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Hybrid knowledge-based/deep learning reduced order modelling of high dimensional chaotic systems

Veerman, Jochem (author)

Chaotic systems are widespread and can be found everywhere, from small scale processes inside the human body to the large scale dynamics of the entire atmosphere. However, modelling these high dimensional chaotic systems is a difficult task due to the intrinsic nonlinear nature of chaos as well as the accompanied computational cost. Therefore,...

master thesis 2023

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Output Error Estimation for Unsteady Flows Using Reconstructed Solutions: Effect of Compression and Reconstruction of Unsteady CFD Data using Neural Networks and PODs on Error Estimates

Sitaram, Arnish (author)

Unsteady numerical simulation has been proven to be an essential tool for research. The quality of the results can be improved by using mesh adaptation. Mesh adaptation uses error indicators to refine the mesh in regions with high errors. The error indicators used are output errors with the most accurate output error estimation method being...

master thesis 2023

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Wake Dynamics of a Cylinder with a Flexible Splitter Plate: An Experimental Study

Paranjape, Aniruddha (author)

As the world grapples with the challenges of global warming, there has been a significant push for the development of new methods of harnessing renewable energy. Energy harvesters have been a new avenue to generate electrical power locally for remote applications. An energy harvester is a device that taps into aeroelastic phenomena to convert...

master thesis 2023

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Non-Intrusive Multi-Fidelity Reduced Order Modeling using Adaptive Sparse Grids: Analysis of Nuclear Reactors using Non-Intrusive Adaptive Multi-Fidelity Reduced Order Modeling Techniques

Bohnenn, Sylvie (author)

Computational power is a challenge when it comes to the high-fidelity modeling of nuclear reactors. Detailed simulations of reactor physics involve complex calculations that require significant computing resources, which can be time-consuming and expensive. Reduced Order Modeling (ROM) allows for an approximation of a complex model by only...

master thesis 2023

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Deflation techniques applied on mixed model equations

Vandenplas, Jeremie (author), Nguyen, B. (author), Vuik, Cornelis (author)

In this paper, we consider a block Jacobi preconditioner and various deflation techniques applied in the Deflated Preconditioned Conjugate Gradient (DPCG) method for solving a sparse system of linear equations derived from a statistical linear mixed model that analyses simultaneously phenotypic and pedigree information of genotyped and...

journal article 2023

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4D Flow Patterns and Relative Pressure Distribution in a Left Ventricle Model by Shake-the-Box and Proper Orthogonal Decomposition Analysis

Wu, X. (author), Saaid, Hicham (author), Voorneveld, J.D. (author), Claessens, Tom (author), Westenberg, Jos J.M. (author), de Jong, N. (author), Bosch, Johan G. (author), Kenjeres, S. (author)

Purpose: Intraventricular blood flow dynamics are associated with cardiac function. Accurate, noninvasive, and easy assessments of hemodynamic quantities (such as velocity, vortex, and pressure) could be an important addition to the clinical diagnosis and treatment of heart diseases. However, the complex time-varying flow brings many...

journal article 2023

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POD-based surface pressure reconstructions from sparse sensing: An experimental investigation

Hendriksen, Luuk (author)

Aerodynamic load determination through integration of the surface pressure distribution is limited in accuracy by the discrete number of measurements taken. Therefore, typically relatively large numbers of spatially distributed measurements are required which increase setup complexity, latency, cost and weight while requiring physical access in...

master thesis 2022

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Machine Learning Based local Reduced Order Modeling for the prediction of Unsteady Aerodynamic Loads

Catalani, Giovanni (author)

Advancements in aircraft performance require increasingly complex design processes and tools. Simulating the unsteady non-linear aerodynamic interaction between a maneuvering aircraft and the surrounding flowfield poses serious challenges. High-Fidelity Computational Fluid Dynamics (CFD) methods, based on the numerical solution of the Navier...

master thesis 2022

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An Experimental Study of the Aeroacoustic Properties of a Propeller in Energy Harvesting Configuration

Candeloro, Paolo (author), Martellini, Edoardo (author), Nederlof, R. (author), Sinnige, T. (author), Pagliaroli, Tiziano (author)

The aim of the present manuscript is to investigate the noise footprint of an isolated propeller in different flight configurations for the propulsion of a hybrid-electric aircraft. Experimental tests were performed at the Low-Turbulence Tunnel located at Delft University of Technology with a powered propeller model and flush-mounted...

journal article 2022

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POD–Kalman filtering for improving noninvasive 3D temperature monitoring in MR-guided hyperthermia

VilasBoas-Ribeiro, Iva (author), Nouwens, Sven A.N. (author), Curto, Sergio (author), Jager, Bram de (author), Franckena, Martine (author), van Rhoon, G.C. (author), Heemels, W. P.M.H. (author), Paulides, Margarethus M. (author)

Background: During resonance frequency (RF) hyperthermia treatment, the temperature of the tumor tissue is elevated to the range of 39–44°C. Accurate temperature monitoring is essential to guide treatments and ensure precise heat delivery and treatment quality. Magnetic resonance (MR) thermometry is currently the only clinical method to...

journal article 2022

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An enhanced algorithm for online Proper Orthogonal Decomposition and its parallelization for unsteady simulations

Li, X. (author), Hulshoff, S.J. (author), Hickel, S. (author)

Proper Orthogonal Decomposition (POD) plays an important role in the analysis of complex nonlinear systems governed by partial differential equations (PDEs), since it can describe the full-order system in a simplified but representative way using a handful of dominant dynamic modes. However, determining a POD from the results of complex...

journal article 2022

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A Weighted Surrogate Model for Spatio-Temporal Dynamics with Multiple Time Spans: Applications for the Pollutant Concentration of the Bai River

Huan, Y. (author), Tian, Yubin (author), Wang, Dianpeng (author)

Simulations are often used to investigate the flow structures and system dynamics of complex natural phenomena and systems, which are significantly harder to obtain from experiments or theoretical analyses. Surrogate models are employed to mimic the results of simulations by reducing computational costs. In order to reduce the amount of...

journal article 2022

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Analyzing the wake of a wind turbine actuated with Dynamic Induction Control

de Boer, Emiel (author)

To meet current renewable energy demands, there is a major increase in global wind power capacity. Wind turbines are commonly clustered in wind farms. When operating, wind turbines extract energy from the wind, creating a region characterized by a low velocity, called the wake. The wake negatively impacts the power production of downstream wind...

master thesis 2021

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Two-level preconditioning applied on the ssSNPBLUP model

Nguyen, Buu-Van (author)

The single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) model can potentially be used in animal breeding for genetic evaluations. It has been reported that this model has convergence issues when the Preconditioned Conjugate Gradient (PCG) method is applied. This is due to the linear system being ill-conditioned...

master thesis 2021

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Analysis of the Molten Salt Fast Reactor using reduced-order models

Alsayyari, F.S. (author), Tiberga, M. (author), Perko, Z. (author), Kloosterman, J.L. (author), Lathouwers, D. (author)

In this paper, we present a reduced-order modeling approach to study the Molten Salt Fast Reactor (MSFR). Our approach is nonintrusive and based on the proper orthogonal decomposition method. We include adaptivity in selecting the sampling points both in time and parameter space. Steady-state and transient analysis were both performed using...

journal article 2021

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

Lesjak, Mathias (author), Doan, N.A.K. (author)

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...

journal article 2021

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A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems

Alsayyari, F.S. (author), Perko, Z. (author), Tiberga, M. (author), Kloosterman, J.L. (author), Lathouwers, D. (author)

We present an approach to build a reduced-order model for nonlinear, time-dependent, parametrized partial differential equations in a nonintrusive manner. The approach is based on combining proper orthogonal decomposition (POD) with a Smolyak hierarchical interpolation model for the POD coefficients. The sampling of the high-fidelity model to...

journal article 2021

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Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Alsayyari, F.S. (author)

Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and...

doctoral thesis 2020

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A nonintrusive adaptive reduced order modeling approach for a molten salt reactor system

Alsayyari, F.S. (author), Tiberga, M. (author), Perko, Z. (author), Lathouwers, D. (author), Kloosterman, J.L. (author)

We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced model for a molten salt reactor system. Our approach is based on Proper Orthogonal Decomposition combined with locally adaptive sparse grids. Our reduced model captures the effect of 27 model parameters on k_eff of the system and the spatial...

journal article 2020

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Plasma Actuation for Mitigation of Fluctuating Loads on Airfoils: An Experimental Study

Rezaeiha, Abdolrahim (author), Kotsonis, M. (author)

Wind tunnel measurements are performed to investigate the potential for mitigation of aerodynamic load fluctuations on airfoils, as the main source of fatigue for wind turbines, using plasma actuators. The experiment consists of aerodynamic force measurements using six strain gauges and 2-component velocity measurements using particle image...

journal article 2020