Computational Model for Drag Reducing Polymer Characterization
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Solution Overview
Problem
Current methods for testing drag reducers in fluids, such as those using the Taylor-Couette device, face limitations due to inaccurate torque measurements from bearing friction, which affect the characterization of drag reduction efficiency across various Reynolds numbers.
Innovation Solution
A computational model is developed to characterize pipe flow by utilizing an empirical parameter derived from experimental data from a Couette device, relating drag reduction parameters to dimensionless pipe radius and friction factor, providing more accurate predictions of pressure drop and friction loss in pipes with drag reducing polymers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If torque measurement is used to evaluate drag reduction in Couette device, then drag reduction efficiency can be assessed, but measurement accuracy deteriorates due to bearing friction
Solution Approach 1:
The patent replaces the mechanical torque measurement system with a computational fluid dynamics (CFD) model that numerically simulates the flow behavior in the Couette device. This substitution eliminates the need for physical torque sensors and bearing assemblies, thereby removing the harmful effect of bearing friction on measurement accuracy while still enabling drag reduction assessment through virtual experimentation.
Solution Approach 2:
The patent creates a virtual copy of the physical Couette device through a detailed CFD model that replicates the geometry, boundary conditions, and flow characteristics. This digital twin allows for accurate measurement of drag reduction effects without the interference of physical bearing friction, enabling precise assessment of polymer additive performance.
2Reliability
If traditional flow loop testing is used to characterize drag reducers, then pressure drop can be measured, but the testing complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary action by conducting comprehensive CFD simulations to establish accurate drag reduction characteristics before physical testing. The CFD model pre-characterizes the drag reduction behavior across various Reynolds numbers and polymer concentrations, creating a predictive framework that reduces the need for extensive iterative physical testing and accelerates the overall characterization process.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying Reynolds number, polymer concentration, and flow rate in the CFD model to comprehensively characterize drag reducer performance. This computational approach allows rapid exploration of multiple parameter combinations without the time-consuming setup and measurement requirements of physical flow loop testing for each condition.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for precise characterization of fluid flow in pipes with drag reducing polymers, enhancing the accuracy of drag reduction efficiency assessment and pressure drop predictions, overcoming the limitations of traditional torque-based measurements.
Implementation Method 1
studies turbulent drag reduction in a device of a similar design
Implementation Method 2
A computational model is developed to characterize pipe flow by utilizing an empirical parameter derived from experimental data from a Couette device
Implementation Method 3
Drag reducers are chemical additives, which being added to a fluid, significantly reduce friction pressure losses on fluid transport in a turbulent regime through pipelines
Data Source
AI summary
A method is provided for characterizing fluid flow in a pipe where the fluid includes a drag reducing polymer of a particular type and particular concentration. A computational model is configured to model flow of a fluid in a pipe. The computational model utilizes an empirical parameter for a drag reducing polymer of the particular type and the particular concentration. The computational model can be used to derive information that characterizes the flow of the fluid in the pipe. The empirical parameter for the particular type and the particular concentration of the drag reducing polymer can be identified by solving another computational model that is configured to model turbulent Couette flow in a Couette device for a fluid that includes a drag reducing polymer of the particular type and the particular concentration. The empirical data needed for identification of the empirical parameter are obtained from Couette device experiments.