Dr Chaitanya Patil

Lecturer

Naval Architecture, Ocean and Marine Engineering

Contact

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Personal statement

Dr. Chaitanya Patil is a lecturer and researcher in the Department of Naval Architecture, Ocean and Marine Engineering (NAOME), with a focus on advancing intelligent maritime systems. His work bridges cutting-edge modelling and simulation techniques including Artificial Intelligence (AI) to develop reliable and resilient digital twins of marine operational systems. These digital twins are designed to enhance how marine systems perceive their environment, make decisions, and operate autonomously in an increasingly digital maritime industry.

Dr. Patil’s core expertise lies in the modelling and simulation of marine systems, using both physics-based (first-principles) and data-driven approaches. His research enhances three key areas:

  • Sensing and Perception: Developing neuromorphic sensing and sensor fusion techniques for underwater object detection, improving how marine systems perceive their surroundings.
  • Cognition: Applying AI for diagnostics and prognostics to detect faults and assess the health of marine machinery, enabling predictive maintenance.
  • Decision-Making and Optimisation: Optimising marine engine performance and reducing emissions through intelligent control and data-driven strategies.

He has played leading roles in several funded research projects (EPSRC, Innovate UK, SPF) and has published extensively in top-tier journals on topics such as marine engine diagnostics, digital twins, and AI applications in engineering.

With academic and research experience across the UK, Spain, France, China and India, Dr. Patil brings a strong international and interdisciplinary perspective to his work. He is actively seeking collaborations with researchers and industry partners interested in the future of intelligent, autonomous, and sustainable marine systems.

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Professional Activities

Digitalisation of Maritime Industry in India
Speaker
22/11/2023
Application of AI in engineering
Organiser
22/9/2022
British Standards Institution (BSI) (External organisation)
Member
17/6/2025
Abdus Samad
Host
9/5/2025
M.Phil: A Hybrid Modelling Approach to Evaluating the Effects of a Vessel’s Biofouling State on Hull & Propeller Performance
Examiner
29/4/2025
M.Phil: Impact of Alternative Fuels on Ultra Large Container Ship Cargo Capacity and Emissions
Examiner
2/3/2025

More professional activities

Projects

Neuromorphic Sensor Fusion for Under Water Object Detection
Patil, Chaitanya (Principal Investigator) Di Caterina, Gaetano (Co-investigator)
The project aims to develop a protocol of multimodal data collection through neuromorphic sensors for underwater object detection.
The underwater object detection is very tricky phenomenon due to variability of environmental
changes like wave motion, salinity as well as visibility under water. Various sensors with different
ranges and properties can be deployed to overcome the adverse environmental conditions for object detection. However, increasing number of sensors increases the data processing power, noise and uncertainty in the process rendering the data fusion from these sensors difficult. The proven neuromorphic methods introduce a paradigm shift in handling these sensor uncertainties through sensor fusion and increasing the autonomy of the data management and decision making within the operating envelope of all the sensors used for the object detection task.
01-Jan-2024 - 01-Jan-2025
AI powered fault detection for marine engines operating with B30 biodiesel.
Patil, Chaitanya (Principal Investigator) Budiyanto, Muhammad Arif (Academic)
The project aims to address challenges in implementing B30 biodiesel engines in
Indonesian maritime transportation due to fuel-related faults, leading to higher
maintenance costs and hindering widespread adoption. It proposes developing a
fault estimation model using machine learning techniques to predict and assess
common biodiesel engine faults, facilitating timely detection and cost-effective
maintenance. The project involves customising thermodynamic models, simulating
healthy and faulty operations, training ML models, and verifying them with real-time
data. Additionally, the project includes organising a workshop on AI in the
Indonesian maritime industry to discuss relevant challenges and solutions. The
project contributes to sustainable development by promoting eco-friendly practices,
reducing operational costs, and fostering economic growth of Indonesia. Key
performance indicators will measure success, including the development of fault
diagnosis models, skill development, dissemination of research outputs, and
awareness creation. The impacts of the project span academic, social, economic,
environmental, and cultural domains, benefiting stakeholders and promoting
research in maritime transportation across Indonesia and south-east Asia.
01-Jan-2024 - 31-Jan-2025
Marine Engine Modelling
Patil, Chaitanya (Principal Investigator) Theotokatos, Gerasimos (Principal Investigator)
18-Jan-2024 - 22-Jan-2024
Incylinder pressure prediction using log-signatures
Patil, Chaitanya (Principal Investigator) Wu, Yue (Principal Investigator)
01-Jan-2022 - 30-Jan-2022
EPSRC IAA: Neuromorphic Sensor Fusion for Under Water Object Detection (SENSE)
Patil, Chaitanya (Co-investigator)
01-Jan-2022 - 31-Jan-2026
Intelligent marine engines health assessment system based on digital twins and data driven models
Patil, Chaitanya (Co-investigator)
The i-HEATS project aims to develop an intelligent condition monitoring and
diagnostics system for marine engines based on first-principles digital twins
and data-driven models. The project has direct application to ship and land
power plants, endeavouring to provide a game-change in the condition
monitoring/diagnostics of internal combustion engines. I4.0 technologies will be
employed leading to the development of intelligent tools, including digital twins,
deep learning, sensors fusion, and cloud computing. The project key objectives
include:
1. Installation of a novel, custom made, data acquisition system for measuring the
instantaneous shaft torque, integrated with the existing monitoring systems to
acquire the required performance parameters.
2. Storage and analysis of acquired data locally and on cloud (edge/cloud
computing).
3. Development of thoroughly validated engine first-principles digital twins,
integrating thermodynamic models with crankshaft/shafting system dynamics
models.
4. Development of data-driven models based on deep learning techniques,
complementing the digital twins to offer real-time predictive capabilities.
5. Use of the developed data-driven model to identify inaccuracies of critical
sensors of the measured parameters and rectify those as needed.
6. Development of intelligent algorithms to monitor engine condition and provide
diagnostics based on the measured instantaneous torque and other critical
parameters.
7. Develop the prototype i-HEATS system by integrating the above tools with
appropriate hardware and user interfaces
8. Extensive testing and verification of the prototype i-HEATS system in lab and
full-scale conditions onboard two ships and improvement of its functionality
leading to a pre-commercial version.
01-Jan-2021 - 31-Jan-2023

More projects

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Contact

Dr Chaitanya Patil
Lecturer
Naval Architecture, Ocean and Marine Engineering

Email: chaitanya.patil@strath.ac.uk
Tel: 574 5359