Hybrid rocket propulsion technology shows promise for the next generation of sounding rockets and small launch vehicles. This paper seeks to provide details on the process of developing hybrid propulsion systems to the academic and amateur rocket communities to assist in future research and development. Scaling hybrid rocket motors for use in sounding rockets has been a challenge due to the inadequacies in traditional boundary layer analysis. Similarity scaling is an amendment to traditional boundary layer analysis which is helpful in removing some of the past scaling challenges. Maintaining geometric similarity, oxidizer and fuel similarity and mass flow rate to port diameter similarity are the most important scaling parameters. Advances in composite technologies have also increased the performance through weight reduction of sounding rockets through and launch vehicles. Technologies such as Composite Overwrapped Pressure Vessels (COPV) for use as fuel and oxidizer tanks on rockets promise great advantages in flight performance and manufacturing cost. A small scale COPV, carbon fiber ablative nozzle and a N class hybrid rocket motor were developed, manufactured and tested to support the use of these techniques in future sounding rocket development. The COPV exhibited failure within 5% of the predicted pressure and the scale motor testing was useful in identifying a number of improvements needed for future scaling work. The author learned that small scale testing is an essential step in the process of developing hybrid propulsion systems and that ablative nozzle manufacturing techniques are difficult to develop. This project has primarily provided a framework for others to build upon in the quest for a method to easily develop hybrid propulsion systems sounding rockets and launch vehicles.

<p>One way of modeling human knowledge is by using multidimensional spaces, in which an object is represented as a point in the space, and the distances among the points reflect the similarities among the represented objects. The distances are measured with some metric, commonly some instance of the Minkowski metric. The instances differ with the magnitude of the so-called r-parameter. The instances most commonly mentioned in the literature are the ones where r equals 1, 2 and infinity.</p><p>Cognitive scientists have found out that different metrics are suited to describe different dimensional combinations. From these findings an important distinction between integral and separable dimensions has been stated (Garner, 1974). Separable dimensions, e.g. size and form, are best described by the city-block metric, where r equals 1, and integral dimensions, such as the color dimensions, are best described by the Euclidean metric, where r equals 2. Developmental psychologists have formulated a hypothesis saying that small children perceive many dimensional combinations as integral whereas adults perceive the same combinations as separable. Thus, there seems to be a shift towards increasing separability with age or maturity.</p><p>Earlier experiments show the same phenomenon in adult short-term learning with novel stimuli. In these experiments, the stimuli were first perceived as rather integral and were then turning more separable, indicated by the Minkowski-r. This indicates a shift towards increasing separability with familiarity or skill.</p><p>This dissertation aims at investigating the generality of this phenomenon. Five similarity-rating experiments are conducted, for which the best fitting metric for the first half of the session is compared to the last half of the session. If the Minkowski-r is lower for the last half compared to the first half, it is considered to indicate increasing separability.</p><p>The conclusion is that the phenomenon of increasing separability during short-term learning cannot be found in these experiments, at least not given the operational definition of increasing separability as a function of a decreasing Minkowski-r. An alternative definition of increasing separability is suggested, where an r-value ‘retreating’ 2.0 indicates increasing separability, i.e. when the r-value of the best fitting metric for the last half of a similarity-rating session is further away from 2.0 compared to the first half of the session.</p>

One way of modeling human knowledge is by using multidimensional spaces, in which an object is represented as a point in the space, and the distances among the points reflect the similarities among the represented objects. The distances are measured with some metric, commonly some instance of the Minkowski metric. The instances differ with the magnitude of the so-called r-parameter. The instances most commonly mentioned in the literature are the ones where r equals 1, 2 and infinity. Cognitive scientists have found out that different metrics are suited to describe different dimensional combinations. From these findings an important distinction between integral and separable dimensions has been stated (Garner, 1974). Separable dimensions, e.g. size and form, are best described by the city-block metric, where r equals 1, and integral dimensions, such as the color dimensions, are best described by the Euclidean metric, where r equals 2. Developmental psychologists have formulated a hypothesis saying that small children perceive many dimensional combinations as integral whereas adults perceive the same combinations as separable. Thus, there seems to be a shift towards increasing separability with age or maturity. Earlier experiments show the same phenomenon in adult short-term learning with novel stimuli. In these experiments, the stimuli were first perceived as rather integral and were then turning more separable, indicated by the Minkowski-r. This indicates a shift towards increasing separability with familiarity or skill. This dissertation aims at investigating the generality of this phenomenon. Five similarity-rating experiments are conducted, for which the best fitting metric for the first half of the session is compared to the last half of the session. If the Minkowski-r is lower for the last half compared to the first half, it is considered to indicate increasing separability. The conclusion is that the phenomenon of increasing separability during short-term learning cannot be found in these experiments, at least not given the operational definition of increasing separability as a function of a decreasing Minkowski-r. An alternative definition of increasing separability is suggested, where an r-value ‘retreating’ 2.0 indicates increasing separability, i.e. when the r-value of the best fitting metric for the last half of a similarity-rating session is further away from 2.0 compared to the first half of the session.