Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms

Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPS-IBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking

Insight into the Design of Aerosol Spray Systems for Cell Therapies for Retinal Diseases using Computational Modelling and Experimental Assessment

Retinal degenerative diseases affect numerous people worldwide and in the UK; they lead to dysfunction of retinal cells and retinal dysfunction, in turn leading to vision loss and in some cases blindness. Existing treatments aim to alleviate current risk factors leading to retinal degeneration, such as increased high pressure. However, these procedures do not restore lost cell, vision nor retinal function, and therefore may still lead to blindness. Developing cell-based therapies to replace lost cells provides one option for retinal tissue repair in order to restore retinal function. These therapies involve delivering stem cells to encourage neural cell-like functions within the retinal tissue. Despite progress in developing stem-cells compatible with the retinal layers, there is also a need to developing a minimal invasive technique for cell delivery, without damaging the neighbouring optical structure. After evaluating several methods of cell delivery, this thesis explores the need for developing aerosol spraying systems for stem-cell delivery into the human eye. Mathematical modelling is used as a tool to define spraying parameters which, alongside experimental work, may accelerate the design of aerosol spraying systems to treat retinal degenerative disease such as glaucoma. Firstly, an organic biomaterial is developed and used as scaffold to spray and protect cells from aerodynamic forces and stresses associated with aerosolization. The rheological properties of this biomaterial are incorporated within a computational model to predict cell-spraying into a human eye. Boundary and initial conditions mimic the experimental spraying conditions, and the parameterised model is used to explore the link between operator-defined conditions (namely volume flow rate of the cell-laden hydrogel, external pressure needed for aerosolization and angle of the spraying) and spraying outputs (surface area of the retina covered, droplets speed, wall shear stress on the retinal surface). Data from both computational and experimental analyses were gathered. Computational modelling is used to explore the impact of spraying parameters (pressure and volume flow rate at the injector nozzle, outer cone angle for the spray) on key outputs of high priority, namely the spatial distribution of the delivered hydrogel on the retinal wall, the surface area of the retina covered and droplet speed. Droplets speed at the retinal wall appeared to increase with increasing pressure conditions and were observed at a constant volume flow rate. Experimental assessments were used to validate the computational data and determine cell viability under set environmental conditions (external pressure and volume flow rate of cell-laden hydrogel) through in-vitro testing. This thesis defines indicative spraying parameters for delivering therapeutic cells to the human retina, based on a combination of computational modelling and experimental studies. Mathematical modelling provides the potential to transfer these findings to other organ systems, aligning with broader effects to develop cell delivery systems to treat organ disease and repair

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

Optical sensing for nondestructive structural evaluation and additive manufacturing process monitoring

Sensing is a significant engineering science which quantify parameters from the physical world and discover the physics running behind the measurement process. Optical sensing makes use of electromagnetic waves from infrared to ultraviolet on the light spectrum as a medium to measure variables, such as position, temperature and strain. Image sensing and fiber sensing are two of the most widely applied optical sensing methods in industries and daily life. They have been studied by the academia for decades, due to their immunity to electromagnetic interference and ease of installation. This dissertation introduced the research on the intelligent and flexible metrology methodologies for real-time structure and process monitoring based on optical sensing. The works focused on two major topics: 1) structural health monitoring for compact heat exchanger (CHE), and 2) bimetallic additive manufacturing process monitoring. For the structural health test, a novel online sensing method capable of detecting internal cracks for Compact Heat Exchanger (CHE) was designed and developed through optical fiber sensor based strain measurement. A crack diagnosis model was built to evaluate crack positions based on limited sampling data in mechanical structure. The model established a physical basis to correlate crack position and distributed strain variation that can be detected by the optical fiber sensors. A physical model quantifying the strain transfer from the sensor embedded mechanical structure to the fiber sensor was built to describe the performance of the sensors at different working conditions. A good match has been observed in the comparison of the data from experimental tests and analytical models, with an average relative error 2.4%. Finally, an experimental platform was designed and setup to validate introduced nondestructive test method. The experimental results showed that strain variations can be detected by optical fiber sensors when crack presented in CHE during elastic deformation, plastic deformation and crack growth process. For bimetallic additive manufacturing process monitoring, an in-situ sensing method for measuring material composition in the printed alloy was modeled and developed based on infrared imaging. The method takes the size of temperature contours surrounding the heated spots during additive manufacturing process as an indicator of the material composition variation. The relationship between material composition and dimensions of the temperature contour was analytically modeled based on Fourier’s law of thermal conduction. The thermal images acquisition by IR camera were processed through a series of designed algorithms to extract geometrical features such as the length and width of the contours, which showed consistent trend through the theoretical analysis. The extracted features and actual weight percentage of copper in the alloy were further used to train an Artificial Neuron Network (ANN) model. The results showed that the accuracy of 94% was achieved when using the trained ANN model to estimate the composition of alloy from the thermal image data. The analytical/numerical models, simulations, experiments, and data analysis included in this thesis were expected to provide solid support for testing the research hypotheses and developing new hardware/software in advanced manufacturing systems