Performance models provided by aircraft manufacturers are used by aircraft operators to perform flight path simulations aiming to reduce aircraft fuel consumption. However, performance models are generic and does not account for the performance deviations of each aircraft individual. The performance deviations, particularly in terms of fuel consumption, will affect the dynamic programming of flight path simulations. This may result in a less optimal flight path and ultimately lead to higher fuel consumption than expected. In hope of reducing this risk, a collection of local performance factors were derived. These factors describe the percentual deviation between the real fuel flow and the levels predicted by the performance model, and are allocated with respect to a range of flight parameters in a data library known as the performance library. A test environment is then constructed to simulate a continuous flow of flight data, where a new performance library is derived from the flight data of every month. The local performance factors of the previous month are then updated with the current; a learning process based on the weighted average ensemble approach. Further, the local performance factors are used in conjunction with the performance model to estimate the aircraft fuel consumption during cruise. The observed average prediction error is noticeably smaller than that of an equivalent global, scalar performance factor used by airlines today. The result also reveals that the prediction accuracy and versatility of the performance library is mainly determined by its resolution - higher resolution generally offers better accuracy at a cost of requiring more flight data, whereas lower resolutions are more versatile but of lower accuracy. Finally, the performance libraries of two identical aircraft are used to trace the performance deviation between them. The weighted average of all local performance factors in the performance library of respective aircraft reveal that the average fuel consumption is roughly -1.9 % and -2.5 % lower than the estimates by the performance model, ultimately proving that it is feasible to detect overall fuel efficiency deviation between two identical aircraft. / Prestandamodeller tillhandhållna av flygplanstillverkarna används oftast av flygbolagen för att utföra flygruttsimuleringar i syfte att bespara bränsle. Dock är prestandamodellerna generiska och tillgodoräknar inte prestandaavvikelserna som förekommer hos varje flygplansindivid. Dessa prestandaavvikelser, speciellt i form av bränsleförbrukning, kommer att påverka den dynamiska programmeringen i flygruttssimulationen. Följde när flygrutter som kan leda till högre förbrukningar än de ursprungligen uppskattades. I hopp om att minimera denna risk beräknades mängder av lokala prestandafaktorer, vilka grundar på prestandamodellens avvikelse från verkliga flygdata. Dessa koefficienter allokerades sedan till ett databibliotek (prestandabibliotek) med avseende på en samling av flygparametrar. En testmiljö konstruerades i följd för att simulera ett kontinuerligt dataflöde. Vidare skapades ett prestandabibliotek för varje månadsflygdata, där de nyskapade lokala prestandafaktorerna viktas med de motsvarandeparterna i föregående månadens prestandabibliotek, vilket är en inlärningsprocessbaserad på viktad medelvärdesensemble. Prestandabiblioteket applicerades sedan över prestandamodellen och det snittliga uppskattning felet observerades vara märkbart mindre än det från en motsvarande global, skalärbaserad prestandafaktor. Resultatet antyder också på att prestandabibliotekets uppskattningsnoggrannhet och allsidighet beror huvudsakligen på dess upplösning - en hög upplösning leder generellt till ökad uppskattningsnoggrannhet med på bekostnad av mer flygdata, medan lägre upplösningar tenderar att vara mer allsidiga men med mindre uppskattningsnoggrannhet. Slutligen användes prestandabiblioteken av två identiska flygplan för att spåra prestandaavvikelser som förekommer mellan dem. Viktat medelvärde av alla prestandafaktorer i respektiveflygplanets prestandabibliotek tyder på att snittförbrukningen är ungefär 1,9 % respektive2,5 % lägre än det som uppskattades av prestandamodellen. Härmed bevisades att det är möjligt att spåra varianser i snittförbrukningen mellan två identiska flygplan.

High Spectral Resolution Lidar (HSRL) is a backscatter lidar technique that employs an optical/spectral filter to distinguish between particulate (Mie) and molecular (Rayleigh) backscattered light. By separating the two types of returns, higher accuracy measurements are possible that will enable improved climate models, air quality measurements, and climate forecasting. A spaceborne HSRL instrument can provide great impact in these areas by enabling near-continuous measurements across the Earth, however the optical filter technology has typically been too complex for reliable long-duration space flight due to the need for complicated and costly electro-optic feedback loops, extra alignment detectors, and additional laser sources. Furthermore, these complexities limit the filter from use in other applications. In this research, a high-performance, ultra-narrowband interferometric optical filter with a specific thermo-optical behavior has been designed and built. The interferometer has been designed such that it can be reliably adjusted/tuned by simply monitoring and adjusting the temperature. The greatly reduced operational complexity was made possible through high-accuracy thermal characterization of the interferometer materials, combined with detailed Structural-Thermal-Optical-Performance (STOP) modeling to capture the complicated interactions between the materials. The overall design process, fabrication procedures, and characterization of the optical filter are presented. / Doctor of Philosophy / LiDAR (an acronym for Light Detection and Ranging) is a technology that can be used to measure properties of the atmosphere. It is similar to radar, but uses much smaller light waves rather than larger radio waves, enabling more detailed information to be obtained. High Spectral Resolution Lidar (HSRL) is a lidar technique that uses a high precision optical filter to distinguish between light that scatters from particulates (such as dust, smoke, or fog) and light that scatters from molecules (such as oxygen, nitrogen, or carbon dioxide) in the atmosphere. By separating the two types of backscattered light, higher accuracy measurements are possible that will enable improvements in climate models, air quality measurements, and climate forecasting. A spaceborne HSRL instrument can provide great impact in these areas by enabling near-continuous measurements across the Earth; however, the optical filter technology has typically been too complex for reliable long-duration spaceflight due to the need for complicated and expensive additional hardware. In this research, a high-performance HSRL optical filter that can be reliably operated by simply monitoring and adjusting the temperature has been designed, built, and tested. The greatly-reduced operational complexity has been made possible through a new process that enables more accurate prediction of the complicated interactions between the materials of the optical filter. This process is based on a combination of high-accuracy characterization of the materials and detailed structural-thermal-optical-performance (STOP) modeling. The overall design process, fabrication procedures, and characterization of the optical filter are presented.

The aim of this thesis was to develop a touchscreen-adapted version of the well established Keystroke-level model, which is a user performance model designed to be a quick and easy way to evaluate user interfaces prior to creating prototypes. A quantitative research method was chosen to measure general execution times for common touchscreen gestures on mobile devices. Over 1000 data points were collected from participants who partook in a observational experiment using a prototype specifically programmed for this study. The results of the thesis involves the analyzed measurements acquired from the experiment, as well as a four important aspect to consider when performing similar experiments, namely: (1) The individual speed of the participants; (2) The participants’ level of expertise; (3) The participants’ methods of performing the gestures; (4) Designing the experiment for medium difficulty. / Målet med denna uppsats var att utveckla en pekskärmsanpassad version av den välkända modellen Keystroke-level model, vilket är en modell som kan användas för att snabbt och enkelt utvärdera gränssnitt utan att behöva utveckla fungerande prototyper. En kvantitativ forskningsmetod användes för att mäta hur lång tid det tar för användare generellt att utföra de vanligaste fingergesterna på mobila pekskärmar. Över 1000 datapunkter samlades in från ett experiment där deltagarna fick använda en prototyp som automatiskt mätte den tid det tog för användarna att utföra de olika fingergesterna. Resultaten av studien består av de mätvärden som räknats ut samt fyra aspekter som är viktiga att ha i beaktning när liknande experiment ska utformas. Dessa är: (1) Varje deltagares individuella hastighet; (2) Deltagarnas pekskärmsvana; (3) Sättet de olika deltagarna utför fingergesterna; (4) Utforma experimentet för att representera en medium svårighetsgrad. / Le but de ce projet fut de créer une version du Keystroke-level model adaptée aux écrans tactiles. Ce modèle de performance utilisateur est une méthode d’évaluation simple et rapide d’interfaces utilisateur avant la création de prototypes. Une étude quantitative fut choisie pour mesurer le temps d’exécution général de mouvements spécifiques effectués sur des écrans tactiles de téléphones. Plus de 1000 points de données furent collectés auprès de participants ayant pris part à une étude observationnelle utilisant un prototype spécialement programmé pour l’occasion. Les résultats de ce projet incluent l’analyse des mesures acquises lors de cette étude ainsi que quatre aspects importants à considérer lors de la réalisation d’études similaires, à savoir: (1) La vitesse individuelle des participants; (2) Le niveau d’habitude d’utilisation d’écrans tactiles des participants; (3) Les méthodes utilisées par les participants lors de la réalisation des mouvements; (4) La conception d’étude pour des mouvements de difficulté dite moyenne.