Sheila Burton

Professional Summary

Sheila Burton is a distinguished marine hydrodynamicist specializing in resistance prediction for biofouling growth on ship hulls. Combining expertise in fluid dynamics, marine biology, and computational modeling, Sheila develops predictive frameworks to quantify how biofouling communities (e.g., barnacles, algae, tube worms) impact vessel performance. Her work enables data-driven hull cleaning schedules, fuel efficiency optimization, and eco-friendly antifouling solutions for the shipping industry.

Core Innovations & Methodologies

1. Biofouling-Hydrodynamics Modeling

  • Pioneers multiscale simulations integrating:

    • Macroscale: CFD (Computational Fluid Dynamics) of hull roughness effects (10μm–10mm)

    • Microscale: Lattice Boltzmann methods for boundary layer-biofilm interactions

    • Biological Dynamics: Growth rate models based on water temperature, salinity, and nutrient flux

2. Operational Impact Prediction

  • Quantifies resistance penalties across fouling stages:

    • Early Stage (1–30 days): 2–8% drag increase from microbial slime

    • Mature Fouling (6+ months): Up to 40% fuel penalty from hard fouling

  • Correlates fouling types with CO₂ emissions using IMO DCS (Data Collection System) benchmarks

3. Sustainable Solutions

  • Develops AI-powered hull monitoring systems using:

    • Underwater drones with hyperspectral imaging

    • Acoustic sensors for real-time fouling thickness detection

  • Advises on non-toxic coating performance through accelerated aging tests

Career Highlights

  • Led the FoulPredict Consortium, reducing fleet fuel costs by $4.2M/year for a 50-vessel operator.

  • Patented a dynamic roughness metric adopted by ClassNK and Lloyd’s Register.

  • Published in Ocean Engineering on barnacle clustering patterns’ hydrodynamic effects.

A foggy scene on a maritime vessel, showing an orange life ring mounted on the green railing. The ship's structure features metal and wooden parts and the ocean is visible in the background.
A foggy scene on a maritime vessel, showing an orange life ring mounted on the green railing. The ship's structure features metal and wooden parts and the ocean is visible in the background.

Fine-tuningGPT-4isessentialforthisresearchbecausepubliclyavailableGPT-3.5

lacksthespecializedcapabilitiesrequiredforanalyzingcomplexbiofoulingand

hydrodynamicdata.Theintricatenatureofbiofoulinggrowth,theneedforprecise

resistanceprediction,andtherequirementforoptimizingshipperformancedemanda

modelwithadvancedadaptabilityanddomain-specificknowledge.Fine-tuningGPT-4

allowsthemodeltolearnfrombiofoulingdatasets,adapttotheuniquechallengesof

thedomain,andprovidemoreaccurateandactionableinsights.Thislevelof

customizationiscriticalforadvancingAI’sroleinmaritimesustainabilityand

ensuringitspracticalutilityinhigh-stakesapplications.

gray computer monitor

Tobetterunderstandthecontextofthissubmission,Irecommendreviewingmyprevious

workontheapplicationofAIinmaritimeandenvironmentalscience,particularlythe

studytitled"EnhancingShipPerformanceUsingAI-DrivenBiofoulingResistance

Prediction."Thisresearchexploredtheuseofmachinelearningandoptimization

algorithmsforimprovinghydrodynamicefficiency.Additionally,mypaper"Adapting

LargeLanguageModelsforDomain-SpecificApplicationsinMaritimeAI"provides

insightsintothefine-tuningprocessanditspotentialtoenhancemodelperformance

inspecializedfields.