On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Cross-linked polymer gel is widely used in the oil and gas industry to block high permeability conduits and reduce water cut. The complex nature of this fluid, especially regarding flow in porous media, makes its numerical simulation very time-consuming. This study presents an approach to designing an Artificial Neural Network (ANN) model...

Surrogate models for the characterization of hydrodynamic loads on perforated monopiles

Perforated monopiles show promise in providing a better alternative to the commonly used jacket-like substructures used in intermediate water depths in the range of 30 to 120 m. By introducing perforations near the vicinity of the splash zone the wave loads on the monopile can be mitigated and the fatigue damage reduced, which is the main...

Data-driven RANS closures for wind turbine wakes under neutral conditions

The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are Reynolds-Averaged Navier–Stokes (RANS) which are two orders of magnitude cheaper, but resolve only mean quantities...

Data-driven boundary layer loss model for organic rankine cycle turbomachines

There is a requirement for localised efficient electricity generation systems that increase the efficiency of power plants and reduce wasted heat from other engineering applications such as cement manufacturing units and brick kilns. Organic Rankine Cycle (ORC) power systems use organic fluids and low-grade heat sources to accomplish this. Lack...

Data driven turbulence modelling

RANS simulation are one of the most used tools for aerodynamic analysis. The advantage of RANS simulations is the reduced computational cost compared to other methods such as LES or DNS. This is because by solving the RANS equations one only solves for the mean flow. However, this reduction in computational cost comes at the price of uncertainty...

Enhancement of data-driven turbulence models for wind turbine wake applications

Wind turbine wakes cause significant reductions in power production and increased fatigue damage for downwind turbines. Thus, they affect the wind levelized cost of energy. Computational Fluid Dynamics (CFD) can be used to quantify the wake characteristics, whereby Reynolds-averaged Navier-Stokes (RANS) has the most potential for industrial...

Machine Learning of Wind Plant Turbulence Anisotropy Fields

In studies of wind plant designs, wake dynamics are of great interests as wakes affect downstream turbine loading that impacts wind plant efficiency. Recent developments of Tensor Basis Decision Tree (TBDT) based machine learning (ML) models in reconstructing the turbulence anisotropy fields of simple flow cases prompt the motivation in applying...

Prediction of flow-fields by combining high-fidelity CFD data and Machine Learning algorithms

High-fidelity optimisation studies are a useful asset in the design of critical components for large gas turbines. These studies require the computation of numerous computationally expensive CFD simulations and result in predominantly optimisation graphs of design objectives (e.g. Pareto-front figure). The quantity of generated data is...

A Study of Global-Coefficient Non-Linear Eddy Viscosity Models

In this research a global-coefficient non-linear eddy viscosity model (NLEVM) is studied. This model stems from the inherent inability of the Boussinesq approximation to model anisotropy and therefore flow features such as: swirl, stream-line curvature and secondary motions (Lumley, 1970; Pope, 1975; Craft et al., 1996). The focus lies on the...