Numerical simulation of Flocculation Behaviour during storm events

Transcription

1 Numerical simulation of Flocculation Behaviour during storm events Peihung Chen 1, Michael Fettweis 2, Federico Maggi 3, Jason C.S. Yu 1 Pei-Hung CHEN 27-Oct National Sun Yat-Sen University, Dep. Marine Environment and Eng., Taiwan 2 Royal Beglian Institute of National Science, Management Unit of the North Sea, Belgium 3 The University of Sydney, School of Civil Eng., Australia

2 Biological Flocculation Model (BFLOC) The floc solid volume V= V M +V B Mineral fraction (V M ): Biomass fraction (V B ): d-1 Ø 32 ø dvm L kc a MG kb ( ςg 1- ) 2 = ( 1-ς ) d Œ - L œ d-3 d-4 d-3 dt L Œ p L œ Œ ( L-L º p ) œß dv dt B aggregation + breakup d-1 Ø 32 2ø L kcg a B kςg b L V =ςd Œ - œ+ηv d-3 d-4 d-3 B 1- L Œ p L œ ( L-Lp ) Ł K Œº œß aggregation + breakup B ł + cells growth/death L: Floc size Lp: Primary particle size * Aggregation rate: * Breakup rate: * Biomass growth rate: k = 1+ a ' ( ςk) a 3-d ( ςk) k = 1+ η=η max 1 L dρ N K +N m p 1 L d Ł ' b b 3-d p * Carrying capacity: K=βV ( 3 p =β L -V) μ F y ł 1 2 1) Ka : aggregation parameter 2) Kb : breakup parameter 3) Fy : floc strength 4) η max : max specific grow rate 5) Km: half saturation concentration 6) N: nutrient concentration 7) β: factor ref: Maggi, F., (29), Biological flocculation of suspended particles in nutrient-rich aqueous ecosystems, Journal of Hydrology

3 The original parameter setting ref: Maggi, F., (29), Biological flocculation of suspended particles in nutrient-rich aqueous ecosystems, Journal of Hydrology

4 The previous BFLOC result 1) Dataset: ( Zeebrugge) 2) Time: 8-Sep 2: ~ 9-Sep 9:, 23 ref: Maggi, F., (29), Biological flocculation of suspended particles in nutrient-rich aqueous ecosystems, Journal of Hydrology

5 Data Set A (MOW1) 8-Nov 18: ~ 9-Nov 7:, 24 Jan. ~ Apr., 28 Previous study: ( Maggi, 29) Full tidal cycle data (13hrs) Long term data (Tripod)

6 Data (1/2) 1. Tidal cycle data (13hrs) 1) Floc Size (um): LISST 1C *spaced size classes range: 2.5-5um * including volume conc. 2) OBS (mg/l): optical backscatter sensor, including water level 3) SPM (mg/l): sampling (frequency: 2min) 4) Organic matter (mg/l): sampling (frequency: 6 min) * particulate organic carbon (POC) * particulate organic nitrogen (PON) 5) Current velocity (m/s) modeled with COHERENS-3D Łestimate the kolmogorov microscale & turbulent shear rate ref: Fettweis, M., (28) Uncertainty of excess density and settling velocity of mud flocs derived from in situ measurements, Estuarine, coastal and shelf science

7 Data (2/2) 2. Long-time data (Tripod) 1) Floc size: LISST 1C *including Volume con. *spaced size classes range: 2.5-5um 2) two OBS: *OBS1 (.3m above bottom,.3mab) *OBS2 (2mab) * Including water level 3) current velocity: SonTek 3 MHz ADP 3. Meteorological data : wave height ref: 1. Fettweis, M.,(28) Uncertainty of excess density and settling velocity of mud flocs derived from in situ measurements, Estuarine, coastal and shelf science 2. Fettweis et al. (21) Storm influence on SPM concentration in a coastal turbidity maximum area with high anthropogenic impact (southern North Sea), Continental shelf research

8 The correlation from tidal cycle data (13hrs) 1) SPM (mg/l) vs. Organic content (POC/SPM, %) 2) OBS (mg/l) vs. SPM (mg/l) Ł to get SPM_OBS time series 3) SPM_Lisst (mg/l) vs. SPM_OBS (mg/l) Ł to get SPM_OBS_calibration time series 4) Current velocity (m/s) vs. Kolmogorov microscale (um)

9 The correlation from tidal cycle data (13hrs) 5 OBS vs.spm (tidal cycle data,13hrs) Y=.88528*X SPM - sampling (mg/l) OBS (mg/l) SPM_OBS =.885 OBS SPM_OBS_ calibration=.81 SPM_LISST+15.49

10 The correlation from tidal cycle data (13hrs) 1 SPM vs. POC/SPM (tidal cycle data,13hrs) Y=8.5117*X Current Velocity vs. Kolmogorov microscale Y= *X POC/SPM (%) 6 4 Kolmogorov microscale (um) SPM (mg/l) Current Velocity (m/s) POC/SPM =8.511 SPM Kolmogorov microscale = Velocity -.618

11 The sensitivity test of BFLOC using 13hrs data 1) Calibrate the SPM data - POC/SPM (%) =8.511 SPM Calibrated kolmogorov microscale - Using the original parameter setting (Maggi, 29) ( D D ) 2 in-situ BFLOC RMSE( um) =, D : floc size n Case1:SPM_OBS (=.885 OBS ) Case2: SPM_OBS_calibration (=.81 SPM_Lisst ) RMSE=31.56 um RMSE=27.57 um

12 The sensitivity test of BFLOC using 13hrs data 2 15 Case1: using SPM_OBS ( ) in-situ model Floc size (um) Nov 18:12 8-Nov 2:1 8-Nov 21:51 8-Nov 23:41 9-Nov 1:31 9-Nov 3:21 9-Nov 5:11 9-Nov 7:1 ( ) 2 15 Case2: using SPM_OBS_Calibration in-situ model Floc size (um) Nov 18:12 8-Nov 2:1 8-Nov 21:51 8-Nov 23:41 9-Nov 1:31 9-Nov 3:21 9-Nov 5:11 9-Nov 7:1

13 The sensitivity test of BFLOC using 13hrs data 2) Parameter sensitivity - POC/SPM (%) =8.511 SPM Calibrated kolmogorov microscale

14 The sensitivity test of BFLOC using 13hrs data 2) Parameter sensitivity analysis Step1: sensitivity test of individual parameter Test range P i -3s ~ P i +3s, i=1~7 one parameter each time BFLOC Model adjust test range P i -3s ~ P i +3s NO RMSE YES Get each best-fit parameter p i_bestfit, i=1,7 p S te 2 The best-fit parameter

15 2) Parameter sensitivity analysis Step2: the optimized-parameter-group START i=2 The 1th best-fit parameter The ith best-fit parameter test range P i -3s ~ P i +3s i=i+1 BFLOC Model adjust test range P i -3s ~ P i +3s NO RMSE YES Get the ith best-fit parameter if i > 7 d ize p u g ro tim p O r te e m - a ra p -

16 The sensitivity test of BFLOC using 13hrs data 2) Parameter sensitivity analysis Parameter symbol Unit Aggregation calibration parameter Breakup calibration parameter Half-saturation concentration Original setting Case 1 SPM_OBS Case 2 SPM_OBS_calibration Ka Kb Km 1-6 mol/l Max specific growth rate η m 1-4 1/s Factor β Nutrient concentration N 1-6 mol/l Floc strength Fy 1-11 N RMSE from original parameter setting RMSE from parameter sensitivity

17 Case2 : SPM_OBS_calibration Ka = A (MOW1): RMSE= um A (MOW1): RMSE= um in-situ in-situ model model Floc size (um) Floc size (um) Ka =.67 Summary: 8-Nov 18:12 8-Nov 2:1 8-Nov 21:51 8-Nov 23:41 9-Nov 1:31 9-Nov 3:21 9-Nov 5:11 9-Nov 7:1 8-Nov 18:12 8-Nov 2:1 8-Nov 21:51 8-Nov 23:41 9-Nov 1:31 9-Nov 3:21 9-Nov 5:11 9-Nov 7:1 The aggregation parameter A (MOW1): (Ka ) is RMSE= um the major factor to 2 15 improve the model in-situ model Floc size (um) Nov 18:12 8-Nov 2:1 8-Nov 21:51 8-Nov 23:41 9-Nov 1:31 9-Nov 3:21 9-Nov 5:11 9-Nov 7:1

18 Model Simulation for long-term time *Wave Condition Jan-28 ~ Feb-24 (Blankenberge) 3 1) Jan-28 ~ Feb-24, wave height (cm) ) Mar-6 ~ Apr-1, 2528 wave height (cm) Jan 29-Jan 3-Jan 31-Jan 1-Feb 2-Feb 3-Feb 4-Feb 5-Feb 6-Feb 6-Feb 7-Feb 8-Feb 9-Feb 1-Feb 11-Feb Mar-6 ~ Apr-1 (Blankenberge) Time (dd-mmm) 5 wave height (cm) ) Apr15 ~ Apr3, 28 Apr-15 ~ Apr-3 (Blankenberge) 6-Mar 8-Mar 1-Mar 12-Mar 15-Mar 17-Mar 19-Mar 21-Mar 23-Mar 25-Mar 28-Mar 3-Mar 1-Apr 3-Apr 5-Apr 7-Apr Time (dd-mmm) 5 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 2-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 3-Apr Time (dd-mmm)

19 Model result: Apr-15 ~ Apr-3 (Tripod) 3 Apr-15 ~ Apr-3 (Blankenberge) wave height (cm) during calm period 5 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 2-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 3-Apr 2 15 RMSE=28.86um Time (dd-mmm) in-situ model Floc size (um) Apr 2-Apr 21-Apr 22-Apr 22-Apr 23-Apr 24-Apr 25-Apr 25-Apr 26-Apr 27-Apr 28-Apr 28-Apr 29-Apr 3-Apr 3-Apr Time (dd-mmm)

20 floc size (um) Calm period Flood to ebb in-situ org(maggi) model SPM (mg/l) velocity (cm/s) 2-Apr : 2-Apr 12: 21-Apr : 21-Apr 12: 21-Apr 23: Apr : 2-Apr 12: 21-Apr : 21-Apr 12: 21-Apr 23: OBS calibrated LISST 2-Apr : 2-Apr 12: 21-Apr : 21-Apr 12: 21-Apr 23:59 water level (m) The SPM measured from LISST follows current current wave - orbital water level 2-Apr : 2-Apr 12: 21-Apr : 21-Apr 12: 21-Apr 23:59 Hs wave orbital velocity (cm/s) wave height (cm)

21 Model result: Mar-6~ Apr-1 (Tripod) 3 Mar-6 ~ Apr-1 (Blankenberge) 25 during Storm period wave height (cm) Mar 8-Mar 1-Mar 12-Mar 15-Mar 17-Mar 19-Mar 21-Mar 23-Mar 25-Mar 28-Mar 3-Mar 1-Apr 3-Apr 5-Apr 7-Apr 2 15 RMSE=41.83um Strong wave in-situ model Floc size (um) Mar 8-Mar 9-Mar 11-Mar 13-Mar 14-Mar 16-Mar 18-Mar 2-Mar 21-Mar 23-Mar 25-Mar 26-Mar 28-Mar 3-Mar 31-Mar Time (dd-mmm)

22 Storm period floc size (um) D5 is about 3~5umŁprobablity are mineral particle in-situ org(maggi) model SPM (mg/l) velocity (cm/s) 21-Mar : 21-Mar 18: 22-Mar 12: 23-Mar 6: 24-Mar : Mar : 21-Mar 18: 22-Mar 12: 23-Mar 6: 24-Mar : OBS calibrated LISST 21-Mar : 21-Mar 18: 22-Mar 12: 23-Mar 6: 24-Mar : water level (m) dominated by Wave orbital velocity current wave - orbital water level 21-Mar : 21-Mar 18: 22-Mar 12: 23-Mar 6: 24-Mar : Hs wave orbital velocity (cm/s) wave height (cm)

23 PSD-during ebb/flood tide ) Time: 31-Jan hr 2) D5: um Ebb tide Max:9.8/Min: Jan28Feb24: (Blankenberge) ) Time: 8-Feb hr 2) D5: 22.3um Flood tide ( g ) 12 1 Volume concentration (ml/l) Volume concentration (ml/l) Particle size (um) Particle size (um)

24 PSD-during strong wave 1) Time: 2-Feb 6hr 2) D5: 38.4 um 3) Hs: ~ cm Jan28Feb24: (Blankenberge) Wave can lift the mineral particle in suspension Volume concentration (ml/l) Particle size (um)

25 Conclusion 1. Using the correlations from 13hrs data,model simulation during calm period is much better than storm period 2. The difference between SPM_LISST and SPM_OBS: the SPM measured by LISST follows current velocity better 3. The particle size becomes small (probably due to breakup mechanism of flocs) during high wave or fast current 4. Wave can lift mineral particles in suspension which can be caught by LISST Orbital velocity can be regard as the combined effect of current & wave Future improvement: orbital velocity may present the combined effect of wave & current, which can be to modify the model.

26 Thanks for your attention