Papers for

automotive simulation teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Joint error metric evaluates cfd accuracy against wind tunnel data

A joint error metric for scoring CFD against wind tunnel data, scaled by the robust spread of the participants (AutoCFD5 Case 2)

Abstract: AutoCFD5 Case 2 compares the participants' CFD results with wind tunnel measurements of surface pressure, velocity profiles and force coefficients. To place each run on a diagram of error against computational cost, these quantities must be combined into one number. This note defines that number, the joint error. Following the practice of interlaboratory proficiency testing (ISO 13528), every deviation from the experiment is expressed in units of the spread of the participants' results at that point, estimated robustly with the standard's Algorithm A. The point-wise spreads are pooled into one scale per component, so that every measurement point carries equal weight within its component. The joint error is a fixed weighted sum of five component errors. The scales are computed once from all AutoCFD5 submissions received by 5 October 2026 and then frozen, so that scores are reproducible and comparable across workshops.

Thu 8 OctComputational Engineering, Finance, and Science

The gist

Comparing computer simulations of airflow with real wind tunnel experiments is tricky because they measure many things differently. The authors created a single combined score that fairly measures how close the simulations are to experiments across various data points. This score uses statistical methods to balance errors by how much variation exists among different simulation runs. It helps researchers and engineers objectively compare accuracy and computational effort of different simulations.

Open → 2610.11522v1