219 research outputs found
Development of a virtual reality milling machine for knowledge learning and skill training
No full textCurrent methods of training personnel on high cost machine tools involve the use of both classroom and hands on practical training. The practical training required the operation of costly equipment and the trainee has to be under close personnel supervision. The main aim of this project is to reduce the amount of practical training and its inherent cost, time, danger, personal injury risk and material requirements by utilising a virtual reality technology.
In this study, an investigation into the use of Virtual reality for training operators and students to use the Milling Machine was carried out. The investigation has been divided into two sections: first the development of Milling Machine in the 3D virtual environment, where the real machine was re-constructed in the virtual space.
This has been carried out by creating objects and assembling them together. The complete Milling machine was then properly modelled and rendered so it could be viewed from all viewpoints.
The second section was to add motion to the virtual world. The machine was made of functions as for the real machine. This was achieved by attaching Superscape Control Language (SCL) to the objects. The developed Milling machine allows the users to choose the material, speed and feed rate. Upon activation, the virtual machine will be simulated to carry out the machining process and instantaneous data on the machined part can be generated.
The results were satisfactory, the Milling Machine was modelled successfully and the machine was able to perform according to task set. Using the developed Virtual Model, the ability for training students and operators to use the Milling Machine has been achieved
Experimental and finite element study of the hydroforming bi-layered tubular components
No full textThe application of finite element method (FEM) in the area of metal forming and material processing has been increasing rapidly during the recent years. The present study has been carried out on one of the unconventional metal forming processes called hydroforming of a multi-layered tube. The study involved both experimental and simulation work using FEA. Multi-layered tubes have extensive advantages in both domestic and industrial uses. The specimen tube consists of two different layers of materials. The outer tube material is brass and the inner tube material is copper. This project is mainly dedicated to the modelling, simulation and advanced study of one of the unconventional metal forming processes called hydroforming in which extremely high fluid pressure is used to deform the metal into desired shape. Various types of complex industrial products can be made by hydroforming. This process is suitable to produce seamless, lightweight, near net shaped industrial components. There are some complex products, which are easier to produce by hydroforming than by conventional technique.
In this research work the main forming load is hydrostatic pressure applied to the internal surface of the tube, together with an in-plane compressive load applied simultaneously. The blank is placed in a pre-shaped die block and due to the action of simultaneous internal pressure and axial load; it is formed into a complex desired shape. If the internal pressure is too high during the process without sufficient axial load it may cause the tube to burst, on the other hand too large axial load without applying sufficient internal pressure may cause wrinkling of the tube. For these reasons, a number of simulations of the hydroforming process have been carried out for different axial load and internal pressure combinations and optimum conditions have been established for the particular process. This simulated hydroforming of composite material tube and the formed product has been analysed on the basis of forming conditions and the simulated forming conditions have been verified by experiment. The simulations of hydroforming process for X or T branch have been carried by using the commercial finite element package ANSYS
Development of a hybrid genetic algorithm based decision support system for vehicle routing and scheduling in supply chain logistics managment
No full textVehicle Routing and Scheduling (VRS) constitute an important part of logistics management. Given the fact that the worldwide cost on physical distribution is evermore increasing, the global competition and the complex nature of logistics problems, one area, which determines the efficiency of all others, is the VRS activities. The application of Decision Support Systems (DSS) to assist logistics management with an efficient VRS could be of great benefit. Although the benefits of DSS in VRS are well documented, however in practice many organisations perform these activities manually using combination of skills, intuition and expertise.
A comprehensive review of literature revealed several drawbacks in the existing methods for addressing VRS. The traditional optimisation approaches have very limited applications and these require high computation time. Also, heuristic approaches are capable only to specific variation, a slight difference in the structure of the problem make the algorithm inefficient. Furthermore, metaheuristics methods require higher computation time and they are context dependent. Also, further investigations on the VRS problem formulations suggest that heuristic approaches usually address a single objective of distance minimisation. However in the real world there may be a number of conflicting objectives. In general, there is a lack of considerations for route selections, resource utilisation, unhlfilled demands, underused capacities, reliability of deliveries, fleet size, human fitness and operational cost. Also, these approaches fail to realise non-linearity within objectives and constraints defined for VRS problems. Furthermore, there are no clear distinctions between hard and soft constraints considered in these methods. Finally, the existing approaches fail to capture stochastic and dynamic nature of the logistics processes.
In order to overcome the above-mentioned drawbacks, this study designed and developed a hybrid DSS to assist logistics managers with VRS tasks. The capabilities of the developed DSS have then been applied to a Liquefied Petroleum Gas (LPG) distribution company. The architecture of this DSS is composed of Genetic Algorithm (GA) optimisation tool and a simulation model. The GA module aims to provide a pool of near optimum transportation schedules. The simulation module is used to further evaluate the generated schedules. The feed back from the simulation module is used to update the GA for reoptimisation. Some unique features of this DSS are such as: development of a multi modal genetic algorithm to address VRS problems; considering supply chain performance measures as part of VRS problem formulation; allowing consideration of different objectives, soft or hard constraints concerning the supply chain, considering linearlnonlinear relationships within objectives and constraints defined and finally, considering stochastic and dynamic behaviours of the supply chain system.
The GA and simulation tool integration provides unique benefits that have not been in the literature such as consideration of practical requirements, uncertainties, dynamic and stochastic behaviours, considering several criteria and producing different alternative solutions. Also, this integration allows the GA model to filter out solutions that are less competitive and therefore reducing the simulation time evaluation, which is computationally expensive. Furthermore, the human interaction with the system assists in generating higher quality of solutions. Finally, the clear benefit of this DSS is the fact that it greatly influences the applicability of the GA generated schedules and provides better confidence in implementation of these solution
Performance-Based Intelligent Diagnostics, Prognostics, and Health Monitoring of Hydrogen Fueled Gas Turbines
No full textGlobal energy transition efforts towards decarbonization requires significant advances within the energy sector. In this regard, hydrogen is envisioned as a long-term alternative fuel for gas turbines. Accordingly, the gas turbine industry has expedited their efforts in developing 100% hydrogen compliant burners and associated auxiliary components for retrofitting the existing gas turbines. The utilization of hydrogen in gas turbines has some underlying challenges such as corrosion mainly originating from increased steam content in the hot gas path. In addition to corrosion, the gas turbine compressor is vulnerable to fouling, which is the most commonly occurring fault in gas turbine operating over certain time window. Both faults are susceptible to performance and health degradation. Owing to these problems, an in-depth thermodynamic performance assessment along with combustor’s flue gas thermophysical properties analysis becomes crucial. In addition to the thermodynamic performance model, performance-based intelligent fault diagnostics and prognostics/ remaining useful life (RUL) estimation of hydrogen fueled gas turbine is point of concern for the gas turbines original equipment manufacturers (OEMs) and operators.
The present thesis therefore addresses the above-mentioned technical challenges by providing a detailed comparative thermodynamic performance assessment of both hydrogen and natural gas (NG) fueled gas turbine scenarios. Besides, a comparative analysis of various artificial intelligence (AI) based approaches have been conducted for performance degradation estimation and fault diagnostics. Furthermore, to avoid expensive asset loss caused by unexpected downtimes and shutdowns, data-statistical prognostics have been employed for both NG and hydrogen fueled scenarios.
The first part of the thesis consists of thermodynamic performance and combustion flue gas analysis. The combustion reactions and detailed analysis of fluid flow properties manifested that H2 fuel utilization results
in an increased steam content (by ~106%) as compared to NG combustion. The combined effect of turbine corrosion severity level and high ambient temperature on the overall performance of the MGT showsthat the increased corrosion severity level at high ambient temperature can lead to deterioration in power and thermal efficiency. The second part deals with fault diagnostics using different machine learning based techniques. To identify an accurate algorithm, various algorithms such as support vector machine, decision tree, random forest algorithm, k-nearest neighbors, and artificial neural network were tested. The findings from fault diagnostics process (classification) revealed that ANN outperformed its counterpart algorithm by giving accuracy of 94.55%. Similarly, ANN also showed higher accuracy in performance degradation estimation process (regression) by showing the MSE of training loss as low as ~0.14. The comparative analysis of all the chosen algorithms in the present study revealed ANN as the most accurate algorithm for fault diagnostics of hydrogen fueled gas turbines. The last part dealt with prognostics/RUL estimation. In this regard, the study incorporated linear and polynomial regression approaches and compared the end of life of gas turbines running on natural gas and hydrogen fuels. It became evident from the study that RUL of a gas turbine running on hydrogen fuel is 6.47% lower than that of natural gas fueled gas turbines. These findings underline the necessity of using strong prediction models, as well as targeted maintenance actions, to limit the consequences of turbine corrosion in hydrogen powered MGTs. The findings of the present study further provide new horizons for design modification and effective health monitoring of hydrogen fueled gas turbines
Investigation into coatings produced from nanoparticle blended feedstock for rotating equipment repair applications
No full textCoating of carbon steel with conventional and nano particle blended feedstock material is considered in relation to repair applications of rotating equipment. Gas Metal Arc Welding (GMAW) and Wire Arc Spray (WAS) processes are used to produce the coatings on carbon steel workpieces. The wire arc sprayed workpieces are heat treated at temperature similar to the operating temperature of hot-path components of power gas turbines. The microstructure and metallurgy of the workpieces are examined using the Scanning Electron Microscope (SEM), Optical Microscope, Energy Dispersive Spectroscopy (EDS), X-ray Diffraction (XRD). The indentation tests are carried out to assess the microhardness variation across the coatings. In the case of coatings produced by GMAW, it is found that fine structures are formed in the coating due to the presence of nano particles and they resulted in increased microhardness of the coatings. In the case of the wire arc sprayed workpieces, the formation of dimples like structure at the surface increases the surface roughness of the coatings. In addition, the microhardness of the resulting coating is significantly higher than that of the base material. The heat treatment does not alter the microstructure and microhardness of the coatings significantly
Investigation into laser re-melting of inconel 625 HVOF coating blended with WC
No full textHigh velocity oxy-fuel (HVOF) spraying of Diamalloy 1005 powders mixed with WC particles onto steel (304) is considered and laser re-melting of the resulting coatings is examined. Laser re-melting process is modeled to determine the melt layer thickness while temperature increase is formulated using the Fourier heating law. The morphological and metallurgical analyses prior and post laser re-melting process are carried out using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). X-ray diffraction (XRD) technique is used to determine the residual stress developed in the coating while the analytical formulation is adopted to predict the residual stress levels at the coating base material interface. The indentation tests are carried out to determine the Young’s modulus and fracture toughness of the coating prior to laser re-melting. Corrosion resistance of coating is measured using potentiodynamic polarization technique prior and post laser treatment process. The predictions of the melt layer thickness are in good agreement with experimental results. The presence of WC particles modifies temperature rise and its gradient in the coating while affecting the Young’s modulus, residual stress levels, and fracture toughness of the coating. The differences in the thermal properties of Inconel 625 powders and WC particles result in formation of small size cellular structure through polyphase solidification. WC dissolution in the central region of the large polycrystalline cells is observed due to the loss of carbon through carbonic gas formation. The results of corrosion tests prevail that significant improvement of corrosion resistance can be achieved after laser treatment process
Healthcare Systems : three studies of patient management and policy change
No full textThesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018Cataloged from PDF version of thesis. "Doctor of Philosophy in Healthcare Systems: Management and Policy Research."Includes bibliographical references.For my PhD thesis, I conducted behavioral science research and wrote three first- author journal format papers, of which one paper has been published and the other two will be submitted to healthcare management journals after completion of my degree. All three papers introduce new information about either the cost or the behaviors of patients in local clinics, filling a gap in the healthcare system's management and policy literature. The first paper studies patients with diabetes who are non-adherent to scheduled appointments with physicians in a specialized diabetes clinic setting in Boston. I developed and introduced new and interesting ''technology comfort" measures and a "Smartphone usage" scale, to evaluate if patients would be able to use smart technologies for their disease self-management. This paper not only suggests that patients with diabetes could potentially benefit from using existing advanced technologies, but that new policies can be introduced to reduce the rate of diabetes patients' appointment-related non-adherence. The second paper examines the system of adherence or self-management in five areas ( diet, exercise, medications, doctor's appointments and regular glucose monitoring), revealing how it is correlated to emergency visits and patient lifestyle satisfaction. I analyze predictors of emergency room visits and propose potential policies to reduce these ER visits through the use of advanced smart technologies. The third paper identifies the incidence and consequences of not practicing non- pharmaceutical interventions, during the time of a pandemic, in a student population at a local university clinic.by Sahar Hashmi, MD.Ph. D. in Engineering SystemsPh.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Societ
Plastohydrodynamic drawing and coating of stainless steel wire using a tapered bore die of no metal to metal contact
No full textChanging Demographic, Social, and Economic Conditions in Karachi City, 1959–94: A Preliminary Analysis
No full textKingsley Davis (1961) had argued that the reason that the ancient cities failed to survive was that they were too deadly. He suggested that “three of their (cities) main traits....the crowding of many people in little space, their dependence on widespread contacts (due to in-migration), and their wealth...laid them open to contagious diseases, environmental contamination, occasional starvation and warfare”. Even in the medieval age, some European cities provide examples of such problems; but especially so following the Industrial Revolution. Do the events of the 1980s and the 1990s in Karachi suggest that the city may be heading in the same direction. Recently, The Times London in a lead article in November 1994, labelled Karachi as a “City of Riches and Shattered Dreams”. It further said that Karachi had grown into a megalopolis where life moved fast and street violence had become a norm. Indeed, more than 65 percent of Pakistan’s industries and 80 percent of its finance, banking, and business are concentrated in the city and people come to it from all over the country to find jobs and fulfil their dreams [Husain (1994)]. During the past decade, street violence in the form of ethnic clashes has become a sort of regular event in Karachi. At times, these clashes have been more frequent and even bloodier than the ones before. According to the local newspaper accounts, between 1985 and 1988 (in four years), about 400 people died in Karachi due to violence, which has increased substantially over time. Thus, while the number of violent deaths remained between 350–500 during 1991–93, in 1994 alone the number exceeded 1,100, and during the first three months of 1995, over 300 persons have died due to violence.
The effect of patients’ preference on outcome in the EVerT cryotherapy versus salicylic acid for the treatment of plantar warts (verruca) trial
No full textBackground
Randomised controlled trials are widely accepted as the gold standard method to evaluate medical interventions, but they are still open to bias. One such bias is the effect of patient’s preference on outcome measures. The aims of this study were to examine whether patients’ treatment preference affected clearance of plantar warts and explore whether there were any associations between patients’ treatment preference and baseline variables in the EverT trial.
Methods
Two hundred and forty patients were recruited from University podiatry schools, NHS podiatry clinics and primary care. Patients were aged 12 years and over and had at least one plantar wart which was suitable for treatment with salicylic acid and cryotherapy. Patients were asked their treatment preference prior to randomisation. The Kruskal-Wallis test was performed to test the association between preference group and continuous baseline variables. The Fisher’s exact test was performed to test the association between preference group and categorical baseline variables. A logistic regression analysis was undertaken with verruca clearance (yes or no) as the dependent variable and treatment, age, type of verruca, previous treatment, treatment preference as independent variables. Two analyses were undertaken, one using the health professional reported outcome and one using the patient’s self reported outcomes. Data on whether the patient found it necessary to stop the treatment to which they had been allocated and whether they started another treatment were summarised by treatment group.
Results
Pre-randomisation preferences were: 10% for salicylic acid; 42% for cryotherapy and 48% no treatment preference. There was no evidence of an association between treatment preference group and either patient (p=0.95) or healthcare professional (p=0.46) reported verruca clearance rates. There was no evidence of an association between preference group and any of the baseline variables except gender, with more females expressing a preference for salicylic acid (p=0.004). There was no evidence that the number of times salicylic acid was applied was different between the preference groups at one week (p=0.89) or at three weeks (p=0.24). Similarly, for the number of clinic visits for cryotherapy (p=0.71)
Conclusions
This secondary analysis showed no evidence to suggest that patients’ baseline preferences affected verruca clearance rates or adherence with the treatment
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