CFD Analysis
jimmylea’s CFD analysis consultants provide advanced CFD analysis services for complex fluid systems, specialising in multiphase, multiphysics and multispecies simulations. Our CFD analysis consultants support the chemical, energy, resources and water industries, delivering CFD analysis for complex process systems involving mass transfer, heat transfer, reaction engineering, multiphase interactions and species transport to improve performance, safety and operational efficiency.
Our CFD studies are led by Dr Jimmy Lea PhD FIEAust CPEng RPEQ, Principal Consultant at jimmylea. Dr Lea has over 30 years of experience in chemical engineering, process engineering and advanced CFD simulation, with particular expertise in multiphase flow, solids suspension, mixing systems, reactor design, mass transfer and process scale-up. He holds Bachelor, Master and Doctoral qualifications in Chemical Engineering from UNSW Sydney and is a Fellow and Chartered Professional Engineer with Engineers Australia. Combining rigorous engineering fundamentals with advanced simulation techniques, we assist clients in evaluating flow distribution, mixing performance, solids transport, heat transfer and process optimisation across a broad range of industrial applications.
The publications on this page are based on the industry knowledge gained through commercial CFD analysis projects undertaken by our CFD analysis consultants. Verifying CFD analysis results can be challenging, while conducting physical validation studies to assess CFD analysis accuracy may be impractical or economically prohibitive. The peer-reviewed papers presented on this page demonstrate methodologies for CFD analysis validation in laboratory environments. We have also included publications showing how CFD analysis can be applied to develop design heuristics and optimise industrial mixing systems.
Disclaimer: While every effort has been made to ensure technical accuracy, jimmylea makes no warranty, express or implied, regarding the completeness, reliability or suitability of the information, CFD analysis methodologies or datasets provided. Users are responsible for verifying the applicability of any CFD analysis results, recommendations or materials to their specific projects, operating conditions and software environments.
Verification and Validation in CFD Analysis
Reliable CFD analysis depends on both verification and validation.
Verification confirms that the governing equations have been solved correctly by assessing factors such as mesh independence, numerical convergence and solution stability.
Validation then compares CFD predictions with experimental measurements or published data to demonstrate that the model accurately represents the physical behaviour of the real system.
As experienced CFD analysis consultants, we apply both verification and validation throughout our simulation workflow. Where appropriate, our models are validated against experimental data, peer-reviewed literature and plant measurements to provide confidence that the predicted performance is technically sound and suitable for engineering decision-making.
The infographic on the right summarises the difference between verification and validation, illustrating why both are essential for producing reliable CFD results.
Understanding CFD Validation Discrepancy
CFD validation compares simulation results with experimental data to assess how well the computational model represents the physical system. In a rigorous CFD analysis, the observed comparison discrepancy between the simulation and experimental measurements can arise from several sources, including modelling assumptions, uncertainty in simulation inputs, numerical solution errors and uncertainty in the experimental data itself.
These sources need to be considered when interpreting validation results. Assumptions used to simplify the physical system may introduce modelling uncertainty, while fluid properties, boundary conditions and other input parameters may contain measurement or estimation uncertainty. Numerical factors such as mesh resolution and solution convergence can also influence the CFD results. At the same time, experimental measurements have their own inherent uncertainty.
The infographic on the right illustrates these sources of discrepancy and how they contribute to the comparison between the simulation result and experimental data.
Validation of CFD Results via Peer-Reviewed Journal
As the leading CFD consulting company in Australia, Singapore and the Asia Pacific region, it is important that we demonstrate the accuracy of our simulation results. There are two computational fluid dynamics (CFD) journals in this folder. These CFD simulation – related journals can be downloaded simply by clicking the icon on the left, namely:
Lea J [2009], Suspension mixing tank-design heuristic, Chemical product and process modelling, Volume 4, Issue 1, Article 17, The Berkeley Electronic Press
Fort I [2009], Comments on Lea J [2009] “Suspension mixing tank-design heuristic” Manuscript 1419, Chemical product and process modelling, The Berkeley Electronic Press
In Lea [2009], a computational fluid dynamics (CFD)-assisted design approach has been employed to study the effectiveness of mixing tank geometrical configurations to suspend particles. In contrast, the paper Fort [2009] deals with the analysis, via physical experimentation, of the process characteristics of agitated system with a pitched blade impeller and radial baffles (impeller power input and impeller pumping capacity) under turbulent regime of flow of agitated batch. Original experimental data are compared with results of CFD simulation in a pilot plant mixing system published in literature. These two papers concluded that the CFD results from Lea [2009] and experimental results from Fort [2009] are in good agreement. Download the article by clicking the icon on the left.
Validation of CFD Results via Particle Image Velocimetry (PIV)
The manufacture of nitrocellulose, which is the basis of most artillery, rocket and missile propellant, is intrinsically risky due to the energetic nature of the product and the sensitivity of the process. To optimise the performance of the nitration unit, computational fluid dynamics (CFD) was employed to characterise the nitration unit and to provide detailed, relevant process data emanating from a new perspective. The actual nitration unit was scaled-down in order to use independent measurements from particle image velocimetry (PIV) to validate the CFD results. This physical validation using PIV was conducted in conjunction with CSIRO Australia, providing additional confidence in the accuracy of the CFD simulation. It was only after the model and simulation results were successfully validated that the characterisation of the actual unit was carried out. With the nitration unit characterised, its optimisation was carried out by performing modelling on different geometrical configurations with a view to selecting one which gives an optimum performance.
Suspension Mixing Tank - Design Heuristic
The design of suspension mixing tanks is often heavily reliant on empirical methods due to an incomplete understanding of the hydrodynamics involved in dense solid-liquid interactions. This limitation is particularly significant when designing mixing tanks or batch reactors for producing high-quality expensive products in the fine chemicals, biological and pharmaceutical industries. In this paper, a computational fluid dynamics (CFD)-assisted design approach is employed to examine the effectiveness of various mixing tank geometries in suspending particles. Ten key variables were analysed, and the impact of altering these variables was thoroughly documented. Additionally, a multivariable study was conducted to determine the most influential variables among them. Based on the findings, a design heuristic was developed, offering a practical guideline applicable across the process industry.
GRI-Mech 3.0 Chemical Kinetic Mechanism for Light Hydrocarbon Combustion
For engineers conducting advanced CFD or process simulations, GRI-Mech 3.0 remains one of the most trusted detailed chemical kinetic mechanisms for light hydrocarbon-air combustion. Developed collaboratively by the Gas Research Institute (GRI) and the University of California, Berkeley, it captures the oxidation chemistry of methane, ethane, ethylene, propane, and related C₁-C₃ species, together with comprehensive NOx formation and reduction pathways.
To support high-fidelity modelling in ANSYS Fluent, Aspen Plus, and other CHEMKIN-compatible solvers, the complete mechanism is provided below. Each file is in standard CHEMKIN format and can be imported directly into your simulation environment.
| File | Description | Typical Use |
|---|---|---|
| grimech30.dat | Reaction mechanism defining over 300 elementary reactions among 53 species, including hydrocarbon oxidation and NOx chemistry | Upload as the main CHEMKIN mechanism in Fluent |
| thermo30.dat | NASA polynomial data providing temperature-dependent thermodynamic properties (Cp, enthalpy, and entropy) for all species | Required by Fluent when importing the mechanism |
| transport.dat | Molecular transport coefficients for viscosity, diffusion, and thermal conductivity | Optional, improves species-diffusion and heat-transfer accuracy |
These files enable the detailed simulation of finite-rate gas-phase oxidation in burners, thermal oxidisers, flares, and reactors where light hydrocarbon fuels are involved.
Introduction to Computational Fluid Dynamics
Computational fluid dynamics (CFD) was born during World War II, when scientists at Los Alamos National Laboratory were not only developing the atomic bomb, but also the numerical tools to describe the violent flow created by such a device. Among them was mathematician J. von Neumann, who contributed the keystone method of artificial viscosity for “capturing” shocks in numerical solutions and is regarded as the father of CFD. In the 65 years of its existence, CFD has earned itself a respectable place alongside the established disciplines of theoretical and experimental fluid dynamics. It is a branch of science that attracts mathematicians, physicists, and engineers alike, possibly because of the strong sense of empowerment it confers on its practitioners.
Developers of CFD methods create a virtual reality that users may populate with anything that flows, regardless of scale. It could be a trickle of molecules finding their way through a micro-electro-mechanical system microchannel or air that lifts an entire airplane; it could be the flame moving through a combustion chamber or the melting core of a nuclear reactor gone unstable; it could be the earth’s restless oceans and atmosphere or the gaseous disk of a spiral galaxy. Moreover, the concept of flowing is broad: traffic flows over multilane highways, and an ensemble of stars flows through phase space; both may be treated with CFD. The bottom line: CFD methods allow the user virtual experiments that in the words of P.L Roe, would be “expensive, difficult, or dangerous, [or] impossible” in the real world.