| Project Id
|
BITS025F001516
|
| Project Detail
|
| Project Title
|
DigitOffshore: Smart Monitoring and Predictive Maintenance for Cost-Efficient Offshore Wind Foundations
|
| Senior Supervision Team (BITS)
|
| Supervisor name and Title
|
Prof. Anasua GuhaRay
|
School or Department (or company, if applicable)
|
BITS PILANI, HYDERABAD CAMPUS
|
|
|
|
Email ID
|
guharay@hyderabad.bits-pilani.ac.in
|
| URL for more info
|
https://universe.bits-pilani.ac.in/hyderabad/guharay/Profile
|
| a) Are you currently supervising a BITS or RMIT HDR student?
|
YES
|
| Please comment how many you are supervising
|
5
|
| b) Have you supervised an offshore candidate before?
|
NO
|
| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
|
| Senior Supervision Team (RMIT)
|
| Supervisor name and Title
|
Dr. Dilan Robert
|
School or Department (or company, if applicable)
|
STEM
|
|
|
|
Email ID
|
dilan.robert@rmit.edu.au
|
| URL for more info
|
https://www.rmit.edu.au/contact/staff-contacts/academic-staff/r/robert-associate-professor-dilan
|
| a) Are you currently supervising a BITS or RMIT HDR student?
|
NO
|
| Please comment how many you are supervising
|
15
|
| b) Have you supervised an offshore candidate before?
|
YES
|
| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
|
|
| Other Supervisors (BITS)
|
| Supervisor name and Title
|
Dr. Raghuram Ammavajjala
|
School or Department (or company, if applicable)
|
BITS PILANI, HYDERABAD CAMPUS
|
| Phone Number (Optional)
|
7893748939
|
Email ID
|
raghuram.ammavajjala@hyderabad.bits-pilani.ac.in
|
| URL for more info
|
https://www.bits-pilani.ac.in/hyderabad/raghuram-ammavajjala/
|
| Other Supervisors (BITS)
|
| Supervisor name and Title
|
Mohammad Aminpour
|
School or Department (or company, if applicable)
|
STEM
|
| Phone Number (Optional)
|
+61416714109
|
Email ID
|
mohammad.aminpour@rmit.edu.au
|
| URL for more info
|
https://academics.rmit.edu.au/mohammad-aminpour
|
| Field of Research (For Codes)
|
| 400502 | Civil geotechnical engineering | 70.00 |
| 461199 | Machine learning not elsewhere classified | 30.00 |
|
| Project Description
|
|
The DigitOffshore project aims to develop a smart, cost-efficient monitoring and predictive maintenance framework for offshore wind turbine foundations. As offshore wind energy moves into deeper waters (>45m), traditional monopile foundations become impractical due to their excessive size, noise pollution, and environmental impact. Jacket foundations with suction buckets offer a sustainable alternative, but high design and maintenance costs present significant challenges. Current foundation designs depend on inaccurate seabed geotechnical estimations, leading to overdesign, inflated costs, and inefficiencies. While sensor data is collected, it is not effectively used for real-time design optimization or predictive maintenance.
Aims
This project aims to:
1. Optimize offshore wind foundation design by integrating real-time sensor data with Physics-Based Data-Driven (PDD) modeling to improve geotechnical parameter estimations.
2. Reduce overdesign and foundation costs by developing accurate computational models for jacket foundations with suction buckets in deep-water environments.
3. Enable predictive maintenance strategies to minimize operational disruptions and extend the lifespan of offshore wind foundations, focusing on on challenging seabed soils, including calcareous sands.
Methodology
1. Computational Modeling
o Development of advanced constitutive models to simulate the long-term cyclic behavior of jacket foundations with suction buckets.
o Integration of numerical simulations with real-time sensor data to update geotechnical estimations dynamically.
o Modular framework ensuring scalability across different offshore environments.
2. Data-Driven Methods
o Application of machine learning and data analytics to optimize 3D numerical simulations, reducing computational costs.
o Real-time analysis of sensor data (e.g., deformations, fatigue) to refine geotechnical design parameters and improve foundation accuracy.
o Development of predictive analytics models to enable proactive maintenance, reducing downtime and operational costs.
3. Experimental Testing and Validation
o Element testing on challenging seabed soils, including calcareous sands, to capture complex soil behavior.
o Direct shear and triaxial tests to investigate cyclic loading responses and stress-strain behavior under offshore conditions.
o Calibration of constitutive models and validation of numerical simulations using laboratory and field data for improved foundation performance predictions.
|
| Project Deliverable/Outcomes
|
|
The DigitOffshore project will deliver key advancements in offshore wind turbine foundation design, monitoring, and maintenance. By integrating real-time sensor data with Physics-Based Data-Driven (PDD) modeling, the project will enhance the efficiency, sustainability, and economic viability of jacket foundations with suction buckets. The expected outcomes include:
1. Cost-Efficient and Optimized Foundation Design
o Improved geotechnical parameter estimations will reduce overdesign, lowering material and construction costs.
o Optimized jacket foundations with suction buckets will enable the development of offshore wind farms in deeper waters while minimizing environmental impact.
2. Predictive Maintenance for Offshore Wind Foundations
o A data-driven platform will predict foundation fatigue and deformation, allowing proactive maintenance.
o Reduced downtime and maintenance costs, improving overall reliability and operational efficiency.
3. Enhanced Understanding of Seabed Soil Behavior
o Element testing on challenging seabed soils, including calcareous sands, will refine models for offshore foundation performance.
o Direct shear and triaxial tests will improve constitutive models, leading to more accurate geotechnical simulations.
4. Environmental and Sustainability Benefits
o Minimized noise pollution and ecological disturbance during foundation installation.
o Reduced CO2 emissions through efficient foundation designs, supporting Australia’s net-zero targets.
5. Economic and Industry Impact
o Lower Levelized Cost of Energy (LCoE) by reducing material usage and optimizing offshore wind infrastructure.
o Strengthened offshore wind competitiveness, attracting investment and creating high-value engineering jobs in Australia.
By combining advanced numerical modeling, machine learning, and real-time monitoring, DigitOffshore will establish a global benchmark for sustainable and cost-effective jacket foundation design, contributing to Australia’s leadership in offshore wind energy.
|
| Research Impact Themes
|
| AI/ML and Data Analytics / Data Science with a focus on applications/translation in | Energy & Utilities |
|
| Which RMIT Sustainable Development Goal (SDG) does your project align to
|
|
AFFORDABLE AND CLEAN ENERGY
|
| Which RMIT Enabling Impact Platform (EIP) does your project align to
|
|
DESIGN AND CREATIVE PRACTICE
|
| Which RMIT Program code will this project sit under?
|
|
DR218 (CIVIL)
|
| Student Capabilities and Qualifications
|
|
Geotechnical engineering
|
|
Programming, Machine Learning
|
|
MTech
|
| Preferred discipline of Student
|
| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Civil Engineering, Structural Engineering |
| Data Science, Data Mining, Data Security & Data Engineering |
|