Measurement of the total conditions downstream of fans is of primary importance to aeroengine development. Historically, these measurements have been acquired with the use of traditional total condition probes mounted to the guidevanes or engine cowling; however, such a setup can have significant impact on the flow. Difficulties in obtaining direct measurements with traditional total conditions probes have led to the development of an embedded shielded probe. In order to support this development, a model was desired to be developed that accurately modelled the recovery using a low-order analysis that could be implemented quickly. The creation and validation of such a model is the primary focus of the present research. Of secondary interest is to prove the hypothesis that aerodynamics will dominate the recovery of such a sensor. Based around the calculations for recovery used by Moffat, the model uses a linear vortex panel method to calculate the aerodynamics of the sensor. Higher order corrections were also suggested to improve the accuracy of the model. Several of these corrections, which take into account compressibility and variance of individual recovery factors, were included in the final model. Other corrections, such as improved paneling for the panel method and the inclusion of pitch angle have not been incorporated at this time but are part of an ongoing effort to improve and expand the capabilities of the model. Model validation was performed in three steps, starting with comparing the calculations for the recovery without aerodynamics to values present in literature for traditional Shielded probes. The aerodynamics and the panel method used to generate them were validated separately using the widely available program Xfoil. Validation of the combined model could only be accomplished via experimental testing. Several sensors, based on the predictions of the model, were 3D printed for use in experimental testing. Three key geometric parameters were identified and varied within the limits of interest to create the set of sensors tested. The purpose of this was two-fold. One: validate the model or identify key missing aerodynamic effects for inclusion. Two: prove the secondary hypothesis that aerodynamics will dominate the recovery. Testing was performed at a range of Mach numbers, yaw angles, and pitch angles commonly present in aeroengines. The data collected for model validation were simultaneously used to prove the hypothesis that aerodynamic effects dominated the recovery. This hypothesis was concluded to be true for the range of parameters tested. The model was determined to be valid for the range of parameters tested, although with the caveat that not all aerodynamic effects are fully accounted for and physical testing or CFD analysis is advised to verify results once design parameters have been narrowed down sufficiently. Further refinement of the experimental data and investigation of the aerodynamic effects are the subject of further study. / Master of Science

The new advancement in well monitoring tools have increased the amount of data that could be retrieved with great accuracy. Downhole pressure and temperature could be precisely determined now by using modern instruments. The new challenge that we are facing today is to maximize the benefits of the large amount of data that is being provided by these tools and thus justify the investment of more capital in such gadgets. One of these benefits is to utilize the continuous stream of temperature and pressure data to determine the flow rate in real time out of a multilateral well. Temperature and pressure changes are harder to predict in horizontal laterals compared with vertical wells because of the lack of variation in elevation and geothermal gradient. Thus the need of accurate and high precision gauges becomes critical. The trade-off of high resolution sensors is the related cost and resulting complication in modeling. Interpreting measured data at real-time to a downhole flow profile in multilateral and horizontal wells for production optimization is another challenge. In this study, a theoretical model is developed to predict temperature and pressure in trilateral wells based on given flow conditions. The model is used as a forward engine in the study and inversion procedure is then added to interpret the data to flow profiles. The forward model starts from an assumed well flow pressure in a specified reservoir with a defined well structure. Pressure, temperature and flow rate in the well system are calculated in the motherbore and in the laterals. These predicted temperature and pressure profiles provide the connection between the flow conditions and the temperature and pressure behavior. Then we use an inverse model to interpret the flow rate profiles from the temperature and pressure data measured by the downhole sensors. A gradient-based inversion algorithm is used in this work, which is fast and applicable for real-time monitoring of production performance. In the inverse model, the flow profile is calculated until the one that generates the matching temperature and pressure profiles in the well is identified. The production distribution from each lateral is determined based on this approach. At the end of the study, the results showed that we were able to successfully predict flow rates in the field within 10% of the actual rate. We then used the model to optimize completion design in the field. In conclusion, we were able to build a dependable model capable of predicting flow rates in trilateral wells using pressure and temperature data provided by downhole sensors.

It is well known that the efficiency of a photovoltaic (PV) module is strongly impacted by its temperature such that higher temperatures lead to lower energy conversion efficiencies. An accurate measurement of the temperature de-rating effect, therefore, is vital to the correct interpretation of PV module performance under varied environmental conditions. The current work investigates and compares methods for performing measurements of module temperature both in the lab and in field-test environments. A comparison of several temperature measurement devices was made in order to establish the ideal sensor configuration for quantifying module operating temperature. Sensors were also placed in various locations along a string of up to eight photovoltaic modules to examine the variance in operating temperature with position in the string and within a larger array of strings.

The physical and optical properties of sapphire fiber has been investigated in an effort to create a high temperature optical fiber sensor. Sapphire fiber demonstrates high optical attenuation. This attenuation is very sensitive to injection conditions, and roughly proportional to the cube of the fiber length. The loss was found to be largely due to surface scattering, which causes the fiber to deviate from a perfect cylindrical waveguide. Because of the high optical losses (and high cost) of sapphire fiber, it is desirable to fashion a splice between the sapphire and an inexpensive, low-loss silica fiber so that sapphire is only used in the sensor head. The great physical disparities between sapphire and silica make this a challenging proposition. One solution demonstrated here is the sapphire capillary tube splice, in which the two fibers are aligned in a sapphire capillary tube and bound together with alumino-silicate glass. Sapphire fiber optical sensors cannot use standard interferometric techniques used with silica fibers because sapphire fibers are not clad, making a strongly guiding, highly multimode waveguide that introduces a great deal of modal distortion to interferometric signals. Consequently a simple intensity-based sensor was developed and tested using sapphire. More exotic intensity-based sensors are explored with their applicability to a sapphire fiber sensor head. / Master of Science

Em um reator nuclear PWR a temperatura do refrigerante do circuito primário e a da água de realimentação são medidas usando RTD (Resistance Temperature Detectors), ou termômetros de resistência. Estes RTDs alimentam os sistemas de controle e segurança da usina e devem, portanto, ser muito precisos e ter bom desempenho dinâmico. O tempo de resposta dos RTDs é caracterizado por um parâmetro denominado de Constante de Tempo, definido como sendo o tempo que o sensor leva para atingir 63,2% do seu valor final após sofrer uma variação de temperatura em forma de degrau. Este valor é determinado em laboratório, porém as condições de operação de reatores nucleares são difíceis de ser reproduzidas. O método LCSR (Loop Current Step Response), ou teste de resposta a um degrau de corrente, foi desenvolvido para medir remotamente o tempo de resposta dos RTDs. A partir desse teste, a constante de tempo do sensor é calculada através de uma transformação LCSR que envolve a determinação das constantes modais do modelo de transferência de calor. Este cálculo não é simples e requer pessoal especializado. Por este motivo, utilizou-se a metodologia de Redes Neurais Artificiais para estimar a constante de tempo do RTD a partir do LCSR. Os testes LCSR foram usados como dados de entrada da RNA; os testes de Imersão Rápida foram usados para determinar a constante de tempo dos sensores, sendo estes os valores desejados de saída da rede. Esta metodologia foi aplicada inicialmente a dados teóricos, simulando dez sensores com diferentes valores de constante de tempo, resultando em um erro médio de aproximadamente 0,74 %. Dados experimentais de 3 diferentes RTDs foram usados para estimar a constante de tempo, resultando em um erro máximo de 3,34 %. Os valores de constante de tempo estimados pelas RNAs foram comparados com aqueles obtidos pelo método tradicional, obtendo-se um erro médio de 18 % o que mostra que as RNAs são capazes de estimar a constante de tempo de uma forma precisa. / In a PWR nuclear power plant, the primary coolant temperature and feedwater temperature are measured using RTDs (Resistance Temperature Detectors). These RTDs typically feed the plants control and safety systems and must, therefore, be very accurate and have good dynamic performance. The response time of RTDs is characterized by a single parameter called the Plunge Time Constant defined as the time it takes the sensor output to achieve 63.2 percent of its final value after a step change in temperature. Nuclear reactor service conditions are difficult to reproduce in the laboratory, and an in-situ test method called LCSR (Loop Current Step Response) test was developed to measure remotely the response time of RTDs. From this test, the time constant of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat-transfer model. This calculation is not simple and requires specialized personnel. For this reason an Artificial Neural Network has been developed to predict the time constant of RTD from LCSR test transient. It eliminates the transformations involved in the LCSR application. A series of LCSR tests on RTDs generates the response transients of the sensors, the input data of the networks. Plunge tests are used to determine the time constants of the RTDs, the desired output of the ANN, trained using these sets of input/output data. This methodology was firstly applied to theoretical data simulating 10 RTDs with different time constant values, resulting in an average error of about 0.74 %. Experimental data from three different RTDs was used to predict time constant resulting in a maximum error of 3,34 %. The time constants values predicted from ANN were compared with those obtained from traditional way resulting in an average error of about 18 % and that shows the network is able to predict accurately the sensor time constant.

No presente trabalho, foi desenvolvida uma situação de aprendizagem envolvendo a Modelagem Matemática aplicada a uma atividade experimental, a partir de dados coletados relacionados a sensores de temperatura em uma escola técnica. Esta atividade possui uma abordagem interdisciplinar numa perspectiva teórico-prática, a qual envolve conteúdos de Eletricidade, Física e Matemática, através de uma organização curricular flexível, caracterizando a ruptura do currículo linear que se percebe na escola tradicional. Nesta atividade de modelagem, têm-se como objetivos específicos oferecer condições para que os alunos percebam a importância da coleta e do tratamento de dados e, com isso, passem a identificar a simbologia utilizada no estudo dos circuitos eletrônicos. Que também compreendam e utilizem as principais leis da eletricidade na análise da atividade e na resolução de problemas e empreguem os conhecimentos de Matemática para descrever e interpretar os resultados da atividade. O referencial teórico baseia-se na Modelagem Matemática de Barbosa, Burak e Biembengut & Hein e na proposta de Cenários para Investigação de Skovsmose. Além dos objetivos específicos, há a intenção de criar um ambiente de discussão que favoreça e incentive a participação dos alunos na construção do conhecimento. / In this paper, we develop a learning situation involving mathematical modeling applied to an activity from collected experimental data involving temperature sensors in a technical school. This activity has an interdisciplinary approach in a theoretical - practice perspective engaging contents of Electricity, Physics and Mathematics through a flexible curriculum, featuring the disruption of linear curriculum that is perceived in the traditional school. In this modeling activity we have specific objectives, provide conditions so that students understand the importance of collecting and processing data and thereby identify the symbology used in the study of electronic circuits, understand and use the major laws of electricity in activity analysis and problem solving and also employ the knowledge of mathematics to describe and interpret the results of the activity. The theoretical framework is based on mathematical modeling by Barbosa, and Burak Biembengut & Hein and the proposed Scenarios for Research by Skovsmose. In addition to the specific objectives, we intend to create an environment that encourages discussion and student participation in the construction of knowledge.

Em um reator nuclear PWR a temperatura do refrigerante do circuito primário e a da água de realimentação são medidas usando RTD (Resistance Temperature Detectors), ou termômetros de resistência. Estes RTDs alimentam os sistemas de controle e segurança da usina e devem, portanto, ser muito precisos e ter bom desempenho dinâmico. O tempo de resposta dos RTDs é caracterizado por um parâmetro denominado de Constante de Tempo, definido como sendo o tempo que o sensor leva para atingir 63,2% do seu valor final após sofrer uma variação de temperatura em forma de degrau. Este valor é determinado em laboratório, porém as condições de operação de reatores nucleares são difíceis de ser reproduzidas. O método LCSR (Loop Current Step Response), ou teste de resposta a um degrau de corrente, foi desenvolvido para medir remotamente o tempo de resposta dos RTDs. A partir desse teste, a constante de tempo do sensor é calculada através de uma transformação LCSR que envolve a determinação das constantes modais do modelo de transferência de calor. Este cálculo não é simples e requer pessoal especializado. Por este motivo, utilizou-se a metodologia de Redes Neurais Artificiais para estimar a constante de tempo do RTD a partir do LCSR. Os testes LCSR foram usados como dados de entrada da RNA; os testes de Imersão Rápida foram usados para determinar a constante de tempo dos sensores, sendo estes os valores desejados de saída da rede. Esta metodologia foi aplicada inicialmente a dados teóricos, simulando dez sensores com diferentes valores de constante de tempo, resultando em um erro médio de aproximadamente 0,74 %. Dados experimentais de 3 diferentes RTDs foram usados para estimar a constante de tempo, resultando em um erro máximo de 3,34 %. Os valores de constante de tempo estimados pelas RNAs foram comparados com aqueles obtidos pelo método tradicional, obtendo-se um erro médio de 18 % o que mostra que as RNAs são capazes de estimar a constante de tempo de uma forma precisa. / In a PWR nuclear power plant, the primary coolant temperature and feedwater temperature are measured using RTDs (Resistance Temperature Detectors). These RTDs typically feed the plants control and safety systems and must, therefore, be very accurate and have good dynamic performance. The response time of RTDs is characterized by a single parameter called the Plunge Time Constant defined as the time it takes the sensor output to achieve 63.2 percent of its final value after a step change in temperature. Nuclear reactor service conditions are difficult to reproduce in the laboratory, and an in-situ test method called LCSR (Loop Current Step Response) test was developed to measure remotely the response time of RTDs. From this test, the time constant of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat-transfer model. This calculation is not simple and requires specialized personnel. For this reason an Artificial Neural Network has been developed to predict the time constant of RTD from LCSR test transient. It eliminates the transformations involved in the LCSR application. A series of LCSR tests on RTDs generates the response transients of the sensors, the input data of the networks. Plunge tests are used to determine the time constants of the RTDs, the desired output of the ANN, trained using these sets of input/output data. This methodology was firstly applied to theoretical data simulating 10 RTDs with different time constant values, resulting in an average error of about 0.74 %. Experimental data from three different RTDs was used to predict time constant resulting in a maximum error of 3,34 %. The time constants values predicted from ANN were compared with those obtained from traditional way resulting in an average error of about 18 % and that shows the network is able to predict accurately the sensor time constant.

No presente trabalho, foi desenvolvida uma situação de aprendizagem envolvendo a Modelagem Matemática aplicada a uma atividade experimental, a partir de dados coletados relacionados a sensores de temperatura em uma escola técnica. Esta atividade possui uma abordagem interdisciplinar numa perspectiva teórico-prática, a qual envolve conteúdos de Eletricidade, Física e Matemática, através de uma organização curricular flexível, caracterizando a ruptura do currículo linear que se percebe na escola tradicional. Nesta atividade de modelagem, têm-se como objetivos específicos oferecer condições para que os alunos percebam a importância da coleta e do tratamento de dados e, com isso, passem a identificar a simbologia utilizada no estudo dos circuitos eletrônicos. Que também compreendam e utilizem as principais leis da eletricidade na análise da atividade e na resolução de problemas e empreguem os conhecimentos de Matemática para descrever e interpretar os resultados da atividade. O referencial teórico baseia-se na Modelagem Matemática de Barbosa, Burak e Biembengut & Hein e na proposta de Cenários para Investigação de Skovsmose. Além dos objetivos específicos, há a intenção de criar um ambiente de discussão que favoreça e incentive a participação dos alunos na construção do conhecimento. / In this paper, we develop a learning situation involving mathematical modeling applied to an activity from collected experimental data involving temperature sensors in a technical school. This activity has an interdisciplinary approach in a theoretical - practice perspective engaging contents of Electricity, Physics and Mathematics through a flexible curriculum, featuring the disruption of linear curriculum that is perceived in the traditional school. In this modeling activity we have specific objectives, provide conditions so that students understand the importance of collecting and processing data and thereby identify the symbology used in the study of electronic circuits, understand and use the major laws of electricity in activity analysis and problem solving and also employ the knowledge of mathematics to describe and interpret the results of the activity. The theoretical framework is based on mathematical modeling by Barbosa, and Burak Biembengut & Hein and the proposed Scenarios for Research by Skovsmose. In addition to the specific objectives, we intend to create an environment that encourages discussion and student participation in the construction of knowledge.

No presente trabalho, foi desenvolvida uma situação de aprendizagem envolvendo a Modelagem Matemática aplicada a uma atividade experimental, a partir de dados coletados relacionados a sensores de temperatura em uma escola técnica. Esta atividade possui uma abordagem interdisciplinar numa perspectiva teórico-prática, a qual envolve conteúdos de Eletricidade, Física e Matemática, através de uma organização curricular flexível, caracterizando a ruptura do currículo linear que se percebe na escola tradicional. Nesta atividade de modelagem, têm-se como objetivos específicos oferecer condições para que os alunos percebam a importância da coleta e do tratamento de dados e, com isso, passem a identificar a simbologia utilizada no estudo dos circuitos eletrônicos. Que também compreendam e utilizem as principais leis da eletricidade na análise da atividade e na resolução de problemas e empreguem os conhecimentos de Matemática para descrever e interpretar os resultados da atividade. O referencial teórico baseia-se na Modelagem Matemática de Barbosa, Burak e Biembengut & Hein e na proposta de Cenários para Investigação de Skovsmose. Além dos objetivos específicos, há a intenção de criar um ambiente de discussão que favoreça e incentive a participação dos alunos na construção do conhecimento. / In this paper, we develop a learning situation involving mathematical modeling applied to an activity from collected experimental data involving temperature sensors in a technical school. This activity has an interdisciplinary approach in a theoretical - practice perspective engaging contents of Electricity, Physics and Mathematics through a flexible curriculum, featuring the disruption of linear curriculum that is perceived in the traditional school. In this modeling activity we have specific objectives, provide conditions so that students understand the importance of collecting and processing data and thereby identify the symbology used in the study of electronic circuits, understand and use the major laws of electricity in activity analysis and problem solving and also employ the knowledge of mathematics to describe and interpret the results of the activity. The theoretical framework is based on mathematical modeling by Barbosa, and Burak Biembengut & Hein and the proposed Scenarios for Research by Skovsmose. In addition to the specific objectives, we intend to create an environment that encourages discussion and student participation in the construction of knowledge.

Cytiva i Umeå producerar medicinskteknologisk utrustning och ett av dessa system som produceras är Biacore. Dessa system används inom läkemedelsutveckling och forskning inom proteinanalys. De temperaturgivare som används i Biacore ska kalibreras noggrant då det är viktigt att det inte blir några mätfel. Målet med detta examensarbete är att utveckla nuvarande kalibreringsstationen för temperaturgivare eftersom den har blivit omodern. Fokuset ligger mest på att utveckla en ny mjukvara, dock potentiell utveckling av hårdvaran är också av intresse. Mjukvaran ska beräkna kalibreringsfaktorer för de temperaturgivare som kalibreras på ett automatiserat och effektivt sätt. Utveckling av hårdvaran kan göras genom att byta ut nuvarande referenstermometern till en ny och integrera den i kalibreringsstationen. Ett program har utvecklats med ett användargränssnitt där användaren väljer vilken typ av temperaturgivare som kalibreras och dess identifikationsnummer. Baserat på denna information hittar programmet alla relevanta filer med mätvärdena, beräknar kalibreringsfaktorer för varje temperaturgivare och sparar resultatet. Detta automatiserar beräkning och sparande av data samt effektiviserar kalibreringsprocessen. Utvecklingen av mjukvaran gjordes med kodspråket Python och biblioteket Tkinter. Hårdvaruutvecklingen har påbörjats men inte uppnåtts på grund av tidsbrist. / Cytiva in Umeå manufactures medical technological equipment. One of those systems that are produced is Biacore. Those systems are used in drug development and research in protein analysis.The temperature sensors that are used in Biacore systems must be accurately calibrated since it is of high importance to avoid errors in the measurements. The goal of this thesis is to improve current calibration station since it has gotten outdated. The focus is going to lie mostly on developing the software, although the potential development of the hardware is of interest as well. The software should be able to calculate the calibration factors for each of the temperature sensors that are calibrated in an automated and an effective way. The development of the hardware can be done by upgrading current reference thermometer to a new one and integrate it in the calibration station. A program has been developed with a user interface where the user choses what type of thetemperature sensor is to be calibrated and its identification number. Based on this information the program finds all the relevant files with the measured values, calculates calibration factors for each of the temperature sensors and saves the result. It automises calculation and saving of data as well as makes the calibrating process more effective. Development of the software was done by using Python programming language and Tkinter library. Development of the hardware has been started but was not achieved due to the lack of time.