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Guidelines for Preparing Master's Theses
LIQUID FORCE KITE CONTROL SYSTEM DESIGN A Senior Project submitted to the Faculty of California Polytechnic State University, San Luis Obispo In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Industrial and Mechanical Engineering by Brendan Kerr, Alden Simmer, Brodie Sutherland March 2017 ABSTRACT Liquid Force Kite Control System Design Brendan Kerr, Alden Simmer, Brodie Sutherland Kiteboarding is an ocean sport wherein the participant, also known as a kiter, uses a large inflatable bow shaped kite to plane across the ocean on a surfboard or wakeboard. The rider is connected to his or her kite via a control bar system. This control system allows the kiter to steer the kite and add or remove power from the kite, in order to change direction and increase or decrease speed. This senior project focused on creating a new control bar system to replace a control bar system manufactured by a kiteboarding company, Liquid Force. The current Liquid Force control bar has two main faults, extraneous components and a lack of ergonomic design. Our team aimed to eliminate unneeded components and create a more ergonomic bar. By eliminating components, the bar would also be more cost effective to produce by using less material and requiring less time to manufacture. We first conducted a literature review into the areas of kiteboarding control systems and handle ergonomics. Based on studies done on optimal grip diameters for reducing forearm stress we concluded that the diameter for the bar grip should be at least a centimeter less than the maximum grip of the user. -
Renewable Energy in Small Islands
Renewable Energy on Small Islands Second edition august 2000 Sponsored by: Renewable Energy on Small Islands Second Edition Author: Thomas Lynge Jensen, Forum for Energy and Development (FED) Layout: GrafiCO/Ole Jensen, +45 35 36 29 43 Cover photos: Upper left: A 55 kW wind turbine of the Danish island of Aeroe. Photo provided by Aeroe Energy and Environmental Office. Middle left: Solar water heaters on the Danish island of Aeroe. Photo provided by Aeroe Energy and Environmental Office. Upper right: Photovoltaic installation on Marie Galante Island, Guadeloupe, French West Indies. Photo provided by ADEME Guadeloupe. Middle right: Waiah hydropower plant on Hawaii-island. Photo provided by Energy, Resource & Technology Division, State of Hawaii, USA Lower right: Four 60 kW VERGNET wind turbines on Marie Galante Island, Guadeloupe, French West Indies. Photo provided by ADEME Guadeloupe. Printing: Vesterkopi Printing cover; Green Graphic No. printed: 200 ISBN: 87-90502-03-5 Copyright (c) 2000 by Forum for Energy and Development (FED) Feel free to use the information in the report, but please state the source. Renewable Energy on Small Islands – Second Edition August 2000 Table of Contents Table of Contents Foreword and Acknowledgements by the Author i Introduction iii Executive Summary v 1. The North Atlantic Ocean Azores (Portugal) 1 Canary Island (Spain) 5 Cape Verde 9 Faeroe Islands (Denmark) 11 Madeira (Portugal) 13 Pellworm (Germany) 17 St. Pierre and Miquelon (France) 19 2. The South Atlantic Ocean Ascension Island (UK) 21 St. Helena Island (UK) 23 3. The Baltic Sea Aeroe (Denmark) 25 Gotland (Sweden) 31 Samsoe (Denmark) 35 4. -
Renewable Generation Data for European Energy System Analysis
Renewable Generation Data for European Energy System Analysis Alexander Kies1, Bruno U. Schyska2, Mariia Bilousova1;3, Omar El Sayed1;3, Jakub Jurasz4;5;6, Horst Stoecker1;3;7 1Frankfurt Institute for Advanced Studies, Goethe-University Frankfurt, Ruth-Moufang-Str. 1, 60438 Frankfurt, Germany 2German Aerospace Center (DLR), Institute of Networked Energy Systems, Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany 3Goethe-University, Frankfurt, Germany 4Faculty of Environmental Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland 5Faculty of Management, AGH University, 30-059 Cracow, Poland 6School of Business, Society and Engineering, M¨alardalens H¨ogskola,72113 V¨aster˚as, Sweden 7GSI Helmholtzzentrum f¨urSchwerionenforschung, Planckstraße 1, 64291 Darmstadt, Germany Abstract In the process of decarbonization, the global energy mix is shifting from fossil fuels to renewables. To study decarbonization pathways, large-scale energy system models are utilized. These models require accurate data on renewable generation to develop their full potential. Using different data can lead to conflicting results and policy advice. In this work, we compare several datasets that are commonly used to study the transition towards highly renewable European power system. We find significant differences between these datasets and cost-difference of about 10% result in the different energy mix. We conclude that much more attention must be paid to the large uncertainties of the input data. Keywords: Energy System Analysis, Renewable Generation Data, Energy arXiv:2101.08741v1 [physics.soc-ph] 21 Jan 2021 Meteorology, MERRA-2, ERA5, renewables.ninja, EMHires Preprint submitted to Journal Name January 22, 2021 1. Introduction Sustainable energy sources are a major solution for the imminent thread of climate change [1, 2]. -
Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains
energies Article Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains Rebecca J. Barthelmie 1,* , Tristan J. Shepherd 2 , Jeanie A. Aird 1 and Sara C. Pryor 2 1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA; [email protected] 2 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA; [email protected] (T.J.S.); [email protected] (S.C.P.) * Correspondence: [email protected] Received: 13 July 2020; Accepted: 11 August 2020; Published: 18 August 2020 Abstract: Continued growth of wind turbine physical dimensions is examined in terms of the implications for wind speed, power and shear across the rotor plane. High-resolution simulations with the Weather Research and Forecasting model are used to generate statistics of wind speed profiles for scenarios of current and future wind turbines. The nine-month simulations, focused on the eastern Central Plains, show that the power scales broadly as expected with the increase in rotor diameter (D) and wind speeds at hub-height (H). Increasing wind turbine dimensions from current values (approximately H = 100 m, D = 100 m) to those of the new International Energy Agency reference wind turbine (H = 150 m, D = 240 m), the power across the rotor plane increases 7.1 times. The mean domain-wide wind shear exponent (α) decreases from 0.21 (H = 100 m, D = 100 m) to 0.19 for the largest wind turbine scenario considered (H = 168 m, D = 248 m) and the frequency of extreme positive shear (α > 0.2) declines from 48% to 38% of 10-min periods. -
3.8.5.Post0+Ug.Gdd887c5.D20201118
InVEST User’s Guide, Release 3.8.5.post0+ug.gdd887c5.d20201118 InVEST User’s Guide Integrated Valuation of Ecosystem Services and Tradeoffs Version 3.8.5.post0+ug.gdd887c5.d20201118 Editors: Richard Sharp, James Douglass, Stacie Wolny. Contributing Authors: Katie Arkema, Joey Bernhardt, Will Bierbower, Nicholas Chaumont, Douglas Denu, James Douglass, David Fisher, Kathryn Glowinski, Robert Griffin, Gregory Guannel, Anne Guerry, Justin Johnson, Perrine Hamel, Christina Kennedy, Chong-Ki Kim, Martin Lacayo, Eric Lonsdorf, Lisa Mandle, Lauren Rogers, Richard Sharp, Jodie Toft, Gregory Verutes, Adrian L. Vogl, Stacie Wolny, Spencer Wood. Citation: Sharp, R., Douglass, J., Wolny, S., Arkema, K., Bernhardt, J., Bierbower, W., Chaumont, N., Denu, D., Fisher, D., Glowinski, K., Griffin, R., Guannel, G., Guerry, A., Johnson, J., Hamel, P., Kennedy, C., Kim, C.K., Lacayo, M., Lonsdorf, E., Mandle, L., Rogers, L., Toft, J., Verutes, G., Vogl, A. L., and Wood, S. 2020, InVEST 3.8.5.post0+ug.gdd887c5.d20201118 User’s Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund. 1 InVEST User’s Guide, Release 3.8.5.post0+ug.gdd887c5.d20201118 2 CONTENTS 1 Introduction 5 1.1 Data Requirements and Outputs Summary Table............................5 1.2 Why we need tools to map and value ecosystem services........................5 1.2.1 Introduction...........................................5 1.2.2 Who should use InVEST?...................................5 1.2.3 Introduction to InVEST..................................... 10 1.2.4 Using InVEST to Inform Decisions.............................. 12 1.2.5 A work in progress....................................... 15 1.2.6 This guide............................................ 15 1.3 Getting Started............................................. -
Wind Power Prediction Model Based on Publicly Available Data: Sensitivity Analysis on Roughness and Production Trend
WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND Dissertation in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT Uppsala University Campus Gotland Department of Earth Sciences Gireesh Sakthi [10th December, 2019] WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND Dissertation in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE WITH A MAJOR IN WIND POWER PROJECT MANAGEMENT Uppsala University Campus Gotland Department of Earth Sciences Approved by Supervisor, Dr. Karl Nilsson Examiner, Dr. Stefan Ivanell 10th December, 2019 II Abstract The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU to get a distributed wind speed. The distributed wind speed is then used to interpolate the wind power with the help of the power curve of the wind turbine. The interpolated wind power is then compared with the Actual Production Data (APD) to validate the prediction model. The simulation results show that the model works fairly predicting the Annual Energy Production (AEP) on monthly averages for all sites but the model could not follow the APD trend on all cases. -
The CLIMIX Model a Tool to Create and Evaluate Spatially-Resolved
Renewable and Sustainable Energy Reviews 42 (2015) 1–15 Contents lists available at ScienceDirect Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser The CLIMIX model: A tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development S. Jerez a,b,n, F. Thais c, I. Tobin a, M. Wild d, A. Colette e, P. Yiou a, R. Vautard a a Laboratoire des Sciences du Climat et de l’Environnement (LSCE), IPSL, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France b Department of Physics, University of Murcia, 30100 Murcia, Spain c Institut de Technico-Economie des Systèmes Energétiques (I-Tésé), CEA/DEN/DANS, 91191 Gif sur Yvette, France d Institute for Atmospheric and Climate Science, ETH Zurich, 8092 Zurich, Switzerland e Institut National de l’Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, 60550 Verneuil en Halatte, France article info abstract Article history: Renewable energies arise as part of both economic development plans and mitigation strategies aimed Received 16 April 2014 at abating climate change. Contrariwise, most renewable energies are potentially vulnerable to climate Received in revised form change, which could affect in particular solar and wind power. Proper evaluations of this two-way 5 August 2014 climate–renewable energy relationship require detailed information of the geographical location of the Accepted 26 September 2014 renewable energy fleets. However, this information is usually provided as total amounts installed per administrative region, especially with respect to future planned installations. To help overcome this Keywords: limiting issue, the objective of this contribution was to develop the so-called CLIMIX model: a tool that Wind power performs a realistic spatial allocation of given amounts of both photovoltaic (PV) and wind power Solar photovoltaic power installed capacities and evaluates the energy generated under varying climate conditions. -
Modelo Para a Formatação Dos Artigos a Serem Submetidos À Revista Gestão Industrial
PERMANENT GREEN ENERGY PRODUCTION Relly Victoria V. Petrescu1, Aversa Raffaella2, Apicella Antonio2; Florian Ion T. Petrescu1 1ARoTMM-IFToMM, Bucharest Polytechnic University, Bucharest, 060042 (CE) Romania [email protected]; [email protected] 2Advanced Material Lab, Department of Architecture and Industrial Design, Second University of Naples, Naples 81031 (CE) Italy [email protected]; [email protected] Abstract After 1950, began to appear nuclear fission plants. The fission energy was a necessary evil. In this mode it stretched the oil life, avoiding an energy crisis. Even so, the energy obtained from oil represents about 60% of all energy used. At this rate of use of oil, it will be consumed in about 60 years. Today, the production of energy obtained by nuclear fusion is not yet perfect prepared. But time passes quickly. We must rush to implement of the additional sources of energy already known, but and find new energy sources. Green energy in 2010-2015 managed a spectacular growth worldwide of about 5%. The most difficult obstacle met in worldwide was the inconstant green energy produced. Key-words: environmental protection, green energy, wind power, hydropower, pumped-storage. 1. Introduction Energy development is the effort to provide sufficient primary energy sources and secondary energy forms for supply, cost, impact on air pollution and water pollution, mitigation of climate change with renewable energy. Technologically advanced societies have become increasingly dependent on external energy sources for transportation, the production of many manufactured goods, and the delivery of energy services (Aversa et al., 2017 a-e, 2016 a-o; Petrescu et al., 2017, 2016 a-e). -
Kitesurfing a - Z
Kitesurfing A - Z A Airfoil (aerofoil): a wing, kite, or sail used to generate lift or propulsion. Airtime: the amount of time spent in the air while jumping. AOA, Angle of Attack: also known as the angle of incidence (AOI) is the angle with which the kite flies in relation to the wind. Increasing AOA generally gives more lift. AOI, Angle of Incidence: angle which the kite takes compared to the wind direction Apparent wind, AW: The wind felt by the kite or rider as they pass through the air. For instance, if the true wind is blowing North at 10 knots and the kite is moving West at 10 knots, the apparent wind on the kite is NW at about 14 knots. The apparent wind direction shifts towards the direction of travel as speed increases. Aspect Ratio, AR: the ratio of a kites width to height (span to chord). Kites can range between a high aspect ratio of about 5.0 or a low aspect ratio of about 3.0. AR5: The legendary first 4 line inflatable kite manufactured by Naish. ARC: a foil kite manufactured by Peter Lynn B Back Loop: a kitesurfing trick where the kiter rotates backward (begins by turning their back toward the kite) while throwing his/her feet above the level of his/her head. Back Roll: same as a back loop but without getting their feet up high. Batten: a length of carbon or plastic which adds stiffness or shape to the kite or sail. Bear Away / Bear Off: change your direction of travel to a more downwind direction. -
Geographical Location Optimisation of Wind and Solar Photovoltaic Power Capacity in South Africa Using Mean- Variance Portfolio Theory and Time Series Clustering
Geographical Location Optimisation of Wind and Solar Photovoltaic Power Capacity in South Africa using Mean- variance Portfolio Theory and Time Series Clustering By Christiaan Johannes Joubert Thesis presented in partial fulfilment of the requirements for the degree Master of Engineering in Electrical Engineering at Stellenbosch University Supervisor: Prof. H.J. Vermeulen Department of Electrical and Electronic Engineering November 2016 Declaration By submitting this thesis/dissertation electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. C.J. Joubert November 2016 Copyright © 2016 Stellenbosch University All rights reserved i Acknowledgements I would like to thank the following people for their contribution during this project: My study leader, Prof H.J. Vermeulen, for his valuable guidance and inputs throughout my two years at Stellenbosch University. Thanks are also due to Prof H.J. Vermeulen’s family who opened up their home to the crowd of master’s students and their circle of friends on many occasions. My mother, Marina Joubert, father, Stefan Joubert, brother, Peter Joubert, aunt, Juanita Du Toit, grandmother, Irene Du Toit, as well as my late grandparents Pieter du Toit, Chris Joubert and Rita Joubert. To the extent that I have achieved anything in my life it would undoubtedly not have been possible without their unwavering love, sacrifice and support. -
Arxiv:1703.06553V1 [Physics.Flu-Dyn]
An entrainment model for fully-developed wind farms: effects of atmospheric stability and an ideal limit for wind farm performance Paolo Luzzatto-Fegiza1 and Colm-cille P. Caulfieldb,c a Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA b BP Institute for Multiphase Flow, Madingley Rise, Madingley Road, Cambridge, CB3 0EZ, UK c Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Rd, CB3 0WA, UK Abstract While a theoretical limit has long been established for the performance of a single turbine, no corresponding upper bound exists for the power output from a large wind farm, making it difficult to evaluate the available potential for further performance gains. Recent work involving vertical-axis turbines has achieved large increases in power density relative to traditional wind farms (Dabiri, J.O., J. Renew. Sust. Energy 3, 043104 (2011)), thereby adding motivation to the search for an upper bound. Here we build a model describing the essential features of a large array of turbines with arbitrary design and layout, by considering a fully-developed wind farm whose upper edge is bounded by a self-similar boundary layer. The exchanges between the wind farm, the overlaying boundary layer, and the outer flow are parameterized by means of the classical entrainment hypothesis. We obtain a concise expression for the wind farm’s power density (corresponding to power output per unit planform area), as a function of three coefficients, which represent the array thrust and the turbulent exchanges at each of the two interfaces. Before seeking an upper bound on farm performance, we assess the performance of our simple model by comparing the predicted power density to field data, laboratory measurements and large-eddy simulations for the fully-developed regions of wind farms, finding good agreement. -
Application of Phase Averaging Method for Mea- Suring Kite Performance: Onshore Results
Journal of Sailing Technology, Article 2018-05. © 2018, The Society of Naval Architects and Marine Engineers. APPLICATION OF PHASE AVERAGING METHOD FOR MEA- SURING KITE PERFORMANCE: ONSHORE RESULTS Morgan Behrel ENSTA Bretagne - IRDL UMR CNRS 6027, France Kostia Roncin ENSTA Bretagne - IRDL UMR CNRS 6027, France Jean-Baptiste Leroux ENSTA Bretagne - IRDL UMR CNRS 6027, France Frederic Montel Downloaded from http://onepetro.org/jst/article-pdf/3/01/1/2205361/sname-jst-2018-05.pdf by guest on 30 September 2021 ENSTA Bretagne - IRDL UMR CNRS 6027, France Romain Hascoet ENSTA Bretagne - IRDL UMR CNRS 6027, France Alain Neme ENSTA Bretagne - IRDL UMR CNRS 6027, France Christian Jochum ENSTA Bretagne - IRDL UMR CNRS 6027, France Yves Parlier beyond the sea, France Manuscript received January 26, 2018; accepted September 17, 2018. Abstract: This paper describes an experimental set-up aiming to control and measure per- formances of small leading edge inflatable tube kites, with surface area of less than 35 m². This set-up can be deployed either onshore or on a dedicated boat. This article focuses on the onshore results. A 3D load cell is used to obtain the position of the kite under a straight- line assumption. A wind profiler (SODAR) is deployed to determine the wind speed and direction at the kite position. A specific post-processing of the data is presented, including phase averaging. The guideline of this work is to estimate the variation of the aerodynamic lift coefficient and lift to drag ratio along figure-of-eight trajectories. Results, for a chosen particular case, show a decrease of lift coefficient of about 20% of the maximum value dur- ing turning maneuvers of the kite.