Comparison of wind turbine tower failure modes under seismic and wind loads

This paper studies the structural responses and failure modes of a 1.5-MW horizontal-axis wind turbine under strong ground motions and wind loading. Ground motions were selected and scaled to match the two design response spectra given by the seismic code, and wind loads were generated considering tropical cyclone scenarios. Nonlinear dynamic time-history analyses were conducted and structural performances under wind loads as well as short- and long-period ground motions compared. The results show that under strong wind loads the collapse of the wind turbine tower is driven by the formation of a plastic hinge at the lower section of the tower. This area is also critical when the tower is subject to most ground motions. However, some short-period earthquakes trigger the collapse of the middle and upper parts of the tower due to the increased contribution of high-order vibration modes. Although long-period ground motions tend to result in greater structural responses, short-period earthquakes may cause brittle failure modes in which the full plastic hinge develops quickly in regions of the tower with only a moderate energy dissipation capacity. Based on these results, recommendations for future turbine designs are proposed

Empiricism and stochastics in cellular automaton modeling of urban land use dynamics

An increasing number of models for predicting land use change in regions of rapidurbanization are being proposed and built using ideas from cellular automata (CA)theory. Calibrating such models to real situations is highly problematic and to date,serious attention has not been focused on the estimation problem. In this paper, wepropose a structure for simulating urban change based on estimating land usetransitions using elementary probabilistic methods which draw their inspiration fromBayes' theory and the related ?weights of evidence? approach. These land use changeprobabilities drive a CA model ? DINAMICA ? conceived at the Center for RemoteSensing of the Federal University of Minas Gerais (CSR-UFMG). This is based on aneight cell Moore neighborhood approach implemented through empirical land useallocation algorithms. The model framework has been applied to a medium-size townin the west of São Paulo State, Bauru. We show how various socio-economic andinfrastructural factors can be combined using the weights of evidence approach whichenables us to predict the probability of changes between land use types in differentcells of the system. Different predictions for the town during the period 1979-1988were generated, and statistical validation was then conducted using a multipleresolution fitting procedure. These modeling experiments support the essential logicof adopting Bayesian empirical methods which synthesize various information aboutspatial infrastructure as the driver of urban land use change. This indicates therelevance of the approach for generating forecasts of growth for Brazilian citiesparticularly and for world-wide cities in general

Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Explainable Metamodels for ATM Performance Assessment

Fast-time simulation constitutes a well-known and long-established technique within the Air Traffic Management (ATM) community. However, it is often the case that simulation input and output spaces are underutilized, limiting the full understandability, transparency, and interpretability of the obtained results. In this paper, we propose a methodology that combines simulation metamodeling and SHapley Additive exPlanations (SHAP) values, aimed at uncovering the intricate hidden relationships among the input and output variables of a simulated ATM system in a rather practical way. Whereas metamodeling provides explicit functional approximations mimicking the behavior of the simulators, the SHAP-based analysis delivers a systematic framework for improving their explainability. We illustrate our approach using a state-of-the-art ATM simulator across two case studies in which two delay-centered performance metrics are analyzed. The results show that the proposed methodology can effectively make simulation and its results more explainable, facilitating the interpretation of the obtained emergent behavior, and additionally opening new opportunities towards novel performance assessment processes within the ATM research field

Nonlinear response history analysis and collapse mode study of a wind turbine tower subjected to tropical cyclonic winds

The use of wind energy resources is developing rapidly in recent decades. There is an increasing number of wind farms in high wind-velocity areas such as the Pacific Rim regions. Wind turbine towers are vulnerable to tropical cyclones and tower failures have been reported in an increasing number in these regions. Existing post-disaster failure case studies were mostly performed through forensic investigations and there are few numerical studies that address the collapse mode simulation of wind turbine towers under strong wind loads. In this paper, the wind-induced failure analysis of a conventional 65m hub high 1.5-MW wind turbine was carried out by means of nonlinear response time-history analyses in a detailed finite element model of the structure. The wind loading was generated based on the wind field parameters adapted from the cyclone boundary layer flow. The analysis results indicate that this particular tower fails due to the formation of a full-section plastic hinge at locations that are consistent with those reported from field investigations, which suggests the validity of the proposed numerical analysis in the assessment of the performance of wind-farms under cyclonic winds. Furthermore, the numerical simulation allows to distinguish different failure stages before the dynamic collapse occurs in the proposed wind turbine tower, opening the door to future research on the control of these intermediate collapse phases

Quantum Capacity Approaching Codes for the Detected-Jump Channel

The quantum channel capacity gives the ultimate limit for the rate at which quantum data can be reliably transmitted through a noisy quantum channel. Degradable quantum channels are among the few channels whose quantum capacities are known. Given the quantum capacity of a degradable channel, it remains challenging to find a practical coding scheme which approaches capacity. Here we discuss code designs for the detected-jump channel, a degradable channel with practical relevance describing the physics of spontaneous decay of atoms with detected photon emission. We show that this channel can be used to simulate a binary classical channel with both erasures and bit-flips. The capacity of the simulated classical channel gives a lower bound on the quantum capacity of the detected-jump channel. When the jump probability is small, it almost equals the quantum capacity. Hence using a classical capacity approaching code for the simulated classical channel yields a quantum code which approaches the quantum capacity of the detected-jump channel