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Dynamic Response Characterization of Floating Structures Based on Numerical Simulations
Output-only methods are widely used to characterize the dynamic behavior of very diverse structures. However, their application to floating structures may be limited due to their strong nonlinear behavior. Therefore, since there is very little experience on the application of these experimental tools to these very peculiar structures, it is very important to develop studies, either based on numerical simulations or on real experimental data, to better understand their potential and limitations. In an initial phase, the use of numerical simulations permits a better control of all the involved variables. In this work, the Covariance-driven Stochastic Subspace Identification (SSI-COV) algorithm is applied to numerically simulated data of two different solutions to Floating Offshore Wind Turbines (FOWT) and for its capability of tracking the rigid body motion modal properties and susceptibility to different modeling restrictions and environmental conditions tested. The feasibility of applying the methods in an automated fashion in the processing of a large number of datasets is also evaluated. While the structure natural frequencies were consistently obtained from all the simulations, some difficulties were observed in the estimation of the mode shape components in the most changeling scenarios. The estimated modal damping coefficients were in good agreement with the expected results. From all the results, it can be concluded that output-only methods are capable of characterizing the dynamic behavior of a floating structure, even in the context of continuous dynamic monitoring using automated tracking of the modal properties, and should now be tested under uncontrolled environmental loads.
Journal Article
Resilience of Services
Failures of digital systems cause service outages and data breaches that have major negative effects both on the economy and on society, internationally. The number and scale of these issues continues to grow. This book brings together data and thinking from more than 200 experts to provide both an understanding of the consequences of failures in digital systems and an approach to reducing these failures and their impacts. Written in accessible language it aims to be a 'go to' guide for decision makers in the private, government and not-for-profit sectors. Beginning with the question 'What is the impact of these failures on real people and the economy?', it brings together ideas from management, financial services and the IT industry to create a framework and language for leaders in organizations to enable them to identify priorities, align objectives and take action. The authors have wide and deep experience. Gill has had responsibility for software businesses and for strategy in the IT industry, and is an author and consultant on foresight. Ed has been a researcher and teacher at Stanford, Maastricht and Sussex Universities, consistently engaging with emerging issues spanning both IT and economics. As the reliance on digital systems increases through the use of AI, smart infrastructure and robots, it becomes ever more important to plan for and manage the inevitability of outages and errors. This book provides both the motivation to take action and the methods to achieve IT service resilience.
eBook
Lowering post‐construction yield assessment uncertainty through better wind plant power curves
Abstract
Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post‐construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine‐learning algorithms. We utilize the National Renewable Energy Laboratory's open‐source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0 % and 1.2 % (corresponding to a −59 % and −80 % relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long‐term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5 % (−43 % relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine‐learning model at daily resolution can further reduce regression uncertainty, with up to a −10 % relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.
Journal Article
Lowering Post-Construction Yield Assessment Uncertainty Through Better Wind Plant Power Curves
Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post-construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine-learning algorithms. We utilize the National Renewable Energy Laboratory's open-source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0% and 1.2% (corresponding to a -59% and -80% relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long-term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5% (-43% relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine-learning model at daily resolution can further reduce regression uncertainty, with up to a -10% relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.
Journal Article
Fuzzy Multi-Criteria Decision Making
In summarizing the concepts and results of the most popular fuzzy multicriteria methods, using numerical examples, this work examines all the most recently developed methods. Each one of the 22 chapters include practical applications along with new results.
eBook
Exploring the Impact of Regional Integrated Energy Systems Performance by Energy Storage Devices Based on a Bi-Level Dynamic Optimization Model
In the context of energy transformation, the importance of energy storage devices in regional integrated energy systems (RIESs) is becoming increasingly prominent. To explore the impact of energy storage devices on the design and operation of RIESs, this paper first establishes a bi-level dynamic optimization model with the total system cost as the optimization objective. The optimization model is used to optimize the design of three RIESs with different energy storage devices, including System 1 without an energy storage device, System 2 with a thermal energy storage (TES) device, and System 3 with TES and electrical energy storage (EES) devices. According to the design and operation results, the impact of energy storage devices on the operational performance of RIESs is analyzed. The results show that under the design conditions, energy storage devices can significantly increase the capacity of the combined heating and power units and absorption chillers in System 2 and System 3 and reduce the capacity of the ground source heat pumps and gas boilers; the impact of the TES device on System 3 is more significant. Affected by systems’ configuration, the operating cost, carbon tax, and total cost of System 2 are reduced by 2.9%, 5.5%, and 1.5% compared with System 1, respectively. The EES device can more significantly reduce the operating cost of System 3, with a reduced rate of 5.7% compared with that in System 1. However, the higher equipment cost makes the total cost reduction rate of System 3 less than that of System 1, which is 1.75%. Similar to the design conditions, under the operation conditions, the TES device can effectively reduce the carbon tax, operating cost, and total cost of System 2, while System 3 with an EES device can significantly reduce its operating cost regardless of whether the energy price changes or not. To some extent, this study systematically elucidated the impact of TES and EES devices on the optimal design and operation performance of RIESs and provided a certain reference for the configuration of energy storage devices.
Journal Article
Investigación de Operaciones
Este libro especializado muestra cómo transformar problemas reales de economía, negocios y finanzas en decisiones óptimas mediante modelos de Investigación de Operaciones.Con un enfoque claro y aplicado, integra fundamentos matemáticos con procedimientos paso a paso, problemas resueltos y ejercicios de dificultad progresiva, apoyados por software.
eBook
Lowering post‐construction yield assessment uncertainty through better wind plant power curves
Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post‐construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine‐learning algorithms. We utilize the National Renewable Energy Laboratory's open‐source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0% and 1.2% (corresponding to a −59% and −80% relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long‐term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5% (−43% relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine‐learning model at daily resolution can further reduce regression uncertainty, with up to a −10% relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.
Journal Article
Experimental and numerical investigations on the operational characteristics of a hollow electrode plasma torch with an exit nozzle and reverse polarity discharge structure
A plasma torch consisting of a rear hollow electrode, a cylindrical front electrode and a nozzle, all of which has a diameter of 10 mm, was designed and its operational characteristics in the electrical connection of reverse polarity were investigated experimentally and numerically. Assuming that the cathode spot is formed on the end part of the front and the arc plasma inside the torch is laminar flow, numerical analysis was carried out, which agreed well with the experimental results. For example, both results showed that the arc voltages and thermal efficiencies of the torch were increasing linearly when decreasing the arc current or increasing the flow rate of G1 gas, which is introduced between the rear and front electrodes. However, G2 gas injected between the front electrode and exit nozzle has little effect on the arc voltages and thermal efficiencies of the torch. This indicates that when the cathodic arc roots are anchoring on the end of the front electrode due to the electrical connection of reverse polarity, G2 gas primarily affects their axial movement into the nozzle section rather than arc length or temperature fields inside the electrodes, playing a role in stabilizing the plasma jet. As a result, the proposed plasma torch can produce a stable N
2
plasma jet with the thermal efficiency of the torch higher than 70% and the arc voltages higher than 250 V at the arc currents lower than 70 A and total gas flow rates of 50 L/min.
Journal Article