Toward a Strong-Sense Validation Benchmark Database for Numerical Ladle Flow Models

Abstract

In numerical computation of ladle metallurgy, multiphase models are essential. Still, these models are afflicted with great uncertainty, making a validation with experimental data mandatory. Validation experiments fundamentally differ from physical modeling experiments because emphasis is on a complete documentation of all boundary conditions and a detailed uncertainty assessment. For this work, the experimental design for a comprehensive accuracy assessment of numerical models and reported quantities were jointly determined with international numerical experts. The framework comprises a plume analysis and flow measurements in the single- and multiphase regions of a water model with defined conditions. All influencing factors are documented extensively and their contribution to the overall data uncertainty is quantified. Detailed data are made publicly available within a validation benchmark database for isothermal multiphase flow in metallurgical ladles. A first draft of a standardized validation procedure, including a single number validation score, is proposed. Using the available data, the accuracy of numerical models can be quantified more accurately and comparably, which helps in advancing the model’s further development. It also lays the foundation for a standardization of the validation process, which can lead to greater acceptance of the numerical results.

A Review of Bubble Dynamics in Liquid Metals

“…For a precise validation of such PBMs, however, exact experimental benchmark cases are necessary. For these benchmark cases it is important that all boundary conditions are reported in detail and the measurement uncertainty can be quantified [14].…”

“…However, Irons and Guthrie [18] point out that the poor agreement with the water model correlations may also be due to different nozzles. Unfortunately, these parameters are not given in [14], so this cannot be further verified. Iguchi et al [26] measured the bubble size by X-ray imaging.…”

“…Their segmentation is usually achieved by multi-staged image processing techniques [6][7][8][9][10] or machine learning [11,12]. The accuracy of the different approaches are discussed in [14]. However, no generally applicable workflow was proposed yet.…”

Mixing Time Prediction in a Ladle Furnace

“…To scale down the model from a full-sized industrial ladle, dynamic similarity analysis was carried out by a comparison of the Froude number and Reynolds number between the prototype and the model. However, achieving dynamic and geometric similarities between the prototype and model proved to be exceedingly challenging [19]. The Reynolds number is the ratio of inertial force to viscous force.…”

Full‐Scale Large Eddy Simulation of a 185 t Ladle Optimizing a Hybrid Eulerian Grid and a Discrete Bubble Mapping Approach

“…These measurements were combined with particle image velocimetry (PIV) in the single‐phase and multiphase regions to form a validation database. [ 20 ] In addition to a comprehensive experimental documentation, this database includes a detailed uncertainty estimate of the employed measurement techniques. In order to assess the model's accuracy using all validation data available in the database simultaneously and to use a quantitative rather than a qualitative metric, a validation score was derived.…”