Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Design and Detailed Analysis of Turbomachinery Blades using Truncated Domains

With the continuous growth in air traffic that we are seeing nowadays comes an increase in the requirements needed to be satisfied by an aircraft for it to be certified to fly. These stricter regulations affect aspects such as CO2 emissions, sound pollution and so on, pushing manufacturers to aim for lighter, more efficient, more robust designs. These required improvements needed to keep up with the regulations might come in two different ways; by improving/optimizing existing technology, or by developing new technological concepts. In either of the two scenarios, numerical tools, such as optimization methods or reliable fluid flow simulations play a paramount role.In this thesis, the new capabilities implemented into the in-house Computational Fluid Dynamics (CFD) solver, G3D::Flow, are described. These new additions have been put in place with the objective in mind of performing broadband noise predictions of a fan/OGV configuration using hybrid RANS/LES simulations. Some of the additions to G3D::Flow include: phase-lagged pitch-wise and rotor-stator interfaces, sliding grids and synthetic turbulence injection. These methods have been then put together in order to simulate the flow around the ACAT 1 fan/OGV geometry.In this work, an optimization framework called HAMON is presented. It is based on evolutionary algorithms and can be coupled with meta-modeling techniques to speed up processes where computationally expensive simulations need to be performed, such as 3D CFD simulations. HAMON can be used to fine tune an existing design, or as it has been used here, a black-box approach. It has been able to design counter rotating open rotors with more than acceptable performance were no knowledge about propeller aerodynamics was assumed, giving all the design variables more freedom than probably needed. This black-box approach might be specially useful when optimizing new technologies for which no prior knowledge exist, allowing not only to hopefully find good designs but also to show the trends of what a good design should be like

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Detached Eddy Simulation for Aerospace Applications

With the continuous growth in air traffic that we see nowadays, comes an increase in the requirements needed to be satisfied in order to certify an aircraft for operation. These stricter regulations affect aspects such as CO2 emissions, sound pollution and so on, pushing manufacturers to aim for lighter, more efficient, more robust designs. These improvements might be achieved in two different ways; by improving/optimizing existing technology, or by developing new technological concepts. In either of the two scenarios, numerical tools, such as optimization methods or reliable fluid flow simulations play a paramount role. In this thesis, new functionalities implemented into the in-house compressible Computational Fluid Dynamics (CFD) solver, G3D::Flow, are described. These new additions have been put in place with the objective of performing turbomachinery simulations using hybrid RANS/LES methods as well as nozzle flow simulations. Some of the additions to G3D::Flow include: phase-lagged pitch-wise and rotor-stator interfaces based on the chorochronic method as well as a method based on Proper Orthogonal Decomposition (POD), sliding grid interface and synthetic turbulence injection. The added capabilities, enable G3D::Flow to perform high-fidelity turbomachinery CFD simulations, which were not affordable before due to their high computational cost, since truncated domains can be used.A hybrid RANS/LES simulation of the VOLVO S6 nozzle contour operating under overexpanded conditions is performed. This same geometry, under the same conditions, was previously simulated and reported using a different hybrid RANS/LES methodology. A reduction of over $50\%$ in the difference between the predicted standard deviation of the side loads and those measured in a previous experimental is observed in the current simulation.In this work, an optimization framework called HAMON is also presented, which is based on evolutionary algorithms. In cases where the optimization is based on computationally heavy tasks, such as 3D CFD simulations, meta-modeling techniques can be used to speed up the optimization processes. HAMON can be used to fine tune an existing design, or as it has been used here, as black-box approach. It has been able to design counter rotating open rotors with more than acceptable performance where no knowledge about propeller aerodynamics was assumed, giving all the design variables more freedom than probably needed. This black-box approach might be specially useful when optimizing new technologies for which no prior knowledge exist, allowing not only to, hopefully, find good designs but also to show the trends of what a good design should be like