Marine Operational Guidance Using Large-Scale Phase-Resolved Wave Prediction & Networked Field Sensing ―

Predictability & Capacity

Dick K.P. YuePhilip J. Solondz Professor of EngineeringProfessor of Mechanical & Ocean EngineeringMIT

Roland BouffanaisAssistant ProfessorEngineering & Product Development PillarSUTD

Physical Ship

Phase-resolved Wave Reconstruction &

Forecasting

Waves & Wind

Sensors

Ship Dynamics Prediction

Enabling Technologies for Marine Operational Guidance & Optimization

EXPERIMENT

Body-Linear Time Domain

Frame 174

Incident Wave

Heave

Pitch

Vertical Bending Moment

EXPERIMENT

Body-Linear Time Domain

Frame 174

Incident Wave

Heave

Pitch

Vertical Bending Moment

Increase safetyExpand operational envelope Increase efficiency (fuel & time)Higher speed & maneuverability Intelligent control, autonomous

operations & fleet coordination Operation Guidance & Optimization

ExampleSide Launch & Recovery

OBJECTIVE:

• Find windows of operational opportunity (> 10 second durations) for two

principal crane moves of the RHIB during the launch sequence:

• Extend boom crane so the RHIB does not hit the walls in the storage bay.

• Lower the RHIB to water for release when relative motions are small.

DESCRIPTION:

• Frigate in short-crested seaway, sea state 6, head seas at 10 knots forward

speed.

• Color indicators (in RHIB storage bay in demo) forecast whether operational

criteria will be below, at or near threshold limits.

• Operational criteria for launch/recovery of RHIB:

Criteria

Vertical

Acceleration

Lateral

Acceleration

Roll

angle

Pitch

angle

Launch/Recovery 0.2G 0.2G 8.0 deg 2.5 deg

Criteria Presently Satisfied Criteria Presently Not Satisfied

Side Launch & Recovery of a RHIB from a Frigate

Underway in Head Seas

Launch Criteria Satisfied?NOMARGINALYESpast ← TIME → future

PREDICTED →ACTUAL →

Predicted Windows of

Opportunity for Crane

Launch Operation

Example: Side Launch and Recovery

Phase-Resolved Prediction of Irregular Waves*

Given specific phase-resolved wave measurements in space-time, obtain

deterministic reconstruction and forecasting of nonlinear ocean surface wave-field

evolution.

1. Theoretical determination of predictable zone in space-time for multiple

fixed or moving probes

2. Reconstruction and prediction of nonlinear phase-resolved wave-field within

t/Tp = 0

*Qi, Y., Wu, G., Liu, Y, & Yue, DKP, Part 1 & Part 2, J. of Fluid Mech. 2016.

Error in the predicted phase-resolved wave field

t/Tp = 11

Theoretical predictable zone

reconstruction forecast

t

Actual surface elevation

Predicted surface elevation

Ocean wave field

7 sensors

Predictable Zone of Two Sensors is Larger than Union of Individual Predictable Zones of Each Sensor

Theoretical Predictable Zone Nonlinear Wave Simulation

space

time

• For sensors:

Error in the predicted phase-resolved irregular waves

Predictable Zone of Moving Sensors is Larger than that of Stationary Sensors

space

time

Predictable zone for 4 moving and stationary sensors

Sensor trajectory 2

Sensor trajectory 1

Given a directional wave field, optimally moving sensors can significantly increase the predictable zone

Stationary sensors

Moving sensors

Optimal Sensor Deployment Significantly Increases Predictable Zone

Theory:

Nonlinear Wave Simulation:Error in the predicted phase-resolved wave field

Wave Basin Experiments Verify the Wave Reconstruction and Forecasting

P: Wave data used in reconstruction

Q: Wave data used for comparison

with prediction

OTRC Wave Basin Experiment

Measured record

Predicted record

Comparison of Reconstructed vs.

Measured 3D Bull’s Eye WaveQ

P

Decentralized Mobile Sensory Network*

Given a large number of mobile networked buoys, determine a swarming strategy

for optimal deployment based on distributed computation of the sensed data.

1. Swarming: Decentralized collective behavior emerging from interactions

between units and the environment

2. Benefits of swarming:

• Robustness: with respect to the loss of many agents

• Flexibility: in adapting to dynamic environments

• Scalability: continued

effective operation with a wide range of swarm sizes

*Bouffanais, Springer Complexity Series. 2016. Chamambaz, Bouffanais et al. IEEE Trans Net Cont Sys, 2016 (Pend. Rev.)

Effective Swarming Requiresto Maintain the Network Connectedness

Heading of Sensing BuoysDecentralized Communication Channel

k = 7 nearest neighbors

Swarm Signaling Network:

Dynamic and switching network – must remain strongly connected for sufficient

information flow

Communication channel enabling distributed collective information processing

Minimum Information Capacity Requiredfor Effective Collective Operations

Quantifying and Maintaining Information Flow:

Capacity of the Sensory Network – given by the Shannon–Hartley theorem

B: Network bandwidth

SNR: Signal-to-Noise ratio

Condition for an emergent swarming behavior under limited bandwidth:

M. Komareji, Y. Shang, M. Chamanbaz & R. Bouffanais, Consensus in networked multiagent systems under

communication constraints and dynamically changing topologies, IEEE Trans. Net. Cont. Sys (Pend. Rev.) 2016

Information Theory:

Bandwidth

Network Theory:

Eigenvalues of the graph

Laplacian of the swarm network

An intelligent swarm of autonomous networked mobile sensing buoys monitoring the ocean surface environment

Marine Operational Guidance Using Large-Scale Phase-Resolved Wave Prediction & Networked Field Sensing ―

Predictability & Capacity