The design of toolhead changing manipulator is highly dependent on the specific application. The geometry, mobility of the existing system, the toolhead robot interface and the toolhead size and shape all are major factors in the development of an effective solution. The addition of other functional requirements increase the complexity of the design. The platform maintenance functions required in the BWNS problem increased the complexity to a 5 DOF feeding manipulator. This solution, while complex, is an effective and efficient solution to this specific application. The result should be a significant reduction in human radiation exposure and reduced steam generator service time. / Master of Science

The objective of this thesis is the algorithmic enhancement and initial evaluation of an underwater acoustic positioning system which is designed to determine the position and orientation of a mobile nuclear reactor vessel inspection robot. Although a great deal of research has been done in the area of underwater acoustic positioning, this work differs from previous work in three significant ways. First, most applied acoustic positioning systems have been designed for the offshore oil drilling industry, and thus their requirements and restrictions are dictated by an oceanic environment. Second, most previous work has focused only upon acquiring the position of a point from the acoustic system. The inspection robot operation requires accurate positioning and orientation. Finally, the accuracy of acoustic positioning systems is generally dependent upon an evaluation of the speed of sound. However, this parameter is highly dependent upon water temperature. As will be discussed, the reactor vessel water temperature may not be uniform or constant, which makes the design of a precise positioning system difficult. Original methods to overcome this obstacle are discussed and evaluated. Also examined are configurations and constraints of the acoustic transceivers, the numerical solution procedures utilized, and the resulting errors associated with the developed methods. / Master of Science

Thesis: M.S., Massachusetts Institute of Technology, Department of Nuclear Engineering, 1977 / Includes bibliographical references. / M.S. / M.S. Massachusetts Institute of Technology, Department of Nuclear Engineering

Typescript (photocopy). / Digitized by Kansas Correctional Industries

published_or_final_version / abstract / Physics / Master / Master of Philosophy

A research report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg in partial fulfilment of the requirements for the degree of Master of Science in Engineering, Johannesburg, August 2017 / The objective of this research work is to investigate the use of a high reliability micro grid to supply emergency electrical power to a nuclear facility following loss of offsite power (LOOP) accident. Most of the nuclear facilities around the world utilize diesel generators and battery banks as emergency power to back up the grid power supply. This power supply configuration represents the concept of the micro-grid system. The research work proposes reliability improvement of the emergency power supply by introducing diverse energy sources and energy storage systems. The energy sources and storage systems that were investigated include renewable energy sources, decay heat recovery system and large scale energy storage systems. The investigation results presented include information on the suitable energy sources and energy storage system, establishment of the reliable architectural layout and evaluation of the micro-grid system in terms of capacity adequacy and reliability. / XL2018

Um desafio recorrente em processos produtivos é o desenvolvimento de sistemas de monitoração e diagnóstico. Esses sistemas ajudam na detecção de mudanças inesperadas e interrupções, prevenindo perdas e mitigando riscos. Redes Neurais Artificiais (RNA) têm sido largamente utilizadas na criação de sistemas de monitoração. Normalmente as RNA utilizadas para resolver este tipo de problema são criadas levando-se em conta apenas parâmetros como o número de entradas, saídas e quantidade de neurônios nas camadas escondidas. Assim, as redes resultantes geralmente possuem uma configuração onde há uma total conexão entre os neurônios de uma camada e os da camada seguinte, sem que haja melhorias em sua topologia. Este trabalho utiliza o algoritmo de Otimização por Colônia de Formigas (OCF) para criar redes neurais otimizadas. O algoritmo de busca OCF utiliza a técnica de retropropagação de erros para otimizar a topologia da rede neural sugerindo as melhores conexões entre os neurônios. A RNA resultante foi aplicada para monitorar variáveis do reator de pesquisas IEA-R1 do IPEN. Os resultados obtidos mostram que o algoritmo desenvolvido é capaz de melhorar o desempenho do modelo que estima o valor de variáveis do reator. Em testes com diferentes números de neurônios na camada escondida, utilizando como comparativos o erro quadrático médio, o erro absoluto médio e o coeficiente de correlação, o desempenho da RNA otimizada foi igual ou superior ao da tradicional. / A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANN) have been extensively used in creating monitoring systems. Usually the ANN used to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and number of neurons in the hidden layers. This way, the result networks are generally fully connected and have no improvements in its topology. This work uses an Ant Colony Optimization (ACO) algorithm to create a tuned neural networks. The ACO search algorithm uses Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The outcome ANN was applied to monitoring the IEA-R1 research reactor at IPEN. The results show that the algorithm is able to improve the performance of the model which estimates the values of the reactor variables. In tests with different numbers of neurons in the hidden layer, using as comparison the mean squared error, the mean absolute error, and the correlation coefficient, the performance of the optimized ANN proved equal or better than the equivalent traditional neural networks.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Political Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Vita. / Bibliography: leaves 398-409. / by James Allen Cooney. / Ph.D.

The objective of this thesis is to determine and analyze the dose rate to personnel throughout the proposed IRIS nuclear power plant. To accomplish this objective, complex models of the IRIS plant have been devised, advanced transport theory methods employed, and computationally intense simulations performed. IRIS is an advanced integral, light water reactor with a 335 MWe expected power output (1000 MWth). Due to its integral design, the IRIS pressure vessel has a large downcomer region. The large downcomer and the neutron reflector provide a great deal of additional shielding. This increase in shielding ensures that the IRIS design easily accomplishes the regulatory dose limits for radiation workers. However, The IRIS project set enhanced objectives of further reducing the dose rate to significantly lower levels, comparable or below the limit allowed for general public. The IRIS nuclear power plant design is very compact and has a rather complex geometric structure. Programs that use conventional methods would take too much time or would be unable to provide an answer for such a challenging deep penetration problem. Therefore, the modeling of the power plant was done using a hybrid methodology for automated variance reduction implemented into the MAVRIC sequence of the SCALE6 program package. The methodology is based on the CADIS and FW-CADIS methods. The CADIS method was developed by J.C. Wagner and A. Haghighat. The FW-CADIS method was developed by J.C. Wagner and D. Peplow. Using these methodologies in the MAVRIC code sequence, this thesis shows the dose rate throughout most of the inhabitable regions of the IRIS nuclear power plant. This thesis will also show the regions that are below the dose rate reduction objective set by the IRIS shielding team.

A new solution technique is derived for the time-dependent transport equation. This approach extends the steady-state coarse-mesh transport method that is based on global-local decompositions of large (i.e. full-core) neutron transport problems. The new method is based on polynomial expansions of the space, angle and time variables in a response-based formulation of the transport equation. The local problem (coarse mesh) solutions, which are entirely decoupled from each other, are characterized by space-, angle- and time-dependent response functions. These response functions are, in turn, used to couple an arbitrary sequence of local problems to form the solution of a much larger global problem. In the current work, the local problem (response function) computations are performed using the Monte Carlo method, while the global (coupling) problem is solved deterministically. The spatial coupling is performed by orthogonal polynomial expansions of the partial currents on the local problem surfaces, and similarly, the timedependent response of the system (i.e. the time-varying flux) is computed by convolving the time-dependent surface partial currents and time-dependent volumetric sources against pre-computed time-dependent response kernels.