Mass and power modeling of communication satellites

Analytic estimating relationships for the mass and power requirements for major satellite subsystems are described. The model for each subsystem is keyed to the performance drivers and system requirements that influence their selection and use. Guidelines are also given for choosing among alternative technologies which accounts for other significant variables such as cost, risk, schedule, operations, heritage, and life requirements. These models are intended for application to first order systems analyses, where resources do not warrant detailed development of a communications system scenario. Given this ground rule, the models are simplified to 'smoothed' representation of reality. Therefore, the user is cautioned that cost, schedule, and risk may be significantly impacted where interpolations are sufficiently different from existing hardware as to warrant development of new devices

Evolution of the mass, size, and star formation rate in high-redshift merging galaxies MIRAGE - A new sample of simulations with detailed stellar feedback

We aim at addressing the questions related to galaxy mass assembly through major and minor wet merging processes in the redshift range 1<z<2. A consequent fraction of Milky Way like galaxies are thought to have undergone an unstable clumpy phase at this early stage. Using the adaptive mesh refinement code RAMSES, with a recent physically-motivated implementation of stellar feedback, we build the Merging and Isolated high-Redshift Adaptive mesh refinement Galaxies (MIRAGE) sample. It is composed of 20 mergers and 3 isolated idealized disks simulations with global physical properties in accordance with the 1<z<2 mass complete sample MASSIV. The numerical hydrodynamical resolution reaches 7 parsecs in the smallest Eulerian cells. Our simulations include: star formation, metal line cooling, metallicity advection, and a recent implementation of stellar feedback which encompasses OB-type stars radiative pressure, photo-ionization heating, and supernovae. The initial conditions are set to match the z~2 observations, thanks to a new public code DICE. The numerical resolution allows us to follow the formation and evolution of giant clumps formed in-situ from Jeans instabilities triggered by high initial gas fraction. The star formation history of isolated disks shows stochastic star formation rate, which proceeds from the complex behavior of the giant clumps. Our minor and major gas-rich merger simulations do not trigger starbursts, suggesting a saturation of the star formation in a turbulent and clumpy interstellar medium fed by substantial accretion from the circum-galactic medium. Our simulations are close to the normal regime of the disk-like star formation on a Schmidt-Kennicutt diagram. The mass-size relation and its rate of evolution matches observations, suggesting that the inside-out growth mechanisms of the stellar disk do not necessarily require to be achieved through a cold accretion.Comment: 18 pages, 12 figures. Accepted in A&

Manufacturing System Energy Modeling and Optimization

World energy consumption has continued increasing in recent years. As a major consumer, industrial activities uses about one third of the energy over the last few decades. In the US, automotive manufacturing plants spends millions of dollars on energy. Meanwhile, due to the high energy price and the high correlation between the energy and environment, manufacturers are facing competing pressure from profit, long term brand image, and environmental policies. Thus, it is critical to understand the energy usage and optimize the operation to achieve the best overall objective. This research will establish systematic energy models, forecast energy demands, and optimize the supply systems in manufacturing plants. A combined temporal and organizational framework for manufacturing is studied to drive energy model establishment. Guided by the framework, an automotive manufacturing plant in the post-process phase is used to implement the systematic modeling approach. By comparing with current studies, the systematic approach is shown to be advantageous in terms of amount of information included, feasibility to be applied, ability to identify the potential conservations, and accuracy. This systematic approach also identifies key influential variables for time series analysis. Comparing with traditional time series models, the models informed by manufacturing features are proved to be more accurate in forecasting and more robust to sudden changes. The 16 step-ahead forecast MSE (mean square error) is improved from 16% to 1.54%. In addition, the time series analysis also detects the increasing trend, weekly, and annual seasonality in the energy consumption. Energy demand forecasting is essential to production management and supply stability. Manufacturing plant on-site energy conversion and transmission systems can schedule the optimal strategy according the demand forecasting and optimization criteria. This research shows that the criteria of energy, monetary cost, and environmental emission are three main optimization criteria that are inconsistent in optimal operations. In the studied case, comparing to cost-oriented optimization, energy optimal operation costs 35% more to run the on-site supply system. While the monetary cost optimal operation uses 17% more energy than the energy-oriented operation. Therefore, the research shows that the optimal operation strategy does not only depends on the high/low level energy price and demand, but also relies on decision makers’ preferences. It provides not a point solution to energy use in manufacturing, but instead valuable information for decision making. This research complements the current knowledge gaps in systematic modeling of manufacturing energy use, consumption forecasting, and supply optimization. It increases the understanding of energy usage in the manufacturing system and improves the awareness of the importance of energy conservation and environmental protection