Particle and Bacteria Sorting in Viscoelastic Fluids Using an Elasto-Inertia-Magnetic Fractionation Method

Detection of harmful biological substances in food at the Point of Use (PoU) is very important for the prevention of foodborne diseases. Sample and reagent preparation at the PoU, as a necessary step before detection, is urgently needed. Portable and field-deployable sample preparation microfluidic devices for manipulating particles and biological substances in water have been widely studied. During the recent years, more attention has been given to particles separation in non-Newtonian fluids due to their rheological similarity to the prominent fluids such as food (e.g. milk) and bodily fluids (e.g. blood). However, the mechanism of particle focusing and separation in non-Newtonian fluids is less understood, mainly due to the dominance of elastic forces in such flows. Accordingly, we developed a microfluidic device to investigate the effect of elastic, inertial, and magnetic forces on the focusing of magnetic (9 and 15 m) and non-magnetic (15 m) particles in synthetic viscoelastic fluids with various viscosities. The device included a square microchannel with a side permanent magnet, expanding symmetrically downstream to a wider channel to drop the particles velocity for on-chip imaging. We investigated the effect of multiple parameters on the focusing of each particle experimentally and analytically, in order to obtain physical understanding and the best recipe in which multiplex particle or bacteria separation could be achieved with high efficiency. The studied parameters included the microchannel cross-sectional size, flow rate, fluid viscoelasticity, and magnetic field strength and exposure time. We then used the results of the parametric study to perform Triplex-Inertia-Magneto-Elastic (TIME) sorting of magnetic and non-magnetic particles with >92% purity and efficiency. To demonstrate the potential use of this method in biological applications, we immunologically conjugated two types of bacteria to magnetic and non-magnetic particles and separated them from each other in the microfluidic device with a purity and efficiency of >99%. This study provides the foundation for development of devices for separation of bio-substances in viscoelastic fluids, immunologically attached to microparticles. Our device has the potential to be used for on-site sample preparation along with a variety of biosensors to render biodetection possible at the PoU

Damage Diagnosis of Structures Using Modal Data and Static Response

This paper is aimed at presenting three methods to detect and estimate damage using modal data and static response of a damaged structure. The proposed methods use modal data with and without noise or static displacement to formulate objective functions. Damage location and severity in structural elements are determined using optimization of the objective functions by the simulated annealing algorithm. These methods have been applied to three examples, namely a three-story plane frame, cantilever plate and benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested system has been investigated. The obtained results indicate that the proposed methods can be viewed as a powerful and reliable method for structural damage detection and estimation

Study on corrosion protection of organic coatings using electrochemical techniques: developing electrochemical noise method, effective of surface preparation and inhomogeneity of organic coatings

This study looks into two important aspects of corrosion protection of steel by organic coatings, steel surface preparation and ionic conduction through the coating, as well as development of the electrochemical noise method as an effective assessment method. Surprising and somehow controversial previous findings at the University of Northampton showed an inverse relationship between the roughness of metal substrate and performance of paint coating. So this study was initially launched to further study the effect of metal surface preparation. Four conventional surface preparation methods including ultra high pressure (UHP) hydroblasting, wet abrasive blasting, acid pickling and emery abrasion were studied and compared to an as received control surface. A particular interest of this work was the high demand for an environmentally friendly surface preparation method, e.g. as afforded by UHP hydroblasting, to replace the traditional wet abrasive blasting method. Results of this study revealed the important role of the innate native oxide film and the deleterious effect of contaminants on the protective performance of organic coating. Also it was shown that a highly active surface and large surface profile can be deleterious if an appropriate interaction between paint and metal is not achieved. Results of this study confirmed the earlier findings and suggested the UHP hydroblasting is a successful, cost effective and environmentally friendly surface preparation method and a modern replacement for wet abrasive blasting method. In addition to the effectiveness of metal surface preparation, the ability of organic coating in preventing ions access to metal plays an equally important role in defining the anti-corrosion performance of a coated metal. Hence the mechanism of ionic conduction through organic coatings and their inhomogeneity which are normally formed in crosslinking systems was extensively studied with the aim of finding the cause of formation of the more permeable areas and the ways by which they can be prevented. Several structural and environmental parameters were examined including the coating thickness, multi-layer paint application, curing temperature, partially non-functional resin, pigmentation and solvent. Experimental results showed that the solvent degree to which can escape, the non-functional polymer parts and inherently hydrophilic functional groups of organic coatings are the main parameters causing inhomogeneity and highly ion permeable areas. A statistical model was also developed that can be used to estimate number of permeable areas or corrosion initiation sites in a large area of coating. A particular concern of this work throughout the entire study was development of the electrochemical noise measurement (ENM) in the sense of a good assessing technique for protection efficiency of a coating system. Previous studies have shown great potential of ENM as a practical technique in the field. However, the technique always involved measuring the electrochemical noise between two or three isolated electrodes which cannot be easily provided in certain applications such as submerged structures or inside storage tanks. Also involvement of two or three electrodes in the measurement induces an ambiguity in regards to which electrode dominates the result. Here an attempt was made to perform the noise measurement on a single working electrode so that it can be used in more practical situations. Preliminary results indicate this approach holds promise

Developing a Smart Proxy for Predicting the Fluid Dynamic in DamBreak Flow Simulation by Using Artificial Intelligence

Multiphase flow simulations are essential methods for providing information such as the evolution of phase fraction (gas, liquid and solid), velocities, pressure, temperature and flow regimes at every time during a process. Dynamic flow simulations also help reservoir, drilling, and production engineers to develop a proper well design. DamBreak problem is one of the most well-known problems in computational fluid dynamics (CFD); it is a dynamic hydraulic phenomena and the numerical simulation requires sophisticated mathematical modeling. OpenFOAM, is used to run CFD simulations in this thesis.;One of the main issues in CFD is that the simulations are time-consuming. In this work, will use artificial intelligence (AI) to predict the behavior of the system at each time-step of the process at a lower run time. DamBreak problem is defined base on a two-dimensional rectangular tank with a barrier at the bottom, the liquid column (water in this study) at the left side of the tank behind the wall. As soon as the wall collapse, the water will pour down, resulting in complicated fluid dynamics. The main data-set, generated by OpenFOAM flow simulations, is used for building the smart proxy model (SPM), using the network toolbox in MATLAB. Neural network (NN) is applied with feed-forward back propagation method and the training algorithm is Levenberg Marquardt.;Results indicate that the smart proxy can run 3 seconds of the DamBreak process, which takes 8 hours of computational time with 4 processors when is done by using OpenFOAM, takes less than 2 minutes using the developed SPM on one processor. SPM is also capable of predicting the CFD results in non-cascading condition and up to around 40 time-steps in cascading condition with acceptable error (less than %10)

Principles and essentials of strategic decisions

Decision making is one of the processes which managers are constantly dealing with. Inseparability of management from decision making is to the extent that both issues are known as each other’s synonym. In the matter of decision making, the important point is that most managers are unable to analyze their decisions and consider it as a static and not a dynamic activity. Considering this and given the importance and role of strategic decisions in an organization’s success or failure, in this study, first we are going to explain the concept of decision making and its characteristics, thoughts and opinions about it, and then to study the principles and essentials of strategic decision making and general aspects of the views stated in this regard

Principles and essentials of strategic decisions

Decision making is one of the processes which managers are constantly dealing with. Inseparability of management from decision making is to the extent that both issues are known as each other’s synonym. In the matter of decision making, the important point is that most managers are unable to analyze their decisions and consider it as a static and not a dynamic activity. Considering this and given the importance and role of strategic decisions in an organization’s success or failure, in this study, first we are going to explain the concept of decision making and its characteristics, thoughts and opinions about it, and then to study the principles and essentials of strategic decision making and general aspects of the views stated in this regard

Fault Detection and Isolation of Jet Engines Using Neural Networks

The main objective of this thesis is to design a fault detection and isolation (FDI) scheme for the aircraft jet engine using dynamic neural networks. Toward this end two different types of dynamic neural networks are used to learn the engine dynamics. Specially, dynamic neural model (DNM) and time delay neural network (TDNN) are utilized. The DNM is constructed by using dynamic neurons which utilize infinite impulse response (IIR) filters to generate dynamical behaviour between the input and output of the network. On the other hand, TDNN uses several delays associated with the inputs of the neurons to achieve a dynamic input-output map. We have investigated the fault detection performance of each structure. A bank of neural networks consisting of a set of 12 networks that are trained separately to capture the dynamic relations of all the 12 engine parameters are considered in this study. The results show that certain engine parameters have better detection capabilities as compared to the others. Moreover, the fault detection performance was improved by introduction of the concept of "enhanced fault diagnosis scheme" which employs several networks and monitors several engine parameters simultaneously to enhance and improve the accuracy and performance of the diagnostic system. The fault isolation task is accomplished by using a multilayer perception (MLP) network as a classifier. The concept behind the isolation is motivated by the fact that there is a specific map between the residuals of different networks and a particular fault scenario. We show that the MLP has good capability in learning this map and isolates the faults that are occurring in the jet engine. To demonstrate our diagnostic scheme capabilities, 8 different fault scenarios are simulated and according to the simulation results, our proposed FDI scheme represents a promising tool for fault detection as well as fault isolation requirements. ii