Wind Energy Applications Research Articles

Intercomparing the quality of recent reanalyses for offshore wind farm planning in Germany's exclusive economic zone of the North Sea

Abstract. In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events. Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.

Composite materials in wind energy: design, manufacturing, operation, and end-of-life

Wind energy has consistently grown over recent decades and has grown in many aspects including in terms of installed capacities, turbine size, blade length, and grid penetration. Along with this, wind energy is one of the largest producers of composite structures, and as a result is one of the largest users of composite materials. For wind energy applications, composite materials require high reliability, low-cost, and near-term and future industry goals are to reuse or recycle composite materials. These requirements are quite challenging as wind energy faces a challenging operating environment, which puts great pressure on wind blade materials over their entire life cycle. This paper aims to examine the challenges of composite materials for wind energy applications, and to highlight a few research studies that offer potential new solutions and new insights across the entire life cycle of composites for wind energy systems ranging from the design phase, to manufacturing, to operation, and finally to the end-of-life phase.

An Example of Wind Energy Applications: Türkiye (Kirsehir Province, Geycek Wind Power Plant)

An Example of Wind Energy Applications: Türkiye (Kirsehir Province, Geycek Wind Power Plant)

Techno-economic analysis of hybrid solar-biomass-wind for water pumping considering carbon tax and renewable energy rebate

There is a need for increased penetration of renewable energy resources (RES) in the energy mix of South Africa that is currently bedevilled by recurring load shedding thus affecting sustainable critical infrastructures such as water pumping and water use. This paper assesses the viability of three RES of solar, biomass, and wind for water pumping at two locations (University of Johannesburg, APK campus – L1 and Zuikerbosch pumping station – L2) in Johannesburg, South Africa by utilising daily data from 2003 to 2020. A detailed feasibility analysis of the three RES was conducted, after which a rebate-based novel model for reducing the unit cost with zero greenhouse gas (GHG) was implemented for solar PV. Key findings reveal that the power requirement in L1 and L2 booster stations are 91 kW and 71,331 kW, respectively while the produced power from a 350 W monocrystalline PV-module utilized is 0.103 kW in L1 and L2 using 882 and 692,534 modules. The biomass power of 47.68 GW in L1 and 99.42 GW in L2 was produced from an average amount of food waste estimate of 1641 and 1248,800 tonnes evaluated from 3729 and 2838,100 tonnes of putrescible waste using a population model. The power density results depict a very weak wind energy application capability in the two locations based on National Renewable Energy Laboratory indices after using 11 differently sized wind turbines in the two locations. Finally, the result from the rebated unit-cost model reveals that the unit cost of water dropped by 17.09$/kWhr from 0.33$/kWhr in L1 and 21.20$/kWhr from 0.66$/kWhr in L2 using the solar PV modules. The study aligns with similar study done in Qatar, and the Springer report on Energy Democracy Evaluation for 100% Renewable Energy Policies and Projects showing an increased penetration of affordable and carbon-free RES which will in turn reduce climate change effects globally.

Progress in 3D printing in wind energy and its role in achieving sustainability

The application of 3D printing technology in the wind energy sector has garnered considerable interest recently owing to its ability to curtail the waste of materials, reduce manufacturing costs, and enhance performance. Although previous studies have investigated the impact of 3D printing in various industries, there is still a notable knowledge gap regarding its potential role in advancing the Sustainable Development Goals (SDGs) specifically in the field of wind turbine technology. This study sheds light on 3D printing's potential to advance SDGs through a bibliometric analysis. It also aims to examine the materials and techniques employed in 3D printing technology for wind energy applications and evaluate the extent to which SDGs have been integrated into the existing literature. Based on the bibliometric analysis, the results indicated that SDG 7, which pertains to affordable and clean energy, has been the subject of the most extensive research in this particular domain, accounting for 58% of the total contributions related to the SDGs. This can be justified by the contribution of 3D printing to energy and emissions reductions, with 54.8% energy recovery. Additionally, the impact of wind turbine 3D printing on the SDGs was only noticeable for three other goals that are SDGs 8, 12, and 13, with percentages of 8%, 8%, and 9%, respectively. The impact of 3D printing was also clear on SDG 13, as it reduces CO2 emissions by ∼25% in comparison to conventional manufacturing technologies. On the other hand, the involvement of all remaining SDGs was insignificant where no goal accounted for more than 3%. This highlights the necessity for additional investigation in these domains to enhance the sustainability and social impacts of wind turbines produced through 3D printing.

Numerical thermal analysis of synchronous reluctance generator for wind energy application

AbstractThis paper focuses on the thermal analysis of the synchronous reluctance generator with a rating of 2.1 kW. It mainly uses explicit, and implicit finite difference methods for thermal analysis to reduce the complexity of thermal calculation for the machine's components. It compares the results with the results obtained using a finite element analysis (FEA) and includes the experimental verification of the obtained results. The explicit, and implicit finite difference thermal analysis is relatively simple and computationally fast. Once the design parameters are known, the electric losses and iron losses of the synchronous reluctance generator are evaluated. These machine parameters are utilized in developing the explicit finite difference (EFD), an implicit finite difference (IFD), and a 3D FEA model for thermal analysis. It is observed that the obtained results from the EFD, IFD, FEA, and experiments are very close to each other, and the temperature rise for the designed machine is within the desired and acceptable range.

New White Etch Cracking resistant martensitic stainless steel for bearing applications by high temperature solution nitriding

White Etch Cracking (WEC) is a severe and unpredictable failure mode that has been frequently observed in, among others, bearings for wind energy applications. In this work a novel, patented, WEC resistant bearing steel solution is developed through high temperature solution nitriding, followed by a custom heat treatment. Conventional medium carbon, 13 wt% Cr martensitic stainless steels were subjected to the novel process to obtain a nitrogen-rich case. A series of soft annealing, austenitization, cryogenic and tempering treatments converted the nitrogen-rich region into a fine-grained case containing nano-sized precipitates. Test bearings and tribometer specimens were treated with the same sequence of treatments. These specimens were tested under two different accelerated WEC failure promoting conditions. In contrast to standard through-hardened bearing steel the nitrided specimens showed no WEC formation, not even after prolonged testing.

Assessing the performance of several numerical methods for estimating Weibull parameters for Wind Energy Applications: A case study of Al-Hodeidah in Yemen

The estimation of wind potential is generally assessed using the two parameters of Weibull distribution (k, c). Relative Percentage Error (RPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R), and Index of Agreement (IA) are calculated to comparatively analyze five numerical methods of computing Weibull parameters. The five selected methods are (Empirical Justus (EMJ), Energy Pattern Factor (EPFM), Maximum Likelihood (MLM), Method of Moment (MOM), and L-moment Estimation Method (L-MOM)). With the daily wind speed dataset for the year 2014 measured at 10 m height, the five methods have been applied on the study site Al-Hodeidah city in Yemen. Therefore, compared to other methods, the EPFM method is the most accurate method for estimating the Weibull parameters, while the L-MOM method is the least accurate at 10, 30, and 50 m heights. Moreover, this study reveals that the parameter remains the same with height, while the c parameter increases with height. The values of the skewness and kurtosis of wind speed and power density do not change as height changes.

Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

The Weibull distribution function is essential for planning and designing wind-farm implementation projects and wind-resource assessments. However, the Weibull distribution is limited for those areas with high frequencies of calm winds. One solution is to use the hybrid Weibull distribution. In fact, when the wind speed data present heterogeneous structures, it makes sense to group them into classes that describe the different wind regimes. However, the single use of the Weibull distribution presents fitting errors that should be minimized. In this context, mixture distributions represent an appropriate alternative for modelling wind-speed data. This approach was used to combine the distributions associated with different wind-speed classes by weighting the contribution of each of them. This study proposes an approach based on mixtures of Weibull distributions for modelling wind-speed data in the West Africa region. The study focused on mixture Weibull (WW-BAY) and mixture hybrid Weibull (WWH-BAY) distributions using Bayes' theorem to characterize the wind speed distribution over twelve years of recorded data at the Abuja, Accra, Cotonou, Lome, and Tambacounda sites in West Africa. The parameters of the models were computed using the expectation-maximization (E-M) algorithm. The parameters of the models were estimated using the expectation-maximization (E-M) algorithm. The initial values were provided by the Levenberg-Marquardt algorithm. The results obtained from the proposed models were compared with those from the classical Weibull distribution whose parameters are estimated by some numerical method such as the energy pattern factor, maximum likelihood, and the empirical Justus methods based on statistical criteria. It is found that the WWH-BAY model gives the best prediction of power density at the Cotonou and Lome sites, with relative percentage error values of 0.00351 and 0.01084. The energy pattern factor method presents the lowest errors at the Abuja site with a relative percentage error value of -0.54752, Accra with -0.55774, and WW-BAY performs well for the Tambacounda site with 0.19232. It is recommended that these models are useful for wind energy applications in the West African region.

An Efficient Neural Network Design Incorporating Autoencoders for the Classification of Bat Echolocation Sounds.

Bats are widely distributed around the world, have adapted to many different environments and are highly sensitive to changes in their habitat, which makes them essential bioindicators of environmental changes. Passive acoustic monitoring over long durations, like months or years, accumulates large amounts of data, turning the manual identification process into a time-consuming task for human experts. Automated acoustic monitoring of bat activity is therefore an effective and necessary approach for bat conservation, especially in wind energy applications, where flying animals like bats and birds have high fatality rates. In this work, we provide a neural-network-based approach for bat echolocation pulse detection with subsequent genus classification and species classification under real-world conditions, including various types of noise. Our supervised model is supported by an unsupervised learning pipeline that uses autoencoders to compress linear spectrograms into latent feature vectors that are fed into a UMAP clustering algorithm. This pipeline offers additional insights into the data properties, aiding in model interpretation. We compare data collected from two locations over two consecutive years sampled at four heights (10 m, 35 m, 65 m and 95 m). With sufficient data for each labeled bat class, our model is able to comprehend the full echolocation soundscape of a species or genus while still being computationally efficient and simple by design. Measured classification F1 scores in a previously unknown test set range from 92.3% to 99.7% for species and from 94.6% to 99.4% for genera.

Rethinking the patent domains: An application to wind energy

Wind energy patents are conventionally defined using Cooperative Patent Classification (CPC) and International Patent Classification (IPC) codes that represent wind motors (F03D) and wind energy (Y02E 10/70). This study examines whether these codes sufficiently represent the wind energy patent domain. Using a combination of keywords and classification codes identified through expert input and manual review, we construct an expanded domain with a 7.5% increase in patent count, a 6% increase in recall, and only a 1% decrease in precision for wind energy patents. We also found that the conventional domain is not unbiased; it underrepresents patenting from China and patents published since 2010. This is important because an improved patent domain allows for a holistic patent data set, which is necessary to complete a thorough analysis of the wind industry. Through this wind energy application, we extend our methodology to form a generalized patent search process that can be used to target technology domains within a patent data set.

A Review on Wind Speed Extreme Values Modeling and Bayes Estimation for Wind Power Plant Design and Construction

Rapid growth of the use of wind energy calls for a more careful representation of wind speed probability distribution, both for identification and estimation purposes. In particular, a key point of the above identification and estimation aspects is representing the extreme values of wind speed probability distributions, which are of great interest both for wind energy applications and structural tower reliability analysis. The paper reviews the most adopted probability distribution models and estimation methods. In particular, for reasons which are properly discussed, attention is focused on the evaluation of an opportune “safety index” related to extreme values of wind speeds or gusts. This topic has gained increasing attention in recent years in both wind energy generation assessment and also in risk and structural reliability and safety analysis. With regard to wind energy generation, there is great sensitivity in the relationship between wind speed extreme upper quantiles and the corresponding wind energy quantiles. Concerning the risk and reliability analysis of structures, extreme wind speed value characterization is useful for a proper understanding of the destructive wind forces that may affect structural tower reliability analysis and, consequently, the proper choice of the cut off wind speed value; therefore, the above two kinds of analyses are somewhat related to each other. The focus is on the applications of the Bayesian inference technique for estimating the above safety index due to its effectiveness and usefulness.

Fabrication and characterization of Ag coated Al2O3/GNs reinforced Cu nanocomposites for renewable energy applications

The utilization of renewable energy has become increasingly significant recently, yet it encounters great challenges. The present study focuses on enhancing the performance of copper-based nanocomposites for wind energy applications by achieving a desirable combination of wear resistance and electrical properties. The investigation explores the effects of graphite exfoliation and silver reinforcement coating on the properties of the composite. Cu–Ag-(C/Al2O3) nanocomposites are fabricated, varying the percentage of Al2O3 from 0 to 12.5% in increments of 2.5%. The study involves physical, electrical, mechanical, and tribological analyses, along with extensive metallography using techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and mapping. The results demonstrate significant enhancements in the composite properties with higher percentages of Al2O3, up to a limit of 10%. The inclusion of a hybrid reinforcement consisting of 10% Al2O3 and 5% exfoliated graphene, along with a 20% silver coating, results in a lighter-weight Cu-composite with 98% of the electrical conductivity of copper (IACS). Moreover, this composite exhibits the highest hardness (365 HV), which is six times greater than the hardness of the composite without the reinforcement (60 HV). The composite containing 10% Al2O3 exhibits the lowest wear rate (3.5 × 10−6 (mm3/m)), while the composite without Al2O3 demonstrates the highest wear rate (6.7 × 10−6 (mm3/m)). Metallographic analysis confirms the formation of graphene through exfoliation. The presence of graphene not only improves the coefficient of thermal expansion (CTE) stability due to its lubricating effect but also enhances the electrical conductivity of the composites when combined with the silver reinforcement coating.

Trading rights to consume wind in presence of farm-farm interactions

Michiel Kenis is a PhD researcher at the Energy Systems Integration & Modeling Group at the University of Leuven with a doctoral mandate from the Flemish Institute for Technological Research (VITO). He was a visiting researcher at the Massachusetts Institute of Technology. His research focuses on cross-border electricity markets. He holds a MSc in energy engineering and a MSc in policy economics, both from the University of Leuven. Luca Lanzilao completed his MSc degree in mathematical engineering from Politecnico di Torino in 2018. Currently, he is pursuing a PhD at KU Leuven. His research focuses on studying the response of the atmospheric boundary layer to wind farm forcing, with particular emphasis on meso-scale phenomena, such as gravity waves. Kenneth Bruninx received a MSc degree in energy engineering in 2011, a MSc in management, and a PhD degree in mechanical engineering in 2016, all from the University of Leuven (KU Leuven), Belgium. Currently, he is an assistant professor at the Faculty of Technology, Policy, and Management of TU Delft, Netherlands and a research fellow at the Department of Mechanical Engineering, KU Leuven, Belgium. His research interests include market design, policies, and regulation for integrated energy systems. Johan Meyers is a professor of mechanical engineering at KU Leuven since 2009. His research focuses on the simulation of turbulent flows and the atmospheric boundary layer with applications in wind energy. In 2012, he obtained an ERC grant on wind farm control and has been involved in various European projects on wind energy since. He served as the vice president of the European Academy of Wind Energy from 2017 to 2019 and as its president from 2019 to end of 2021. He has been active as an associate editor for Computers & Fluids and is currently an associate editor for Wind Energy Science. Erik Delarue received MSc and PhD degrees in mechanical engineering from the University of Leuven, Belgium, in 2005 and 2009, respectively. He is currently an associate professor with the University of Leuven, TME Branch (energy conversion) and active with EnergyVille. His research focus and expertise are on quantitative tools, supporting an efficient operation of, and transition toward, a low-carbon energy system (mathematical modeling of energy systems). Applications relate to flexibility through energy systems integration, market design, and energy policies. Michiel Kenis is a PhD researcher at the Energy Systems Integration & Modeling Group at the University of Leuven with a doctoral mandate from the Flemish Institute for Technological Research (VITO). He was a visiting researcher at the Massachusetts Institute of Technology. His research focuses on cross-border electricity markets. He holds a MSc in energy engineering and a MSc in policy economics, both from the University of Leuven. Luca Lanzilao completed his MSc degree in mathematical engineering from Politecnico di Torino in 2018. Currently, he is pursuing a PhD at KU Leuven. His research focuses on studying the response of the atmospheric boundary layer to wind farm forcing, with particular emphasis on meso-scale phenomena, such as gravity waves. Kenneth Bruninx received a MSc degree in energy engineering in 2011, a MSc in management, and a PhD degree in mechanical engineering in 2016, all from the University of Leuven (KU Leuven), Belgium. Currently, he is an assistant professor at the Faculty of Technology, Policy, and Management of TU Delft, Netherlands and a research fellow at the Department of Mechanical Engineering, KU Leuven, Belgium. His research interests include market design, policies, and regulation for integrated energy systems. Johan Meyers is a professor of mechanical engineering at KU Leuven since 2009. His research focuses on the simulation of turbulent flows and the atmospheric boundary layer with applications in wind energy. In 2012, he obtained an ERC grant on wind farm control and has been involved in various European projects on wind energy since. He served as the vice president of the European Academy of Wind Energy from 2017 to 2019 and as its president from 2019 to end of 2021. He has been active as an associate editor for Computers & Fluids and is currently an associate editor for Wind Energy Science. Erik Delarue received MSc and PhD degrees in mechanical engineering from the University of Leuven, Belgium, in 2005 and 2009, respectively. He is currently an associate professor with the University of Leuven, TME Branch (energy conversion) and active with EnergyVille. His research focus and expertise are on quantitative tools, supporting an efficient operation of, and transition toward, a low-carbon energy system (mathematical modeling of energy systems). Applications relate to flexibility through energy systems integration, market design, and energy policies.

Coordinated Predictive Control of Offshore DC Collection Grid and Wind Turbines for Frequency Response: A Scheme Without Secondary Frequency Drop

From both technical and economical perspectives, all-dc offshore wind farm (OWF) is a trend for future offshore wind energy applications. Furthermore, in an all-dc OWF, both the capacitors in dc collection grid and offshore wind turbines have the potential for onshore grid frequency response. In light of this, this paper presents a model predictive control scheme, which coordinates the operation of offshore dc collection grid capacitors (dc-grid capacitors) and offshore wind turbines for the onshore frequency support. With this scheme, offshore dc-grid capacitors provide fast inertia support right after the frequency event, while offshore wind turbines provide primary frequency response for a longer period. A key feature of the proposed scheme lies in the ability to suppress the secondary frequency drop issue, which is caused by sudden power reduction of offshore dc-grid capacitors during the frequency recovery phase. An analytical framework is derived to validate this ability. Finally, simulation results verify the effectiveness of the proposed scheme.

Interdependence of Surface Roughness on Icephobic Performance: A Review

Ice protection techniques have attracted significant interest, notably in aerospace and wind energy applications. However, the current solutions are mostly costly and inconvenient due to energy-intensive and environmental concerns. One of the appealing strategies is the use of passive icephobicity, in the form of coatings, which is induced by means of several material strategies, such as hydrophobicity, surface texturing, surface elasticity, and the physical infusion of ice-depressing liquids, etc. In this review, surface-roughness-related icephobicity is critically discussed to understand the challenges and the role of roughness, especially on superhydrophobic surfaces. Surface roughness as an intrinsic, independent surface property for anti-icing and de-icing performance is also debated, and their interdependence is explained using the related physical mechanisms and thermodynamics of ice nucleation. Furthermore, the role of surface roughness in the case of elastomeric or low-modulus polymeric coatings, which typically instigate an easy release of ice, is examined. In addition to material-centric approaches, the influence of surface roughness in de-icing evaluation is also explored, and a comparative assessment is conducted to understand the testing sensitivity to various surface characteristics. This review exemplifies that surface roughness plays a crucial role in incorporating and maintaining icephobic performance and is intrinsically interlinked with other surface-induced icephobicity strategies, including superhydrophobicity and elastomeric surfaces. Furthermore, the de-icing evaluation methods also appear to be roughness sensitive in a certain range, indicating a dominant role of mechanically interlocked ice.

Infusible thermoplastic composites for wind turbine blade manufacturing: Static characterization of thermoplastic laminates under ambient conditions

The necessity for recyclable materials in wind energy applications has fueled research in glass fiber reinforced thermoplastic composites to replace their thermoset counterparts. Toward demonstrating that infusible acrylic resins can replace epoxy based composite systems in wind blade manufacturing, comprehensive static test protocols were performed, and the resulting data are presented. Specifically, unidirectional and biaxial (±45) continuous E-glass reinforced thermoplastic and epoxy laminates were prepared in four and eight ply laminates. Physical properties were characterized including density, fiber volume fraction, and glass transition temperatures, together with mechanical properties for tensile, compression, and shear responses. Comprehensive evaluation of these data supports infusible acrylic thermoplastic resin systems as viable alternative prospects to replace epoxies in E-glass reinforced wind blades as verified by comparable results for both composite systems.

A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems

Load frequency control (LFC) has recently gained importance due to the increasing integration of wind energy in contemporary power systems. Hence, several power system models, control techniques, and controllers have been developed to improve the efficiency, resilience, flexibility, and economic feasibility of LFC. Critical factors, such as energy systems, resources, optimization approaches, resilience, and transient stability have been studied to demonstrate the uniqueness of the proposed design. This paper examines the most recent advances in LFC techniques for wind-based power systems. Moreover, the use of classical, artificial intelligence, model predictive control, sliding mode control, cascade controllers, and other newly designed and adopted controllers in the LFC area is thoroughly examined. Statistical analysis and a comparison table are used to evaluate the advantages, disadvantages, and applications of various controllers. Finally, this paper presents a comprehensive overview of contemporary and other widely used soft computing tools for the LFC issue. This detailed literature review will assist researchers in overcoming the gap between current progress, application, limitations, and future developments of wind energy in LFC.

Probabilistic and Reliability Analysis of an Intelligent Power Control for a Doubly Fed Induction Generator-Based Wind Turbine System

The wind energy system's complicated characteristics have motivated many researchers to develop advanced and intelligent control strategies to ensure reliable and efficient system operation. Over the past few years, Artificial Neural Networks (ANN) have been widely employed in wind energy applications, and they are strongly recommended as a powerful and adequate tool for controlling wind turbine systems.The generator control block is a critical subsystem in the wind turbine chain, where a failed generator power control leads to an unstable operation process. The current work aims to verify the reliability of an ANN control of a DFIG integrated into a wind turbine system by considering the uncertainties in the machine parameters that affect the control's performance requirements. The major challenges encountered when applying reliability analyses are the system's high complexity and the expensive computational time required to achieve accurate results. In this context, the reliability assessment methodology adopted in this study combines Machine Learning techniques and advanced reliability approximation algorithms to optimize the computing time and ensure accurate results despite the system complexity.The obtained results demonstrate the reliability and effectiveness of ANN controllers in coping with machine parameter variations and maintaining the system at its optimal operating point. In addition, the results demonstrate the effectiveness of the reliability methodology implemented in this work by offering accurate approximations with a reasonable computing time.

CPT-based design method for helical piles in sand

Helical piles are used extensively for low and medium rise developments and are a popular foundation solution in cases where resistance to significant uplift loads is required. The ability to re-use helical piles and recent interest in their use as foundations in offshore wind and solar energy applications has renewed interest in improving existing design methods. This paper presents the results from field investigations in medium dense and dense sand that examine effects on the axial tension and compression capacity of varying the helix pitch, shaft diameter, advancement ratio and shaft tip geometry. The results from the 20 pile tests conducted (many of which included instrumentation) indicate low sensitivity to a range of the investigated parameters and a strong correlation of the capacity to the cone penetration test resistance. A design method based on these findings is proposed for typical helical pile geometries and is shown to provide predictions that are generally within 10 to 15% of the axial capacities reported for 30 other well documented pile tests reported in the literature. The method, referred to as UWASP-22, incorporates a simple means of estimating the load-displacement response of a helical pile as well as a formulation enabling prediction of the installation torque which is a common quality control measure for helical piles.