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

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...

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

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...

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

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...

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...

Use of reduced-order models in well control optimization

Many aspects of reservoir management can be expected to benefit from the application of computational optimization procedures. The focus of this review paper is on well control optimization, which entails the determination of well settings, such as flow rates or bottom hole pressures, that maximize a particular objective function. As is the case...

Model-reduced gradient-based history matching

Gradient-based history matching algorithms can be used to adapt the uncertain parameters in a reservoir model using production data. They require, however, the implementation of an adjoint model to compute the gradients, which is usually an enormous programming effort. We propose a new approach to gradient-based history matching which is based...

Unsteady Flow Organization of a Shock Wave/Boundary Layer Interaction

A fundamental experimental study is carried out to investigate the unsteady flow organization of an incident shock wave/turbulent boundary layer interaction at Mach 2.1. Planar and tomographic particle image velocimetry (PIV) are used in combination with data processing using the proper orthogonal decomposition (POD), complemented with hot-wire...

Use of POD in control of flow through porous media

During the design of development concepts for the exploitation of oil and gas reservoirs, frequent use is made of numerical simulation of the flow of multi-phase fluids through porous rock. Recently, increased attention has been paid to systematic optimization of well positions and operating parameters (rates, pressures) over the life of the...

Use of POD in control of flow through porous media

During the design of development concepts for the exploitation of oil and gas reservoirs, frequent use is made of numerical simulation of the flow of multi-phase fluids through porous rock. Recently, increased attention has been paid to systematic optimization of well positions and operating parameters (rates, pressures) over the life of the...

Model reduction by proper orthogonal decomposition for lambda-omega systems

Proper orthogonal decomposition (POD) is a powerful technique for model reduction of linear and non-linear systems. It is based on a Galerkin type discretization with basis elements created from the system itself. In this work POD is used to derive low-order models for a so-called lambda-omega system that is a universal model to investigate two...

Model reduction by proper orthogonal decomposition for estimation of scalar parameters in elliptic PDEs

Proper orthogonal decomposition (POD) is a powerful technique for model reduction of linear and non-linear systems. It is based on a Galerkin type discretization with basis elements created from the system itself. In this work POD is applied to estimate scalar parameters in elliptic partial differential equations. The parameter estimation is...