Modelling the impact of supersonic aviation emissions on atmospheric ozone concentrations using data-driven methods

With renewed interest in the development of civil supersonic aircraft, their return in the future is becoming more ever more likely. The environmental impact of emissions in the stratosphere on climate and the ozone layer therefore needs to be explored. The stratospheric ozone levels determine the amount of harmful ultraviolet radiation reaching...

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

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,...

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

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...

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

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...

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

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...

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

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...

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

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...

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

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...

Two-level preconditioning applied on the ssSNPBLUP model

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...

A modified POD procedure with patterns in time for parameter estimation

Proper orthogonal decomposition (POD) is a well established model order reduction technique, however its efficiency is limited for advection dominated model. In this report, we explore a modified proper orthogonal decomposition procedure with patterns in time instead of the usual patterns in space. This modified procedure is more suitable for...

Characteristics of flow through orifices in pipes: An experimental investigation

Orifice plates are key components used for flow measurement and control in several industries. For instance, they find applications in gas and liquid circuits of lithography machines, nuclear power plants and aerospace propulsion systems. They are used typically either for measuring flow-rate or to introduce a pressure drop for purposes of flow...

Nonlinear Model Order Reduction using POD/DEIM for Optimal Control of Burgers' Equation

The model-order reduction techniques Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM) have been applied for the optimal control of Burgers' equation. Accuracy and performance of the reduced models have been studied in detail for different values of the viscosity parameter and different sizes of the...

Model-Reduced Gradient Based Production Optimization: Computational Analysis of POD-Based Model-Order Reduction in the Context of Production Optimization

Reduced-order modelling procedures, seen as an alternative to the classical gradient based approach, in which the adjoint of the tangent linear approximation of the forward model is replaced by the adjoint of a linear approximation of a reduced forward model. Proper orthogonal decomposition is used to compute the reduced model, by using the...