Visualization of thermal characteristics around the absorber tube of a standard parabolic trough thermal collector by 3D simulation

Three dimensional conjugate heat transfer simulation of a standard parabolic trough thermal collector receiver is performed numerically in order to visualize and analyze the surface thermal characteristics. The computational model is developed in Ansys Fluent environment based on some simplified assumptions. Three test conditions are selected from the existing literature to verify the numerical model directly, and reasonably good agreement between the model and the test results confirms the reliability of the simulation. Solar radiation flux profile around the tube is also approximated from the literature. An in house macro is written to read the input solar flux as a heat flux wall boundary condition for the tube wall. The numerical results show that there is an abrupt variation in the resultant heat flux along the circumference of the receiver. Consequently, the temperature varies throughout the tube surface. The lower half of the horizontal receiver enjoys the maximum solar flux, and therefore, experiences the maximum temperature rise compared to the upper part with almost leveled temperature. Reasonable attributions and suggestions are made on this particular type of conjugate thermal system. The knowledge that gained so far from this study will be used to further the analysis and to design an efficient concentrator photovoltaic collector in near future.

Autologous vs. allogenic mesenchymal progenitor cells for the reconstruction of critical-sized segmental tibial bone defects in aged sheep

Mesenchymal progenitor cells (MPCs) represent an attractive cell population for bone tissue engineering. Their special immunological characteristics suggest that MPCs may be used in an allogenic application. The objective of this study was to compare the regenerative potential of autologous vs. allogenic MPCs in an ovine critical-sized segmental defect model. Ovine MPCs were isolated from bone marrow aspirates, expanded and cultured with osteogenic media for two weeks before implantation. Autologous and allogenic transplantation was performed by using the cell-seeded scaffolds, unloaded scaffolds and the application of autologous bone grafts served as control groups (n=6). Bone healing was assessed twelve weeks after surgery by radiology, micro computed tomography, biomechanical testing and histology. Radiology, biomechanical testing and histology revealed no significant difference in bone formation between the autologous and allogenic group. Both cell groups showed more bone formation than the scaffold alone, whereas the biomechanical data showed no significant differences between the cell-groups and the unloaded scaffolds. The results of the study suggest that scaffold based bone tissue engineering using allogenic cells offers the potential for an off the shelf product. Therefore, the results of this study serve as an important baseline for the translation of the assessed concepts into clinical application.

A neural-network based flight controller for UASs

This paper presents a nonlinear gust-attenuation controller based on constrained neural-network (NN) theory. The controller aims to achieve sufficient stability and handling quality for a fixed-wing unmanned aerial system (UAS) in a gusty environment when control inputs are subjected to constraints. Constraints in inputs emulate situations where aircraft actuators fail requiring the aircraft to be operated with fail-safe capability. The proposed controller enables gust-attenuation property and stabilizes the aircraft dynamics in a gusty environment. The proposed flight controller is obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equations based on an policy iteration (PI) approach. Performance of the controller is evaluated using a high-fidelity six degree-of-freedom Shadow UAS model. Simulations show that our controller demonstrates great performance improvement in a gusty environment, especially in angle-of-attack (AOA), pitch and pitch rate. Comparative studies are conducted with the proportional-integral-derivative (PID) controllers, justifying the efficiency of our controller and verifying its suitability for integration into the design of flight control systems for forced landing of UASs.

Damage identification and condition assessment of building structures using frequency response functions and neural networks

This thesis investigated the viability of using Frequency Response Functions in combination with Artificial Neural Network technique in damage assessment of building structures. The proposed approach can help overcome some of limitations associated with previously developed vibration based methods and assist in delivering more accurate and robust damage identification results. Excellent results are obtained for damage identification of the case studies proving that the proposed approach has been developed successfully.

Quantitative and qualitative assessment of the regenerative potential of osteoblasts versus bone marrow derived mesenchymal stem cells in the reconstruction of critical sized segmental tibial bone defects in a large animal model

This thesis is about the use of different cells for bone tissue engineering. The cells were used in combination with a novel biomaterial in a large tibial bone defects in a sheep model. Furthermore this study developed a novel cell delivery procedure for bone tissue engineering. This novel procedure of cell delivery could overcome the current problems of cell-based tissue engineering and serve as a baseline for the translation of novel concepts into clinical application.

The use of artificial neural networks for identifying sustainable biodiesel feedstocks

Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer term due to the rising food prices and the concerns about automobile engine durability. Therefore, there is an urgent need for researchers to identify and develop sustainable biodiesel feedstocks which overcome the disadvantages of current ones. On the other hand, artificial neural network (ANN) modeling has been successfully used in recent years to gain new knowledge in various disciplines. The main goal of this article is to review recent literatures and assess the state of the art on the use of ANN as a modeling tool for future generation biodiesel feedstocks. Biodiesel feedstocks, production processes, chemical compositions, standards, physio-chemical properties and in-use performance are discussed. Limitations of current biodiesel feedstocks over future generation biodiesel feedstock have been identified. The application of ANN in modeling key biodiesel quality parameters and combustion performance in automobile engines is also discussed. This review has determined that ANN modeling has a high potential to contribute to the development of renewable energy systems by accelerating biodiesel research.

Mutations in cardiac T-Box Factor gene TBX20 are associated with diverse cardiac pathologies, including defects of septation and valvulogenesis and cardiomyopathy

The T-box family transcription factor gene TBX20 acts in a conserved regulatory network, guiding heart formation and patterning in diverse species. Mouse Tbx20 is expressed in cardiac progenitor cells, differentiating cardiomyocytes, and developing valvular tissue, and its deletion or RNA interference-mediated knockdown is catastrophic for heart development. TBX20 interacts physically, functionally, and genetically with other cardiac transcription factors, including NKX2-5, GATA4, and TBX5, mutations of which cause congenital heart disease (CHD). Here, we report nonsense (Q195X) and missense (I152M) germline mutations within the T-box DNA-binding domain of human TBX20 that were associated with a family history of CHD and a complex spectrum of developmental anomalies, including defects in septation, chamber growth, and valvulogenesis. Biophysical characterization of wild-type and mutant proteins indicated how the missense mutation disrupts the structure and function of the TBX20 T-box. Dilated cardiomyopathy was a feature of the TBX20 mutant phenotype in humans and mice, suggesting that mutations in developmental transcription factors can provide a sensitized template for adult-onset heart disease. Our findings are the first to link TBX20 mutations to human pathology. They provide insights into how mutation of different genes in an interactive regulatory circuit lead to diverse clinical phenotypes, with implications for diagnosis, genetic screening, and patient follow-up.

The search for migraine genes : an overview of current knowledge

Migraine is a complex familial condition that imparts a significant burden on society. There is evidence for a role of genetic factors in migraine, and elucidating the genetic basis of this disabling condition remains the focus of much research. In this review we discuss results of genetic studies to date, from the discovery of the role of neural ion channel gene mutations in familial hemiplegic migraine (FHM) to linkage analyses and candidate gene studies in the more common forms of migraine. The success of FHM regarding discovery of genetic defects associated with the disorder remains elusive in common migraine, and causative genes have not yet been identified. Thus we suggest additional approaches for analysing the genetic basis of this disorder. The continuing search for migraine genes may aid in a greater understanding of the mechanisms that underlie the disorder and potentially lead to significant diagnostic and therapeutic applications.

Bending properties of Ag nanowires with pre-existing surface defects

Materials used in the engineering always contain imperfections or defects which significantly affect their performances. Based on the large-scale molecular dynamics simulation and the Euler–Bernoulli beam theory, the influence from different pre-existing surface defects on the bending properties of Ag nanowires (NWs) is studied in this paper. It is found that the nonlinear-elastic deformation, as well as the flexural rigidity of the NW is insensitive to different surface defects for the studied defects in this paper. On the contrary, an evident decrease of the yield strength is observed due to the existence of defects. In-depth inspection of the deformation process reveals that, at the onset of plastic deformation, dislocation embryos initiate from the locations of surface defects, and the plastic deformation is dominated by the nucleation and propagation of partial dislocations under the considered temperature. Particularly, the generation of stair-rod partial dislocations and Lomer–Cottrell lock are normally observed for both perfect and defected NWs. The generation of these structures has thwarted attempts of the NW to an early yielding, which leads to the phenomenon that more defects does not necessarily mean a lower critical force.

Lateral impact response and parametric studies of axially loaded square concrete filled steel tube columns

This paper presents a numerical study on the response of axially loaded slender square concrete filled steel tube (CFST) columns under low velocity lateral impact loading. A finite element analysis (FEA) model was developed using the explicit dynamic nonlinear finite element code LS -DYNA in which the strain rate effects of both steel and concrete, contact between steel tube and concrete and confinement effect provided by the steel tube for the concrete were considered. The model also benefited from a relatively recent feature of LS-DYNA for applying a pre-loading in the explicit solver. The developed numerical model was verified for its accuracy and adequacy by comparing the results with experimental results available in the literature. The verified model was then employed to conduct a parametric study to investigate the influence of axial load level, impact location, support conditions, and slenderness ratio on the response of the CFST columns. A good agreement between the numerical and experimental results was achieved. The model could reasonably predict the impact load-deflection history and deformed shape of the column at the end of the impact event. The results of the parametric study showed that whilst impact location, axial load level and slenderness ratio can have a significant effect on the peak impact force, residual lateral deflection and maximum lateral deflection, the influence of support fixity is minimal. With an increase of axial load to up to a certain level, the peak force increases; however, a further increase in the axial load causes a decrease in the peak force. Both residual lateral deflection and maximum lateral deflection increase as axial load level increases. Shifting the impact location towards the supports increases the peak force and reduces both residual and maximum lateral deflections. A rise in slenderness ratio decreases the peak force and increases the residual and maximum lateral deflections.

The role of defects, or active states, in surface electrochemistry with particular reference to gold in neutral solution

Metastable, active, or nonequilibrium states due to the presence of abnormal structures and various types of defects are well known in metallurgy. The role of such states at gold surfaces in neutral aqueous media (an important electrode system in the microsensor area) was explored using cyclic voltammetry. It was demonstrated that, as postulated in earlier work from this laboratory, there is a close relationship between premonolayer oxidation, multilayer hydrous oxide reduction and electrocatalytic behaviour in the case of this and other metal electrode systems. Some of the most active, and therefore most important, entities at surfaces (e.g., metal adatoms) are not readily imageable or detectable by high resolution surface microscopy techniques. Cyclic voltammetry, however, provides significant, though not highly specific, information about such species. The main conclusion is that further practical and theoretical work on active states of metal surfaces is highly desirable as their behaviour is not simple and is of major importance in many electrocatalytic processes.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Effect of defects on fracture strength of graphene sheets

With a hexagonal monolayer network of carbon atoms, graphene has demonstrated exceptional electrical 22 and mechanical properties. In this work, the fracture of graphene sheets with Stone–Wales type defects and vacancies were investigated using molecular dynamics simulations at different temperatures. The initiation of defects via bond rotation was also investigated. The results indicate that the defects and vacancies can cause significant strength loss in graphene. The fracture strength of graphene is also affected by temperature and loading directions. The simulation results were compared with the prediction from the quantized fracture mechanics.

Strong and bioactive tri-calcium phosphate scaffolds with tube-like macropores

Calcium phosphate ceramic scaffolds have been widely investigated for bone tissue engineering due to their excellent biocompatibility and biodegradation. Unfortunately, they have the shortcoming of low mechanical properties. In order to provide strong, bioactive, and biodegradable scaffolds, a new approach of infiltrating the macro-tube ABS (acrylontrile butadiene styrene) templates with a hydroxyapatite/bioactive glass mixed slurry was developed to fabricate porous Si-doped TCP (tri-calcium phosphate) scaffolds. The porous Si-doped TCP ceramics with a high porosity (~65%) and with interconnected macrotubes (~0.8mm in diameter) and micropores (5-100 m) had a high compressive strength (up to 14.68+0.2MPa), which was comparable to that of a trabecular bone and was much higher than those of pure TCP scaffolds. Additional cell attachment study and MTT cytotoxicity assay proved the bioactivity and biocompatibility of the new scaffolds. Thus a potential bioceramic material and a new approach to make the potential scaffolds were developed for bone tissue engineering.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.