CFD Geometry Verification for Tundish Process Optimization
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Solution Overview
Problem
Conventional systems face challenges in efficiently optimizing the geometry and operational parameters of industrial processes, such as the Tundish process in steelmaking, due to the lack of automated methods, leading to time-consuming and resource-intensive simulations and potential errors from faulty geometries.
Innovation Solution
A method and system that utilize numerical techniques to receive and process model parameters, generate objective function data, identify significant variables, create a Design of Experiments (DOE) table, and develop a surrogate model to optimize industrial processes, specifically detecting and correcting inconsistencies in geometry formation to ensure valid geometries for simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional manual methods are used to optimize geometry and operational parameters, then flexibility and control are maintained, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs self-verification of geometry parameters through automated rule-checking mechanisms. The geometry verification module automatically detects inconsistencies in generated geometries without requiring manual intervention, enabling the optimization process to proceed autonomously and efficiently.
Solution Approach 2:
Manual optimization processes are replaced with automated computational systems. The system uses numerical techniques, surrogate models, and automated geometry verification to substitute human-operated manual optimization, dramatically reducing time consumption while maintaining or improving optimization quality.
2Reliability
If comprehensive simulations are performed to ensure geometry validity, then accuracy is improved, but computational resources and time are increased
Solution Approach 1:
The system performs preliminary verification of geometry parameters before conducting full simulations. By checking geometric consistency rules in advance, the system identifies and corrects potential issues early, preventing wasted computational resources on invalid geometries and ensuring only valid configurations proceed to resource-intensive simulations.
Solution Approach 2:
A geometry verification module acts as an intermediary between geometry generation and simulation execution. This intermediate verification step filters out invalid geometries before they consume computational resources, ensuring that simulations are performed only on geometrically valid configurations.
3Productivity
If automated optimization methods are implemented, then productivity is improved, but complexity of the system increases
Solution Approach 1:
The automated optimization system is divided into distinct functional modules: geometry generation module, geometry verification module, parameter optimization module, and simulation module. This segmentation allows each module to perform its specific function independently, making the overall complex system manageable and maintainable while achieving high productivity.
4Measurement precision
If iterative parameter variation is performed to generate objective function data, then optimization accuracy is improved, but the number of simulations and resource consumption increase
Solution Approach 1:
The system uses surrogate models to predict objective function values based on parameter variations without performing full simulations for each parameter set. This approach maintains optimization accuracy by using statistically rigorous parameter sampling methods while dramatically reducing the number of actual simulations required, thus improving data generation efficiency.
Data Source
AI summary
This disclosure relates generally to system and method for optimization of industrial processes, for example a tundish process. Typically geometries for industrial processes are simulated in a numerical analysis model such as a CFD. In order to simulate a physical phenomenon (such as tundish process) numerically, the domain thereof is discretized in order to convert the differential equations to be solved in the domain into linear equations. The accuracy of a CFD solution is dependent on a mesh of the domain, which in turn depends on a geometry thereof. For setting up an optimization task, the disclosed method provides first a CFD friendly base geometry, so that a faulty geometry can be detected before forming the complete geometry.