Accurate Load Estimation Research Articles

Graph convolutional network-based aggregated demand response baseline load estimation

Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR event day fluctuates largely compared with that in historical days. To this end, this paper proposes a Graph Convolutional Network (GCN)-based ABL estimation method to improve the estimation accuracy. The basic idea is to enhance the estimator's ability to capture load uncertainty by sharing load fluctuation information between different customers. The proposed method contains three main steps: First, all customers are grouped into different clusters by the K-means algorithm according to their historical typical load patterns (TLPs). Second, these clusters are transformed into an undirected graph based on an adjacency matrix reflecting the spatial correlations, which are constructed according to the difference between the TLPs in different clusters. Third, the ABL estimation is transformed into a node regression problem of the graph. Case studies on a real load dataset verify the effectiveness and superiority of the proposed method.

Hydrodynamic modeling effect analysis of a fully submerged tension leg concept integrating the DTU 10 MW offshore wind turbine

Efficient and accurate hydrodynamic load estimation is important for the dynamic response analyses of large-scale offshore wind turbines to achieve a reliable and cost-effective structural design. In this study, the hydrodynamic modeling effects based on a fully submerged tension leg platform (FSTLP) integrating the DTU 10 MW wind turbine are assessed. The linear potential flow theory combined with viscous drag force and the Morison's equation with added mass coefficients tuned by two different methods are investigated to assess their feasibility. The numerical simulations using different hydrodynamic models are performed and the results are validated by a series of hydrodynamic model tests. The dynamic motions and the structure forces of FSTLPWTs are analyzed in a range of wave-only and joint wind-wave conditions considering varying water depth. It shows that reliable predictions of the dynamic responses can be provided by the potential flow model with viscous drag force. Results obtained from Morison's equation with properly tuned added mass coefficients can match well with those provided by the potential flow model. The correct added mass coefficients are not necessarily obtained from the potential flow solutions. It is also found that the relative dominance of potential and viscous damping varies in different modes and can be affected by water depth.

Two-Stage Decoupled Estimation Approach of Aggregated Baseline Load Under High Penetration of Behind-the-Meter PV System

Accurate aggregated baseline load (ABL) estimation is critical for the implementation of incentive-based demand response (DR). The increasing penetration of invisible behind-the-meter photovoltaic (BTM-PV) systems makes the net load less predictable, thus posing a significant challenge to ABL estimation. Current direct estimation approaches will produce large errors when the weather changes suddenly on DR event day, which is very likely to happen especially under high PV penetration scenarios. This paper proposes a two-stage decoupled estimation approach to improve the accuracy of ABL estimation under BTM-PV penetration. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stage I</i> , an optimal PV-Load decoupling method is proposed to decouple the PV power and the actual customer load from the net load only using a small number of observable PVs power and smart meter data. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stage II</i> , PV power and customer load within the DR event are individually estimated. Particularly, considering ABL is an ex-post estimation, a weather classification-based PV power estimation method is proposed to fully utilize the information not only before but also after the DR event to further improve the accuracy. The effectiveness and superiority of the proposed approach have been verified using a real smart meter dataset.

An inverse approach for load identification of cracked wind turbine components

ABSTRACT To assess the integrity of a structure, it is necessary to be familiar with the applied loads on its components. In this paper, an inverse modeling procedure was developed to identify the unknown static loads of a cracked component made from a brittle material, using a recently developed phase-field fracture modeling approach. The main goal was to define the optimal number, position, and orientation of the strain gauges on the structural component needed to accurately determine the imposed loads leading to different crack sizes and crack orientations. This enables an accurate assessment of the structural integrity of the components throughout their entire service life, including the process of crack initiation and its stable growth to a critical length. To verify the inverse approach, several numerical benchmark examples as well as the vital components of the wind turbine, such as the axle pin were analyzed. It is shown that the proposed approach with optimally placed strain gauges enables the accurate estimation of complex loads in a fractured component.

A novel upper bound finite-element for the limit analysis of plates and shells

Shear-locking is a classical issue in displacement-based finite-element approaches for thick plates and shells. It is even more important when considering kinematic limit analysis approaches which attempt at producing an upper bound estimate of the structure collapse load. Previous locking-free finite-element discretizations for thick plates either rely on mixed approaches or approximate strain compatibility relations which inevitably loose the upper bound status of the solution or on discontinuous interpolations which are difficult to implement and have a much higher computational cost. In this contribution, we investigate the use of a simple element with a continuous quadratic displacement and a piecewise linear rotation with continuity at the element mid-edges only. We show that this element can either produce strict upper-bound estimates taking into account the contribution of rotation jumps or a pseudo-upper bound when neglecting this contribution. Although the upper bound status is lost a priori in this case, numerical evidence indicate that limit loads usually converge from above and have a very good accuracy. Finally, we also use this element for shell problems and discuss in particular the formulation of strength criteria for thick shells. Illustrative applications show that the proposed element is free from any shear locking and produces very accurate limit load estimates for plate as well as shell problems.

Bayesian updating methodology for probabilistic model of bridge traffic loads using in-service data of traffic environment

The traffic environment of a bridge generally varies over its lifetime and can be affected by unexpected changes in the surroundings such as the construction of new roads. Therefore, for accurate estimation of traffic loads, changes in the traffic environment need to be continuously monitored and incorporated into traffic load predictions. To this end, this study first further develops the comprehensive probabilistic model of bridge traffic loads by introducing micro-simulation models to describe accurately congestion state. Next, a Bayesian methodology is proposed to update the parameters of the distributions in the probabilistic model of bridge traffic loads based on in-service data representing the traffic environment. Three Bayesian inference methods are used: conjugate prior distributions, Bayesian linear regression, and Gibbs sampling. Hyper-parameters of the prior model are set up appropriately based on the measurement accuracy and the degrees of belief in the prior model. The proposed Bayesian updating methodology is demonstrated by numerical examples with various scenarios of traffic environment changes and in-service weigh-in-motion (WIM) data measured on a real bridge. The results confirm that the proposed methodology can successfully incorporate changes of the traffic environment into the estimation of traffic load effects on bridges in operation.

Advanced optimal sensor placement for Kalman-based multiple-input estimation

The direct measurement of the external loads acting on a mechanical component represents often a challenge in many engineering applications. In this context, the attention of many researchers is focused on the field of inverse load identification. Several techniques are proposed in literature to address this problem by means of experimental methodologies, often coupled with simulation solutions. In this paper, a Kalman-based methodology is considered, which solves the problem of inverse load identification in a predictive manner. The common issue of most of the techniques is the selection of an optimal set of sensors which gives the best load estimation. In the Kalman-filtering framework, an Optimal Sensor Placement (OSP) strategy has been proposed by the authors and it aims to find the best set of sensors in terms of system observability, which is the minimum requirement of a stable estimation. However, this does not guarantee the most accurate load estimation. In this contribution, two alternative metrics are proposed, based on: i) steady-state error covariance of the estimation, ii) estimator bandwidth, with respect to the available set of measurements. Both criteria will be included in the existing OSP to improve the sensors selection. A comparison of the three strategies for multiple input/state estimation is discussed on an industrial-scale Finite Element Model, in order to show the improvement in the accuracy of the estimated quantities.

Rapid Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced Fidelity Models

Balancing accurate and efficient estimation of aerothermodynamic loads is a significant challenge for multidisciplinary modeling and analysis of high-speed vehicles. This study focuses on this issue by studying the impact of analytical and data-driven model reductions over a broad operational space. Reduced models are compared with Reynolds-averaged Navier–Stokes predictions for steady-state aerodynamic pressure and heat flux on the surface of a complete hypersonic vehicle geometry over a broad operational space. Both accuracy and computational requirements are considered so as to provide guidance on tradeoffs when carrying out multidisciplinary simulation of high-speed vehicles.

A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling.

(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r2 = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.

Viral Load in Non-Surviving Liberian Ebola Virus Disease Patients Is Underestimated by Diagnostic qRT-PCR: A Retrospective Observational Study

Background: Viral load in Ebola virus disease (EVD) correlates with case fatality rate (CFR) and is an important parameter used for diagnostic cutoffs, stratification in randomized controlled trials, and epidemiologic investigation. However, it is currently only estimated using cycle threshold (Ct) values from diagnostic qRT-PCR tests that were not developed or validated for quantitative purposes. Furthermore, during the 2013-2016 EVD epidemic in west Africa, numerous different diagnostic platforms and protocols were used and yielded inconsistent, often conflicting data. Methods: 727 whole blood samples, both positive and negative for Ebola virus (EBOV), that were collected for diagnostic purposes in Monrovia, Liberia between August 28 and December 18, 2014 were reanalyzed under highly controlled biosafety level 4 laboratory conditions. An optimized TRIzol-based extraction protocol was utilized, followed by a novel semi-strand specific two-step droplet digital PCR assay to quantify viral load as log10 EBOV RNA copies/mL. These data were then compared to the cycle threshold (Ct) values from the original diagnostic qRT-PCR testing, and a subset of the samples was also reanalyzed following re-extraction with the same diagnostic one-step PCR and instruments employed at the Ebola treatment unit for further assessment. Finally, for EBOV-positive patients, correlations between viral load, outcome, time from symptom onset, and other epidemiological parameters were evaluated utilizing a deidentified database. Findings: Survivors had significantly lower mean viral loads at presentation than non-survivors in both the reanalyzed dataset (5.61 [95% CI 5.34 to 5.87] versus 7.19 [95% CI 6.99 to 7.38] log10 EBOV RNA copies/mL, respectively; p < 0.0001) and diagnostic dataset (Ctvalue 28.72 [95% CI 27.97 to 29.47] versus 26.26 [95% CI 25.72 to 26.81], respectively; p < 0.0001). However, receiver operating characteristic curve analysis demonstrated the reanalyzed dataset to be superior to the diagnostic dataset (area under curve 0.80 [95% CI 0.75 to 0.85] versus 0.66 [95% CI 0.59 to 0.72], respectively) in distinguishing between survivors and non-survivors and the prognostic capacity of viral load increased substantially upon reanalysis (odds ratio [OR] of death 8.06, 95% CI 4.81 to 13.53, p < 0.0001 for viral loads above 6.71 log10 EBOV RNA copies/mL versus OR of death 2.02, 95% CI 1.27 to 3.20, p = 0.0028 for Ct values below 27.37). Diagnostic Ct values underestimated viral load in non-survivors by nearly 100-fold on average (mean difference between diagnostic Ct value and reanalyzed Ct value of 5.15 [95% CI 4.43 to 5.87]); substantially less discrepancy was observed for survivors (mean difference between diagnostic Ct value and reanalyzed Ct value of 1.79 [95% CI 1.16 to 2.43]). Six samples that were reported negative by diagnostic testing were found to be positive upon reanalysis, and had high viral loads. Non-survivors presented to the ETU significantly earlier as the epidemic progressed, from 6.8 days following symptom onset (95% CI 5.7 to 7.9), on average, during the first three weeks to 3.7 days (95% CI 2.4 to 5.0) during the last three weeks (p = 0.0006) and had significantly lower viral loads (7.6 log10 EBOV RNA copies/mL [95% CI 7.3 to 7.9] to 6.5 log10 EBOV RNA copies/mL [95% CI 5.8 to 7.2] for the same intervals (p = 0.0046). However, the CFR remained steady at approximately 60% throughout. Interpretation: Inaccurate viral load estimation from diagnostic Ct values is likely multifactorial, but unaddressed PCR inhibition from tissue damage in patients with fulminant EVD may largely account for the discrepancies between the datasets in this study. In light of these findings, testing protocols for EVD require further standardization and validation to produce accurate viral load estimates, minimize false negatives, and allow for reliable epidemiological investigation. Funding Information: This research was funded by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH). Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: The deidentified samples and data utilized for this study received a determination of ‘not human subjects research’ by the NIH Office of Human Subjects Research Protection and has been approved by the University of Liberia-Pacific Institute for Research and Evaluation IRB.

Comparison of Long Short-Term Memory and Weighted Regressions on Time, Discharge, and Season Models for Nitrate-N Load Estimation

The long short-term memory (LSTM) model has been widely used for a broad range of applications entailing the estimation of variables in different fields to improve water quality management in rivers. The main objectives of this study are (1) to develop a novel LSTM-based model for the estimation of nitrate-N loads, which adversely affect water resources, and (2) to evaluate the performance of the model by comparing it with that of Monte Carlo sub-sampling and the weighted regressions on time discharge and season (WRTDS) model. We evaluated the model performance using various numbers of hidden layers, ranging from one to four, in the LSTM model to determine the appropriate number of hidden layers; furthermore, we applied the sampling frequencies of 6, 12, and 24 to assess their impact. Seven polluted river basins in the United States were used for analysis, and the relative root mean squared error (rRMSE) and the mean percentage error (MPE) metrics were applied for the validation of the model estimates. The proposed model achieved accurate nitrate-N load estimates using three to four hidden layers, and improved model performance was observed when the sampling frequency was increased. The differences among the results obtained using the LSTM model were examined based on a binning technique via a log-log plot of nitrate-N concentration against discharge. The binning analysis showed that the slope obtained from the average rates of discharge and low discharge values apparently influenced the estimates. Furthermore, box plot analyses of the statistical indices such as rRMSE and MPE demonstrate that the LSTM model seems to exhibit better performance than the WRTDS model. The results of the examination demonstrate that the LSTM model may be a good alternative with regard to estimating nitrate-N loads for the control of water quality constituents.

Induction Motor Adaptive Backstepping Control and Efficiency Optimization Based on Load Observer

In this paper, an adaptive load torque observer based on backstepping control is designed, which achieves accurate load estimation where the load is unknown. Based on this, in order to reduce the loss of the motor at low load, a smooth switching strategy of rotor flux based on speed error is designed. According to the real-time speed error of the induction motor, the smooth switching strategy achieves dynamic flux switching. Firstly, when the uncertain load occurs for the first time in the recursive design, the adaptive law of the load is designed, and a novel adaptive load torque observer is obtained, which accurately estimates the uncertain load torque in real time. Secondly, the relationship between the loss and the rotor flux is established by analyzing the loss model of induction motor, and the optimal rotor flux is obtained. The smooth switching control strategy based on speed error is designed to realize the efficiency optimization of induction motor. Finally, the control strategy proposed in this paper is experimentally verified on the LINKS-RT platform. The results show that the proposed control strategy has excellent load disturbance attenuation performance and reduces the energy loss.

A Method for the Prediction of Extreme Ship Responses Using Design-Event Theory and Computational Fluid Dynamics

The design of a naval vessel requires accurate estimation of the extreme loads and motions that it will experience during its lifetime. Operation in large seaways in which the ship-wave interaction is highly nonlinear and transient leads to design events such as maximum internal loads due to global wave bending, local slamming loads, extreme roll, combinations of the global wave bending and local slamming, and many others. In this article, a method is presented that allows for nonlinear analysis to be used to predict events with user-specified rareness. The core of the method combines probability, frequency, and time-domain analyses to generate short time-window sea environments that lead to extreme dynamical events. The Office of Naval Research Tumblehome geometry is analyzed for the extreme roll angle when advancing in stern quartering irregular seas. 1. Introduction The design of a naval vessel requires accurate estimation of extreme loads and motions that it will experience during its lifetime. Specific quantities of interest are the maximum slamming load during wet-deck impact, maximum acceleration at different locations on the vessel, maximum green-water load on the bow structure or helicopter deck, maximum roll angle, or frequency of occurrence of capsize, to name a few. It is important to recognize that a ship lifetime is decades long, and the exposure time in different severe storms over the lifetime is of the order of weeks, if not months. Furthermore, because of the random nature of the sea and, hence, the dynamical response of the ship, the extreme response is also random and should be characterized statistically. This means that a single lifetime realization in a given seaway by either model tests or numerical simulation only gives one sample of the extreme response, and multiple lifetime realizations are required to characterize the extreme response.

CFD validation of wind pressure distribution on a tall building under the influence of upstream terrain

It is essential to investigate the pressure distribution, aerodynamic coefficients and wind flow fields in-order to predict the wind loads and wind behaviour in building environments. Most of the investigations and international wind load standards haven't highlighted the wind load and wind environment on-and-around buildings under different terrains. The main objective of this investigation is to explore a practical numerical approach for the accurate estimation of wind loads and their impact on buildings under different upstream conditions. In this investigation, simulations are done in IDDES turbulence model along with the formulation of k-ω SST using CFD. Results indicated the influence of ground roughness in pressure distribution on building faces. Furthermore, CFD results over-predicted the experimental values near the edges due to strong shear-flow and expanding wake-vortices. The cross-validation of the mean drag coefficients showed good-agreement. Hence this investigation promotes the practice of IDDES in wind-resistant designs for tall buildings.

Subject-specific regression equations to estimate lower spinal loads during symmetric and asymmetric static lifting

Workplace safety assessment, personalized treatment design and back pain prevention programs require accurate subject-specific estimation of spinal loads. Since no noninvasive method can directly estimate spinal loads, easy-to-use regression equations that are constructed based on the results of complex musculoskeletal models appear as viable alternatives. Thus, we aim to develop subject-specific regression equations of L4-L5 and L5-S1 shear and compression forces during various symmetric/asymmetric tasks using a nonlinear personalized finite element musculoskeletal trunk model. Kinematics and electromyography (EMG) activities of 19 young healthy subjects were collected during 64 different symmetric/asymmetric tasks. To investigate the reliability and accuracy of the musculoskeletal model and regression equations, we compared estimated trunk muscle activities and L4-L5 intradiscal pressures (IDPs) respectively with our own electromyography data (EMGs) and reported in vivo pressure measurements. Although in general, six independent rotation components (three trunk T11 rotations and three pelvic S1 rotations) are required to determine kinematics along the spine, only two surrogate variables (trunk flexion and its asymmetric angles) satisfactorily predicted all six rotation components (R2 > 0.94). Regression equations, developed based on subject-specific inputs, predicted spinal loads in satisfactory agreement with IDP measurements (R2 = 0.85). Predicted muscle activities in the personalized musculoskeletal models were in moderate to weak agreements with our measured EMGs in 19 participants. Based on dominance analysis, trunk flexion and its asymmetry angle, hand-load weight, hand-load lever arm, and body weight were the most important variables while the effects of body height and sex on spinal loads remained small.

Data reconstruction at surface in immersed-boundary methods

This work compares interpolation techniques for data reconstruction at the surface in an immersed-boundary method. Three different methods of surface pressure reconstruction based on inverse distances are presented, which are christened as: Inverse Distance Weight (IDW) method, Inverse Distance Weight at Interpolation Point method (IDW-IP) and Inverse Distance Weight based on Upwinding (IDW-Upwind) method. Additionally, shear stress at the immersed surface is determined using two approaches: direct interpolation of velocity gradient at the surface using IDW method, and interpolation of velocity at a point along the surface normal using IDW-IP method. The interpolation methods are verified against analytic solutions of ideal flow past a circular cylinder and subsonic-supersonic inviscid flow in a convergent-divergent nozzle, and validated against laminar flow simulations of Mach 0.5 flow past a NACA0012 airfoil, Mach 2.0 flow past a circular cylinder, and Mach 3.0 flow past a 10∘ ramp. The verification cases show that while the pressure values reconstructed at the surface by the three interpolation methods are very similar for the incompressible flow, the IDW-Upwind method produces the sharpest pressure rise across the normal shock in the convergent-divergent nozzle. Comparisons of the reconstructed surface pressure coefficient (Cp) and skin-friction coefficient (Cf) with values available from literature or ANSYS-Fluent simulations conducted as part of the validation study show good match, but indicate that the reconstructed pressure and shear stress values at the immersed surface has noise, which, however, reduces with grid refinement. Further, the IDW and IDW-Upwind method for pressure reconstruction, and the gradient reconstruction based method for shear stress calculation are shown to produce less noise in computed values. Integrated drag and lift values using the reconstructed surface pressure and shear stress indicate that while the different methods used for pressure reconstruction result in similar values of aerodynamic loads, the gradient-based shear stress calculations result in more accurate load estimation. Finally, one of the interpolation methods (IDW-Upwind) is used to investigate the variation of the surface pressure coefficient with time for a NACA0012 airfoil undergoing non-periodic plunge motion in a Mach 0.2 flow. The computed surface pressure coefficients are correlated with the leading and trailing edge vortices in the flow field.

Efficient and accurate technology for aircraft loads estimation

In this paper, a novel high-precision aircraft loads estimator (named EAGLE) will be introduced. EAGLE is based on the hybridization of two state-of-the-art loads estimation methods (a Luenberger observer and the local model network approach) and combines the individual advantages of the underlying methods. The hybrid method features an accurate estimation of structural loads (such as bending moments, shear forces, and torsional moments) due to maneuvers and gusts even for high load events and can be set up for a given flight test database in shortest time. Therefore, the hybrid method provides a cost-efficient setup of an accurate aircraft loads monitoring system. The paper focuses on the introduction of the new loads observer method and its validation by means of an extensive flight test database containing high maneuver and gust load events, which has been acquired using the ultra-light test aircraft UW-9 Sprint.

Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

Accurate customer baseline load (CBL) estimation is critical for implementing incentive-based demand response (DR) programs. The increasing penetration of grid-tied distributed photovoltaic systems (DPVS) complicates customers’ load patterns, making the CBL estimation more difficult because the volatile actual load and the intermittent PV output power are coupled together. A PV-load decoupling framework is proposed in this paper to address the above issue. The basic idea is to decouple the actual load power and the PV output power, then estimate them separately. To this end, historical PV output power data of each individual DPVS is required. However, pure historical PV output power data is usually unavailable for small-scale DPVSs, since they are normally located behind the meter, thus only the net load (i.e. actual load power minus PV output power) data is metered. Therefore, this paper proposes a machine learning approach to disaggregate the output power of each individual DPVS from net load data. The proposed approach includes two stages: DPVS capacity estimation and PV output power estimation. The first stage consists of two steps. First, a net load curve optimal pairing-based feature extraction method is proposed to extract features from the discrepancy between two different net load curves of the customers under heterogeneous weather conditions. Second, a multiple support vector regression-based ensemble model with the input features extracted in the first step is established to estimate the DPVS capacity. In the second stage, the output power of each DPVS is estimated by its capacity multiplied by the output power of a standard DPVS. Case studies using a real dataset from Sydney indicate that the proposed approach shows a promising performance on PV output power estimation and can significantly improve the CBL estimation accuracy for customers with DPVSs.

Measurement of unsteady wind loads in a wind tunnel: Scaling of turbulence spectra

Mismatch of turbulence spectra from the corresponding full-scale conditions is a common challenge in wind tunnel modelling of unsteady wind loads on small-scale structures, such as solar panels, heliostats and low-rise buildings. Understanding the effect of this mismatch on the unsteady wind loads is necessary for providing an accurate estimation of wind loads on full-scale structures. The correlation between the turbulence spectra and the unsteady wind loads in wind tunnel measurements is investigated in this study through measurement of unsteady lift and drag forces on horizontal and vertical flat plates. It was found through spectral analysis that the turbulent eddies in the range of reduced frequencies between 0.01 and 1 contributed the most to the unsteady wind loads. An approach for wind tunnel modelling was proposed in which the geometric scaling ratio of each model is determined based on the analysis of the turbulence power spectrum as a function of reduced frequency. The suitable geometric scaling ratio should be then chosen such that the turbulence spectrum as a function of reduced frequency is the closest match to that at full-scale for reduced frequencies between approximately 0.01 and 1.

Wind spectral characteristics on fatigue responses of towerbase and moorings of a floating offshore wind turbine

The tower-platform interface and mooring system of floating offshore wind turbines (FOWTs) are some of the most critical components with significant influences on overall project costs. In addition to satisfying strength requirements, it is typical and vital to meet fatigue criteria for a service life of 25 years or more. Wind spectra characteristics considered in analysis can penalize fatigue designs, leading to unnecessary costs. The International Electrotechnical Commission (IEC, 2009) recommends the use of site-specific wind data (spectrum, turbulence intensity, etc.) in design of FOWTs, but for offshore sites it is often the case that such data is unavailable and land-based data are used as surrogates in design. For such scenarios, it is worth investigating whether such alternative approach is suitable and accurate, and understanding the consequence of the selection of wind spectral characteristics on fatigue design. This paper addresses the impact of the subsequent selection on fatigue responses of towerbase and mooring system in a FOWT, as a sequel to the paper by Udoh and Zou (2018) which focused on impacts on strength design. The 5 MW semi-submersible FOWT platform with six mooring lines implemented in the preceding study is applied in analysis. Results indicate significant variations in resulting fatigue life with considered wind parameters. Thus, it is critical to apply proper wind spectra characteristics for analysis and design of FOWTs to avoid unnecessary conservatism and costs. Based on the findings of this study, more explicit guidance on the application of turbulence intensities for IEC-recommended models in offshore sites could lead to more accurate load estimates in design of FOWTs.