Wind Load Data Research Articles

Probabilistic modeling of multivariate extreme non-Gaussian wind loads and its applications in envelope engineering

Probabilistic modeling for non-Gaussian wind load fields under extreme winds is crucial to the quantitative risk and reliability analysis of envelope structures. Based on the multi-point synchronous wind load data during typhoons, a probabilistic model is proposed to characterize multivariate non-Gaussian wind loads to improve the accuracy of the risk analysis considering data dimension reduction. In the proposed model, the Pareto distribution function is utilized to fit the marginal distribution, and the copula function is employed to construct the dependent structure of wind loads from different locations. In the marginal fitted results, the extreme wind load value in the edge region is about 0.10–0.15 higher than those from inner measurement points at quantile p = 95 %. In addition, the variation gradient from the different variable directions is approximately equal, and a sharp variation occurs at the point (0.10, 0.10) for bivariate joint distributions. Engineering applications given in this article are composed of three parts: calculating regional exceeding probability, estimating wind load vector under a specific regional exceeding probability, and determining the location of joint extreme wind loads using the joint gradient value. Finally, risk analysis of existing roof coverings is carried out, the edge corner region is weaker, and two engineering recommendations are given. For determining the regional design wind load, a comprehensive calculation framework considering the temporal dependence of different locations is established, and the reduction rates are in the range (5 %, 25 %).

Identification of full-field wind loads on buildings using a mechanism-inspired recursive convolutional neural network with partial structural responses

Indirect identification approaches through structural responses have proven effective for wind load estimation in real-world engineering. Currently, methods for identifying wind loads mainly rely on theoretical inverse identification, with rare research based on the mapping relationship between structural responses and wind loads through machine learning. In this paper, a scheme for identifying full-field wind loads using a recursive convolutional neural network (CNN) inspired by physical mechanisms is proposed. The recursive form of the network, as well as the inspiration for its inputs and outputs, is inspired by the spatial correlation and the mapping relationship between wind loads and structural responses. Thus, the network inputs comprise a fusion of structural acceleration and inter-story displacement responses, while the network outputs represent the independent wind loads on structures. Notably, mismatch test is employed by the network, wherein the training and testing datasets originate from entirely different sources. Specifically, during training, Gaussian white noises that simulate wind loads are utilized, while real wind load data are used for testing. The generalization of the proposed scheme is demonstrated through the identification of full-field wind loads generated by different stationary or non-stationary wind spectra of the 76-story wind-excited benchmark building. Furthermore, the proposed scheme is validated by identifying the full-field wind loads of a 67-story shear wall structure with wind tunnel test data.

Design of a wind-PV system integrated with a hybrid energy storage system considering economic and reliability assessment

Hybrid energy systems (HESs) have garnered significant attention as a sustainable solution to meet the world's growing energy demands while minimizing environmental impact. Achieving cost-effective and resilient HES solutions requires considering economic and reliability factors for optimal design. This research delves into the optimization and design of a wind-PV system integrated with a hybrid energy storage system using the Multi-Objective African Vultures Optimization Algorithm (MOAVOA) in both standalone and grid-connected modes. A comprehensive case study is conducted in Tabuk, Saudi Arabia, focusing on a microgrid and incorporating wind speed, solar radiation, and load data. The effectiveness of MOAVOA is verified using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) methods in achieving the Pareto front of optimal system designs. Then, the mean, entropy, and criteria importance using the inter-criteria correlation (CRITIC) methods are utilized to assign weights to each objective in the obtained Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method is employed to select the optimal system design from the Pareto front. The results underscore the significance of the optimization approach in assessing the cost, dependability, and ideal configurations of the system. Furthermore, they highlight the diverse outcomes produced by different optimization techniques. While the entropy method yields more cost-effective designs, the mean and CRITIC methods produce comparable results. However, the mean and CRITIC approaches exhibit higher levels of reliability. For instance, in the grid-connected mode, the entropy weighting method yields an optimal system design with a total cost of M$ 42.2 and a reliability index of 91.73 %, while using the mean method, the selected system configuration has a total cost of M$ 42.98 and a reliability index of 92.89 %. The study emphasizes the benefits of diversifying renewable resources by considering different scenarios involving wind and solar generation. For example, in the wind-PV grid-connected system, the total cost is 22.65 % less than in the PV-only grid-connected system with a higher system reliability. The findings provide valuable guidance for system designers in selecting optimal optimization techniques and promoting the integration of renewable energy sources in hybrid energy systems. By presenting a comprehensive framework that incorporates economic and reliability assessments through the utilization of MOAVOA, this study contributes to the field of HES design.

Research on key evaluation indexes of grid-connected characteristics of new energy based on historical data

To fully excavate the key information of the historical data of the power grid which has been sleeping for a long time, grasp the operation characteristics of the power grid, and further solve the problem of new energy generation in the power grid, the grid-connected characteristics of new energy sources in Liaoning province are studied based on the actual wind and solar power load data. Taking the actual data of Liaoning province as an example, the above analysis is carried out, the grid load of PV and wind power is classified and compared, and the typical load curve is drawn. The results show that the key evaluation index system can be easily calculated, and the results can be used as a reference for the scheduling and planning of a regional new energy system.

Peak factor estimation method of non‐Gaussian wind pressures on long‐span tri‐centered cylindrical roof structures

SummaryCladdings are susceptible to damage due to underestimation of extreme wind‐induced surface pressures. Commonly accepted methods for estimating the peak factor (an input used to determine cladding design loads) involve complex calculation‐intensive procedures. This research develops a four‐parameter unified auto‐spectral model of wind pressure to simplify peak factor estimation of wind‐induced surface pressure via analysis of wind tunnel wind load data on tri‐centered cylindrical roofs. Values of the model parameters were identified via statistical analysis of wind tunnel wind pressure measurement on two long‐span tri‐centered cylindrical roof structures with different curvatures. The study identified roof regions with non‐Gaussian features by inspecting probabilistic density functions of the standardized wind‐induced roof pressures and the third‐ and fourth‐order statistical moments of wind pressure time histories. The paper ultimately proposed and evaluated a simplified method for estimating the peak factors in the non‐Gaussian regions, the Three‐parameter Hermite Model, derived through the moment‐based Hermite Model, the Revised Hermite Model, and the parameter simplification accomplished in this study. The results show that the auto‐spectral model of wind‐induced roof pressures can accurately estimate the zero‐ and second‐order spectral moments, which reflects the wind pressure fluctuating characteristics and geometric features of spectral curves. Compared with the peak factors of the moment‐based Hermite Model and the Revised Hermite Model, the peak factor errors estimated by the Three‐parameter Hermite Model are all less than 10%. These results suggest that the Three‐parameter Hermite Model simplifies the calculation with acceptable accuracy.

Design of an isolated renewable hybrid energy system: a case study

In addition to the fact that most renewable energies such as solar and wind energy have become more competitive in the global energy market, thanks to the great development in conversion technologies, it believes that renewable energy can play a crucial role in global environmental issues. However, in Palestine, the situation is different from anywhere else; renewable energy is not only an economic option, but an absolute necessity to get out of the energy crisis that Palestinian cities suffer from long years ago and continue nowadays. The cornerstone of the present research is focusing on the availability of renewable energy resources in Jenin Governorate (JG)—West Bank (WB)—Palestine. Two-year time-series of hourly solar, wind, biomass, and 1-year hourly electrical load data are used in the analysis in this paper. The energy potentials were estimated using System Advisor Model software (SAM), and the optimum combination and sizing of the hybrid renewable energy system were determined using Hybrid Optimization of Multiple Energy Resources (HOMER). The proposed Hybrid Renewable Energy System (HRES) consists of an 80 MW PV solar field, 66 MW wind farm, and 50 MW biomass system with an initial investment of $323 M. The proposed HRES generates 389 GWh/yr and is enough to meet 100% of the electrical demand of JG (372 GWh/yr) with excess in electricity generation of about 4.57% and the unmeet electric load is about 109.6 MWh/yr which is equivalent to less than 2 h off in a year. The estimated Levelized Cost of Energy (LCOE) was found as 0.313 $/kWh.

Effect of corner modifications on wind-induced responses of tall buildings

Any tall building subjected to high-speed winds will tilt and crack unless precautionary measures are taken. Various active and passive control methods are proposed to control the wind induced responses of the tall buildings. These tall structures can be made aerodynamic by modifying the external geometry of the building. In the present study, minor geometrical modifications are rendered to the structures. G+30 storeys are considered for each model with constant plan layout. The location of the building is considered to be Bengaluru and corresponding wind load data is considered. Modeling and analysis is carried out using ETABS 2017 software. Wind load analysis is carried out to know the effect of these aerodynamic modifications. The parameters considered for the comparison are storey displacement, natural time period and overturning moment. On comparative analysis, it was observed that wind-induced responses of these geometrically modified buildings have reduced by 5% to 10%. It can also be concluded that minor modifications have shown significantly better performance than a conventional square-plan building in addition to an innovative architectural look.

PERENCANAAN DAN PEMODELAN 3D STRUKTUR GEDUNG CO-WORKING SPACE 4 LANTAI SOEKARNO HATTA KOTA MALANG BERBASIS BUILDING INFORMATION MODELING (BIM)

The rapid development of information technology provides lots of benefits for construction. In several construction works, related to various scientific disciplines, there are often inefficiency and ineffectiveness during the process of planning and project implementation. Thus, we need a certain technology as a solution for the more efficient and integrated building planning, which is Building Information Modeling (BIM). One of the BIM-based software that is able to analyze the structures and the detailing structural elements is called Tekla Structural Designer 2019 and Tekla Structures 2020. The purpose of this study is to find out the result of analysis and the design of roof structural elements, slabs, beams, columns, stairs, bored pile foundations, pile caps, and tie beams in the 4-storey Building Structures of Co-working space Soekarno Hatta Malang City based on Building Information Modeling (BIM). The data needed was the dead load data, live load, wind load, earthquake load, technical drawings, and soil data. The analysis and the design of reinforced concrete structural elements based on the Tekla Structural Designer 2019 and rebar detailing using the Tekla Structures 2020. From the calculation data, the result obtained for the seismic base shear force of 1673 kN. The structures of the roof, slabs, beams, stairs, and tie beams using fc’ 25 MPa quality concrete. Columns structure, bored pile foundations, and pile caps using quality concrete fc’ 30 MPa. The quality of reinforcing steel for all concrete structural elements using fy 400 MPa

Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.

Reliability model for wind turbine blade composites under alternate action of normal and extreme wind loads

Wind turbine blade is the key component to capture wind power, and it is mainly composed of glass fiber reinforced polymer (GFRP). Due to the randomness and volatility of wind speed in nature, wind turbine blade is usually subjected to the alternate action of normal and extreme wind loads during its long-term service. In this study, a reliability model that considering the stochastic wind loads and strength degradation of GFRP is proposed. Firstly, a residual strength model is developed based on the Palmgren-Miner (P-M) damage theory and the same damage state (SDS) principle, which is capable of characterizing the strength degradation law of GFRP under the normal wind load. Then, the alternate actions of normal and extreme wind loads are considered, and a dynamic reliability model is presented based on Poisson process and mathematical derivation. Finally, the traditional discrete stress-strength interference (DSSI) model is extended to calculate the dynamic reliability when probability distributions of stochastic wind loads and residual strength of GFRP are unknown. The wind load data and GFRP test data are utilized to demonstrate the effectiveness of the proposed model. The result shows that the reliability of GFRP keeps at a high level in the early stage, then it rapidly decreases due to the accumulation of fatigue damage.

Development and validation of a mobile bluff-body to understand extreme wind loading

The collection of valuable wind loading data from winds generated by convective events presents a significant challenge to the wind engineering field. The spatial and temporal characteristics of these events make data acquisition challenging. A mobile bluff-body capable of recording wind-induced pressure data during extreme wind events was developed to solve this issue. The bluff-body is called mSWERF3 for mobile Smart Wind Engineering Research Facility. This paper introduces mSWERF3, discussing its design, the scaling techniques, and validation using full-scale data from the Silsoe cube and the Wind Engineering Research Field Laboratory (WERFL) building, and wind tunnel data from the Tokyo Polytechnic University (TPU) Aerodynamic Database. The validation includes a comparison of mean, standard deviation, minimum, and maximum pressure coefficients using the Silsoe and the TPU data, and a comparison of power spectra density of surface pressures on similar flow regimes using the WERFL and the TPU data. The results of these comparisons show that the aerodynamics of mSWERF3 is similar to those in larger full-scale buildings and wind tunnel models. Therefore, it is shown that mSWERF3 can provide valuable data to improve our understanding of extreme wind loading.

The physical simulation of a transient, downburst-like event – How complex does it need to be?

There has been much debate of late regarding the physical simulation of downbursts and, in particular, the need to construct large-scale, relatively expensive facilities in order to obtain wind loading data. For the first time, this paper illustrates that, through the use of partial turbulence simulations and quasi-steady analysis, it is possible to capture the both the loading due to the large-scale features present in downburst flows and the local peaks due to smaller scales of turbulence. These findings have considerable implications for future analysis of transient winds.

Failure probability estimation of the gas supply using a data-driven model in an integrated energy system

Probabilistic security evaluation is one of the academic frontiers in the research on energy system reliability. It is very important to evaluate the impact of gas systems on the power/heat system for practical engineering in gas turbine engine-based integrated energy systems. This paper proposes a data-driven model instead of a physical model to estimate the probabilities of the incident of insufficient gas supply suffered from weather uncertainty, which affects the reliability of gas turbine engine-based integrated energy systems. According to actual energy projections, it can be assumed that the uncertainty of intermittent wind power, load fluctuations, and variations in gas deliverability derives from fluctuating weather conditions such as the temperature and wind. The wind power, load, and gas consumption data in the integrated energy system and the gas supply data of the station are sufficient to accurately build a data-driven model. Traditional methods based on physical models include the Iman and Stein methods, the first-order reliability method, and the mixed Monte Carlo algorithm to judge the effectiveness of the proposed method. The results from three cases are a testimony to the accuracy and engineering feasibility of the proposed method. The calculation of a data-driven model is easier than that of a physical model, and its simplification is conducive to failure probability estimation in a real application.

Preventive maintenance of wood-framed buildings for hurricane preparedness

This paper presents a time-dependent reliability modeling and optimal maintenance planning approach for residential buildings subject to hurricane winds. A gamma process is employed to model the stochastic degradation of building components. The time-dependent fragility function and the failure probability of the roof system obtained from the degradation model are used to determine the retrofit timing of components that optimize an average cost per cycle time. The method is illustrated on determining the retrofit times of roof-to-wall connections based on actual component failure and hurricane wind load data. Effectiveness of the optimal retrofit age is compared to a replacement based on time-invariant failure probabilities. A simulation approach is presented to quantify the effect of wind speed uncertainty on maintenance cost.

Risk-Informed Mean Recurrence Intervals for Updated Wind Maps in ASCE 7-16.

ASCE 7 is moving toward adopting load requirements that are consistent with risk-informed design goals characteristic of performance-based engineering (PBE). ASCE 7-10 provided wind maps that correspond to return periods of 300, 700, and 1,700 years for Risk Categories I, II, and combined III/IV, respectively. The risk targets for Risk Categories III and IV buildings and other structures (designated as essential facilities) are different in PBE. The reliability analyses reported in this paper were conducted using updated wind load data to (1) confirm that the return periods already in ASCE 7-10 were also appropriate for risk-informed PBE, and (2) to determine a new risk-based return period for Risk Category IV. The use of data for wind directionality factor, Kd , which has become available from recent wind tunnel tests, revealed that reliabilities associated with wind load combinations for Risk Category II structures are, in fact, consistent with the reliabilities associated with the ASCE 7 gravity load combinations. This paper shows that the new wind maps in ASCE 7-16, which are based on return periods of 300, 700, 1,700, and 3,000 years for Risk Categories I, II, III, and IV, respectively), achieve the reliability targets in Section 1.3.1.3 of ASCE 7-16 for nonhurricane wind loads.

Application of a pattern recognition method to estimate wind loads on ships and marine objects

AbstractThis paper presents an extension of the application capabilities of elliptic Fourier descriptors from the usual pattern recognition and classification problems to problems of very complex nonlinear multivariable approximations of multi‐input and multi‐output functions. Wind loads on ships and marine objects are a complicated phenomenon because of the complex configuration of the above‐water part of the structure. The proposed approach of the wind load estimation method presented in this paper consists of four basic parts: acquisition and pre‐processing of vessel images; image editing; data preparation for neural network training; validating and testing of the created neural network. The method is based on elliptic Fourier features of a closed contour which are used for the frontal and lateral closed contour representation of ships. Therefore, this approach takes into account all aspects of the variability of the above‐water frontal and lateral ship profile. For the purpose of multivariate nonlinear regression, the generalized regression radial basis neural network is trained by elliptic Fourier features of closed contours and wind load data derived from wind tunnel tests. The trained neural network is used for the estimation of non‐dimensional wind load coefficients. The results for a group of car carriers are presented and compared with the experimental data.

Non-working day estimation in high-rise building construction with wind load data by radiosonde and weibull distribution

Optimum scheduling and risk prediction in high-rise building projects are among the most important factors that can determine a project's success or failure. Non-working days due to strong wind should be accurately reflected in the initial construction planning and scheduling process, but existing methods only use the wind speed at ground level or make calculations based on the power distribution law. Thus, in this study, a method of using actual measured data via radiosonde and the Weibull distribution was proposed to calculate the non-working probability for height sections at various altitudes and predict the number of non-working days. This approach has advantages over existing methods. In particular, the Weibull distribution can be used to increase the reliability of wind speed prediction when measured data are not sufficient. Finally, the derived results using the Weibull distribution empirically showed more non-working days, which imply that potential risk due to wind speed can be reduced by the proposed method in the construction phase for high-rise buildings.

An Experimental Study on the Effects of Base Motion on the Aeromechanic Performance of Floating Wind Turbines

An experimental study was conducted to investigate the effects of the wave-induced base motions experienced by floating wind turbines sited in offshore wind farms on their aeromechanic performance and wake characteristics, in comparison with those of a bottom- fixed wind turbine. The experimental study was performed in a large-scale atmospheric boundary layer (ABL) wind tunnel with a scaled wind turbine model placed in a turbulent boundary layer flow with similar mean and turbulence characteristics as those over a typical offshore wind farm. During the experiments, a scaled wind turbine model was mounted on a translational and rotational stage, which can generate translation and/or rotation motions to simulate the dynamic wave-induced motions (i.e., surge, pitch and heave motions) experienced by floating wind turbines in offshore wind farms. In addition to measuring dynamic wind loadings (both forces and moments)acting on the model turbine, a high-resolution Particle Image Velocity (PIV) system was also used to conduct detailed flow field measurements to characterize the turbine wakes with the turbine base in motion. The detailed flow field measurements were correlated with the dynamic wind load data to elucidate underlying physics for higher total power yield and better durability of floating offshore wind turbines.

Hybrid method for estimating wind loads on ships based on elliptic Fourier analysis and radial basis neural networks

Wind loads on ships and marine objects are complicated phenomena because of the complex configuration of the above-water part of the structure. The estimation of wind loads on ships and other marine objects represents a challenge because of its implications for various analyses related to ship stability, ship speed estimation, maneuvering, station keeping, and berthing. This paper presents a new approach to wind load estimation on ships and other marine objects. The method is based on elliptic Fourier features of a closed contour which are used for ship frontal and lateral closed contour representation. Therefore, this approach takes into account all aspects of the variability of the above-water frontal and lateral ship profile. For the purpose of multivariate nonlinear regression, the radial basis neural network is trained by elliptic Fourier features of closed contours and wind load data derived from wind tunnel tests for three groups of ships: offshore supply vessels, car carriers and container ships. The trained neural network is used for the estimation of non-dimensional wind load coefficients. The results are compared with experimental data.

Feasibility analysis of a renewable hybrid energy system with producer gas generator fulfilling remote household electricity demand in Southern Norway

Hybrid energy system is increasingly emerging as an option to produce energy for the remote areas. This paper presented an economic feasibility analysis of a single standalone house operating with a hybrid power plant consisting of a fixed capacity producer gas generator (2 kWe) and other renewable energy sources (Photovoltaic and wind). The National Renewable Energy Laboratory's Hybrid Optimization Model for Electric Renewable (HOMER) was employed which evaluated techno-economic analysis based on the criteria of net present cost and levelized cost of electricity. Taking the site specific daily average solar radiation, average wind speed and load data into account, renewable hybrid model consisting of Bio/PV (Photovoltaic)/wind/battery/capacitor was found feasible giving 19,866 kWh/yr. of energy with a levelized cost of electricity of 0.306 kWh/yr. While comparing the hybrid system with a diesel or, natural gas generator alone, the maximum savings from CO2 emissions worth 22,626 kg/yr. was achieved. The sensitivity analysis over a range of diesel/natural/producer gas price (0.1 $/L to 1 $/L or, 0.1 $/m3 to 1.0 $/m3) showed that in addition to the environmental benefits hybrid energy configuration could result economic advantages when the producer gas price does not exceed the threshold of 0.1 $/m3.