Evaluation of ship icing algorithms against unique ice accumulation data in Norwegian waters

Transcription

1 Evaluation of ship icing algorithms against unique ice accumulation data in Norwegian waters Eirik Mikal Samuelsen 1,2 1 UiT The Arctic University of Norway 2 MET Norway Norwegian Meteorological Institute Wednesday, September 9, 2015 Photo: Sveinung Løset

2 Personal information Eirik Mikal Samuelsen 1. PhD Candidate, Department of safety and engineering, UiT «Prediction of vessel icing in Arctic waters» 2. Senior Meteorologist/ Developer in Polar Meteorology at MET Norway, Tromsø Figure 1: Samuelsen, E. M., Løset, S. and Edvardsen, K. (2015)

3 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

4 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

5 Part 1: cont. Background/Motivation Marine icing hazardous/safety risk: 81 vessels capsized due to ice accretion (Shellard, 1974) slippery decks, ladders, handrails, disabled life boats, hamper radio comm. (Løset et al., 2006, MAROFF (2013)) Barents sea especially exposed to severe marine icing (Jørgensen, 1981) Figure 2: Stairboard walk and railing on KV Nordkapp during a severe icing event Photo: Sveinung Løset

6 Part 1: cont. Operational icing algorithms Last 40 years: several algorithms/models forecasting wave-ship collision generated sea-spray icing Kachurin (1974), Stallabrass (1980), Overland (1986, 1990), Makkonen (1987), Horjen (1990), Henry (1995), Lozowski et al. (2000), and many more Only few in operational use inside Meteorological society: Overland (1986, 1990) Modified Stallabrass (Henry, 1995) Mertins (1968)/Forecasters reference book (1997) Problem: - Little data available for verification - How good are the algorithms? Figure 3: 39 cases with ice accumulation from 1983 to 1998 from the same ship type.

7 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

8 Part 2: cont. Weather situation during icing conditions Low pressure system in the Barents sea Cold air outbreak T 850hPa mainly < -12 Icing mainly on W/NW side of the low Svalbard L Median values observed: T = 11 RH = 87% v = 15 m/s SST = 3 H s = 3 m v s = 4 m/s (calc.) c(d p, P w ) = 9 m/s (calc.) S w = 35 ppt (hindc.) Figure 4: Example of the weather situation in one of the cases. MSLP (blue lines), T850 (dashed lines), 3h acc. prec. (blue shading)

9 Part 2: cont. Ship data 4.0 m Figure 5: Ship dimensions (Samuelsen, E. M., Løset, S. and Edvardsen, K., 2015) and ice accumulation z = 3 to 5 m (z = 4 m) from deck. L = 105 m D = 4 m dh dt = 0.1 to 2.3 cm/h Measured total ice accretion, h on fixed position of the ship Icing rate, dh dt = h t 2 h(t 1 ) t 2 t 1 t pos ~ 3 h (31 cases) 6 h (5 cases) 9 h (3 cases)

10 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

11 Part 3: cont. Wave-ship collision icing s, t Q f = Q c + Q e + Q d Q f = L fs R i Q c = h a T s T a Q e = h e e T s RH e T a Q d = R w c p T s T d R i = nr w R i = ρ i dh dt Icing rate! Conduction and radiation neglected! Figure 6: Sketch of wave-ship collision icing process, heat balance equation of the freezing brine and important parameters used in calculations of the icing rate.

12 Part 3: cont. 17 algorithms testet 6 variants of Overland (1990, 1986) algorithm. O1-O6 5 variants of Stallabrass (1980)/Modified Stallabrass (Henry, 1995) algorithm. S, M1-M4 4 variants of Test model algorithm (Makkonen (1987) + ModStall). T1-T4 R i = 0.06 R w R w = 17 R i h a = 42 v dh dt v T f T a 1 + c w SST T nl f fs R w = v lwc (V 2 r, H s, z) h a = 5 v 0.8, long t T s = 1 + n T f R w = W r E lwc t dur N T s = T s S b, shorter t 2 variants of Mertins (1968) nomogram verified categorically (MERTINS, MERTINS2) Figure 7: Example of Mertins nomogram

13 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

14 Part 4: cont. a) Continuous verification b) Figure 8: a) Observed and calculated icing rate from all algorithms. b) The mean value of the observed and calculated icing rate (cm/h), BIAS and MAE

15 Part 4: cont. Categorical verification Figure 9: Contingency table from Overland (1990) and TestModel (Samuelsen et al.2015) when including 42 no accretion cases. None < 0.05 cm/h 0.05 cm/h Light < 0.7 cm/h 0.7 cm/h Moderate < 2.0 cm/h 2.0 cm/h Severe

16 Part 4: cont. Multi-categorical verification scores a) b) PC = 1 n 4 a ii i=1 c) Figure 10: a) PC (percent correct) for all algorithms. b) Scoring weight matrix used by GSS c) GSS (Gerrity Skill Score) for all algorithms 4 4 GSS = p(y i, o j ) i=1 j=1 s i,j

17 Outline of talk Part 1: Background/Motivation Part 2: Metocean, ship and icing data Part 3: Icing algorithms Part 4: Results Part 5: Discussion/Conclusions

18 Part 5: cont. Discussion 1. Obs. dh dh is comparable to inst. dt dt input parameters? calc. from mean values of 2. LWC of an MFV can be used for KV Nordkapp class? 3. Visual estimated H s and P w fair to use? - Correction methods adviced by Gulev and Hasse (1998) 4. That all droplets have same t and same T d. 5. Fog and snow could have contributed to icing.

19 Part 5: cont. Conclusions 1. The algorithms used in MET-society Overland (O1-O6) and ModStall (M3) using realistic values for relative speeds too high icing rate for these cases Possible reason for this: too large heat transfer coefficient with a h a ~ x v assumption in the Overland algorithms (instead of h a ~ 5 x v 0.8 ) very long droplet cooling time in the ModStall algorithm low T d 2. Small changes in the ModStall algorithm (calc. S b, shorter t, time aver. R w and using relative speeds), give possibly a better algorithm (T1 and T3) both seen when verifying continuous and categorical icing rates. Christmas wish: Continuous automatic observation of spray flux and ice accretion, combined with met-ocean parameters for different ship types!

20 Thank you for listening! Questions? uit.no 9/17/

21 Complementary slides

22 O1 M1 T1 Figure 11: Example of O1, M1 and T1 by using NWP as input for an icing case 24th March 2015 in the Barents Sea.

23 The basis of the Overland algorithms O1,O4-O6 (1990) n = Overland O2 (1986) n = O3 n = solved dh dt = K v(t f T a ) 1 + c w nl fs (SST T f ) Q e = 0 R w = ρ i n dh dt R w incr. T a decr. t = 0 h a = K v T s = T f v T f T a 1 + c w nl fs SST T f

24 The basis of the ModStall/Stall algorithms M1(org), M2 v, V r = const./obs ModStall M3 W r, V r = obs M4 Time-aver. R w + shorter t Stall S Diff. lwc(h s ) + shorter t L fs = L f 2 lwc = w 0 V r H s e 0.55z (Zakrzewski,1986,1987) R w = v lwc t = v 4 (Zakrzewski, 1986) h a = 5.18 v 0.8 (Stallabrass, 1980) T s = 1 + n T f (Tabata, 1963)

25 The basis of the Test Model algorithms T1, T4 T s S b, brine entr. T4: t (Stall) TestMod T2 T s = (1 + n)t f T3 lwc(h s 2.5 ) L fs = 0.7L f (30% brine entrapment) R w = W r E lwc t dur N t = τ + t dur 2 h a = 4.85 W0.8 r D = 4 m T s = T s S b S b = S w 1 0.7n (Makkonen, 1987)

26 Meteorological data: T a = 1.4, 11.1, 21.5 v = [2.1, 15.4, 30.9] m/s DD = 300, 090 (1 outside) RH = [67, 87, 100] % p = [980, 1005, 1031] hpa Oceanographical data: SST = 6.6, 2.6, 2.1 H s = [0, 3.0,12.8] m P w = [0, 6.0, 10.1] s S w = [34.5, 34.9, 35.1] ppt D p = [10, 342, 2701] m c = [0, 9.3, 15.9] m/s Ship data: Latitude = [69.6, 74.7, 79.9] Longitude = [8.3, 18.0, 34.3] v s = [0, 4.2, 8.6] m/s Dir = 107, 318 (1 outside) Relative speed: V r = c v s cos α W r = v 2 + v s 2 2v v s cos α

27 Figure 12: The mean value of the observed and calculated icing rate (cm/h), BIAS and MAE, droplet cooling time (s), droplet temperature ( ), heat transfer coeff. (W/m 2 ), spray flux (g/m 2 s) and total accretion (cm).

28 Figure 13: Observed and calculated icing rate from some of the algorithms with lowest icing.

29 Figure 14: Contingency table of T4 algorithm T4 has highest PC but lowest GSS!!! T4 s short DCT making T d closer to SST and give less icing in severe conditions.

30 Figure 15: Derivation of heat transfer coeff. in Stallabrass (1980)

31 Figure (3 next slides): Weather situation during all 39 cases with accumulated ice on KV Nordkapp-class (35 weather situations) MSLP (blue lines), T850 (dashed lines), 3h acc. prec. (blue shading)

32 Figure 16

33 PL Samuelsen et al. (2015) Figure 17

34 Figure 18