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Electrical power generated from wind turbines inherently fluctuates due to changing wind speeds. Without proper control, disturbances such as changing wind speeds can degrade the power quality factor and robustness of the electrical grid. To ensure good power quality factor, high performance and robustness of the grid against internal and external disturbances, the use of Active Disturbance Rejection Control with an extended state observer ESO for a PMSG Wind Energy Conversion System is investigated. The system has been simulated in MATLAB/Simulink at various wind speeds. The obtained simulation results indicate that the controller maintains constant DC voltage at the interface of the generator-side converter and grid-side converters and achieves maximum power. The results also show that the system performance has good stability, precision and rejection of internal disturbances, with an overall system efficiency of 98.65%.

Biopolymers are an attractive green alternative to conventional polymers, owing to their excellent biocompatibility and biodegradability. However, their amorphous and nonconductive nature limits their potential as active biosensor material/substrate. To enhance their bio-analytical performance, biopolymers are combined with conductive materials to improve their physical and chemical characteristics. We review the main advances in the field of electrochemical biosensors, specifically the structure, approach, and application of biopolymers, as well as their conjugation with conductive nanoparticles, polymers and metal oxides in green-based noninvasive analytical biosensors. In addition, we reviewed signal measurement, substrate bio-functionality, biochemical reaction, sensitivity, and limit of detection (LOD) of different biopolymers on various transducers. To date, pectin biopolymer, when conjugated with either gold nanoparticles, polypyrrole, reduced graphene oxide, or multiwall carbon nanotubes forming nanocomposites on glass carbon electrode transducer, tends to give the best LOD, highest sensitivity and can detect multiple analytes/targets. This review will spur new possibilities for the use of biosensors for medical diagnostic tests.

In this paper, a new roll and pitch control mechanism for an underwater glider is described. The mechanism controls the glider’s pitch and roll without the use of a conventional buoyancy engine or movable mass. The mechanism uses water as trim mass, with a high flow rate water pump to shift water from water bladders located at the front, rear, left, and right of the glider. By shifting water from the left water bladder to the right water bladder, a roll moment is induced. Similarly, pitch is achieved by controlling the water flow between the front and rear water bladder using a water pump. The water bladders act not only as a means for roll and pitch control but as a buoyancy engine as well. While this mechanism reduces the need for a dedicated buoyancy engine, as well as internal moving masses, motion control is more complicated, as buoyancy, roll, and pitch must be considered simultaneously. The dynamics of the system were derived and simulated, as well as validated experimentally. The glider is able to move in a sawtooth pattern with a pitch angle of 43.5?, as well as a maximum roll angle of 43.6?. Additionally, the effect of pump rate on pitch and roll rate was investigated. Both pitch and roll rates increase with increasing pump rate.

In this work, a novel aptamer-based CA-MoS2 hybrid biosensor was developed for diagnosis of Acute Myocardial Infraction (AMI). Specifically, a novel petal-like MoS2 nanoflower was used as the active material on the Screen-Printed Electrode (SPE) for AMI biomarker determination. The aptamer-based CA-MoS2–24 hybrid was able to attain a low limit of detection (LOD) and sensitivity to 10 fM at a linear range from 10 fM to 1 nM with good reproducibility based on an electrochemical impedance spectroscopy and a ~4 folds higher selectivity of troponin I compared to the signal from other biomolecules in human serum, retaining a 91% stability after 6 weeks. This novel CA-MoS2 aptamer-based biosensor with a 2.3% RSD value has the potential to revolutionize medical diagnosis by conducting multiple biomolecule detection on a single sensing platform.

Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis.

This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of 71 sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error (within 15%). Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.

This paper describes the application of finite element method (FEM) to predict the failure pressure of pipe with circumferential groove corrosion subjected to internal pressure. The FEM incorporated material nonlinearity and employed true UTS to determine the failure point. Finite element analysis (FEA) results were used to determine the failure trends of circumferential groove corrosion. Since DNV-RP-F101 has limited parametric range for predicting the failure pressure of circumferential groove corrosion subjected to internal pressure, several equations were then developed by utilizing Buckingham’s π theorem and nonlinear multivariate regression techniques. These equations provide failure pressure predictions with errors less than 5%.

This paper describes the effects of different stacking sequences and fiber content ratios on the compression and the compression-after-impact (CAI) behavior of hybrid carbon�basalt (C�B) fiber-reinforced polymer pipes. Hybrid composite pipes composed of eight layers of ± 55°/90°2/± 55°/90°2 were manufactured utilizing the filament winding technique. A series of axial compression tests were carried out on the composite pipes based on ASTM D3410 standard for the nonimpacted and impacted specimens at 100-J impact energy. Residual compressive strength, damage tolerance and failure modes were examined and analyzed for different composite pipe configurations before and after an impact. The results showed that the specimens with fiber content ratio 50:50 C�B fibers exhibited better impact resistance as compared with specimens of fiber content ratio 75:25 C�B fibers, under the same conditions of impact energy. Furthermore, despite carbon fiber sustaining highest compressive force among nonimpacted samples, it exhibited the highest percentage reduction in residual compressive force after impact, at 62%, as such having the worst impact damage tolerance for a given energy level. Specimens with basalt fiber on the outer surface (i.e., Nos. 3 and 6) and alternative fiber specimens (i.e., Nos. 5 and 8), which showed a good response to the impact, sustain high values of the compressive loads for both content ratios. © 2020 by Begell House, Inc.

The bovine milk allergenic protein, ‘β-lactoglobulin’ is one of the leading causes of milk allergic reaction. In this research, a novel label-free non-faradaic capacitive aptasensor was designed to detect β-lactoglobulin using a Laser Scribed Graphene (LSG) electrode. The graphene was directly engraved into a microgapped (~ 95 µm) capacitor-electrode pattern on a flexible polyimide (PI) film via a simple one-step CO2 laser irradiation. The novel hybrid nanoflower (NF) was synthesized using 1,1′-carbonyldiimidazole (CDI) as the organic molecule and copper (Cu) as the inorganic molecule via one-pot biomineralization by tuning the reaction time and concentration. NF was fixed on the pre-modified PI film at the triangular junction of the LSG microgap specifically for bio-capturing β-lactoglobulin. The fine-tuned CDI-Cu NF revealed the flower-like structures was viewed through field emission scanning electron microscopy. Fourier-transform infrared spectroscopy showed the interactions with PI film, CDI-Cu NF, oligoaptamer and β-lactoglobulin. The non-faradaic sensing of milk allergen β-lactoglobulin corresponds to a higher loading of oligoaptamer on 3D-structured CDI-Cu NF, with a linear range detection from 1 ag/ml to 100 fg/ml and attomolar (1 ag/ml) detection limit (S/N = 3:1). This novel CDI-Cu NF/LSG microgap aptasensor has a great potential for the detection of milk allergen with high-specificity and sensitivity.

This paper investigates the effect of waves and ocean currents on the dynamics and motion control of underwater gliders. Waves and ocean currents affect a glider’s performance by deviating from its desired path and reducing its operational range. The influence of wave disturbances and ocean currents is estimated using mathematical models for sea state, potential flow theory and linear wave theory. The effect of waves at various heights, frequency and period were studied for a nonlinear dynamic model of the underwater glider. The wave characteristics have been simulated to investigate their effect on the depth, velocity and range of the glider. Subsequently, a line of sight algorithm in conjunction with controller was designed for the newly designed prototype of the Universiti Teknologi PETRONAS autonomous underwater glider to investigate the action of internal actuators in performing a saw tooth trajectory in the presence of waves and current disturbances. The glider was slightly perturbed due to the shallow and deep waves, with the more pronounced effect at increasing wave amplitude. However, the range of the vehicle remains relatively unchanged.

Flow rate estimation of an underwater oil spill is challenging due to the complex flow dynamics of an oil spill. Existing techniques such as particle image velocimetry (PIV) and feature tracking velocimetry (FTV) were unable to accurately estimate the flow rate of the oil spill in the Macondo well incident, with the most accurate technique being optical plume velocimetry (OPV). OPV estimates the velocity field based on temporal averaging algorithm, which has a better temporal resolution compared to PIV and FTV. This is useful for flow estimation where the temporal evolution of turbulent flow is significant. However, the classical temporal cross correlation algorithm used in OPV is not appropriate to deal with multi-scale flow behaviour associated with a turbulent flow such as that of oil spills. Several methods were previously used to multi-scale representation, such as Short-Time Fourier Transform, Wavelet Transform (WT) and Empirical Mode Decomposition. Among all of these methods, wavelet transform (WT) is more appropriate for turbulent flow applications, since the interesting point is the presentation of flow spectral changes over time. This is because the wavelet convolution has the ability to balance between temporal and frequency information. This paper proposed an alternative technique called wavelet-based optical velocimetry (WOV). WOV estimates the velocity field based on cross correlation of wavelet coefficients. Continuous wavelet transform was first utilized to transform two intensity signals into the wavelet domain. The Fast Fourier Transform (FFT) algorithm was then used for wavelet coefficient correlation, from which the velocity was estimated. To validate the accuracy of the WOV technique, a turbulent jet flow was experimentally simulated at Reynold's number ranging from 1847 to 11,656 at the nozzle. In our experimental work, WOV estimated the nozzle flow rate with an error of 8.3%, while the OPV estimated the nozzle flow rate with an error of 19.7%. The better estimation of WOV technique is due to its advantage to deal with the multi-scale behaviour of turbulent flow, since the WT has a multi-resolution scheme. The accuracy of WOV algorithm, however is sensitive to the selection of wavelet scaling, as well as the wavelet function. A significant influence was observed for the wavelet scaling, due to the difference of the frequency bandwidth width of WT, while the influence of wavelet function is less significant.

In this paper, the effect of carbon fiber hybridization on the compressive strength of glass-carbon/epoxy hybrid filament wound pipes before and after low velocity impact was investigated experimentally. Specifically, the effects of different stacking sequence and fiber content ratio on the compression and compression after impact (CAI) behavior of hybrid glass-carbon fiber reinforced polymer pipes were analyzed. Hybrid composite pipes composed of eight layers of / reinforced with thin HDPE liner were manufactured utilizing filament winding technique. A series of axial compressive tests were carried out on the composite pipes for the non-impacted and impacted specimens under 100 J of impact energy. Residual compressive strength, damage tolerance and failure modes were examined and analyzed for different pipe configurations, before and after the impact. The failure modes of non-impacted and impacted composite pipes under compressive loading were analyzed visually. The results show that, under the same conditions of impact energy, specimens with alternative fibers exhibited better impact resistance regardless of fiber content ratio. Moreover, carbon fiber reinforced epoxy specimens exhibited the worst impact damage tolerance for a given impact energy level although having the highest compressive strength before impact among the samples, with the highest percentage reduction of 62% in residual compressive force after impact.

Opaque fluid flow estimation is a challenging problem due to the complex nature of this flow type. Deepwater Horizon oil spill is one of the real examples of opaque fluid flow. Due to the complicated spill flow and the lack of dedicated flow measurement technique its flow rate was estimated with high uncertainty. In this paper, a simulation of jet flow is conducted experimentally and numerically. This is in order to analyze the difference between them. First, a turbulent buoyant jet was experimentally simulated considering various ranges of nozzle flow rates including laminar and turbulent flow. A video camera was used to capture the jet flow. Then, Fast Fourier Transform (FFT) based method was developed to estimate velocity field from video sequence. The outcomes of experimental results were compared to the outcomes of numerical simulation. As a result, the FFT-based method was estimated the nozzle flow rates with a relative error of 18.2% when it was compared to the measured experimental values. Despite this poor accuracy, a good agreement between experimental and numerical simulation outcomes was found in term of overall velocity field, centerline velocity, axial velocity as well as the distribution of radial velocity.

Elevated levels of Troponin I, a cardiac biomarker, is indicative of an Acute Myocardial Infarction. However, current immunosensing methods to detect the presence of Troponin I such as Enzyme-Linked Immunosorbent Assay (ELISAs) has poor detection accuracy and unreliable quantitative results due to its single model readout. On the other hand, impedance spectroscopy detection using aptamer-modified electrodes is a more reliable and robust detection method, with higher sensitivity and better selectivity. In this paper, we propose a molybdenum disulphide/cellulose acetate (MoS2/CA) nanofiber composite on screen printed electrodes for detecting troponin I by Electrical Impedance Spectroscopy. The MoS2/CA nanofiber was electrospin at various amount of MoS2 nanosheets (0.025 g, 0.05 g, 0.1 g and 0.2 g). Morphological, structural and optical characterization was conducted to discover the functional group as well as crystallinity formed in the MoS2/CA composite nanofiber. The developed electrochemical nano-biosensor can detect up to 10 fM of Troponin I with 90% stability after 6 weeks, ~ 5 folds improvement compared to other proteins (selectivity), 0.89 µA mM−1 cm− 2 sensitivity of sensor and RSD value of 3.8% (repeatability).

This chapter provides recommendations for the implementation of the virtual labs considered in Chapter 1. This chapter is to be used when encountering difficulties in Chapter 1. The instructions contain the necessary reference material that is needed to complete the tasks. For each lab from the Chapter 1, there are guidelines and recommendations on how to construct the diagrams of component models and to perform the model simulation. In addition, example answers to the tasks set in each lab are provided.

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. Cardiac troponin I (cTn1) is a commonly used biomarker for the diagnosis of AMI. Although there are various detection methods for the rapid detection of cTn1 such as optical, electrochemical, and acoustic techniques, electrochemical aptasensing techniques are commonly used because of their ease of handling, portability, and compactness. In this study, an electrochemical cTn1 biosensor, MoS2 nanoflowers on screen-printed electrodes assisted by aptamer, was synthesized using hydrothermal technique. Field emission scanning electron microscopy revealed distinct 2D nanosheets and jagged flower-like 3D MoS2 nanoflower structure, with X-ray diffraction analysis revealing well-stacked MoS2layers. Voltammetry aptasensing of cTn1 ranges from 10 fM to 1 nM, with a detection limit at 10 fM and a sensitivity of 0.10 nA µM�1 cm�2. This is a �fivefold improvement in selectivity compared with the other proteins and human serum. This novel aptasensor retained 90 of its biosensing activity after 6 weeks with a 4.3 RSD and is a promising high-performance biosensor for detecting cTn1. © 2020 International Union of Biochemistry and Molecular Biology, Inc.

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. Cardiac troponin I (cTn1) is a commonly used biomarker for the diagnosis of AMI. Although there are various detection methods for the rapid detection of cTn1 such as optical, electrochemical, and acoustic techniques, electrochemical aptasensing techniques are commonly used because of their ease of handling, portability, and compactness. In this study, an electrochemical cTn1 biosensor, MoS

This paper describes the application of finite element method (FEM) to predict the failure pressure of corrosion-affected pipes, specifically for pipes with multiple internal (2 and 3 defects) longitudinally aligned interacting corrosion defects subjected to internal pressure and axial compressive stress. The FEM was verified against results of full-scale burst tests. The developed FEM incorporated material nonlinearity, which allows for large strains and displacements. Furthermore, true ultimate tensile strength (UTS) instead of the typical engineering UTS was employed to determine the point of failure. The pipes used in the finite element analysis (FEA) were modeled based on API 5L X52 with a length of 2000 mm, a nominal outer diameter of 300 mm, and a nominal wall thickness of 10 mm. The results obtained from the FEA were presented in a graph form and then used to determine the failure pressure trendlines of multiple longitudinally aligned interacting corrosion defects for varying defect depths and spacing between defects. In addition, the failure pressure trendlines of multiple longitudinally aligned interacting defects were compared to determine their differences. As expected, 3 longitudinally aligned interacting defects showed lower failure pressure compared to 2 longitudinally aligned interacting defects.

This chapter describes Wolfram SystemModeler (WSM), an interactive graphical modeling and simulation environment. The WSM is intended for computer simulation of complex multi-domain physical and engineering systems and processes based on the Modelica language. It allows to simulate the developed models and provides a wide range of tools for analyzing the results of the simulation. The reader gets acquainted with the basics of writing program code with the use of the object-oriented language Modelica. In addition, the basics of working in the Model Center to create a component model using the Modelica standard library are demonstrated. The principles of creating custom components as well as the rules for performing a computational experiment in the Simulation Center are described in detail. The chapter contains a large number of simple and useful examples.