Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning

The Wind Farm Layout Optimization problem involves finding the optimal positions for wind turbines on a wind farm site. Current Metahueristic based methods make use of a combination of turbine specifications and parameters, mathematical models and empirically produced power production equations to estimate the energy output of a real wind farm [15]. The overarching variable in any optimisation function is wind speed - this is what used to determine the power generated. Therefore, accurate predictions of wind speeds at specific points across the volume of the site are needed. In this paper, Computational Fluid Dynamics (CFD) was used to simulate a full scale rotating wind turbine blade with fluid (air) at various wind speeds flowing past the turbine. The wake effect can be observed and leads to decrease in wind speeds, as expected. Wind speed at specific x,y and z (3D) coordinates were sampled and used as input to common Machine Learning regression algorithms to create different surrogate models. This was needed as each individual CFD experiment takes approximately 8 hours to complete, so it is not feasible to continuously repeat these simulations inside a metaheuristic optimiser

Development of a stochastic computational fluid dynamics approach for offshore wind farms

In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction

Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model

Over the course of the last four decades, the rotor diameter of Horizontal Axis Wind Turbines (HAWTs) has undergone a substantial increase, expanding from 15 m (30 kW) to an impressive 240 m (15MW), primarily aimed at enhancing their power generation capacity. This growth in blade swept area, however, gives rise to heightened loads, stresses and deflections, imposing more rigorous demands on the structural robustness of these components. To prevent sudden failure and to plan effective inspection, maintenance, and repair activities, it is vital to estimate the reliability of the rotor blades by considering all the forces (aerodynamic and structural dynamics) acting on them over the turbine’s lifespan. This research proposes a comprehensive methodology that seamlessly combines fluid-structure interaction (FSI) simulation, Kriging model/algorithm and Adaptive Kriging Monte Carlo Simulation (AKMCS) to assess the reliability of the HAWT rotor blades. Firstly, high-fidelity FSI simulations are performed to investigate the dynamic response of the rotor blade under varying wind conditions. Recognizing the computationally intensive nature and time-consuming aspects of FSI simulations, a judicious approach involves harnessing an economical Kriging model as a surrogate. This surrogate model adeptly predicts blade deflection along its length, utilizing training and testing data derived from FSI simulations. Impressively, the Kriging model predicts blade deflection 400 times faster than the FSI simulations, showcasing its enhanced efficiency. The optimized surrogate model is then used to estimate the flap wise blade tip deflection for one million wind speed samples generated using Weibull distribution. Thereafter, to evaluate the reliability of the blades, statistical modeling using methods such as Monte Carlo Simulation (MCS), AKMCS is performed. The results demonstrate the faster convergence of AKMCS requiring only 21 samples, as opposed to 1 million samples for MCS with minimal reduction in the precision of the estimated probability of failure (Pf) and reliability index (β). Demonstrated on the backdrop of an IEA-15MW offshore reference WT rotor blade, the proposed methodology underscores its potential to be seamlessly incorporated into the creation of WT digital twins, due to its near real-time predictive capabilities for Pf and β assessments.Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging modelacceptedVersio

Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization

Converting energy from the currents found within tidal channels, open ocean, rivers, and canals is a promising yet untapped source of renewable energy. In order to permit current energy converters for installation in the environment, the CECs must be shown to non-negatively impact the environment. While developing these model increased utility may be gained if researchers may optimize mechanical power while constraining environmental effects. Surrogate models have garnered interest as optimization tools because they maximize the utility of expensive information by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence. An OpenFOAM model was developed where actuator-disc $k$-$\epsilon$ turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the $k$-$\epsilon$ equations. Parameter influences on longitudinal wake profiles were estimated using Gaussian-process regression with subsequent optimization achieving results within 3\% of those obtained using the full model representation, but for as low as 27\% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data

Development of Methods for Uncertainty Quantification in CFD Applied to Wind Turbine Wake Prediction

The CFD 2030 vision aims to improve computer simulations of fluid dynamics in fields like aerospace and energy. They focus on managing uncertainties in these simulations. This study presents two methods:1. Intrusive Polynomial Chaos (IPC) Stochastic Solver: This method employs Polynomial Chaos expansion to tackle uncertainties linked to fluid flow simulations. It characterizes parametric uncertainties, studying their nonlinear effects. The solver is tested on various scenarios, showing its promise for reliable Uncertainty Quantification (UQ) analysis in CFD without being overly intrusive or costly.2. Surrogate Based Uncertainty Quantification (SBUQ) using Deep Learning: A novel approach involves constructing a surrogate model using a neural network, capable of predicting wind flow within a wind farm based on single wind turbine data. This model is used to assess uncertainty in wind farm predictions, accounting for parameter and model form uncertainties.These techniques were tested on different scenarios and demonstrated their capability to analyze complex CFD simulations under various uncertainties. They contribute to the potential of enhancing accuracy and efficiency in UQ analysis. The IPC-based stochastic solver integrates efficiently with existing code, while the SBUQ approach utilizes data from individual wind turbine simulations to predict flow patterns in wind farms.Both methods enhance the accuracy of fluid simulations under different uncertainties. This research contributes to more dependable simulations for aerospace, energy, and environmental engineering applications

A Supportive Framework for the Development of a Digital Twin for Wind Turbines Using Open-Source Software Tiril Malmedal Mechanics and Process Technology

The world is facing a global climate crisis. Renewable energy is one of the big solutions, nevertheless, there are technological challenges. Wind power is an important part of the renewable energy system. With the digitalization of industry, smart monitoring and operation is an important step towards efficient use of resources. Thus, Digital Twins (DT) should be applied to enhance power output. Digital Twins for energy systems combine many fields of study, such as smart monitoring, big data technology, and advanced physical modeling. Frameworks for the structure of Digital Twins are many, but there are few standardized methods based on the experience of such developed Digital Twins. An integrative review on the topic of Digital Twins with the goal of creating a conceptual development framework for DTs with open-source software is performed. However, the framework is yet to be tested experimentally but is nevertheless an important contribution toward the understanding of DT technology development. The result of the review is a seven-step framework identifying potential components and methods needed to create a fully developed DT for the aerodynamics of a wind turbine. Suggested steps are Assessment, Create, Communicate, Aggregate, Analyze, Insight, and Act. The goal is that the framework can stimulate more research on digital twins for small-scale wind power. Thus, making small-scale wind power more accessible and affordable

Development of numerical and data models for the support of digital twins in offshore wind engineering

Error on title page. Date of award is 2022.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets.As offshore wind farms grow there is a continued demand for reduced costs. Maintenance costs and downtime can be reduced through greater information on the asset in relation to its operational loads and structural resistance to damage and so there is an increasing interest in digital twin technologies. Through digital twins, an operational asset can be replicated computationally, thus providing more information. Modelling these aspects requires a wide variety of models in different fields. To advance the feasibility of digital twin technology this thesis aims to develop the multi-disciplinary set of modelling domains which help form the basis of future digital twins. Throughout this work, results have been validated against operational data recorded from sensors on offshore structures. This has provided value and confidence to the results as it shows how well the mix of state-of-the art models compare to real world engineering systems. This research presents a portfolio of five research areas which have been published in a mix of peer-reviewed journal articles and conference papers. These areas are: 1) A computational fluid dynamics (CFD) model of an offshore wind farm conducted using a modified solver in the opensource software. This work implements actuator disk turbine models and uses Reynolds averaged Naiver Stokes approaches to represent the turbulence. This investigates the impact of modelling choices and demonstrates the impact of varied model parameters. The results are compared to operational site data and the modelling errors are quantified. There is good agreement between the models and site data. 2) An expansion on traditional CFD approaches through incorporating machine learning (ML). These ML models are used to approximate the results of the CFD and thereby allow for further analysis which retains the fidelity of CFD at comparatively negligible computational cost. The results are compared to operational site data and the errors at each step are quantified for validation. 3) A time-series forecasting of weather variables based on past measured data. A novel approach for forecasting time-series is developed and compared to two existing methods: Markov-Chains and Gradient Boosting. While this new method is more complex and requires more time to train, it has the desirable feature of incorporating seasonality at multiple timescales and thus providing a more representative time-series. 4) An investigation of the change in modal parameters in an offshore wind jacket structure from damages or from changing operational conditions. In this work the detailed design model of the structure from Ramboll is used. This section relates the measurable modal parameters to the operational condition through a modelling approach. 5) A study conducted using accelerometer data from an Offshore Substation located in a wind farm site. Operational data from 12 accelerometers is used to investigate the efficacy of several potential sensor layouts and therefore to quantify the consequence of placement decisions. The results of these developments are an overall improvement in the modelling approaches necessary towards the realisation of digital twins as well as useful development in each of the component areas. Both areas related to wind loading as well as structural dynamics have been related to operational data. The validation of this link between the measured and the modelled domains facilitates operators and those in maintenance in gaining more information and greater insights into the conditions of their assets

Learning to Optimise Wind Farms with Graph Transformers

This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions. The proposed model functions by encoding a wind farm into a fully-connected graph and processing the graph representation through a graph transformer. The graph transformer surrogate is shown to generalise well and is able to uncover latent structural patterns within the graph representation of wind farms. It is demonstrated how the resulting surrogate model can be used to optimise yaw angle configurations using genetic algorithms, achieving similar levels of accuracy to industrially-standard wind farm simulation tools while only taking a fraction of the computational cost