Development of UV Reactor Design Code using Potential Flow Theory and MSSS

Transcription

1 IUVA America Conference 2018 Development of UV Reactor Design Code using Potential Flow Theory and MSSS Jeong-Gyu Bak, Hyosun Kim 28 Feb. 2018

2 Acknowledgements 2 Neotec UV Inc. Woochul Hwang, Chief engineer Hanyang Univ. Prof. Jinsoo Cho, Dept. of ME.

3 Outline 3 Introduction Object UV-CFD - Potential Flow - UV Radiation Model - Dose Calculation Case Study1 Case Study2 Summary

4 Introduction 4 Potential Flow Code. Early CFD codes based on potential flow theory were developed for aerodynamic/hydrodynamic fields from the late 1960s to early 1970s These codes were replaced with higher order method subsequently It s considered as an old method, but is still used in preliminary design Less computational time (get results in few seconds) Formula 1 Motor Racing CFD Trends illustrated by Hanna & Parry, 2011.

5 Introduction 5 Role of CFD in UV Reactor Design Pros. Detailed flow behavior and fluence distribution in the reactor Study on interaction of hydraulics, optics, and microbial inactivation kinetics easily Enabling efficient and effective design of a UV reactor

6 Introduction 6 Role of CFD in UV Reactor Design Cons. Computational cost is still expensive. - Few hours for small systems 1) and, - Few weeks for large systems 2) using a modern workstation. Not suitable for initial design (system sizing) due to computational time. Mesh creation effort increases in large and complex UV system. Convergence is impossible in some cases (due to unsteadiness). 1) Close conduit reactor (4 lamps) using 4-core Xeon E5-1620V4 workstation 2) Open channel reactor (32 lamps) using 48-core Xeon E5-2690V3 cluster

7 Objective 7 Research Motivation and Objective Apply/modify the potential code for UV analysis Developing UV system sizing (design) code that requires less computation time. Finding a correction factor for reliable results.

8 UV-CFD 8 Modeling Approach Preparation of a Geometric Model - SRS 1) - RANS 2) - Euler - Potential Hydraulic Calculation UV Radiation Calculation - MPSS 3) - MSSS 4) - LSI 5) - DO 6) - Bolton UVCalc Dose Calculation 1) Scale-Resolving Simulation (eg. LES) 2) Reynolds Averaged Navier-Stokes 3) Multiple Point Source Summation 4) Multiple Segment Source Summation 5) Line Source Integration Model 6) Discrete Ordinate Model RED (Reduction Equivalent Dose)

9 Potential Flow 9 Major Characteristics/Assumptions Linearized flow equation: simple implementation of a computer code. A series of singularities as sources, sinks, vortexes and doublets are used to model the flow. Inviscid Steady tt = 0 Irrotational flow field VV = 0 Incompressible VV = 0 Flow around circular cylinder by Thierry Dugnolle is licensed under CC0 by 1.0

10 UV Radiation Model 10 Multiple Segment Source Summation Calculation of the UV irradiance proposed by Liu et al 1). Necessary to parallel computation to reduce the computation time in large problems (eg. more than 10 lamps) ll PP JJ 3 ll 2 II jj = (1 RR 1 )(1 RR 2 ) 4ππ(ll 1 + ll 2 + ll 3 ) 2 TT ww 0.01 TT 0.01 qq AA ff cos θθ 1 Where, ss 1 + ss 2 + ss 3 = rr 1 tan θθ 1 +rr 2 nn aa sin θθ 1 nn qq 1 nn aa 2 nn qq 2 sin2 θθ 1 + rr 3 nn aa sin θθ 1 nn ww 1 nn aa 2 nn ww 2 sin2 θθ 1 AA ff = (ll 1 + ll 2 + ll 3 ) 2 θθ cos θθ 1 rr(h 2 h 1 ) 1) Liu, D., Ducoste, J. J., Jin, S., and Linden, K. (2004). Evaluation of alternative fluence rate distribution models. Journal of Water Supply: Research and Technology AQUA, 53(6):

11 Dose calculation 11 Lagrangian Particle Tracking Method Assume pathogen as particles Accumulate the dose at each position along the particle path Using MS2 dose response curve by collimated beam test Instantaneous dose Accumulated dose DD pp = pp II dddd nn 0 1 log II = log 10 ( NN NN nn 0 ) pp 0 pp=1 1) USEPA. (2006). Ultraviolet Disinfection Guidance Manual for the Final Long Term2 Enhanced Surface Water Treatment Rule; EPA office of Water: Wasington, DC, USA.

12 Case Study[1] 12 Close Conduit Reactor Lab scale pilot UV reactor with 125W (UVC 41W) 4 LP lamps Only comparison with 3D RANS 1) and 2D potential flow Flow 1) Solver: Ansys CFX - Turbulent model: k-w SST - Number of particles: 3000 Computational mesh (upper: potential flow, lower: RANS)

13 Case Study[1] 13 Velocity and UV Intensity Velocity: show the different patterns due to equations (idle vs real flow) UV intensity: same value and patterns Unit: [W m -2 ] = 0.1 [mw cm -2 ] Flow Velocity [m s -1 ] (upper: potential flow, lower: RANS) UV Intensity [W m -2 UVT 85% (upper: potential flow, lower: RANS)

14 Case Study[1] 14 Particle tracking Particle tracks never cross each other in a potential flow case. Uniformly distributed in the entire reactor chamber. Unit: [J m -2 ] = 0.1 [mj cm -2 ] Unit: [J m -2 ] = 0.1 [mj cm -2 ] Flow Particle track by potential flow (colored by dose) Particle track by RANS (colored by dose)

15 Case Study[1] 15 UV Dose Delivery Potential flow predicts 1.4 times higher RED 1) than that of RANS In the potential flow cases, dose distributions are more uniform. y=1.4275x R 2 = ) RED: Reduction Equivalent Dose Delivery Dose Prediction: Potential Flow vs. RANS

16 Case Study[1] 16 Comparison of Computation Time Potential code with MSSS approach can reduce computation time by 60 times in the presented model. By potential flow code - 9 cases - General desktop computer (4 cores) By RANS code (Ansys CFX) - 9 cases - Mini cluster (24 cores) Total 30 min. Total 5 hours 2 cpu core hours. 120 cpu core hours.

17 Case Study[2] 17 Parallel Flow Open Channel Pilot reactor based on Neotec NOL-H series With 8 LP Lamps (320 W) per module, 4 modules were used. Comparison with RANS and potential flow

18 Case Study[2] 18 Velocity and UV Intensity Flow is more uniform in the potential flow case, but velocity is slightly faster. UV intensity show the same values and patterns in both cases. Unit: [m s -1 ] Unit: [W m -2 ] = 0.1 [mw cm -2 ] Unit: [m s -1 ] Flow Unit: [W m -2 ] = 0.1 [mw cm -2 ] Velocity [m s -1 ] (upper: potential flow, lower: RANS) UV Intensity [W m -2 UVT 73.7% (upper: potential flow, lower: RANS)

19 Case Study[2] 19 UV Dose Delivery Potential flow predicts 0.9 times lower RED 1) than that of RANS. In the potential flow case, it shows the locally high dose delivery value, because there is no mixing. y=0.9299x R 2 = Delivery Dose Prediction: Potential Flow vs. RANS

20 Case Study[2] 20 Comparison of Computation Time Potential code with MSSS approach can reduce computation time by 7260 times in the presented model. By potential flow code - 5 cases - General desktop computer (4 cores) By RANS code (Ansys CFX) - 5 cases - Mini cluster (48 cores) Total 50 min. Total 3 weeks 3.3 cpu core hours cpu core hours.

21 Summary 21 Combination of Potential Flow and MSSS It shows 1.4 times higher RED values in closed conduit type reactor, and 0.9 times lower RED values in open channel type reactor. Further studies are required for finding the multiplier (correction factor) for various reactor types. Potential flow code can be used for initial UV system sizing, and prototype design as the time required for an analysis is significantly less.

22 IUVA America Conference 2018 THANK YOU Jeong-Gyu Bak, Hyosun Kim 28 Feb. 2018