• Wind and Structures
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• v.28 no.5
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• pp.271-284
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• 2019

This paper describes the application of a novel virtual prototyping methodology to wind turbine blade design. Numeric modelling data and experimental data about turbine blade geometry and structural/dynamical behaviour are combined to obtain an affordable digital twin model useful in reducing the undesirable uncertainties during the entire turbine lifecycle. Moreover, this model can be used to track and predict blade structural changes, due for example to structural damage, and to assess its remaining life. A new interactive and recursive process is proposed. It includes CAD geometry generation and finite element analyses, combined with experimental data gathered from the structural testing of a new generation wind turbine blade. The goal of the research is to show how the unique features of a complex wind turbine blade are considered in the virtual model updating process, fully exploiting the computational capabilities available to the designer in modern engineering. A composite Sandia National Laboratories Blade System Design Study (BSDS) turbine blade is used to exemplify the proposed process. Static, modal and fatigue experimental testing are conducted at Clarkson University Blade Test Facility. A digital model was created and updated to conform to all the information available from experimental testing. When an updated virtual digital model is available the performance of the blade during operation can be assessed with higher confidence.

• Steel and Composite Structures
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• v.39 no.3
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• pp.261-274
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• 2021

In a railway bridge, the CRTS II slab ballastless track is subjected to interlayer connection failures, such as void under slab, mortar debonding, and fastener fracture. This study investigates the influences of interlayer connection failure on the safe operation of high-speed trains. First, a train-track-bridge coupled vibration model and a bridge-track deformation model are established to study the running safety of a train passing a deformed bridge with interlayer connection failure. For each type of the interlayer connection failure, the effects of the failure locations and ranges on the track irregularity are studied using the deformation model. Under additional bridge deformation, the effects of interlayer connection failure on the dynamic responses of the train are investigated by using the track irregularity as the excitation to the vibration model. Finally, parametric studies are conducted to determine the thresholds of additional bridge deformations considering interlayer connection failure. Results show that the interlayer connection failure significantly affects the running safety of high-speed train and must be considered in determining the safety thresholds of additional bridge deformation in the asset management of high-speed railway bridges.

• Smart Structures and Systems
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• v.24 no.4
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• pp.435-446
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• 2019

The existing piezoelectric spherical transducers with fixed prescribed dynamic characteristics limit their application in scenarios with multi-frequency or frequency variation requirement. To address this issue, this work proposes an improved design of piezoelectric spherical transducers using the resistance tuning method. Two piezoceramic shells are the functional elements with one for actuation and the other for tuning through the variation of load resistance. The theoretical model of the proposed design is given based on our previous work. The effects of the resistance, the middle surface radius and the thickness of the epoxy adhesive layer on the dynamic characteristics of the transducer are explored by numerical analysis. The numerical results show that the multi-frequency characteristics of the transducer can be obtained by tuning the resistance, and its electromechanical coupling coefficient can be optimized by a matching resistance. The proposed design and derived theoretical solution are validated by comparing with the literature given special examples as well as an experimental study. The present study demonstrates the feasibility of using the proposed design to realize the multi-frequency characteristics, which is helpful to improve the performance of piezoelectric spherical transducers used in underwater acoustic detection, hydrophones, and the spherical smart aggregate (SSA) used in civil structural health monitoring, enhancing their operation at the multiple working frequencies to meet different application requirements.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

• Geomechanics and Engineering
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• v.24 no.5
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• pp.419-430
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• 2021

Investigation on the compressibility and shear strength of compacted loess is of great importance for the design and operation of engineering infrastructures in filling area. In this study, the mechanical behaviors of Yan'an compacted loess are investigated at various dry densities and water contents by conducting one dimensional compression and direct shear tests. And the elastic compressibility, plastic compressibility, yield stress and strength are obtained from the experiments. Results show that when water content increases, plastic compressibility parameter increases, but yield stress decreases. However, the increase of dry density leads to a decrease in plastic compressibility parameter but an increase in yield stress. In addition, elastic compressibility parameter is found to be a constant which is irrelevant to water content and dry density. As for strength, cohesion and internal friction angle is directly proportional to dry density, but inversely proportional to water content. Moreover, the mercury intrusion porosimetry (MIP) and scanning electron microscope (SEM) tests were also performed to observe the pore size distribution and microstructure of the specimens. Finally, by using results of MIP and SEM tests, the compressibility and strength behaviours of Yan'an compacted loess are explained from the perspective of pore-size distribution and microstructure.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Membrane and Water Treatment
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• v.13 no.4
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• pp.183-189
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• 2022

This study seeks towards an optimal way to control sulfate ions in semiconductor wastewater effluent with potential eco-toxicity. We developed a system based on ettringite (Ca₆Al₂(SO₄)₃(OH)₁₂·26H₂O). The basic idea is that the pH of the water is raised to approximately 12 with Ca(OH)₂. After, aluminium salt is added, leading to the precipitation of ettringite. Lab-scale batch and continuous experiment results with real semiconductor wastewater demonstrated that 1.5 and 1 of stoichiometric quantities for Ca²⁺ and A³⁺ with pH above 12.7 could be considered as the optimal operation condition with 15% of sludge recycle to the influent. A mixed AlCl₃ + Fe reagent was selected as the beneficial Al³⁺ source in ettringite process, which resulted in 80% of sludge volume reduction and improved sludge dewaterability. The results of continuous experiment showed that with precipitation as ettringite, sulfate concentration can be stably reduced to less than 50 mg/L in effluent from the influent 2,050 ± 175 mg/L on average (1,705 ~ 2,633 mg/L).

• Steel and Composite Structures
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• v.42 no.2
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• pp.209-219
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• 2022

Offshore platforms in seismically active areas must be designed to survive in the face of intense earthquakes without a global structural collapse. This paper scrutinizes the seismic performance of a newly designed and established jacket type offshore platform situated in the entrance of the Gulf of Suez region based on the API-RP2A normalized response spectra during seismic events. A nonlinear finite element model of a typical jacket type offshore platform is constructed taking into consideration the effect of structure-soil-interaction. Soil properties at the site were manipulated to generate the pile lateral soil properties in the form of load deflection curves, based on API-RP2A recommendations. Dynamic characteristics of the offshore platform, the response function, output power spectral density and transfer functions for different elements of the platform are discussed. The joints deflection and acceleration responses demands are presented. It is generally concluded that consideration of the interaction between structure, piles and soil leads to higher deflections and less stresses in platform elements due to soil elasticity, nonlinearity, and damping and leads to a more realistic platform design. The earthquake-based analysis for offshore platform structure is essential for the safe design and operation of offshore platforms.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.249-258
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• 2022

A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

• Advances in aircraft and spacecraft science
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• v.8 no.4
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• pp.289-302
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• 2021

Ensuring flight safety for passengers as well as crew is the most important aspect of modern aviation, and in order to achieve this, it is necessary to be able to forecast the durability of individual components. The present contribution illustrates the results of a computational analysis to determine the possibility of analysing the prediction of bearing durability in on-board rotating equipment from the point of view of thermal fatigue.In this study, a method developed at the Air Force Institute of Technology was used for analysis, which allowed to determine the bearing durability from the flight altitude profile. Two aircraft have been chosen for analysis - a military M-28 and a civilian Embraer. As a result of the analysis were obtained: the bearing durability in on-board rotating devices, average operation time between failures, as well as failure rate. In conclusion, the practical applicability of this approach is demonstrated by the fact that even with a limited number of flight parameters, it is possible to estimate bearing durability and increase flight safety by regular inspections.