Transmission line towers are usually analyzed using linear elastic idealized truss models. Due to the assumptions used in the analysis, there are discrepancies between the actual results obtained from full-scale prototype testing and the analytical results. Therefore, design engineers are interested in assessing the actual stress levels in transmission line towers. Since it is costly to place sensors on every member of a tower structure, the best locations for sensors need to be carefully selected. This study evaluates a methodology for sensor placement in transmission line towers. The objective is to find optimal locations for sensors such that the real behavior of the structure can be explained from measurements. The methodology is based on the concepts of entropy and model falsification. Sensor locations are selected based on maximum entropy such that there is maximum separation between model instances that represent different possible combinations of parameter values which have uncertainties. The performance of the proposed algorithm is compared to that of an intuitive method in which sensor locations are selected where the forces are maximum. A typical 220-kV transmission tower is taken as case study in this paper. It is shown that the intuitive method results in much higher number of non-separable models compared to the optimal sensor placement algorithm. Thus, the intuitive method results in poor identification of the system. © 2015 Raphael and Jadhav.

Benny Raphael

Department Of Civil Engineering

Fulltext

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IIT Madras

Frontiers in Built Environment

Although Computational Fluid Dynamics (CFD) simulations are often used to assess wind conditions around buildings, the accuracy of such simulations is often unknown. This paper proposes a data-interpretation framework that uses multiple simulations in combination with measurement data to improve the accuracy of wind predictions. Multiple simulations are generated through varying sets of parameter values. Sets of parameter values are falsified and thus not used for predictions if differences between measurement data and simulation predictions, for any measurement location, are larger than an estimate of uncertainty bounds. The bounds are defined by combining measurement and modeling uncertainties at sensor locations. The framework accounts for time-dependent and spatially-distributed modeling uncertainties that are present in CFD simulations of wind. The framework is applied to the case study of the CREATE Tower located at the National University of Singapore. Values for time-dependent inlet conditions, as well as values for the roughness of surrounding buildings, are identified with measurements carried out around the CREATE Tower. Results show that, on average, ranges of horizontal wind-speed predictions at an unmeasured location have been decreased by 65% when measurement data are used. © 2015 Elsevier Ltd.

Journal of Wind Engineering and Industrial Aerodynamics

A model-based data-interpretation framework for improving wind predictions around buildings

Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy. © 2014 by the authors; licensee MDPI, Basel, Switzerland.

Entropy

Hierarchical sensor placement using joint entropy and the effect of modeling error

Lattice microwave towers and transmission towers are frequently made of angles bolted together directly or through gussets. Such towers are normally analysed to obtain design forces using the linear static methods, assuming the members to be subjected to only axial loads and the deformations to be small. The effects of the end restraints, eccentricity of connections and secondary bracings (redundants) on the strength of the compression members are usually accounted for in the codal recommendations by modifying the effective length of the members and thus the design compressive strength. Hence, forces in the redundants are not known from the analysis and their design is empirical. In this study, non-linear analysis of angle compression members and the single panel of angle planar as well as three-dimensional lattice frames, as in typical lattice towers, are carried out using MSC-NASTRAN software. Account is taken of member eccentricity, local deformation as well as rotational rigidity of joints, beam-column effects and material non-linearity. The analytical models are calibrated with test results. Using this calibrated model, parametric studies are carried out to evaluate the forces in the redundants. The results are compared with codal provisions and recommendations for the design of redundants are presented. © 2001 Elsevier Science Ltd. All rights reserved.

Journal	Frontiers in Built Environment
Publisher	Frontiers Media S.A.
ISSN	22973362
Open Access	Yes