Transition is an important phenomenon in large scale, commercial, wind tunnel testing at low speeds because it is an excellent indicator of an airfoil performance. It is difficult to estimate transition through numerical techniques because of the complex nature of viscous flow. Therefore experimental techniques can be essential. Over the transition region the rate of heat transfer shows significant increases which can be detected using infrared thermography. This technique has been used predominantly at high speeds, on small models made of insulated materials, and for short test runs. Large scale testing has not been widely undertaken because the high sensitivity of transition to external factors makes it difficult to detect. The present study records the process undertaken to develop, implement and validate a transition detection system for continual use in the Virginia Tech Stability Wind Tunnel: a low speed, commercial wind tunnel where large, aluminium models are tested. The final system developed comprises of two high resolution FLIR A655sc infrared cameras; four 63.5-mm diameter circular windows; aluminium models covered in 0.8-mm silicone rubber insulation and a top layer of ConTact© paper; and a series of 25.4-mm wide rubber silicone fiberglass insulated heaters mounted inside the model and controlled externally by experimenters. This system produces images or videos of the model and the associated transition location, which is later extracted through image processing methods to give a final transition location in percentage chord. The system was validated using two DU96-W-180 airfoils of different chord lengths in the Virginia Tech Stability Wind Tunnel, each tested two months apart. The system proved to be robust and efficient, while not affecting the airfoil performance or any other system in use in the wind tunnel. Transition results produced by the system were compared to measurements obtained from pressure data and stethoscope tests as well as the numerical predictions of XFOIL. The transition results from all four methods showed excellent agreement with each other for the two models, for at least two Reynolds numbers and for several angles of attack on both suction and pressure side of the model. The agreement of data obtained under such different conditions and at different times suggests that the infrared thermography system efficiently and accurately detects transition for large aluminium models at low speeds. / Master of Science

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The research presented in this thesis serves to provide a tool to autonomously screen for cardiovascular disease in the rural areas of Africa. Vital information thus obtained from patients can be communicated to advanced medical centres by Telemedicine. Cardiovascular disease is then detected in its initial stages, which is essential to its effective treatment. The system developed in this study uses recorded heart sounds and electrocardiogram signals to distinguish between normal and abnormal heart conditions. This system improves on standard diagnostic tools in that it does not require cumbersome and expensive imaging equipment or a highly trained operator. Heart sound- and electrocardiogram signals from 62 volunteers were recorded with the prototype Precordialcardiogram device as part of a clinical study to aid in the development of the autonomous auscultation software and to screen patients for cardiovascular disease. These volunteers consisted of 28 patients of Tygerberg Hospital with cardiovascular disease and, for control purposes, 34 persons with normal heart conditions. The autonomous auscultation system developed during this study, interprets data obtained with the Precordialcardiogram device to autonomously acquire a normal or abnormal diagnosis. The system employs wavelet soft thresholding to denoise the recorded signals, followed by the segmentation of heart sound by identifying peaks in the electrocardiogram. Novel frequency spectral information was extracted as features from the heart sounds, by means of ensemble empirical mode decomposition and auto regressive modelling. These features proved to be particularly significant and played a major role in the screening capability of the system. New time domain based features were identified, established on the specific characteristics of the various cardiovascular diseases encountered during the study. These features were extracted via the energy ratios between different parts of ventricular systole and diastole of each recorded cardiac cycle. The respective features were classified to characterise typical heart diseases as well as healthy hearts with an ensemble artificial neural network. Herein the decisions of all the members were combined to obtain a final diagnosis. The performance of the autonomous auscultation system used in concert with the Precordialcardiogram device prototype, as determined through the leave-one-out crossvalidation method, had a sensitivity rating of 82% and a specificity rating of 88%. These results demonstrate the potential benefit of the Precordialcardiogram device and the developed autonomous auscultation software in a Telemedicine environment. / AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die navorsing van 'n outonome toetsing en sifting stelsel vir kardiovaskulêre siektes in landelike dele van Afrika, vanwaar mediese inligting per telefoon versend kan word. Die apparaat maak vroeë opsporing van kardiovaskulêre siektes moontlik, wat essensieel is vir effektiewe behandeling daarvan en ook die koste-effek van hierdie siektes verminder. In die huidige ontwikkelde stelsel word normale sowel as abnormale hart-toestande getipeer met opnames van hartklanke sowel as elektrokardiogram-seine. Voordele wat hierdie stelsel bo standaard diagnostiese metodes het, sluit die hanteerbare formaat van die hele apparaat sowel as die nie-noodsaaklikheid van duur beeldskeppende apparaat, of hoogs opgeleide personeel. Hartklank- en elektrokardiogramseine van 62 vrywilligers is met die prototipe "Precordialcardiogram" apparaat opgeneem om by te dra tot die ontwikkeling van die rekenaar sagteware vir die outonome auscultatsie stelsel en om die pasiëntsiftingsvermoë daarvan te toets. Die vrywilligers het 28 pasiënte van Tygerberg hospitaal met abnormale harttoestande ingesluit, sowel as ‘n kontrolegroep van 34 persone met normale harttoestande. Die outonome auskultasie-stelsel wat tot stand gekom het deur hierdie ondersoek maak gebruik van “wavelet” sagte drempeling om geraas uit die opgeneemde seine te verwyder. Daarna word die hartklanke gesegmenteer deur die pieke van die elektrokardiogram te identifiseer. Deur middel van "ensemble empirical mode decomposition" en outoregressiewe modellering, is nuwe inligting aangaande die frekwensie spektra van hartklanke, aanwysend van spesifieke harttoestande, verkry. Die beduidendheid van hierdie eienskappe is bewys en het 'n belangrike rol in die siftingsvermoë van die stelsel gespeel. Hierbenewens is nuwe tyd-gebaseerde eienskappe van die onderskeie kardiovaskulêre siektes wat tydens die ondersoek bestudeer is, geïdentifiseer. Hierdie eienskappe is geëien deur die energie-verhoudings tussen verskillende dele van die ventrikulêre sistolie en diastolie van elke opgeneemde hartsiklus te ontleed. 'n "Ensemble artificial neural network" is gebruik om die geïdentifiseerde eienskappe van hartsiektes sowel as normale harttoestande, te klassifiseer. Hierin is besluite van al die lede van die netwerk gekombineer, ten einde ‘n finale diagnose te maak. Die klassifiseerder se geldigheid is kruis-bevestig deur middel van die laat-een-uit kruisbevestigings-metode. Deur middel van die kruis-bevestigingsmetode is die bedryfsvermoëns van die outonome auskultasie-stelsel, toegerus met die "Precordialcardiogram" apparaat, repektiewelik op 82% vir sensitiwiteit en 88% vir spesifisiteit vasgestel. Hierdie resultate demonstreer die benuttingspotensiaal van die apparaat in 'n Telemedisyne omgewing.

La presente tesis propone el desarrollo de un sistema prototipo de estetoscopio digital para ser usado en consultas médicas remotas, utilizando transmisión por la red celular (GSM) e Internet, con la finalidad de brindarle a los profesionales en la salud una herramienta con la cual puedan seguir realizando sus consultas cotidianas evitando el contacto directo con el paciente, por ende, previniendo el contagio de una enfermedad infecciosa. Por ello, el presente trabajo se divide en tres partes: la adquisición y transmisión de las señales de auscultación, el servidor Web y el software de control. El primer paso que se debe realizar es la adquisición de la señal acústica proporcionada por el estetoscopio. Las señales ingresan al controlador para su grabación y ser transmitidas vía Internet al servidor Web. Además, previo a la grabación de las señales, el médico, utilizando el software de control, podrá escuchar la señal acústica en tiempo real mediante una llamada a celular para indicar al paciente la correcta posición del estetoscopio. En el servidor se encuentran las grabaciones ordenadas en carpetas con los datos del paciente, dichos archivos son descargables y reproducibles. El método de validación se realizó con una encuesta y pruebas del prototipo con diferentes médicos. Los resultados obtenidos demostraron que el prototipo brinda una buena calidad de los sonidos auscultados, siendo así útil para poder realizar un diagnóstico preliminar certero de manera remota. / The present thesis proposes the development of a digital stethoscope prototype system to be used in remote medical attentions using the celular network transmittion (GSM) and Internet, in order to provide to health professionals a tool which they can keep making their attentions avoiding physical contact with the patient. Thus, preventing the spread of an infectious disease. Therefore, the present work is divided in three parts: the acquisition and transmisión of auscultation signals, the Web server and the software. The first step to take is the acoustic signal acquisition provided by stethoscope. Signals enter the controller for recording and transmitted via Internet to Web server. Also, prior to recording the signals, the doctor, using the software, can hear the eliminate signals in real time using the call in order to indicate the correct position of stethoscope to patient. The recordings are stored on the Web server arranged in folders with data patient, the files are downloadable and playable. The validation method was performed using an inquest and testing prototype with different doctors. The results obtained showed that prototype provides a good quality of auscultated sounds, thus being useful to be able to carry out an accurate preliminary diagnosis remotely. / Tesis

<p dir="ltr">The onset of smart manufacturing signifies a crucial shift in the industrial landscape, underscoring the pressing need for systems capable of adapting to and managing the complex dynamics of modern production environments. In this context, the importance of smart monitoring becomes increasingly apparent, serving as a vital tool for ensuring operational efficiency and reliability. Inspired by the critical role of auditory perception in human decision-making, this study investigated the application of machine sound recognition for practical use in manufacturing environments. Addressing the challenge of utilizing machine sounds in the loud noises of factories, the study employed an Internal Sound Sensor (ISS).</p><p dir="ltr">The study examined how sound propagates through structures and further explored acoustic characteristics of the ISS, aiming to apply these findings in machine monitoring. To leverage the ISS effectively and achieve a higher level of monitoring, a smart sound monitoring framework was proposed to integrate sound monitoring with machine data and human-machine interface. Designed for applicability and cost effectiveness, this system employs real-time edge computing, making it adaptable for use in various industrial settings.</p><p dir="ltr">The proposed framework and ISS deployed across a diverse range of production environments, showcasing a leap forward in the integration of smart technologies in manufacturing. Their application extends beyond continuous manufacturing to include discrete manufacturing systems, demonstrating adaptability. By analyzing sound signals from various production equipment, this study delves into developing machine sound recognition models that predict operational states and productivity, aiming to enhance manufacturing efficiency and oversight on real factory floors. This comprehensive and practical approach underlines the framework's potential to revolutionize operational management and manufacturing productivity. The study progressed to integrating manufacturing context with sound data, advancing towards high-level monitoring for diagnostic predictions and digital twin. This approach confirmed sound recognition's role in manufacturing diagnostics, laying a foundation for future smart monitoring improvements.</p>