Turbulence model performance for ventilation components pressure losses

Karsten Tawackolian; Martin Kriegel

doi:10.1007/s12273-021-0803-x

Building Simulation 2022, 15(3): 389-399 https://doi.org/10.1007/s12273-021-0803-x

Research Article |

Open Access | Issue | Published: 25 June 2021

Turbulence model performance for ventilation components pressure losses

Show Author's Information Hide Author's Information Karsten Tawackolian(

), Martin Kriegel

Hermann-Rietschel-Institut, Energy, Comfort & Health in Buildings, TU Berlin, Germany

Keywords:

simulation, CFD, model calibration, HVAC, turbulence model, ductwork

Cite this article:

Tawackolian K, Kriegel M. Turbulence model performance for ventilation components pressure losses. Building Simulation, 2022, 15(3): 389-399. https://doi.org/10.1007/s12273-021-0803-x

Download citation

EndNote(RIS)

BibTeX

597

Views

Downloads

Citations

Crossref

WoS

Scopus

CSCD

Abstract Full text Electronic supplementary material About this article

Abstract

This study looks to find a suitable turbulence model for calculating pressure losses of ventilation components. In building ventilation, the most relevant Reynolds number range is between 3×10⁴ and 6×10⁵, depending on the duct dimensions and airflow rates. Pressure loss coefficients can increase considerably for some components at Reynolds numbers below 2×10⁵. An initial survey of popular turbulence models was conducted for a selected test case of a bend with such a strong Reynolds number dependence. Most of the turbulence models failed in reproducing this dependence and predicted curve progressions that were too flat and only applicable for higher Reynolds numbers. Viscous effects near walls played an important role in the present simulations. In turbulence modelling, near-wall damping functions are used to account for this influence. A model that implements near-wall modelling is the lag elliptic blending k-ε model. This model gave reasonable predictions for pressure loss coefficients at lower Reynolds numbers. Another example is the low Reynolds number k-ω turbulence model of Wilcox (LRN). The modification uses damping functions and was initially developed for simulating profiles such as aircraft wings. It has not been widely used for internal flows such as air duct flows. Based on selected reference cases, the three closure coefficients of the LRN model were adapted in this work to simulate ventilation components. Improved predictions were obtained with new coefficients (LRNM model). This underlined that low Reynolds number effects are relevant in ventilation ductworks and give first insights for suitable turbulence models for this application. Both the lag elliptic blending model and the modified LRNM model predicted the pressure losses relatively well for the test case where the other tested models failed.

Full text

Abstract

Full text

Outline

Electronic supplementary material

About this article

Turbulence model performance for ventilation components pressure losses

Show Author's information Hide Author's Information Karsten Tawackolian(

), Martin Kriegel

Hermann-Rietschel-Institut, Energy, Comfort & Health in Buildings, TU Berlin, Germany

Abstract

Keywords: simulation, CFD, model calibration, HVAC, turbulence model, ductwork

References(62)

Abraham JP, Sparrow EM, Gorman JM, et al. (2019). Application of an intermittency model for laminar, transitional, and turbulent internal flows. Journal of Fluids Engineering, 141: 071204.

DOI Google Scholar

Ai ZT, Mak CM (2013). Pressure losses across multiple fittings in ventilation ducts. Scientific World Journal, 2013: 195763.

DOI Google Scholar

ANSYS (2019). ANSYS Fluent Theory Guide 2019. Canonsburg, PA, USA: ANSYS Inc.

Argyropoulos CD, Markatos NC (2015). Recent advances on the numerical modelling of turbulent flows. Applied Mathematical Modelling, 39: 693–732.

DOI Google Scholar

Arolla SK, Durbin PA (2013). Modeling rotation and curvature effects within scalar eddy viscosity model framework. International Journal of Heat and Fluid Flow, 39: 78–89.

DOI Google Scholar

ASHRAE (2017). Standard 120-2017: Method of Testing to Determine Flow Resistance of HVAC Ducts and Fittings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Billard F, Laurence D (2012). A robust elliptic blending turbulence model applied to near-wall, separated and buoyant flows. International Journal of Heat and Fluid Flow, 33: 45–58.

DOI Google Scholar

Biswas R and Durbin PA (2019). Assessment of viscosity models that incorporate lag parameter scaling. International Journal of Heat and Fluid Flow, 78: 108427.

DOI Google Scholar

Biswas R, Durbin PA, Medic G (2019). Development of an elliptic blending lag k-ω model. International Journal of Heat and Fluid Flow, 76: 26–39.

DOI Google Scholar

Bredberg J, Peng SH, Davidson L (2002). An improved k-ω turbulence model applied to recirculating flows. International Journal of Heat and Fluid Flow, 23: 731–743.

DOI Google Scholar

CIBSE (2007). CIBSE Guide C: Reference Data. London: The Chartered Institution of Building Services, Engineers.https://doi.org/10.4324/9780080523088

DOI

Davidson L (2003). Modification of the V2F model for computing the flow in a 3D wall jet. Turbulence, Heat and Mass Transfer, 4: 577–584.

Google Scholar

Doumbia M, Kriegel M (2016). Influence of the Cross-sectional Shape on the Pressure Drop at 90 Degree Elbow Duct Fittings. In: Proceedings of the12th REHVA World Congress (CLIMA 2016), Aalborg, Denmark.

Durbin PA (1991). Near-wall turbulence closure modeling without "damping functions". Theoretical and Computational Fluid Dynamics, 3: 1–13.

Google Scholar

Durbin PA (2018). Some recent developments in turbulence closure modeling. Annual Review of Fluid Mechanics, 50: 77–103.

DOI Google Scholar

Gan G, Riffat SB (1999). Determination of energy loss characteristics of dampers. International Journal of Energy Research, 23: 61–69.

DOI

Gao R, Chen S, Zhao J, et al. (2016). Coupling effect of ventilation duct bend with different shapes and sizes. Building Simulation, 9: 311–318.

DOI Google Scholar

Genç MS (2012). Low Reynolds Number Flows and Transition. Rijeka, Croatia: InTech.https://doi.org/10.5772/2398

DOI

Gibson MM, Launder BE (1978). Ground effects on pressure fluctuations in the atmospheric boundary layer. Journal of Fluid Mechanics, 86: 491–511.

DOI Google Scholar

Hrenya CM, Bolio EJ, Chakrabarti D, et al. (1995). Comparison of low Reynolds number k-ε turbulence models in predicting fully developed pipe flow. Chemical Engineering Science, 50: 1923–1941.

DOI Google Scholar

Iacovides H, Launder BE, Li HY (1996). Application of a reflection-free DSM to turbulent flow and heat transfer in a square-sectioned U-bend. Experimental Thermal and Fluid Science, 13: 419–429.

DOI Google Scholar

Idelchick IE (2008). Handbook of Hydraulic Resistance. Mumbai, India: Jaico Publishing House.

Jones WP, Launder BE (1972). The prediction of laminarization with a two-equation model of turbulence. International Journal of Heat and Mass Transfer, 15: 301–314.

DOI Google Scholar

Kalpakli Vester A, Örlü R, Alfredsson PH (2016). Turbulent flows in curved pipes: Recent advances in experiments and simulations. Applied Mechanics Reviews, 68: 050802.

DOI Google Scholar

Karbon M, Sleiti AK (2020). Large-eddy simulation of the flow in Z-Shape duct. Cogent Engineering, 7: 1778349.

DOI Google Scholar

Koch P (2006). The influence of Reynolds Number and size effects on pressure loss factors of ductwork components. Building Services Engineering Research and Technology, 27: 261–283.

DOI Google Scholar

Kriegel M (2005). Numerische Simulation von Quellluftsystemen. PhD Thesis, Hermann Rietschel Institut, Germany. (in German)

Kriegel M, Doumbia M, Schaub M, et al. (2018). EnEff: Luft-Energieeffiziente Luftkonditionierung und Kanalnetzauslegung für Neu-und Bestandsgebäude: gemeinsamer Endbericht. Technische Universität Berlin, Fachgebiet Gebäudeenergiesysteme, Hermann-Rietschel-Institut (HRI), Deutschland. (in German)

Lardeau S, Manceau R (2014). Computations of complex flow configurations using a modified elliptic-blending reynolds-stress model. In: Proceedings of the 10th International ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurements, Marbella, Spain.

Lardeau S, Billard F (2016). Development of an elliptic-blending lag model for industrial applications. In: Proceedings of the 54th AIAA Aerospace Sciences Meeting, San Diego, CA, USA.https://doi.org/10.2514/6.2016-1600

DOI

Leutheusser HJ (1984). Velocity distribution and skin friction resistance in rectangular ducts. Journal of Wind Engineering and Industrial Aerodynamics, 16: 315–327.

DOI Google Scholar

Lien FS, Chen WL, Leschziner MA (1996). Low-Reynolds-number eddy-viscosity modelling based on non-linear stress–strain/vorticity relations. In: Proceedings of the 3rd Symposium on Engineering Turbulence Modeling and Measurements, Crete, Greece.https://doi.org/10.1016/B978-0-444-82463-9.50015-0

DOI

Liu W, Long Z, Chen Q (2012). A procedure for predicting pressure loss coefficients of duct fittings using computational fluid dynamics (RP-1493). HVAC & R Research, 18: 1168–1181.

Google Scholar

Mangeon G, Benhamadouche S, Wald JF, et al. (2020). Extension to various thermal boundary conditions of the elliptic blending model for the turbulent heat flux and the temperature variance. Journal of Fluid Mechanics, 905: 1–34.

DOI Google Scholar

Manning A, Wilson J, Hanlon N, et al. (2013). Prediction of duct fitting losses using computational fluid dynamics. HVAC & R Research, 19: 400–411.

Google Scholar

Menter FR (2009). Review of the shear-stress transport turbulence model experience from an industrial perspective. International Journal of Computational Fluid Dynamics, 23: 305–316.

DOI Google Scholar

Menter FR, Smirnov PE, Liu T, et al. (2015). A one-equation local correlation-based transition model. Flow, Turbulence and Combustion, 95: 583–619.

DOI Google Scholar

Moujaes SF, Deshmukh S (2006). Three-dimensional CFD predications and experimental comparison of pressure drop of some common pipe fittings in turbulent flow. Journal of Energy Engineering, 132: 61–66.

DOI Google Scholar

Norris LH, Reynolds WC (1975). Turbulent channel flow with a moving wavy boundary. Report No. FM-10, Stanford University, USA.

Patel VC, Rodi W, Scheuerer G (1985). Turbulence models for near-wall and low Reynolds number flows—A review. AIAA Journal, 23: 1308–1319.

DOI Google Scholar

Peng SH, Davidson L, Holmberg S (1997). A modified low-Reynolds-number k-ω model for recirculating flows. Journal of Fluids Engineering, 119: 867–875

DOI Google Scholar

Pirozzoli S, Modesti D, Orlandi P, et al. (2018). Turbulence and secondary motions in square duct flow. Journal of Fluid Mechanics, 840: 631–655.

DOI Google Scholar

Pruvost J, Legrand J, Legentilhomme P (2004). Numerical investigation of bend and torus flows, part Ⅰ: Effect of swirl motion on flow structure in U-bend. Chemical engineering science, 59: 3345–3357.

DOI Google Scholar

Red Cedar Tech. (2008). SHERPA—An Efficient and Robust Optimization/Search Algorithm. Available at http://www.redcedartech.com/pdfs/SHERPA.pdf.

Revell AJ, Craft TJ, Laurence DR (2011). Turbulence modelling of unsteady turbulent flows using the stress strain lag model. Flow, Turbulence and Combustion, 86: 129–151.

DOI Google Scholar

Rodi W (1991). Experience with two-layer models combining the k-epsilon model with a one-equation model near the wall. In: Proceedings of the 29th Aerospace Sciences Meeting, Reston, VA, USA.https://doi.org/10.2514/6.1991-216

DOI

Schultz MP, Flack KA (2013). Reynolds-number scaling of turbulent channel flow. Physics of Fluids, 25: 025104.

DOI Google Scholar

Shang W, Agarwal RK (2020). Development and validation of an elliptic blending lag SST k-ω turbulence model. In: Proceedings of the AIAA AVIATION 2020 FORUM, Reston, VA, USA.https://doi.org/10.2514/6.2020-2976

DOI

Shao L, Riffat SB (1995). Accuracy of CFD for predicting pressure losses in HVAC duct fittings. Applied Energy, 51: 233–248.

DOI Google Scholar

Shih TH, Liou WW, Shabbir A, et al. (1994). A new k-epsilon eddy viscosity model for high Reynolds number turbulent flows: Model development and validation. NASA Sti/recon Technical Report N, 95.

Siemens (2020). Simcenter STAR-CCM+ user guide, Version 15.04.

Sleiti AK, Zhai J, Idem S (2013). Computational fluid dynamics to predict duct fitting losses: Challenges and opportunities. HVAC & R Research, 19: 2–9.

Google Scholar

Smith SJ (1998). Determination of k-factors of HVAC system components using measurement and CFD modelling. PhD Thesis, University of Nottingham, UK.

Sprenger H (1969). Druckverluste in 90°-Krümmern für Rechteckrohre. Schweizerische Bauzeitung, 87: 223–231. (in German)

Tawackolian K, Sagheby H, Brandt D, et al. (2016). Development and tests of bionic fittings for heating nets. In: Proceedings of the 12th REHVA World Congress (CLIMA 2016), Aalborg, Denmark.

VDI (2006). VDI 2087: Air Ducts—Operating and Construction Fundamentals. VDI-Gesellschaft Bauen und Gebäudetechnik.

Wagner W (2012). Strömung und Druckverlust. Germany: Vogel-Fachbuch (in German)

Wilcox DC (1992). The remarkable ability of turbulence model equations to describe transition. In: Proceedings of the 5th Symposium on Numerical and Physical Aspects of Aerodynamic Flows. Long Beach, CA, USA.

Wilcox DC (1994). Simulation of transition with a two-equation turbulence model. AIAA Journal, 32: 247–255.

DOI Google Scholar

Wilcox DC (2006). Turbulence Modelling for CFD, 3rd edn. La Canada, CA, USA: DCW Industries.

Wolfshtein M (1969). The velocity and temperature distribution in one-dimensional flow with turbulence augmentation and pressure gradient. International Journal of Heat and Mass Transfer, 12: 301–318.

DOI Google Scholar

Wu P, Feng Z, Cao, SJ (2018). Fast and accurate prediction of airflow and drag force for duct ventilation using wall-modeled large-eddy simulation. Building and Environment, 141: 226–235.

DOI Google Scholar

Electronic supplementary material

File

bs-15-3-389_ESM.pdf (798.9 KB)

About this article

Publication history

Acknowledgements

Rights and permissions

Publication history

Received: 15 January 2021

Revised: 01 April 2021

Accepted: 02 April 2021

Published: 25 June 2021

Issue date: March 2022

Copyright

Acknowledgements

This work is funded by the German Federal Ministry for Economic Affairs and Energy in the framework of the research program LuftKonVerTeR/BMWi 03ET1606A.

Rights and permissions

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/