M&TT Colloquia 2024-2025
19-12-2024 - Lecture Hall E
PostDoc presentation
Challenges in High-Fidelity Simulations of Two-Phase Flows Undergoing Phase Change
Jordi Poblador Ibanez
Multiphase flows undergoing phase change are present in many engineering applications. In particular, they play an important role in the energy transition landscape. Some examples include the injection of biofuels, the boiling of coolants in heat exchangers, or the formation of bubbles in water electrolysis. A common approach to study numerically these systems is based on the one-fluid formulation of the Navier-Stokes equations, which treats the phase discontinuity as a single fluid with varying properties and adds additional source terms to impose interfacial jump conditions, e.g., surface tension. Many legacy codes used to study non-evaporative conditions have been extended to phase-change simulations. However, the numerical discretization of the one-fluid governing equations under such conditions introduces errors that must be corrected to ensure the physical consistency of the results..
19-12-2024 - Lecture Hall E
PostDoc presentation
CaNS-Fizzy. A GPU-accelerated solver for two-phase turbulent flows
Giandomenico Lupo
Direct Numerical Simulations of two-phase flows with high levels of interface dispersion, such as bubble or droplet-laden turbulent flows, remain computationally expensive due to the high-resolution requirement imposed throughout the whole computational domain by the direct solution of the interface morphology and of the transport at the interface scale. The finite different solver CaNS-Fizzy has been developed to enable these simulations by exploiting the computational capacity of modern GPU clusters. The key features that keep the computational cost affordable are the one-fluid formulation of the two-phase flow, the use of a fast direct solver for the pressure Poisson equation, and the diffuse representation of the phase interface. The latter allows for continuous and smooth mapping of the physical fields and requires no explicit interface reconstruction, keeping the computational load constant regardless of the local interface topology and thus making the algorithm particularly suited for parallelization on GPU architecture.
05-12-2024 - Lecture Hall E
PhD presentation
Degradation-Conscious Model predictive Control For Marine Solid Oxide Fuel Cells
Andrea Caspani
The AmmoniaDrive project investigates the adoption of ammonia fueled hybrid powertrains for cargo vessels, based on the combination of Solid Oxide Fuel Cells (SOFCs) and internal combustion engines technology. SOFCs represent a promising technology in electric power generation, especially for alternative fuels and large-scale applications. However, their widespread adoption still requires improved reliability, particularly in addressing cell degradation, which directly affects its functional lifetime.
This presentation introduces a Degradation-Conscious control strategy for SOFCs, which combines the information of the dynamical and of the degradation models of the cell to meet power demand while reducing cell’s deterioration throught time. The approach involves a new state space model that integrates a reduced-order SOFC dynamics model with a voltage degradation model, which are inserted in a Nonlinear Model Predictive Control framework. Simulation results demonstrate the effectiveness of this strategy in reducing degradation while maintaining the required dynamic performance.
05-12-2024 - Lecture Hall E
PhD presentation
Ammonia-hydrogen as fuel for internal combustion engines
Isabelle Jacobs
My research is part of the AmmoniaDrive project, which investigates an ammonia-fueled power plant concept consisting of a solid oxide fuel cell (SOFC) and a reciprocating internal combustion engine (ICE).
My research puzzle piece within this project is the in-cylinder combustion of ammonia-hydrogen (NH3-H2). In this presentation, I will provide an overview of combustion properties, in-cylinder combustion strategies and the process of developing a model with predictive capabilities while limiting the computational costs.
21-11-2024 - Lecture Hall E
PhD presentation
Predictive Maintenance of Marine Diesel Engines: Integrating In-Cylinder Pressure Measurements, Machine Learning, and First-Principles Models
Youri Linden
This study addresses the pressing need for advanced maintenance strategies to enhance operational readiness of critical assets, particularly Marine Diesel Engines in naval vessels. Against a backdrop of heightened geopolitical tensions and increased defense budgets, optimizing maintenance through data-driven and condition-based approaches has become essential. The research focuses on leveraging advanced condition monitoring techniques, such as in-cylinder pressure measurements, to detect early signs of engine degradation. A key objective is to adapt these methods to real-world operational settings characterized by noisy, incomplete data and environmental variability. By bridging gaps in existing literature, the study aims to develop hybrid models integrating machine learning and physical principles for real-time fault detection and degradation prediction. With access to both controlled experimental and operational naval data, this work seeks to deliver robust, practical solutions, enhancing the predictive maintenance capabilities essential for naval operational efficiency and asset reliability.
21-11-2024 - Lecture Hall E
PhD presentation
Deep sea mining turbidity current dispersion on a slope
Shaheen Wahab
The global transition to clean energy technologies, critical for achieving a low-carbon future, intensifies the demand for essential raw materials such as nickel, cobalt, and rare earth metals. Land-based mining faces challenges like ore grade depletion and environmental concerns, prompting exploration of alternative sources like deep-sea mining (DSM). DSM, particularly in regions like the Clarion-Clipperton Zone (CCZ), offers access to critical metals in polymetallic nodules but raises environmental concerns, notably sediment plumes that threaten deep-sea ecosystems. This research investigates the near-field dispersion dynamics of turbidity currents from DSM activities, focusing on slope angle and velocity effects. Experiments conducted in TU Delft’s Offshore and Dredging Laboratory used a flume tank setup, simulating turbidity currents with glass beads as sediment. Various measurement techniques such as Acoustic Doppler velocimeters, ultrasonic velocity profilers, and multi-angle cameras revealed that steeper slopes and higher velocity ratios increased sediment bulge formation and dispersion. Downhill experiments exhibited wider dispersion angles with higher velocity ratios, while uphill conditions showed inverse trends. Findings emphasize slope angle and discharge velocity as key factors influencing sediment plume behaviour.
07-11-2024 - Lecture Hall F
PhD presentation
The Potential of Water Jet Stimulation for Flatfish Trawling
Sterre Bult
Commercial demersal trawl fisheries are a large source of physical disturbance to marine habitats. The tickler chain beam trawl causes large bed disturbances, in addition to high fuel costs owing to high drag forces and a low catch efficiency and selectivity. The objective of this research is therefore to develop a new catching technique without electrical stimuli for sole and plaice that minimizes the bed disturbance and increases the catch efficiency. The project is a collaboration between Wageningen University & Research (WUR) and Delft University of Technology (TUD), with the aim to combine the insights on the avoidance behaviour of flatfish and the impact of various stimuli on the seabed for the design of the new catching technique. At TUD, the bed disturbance as a result of various mechanical and hydrodynamical stimuli will be quantified. The focus will be on the effect of hydraulic stimuli on the seabed, more specifically water jets. I will combine small-scale laboratory experiments and computational fluid dynamics modelling to optimize the bed disturbance, while applying sufficient stimulus to initiate a startle response in flatfish. In this talk, I will discuss preliminary experimental and numerical results, as well as our objectives for the coming years.
07-11-2024 - Lecture Hall F
PhD presentation
Acoustic emission monitoring of corrosion damage in marine structures
Romain Habiyaremye
Corrosion is amongst the main driving damage mechanisms for the degradation of marine assets. For ships, periodic inspections every five years in a dry dock are mandated by classification societies in order to prevent failures. These surveys are costly and leave the ship non-operational for an extended period of time. Corrosion damage can develop in crevices, under aged coatings, and in hard-to-reach places for traditional inspection methods. Complementary to these inspections, recently, the implementation of real-time inspection methods, so-called structural health monitoring systems (SHM) are becoming more prevalent. Acoustic emission (AE) monitoring is a SHM technique that consists in measuring elastic stress waves caused by a rapid release of energy when irreversible changes occur in a material. When these stress waves occur in thin-walled structures, such as ship hulls, these AE signals can travel over relatively large distances, making it possible to monitor large structures with a relatively low number of sensors. This presentation elaborates on the application of AE monitoring for corrosion detection, the challenges facing this technique, and how my research approaches some of these challenges.
31-10-2024 - Lecture Hall F
PhD presentation
Fatigue Data FUSION: Advancing Lifetime Prediction Models Toward a Unified Resistance Curve
Ruben Slange
Accurate fatigue life assessment is essential for extending the operational life of maritime structures, particularly for fatigue-sensitive details like welded joints. Compared to current practice, improvements can be made regarding the uncertainty of the estimation by incorporating a more advanced Fatigue strength parameter. The parameter includes more information about the stress state, resulting in similarity between different joints, allowing for a unified mid to high-cycle resistance curve for constant amplitude (CA) loading. In this talk, I will discuss how this resistance curve is obtained and how a design curve can be derived.
Extending our aim to variable amplitude (VA) loading conditions introduces the need for a damage accumulation model. The aim is to advance the typically adopted linear damage accumulation model and combine it with the effective notch stress as the fatigue strength criterion and a resistance curve formulation containing a fatigue limit. This will allow exploring the similarities between CA and VA resistance.
In the introduced accumulation model, resistance-induced damage non-linearities are accounted for, decreasing the fatigue limit for accumulating damage. Adopting a random damage resistance description and damage evolution exponent, the model can implicitly reflect sequence effects for different loading spectra, with damage accumulation behaviour ranging from concave upwards to downwards, implying an accelerating and decelerating damage accumulation process. This presentation will discuss preliminary results from this model and ongoing efforts to refine these methods for application in structural health monitoring and fatigue life prediction.
31-10-2024 - Lecture Hall F
PhD presentation
Navigating the maritime energy transition: preparing for tomorrows challenges
Jesper Zwaginga
The maritime industry is under increasing pressure to adopt effective emission reduction measures in response to global targets and the drive for sustainability. However, several challenges complicate decision-making, including: the industry's global nature, economic barriers, the diversity and complexity of vessel types, long asset lifespans, and uncertainties surrounding future regulations and technological development.
This presentation covers decision-making while facing such multi-challenge problems and how these can be addressed. It focuses on three core aspects of my research. First, I present a decision-making framework based on a literature review that guides decision-makers in identifying and categorising key challenges and determining suitable approaches. Second, decision-making over the lifecycle of a fixed or fuel-flexible ship design is explored in response to uncertainties like expected fuel prices and carbon taxation. Finally, I discuss my research into the practical implications of a “changeable” ship design and show an example of preparing a ship design for methanol conversion, illustrating how decisions made today can support tomorrows goals.
17-10-2024 - Lagerhuysch
PostDoc presentation
A validated DEM modelling framework on plate and pile penetrations in a double-layer scour protection
Hao Shi
Monopiles are the dominant foundation type for offshore wind turbines, accounting for approximately 80% of the installed capacity. Installing offshore monopile foundations requires monopiles to penetrate several pre-installed scour protection rock layers before securing them into the seabed. With the increasing power of the wind turbines, both the size of the monopile and the scour protection rocks increase. The accurate prediction of the penetration resistance is crucial to ensure successful monopile installations. To facilitate or even replace the costly and labour-intensive experimental small-scale penetration tests, a numerical model has been developed using the Discrete Element Method (DEM) that captures the discrete nature of interactions between rocks and piles and predicts the resistance during the penetration process. The developed DEM model includes armour and filter rocks represented by multispheres and sand particles by spheres. A multistage calibration, verification and validation DEM modelling framework is proposed and examined with small-scale penetration tests conducted using plates and piles in a bilayer scour protection configuration. The sand material model is calibrated and verified using penetrometer tests and the rock material models are calibrated and verified using a plate penetration test. The verified model predicts the penetration resistance well in small-scale pile penetration tests and proves the validity of the proposed framework. The proposed framework facilitates the modelling area where traditional continuum-based numerical methods give less accurate predictions and allow us to achieve further insights that are hardly or nearly impossible to obtain by experiments.
17-10-2024 - Lagerhuysch
PostDoc presentation
Efficient Constraint Multi-Objective Optimization with Applications in Ship Design
Roy de Winter
In this talk I will introduce the work that I conducted during my PhD at C-Job Naval Architects and the Natural Computing group of Leiden University. The main contribution of my thesis are efficient multi-objective optimization algorithms that can also handle constraints. I worked on and discovered several different strategies that speed up the optimization process despite the expensive nature of the objective and constraint functions. The algorithm that I will introduce in this talk is the IOC-SAMO-COBRA algorithm that has been used to optimize the damage stability criteria and the cargo hold volume of a single hold cargo ship by adjusting the bulkhead positions. The IOC-SAMO-COBRA algorithm incorporates all the techniques and recommendations discovered during my PhD, which include: 1) Use an as small as possible initial design of experiments for faster convergence, 2) Adaptively chose the best surrogate fit, 3) The use of uncertainty quantification methodologies is redundant when doing multi-objective optimization, 4) I t is possible to propose multiple solutions per iteration by using the hypervolume performance indicator, and finally 5) Combining inexpensive constraint and objective functions with surrogate models for expensive functions leads to better Pareto frontier approximations.
03-10-2024 - Lecture Hall A
PhD presentation
Towards sub-grid scale bubble modeling and Characterizing numerical artifacts in multiphase simulation
Tzu-Yao Huang
Simulating bubbly flows is challenging, particularly at high Reynolds (Re) and Weber (We) numbers, where bubble sizes vary significantly. Direct numerical simulation (DNS) is computationally infeasible for high-momentum cases like those induced by Olympic divers. To address this, we aim to develop a sub-grid scale (SGS) bubble model that ensures coarse grid solutions align with real-world data. Along the way, we identified an intriguing numerical artifact in sharp interface simulations, which we call ‘numerical shape diffusion.’ This presentation will cover our progress toward creating an SGS bubble model and our insights into these numerical artifacts in multiphase simulations.
03-10-2024 - Lecture Hall A
PhD presentation
Towards Adaptive Manufacturing Systems: Planning and scheduling methods for more sustainable and resilient factories
Rafael Leite Patrão
The manufacturing sector faces increasing uncertainty due to systemic changes like the green and digital transitions. These changes offer opportunities for factories to become more sustainable and resilient, but they also create significant challenges for decision-makers at strategic, tactical, and operational levels. To help factories adapt while maintaining production goals, we explored two key technological enablers: reconfigurable machines and machine learning techniques. We defined a manufacturing system that is both highly sustainable and resilient, using such enablers, as an adaptive manufacturing system. To drive existing factories towards greater adaptability over time, while minimizing production disruptions, we develop production planning and scheduling models that consider sustainability and resiliency metrics and integrate both technological enablers.
19-09-2024 - Lecture Hall A
PhD presentation
On physical-constraints in Scientific Machine Learning for fast flow field predictions
Manuel Cabral
Traditional fluid simulations, while highly accurate, are often too slow for many practical applications. Recently, machine learning has emerged as a promising alternative due to its ability to deliver fast predictions. However, ensuring the accuracy and physical consistency of these models requires the incorporation of physical priors. In this talk, we explore methods for embedding physical laws — such as symmetries and governing equations — into machine learning models, with a focus on fluid dynamics. By integrating these priors, we develop models that are not only more data-efficient but also smaller, making them well-suited for real-world applications where data and computational resources are often limited. Furthermore, we show that these physics-based models generalize better to previously unseen flow regimes, offering improved performance over purely data-driven approaches.
19-09-2024 - Lecture Hall A
PhD presentation
Digital Potential in Shipyards: A Path to Transformation
Miguel Calvache
Before diving into academia, I spent several years in the navy and the shipbuilding industry, where I saw firsthand the challenges shipyards face in keeping up with digital transformation. Now, as a PhD researcher, my work explores how digital technologies can reshape shipbuilding processes, addressing declining throughput and competitiveness. Using a mathematical model, I (try to) analyze the financial benefits of various technologies and apply innovation theories to understand why shipyards have been slow to adopt new systems. Join me this week as I share my journey from shipbuilding to digital transformation, and discuss the challenges and opportunities for the industry in the digital age.
12-09-2024 - Lecture Hall A
Mini-Symposium: Technical Talk
Perspective and applications of data-informed CFD
Bernat Font
The widespread adoption of data-driven modeling tools and advances in hardware architectures across the scientific computing community have also shaped the future of CFD solvers. In this context, we will review the opportunities and challenges arising from the use of ML models in scale-resolving turbulent flow simulations. As examples, the cases of data-driven turbulence modelling, and active flow control using reinforcement learning will be discussed. We will also introduce our flagship CFD solver, WaterLily, and discuss future research directions.