1 / 11

JCOMMOPS database contains life-time information GDP log Buoy metadata collection scheme

A simple model for drifting buoy life-times, and a method for estimating evolution of a network's size Etienne Charpentier * , Mathieu Belbéoch * , Julien Bourcier ** * JCOMMOPS ** Student, UN. Paul Sabatier, Toulouse. JCOMMOPS database contains life-time information GDP log

winka
Download Presentation

JCOMMOPS database contains life-time information GDP log Buoy metadata collection scheme

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A simple model for drifting buoy life-times, anda method for estimating evolution of a network's sizeEtienne Charpentier*, Mathieu Belbéoch*, Julien Bourcier*** JCOMMOPS** Student, UN. Paul Sabatier, Toulouse • JCOMMOPS database contains life-time information • GDP log • Buoy metadata collection scheme • EGOS historical database • Argo • Assess drifting buoy life-time using model • Simple exponential model proposed • Predict evolution of network size • Using model • Assuming constant deployment rate • Use this a management tool

  2. Product reliability • Probability density function, f(t) • Cumulative distribution function F(t) • Probability that a randomly selected unit will fail by time t • Proportion of population that fails by time t • Reliability function • Probability that randomly selected unit survive beyond time t • Proportion of population that survives beyond time t

  3. The Bathtube Curve • h(t)=instantaneous failure rate for the survivors at time t • h(t)= f(t)/R(t) • Early failures can be considered separately • Non repairable system; wearout failure period usually not reached • Will assume Intrinsic Failure Period only with constant failure rate ⇒ Exponential model

  4. Example AOML Argos programme #6325 • Because of missing information • Only date of last location used to assess age ⇒ Optimistic approach; are considered alive: • Beach platforms still active • Buoys picked up still located • Platforms removed from GTS still located • 452 buoys deployed between 1/1/2002 and 30/6/2004 • Average age = 444.9 days • Maximum age = 961 days • 172 buoys still active

  5. Survivability • Real half life between 465 and 522 days • 25% buoys died before 275 days • Some buoys still active, life-time unknown: • Optimistic : Active buoys survive forever • Pessimistic: Active buoys die Today

  6. α=0.0012301 Correlation coefficient=0.9965 Least square fit, model: R(t) = N(t)/N0= e-αt Model half life = ln(2)/α=563 days ⇒ Actual failure rate higher than model for units which died after 414 days

  7. Model validation using hind-cast • Buoys deployed as in reality • Buoys dying according to the model Last deployment

  8. Network size evolution • Units deployed at the same time, no further deployments • M0 Units deployed at the same time at time t=-t0, Constant deployment rate Rx between time -t0 and 0, no further deployment after time t=0 • Units deployed as in (2) before t=0, new constant deployment rate Rx after t=0

  9. Network management example Rate to maintain network at target level = 562 units/year • Rx: Deployment rate (units/day) • Initial number of buoys: 1000 • Target: 1250 in 1 year 1250 in 1 year at rate = 760 units/year 1250 target

  10. Tool available from http://www.jcommops.org

More Related