High-efficiency heat transfer devices by innovative manufacturing techniques

In the present thesis, novel methods devoted to develop high heat transfer efficiency devices have been presented. These methods rely on both novel manufacturing techniques, belonging to the class of additive manufacturing (AM), and thermal and fluid-dynamics studies and optimization procedures. As a first result, optimization of a traditional heat exchanger from a real application, i.e. million of units produced per year, is presented; That is manufactured by extrusion. A thermal fluid-dynamic model is experimentally validated (from an industrial experimental test rig) and used for optimization purposes. Results demonstrate there is room for efficiency optimization even in well established heat transfer devices configurations based on traditional manufacturing techniques. Then, an experimental rig for ''in house'' thermal characterization is designed. It guarantees high precision measurement of small convective heat fluxes (forced air) on enhanced solutions investigated hereinafter, namely micro-structured surfaces and small heat transfer devices. To deal with that challenge, an innovative convective heat flux sensor is developed. That exploits the concept of thermal guard to avoid any spurious perturbation between the flow field and investigated surfaces, while it allows to cancel out terms due to spreading conduction phenomenon. Results demonstrate remarkable accuracy in direct measurement of convective heat fluxes through this novel concept. Relying on the proposed experimental rig, various methods for enhanced convective heat transfer are experimentally investigated. Firstly, regular patterns of micro-protrusions are studied. Effect of fluid-dynamics and geometrical length on heat transfer performances are discussed. More important, they have been applied to develop an optimization procedure tailored to deal with AM techniques. Results from both experimental investigation and optimization procedure suggest the existence of an optimal value of protrusion height, that maximize performance-to-cost ratio for patterns made by AM. Then, surface roughness of components built by DMLS has been investigated as an augmentation heat transfer technique. Surface roughness is controlled varying DMLS process parameters and its effect on convective heat transfer is measured. The results demonstrate a remarkable enhancement in convective heat transfer due to DMLS artificial roughness, in the investigated configurations. That preliminary study unveils the potential of AM artificial roughness as an heat transfer enhancement techniques. It has been considered, by academic and industrial institutions, as an important step towards development of next generation gas turbine components and electronic cooling devices. Finally, extreme flexibility in shape of parts built by DMLS is exploited to design and fabricate in one step an unconventional heat transfer device, called Pitot heat exchanger. Enhanced heat transfer efficiency is achieved, with regard to standard heat exchangers. Nevertheless, the most important achievement has been to highlight unusual morphologies allowed by AM can pave the way to revolutionary changes in conceiving and designing heat transfer components

Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

Analysis and study of hospital communication via social media from the patient perspective

Currently, the online interaction between citizens and hospitals is poor, as users believe that there are shortcomings that could be improved. This study analyzes patients’ opinions of the online communication strategies of hospitals in Spain. Therefore, a mixed-method is proposed. Firstly, a qualitative analysis through a focus-group was carried out, so around twenty representatives of national, regional and local patients’ associations were brought together. Secondly, the research is supplemented with a content assessment of the Twitter activity of the most influential hospitals in Spain. The results reveal that the general public appreciate hospitals’ communication potential through social media, although they are generally unaware of how it works. The group says that, apart from the lack of interaction, they find it hard to understand certain messages, and some publications give a biased picture. In order to improve communication, patients and relatives are demanding that their perspective be taken into consideration in the messages issued to enhance the quality of life and well-being of society

A Systematic Review of the Clinical Value and Applications of Three-Dimensional Printing in Renal Surgery

The purpose of this systematic review is to collate and analyse the current literature which examines clinical applications of 3D printing for renal disease, alongside cost and time duration factors associated with the printing process. A comprehensive search of the literature was performed across five di erent databases to identify studies that qualitatively and quantitatively assessed the value of 3D-printed kidney models for renal disease. Twenty-seven studies met the selection criteria for inclusion in the review. Twenty-five were original studies, and two were case reports. Of the 22 studies reporting a qualitative evaluation, the analysis of findings demonstrated the value of the 3D-printed models in areas of clinician and patient education, and pre-surgical simulation for complex cases of renal disease. Of five studies performing a quantitative analysis, the analysis of results displayed a high level of spatial and anatomical accuracy amongst models, with benefits including reducing estimated blood loss and risk of intra-operative complications. Fourteen studies evaluated manufacturing costs and time duration, with costs ranging from USD 1 to 1000 per model, and time duration ranging from 15 min to 9 days. This review shows that the use of customised 3D-printed models is valuable in the education of junior surgeons as well as the enhancement of operative skills for senior surgeons due to a superior visualisation of anatomical networks and pathologic morphology compared to volumetric imaging alone. Furthermore, 3D-printed kidney models may facilitate interdisciplinary communication and decision-making regarding the management of patients undergoing operative treatment for renal disease. It cannot be suggested that a more expensive material constitutes a higher level of user-satisfaction and model accuracy. However, higher costs in the manufacturing of the 3D-printed models reported, on average, a slightly shorter time duration for the 3D-printing process and total manufacturing time