CFD-AI methodology for translating Brookfield measurements into rheometer data

Abstract

Brookfield viscometers are widely used in industrial quality control because they are simple and practical, but the viscosity values obtained are relative to the instrument, spindle, container geometry, and measurement procedure. In contrast, rotational rheometers such as the Anton Paar RheolabQC provide more defined rheological data under controlled shear conditions.
This thesis developed and evaluated a novel workflow combining computational fluid dynamics (CFD) and artificial intelligence (AI) to link Brookfield measurements to rheological model parameters that can be used in CFD simulations. The method combines experimental measurements with the Brookfield DV2T-RV with RV5 spindle and Anton Paar RheolabQC with CC27 geometry, Cross model regression, CFD validation, CFD-generated dataset creation, and artificial neural network prediction into one integrated framework.
An initial Power Law-based approach demonstrated that AI could learn the relationship between CFD-generated torque in the RV5 geometry and rheological parameters, but laboratory comparison showed that the Power Law model was insufficient for transfer between the RV5 and CC27 geometries. Shear rate profile analysis showed that the CC27 geometry mainly samples a higher and narrower shear rate range, while the RV5 includes very low shear rates even at high RPM. The Cross model was therefore selected because it can represent the low-shear plateau, transition region, and shear-thinning behaviour.
CMC30000 solutions at 1.0, 1.4, and 1.5 wt% were used to span a broad torque range measured using the Brookfield viscometer. Due to higher variability in the 1.5 wt% measurements, the main validation and AI evaluation focused on the 1.0 and 1.4 wt% solutions.
CFD validation showed that Cross model parameters regressed from CC27 geometry data reproduced CC27 torque well when end-effect correction was included, reducing the mean torque deviation to approximately 1%. Artificial neural networks were then trained on 5400 CFD-generated RV5 simulations to predict Cross model parameters from Brookfield-type inputs. Both 5-point and 10-point models achieved high accuracy on synthetic test data, but the 10-point model gave lower maximum errors and better torque reconstruction. When AI-predicted parameters were reintroduced into CFD, the 10-point model reproduced measured torque from the Brookfield viscometer with mean deviations of approximately −1.74% to +0.49% for 1.0 wt% and −1.41% to +0.16% for 1.4 wt%.
The results show that the developed CFD-AI workflow is a promising proof-of-concept for obtaining CFD-usable Cross parameter estimates from controlled Brookfield measurements. The method is especially valuable because it demonstrates a practical route from simple industrial viscosity measurements to simulation-ready parameter estimates. Although the AI-predicted parameters did not fully match the CC27 geometry–regressed rheological parameters, the torque agreement achieved with the 10-point model indicates strong potential for further development. With improved low-shear measurements, broader CFD datasets, refined uncertainty modelling, and validation on additional fluids and spindle geometries, the workflow could become a useful tool for industrial rheological characterization and process simulation.

Popular Abstract

What if a simple factory-floor viscosity measurement could help predict how a sauce, cream, or dairy product behaves in a production line? This thesis explores how computer simulations and artificial intelligence can turn simple industrial measurements into more useful process data.
Many food products, such as sauces, creams, dairy products, and other liquid foods, do not flow like water. Some become thinner when they are pumped, stirred, or mixed. This behaviour is important in food production because it affects how products move through pipes, how they are heated, filled into packages, and how stable the final product feels to the consumer.
In industry, viscosity is often measured using simple instruments called Brookfield viscometers. These instruments are practical, robust, and widely used for quality control. However, the values they give depend on the exact measurement setup, such as the rotating measuring tool, container, and procedure. This means that Brookfield measurements are useful for comparing whether a product has changed relative to a previous measurement, but harder to use directly for process design and computer simulations. More advanced laboratory instruments, such as rotational rheometers, can measure flow behaviour under more controlled conditions. These measurements are better suited for describing material properties, but are less common in daily production environments. This creates a gap between practical industrial measurements and the rheological data needed for process calculations.
The aim of this thesis was to investigate whether this gap can be reduced using computer simulations and artificial intelligence. In simple terms, the simulations imitated how the fluid behaves inside the measuring instruments, while the artificial intelligence model was trained to connect simple Brookfield measurements to more useful descriptions of the fluid’s flow behaviour.

At first, a simpler mathematical description of the fluid was tested. This showed that artificial intelligence could learn useful patterns from simulated measurement data. However, the two instruments do not “feel” the fluid in exactly the same way. The Brookfield instrument captures more of the slow-moving behaviour of the fluid, while the laboratory rheometer measures under more controlled conditions. Because many food-like fluids behave differently depending on how strongly they are stirred, a more flexible model was needed.
To test the method, controlled model fluids were used. These fluids become thinner when stirred faster, in a similar way to many food products. The results showed that the developed workflow could reproduce Brookfield measurements with good accuracy, especially when more measurement points were used. This suggests that relatively simple viscosity measurements can be transformed into information that is more useful for process calculations and simulations.
The method is not yet a complete replacement for advanced laboratory measurements. The predicted fluid properties did not always match the laboratory-derived values exactly. However, the results show strong potential for creating a practical bridge between everyday production measurements and advanced process design tools. With more testing on different fluids and measurement setups, this type of workflow could help engineers make better use of simple viscosity measurements when designing and optimizing food production processes.

• author: Bratt, Eric and Falck, Olle
• supervisor: Bernt Nilsson; Dragana Arlov; Sofia Klasen; Hannah Viklund
• organization: Chemical Engineering (M.Sc.Eng.)
• course: KETM05 20261
• year: 2026
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• keywords: CFD, artificial neural networks, shear-thinning fluids, Cross model, CMC, relative viscometry, absolute rheometry, method development, food engineering, chemical engineering
• language: English
• additional info: Authors Eric Bratt and Olle Falck contributed equally to the thesis
• id: 9238731
• date added to LUP: 2026-06-18 12:04:59
• date last changed: 2026-06-18 12:04:59