Airflow Model Testing to Determine the Distribution of Hot Gas Flow and O/F Ratio Across the Space Shuttle Main Engine Main Injector Assembly

Engine 0209, the certification engine for the new Phase 2+ Hot Gas Manifold (HGM), showed severe deterioration of the Main Combustion Chamber (MCC) liner during hot fire tests. One theory on the cause of the damage held that uneven local distribution of the fuel rich hot gas flow through the main injector assembly was producing regions of high oxidizer/fuel (O/F) ratio near the wall of the MCC liner. Airflow testing was proposed to measure the local hot gas flow rates through individual injector elements. The airflow tests were conducted using full scale, geometrically correct models of both the current Phase 2 and the new Phase 2+ HGMs. Different main injector flow shield configurations were tested for each HGM to ascertain their effect on the pressure levels and distribution of hot gas flow. Instrumentation located on the primary faceplate of the main injector measured hot gas flow through selected injector elements. These data were combined with information from the current space shuttle main engine (SSME) power balances to produce maps of pressure, hot gas flow rate, and O/F ratio near the main injector primary plate. The O/F distributions were compared for the different injector and HGM configurations

My Dream of the U. S. A.: Quartette Chorus

https://digitalcommons.library.umaine.edu/mmb-vp/2912/thumbnail.jp

What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value

Fitness is at the core of evolutionary theory, but it is difficult to measure accurately. One way to measure long-term fitness is by calculating the individual's reproductive value, which represents the expected number of allele copies an individual passes on to distant future generations. However, this metric of fitness is scarcely used because the estimation of individual's reproductive value requires long-term pedigree data, which is rarely available in wild populations where following individuals from birth to death is often impossible. Wild study systems therefore use short-term fitness metrics as proxies, such as the number of offspring produced. This study compared two frequently used short-term metrics for fitness obtained at different offspring life stages (eggs, hatchlings, fledglings and recruits), and compared their ability to predict reproductive values derived from the genetic pedigree of a wild passerine bird population. We used twenty years of precise field observations and a near-complete genetic pedigree to calculate reproductive success, individual growth rate and de-lifed fitness as lifetime fitness measures, and as annual de-lifed fitness. We compared the power of these metrics to predict reproductive values and lineage survival to the end of the study period. The three short-term fitness proxies predict the reproductive values and lineage survival only when measured at the recruit stage. There were no significant differences between the different fitness proxies at the same offspring stages in predicting the reproductive values and lineage survival. Annual fitness at one year old predicted reproductive values equally well as lifetime de-lifed fitness. However, none of the short-term fitness proxies were strongly associated with the reproductive values. The commonly used short-term fitness proxies best predict long-term fitness when measured at recruitment stage. Thus, because lifetime fitness measured at recruit stage and annual fitness in the first year of life were the best proxies of long-term fitness in short-lived birds, we encourage their future use

Excited states in lattice QCD with the stochastic LapH method

Progress in computing the spectrum of excited baryons and mesons in lattice QCD is described. Results in the zero-momentum bosonic I=1/2, S=1, T1u symmetry sector of QCD using a correlation matrix of 58 operators are presented. All needed Wick contractions are efficiently evaluated using a stochastic method of treating the low-lying modes of quark propagation that exploits Laplacian Heaviside quark-field smearing. Level identification using probe operators is discussed.Comment: 4 pages, 3 figures, talk presented at the 13th International Conference on Meson-Nucleon Physics and the Structure of the Nucleon (MENU 2013), Sept 30 - Oct 4, 2013, Rome, Ital

Teaching multimodal literacies with digital technologies and augmented reality : a cluster analysis of Australian teachers' TPACK

Despite the proliferation of augmented reality (AR) apps, Australian primary teachers have yet to use them widely for the teaching of multimodal literacies. Conceptualising teachers’ knowledge of using digital technologies to teach multimodal literacies as a form of technological pedagogical content knowledge or TPACK(ML), this study examined teacher differences through a cluster analysis of survey responses collected from a sample of 142 Australian primary school teachers. Two distinct clusters of teachers were derived. The first cluster with lower TPACK(ML) comprised teachers with lower self-reported confidence in facilitating new cultures of learning that are participatory and technology-driven in nature. In their open-ended survey responses, these teachers shared their unfamiliarity with AR, as well as concerns about their personal technical competency and how AR could be integrated into the curriculum. The second cluster of teachers rated themselves higher in TPACK(ML) and in how they used technology to support language learning pedagogies. They were able to propose different pedagogical strategies to engage students’ multimodal literacies meaningfully with AR in their open-ended survey responses. The implications of the study’s findings were discussed, and recommendations were proposed for designing and sustaining differentiated forms of teacher professional development for teaching multimodal literacies with emergent digital technologies

Human-aware navigation for autonomous mobile robots for intra-factory logistics

This paper presents a human-aware navigation system for mobile robots targeted to cooperative assembly in intra-factory logistics scenarios. To improve overall efficiency of the operator-robot ensemble, assembly stations and operators are modelled as cost functions in a layered cost map supporting the robot navigation system. At each new sensory update, the system uses each operator’s estimated location to affect the cost map accordingly. To promote predictability and comfort in the human operator, the cost map is affected according to the Proxemics theory, properly adapted to take into account the layout activity space of the station in which the operator is working. Knowledge regarding which task and station are being handled by the operator are assumed to be given to the robot by the factory’s computational infrastructure. To foster integration in existing robots, the system is implemented on top of the navigation system of the Robot Operating System (ROS).info:eu-repo/semantics/acceptedVersio