Towards the Evolution of Novel Vertical-Axis Wind Turbines

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.Comment: 14 pages, 11 figure

Machine learning based modelling and control of wind turbine structures and wind farm wakes

With the fast development of wind energy, new technological challenges emerge, which calls for new research efforts to further reduce the cost of wind power. A lot of efforts have been spent to tackle the modelling and control of wind turbines and wind farms. However, big research gaps still exist due to the complexity and strong nonlinearity of the underlying structural and fluid systems. On the other hand, machine learning (ML), which is very powerful in handling complex and nonlinear systems, is developing very fast in the past years. Therefore, this thesis aims to tackle the modelling and control issues arising from the fast-developing wind industry, based on both traditional methods (including structural mechanics, control engineering, fluid dynamics, and scientific computing) and ML (including reinforcement learning, supervised ML, dimensionality reduction, generative adversarial network, and physics-informed deep learning). First, at the turbine level, mitigation of dynamic response of a floating wind turbine using active tuned mass dampers is investigated, where a reinforcement learning algorithm is employed and a neural network structure is designed to realize the employed algorithm. Second, at the farm level, novel static and dynamic wind farm wake models are developed by proposing novel ML-based surrogate modelling methods for distributed fluid systems and then training the model based on highfidelity CFD database generated by large eddy simulations. Third, the prediction of the spatiotemporal wind field in the whole domain in front of a wind turbine is investigated by combining data (i.e. LIDAR measurements at sparse locations) and physics (i.e. Navier-Stokes equations) in a unified manner via physics-informed deep learning. The results presented in this thesis fully demonstrate the great performance of the proposed structural controllers, the great accuracy, efficiency & robustness of the developed wind farm models, and the great accuracy of the full spatiotemporal wind field predictions. xi

An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design of wind energy projects.Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models.In this paper, we develop an optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations are carried out to obtain the corresponding AEPs.This wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset.Such adaptive update of wind farm layout dataset continues until the algorithm converges.To evaluate the performance of the proposed SBO framework, we apply it to three representative wind farm cases.Compared to the conventional staggered layout, the optimized wind farm produces significantly higher total AEP.In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method.Further analysis on the velocity fields show that the optimization framework attempts to locate the downstream turbines away from the the wakes of upstream ones.The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects

Final report on the farmer's aid in plant disease diagnoses

This report is the final report on the FAD project. The FAD project was initiated in september 1985 to test the expert system shell Babylon by developing a prototype crop disease diagnosis system in it. A short overview of the history of the project and the main problems encountered is given in chapter 1. Chapter 2 describes the result of an attempt to integrate JSD with modelling techniques like generalisation and aggregation and chapter 3 concentrates on the method we used to elicit phytopathological knowledge from specialists. Chapter 4 gives the result of knowledge acquisition for the 10 wheat diseases most commonly occurring in the Netherlands. The user interface is described briefly in chapter 5 and chapter 6 gives an overview of the additions to the implementation we made to the version of FAD reported in our second report. Chapter 7, finally, summarises the conclusions of the project and gives recommendations for follow-up projects

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

Integrated land use modelling of agri-environmental measures to maintain biodiversity at landscape level

Integrated land use models (ILM) are increasingly applied tools for the joint assessment of complex economic-environmental farming system interactions. We present an ILM that consists of the crop rotation model CropRota, the bio-physical process model EPIC, and the farm optimization model FAMOS[space]. The ILM is applied to analyze agri-environmental measures to maintain biodiversity in an Austrian landscape. We jointly consider the biodiversity effects of land use intensity (i.e. nitrogen application rates and mowing frequencies) and landscape development (e.g. provision of landscape elements) using a rich indicator set and region specific species-area relationships. The cost-effectiveness of agri-environmental measures in attaining alternative biodiversity targets is assessed by scenario analysis. The model results show the negative relationships between biodiversity maintenance and gross margins per ha. The absence of agri-environmental measures likely leads to a loss of semi-natural landscape elements such as orchard meadows and hedges as well as to farmland intensifications. The results are also relevant for external cost estimates. However, further methodologies need to be developed that can jointly and endogenously consider the complexities of the socio-economic land use system at farm and regional levels as well as the surrounding natural processes at sufficient detail for biodiversity assessments.Integrated farm land use modeling, biodiversity indicators, agri-environmental policy, landscape elements, Agricultural and Food Policy, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Food Security and Poverty, Land Economics/Use, Production Economics,

MULTIPLE-OBJECTIVE DECISION MAKING FOR AGROECOSYSTEM MANAGEMENT

Multiple-objective decision making (MODEM) provides an effective framework for integrated resource assessment of agroecosystems. Two elements of integrated assessment are discussed and illustrated: (1) adding noneconomic objectives as constraints in an optimization problem; and (2) evaluating tradeoffs among competing objectives using the efficiency frontier for objectives. These elements are illustrated for a crop farm and watershed in northern Missouri. An interactive, spatial decision support system (ISDSS) makes the MODEM framework accessible to unsophisticated users. A conceptual ISDSS is presented that assesses the socioeconomic, environmental, and ecological consequences of alternative management plans for reducing soil erosion and nonpoint source pollution in agroecosystems. A watershed decision support system based on the ISDSS is discussed.Agribusiness,