Towards a fully distributed multivariable hydrological deep learning model with graph neural networks

Fully distributed hydrological models take into account the spatial variability of a catchment, and allow for assessing its hydrological response at virtually any location. However, these models can be time-consuming when it comes to model runtime and calibration, especially for large-scale catchments. Meanwhile, deep learning models have shown...

Multi-level damage diagnosis on stiffened composite panels based on a damage-uninformative digital twin

In this study, a multi-level Structural Health Monitoring methodology for stiffened composite panels is introduced. A digital twin (DT), that is, a three-dimensional finite element (FE) model, representing the pristine state baseline of the test article, is developed and verified for compressive loading in the post-buckling regime. The...

Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimization of water distribution and urban drainage systems. With the advent of machine learning (ML),...

Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model

We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing...

A Fast Neural Network-Based Computational Framework for the Prediction of Skin Contraction

Burn injuries occur daily and can have severe physical and mental effects both in short and long term, such as disabilities due to severe skin contraction. Even though the mortality rate has decreased over the years, the need for a higher quality of life after severe burns remains. Decreasing the probability of a severe contraction is essential...

MDO with composite aeroelastic tailoring

With the focus on the reduction of fossil fuel emissions, aircraft are continuously growing towards higher fuel efficiency. The traditional limits of aircraft performance can be surpassed through the use of composite materials which offer a reduction in aircraft weight. Due to the multidisciplinary nature of aircraft design, integration of...

Bayesian model calibration with interpolating polynomials based on adaptively weighted leja nodes

An efficient algorithm is proposed for Bayesian model calibration, which is commonly used to estimate the model parameters of non-linear, computationally expensive models using measurement data. The approach is based on Bayesian statistics: using a prior distribution and a likelihood, the posterior distribution is obtained through application...

Automatic tuning of photonic beamformers: A data-driven approach

Beamforming is a signal processing technique used in highly directional antennas. An array of antenna elements transmits the same signal, but with a different time delay for each element. By providing the right time delays for each antenna element, the whole array transmits a high-powered signal in one desired direction. This technique can be...

Engineering an Optimal Wind Farm

The project covers creation of a framework for optimisation of wind farm layouts. The optimisation is done with an objective of maximising the Net Present Value of the farm. With that aim, both power production and the fatigue driven component degradation are considered. The work is split in two main parts. First, in order to circumvent the use...

Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-stationary Gaussian process

This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using...