A new aerodynamic device, based on flow interference effects, is studied in order to significantly improve the cornering performance of racing motorcycles in MotoGP. After a brief overview on why standard downforce devices cannot be used on motorcycles, the new idea is introduced and a simplified mechanic analysis is provided to prove its effectiveness. The concept is based on the use of anhedral wings placed on the front fairing, with the rider acting as an interference device, aiming to reduce the lift generation of one wing. Numerical calculations, based on Reynolds-averaged Navier-Stokes equations, are performed on simplified static 2D and 3D cases, as a proof of concept of the idea and as a preparation for further analysis which may involve experimental wind-tunnel testing. The obtained results show that the flow interference has indeed a significant impact on the lift on a single wing. For some cases the lift can be reduced by 70% to over 90% - which strengthens the possibility of a realistic implementation. / Ett nytt aerodynamisk koncept som nyttjar effekter av flödesinterferenser är utvärderat i syfte att på ett noterbart sätt förbättra en roadracing-motorcykels kurtagningsmöjligheter. Efter en kort genomgång av varför diverse klassiska "downforce" lösningar ej är applicerbara på motorcyklar, presenteras det nya konceptet. Varpå en mekanisk analys genomförs i syfte att se över dess tillämpbarhet. Konceptet bygger på anhedrala vingar som placeras på den främre kåpan, där föraren agerar som ett interferensobjekt, och försöker störa ut lyftkraften som den ena vingen genererar. Numeriska beräkningar baserade på RANS-ekvationer är utförda i förenklade statiska 2D och 3D fall. Som ett vidare steg rekommenderas vindtunneltester. Resultaten visar att flödesinterferenser är ytterst märkbara för vingar och i vissa fall kan lyftkraften reducerats med 70-90%. Detta förstäker möjligheten för en realistisk implementering.

<p>The first force and moment measurements were conducted in the BAM6QT. Three 7-degree half-angle sharp cones were tested, one with base radius of 4.5 in. and two with base radius of 3.5 in. made out of different materials. Models were tested at 0 and 2 degrees angle of attack. Models were tested over a range of burst pressures and Reynolds numbers. Models were fitted onto a strain gauge, 6 component, internal, moment balance. Multiple assemblies were tested that mounted the balance in the BAM6QT. High-speed schlieren video was used to monitor flow conditions and track the movement of the tunnel and model. Three entries were performed in the BAM6QT. The improvement in data quality with each new entry is shown and the startup and running loads from entry 3 are analyzed.</p> <p>Startup loads were measured and are of importance in determining the load range needed to operate in the BAM6QT. Large startup loads up to 40X the running load were identified. Tunnel movement was measured and was used to approximate the inertial loading during startup and the run. The inertial loading was not found to be the cause of the large startup loads. Schlieren video was used to qualitatively review the startup flow. It was found the large startup loads in axial force were plausibly from the high-pressure subsonic flow evacuating the nozzle. For normal force and pitching moment, the startup loads peak at a different time than axial force and appear to be from a shock-shock interaction nearby the model. Trends in startup load with changing model geometry, AoA, and burst pressure were put together to form an empirical estimation for startup loads sharp cones. </p> <p>Running loads were profiled and found to be trending with burst pressure and model geometry similarly to Newtonian flow theory predictions. However, due to the lack of a base pressure measurement, the results are uncorrected for sting effects and differ from Newtonian flow theory by a scalar. A 5.3 Hz oscillation in axial force was identified. The frequency of the oscillation is the same as the frequency of the quasi-steady flow periods caused by the reflection of the expansion fan in the driver tube. Normal force during the running load was found to be measuring positive loads when at 0 degrees angle of attack. Both the axial and normal force phenomena were unexpected and were investigated but both require further research. </p> <p><br></p> <p><br></p> <p><br></p> <p><br></p>

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks. The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model. The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.

No description available.

No description available.

No description available.

This thesis is based around examination of three mainstream inhaled drugs Formoterol, Budesonide and Beclomethasone for treatment of asthma and COPD. The areas investigated are these which have been raised in reports and studies, where there are concern, for drug use and assessment of their use. In reporting this work the literature study sets out a brief summary of the background and anatomy and physiology of the respiratory system and then discuses the mechanism of drug deposition in the lung, as well as the methods of studying deposition and pulmonary delivery devices. This section includes the basis of asthma and COPD and its treatment. In addition, a short section is presented on the role of the pharmacist in improving asthma and COPD patient¿s care. Therefore the thesis is divided into 3 parts based around formoterol, budesonide and beclomethasone. In the first case the research determines the in-vitro performance of formoterol and budesonide in combination therapy. In the initial stage a new rapid, robust and sensitive HPLC method was developed and validated for the simultaneous assay of formoterol and the two epimers of budesonide which are pharmacologically active. In the second section, the purpose was to evaluate the aerodynamic characteristics for a combination of formoterol and the two epimers of budesonide at inhalation flow rates of 28.3 and 60 L/min. The aerodynamic characteristics of the emitted dose were measured by an Anderson cascade impactor (ACI) and the next generation cascade impactor (NGI). In all aerodynamic characterisations, the differences between flow rates 28.3 and 60 were statistically significant in formoterol, budesonide R and budesonide S, while the differences between ACI and NGI at 60 were not statistically significant. Spacers are commonly used especially for paediatric and elderly patients. However, there is considerable discussion about their use and operation. In addition, the introduction of the HFAs propellants has led to many changes in the drug formulation characteristics. The purpose of the last section is to examine t h e performance of different types of spacers with different beclomethasone pMDIs. Also, it was to examine the hypothesis of whether the result of a specific spacer with a given drug/ brand name can be extrapolated to other pMDIs or brand names for the same drug. The results show that there are different effects on aerodynamic characterisation and there are significant differences in the amount of drug available for inhalation when different spacers are used as inhalation aids. Thus, the study shows that the result from experiments with a combination of a spacer and a device cannot be extrapolated to other combination.

Accurately modeling aerodynamic forces and moments are crucial for understanding thebehavior of an aircraft when performing various maneuvers at different flight conditions.However, this task is challenging due to complex nonlinear dependencies on manydifferent parameters. Currently, Computational Fluid Dynamics (CFD), wind tunnel,and flight tests are the most common methods used to gather information about thecoefficients, which are both costly and time–consuming. Consequently, great efforts aremade to find alternative methods such as machine learning. This thesis focus on finding machine learning models that can model the static and thedynamic aerodynamics coefficients for lift, drag, and pitching moment. Seven machinelearning models for static estimation were trained on data from CFD simulations.The main focus was on dynamic aerodynamics since these are more difficult toestimate. Here two machine learning models were implemented, Long Short–TermMemory (LSTM) and Gaussian Process Regression (GPR), as well as the ordinaryleast squares. These models were trained on data generated from simulated flighttrajectories of longitudinal movements. The results of the study showed that it was possible to model the static coefficients withlimited data and still get high accuracy. There was no machine learning model thatperformed best for all three coefficients or with respect to the size of the training data.The Support vector regression was the best for the drag coefficients, while there wasno clear best model for the lift and moment. For the dynamic coefficients, the ordinaryleast squares performed better than expected and even better than LSTM and GPR forsome flight trajectories. The Gaussian process regression produced better results whenestimating a known trajectory, while the LSTM was better when predicting values ofa flight trajectory not used to train the models. / Att noggrant modellera aerodynamiska krafter och moment är avgörande för att förståett flygplans beteende när man utför olika manövrar vid olika flygförhållanden. Dennauppgift är dock utmanande på grund av ett komplext olinjärt beroende av många olikaparametrar. I nuläget är beräkningsströmningsdynamik (CFD), vindtunneltestningoch flygtestning de vanligaste metoderna för att kunna modellera de aerodynamiskakoefficienterna, men de är både kostsamma och tidskrävande. Följaktligen görs storaansträngningar för att hitta alternativa metoder, till exempel maskininlärning. Detta examensarbete fokuserar på att hitta maskininlärningmodeller som kanmodellera de statiska och de dynamiska aerodynamiska koefficienterna för lyftkraft,luftmotstånd och stigningsmoment. Sju olika maskininlärningsmodeller för destatiska koefficienterna tränades på data från CFD–simuleringar. Huvudfokus lågpå den dynamiska koefficienterna, eftersom dessa är svårare att modellera. Härimplementerades två maskininlärningsmodeller, Long Short–Term Memory (LSTM)och Gaussian Process Regression (GPR), samt minstakvadratmetoden. Dessa modellertränades på data skapad från flygbanesimuleringar av longitudinella rörelser. Resultaten av studien visade att det är möjligt att modellera de statiskakoefficienterna med begränsad data och ändå få en hög noggrannhet. Ingen avde testade maskininslärningsmodelerna var tydligt bäst för alla koefficienterna ellermed hänsyn till mängden träningsdata. Support vector regression var bäst förluftmotstånds koefficienterna, men vilken modell som var bäst för lyftkraften ochstigningsmomentet var inte lika tydligt. För de dynamiska koefficienterna presterademinstakvadratmetoden bättre än förväntat och för vissa signaler även bättre än LSTMoch GPR. GPR gav bättre resultat när man uppskattade koefficienterna för enflygbanan man tränat modellen på, medan LSTM var bättre på att förutspå värdenaför en flybana man inte hade tränat modellen på.

No description available.

No description available.