Determining ice loads for OSVs

Transcription

1 Determining ice loads for OSVs LMA, OSVs in Ice London, , Prof. Roger Skjetne Department of Marine Technology Norwegian University of Science and Technology Illustration: Bjarne Stenberg. Copyright: NTNU. 1

2 Contents About CRI SAMCoT Numerical structure-ice model and simulation-based studies o Introduction o Comments on ISO Arctic Offshore Structures standard o NTNU Ice Simulator o New concept for independent verification Effective stationkeeping in ice o Ice management o Ice intelligence o DP in ice Conclusion

3 Sustainable Arctic Marine and Coastal Technology - SAMCoT Vision: Leading international centre for the development of robust technology needed by the industry operating in the Arctic region Duration: Centre started in 2011 and potentially continues to 2019 Strategy: Research team: Strong collaboration between Academia and Industry More than 25 PhD candidates/post docs Turnover: RCN; 10 mill. NOK /year; total revenue 45 mill. NOK/year 3

4 4

5 General Assembly (All Partners) (Leader: Board Chairperson) Board (Majority from IP) (Leader IP) Exploitation and Innovation Advisory Committee (EIAC) (Leader IP, all IP) Centre Management Group (CMG) NTNU SINTEF UNIS (Leader NTNU) Scientific Advisory Committee (SAC) (University lead, leading int. cap.) Modelling (UNIS) WP1: Data Collection & Process (NTNU) WP2: Material Modelling (NTNU) WP3: Fixed Structures in Ice (NTNU) WP4: Floating Structures in Ice Philosophy (NTNU) WP5: Ice Management and Design (NTNU) WP6: Coastal Technology IA1 IA2

6 Engineering Challenges in the Arctic addressed by. WP5: IM&Design Philosophy WP6: Coastal Technology WP4: Floating Structures in Ice WP3: Fixed Structures in Ice WPs 1&2: Quantifying the Physical Environment

7 7 Data Collection

8 8 Full-scale data if possible Oden Arctic Technology Research Cruises: 2012, 2013 and 2015

9 Contents About CRI SAMCoT Numerical structure-ice model and simulation-based studies o Introduction o Comments on ISO Arctic Offshore Structures standard o NTNU Ice Simulator o New concept for independent verification Effective stationkeeping in ice o Ice management o Ice intelligence o DP in ice Conclusion

10 Introduction The Arctic is indeed valuable, but it is also vulnerable. Damage of a chemical tanker s plating caused by multi-year ice (Hanninen, 2005) This may explain the extreme public scrutiny towards any field development in the Arctic. This also demonstrates the need for reliable and trustworthy tools and procedures to verify the designs of Arctic structures and operations.

11 Introduction As offshore activities in the Arctic constitute a relatively new field with only a handful of relevant operations to draw experience from, and since full-scale trials are extremely expensive, there is an expressed need for much more extensive, more detailed, and more cost-efficient analysis of Arctic structures and operations based on numerical simulations. Until recently, simulation tools of sufficient quality to perform such numerical analysis have not existed. The only verification available has been through a limited set of experiments in ice model basins. Today, this has changed, partly through the efforts at NTNU hosting the CRI SAMCoT and prior projects, laying the foundation of a versatile and highly accurate high-fidelity numerical simulator of offshore structures in floe-ice conditions, also including ice ridges.

12 Introduction In this presentation: We will comment on the methods to calculate Ice Actions as presented in the recent standards. Then, we will briefly introduce our approach at NTNU to numerically simulate fixed and floating structures in icy waters. We will discuss a potential use of such a numerical model to verify the designs of Arctic structures and operations. Finally, Arctic DP and challenges related to real-time load detection is presented.

13 ISO19906: Annex A - Fixed vs. Floating

14 ISO19906: Annex A The determination of ice actions depends on the selection of the interaction scenario To setup a scenario, the designer will have to make a choice of: Ice feature Limiting mechanism Failure mode

15 ISO19906: Annex A Question How to characterize a managed-ice field (or a naturally broken-ice field)??

16 ISO19906: Annex A Question How to characterize a managed-ice field (or a naturally broken-ice field)? Answer: A single-feature representation of a managed-ice field is often insufficient. NTNUs complete description includes: Floe size distribution Ice concentration Lateral pressure Ridge frequency etc.

17 ISO19906: Annex A Question What is the limiting mechanism when dealing with a managed-ice field??

18 ISO19906: Annex A Question What is the limiting mechanism when dealing with a managed-ice field? Answer: In fact, the 3 limiting mechanisms coexist during the interaction between managed-ice and offshore structures. It is a very challenging exercise to identify the limiting mechanism that will cause the highest load on the structure. This is because the processes are nonlinear and the outcome depends strongly on the initial and boundary conditions, driving forces, structure response, etc. This gives us no choice but to go for time-domain analysis

19 Full-scale observations of interaction processes Major physical interaction processes: Novel theory developed based on 1 st principles Numerical implementation Continuously > 6 man-years input Full-scale data for observing phenomena and validating models

20 Overview Ice Actions Global The estimate of a time-averaged pressure over a nominal contact area is sufficient Useful for the design of: Foundation Moorings/DP systems Operations: IM, towing, Prop. wash, etc. Local Here, a high resolution image of the pressure over the contact area is needed (Kim, 2014) (Storheim, 2015) Useful for the design of: Structural elements such as plates and beams The model must be adequate for relatively large temporal and spatial scales Simulation time (temporal scale)

21 Numerical simulator - Basis for innovation Design verification Structure Mooring Operation Ice Management Towing/DP Supplement to Ice Tank Tests Lubbad, R., S. Løset, and R. Skjetne, Numerical Simulations Verifying Arctic Offshore Field Activities. In proc. 23rd Int. Conf. Port & Ocean Eng. Arctic Conditions (POAC 15), Trondheim, Norway, June 14-18, 2015.

22 Simulator

23 Modelling of fracture W. Lu Postdoc at NTNU Influence of: 1) floe geometry 2) confinement 3) inertia Lu et al.,(2014a) Lu et al.,(2014b) Awarded with the best doctoral thesis at NTNU in 2014, competing with 330 other PhD theses.

24 Modelling of hydrodynamic forces A. Tsarau PhD student at NTNU c) Hydrodynamic (Aerodynamic) forces: Summary of the current research Ice accumulation and clearing upstream of a structure Propeller wash effect Wake closing behind a structure Photo:

25 Modelling ice ridges M. vd. Berg, PhD student at NTNU/TU Delft ICE RIDGES

26 Simulation-based verification phases Phase 1 Simulation-based verification of structural designs in benchmark ice load conditions and operation cases. Phase 2 Simulation-based verification of proposed structures in realistic operations in sea-ice. Simulation-based verification of operational procedures and emergency response plans. Phase 3 Simulation-based verification of control, monitoring, and decision support systems. Verification of technical barriers. Misc. Proof of concept. Simulation-based offline assessment tools and analysis reports. Simulation-based online decision support tools.

27 Simulation-based verification scope Fieldspecific concepts Control and monitoring systems, technical tools Operations, procedures, and emergency response Structural designs and dimensioning

28 Distribution of responsibilities End client Public scrutiny Classification society Arctic activity Provider of simulation-based verification Ship owner/contractor Certificate Vessel design, concept Concept designer. Solution provider Iterations Validation of a few selected cases Ice tank

29 Parties Public scrutiny 3rd party: Class Society 1st parties: Vessel owner Contractor End Client 2nd parties design: Concept designer Solution provider 2nd parties verification: Simulation provider Ice tank

30 30 VeriArc Verifying Arctic Offshore Field Activities Vision Pioneering verification of Arctic offshore activities. Business Idea Safer and publically accepted Arctic offshore activities through independent verification of structure designs, operational procedures, and decision support tools using superior sea-ice simulation technology. Mission Securing the integrity of your Arctic voyages.

31 31 Contents About CRI SAMCoT Numerical structure-ice model and simulation-based studies o Introduction o Comments on ISO Arctic Offshore Structures standard o NTNU Ice Simulator o New concept for independent verification Effective stationkeeping in ice o Ice management o Ice intelligence o DP in ice Conclusion

32 ACEX: IODP expedition 302

33 ACEX: IODP expedition 302 Video courtesy: Captured by IODP ACEX mission. Digitized by AKAC Inc. (Evan Martin)

34 Review: Open water DP

35 3 DOF DP model by T. I. Fossen (2011) Position and heading:...and velocities: Kinematic equation: Kinetic equation: Current forces: Wind and wave forces assumed additive:

36 Current compensation: Feedback integral action We don t really measure r Simplification for control design: Slowly varying bias force due to currents. Compensated well by integral action in a PID feedback control law: From the integral action on the bias b(t), a «current speed and direction» can be deduced. However, it is frequently referred to as the «DP current» since it really accounts for: Ocean currents. Wave drift forces. Unmodeled wind force dynamics. Unmodeled thruster dynamics. Other unmodeled dynamics.

37 Wind compensation: Feedforward cancellation Assume a static relationship between wind velocity and force To compensate wind force, use feedforward: Measure the wind speed and direction in the mast and assume uniform wind. Calculate corrected wind coefficients based on sensors location above sea level. Calculate an estimate of the wind force/moment: Compensate by directly opposing the wind:

38 Wave compensation: Integral action and filtering Fossen (2011): 1st-order wave-induced loads: wave frequency (WF) motion observed as zero mean oscillatory motions. 2nd-order wave-induced loads: wave drift forces observed as non-zero slowly-varying components. This motivates: Wave forces are highly periodic and described according to a wave spectrum: Courtesy: Fossen (2011) Courtesy: Fossen (2011)

39 Wave compensation: Integral action and filtering For control design it is common to assume that the 1st order wave loads only produce an oscillatory zero-mean wave motion Total ship motion: Slowly-varying wave drift forces incorporated into the bias, yielding: Courtesy: Fossen (2011) Compensation of 2nd-order wave loads: INTEGRAL ACTION Handling of 1st-order wave loads: FILTERING

40 Wave compensation: Integral action and filtering Not desired to let the wave motion thus requiring filtering. enter the feedback loop, Used for PID feedback: Frequency response: or alternatively: Courtesy: Fossen (2011)

41 Review: Open water DP Use thrusters to compensate environmental loads: Typically PID-like control. Current: Feedback by PID, especially integral action (I). Waves: 1st order wave-induced loads: Filtering to avoid that enter the feedback loop. 2nd order wave-induced loads: Feedback by PID. Wind: Feedforward compensation from measurements of wind speed and direction. REACTIVE CONTROL STRATEGIES

42 Arctic DP Ice loads Ice

43 Effective stationkeeping in ice 1. Effective ship design. 2. An effective Ice Management system. 3. Effective strategies for the DP control system to compensate ice forces. Courtesy: J. Haugen, NTNU.

44 (Courtesy: Berg, T. E. et al, Design considerations for an Arctic intervention vessel, OTC Effective ship design

45 1. Effective ship design 22nd Int. Conf. Port and Ocean Engineering under Arctic Conditions (POAC 2013): Ice-going capability assessment and DP-Ice Capability Plot for a double acting intervention vessel in level ice Biao Su (NTNU), Øivind K. Kjerstad (NTNU), Roger Skjetne (NTNU), and Tor E. Berg (MARINTEK) The CIV Arctic vessel (Source: Mainly designed for open-water operations Ice-breaking capabilities with the stern Numerical analysis of the ice-breaking capability and maneuverability for the CIVArctic vessel. Comparison with the ice model tests carried out in the Aker Arctic ice tank in May A static DP-Ice Capability Plot by coupling the numerical simulator (towing simulations) and a thrust allocation algorithm. Collaboration with KMB CIV Arctic: Construction and intervention vessels for Arctic oil and gas.

46 The numerical ice hull interaction model Local ice loads are detected by coupling the computer geometric tools and an empirical ice hull contact model Global ice loads are calculated by integrating the local ice loads around the hull Case studies with different vessels: 3DOF rigid-body equations of surge, sway and yaw are solved by a step-bystep numerical integration method Iterations are performed at each time step to find a balance between the ship s motions and the resulting ice loads Tor Viking II (Su et al., 2010), MT Uikku (Su et al., 2011), MS Kemira (Su et al., 2011) Su, B., Ø. K. Kjerstad, R. Skjetne, and T. E. Berg (2013), Ice-going capability assessment and DP-Ice Capability Plot for a double acting intervention vessel in level ice. In proc. 22nd Int. Conf. Port and Ocean Eng. under Arctic Conditions (POAC 2013).

47 Ice-going capability assessment for the CIV Arctic vessel The speed that the vessel could attain in the ice of increasing thickness (in model-scale) Runing ahead Turning ability (in model-scale) Runing astern Both model tests and numerical simulations agree that the vessel has a much better icebreaking capability when it is running astern. Both model tests and numerical simulations agree that the vessel is difficult to turn in 0.5 m thick ice. Su, B., Ø. K. Kjerstad, R. Skjetne, and T. E. Berg (2013), Ice-going capability assessment and DP-Ice Capability Plot for a double acting intervention vessel in level ice. In proc. 22nd Int. Conf. Port and Ocean Eng. under Arctic Conditions (POAC 2013).

48 What ice load is dimensioning? The quantification levels defined was Peak load Significant load Mean load Kjerstad, Ø. K., R. Skjetne, and B. O. Berge (2013), Constrained Nullspace-Based Thrust Allocation for Heading Prioritized Stationkeeping of Offshore Vessels in Ice. In proc. 22nd Int. Conf. Port and Ocean Eng. under Arctic Conditions (POAC 2013).

49 DP-Ice Capability Plot for the CIV Arctic vessel DP-Ice Capability Plot parameterized by ice drift speed The simulation results reveal that the DP-ice capability of the CIVArctic vessel in level ice is restricted by a narrow relative ice drift direction band. DP-Ice Capability Plot parameterized by ice thickness The main reason for this is that the hull shoulder (middle body) becomes exposed to the ice when the relative drift direction is in the range ( ). As there is no vessel dynamics in a towing experiment, the hull shoulder where the hull surface is vertical can not break the ice by bending, and is rendered to continuously crush the ice.

50 2. An effective Ice Management system Essential for offshore operations in ice Ice Management is the sum of all activities where the objective is to reduce or avoid actions from any kind of ice feature. Illustration: Bjarne Stenberg. Copyright: NTNU.

51 NOTE! Reactive ice compensation needs Ice Management! Reactive ice compensation: Based on instantaneous or slightly delayed sensing. This implies: Thus: Ice loads that accumulate cannot grow beyond the maximum thruster/mooring capacity. Ice loads cannot grow faster than thrust rate limit. Physical Ice Management with guaranteed success of protecting the Protected Vessel is of utmost importance.

52 Ice Management Ice breaker II Football field size ice floes Ice breaker I County size ice floes Ice breaker III Cuts ice into small pieces Ice drift Protected vessel Illustration: Bjarne Stenberg. Copyright: NTNU. Sea-ice management Sea ice observation and monitoring: Ice concentration, floe size distribution, etc. Ice geometry, ice age, density, salinity, etc. Sea ice tracking (drift speed and direction, ice floe positioning) Ridge and iceberg detection and tracking. Threat evaluation (forecasting, threat assessment). Physical ice management. Risk management, procedures, and training. Iceberg management Iceberg detection and tracking. Threat evaluation (forecasting, threat assessment). Iceberg handling (towing, etc.) Risk management, procedures, and training.

53 Max ice load over years Ice actions design philosophy Extreme ice feature Consolidated ridges Typical ice feature Time Slide courtesy: Sveinung Løset

54 Max ice load over years Ice actions design philosophy Max lifetime ice actions = design level? Consolidated ridges Typical ice feature Time Slide courtesy: Sveinung Løset

55 Max ice load over years Ice actions design alternative Disconnection Design level with disconnection Time Slide courtesy: Sveinung Løset

56 Max ice load over years Ice actions design alternative Disconnection Ice Management Design level with disconnection + IM Time Slide courtesy: Sveinung Løset

57 IM: Outer feedback loop IM Supervisory System IM ILRS Ice breakers ++ DP ctrl Arctic ice-infested environment Thr.sys Wind Waves Current Stationkeeping Vessel Sensors Ice observation system: Mobile sensor network and onboard sensors Ice risk management Quantification of safety and reliability of Arctic stationkeeping operations

58 Ice Load Reduction System Incoming ice ILRS Broken ice out Courtesy : Moran

59 Optimal ice load reduction Feedforward Feedback Ice state in Optimal guidance & control problem Ice breaker operation Ice state out Challenges: How to measure and estimate relevant ice parameters for Ice State In and Ice State Out? How to model and simulate the behavior of the ice in the field? How to model and simulate the effect of ice breaking based on given maneuvers by the ice breaker? What to minimize and how to formulate an optimization problem?

60 The challenge of ice drift changes Courtesy: J. Haugen, NTNU.

61 We need ice intelligence! A better instrumented Arctic operation! Courtesy: Met.no Slide courtesy: Morten Mejlaender-Larsen, DNV

62 Regional ice surveillance Iceberg detection and tracking Sea-Ice surveillance Ad-hoc communication network Ice breaker II Football field size ice floes Ice breaker I County size ice floes Protected vessel Ice breaker III Cuts ice into small pieces Ice drift Online ice drift monitoring units Unmanned aerial utility vehicle Local and regional aerial ice observation Local underwater ice observation Illustration: Bjarne Stenberg. Copyright: NTNU.

63 We need ice intelligence: A better instrumented Arctics! A better instrumented ship. Methods for extracting useful information from all sensor data. Courtesy: Met.no Illustration: Bjarne Stenberg

64 Onboard instrumentation for real-time load detection Sensor network, data acquisition, and data analysis of IMU measurements supported by camera images in sea-ice.

65 Arctic expeditions A measurement system has been developed and tested during two field tests on three icebreakers CCGS Amundsen in April 2015 Oden in September 2015 Frej in September 2015 CCGS Amundsen off the coast of Labrador. OATRC 2015 in waters north of Svalbard. Photo: Roger Skjetne (2015)

66 Instrumentation Oden (1988) Swedish icebreaker m / 31 m Shaft-propeller system 250 tonnes BP Scientific vessel Frej (1975) Swedish icebreaker m / 23.8 m Diesel-electric, four propellers 190 tonnes BP Normally operating in Baltic sea

67 Measurement system: IMUs Measurements of linear accelerations and angular velocities. Different measurement locations. Camera system for reference. IMU 1 Radio room, deck 9 IMU 3/4 Fore peak store FREJ IMU 2 Fore dry tank

68 Measurement system: IMUs Oden IMU 3/4 IMU 1 IMU 2

69 Measurement system: Cameras F180S: Bow camera F2 F360P F180F FREJ F360S F180S F180F: Ahead looking camera F360P: Port 360 camera

70 Measurement system: Cameras O3 O2 O360P OITC O180 O4 O1 Oden O360S

71 Measurement system: Data acquisition System based on ADIS and ADIS IMU sensors Measurements of linear accelerations and angular velocities Real time clocks synchronize measurements Local buffer and central storage for data

72 Analysis technique: Ice drift est. g Courtesy of Thor I. Fossen, NTNU Assumption: Accelerometer is aligned with the body frame, no misalignment a sensor = a mp + ω v g + b + n Since we cannot put the sensor at CO, a dependency between the sensor mounting point mp and CO has to be considered: a mp = a co + α l + ω (ω l) Courtesy Øivind Kjerstad, UNIS v = u v w T ω = p q r T

73 Analysis technique: Ice drift est. Assumption: Ship moves at low speed η = ν M RB ν + Dν = τ thr + τ env + τ ice η = x y ψ T ν = u v r T Assumption: Sea ice can be modelled as mass-damping system, no wave interaction M ice ν ice + D ice ν ice = τ ice Ice has a drift speed, relative to the vessel ν ice = ν + ν drift ν drift = 0 Resulting coupled ship-ice system η = ν (M RB +M ice ) ν + D + D ice ν = τ thr + τ env D ice ν drift

74 Example 1: Slow speed in managed ice Data:

75 Analysis technique: Ice load Wigner-(Ville) Distribution: W x t, f = x t + τ 2 x t τ 2 e j2πfτ dτ

76 Example 1: Slow speed in managed ice Data:

77 Example 2: Ship leaving ice channel Data:

78 Example 2: Ship leaving ice channel Data:

79 3. Effective Arctic DP control strategies Reactive control and/or Proactive control?

80 3. Effective Arctic DP control strategies Reactive control strategies: A. Feedback from pos-ref and gyrocompasses, using an ice characteristic model and smarter integral action. B. Feedforward cancellation of ice loads using acceleration measurements. Proactive control strategy: C. Feedforward using a high-fidelity predictive system (with inputs from an ice observation system).

81 3. Effective Arctic DP control strategies Reactive control strategies: A. Feedback from pos-ref and gyrocompasses, using an ice characteristic model and smarter integral action. B. Feedforward cancellation of ice loads using acceleration measurements. Proactive control strategy: C. Feedforward using a high-fidelity predictive system (with inputs from an ice observation system).

82 Slide courtesy: Øivind K. Kjerstad Kjerstad, Ø. K., I. Metrikin, S. Løset, and R. Skjetne, A phenomenological investigation of the managed ice loads on a stationkeeping vessel. Cold Regions Science and Technology, Vol. 111, pp , 2015.

83 Slide courtesy: Øivind K. Kjerstad

84 Slide courtesy: Øivind K. Kjerstad

85 Slide courtesy: Øivind K. Kjerstad

86 Slide courtesy: Øivind K. Kjerstad

87 Slide courtesy: Øivind K. Kjerstad Kjerstad, Ø. K. and R. Skjetne, Modeling and Control for Dynamic Positioned Marine Vessels in Drifting Managed Sea Ice. Modeling, Identification & Control (MIC), 35(4), pp , 2014.

88 Slide courtesy: Øivind K. Kjerstad

89 Slide courtesy: Øivind K. Kjerstad

90 Slide courtesy: Øivind K. Kjerstad

91 Slide courtesy: Øivind K. Kjerstad

92 Slide courtesy: Øivind K. Kjerstad REACTIVE CONTROL STRATEGIES

93 Slide courtesy: Øivind K. Kjerstad

94 Slide courtesy: Øivind K. Kjerstad

95 Slide courtesy: Øivind K. Kjerstad

96 AFF experimental platform on R/V Gunnerus MRU 5+ motion reference unit. Includes three axes high quality MEMS rate gyros and linear accelerometers. Courtesy: Kongsberg Maritime Slide courtesy: Øivind K. Kjerstad

97 Slide courtesy: Øivind K. Kjerstad

98 Slide courtesy: Øivind K. Kjerstad

99 Slide courtesy: Øivind K. Kjerstad

100 3. Effective Arctic DP control strategies Reactive control strategies: A. Feedback from pos-ref and gyrocompasses, using an ice characteristic model and smarter integral action. B. Feedforward cancellation of ice loads using acceleration measurements. Proactive control strategy: C. Feedforward using a high-fidelity predictive system (with inputs from an ice observation system).

101 C) Compensation by feedforward from a supervisory control system Suppose we let the DP system know in advance what ice loads to encounter. DP system can then plan its motion for optimal handling of ice floe impacts while doing adequate stationkeeping. Vidar Viking stationkeeping maneuvers. One can then use high-fidelity numerical ice-ship simulators to predict the future load variations. The predicted loads can be used in a feedforward compensation strategy. A supervisory predictive system requires full overview of the ice field, including detailed high-fidelity knowledge of the local ice properties.

102 Conclusion Standardized load formulas from ISO are based on empirical formulations and are valid only for single ice features and a single limiting load mechanism. Numerical tools giving more realistic and accurate loads on structures are entering the market. This is necessary in order to ensure sufficiently rich and detailed analysis of structures and operations in Arctic sea-ice. Independent verification of Arctic activities is one way to ensure a distribution of responsibilities and a sound «concept review» that can ensure necessary safety. Effective stationkeeping was discussed, including ice management and ice intelligence activities, to ensure DP in ice. Methods for updating the DP control systems to handle ice loads have been studied deeply, and new ice-adapted systems are believed to be issued soon if the market continues to request it. Photo: Roger Skjetne (2015)

103 SAMCoT Illustration: Bjarne Stenberg. Copyright: NTNU.