Optimizing the pump schedule of water distribution systems using a deep learning meta-model: To what extent can algorithm unrolling optimize the pump schedule of an urban water distribution system?

This thesis investigates the integration of algorithm unrolling and genetic algorithms (GA) for optimizing pump scheduling in water distribution systems (WDS), a critical component for ensuring energy-efficient water delivery. In the context of modern civilization’s reliance on clean, affordable water for diverse uses, the operation of a WDS,...

Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based...

Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach

Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs' Operational Design Domain ...

An Efficient Modeling Approach for Probabilistic Assessments of Present-Day and Future Fluvial Flooding

Risk-informed flood risk management requires a comprehensive and quantitative risk assessment, which often demands multiple (thousands of) river and flood model simulations. Performing such a large number of model simulations is a challenge, especially for large, complex river systems (e.g., Mekong) due to the associated computational and...

Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment

Monte Carlo simulation is often used for the reliability assessment of power systems, but it converges slowly when the system is complex. Multilevel Monte Carlo (MLMC) can be applied to speed up computation without compromises on model complexity and accuracy that are limiting real-world effectiveness. In MLMC, models with different complexity...

Energy dissipation response of a rectangular metal tube on axial impact

A surrogate model is used to predict the energy dissipation response for a rectangular beam, initiated by an axial impact. This method is used for one type of steel, after which a generalization is performed to make it available for multiple types of metal.

Stochastic Surrogates for Measurements and Computer Models of Fluids

Both measurements and computer simulations of fluids introduce a prediction problem. A Particle Image Velocimetry (PIV) measurement of a flow field results in a discrete grid of velocity vectors, from which we aim to predict the velocity field or related quantities. In Computational Fluid Dynamics (CFD), the output of the computer code depends...

Manifold mapping optimization with of without true gradients

This paper deals with the Space Mapping optimization algorithms in general and with the Manifold Mapping technique in particular. The idea of such algorithms is to optimize a model with a minimum number of each objective function evaluations using a less accurate but faster model. In this optimization procedure, fine and coarse models interact...

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design...