Comprehensive Approach to Verification and Validation of CFD Simulations—Part 2: Application for Rans Simulation of a Cargo/Container Ship

TLDR

This paper presents a case study applying the comprehensive verification and validation framework introduced in Part 1. The study applies a RANS CFD model to a standard ship‑hydrodynamics benchmark. Verification shows numerical uncertainties of ~2 % and iterative errors below grid uncertainties, whereas validation finds modeling errors of ~8 % in resistance and ~6 % in wave height; reducing experimental uncertainties is required to achieve the desired 3 % and 4 % accuracy, illustrating that the approach can quantify errors and guide improvements.

Abstract

Part 2 of this two-part paper provides an example case study following the recently developed comprehensive verification and validation approach presented in Part 1. The case study is for a RANS simulation of an established benchmark for ship hydrodynamics using a ship hydrodynamics CFD code. Verification of the resistance (integral variable) and wave profile (point variable) indicates iterative uncertainties much less than grid uncertainties and simulation numerical uncertainties of about 2%S1(S1 is the simulation value for the finest grid). Validation of the resistance and wave profile shows modeling errors of about 8%D (D is the measured resistance) and 6%ζmax(ζmax is the maximum wave elevation), which should be addressed for possible validation at the 3%D and 4%ζmax levels. Reducing the level of validation primarily requires reduction in experimental uncertainties. The reduction of both modeling errors and experimental uncertainties will produce verified and validated solutions at low levels for this application using the present CFD code. Although there are many issues for practical applications, the methodology and procedures are shown to be successful for assessing levels of verification and validation and identifying modeling errors in some cases. For practical applications, solutions are far from the asymptotic range; therefore, analysis and interpretation of the results are shown to be important in assessing variability for order of accuracy, levels of verification, and strategies for reducing numerical and modeling errors and uncertainties.