Dr Chaitanya Patil

Publications

Health assessment framework of marine engines enabled by digital twins

Tsitsilonis Konstantinos-Marios, Theotokatos Gerasimos, Patil Chaitanya, Coraddu Andrea

International Journal of Engine Research Vol 24, pp. 3264-3281 (2023)

https://doi.org/10.1177/14680874221146835

Effects of EGR transient operation on emissions and performance of automotive engines during RDE cycles

Patil Chaitanya

(2020)

Comparative analysis of data-driven models for marine engines in-cylinder pressure prediction

Patil Chaitanya, Theotokatos Gerasimos

Machines Vol 11 (2023)

https://doi.org/10.3390/machines11100926

Marine engines combustion diagnostics employing fourier series and ANN

Patil Chaitanya, Theotokatos Gerasimos, Milioulis Konstantinos

11th European Combustion Meeting (2023)

In-cylinder pressure prediction for marine engines using machine learning

Patil Chaitanya, Theotokatos Gerasimos, Milioulis Konstantinos

The 8th International Symposium on Ship Operations, Management and Economics (2023)

https://doi.org/10.5957/SOME-2023-014

EGR transient operations in highly dynamic driving cycles

Galindo Jose, Climent Hector, Pla Benjamin, Patil Chaitanya

International Journal of Automotive Technology Vol 21, pp. 865-879 (2020)

https://doi.org/10.1007/s12239-020-0084-x

More publications

Professional Activities

11th European Combustion Meeting

Participant

26/4/2023

8th International symposium of Ship Operations, Management and Economincs

Participant

7/3/2023

Intelligent marine engine diagnosis and prognosis

Speaker

4/10/2022

Application of AI in engineering

Organiser

22/9/2022

European Automotive Engineering Conference

Participant

2017

More professional activities

Projects

Incylinder pressure prediction using log-signatures

Patil, Chaitanya (Principal Investigator) Wu, Yue (Principal Investigator)

01-Jan-2022 - 30-Jan-2022

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

Model Based Calibration for hybrid and electric vehicles using Data-driven models

Patil, Chaitanya (Co-investigator)

• Model based calibration (Matlab/Simulink) of GPF automate in conventional and ePower concept vehicles to verify yearly customer service regeneration for china and European market.
• Data based Machine Learning models (TWC, GPF, CVT) from scratch with NARX and RNN structures to reduce calibration time (by 60%) using tools like ASCMO (ETAS) and Python.
• Emission Platform development using ML in collaboration with HiLs team for significant reduction in cost and time in powertrain calibration.
• Performance Optimization through Dynamic DOE (Design of Experiment) method using ASCMO tool.
• Automation of various calibration tasks and data analysis using Python (open source platform) bypassing conventional software license requirements.

03-Jan-2020 - 30-Jan-2021

Optimization of EGR split index in diesel engine for passenger vehicles

Patil, Chaitanya (Principal Investigator)

Real-time EGR Split Optimization between High and low-pressure exhaust gas recirculation in GT Power simulations using different controllers to lower NOx emissions from a diesel turbocharged engine.

02-Jan-2018 - 02-Jan-2018

EGR transient operation in highly dynamic transient cycles

Patil, Chaitanya (Principal Investigator) Galindo, Jose (Principal Investigator) Climent, Hector (Principal Investigator)

01-Jan-2017 - 28-Jan-2019

More projects