Marco Turrini

Marco Turrini

Engineer & Researcher | Wind Energy · Fluid Mechanics · Computational Physics

With an international experience, a background in mechanical engineering and a passion for understanding nature, I enjoy taking on challenges, utilising computational modeling to data-driven insights. My work spans scientific research, consulting, and project management. I am committed to sustainability, ethical impact, and bringing diverse perspectives together to deliver meaningful results.

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Featured Projects

Selected research and engineering projects demonstrating expertise in wind energy and computational fluid dynamics.

LiDAR Wind Field Reconstruction (GloBE)

2021-2024 | Research Project

Developed machine learning algorithms using Gaussian Processes to optimize Lidar scanning patterns and reconstruct 3D wind fields for characteristic atmospheric events.

Python Gaussian Processes Lidar Data Analysis

Wake Modeling for Large Wind Farms (SAWOP, CliF)

2022-2023 | Research Project

Wind farm flow control optimization for optimized yield in offshore wind farms. Developed a flow estimation model using SCADA data from turbines

Optimisation Wake modeling MATLAB

Anomaly Detection Tool

2022-2024 | Research Project

Developed tools for outliers and trend detection using Isolation Forest and Clustering algorithms for data processing.

Machine Learning Sensor fusion Data Integration

Settling Velocity Density Function of Cohesive Particles from Turbidity Measurements

2023-2025 | Research MSc Thesis

Designed measurement set up and data analysis procedure to estimate Particle Velocity Density function of deep sea water sediment samples.

Deep/Convolutional Neural Networks Python Optical Measurements

Selected Publications

Peer-reviewed papers.

Advancements in Wind Turbine Wake Modelling using 3D scanning LiDAR measurements

M. Turrini, Kisorthman Vimalakanthan, Edwin Bot

Journal of Physics: Conference Series, 2025 | Downloads: 88

📄 PDF 🔗 DOI 📊 IOP Science

Conference presentations:

Wind Direction Measurement Uncertainty Driven Analyis in a Sensor-Equipped Wind Farm

M. Turrini, N. Cassamo, H. Links, K. Hermans, L. Laas

📄 PDF

MSc Thesis:

Settling Velocity Density Function of Cohesive Particles from Turbidity Measurements

M. Turrini; Supervisors: R. Ouillon, F. Gallaire, T. Peacock

📄 PDF 📊 EPFL InfoScience

Research reports.

Application of Gaussian Processes to Dual-Doppler LiDAR scanning measurements for high frequency wind field reconstruction

N. Cassamo, M. Turrini, D. Wouters, A. van der Werff,W. Castricum, J.W. Wagenaar

📄 PDF

Technical Skills & Expertise

Programming & Data Science

Python
MATLAB
R
SQL

Machine Learning & AI

TensorFlow
PyTorch
scikit-learn
Gaussian Processes
Deep Learning
Neural Networks

Wind Energy Tools

Flow and Wake Modeling
Wind Farm Control
SCADA, LiDAR, sensor Analysis
Resource Assessment

Optimization & Control

Optimization Algorithms
Control Theory
Energy Management
Grid Integration
Cost Analysis

HPC & Computing

HPC Clusters
Git

Research Areas

Core domains of expertise spanning atmospheric sciences, machine learning, and sustainable energy systems.

Atmospheric & Wake Flow Modeling

I work on improving our understanding of wind behavior in and around wind farms, especially focusing on wake dynamics, turbulence, and mesoscale coupling. This couples with CFD, and mesoscale models like WRF to reduce uncertainty in flow and predictions and turbine behavior.

Lidar Measurements & Smart Scanning

I develop strategies for LiDAR data analysis. These methods aim to capture critical phenomena such as blockage, gusts, jets, and shear layers with higher resolution and relevance for wind turbine and farm operation and research.

Machine Learning

I'm exploring data-driven models for forecasting and data analysis applied to various data types. By integrating ML with physical models, I aim to build hybrid solutions that are both accurate and interpretable.

Curriculum Vitae

Download my complete CV for detailed information about my education, experience, publications, and achievements.

📥 Download CV (PDF)

Last updated: November 2025