocities, outlet pressures, or heat fluxes. These conditions are critical for accurately representing real-world scenarios in your simulation. Additionally, specify initial parameters like temperature distributions or initial flow velocities that will serve as starting points for your numerical solver.

Next, choose a suitable numerical method for solving the non-linear equations governing fluid behavior. Non-linear CFD problems often require advanced solvers capable of handling complex interactions between variables.

Popular methods include Euler time marching schemes or implicit solvers that are well-suited for non-linear problems involving turbulence or transient flows. The choice of solver depends on factors such as the nature of the fluid (compressible vs. incompressible), flow regime (laminar vs. turbulent), and whether you’re dealing with steady-state or transient conditions.

Mesh generation is a critical step in CFD simulations. The geometry of your product must be broken down into smaller elements (or cells) that can be analyzed computationally. For non-linear CFD problems, finer meshes are typically required in areas where rapid changes occur—such as near walls, sharp edges, or regions with high gradients in velocity or temperature. A well-refined mesh ensures that the solver can capture detailed fluid behaviors accurately without missing critical phenomena like boundary layer separation or shock waves.

With your mesh and numerical method in place, it’s time to run iterative solvers that will gradually converge on a solution. Non-linear CFD problems typically require multiple iterations to solve due to their complexity. During each iteration, the solver updates values for variables like velocity and pressure until they meet predefined accuracy thresholds. It’s important to monitor convergence criteria closely to ensure that the solution is stable and accurate.

Once the simulation has converged on a solution, post-process the results to analyze KPIs (key performance indicators) such as drag coefficients, heat transfer rates, or pressure drops across components.

Visualization tools can help you interpret complex flow patterns by generating contour plots, streamlines, or vector fields that illustrate how fluids behave within your design. This analysis will provide insights into potential design improvements and help validate whether your objectives have been met.

CFD is an iterative process by nature. Based on your analysis of the initial results, you may need to adjust boundary conditions, refine mesh resolution in critical areas, or explore alternative design configurations. Each iteration brings you closer to an optimized solution that meets performance goals while minimizing costs and risks associated with physical prototyping.

For high-stakes projects where accuracy is paramount—such as aerospace or automotive applications—it’s often beneficial to validate CFD results through physical testing. Wind tunnel tests, or thermal experiments, can provide real-world data that helps verify whether your non-linear CFD model accurately predicts product behavior under operational conditions.