Proposed construction of or changes to a radiological effluent stack must meet the mixing criteria at the sampled stack location required by the standards ANSI/HPS N13.1–2011 and ISO 2889. For more than a decade, threedimensional computational fluid dynamics (CFD) models of such stacks have been used to assist in characterizing the sample location suitability.
Progress in Nuclear Energy 124 (2020) 103338 Contents lists available at ScienceDirect Progress in Nuclear Energy journal homepage: http://www.elsevier.com/locate/pnucene Modeling filtered building effluent stack sampling points for qualification criteria J Matthew Barnett *, Xiao-Ying Yu , Sarah R Suffield , Kurtis P Recknagle Pacific Northwest National Laboratory, P.O Box 999, Richland, WA, USA A R T I C L E I N F O A B S T R A C T Keywords: CFD Standards Modeling Radioactive air Proposed construction of or changes to a radiological effluent stack must meet the mixing criteria at the sampled stack location required by the standards ANSI/HPS N13.1–2011 and ISO 2889 For more than a decade, threedimensional computational fluid dynamics (CFD) models of such stacks have been used to assist in characterizing the sample location suitability When inadequate mixing in the stack at the sampling location has been identified, additional modeling was used Testing results have confirmed the CFD model predictions with similar values in both scale model and full-scale stack tests Simulations of a stack scheduled for modifications can evaluate mixing at the limits of operation, recommend modifications, and indicate if the retrofitted stack passes criteria for baseline operating conditions This work demonstrates CFD modeling is an effective design tool and should be considered to facilitate qualification of a stack sampling location to meet standards Introduction The U.S Department of Energy has a number of research and development facilities throughout the United States, one of which is Pacific Northwest National Laboratory (PNNL) located in southeastern Washington State For buildings with radiological capabilities, the air discharged from the exhaust stack system may be monitored for radio nuclides Furthermore, the air monitoring systems must comply with applicable federal regulations, which require a sampling probe in the exhaust stream, aligned with the duct axis, to conform to the criteria of the ANSI/HPS N13.1–2011 (Health Physics Society HPS, 2011) stan dard This standard requires that the sampling location be well-mixed and stipulates specific requirements for cyclonic flow, velocity, and gas and aerosol concentration uniformity to verify the extent of mixing In qualification testing, quantities are measured manually along hori zontal and vertical traverses at predetermined distances established by the standard Uniformity is determined by the coefficient of variance (COV), which is defined as the standard deviation divided by the mean, expressed as a percent The criteria require the maximum average cyclonic flow angle be � 20� , COV for the flow velocity uniformity be � 20%, COV for the gas concentration uniformity be � 20% and at no point in the sampling plane does the gas concentration vary from the mean by > 30%, and COV for the particle tracer concentration unifor mity be � 20% ISO 2889 (International Standards Organization (ISO), 2010) is the international version of ANSI/HPS N13.1 Testing must be performed on all new and retrofitted stacks to demonstrate system compliance Testing options are provided in the ANSI/HPS N13.1–2011 standard One of three approaches may be taken: 1) perform a full test series on the actual exhaust system, 2) perform the full test series on a scale model of the exhaust system, fol lowed by a partial test of the actual exhaust system to verify the scale model results, or 3) adopt results from previously performed full test series for a system with a similar configuration, followed by a partial test of the actual exhaust system to verify the applicability of the previous test results Options and can be very expensive, and the viability of Option depends upon the available data If stack compliance testing reveals the need for stack alterations to comply with the standard, additional costs are incurred for retrofitting and retesting Properly implemented, CFD modeling is much less expensive than the physical testing to evaluate the system performance When exercising any of the three options, modeling can and has been employed to preview and evaluate stack mixing performance to inform stack design and testing decisions Various CFD application to radioactive air emissions has been considered over the past one to two decades In particular, Tang and Guo (2011) reported on aerosol transport and deposition, while Vijayar aghavan (2006) simulated turbulent mixing in tubing, which was compared to experimental results for velocity and tracer gas * Corresponding author E-mail address: matthew.barnett@pnnl.gov (J.M Barnett) https://doi.org/10.1016/j.pnucene.2020.103338 Received June 2019; Received in revised form March 2020; Accepted 20 March 2020 Available online April 2020 0149-1970/© 2020 Elsevier Ltd This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 Fig Stack model of the preliminary (four-fan) system showing (a) the layout with the sampling point (location) and fan exit plane feature locations, and (b) computational mesh at the inlet (fan exit plane) which is similar throughout the model domain concentrations Additional CFD works have considered air mixing and particle transport on both scale models as well as for a modified stack (Ballinger et al., 2014; Recknagle et al., 2009; and Barnett et al., 2016) Other applications of CFD modeling have been applied to nuclear reactor accident events to assess fission product deposition in pipes (Dwivedi et al., 2019), and to the volume of fluid method to simulate the gas entrainments process and capture the dynamic liquid-gas interface at a propagating slug front (Hua et al., 2015) which tangentially support this approach of establishing a well-mixed sample location in stacks or ducts of nuclear facilities This work is significant because it supports expanding the physical testing requirements to allow CFD modeling as an option in the stan dards In particular, the more expensive physical scale-model tests or full-scale testing can be supplemented by the use of CFD modeling prior to construction In such cases, the CFD modeled results of the exhaust system are followed by a partial physical test of the actual exhaust system to confirm the well-mixed sample location requirements are met plane of each fan duct An example of such a stack system model domain is shown in Fig 1a, and a partial view of the computational mesh in Fig 1b The models are meshed with sufficient refinement to enable resolution of the turbulent flow field and provide accurate calculations of the particle tracks throughout the system A grid sensitivity (mesh independence) study was conducted to ensure sufficient representation of the model by changing the mesh until the simulation results did not significantly vary (i.e., converge) with further mesh refinement The CFD convergence associated with mesh independence satisfied the conditions of the residual root-mean-square (RMS) error values reduced to an acceptable value, the monitored point(s) of interest reached a steady solution, and low domain imbal ances (e.g., less than 1%) To achieve grid-independence, the resulting typical computational mesh for a nuclear facility stack system with sufficient refinement contains approximately a million or more elements When physical tests are conducted, the stack sampling methodology assumes isothermal conditions exist within the stack Accordingly, this assumption is adopted in the CFD modeling methodology Simulations of the isothermal flow solve the Navier-Stokes conservation of mass and momentum equations, which for steady flow are expressed in Eqs (1) and (2): Modeling methodology The methodology used for the CFD modeling presented here includes defining the system with geometric detail, physics models, and bound ary conditions suitable to represent the flow and mixing that occurs within the actual stack system and distributions of gas and particle tracers throughout the system, and particularly at the sampling point Other CFD work has considered air mixing and particle transport at a smaller scale In particular, Tang and Guo (2011) reported on aerosol transport and deposition, while Vijayaraghavan (2006) simulated tur bulent mixing in tubing, which was compared to experimental results for velocity and tracer gas concentrations To successfully predict the distributions of the tracer gas (e.g., nitrous oxide [N2O]) and particulates (e.g., oil droplets) requires accu rate modeling of the air flow (carrier phase) and transport of the tracers (dispersed phases) The geometry of the typical filtered building exhaust system is complex and three-dimensional (3-D) As such, a boundaryfitted, 3-D flow solution is also required The commercially available, CFD general-purpose flow simulation codes, STAR-CD (2001), and more recently, STAR-CCMỵ (2017), were used for system simulations These codes contain widely tested flow, turbulence, multiphase and multi-physics models, and meshing capabilities for creating highly resolved 3-D computational domains from computer-aided design (CAD) geometry models The CFD models consider the geometric detail of the stacks from just downstream of the inlet fans and dampers to the stack exit While the fans are not included in the model domain, turbulence generated up stream in the ductwork and through the fans is accounted for at the exit ∂ À � ρuj ¼ ∂xj (1) � ∂ À ∂p ρuj ui À τij ¼ À ∂xi ∂xj (2) where the ui are the absolute fluid velocity components in coordinate directions xi (i ¼ 1, 2, 3) while the j subscript, when present with i, in dicates coordinate directions other than i; ρ is the density, p is the pressure, and τij is the fluid stress tensor, which for turbulent flow is represented by Eq (3): τij ¼ 2μσij À ∂uk μ δij À ρu’i u’j ∂xk (3) Here μ is the dynamic viscosity, σij is the rate of strain tensor, δij is the Kronecker delta, uiʹ and ujʹ are fluctuations about the average velocity, and the overbar indicates the averaging of the fluctuations The rightmost term in Eq (3) represents the additional Reynolds stresses due to turbulent motion These are linked to the mean velocity via the turbu lence model being used In the simulations for this work, turbulence is handled with a realizable k-ε model This k-ε model is a widely tested and validated two-equation closure model for the Reynolds-averaged Navier-Stokes (“RANS”) equations and has been shown to be suitable for this class of duct flow simulation (Recknagle et al., 2009; Jensen, J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 of velocity, gas, and aerosol tracers with ANSI/HPS N13.1–1999 criteria (Health Physics Society HPS, 2011) The scale-model test results and the full-scale CFD model results would then be applied to evaluate the sampling location on the full-scale stack In benchmarking against the scale-model results, the 3-D CFD modeling results closely matched most uniformity criteria and conservatively predicted uniformity of gas and particle tracers In simulations of the full-scale stack, the CFD model showed the full stack length was needed for sufficient mixing and thus advised against moving the sampling location to a more advantageous site (Recknagle et al., 2009; Barnett et al., 2013) More details on this earliest modeling work, and the application to an extended set of flow conditions, is available elsewhere (Ballinger et al., 2011) In 2012–2013, an existing PNNL building exhaust stack was to be modified to expand the ventilation capacity The modifications would upgrade the two-fan (as-built) system to a three-fan system to meet additional capacity requirements The mixing performance was first evaluated with CFD (Recknagle et al., 2013) Initial modeling of the as-built system showed good agreement with onsite testing of flow angle and velocity uniformity as shown in Table 1; however, this onsite testing to evaluate the as-built stack did not include tracer gas and particle distribution testing because the as-built stack qualification relied upon previous testing of a similar stack (Glissmeyer and Droppo, 2007) for the tracer mixing qualification, as is permissible in the ANSI/HPS N13.1 standard While simulation results of the three-fan system showed acceptable flow angle and velocity uniformity, the gas and particle tracer COVs were unacceptably high When two or three fans were operated, tracers in the flow showed the streams from each fan were slow to mix; a finding consistent with the previous testing The instal lation of an air blender was proposed to correct the poor mixing indi cated by the modeling and was added to the CFD model downstream of the last fan With the air blender included, the model predicted the stack would pass all mixing criteria of the standard Results of the three-fan system model are summarized in Table 2, columns and 3, and show the striking improvement made by the air blender (Recknagle et al., 2013; Yu et al., 2013) The blind predictions were later confirmed by testing of the completed stack (Yu et al., 2013, 2014) (Table 2, column 4), fulfilling the standard requirements In this work, the CFD modeling indicated the need for the air blender Modeling with the air blender matched the later testing results to within COV%, a requirement for use of surrogate stack testing data Table Comparison of CFD validation model and original configuration test result ranges (Recknagle et al., 2013) Criteria evaluated CFD validation model, Two-fan design Original two-fan full scale test, As built ANSI/HPS N13.1criteria Velocity Uniformity COV (%) Cyclonic Flow Angle (� ) 3.6–7.0 2.4–4.3 �20 5.2–6.4 3.3–11.0 �20 2007) For the tracer gas, a model is used in which species k of a gas mixture, with local mass fraction Yk is governed by a species conservation equation as in Eq (4): � ∂ uj Yk ỵ Fk;j ẳ Sk xj (4) where Fk,j is the gas diffusional flux component and Sk is the gas species source term, which is non-zero at the injection location A Lagrangian multiphase model that considers the interactions of mass, momentum, and energy between the continuum and dispersed phase is used for the tracer droplet/particle transport For the stack modeling work, droplet concentrations are small, thus we only consider momentum transfer from the air to droplets In the model, the mo mentum equation for a droplet, given by Newton’s second law, is shown in Eq (5): md dud ẳ Fdr ỵ Fp ỵ Fb dt (5) where md and ud are the mass and velocity of the dispersed droplet phase, Fdr is the drag force, Fp the pressure force, and Fb is the body force including effect of the gravity and angular velocity vectors Surface vapor pressure and mass transfer between phases are not considered here Because the problem is considered isothermal and is also assumed to not involve electrically charged flow, thermophoresis and electro static effects are not included Also, because of the low droplet con centration, separation and coalescence are neglected as well Mass inflow boundaries are established at the duct inlets with tur bulence intensity and length scale settings to account for upstream turbulence including the fans A pressure boundary with 1.0 atm abso lute pressure is used at the stack exit Duct walls are modeled as smooth surfaces with zero slip flow boundary conditions The particle boundary condition at the walls is established such that droplets with trajectories that impact the duct walls will remain on the surface 2.2 Present stack modeling In 2017–2018, another PNNL building filtered exhaust stack was identified for modification to expand the ventilation capacity In this case, the modifications would upgrade an existing three-fan (as-built) system to a four-fan system to meet capacity requirements As with the previous case, tracer distributions may be adapted from previous testing of a similar system, a practice used for the existing original as-built system (Glissmeyer and Flaherty, 2010) Although previous CFD simu lations provided good results, additional validation of the modeling methodology was desired to increase confidence that CFD would pro vide good predictions for the four-fan system To so, we benchmarked the model results against previous testing data used to qualify the as-built system In the previous tests, the full suite of mixing requirement 2.1 Stack modeling history PNNL has recommended and has been using CFD to model radio logical effluent stacks since the early 2000s (Ballinger et al., 2004, 2014; Barnett et al., 2013) Initially, the commercial CFD code STAR-CD was used to compare the results of physical scale-model tests for cyclonic flow, and uniformity Table Three-fan design results of CFD original modeling, CFD modeling with an air blender, and full-scale stack testing (Barnett et al., 2016) Criteria Evaluated CFD Modeling: Original CFD Modeling: with Air Blender Full-Scale Testing with Air Blender ANSI/HPS N13.1 Acceptance Criteria Maximum Difference Between Model and Test with Air Blender Velocity Uniformity COV (%) Cyclonic Flow Angle (� ) Gas Tracer Uniformity COV (%) Gas Tracer Maximum Deviation from the Mean (% of mean) Particle Tracer Uniformity COV (%) 4.7–6.3 5.8–7.5 4.1–29.0 6.5–54.0 2.2 0.3 1.7 3.0 1.9–2.9 1.4–4.0 0.5–2.8 6.7 �20 �20 �20 �30 0.7 3.7 1.2 3.7 36–58 11.6 6.5–9.9 �20 5.1 J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 (Fig 2) When expansion and contraction sections are added to accommodate the diameter difference, the flow tends to concentrate in the central portion of the air blender, the counter-rotating flow is not present, and the pressure drop is large CFD models of straight ducts with various lengths of expansion and contraction sections were created and used to design a duct section to incorporate the oversized air blender A series of test cases revealed that long, low-angle expansion and contraction sections and short straight sections adjacent to the blender provided the needed counter-rotating flow through the blender and mixing of tracer gas and particles, with a pressure drop of only 0.71-cm (0.28-in.) water column The modified duct incorporating the expan sion, air blender, and contraction was incorporated into the four-fan stack design geometry (see Fig 3) Prior to simulating the full array of expected flow scenarios, a test simulation of the modified four-fan design with four fans operating at 9.4 m3 sÀ (20 kcfm) each for a total exhaust rate of 37.8 m3 sÀ (80 kcfm) was performed to check the effectiveness of the system mixing performance and sampling location Fig shows the mid-duct velocity magnitude profile in the plan view, and the velocity distribution at the sampling location traverse (cell values are interpolated to provide smooth contours) The red dots represent the horizontal and vertical sample point locations for this stack Flows from each fan entering the main duct at an angle creates a swirling flow that remains along the duct, including the expansion section Flow through the air blender es tablishes a counter-rotating flow Once through the contraction section, most mixing is complete and the flow travels towards the sampled sec tion with reduced swirl For this test case, the maximum flow angle, at the sample location, is 6.8� , and the COV of velocity uniformity is 2.1% Table Benchmark comparison of CFD model and testing data (Glissmeyer and Droppo, 2007) Criteria Evaluated Physical Test Results CFD Model Results ANSI/HPS N13.1 Acceptance Criteria Difference Between Test Results and Model Velocity Uniformity COV (%) Cyclonic Flow Angle (� ) Gas Tracer Uniformity COV (%) Particle Tracer Uniformity COV (%) 5.5 4.6 �20 0.9 4.2–4.6 3.3 �20 1.3 10.5 11.0 �20 0.5 17.6 19.5 �20 1.9 data was collected (i.e., flow angle, velocity, and gas and particle tracer uniformity) (Glissmeyer and Droppo, 2007) A boundary-fitted CFD model of the tested stack was constructed and inputs were set to match flow conditions of potentially comparable test cases, particularly those performed without dampers installed The benchmarking results sum marized in Table show that the modeling results agree well with the mixing requirement testing data, thus providing confidence and vali dation of the modeling methodology All potential operating scenarios of the four-fan system must be checked with CFD simulations to ensure ANSI/HPS N13.1–2011 stan dard criteria are met under all operational conditions These operating scenarios include flows involving any number of the four fans and flow rates in the range of 7.8–47.2 m3 sÀ (16.5–100 kcfm) in the 157.5-cm (62-in.) duct For standard operations, any combination of three fans operate with one held in reserve Simulations of the preliminary four-fan system, as pictured in Fig 1, were performed A total of 10 cases (including one-, three-, and four-fan operations) encompassing the full range of flow rates were run; four failed to meet the acceptance criteria for the gas tracer uniformity, and seven failed to meet the acceptance criteria for the particle uniformity None of the two-fan cases were simulated because of the challenges met by the other cases The cases that were run are summarized in Table Because of tracer mixing challenges seen with the preliminary fourfan model, and the subsequent uncertainty that the stack would meet the standard criteria, a stationary air blender was considered for inclusion in the stack system Fig shows a typical stationary air blender Preferably, addition of an air blender would not increase the system pressure drop relative to the initial four-fan design by more than 0.64cm (0.25-in.) of water column for a flow rate of 35.4 m3 sÀ (75 kcfm) An air blender of the same diameter as the main duct would create too large a pressure drop unless the blade angles are substantially decreased Unfortunately, the decreased blade angle would not provide sufficient mixing to enable the system to meet the standard criteria An oversized air blender and stack section of larger diameter than the main duct was subsequently used to maintain a low system pressure drop and provide sufficient mixing When installed in the main duct, the configured air blender has a well-established counter-rotating flow through the central and annular portions of the device, which provides the needed energetic mixing Fig Typical static air blender with turning vanes in the central section, and opposite direction vanes in the annular section Table Summary of CFD model simulations of the preliminary four-fan system and cases that pass or fail the ANSI/HPS N13.1 acceptance criteria Number of Operating Fans Flow (m3 sÀ [kcfm]) 4 7.8 [16.5] 33.0 [70] 37.8 [80] 47.2 [100] Flow Angle (� ) Velocity COV (%) Gas Tracer COV (%) Particle Tracer COV (%) ANSI/HPS N13.1 Criteria, Pass/ Fail 6.3 3.2–3.8 2.9 3.0 6.9 2.4–6.7 4.6 4.2 2.24 6.3–38.6 5.9–31.3 2.24 60.7 13.2–30.1 24.8–55.7 60.7 0/1 3/1 0/4 0/1 J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 Fig The modified four-fan stack system with static air blender The distance from the last fan to the sampling point is 24.3 m (79.6 ft), and the distance from the sampling point (location) to the next transition is 3.5 m (11.6 ft) Fig Velocity contours through the modified duct centerline with fans operating at 37.8 m3 sÀ traverse location (80 kcfm) Shown in: (a) plan view, and (b) at the sampled Table Summary of CFD model simulations of the modified four-fan system with stationary air blender installed and cases that pass or fail the ANSI/HPS N13.1 acceptance criteria Fans Operating Flow (m3 sÀ 4 7.8 [16.5] 15.6 [33] 33.0 [70] 37.8 [80] 47.2 [100] [kcfm]) Flow Angle (� ) Velocity COV (%) Gas Tracer COV (%) Particle Tracer COV (%) ANSI/HPS N13.1 Criteria (Pass/Fail) 0.8–6.6 0.8–2.6 1.8–5.3 6.8 6.9 1.9–4.1 1.6–2.5 1.58–1.62 2.1 2.0 0.1–0.47 0.3–1.9 1.2–3.5 0.9–3.2 0.9–3.0 15.9–19.0 11.4–19.7 13.8–19.4 16.2–18.9 15.6–19.8 4/0 4/0 4/0 4/0 4/0 J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 Fig Mass fraction of N2O tracer gas injected mid-duct at Fan A, with fans operating at 37.8 m3 sÀ traverse location Fig Particle concentrations for tracers injected mid-duct at Fan A, with fans operating at 37.8 m3 sÀ traverse location (80 kcfm) Shown in: (a) plan view, and (b) at the sampled (80 kcfm) Shown in: (a) plan view, and (b) at the sampled J.M Barnett et al Progress in Nuclear Energy 124 (2020) 103338 Table indicates other model data for this and other scenarios Similar effects due to four-fan flow patterns in the duct and blender for gas and particle tracer distributions are shown in Figs and 6, respectively Environmentally friendly N2O tracer gas (Yu et al., 2018) was injected near Fan A Fig shows the mid-duct N2O mass fraction in the plan view and at the sampled section The effect of mixing is most evident downstream of the air blender The gas tracer COV for this case is 2.23% Ten-micron aerodynamic diameter tracer particles also were released near Fan A Particle (parcel) tracks from the Lagrangian solu tion are shown in Fig The tracks are displayed by parcel number to (provide color which helps distinguish and visualize the varying paths Tracks are shown in plan view and at the sampling location The latter shows a good distribution across the sampled traverse The COV for this particle distribution is 16.2% This simulation illustrates how uniform the particle distribution must be to result in particle COV �20% It is necessary to check that all flow conditions will meet the criteria set forth in the ANSI/HPS N13.1–2011 standard While the original compliant three-fan system was designed for normal operations in the range of 23–26 m3 sÀ 1, the modified four-fan system with a stationary air blender is expected to operate in the range of 33–36 m3 sÀ and continue to be well-mixed at the sampling location Thus, the full array of standard flow condition cases were modeled using the modified fourfan system with an air blender The results of these runs are summarized in Table 5, which shows that the modeling results predict that flow angle (1� to 7� ), velocity COV (2%–4%), gas tracer COV (0%–4%), and particle tracer COV (11%–20%) meet criteria established by the ANSI/HPS N13.1–2011 standard when the air blender is installed using the CFDdesigned duct section Laboratory, which is operated for the U.S Department of Energy by Battelle under Contract DE-AC05-76RL01830 Appendix A Supplementary data Supplementary data to this article can be found online at https://doi org/10.1016/j.pnucene.2020.103338 References Ballinger, M.Y., Recknagle, K.P., Barnett, J.M., 2014 Comparison of a computational fluid dynamics model with exhaust flow data from a scale model stack In: Barnett, J M., V� azquez, G.A., Bates, C.J., Anderson, S.V (Eds.), 2003 Annual NESHAP Meeting on Radioactive Air PNNL-SA-101801 Pacific Northwest National Laboratory, Richland, WA Ballinger, M.Y., Barnett, J.M., 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Testing Atmospheric Environment https://doi.org/10.1016/j atmosenv.2017.12.015 (online: December 16, 2017) Summary The added value of a reliable CFD modeling methodology for eval uating filtered building exhaust stack performance has been demon strated Additionally, CFD model results of previously built and tested systems confirm the CFD approach in which the difference of the COVs of each method is not more than 5% For the designs evaluated, modeling results led to a recommendation against relocating the sam pling point in one stack, and clearly identified the need to add an air blender to two other exhaust stack systems Modeling provides conser vative evaluations, especially of particle tracer mixing This adds cer tainty that a stack will perform in compliance with the ANSI/HPS N13.1–2011 standard Results from stack modeling allow decisionmakers to proceed with confidence that a given stack would perform acceptably or realize the existence of performance shortcomings that need to be resolved to prevent expensive rework This work demonstrates the utility of CFD as a predictive tool for evaluating designs to the standards and designing modifications to effluent stack systems and suggests that CFD modeling be included to support the design decisions to qualify a new or retrofitted stack CRediT authorship contribution statement J Matthew Barnett: Conceptualization, Methodology, Investiga tion, Validation, Writing - original draft, Writing - review & editing, Supervision, Project administration, Funding acquisition Xiao-Ying Yu: Writing - original draft, Methodology, Project administration, Writing - review & editing, Formal analysis Sarah R Suffield: Inves tigation, Writing - review & editing, Data curation, Validation, Software Kurtis P Recknagle: Software, Validation, Investigation, Writing original draft Acknowledgement This work was conducted at the Pacific Northwest National ... tests and modeling of effluent stack sampling location mixing Health Phys 96 (2), 164–174 Recknagle, K.P., Suffield, S.R., Barnett, J.M., 2013 Modeling the Air Flow in the 3410 Building Filtered. .. scale-model tests for cyclonic flow, and uniformity Table Three-fan design results of CFD original modeling, CFD modeling with an air blender, and full-scale stack testing (Barnett et al., 2016) Criteria. .. were run; four failed to meet the acceptance criteria for the gas tracer uniformity, and seven failed to meet the acceptance criteria for the particle uniformity None of the two-fan cases were simulated