Computational fluid dynamics predictions of non‐isothermal ventilation flow—How can the user factor be minimized?

Abstract

The use of computational fluid dynamics (CFD) to solve indoor airflow problems has increased tremendously in the last decades. However, the accuracy of CFD simulations depends greatly on user experience, the available validation data, and the effort made to verify solutions. This study presents the results of a conference workshop, which assessed user influence on the CFD results obtained for a generic non-isothermal flow problem; ie, a backward-facing step flow problem with a heated wall below the supply. Fifty-five simulation sets were submitted by 32 teams. The results showed a very large spread in predicted penetration length (x /(H - h)), location of maximum velocity in the lower part of the recirculation cell (x /(H - h)), and maximum velocity at this location (u /u ). The turbulence model seemed to very strongly influence the results, with a statistically significant difference in the predictions yielded by the k-ε and k-ω models. The results obtained using a single turbulence model generally also showed a spread in results; the level of spread depended on factors such as grid size and near-wall treatment. The statistical data strongly indicate the need for validation studies using experimental data (benchmarks) to ensure the accuracy, reliability, and trustworthiness of CFD simulations for indoor airflow problems.

Mixing ventilation driven by two oppositely located supply jets with a time-periodic supply velocity: A numerical analysis using computational fluid dynamics

“…[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] The largest disadvantage of CFD is the need for solution verification and validation [26][27][28][29][30][31] and the large sensitivity of results to the large amount of choices a user needs to make when performing CFD simulations. 32,33 In this paper, unsteady Reynolds-averaged Navier-Stokes (URANS) CFD simulations using the renormalization group (RNG) k-e turbulence model and a large-eddy simulation (LES) using the dynamic Smagorinksy subgrid-scale model were performed for a mixing ventilation case with time-periodic supply velocities, and the results were compared in terms of dimensionless time-averaged velocities and contaminant levels inside the enclosure. The enclosure was ventilated by two oppositely located supply openings (top) and two oppositely located exhaust openings (bottom).…”

“…11–25 The largest disadvantage of CFD is the need for solution verification and validation 26–31 and the large sensitivity of results to the large amount of choices a user needs to make when performing CFD simulations. 32,33…”

Dynamic sequential box modelling of inhalation exposure potential in multi-bed patient ward: Validation and baseline case studies

“…Ferziger et al [30] remarked that the differences between numerical solutions from different authors using similar models exceed that of the same author using different models. Recent studies [31,32] about the user-dependency effects on the accuracy of building performance diagnosis outcomes reported varying results among different teams of simulators.…”

“…[33]. Therefore, validation studies are imperative to ensure the accuracy, reliability, and trustworthiness of numerical simulation for indoor environment studies [31]. It is sufficient, for validating purposes, to employ data from existing studies that are similar to the problem under study.…”

“…These open-source tools are robust in that various users globally contribute freely to their development thereby making the extension of their capabilities continuous. As numerical modelling and simulation involve a series of uncertainties that may impact the accuracy of their results, their validation becomes essential [31].…”