---
title: DCC Project Showcase Projects
---

# TU Delft DCC Showcase 2024

Thank you for your interest in the DCC project Showcase 2024!

During the showcase event, we shared a general overview of our completed projects from 2024 through a series of short presentations. Here, we provide a deeper dive into these projects. We hope you find these resources informative.

Explore the resources below to revisit the solutions, insights, and best practices we discussed. You’ll find project abstracts below and detailed information organized in the (left-most) table of contents for easy navigation.

## [OBeLiX](/@2024-DCC-showcase/rJKFHqAkye)

_**Applicant:** Adarsh Kalikadien (Applied Sciences)_

**Description**

OBeLiX (The Open Bidentate Ligand eXplorer) is a software workflow designed to simplify and automate the generation and analysis of transition-metal-based catalyst structures. Its goal is to make the process of building catalyst structures and calculating chemical properties more efficient, reproducible, and accessible.

**Role of the DCC**

The contributions made by the DCC to the OBeLiX software package were the following:

- Implemented integration tests to validate the different workflows in OBeLiX.
- Set up a Continuous Integration workflow for automated testing of new code contributions to the GitHub repository.
- Established a data management infrastructure in the cloud to facilitate storage and analysis of workflow results.

**Useful links:** [GitHub Repository](https://github.com/EPiCs-group/obelix)

---
## [Flaperon - Flight Mechanics Modeling](/@2024-DCC-showcase/B1HgLqAJJg)

_**Applicant:** Carmine Varriale (Aerospace Engineering)_

**Description**

Sustainable aircraft configurations of the future are going to feature extremely integrated and strongly interacting components. For example, wing aerodynamics impacted by propellers impinging on it, and battery performance impacted by cooling airflow. Because of this complexity, current studies are focused on high-fidelity component-level analyses, which say little or nothing about the behavior of the aircraft in any operational scenario.

This project aims to provide a framework to integrate different component-based models into an aircraft system or sub-system level model, capable of providing interpretable flight performance metrics, and optionally feeding back to a configuration design optimizer.

**Role of the DCC**

The DCC contributed to the project by:

- Setting up a collaborative workflow on GitHub.
- Implementing software testing and integrating continuous integration with GitHub Actions for documentation and testing.
- Managing gitmodules for MATLAB utilities.
- Supporting brainstorming and formalization of project ideas.

**Useful links:** [GitHub Organisation](https://github.com/TUDelft-FPP-Group)

---

## [Illuminator - Energy System Integration Development (ESID) Kit](/@2024-DCC-showcase/Hy3xLqRkyx)

_**Applicant:** Milos Cvetkovic (Electrical Engineering, Mathematics, and Computer Science)_

**Description**

Illuminator is an open-source energy system integration development kit designed to demonstrate the challenges that energy grids face during the energy transition. The software behind Illuminator provides educators, researchers, energy consultants, and engineers with an intuitive tool for explaining energy system concepts to a broader audience. It offers simulations of energy models, scenarios, strategies, and policies, making complex concepts more accessible. The Illuminator can be deployed using Raspberry Pi devices to simulate different nodes in an energy system.

**Role of the DCC**

In this project, our research software engineers:

- Reviewed the Illuminator’s source code and provided advice and hands-on support to enhance its modularity, testability, and maintainability.
- Improved usability and accessibility by preparing a well-documented and easier-to-set-up version of the Illuminator, soon to be available as both a standalone Python package and a Raspberry Pi distributed system.

**Useful links:** [GitHub Repository](https://github.com/Illuminator-team/Illuminator)

---

## [QDSim - Quantum Dot Simulator Package](/@2024-DCC-showcase/H1kWI9Ckyx)

_**Applicant:** Valentina Gualtieri (QuTech)_

**Description**

QDSim (Quantum Dot Simulator) is an open-source Python package used to simulate quantum dot devices. The package is designed to be user-friendly and efficient, allowing for the simulation of charge stability diagrams for large quantum dot devices (even 100+ dots) in a matter of minutes. QDSim was developed by Valentina as part of her PhD project within the Quantum Matter and AI group at QuTech.

**Role of the DCC**

The main goal of the DCC was to parallelize QDSim, enabling multiple devices and configurations per device to be simulated concurrently. Additional contributions included:

- Setting up a ReadTheDocs documentation website.
- Developing tests for both new and existing functionality.
- Creating a conda environment to manage all package dependencies.

**Useful links:** [GitLab Repository](https://gitlab.com/QMAI/papers/qdsim)

---

## [Hi-res Salt Intrusion Modeling](/@2024-DCC-showcase/SJMWLqAyyl)

_**Applicant:** Marlein Geraeds (Civil Engineering and Geosciences)_

**Description**

This project involves developing a detailed coast-delta model that incorporates non-hydrostatic effects. Issues addressed include the influence of extreme weather conditions, human interventions, and climate change on the frequency and severity of salt intrusion events. Better predictions of salt intrusion are a crucial requirement for short-term decision-making and long-term strategies to mitigate risks through countermeasures. Analyzing this high-resolution model often requires more computational resources than available.

**Role of the DCC**

The DCC supported this project by:

- Testing and implementing various data storage, transfer, and analysis options.
- Optimizing performance by reducing or selecting data and parameters.

**Useful links:** [GitHub Repository](https://github.com/mgeraeds/hires-processing)

---

## [AI Models for Water Networks](/@2024-DCC-showcase/HkD-I5Rkkx)

_**Applicant:** Alexander Garzón Díaz (Civil Engineering and Geosciences)_

**Description**

This project focuses on stormwater system infrastructure, specifically on transferable and data-efficient metamodelling of the stormwater system nodal depths using auto-regressive graph neural networks. Utility companies typically rely on computer simulators to design, manage, and operate these systems, but they are time-consuming. Researchers are turning to more cost-effective models, such as meta-models, as alternatives to computationally expensive simulations. By applying inductive biases and transfer learning, this project develops stormwater system metamodels that require minimal data while maintaining high performance across different contexts.

**Role of the DCC**

The DCC contributed to this project by:

- Providing knowledge transfer on best practices for software development, licensing, publishing, and archiving.
- Conducting test-driven refactoring and reproducibility checks of the experimental setup.
- Advising on environment and dependency management, software version control, and documentation improvement.
- Assisting with restructuring project components and implementing data versioning control.

**Useful links:** [GitHub Repository](https://github.com/alextremo0205/SWMM_GNN_Repository_Paper_version), [Peer-Reviewed Research Article](https://doi.org/10.1016/j.watres.2024.122396), [4TU Dataset](https://doi.org/10.4121/fec1e3de-9586-4a61-b3a1-02382592e52c)

---

## [TUdat - Open Science in Space](/@2024-DCC-showcase/HktbL9Akkl)

_**Applicant:** Dominic Dirkx (Aerospace Engineering)_

**Description**

TUdat is an open-source software suite (C++ code with a Python interface) for numerical state propagation and estimation of spacecraft and natural solar system bodies. Its modular and flexible setup allows it to be used in applications ranging from orbit determination and space situational awareness to re-entry vehicle design and space mission optimization. TUdat is used in two MSc courses (approximately 40-50 students per year) and 20-30 MSc thesis projects. It is also currently part of five ongoing PhD projects.

**Role of the DCC**

The contributions made by the DCC to tudat(py) include:

- Implementing a fast Continuous Integration workflow to provide developers with prompt feedback on their code changes, addressing the bottleneck of the approximately 4-hour compilation time for C++ source code across Linux, MacOS, and Windows.
- Reducing the number of steps and dependencies in the code and API documentation process to improve maintainability.
- Simplifying and automating the process for creating and releasing new conda packages for the software.

**Useful links:** [GitHub Repository - Tudat](https://github.com/tudat-team/tudat), [GitHub Repository - Tudatpy](https://github.com/tudat-team/tudatpy)

---

## [GeoDykeMonitor](/@2024-DCC-showcase/r1pZL5Rk1l)

_**Applicant:** Ching-Yu Chao (Civil Engineering and Geosciences)_

**Description**

Researchers and water management stakeholders are working on monitoring the behavior and health of dikes in response to weather conditions and climate change. This involves installing various types of sensors from different companies on the dikes. The project requires a platform to efficiently collect, present, and monitor this data through a user-friendly dashboard.

**Role of the DCC**

The DCC supported this project by:

- Collaborating with researchers to understand field activities and the entire data lifecycle management process.
- Developing accurate data models to reflect real-world activities and measurements.
- Selecting and implementing a backend technology stack using a RESTful API with FastAPI.
- Designing and implementing a relational database to store and manage sensor data.
- Building a frontend dashboard that interacts with the database via API endpoints, providing a user-friendly interface for monitoring and retrieving data.

**Useful links:** [GitHub Repository](https://github.com/TUDelft-GeoDykes/geodykes-fastapi)

---

## [LaPyn](/@2024-DCC-showcase/HJGz8c011e)

_**Applicant:** Bram van den Eijnden (Civil Engineering and Geosciences)_

**Description**

LaPyn (Python Access to Lagamine) is a graphical user interface (GUI) for the LAGAMINE finite element solver. Developed by researchers at TU Delft, LaPyn serves as an alternative to the existing GUI for Lagamine, which was built using Visual Basic. The older Visual Basic-based interface is considered outdated and challenging to maintain or extend due to the limited familiarity with the language among the current Lagamine community.

**Role of the DCC**

The DCC advised Bram on the following aspects:

- Strategies for extending LaPyn to support all materials compatible with Lagamine.
- Delivering the application to end-users via a standard Windows installer.
- Implementing good programming practices and tutorials on collaborative software development.

---

## [AWE Simulation Toolchain](/@2024-DCC-showcase/Sk4MUcCk1e)

_**Applicant:** Jelle Poland (Aerospace Engineering)_

**Description**

Airborne Wind Energy (AWE) uses tethered airborne devices to harness high-altitude wind resources inaccessible to traditional turbines. The largest AWE research group, based at TU Delft, focuses on sharing knowledge with the community and collaborating with industry startups. Their goal is to aid AWE system development and accelerate sector growth using an integrated open-source toolchain, contributing to faster energy harvesting and the energy transition.

**Role of the DCC**

The DCC supported this project by:

- Establishing good software development practices through weekly meetings and seminars.
- Supporting individual projects with best practices, FAIR principles, and updated software design through consultations and hands-on assistance.
- Reviewing and enhancing software architecture to enable modular integration of existing projects.

**Useful links:** [GitHub Repository](https://github.com/awegroup)

---

## [JointAI](/@2024-DCC-showcase/B1Hb8cC1kg)

_**Applicants:** Riccardo Taormina (Civil Engineering), Artur Schweidtmann (Applied Sciences)_

**Description**

Artificial intelligence (AI) is becoming increasingly adopted at TU Delft through initiatives such as the [AI Labs & Talent Programme](https://www.tudelft.nl/en/ai/tu-delft-ai-labs). The research community has a common need for cost-effective tools that enable experts to share their knowledge and improve AI-based approaches. This project aims to build an open-source online community where experts can share and peer-review their data to improve AI’s performance in retrieving accurate domain-specific information.

The project is based on two separate applications which required a structured system to organize and interpret complex data, as well as an integrated workflow to handle it.

**Role of the DCC**

The DCC contributed by:

- Exploring different specialized platforms to host datasets, models, and code.
- Delivering initial prototypes for hosting the outcomes of the project.
- Enabling cross-faculty collaboration and onboarding new DCC members and researchers.
- Developing a platform prototype, which will be exposed to the research community in a second phase.

**Useful links:** [Dataset(s)](https://huggingface.co/draco-ai), [GitHub Repository](https://github.com/rtaormina/DRACO-phase1)